[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-NDVI":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":8,"page_size":9,"items":10},"NDVI","tag",[4],52,1,100,[11,100,172,233,281,329,394,436,498,580,645,689,771,824,882,932,990,1037,1094,1153,1205,1259,1311,1358,1419,1483,1549,1620,1674,1730,1800,1862,1915,1980,2025,2084,2140,2198,2251,2304,2358,2417,2475,2521,2583,2641,2699,2744,2808,2884,2930,2981],{"id":12,"title":13,"url":14,"summary":15,"summary_zh":16,"content":17,"source_name":18,"source_url":14,"published_at":19,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":23,"score_detail":24,"sources":31,"tags":33,"primary_topic":38,"secondary_topics":44,"classification":45,"search_phrases":76,"slug":79,"view_count":80,"doi":81,"paper":82,"created_at":99},5575,"Wetland vegetation dynamics and its linkage with climatic, hydrological, and land-use\u002F land cover changes in the Foket floodplain of the Fetam River Watershed, Northwestern Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01136-x","Timely monitoring of wetland changes is vital for effective conservation and management of the landscape. This study, therefore, examines vegetation dynamics and their climatic, hydrological, and land-use drivers in the Foket floodplain wetland, Upper Blue Nile basin, Ethiopia. The LULC analysis was employed based on 1985, 2002, and 2020 cloud-free Landsat images. The maximum likelihood classification algorithm was applied to classify the images into eight LULC classes. The spatial and temporal dynamics of the floodplain’s surface water were further analyzed using the Global Surface Water Explorer (1984–2020), focusing on water occurrence, seasonality, transitions, and changes in surface-water dynamics. The Normalized Difference Vegetation Index (NDVI) was used to assess the temporal dynamics of wetland vegetation, while annual rainfall, temperature, and evapotranspiration data were used to examine the climatic context of vegetation change. The results reveal that the area coverage of water body and wetland decreased by 63.2% and 29%, respectively, over the study period. In contrast, cultivated land, settlement, and eucalyptus plantation had expanded by 39.8%, 280.6%, and 380%, respectively, at the expense of wetland, woodland, and shrubland. Surface-water dynamics showed a substantial decline in seasonally inundated areas and changes in the spatial distribution and persistence of surface water. This indicates the reduction of the floodplain’s hydrological connectivity. The NDVI analysis also revealed a significant increasing of wetland vegetation over 1985–2020\u002F21. Vegetation growth was very fast during 1985–2000, slowed down between 2001 and 2012, and dropped slightly from 2013 to 2020\u002F21. Analysis of climatic variables showed no major change in annual rainfall or evapotranspiration, whereas temperature exhibited a significant upward trend. The overall findings revealed the association of the floodplain vegetation dynamics with land-use land cover, hydrological, and climatic changes. This indicates that land-use land cover, hydrological, and climatic changes have contributed to the degradation of the floodplain vegetation, highlighting the urgent need for integrated management interventions that regulate agricultural expansion, grazing, and water diversion while maintaining hydrological connectivity and wetland vegetation. Clinical trial number Not applicable (NA).","及时监测湿地变化对于景观的有效保护和管理至关重要。因此，本研究考察了埃塞俄比亚上青尼罗河流域福凯特洪泛平原湿地的植被动态及其气候、水文和土地利用驱动因素。基于1985年、2002年和2020年无云Landsat影像进行土地利用\u002F土地覆盖（LULC）分析，采用最大似然分类算法将影像分为八个LULC类别。进一步利用全球地表水探索器（1984—2020年）分析洪泛平原地表水的时空动态，重点关注水的出现频率、季节性、转换及地表水动态变化。采用归一化差异植被指数（NDVI）评估湿地植被的时间动态，同时利用年降水量、气温和蒸散发数据考察植被变化的气候背景。结果表明，研究期间水体和湿地的面积覆盖率分别下降了63.2%和29%。相比之下，耕地、居民点和桉树种植园分别扩张了39.8%、280.6%和380%，侵占了湿地、林地和灌丛。地表水动态显示季节性淹没区域大幅减少，地表水的空间分布和持续性发生变化，表明洪泛平原水文连通性降低。NDVI分析还揭示1985—2020\u002F21年间湿地植被显著增加。植被生长在1985—2000年间非常迅速，2001—2012年间放缓，2013—2020\u002F21年间略有下降。气候变量分析显示年降水量或蒸散发无重大变化，而气温呈显著上升趋势。总体研究结果揭示了洪泛平原植被动态与土地利用\u002F土地覆盖、水文和气候变化的关联。这表明土地利用\u002F土地覆盖、水文和气候变化共同导致了洪泛平原植被退化，凸显了迫切需要综合管理干预措施，以调控农业扩张、放牧和引水，同时维持水文连通性和湿地植被。临床试验编号：不适用（NA）。",null,"Discover Environment","2026-10-20T00:00:00Z","论文",10,false,65,{"impact":25,"substance":26,"depth":27,"authority":28,"freshness":29,"relevant":8,"comment":30},8,20,16,12,9,"基于Landsat与NDVI的湿地植被动态实证研究，数据扎实但属区域案例，国际参考价值有限，不宜进入每日精选。",[32],{"name":18,"url":14},[4,34,35,36,37],"埃塞俄比亚","土地利用变化","湿地遥感","水文连通性",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":46,"locked":22,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,59,66,71],{"code":39,"score":52,"supported":53,"title":54,"abstract":55,"tags":58},6,true,[],[56,57],"蒸散发","evapotranspiration",[],{"code":60,"score":61,"supported":53,"title":62,"abstract":63,"tags":65},"H7",3,[],[64],"气候变化",[],{"code":67,"score":8,"supported":22,"title":68,"abstract":69,"tags":70},"H6",[],[],[35],{"code":72,"score":8,"supported":22,"title":73,"abstract":74,"tags":75},"I3",[],[],[4],[77,78],"土地利用变化 埃塞俄比亚 水文连通性 湿地遥感","土地利用变化 埃塞俄比亚","土地利用变化埃塞俄比亚水文连通性湿地遥感-5575",0,"10.1007\u002Fs44274-026-01136-x",{"doi":81,"openalex_id":83,"authors":84,"venue":18,"cited_by_count":80,"oa_url":91,"card":92,"direction":96,"ingested_from":98},"W7223064056",[85,88],{"name":86,"orcid":87},"Workiyie Worie Assefa","https:\u002F\u002Forcid.org\u002F0000-0002-4029-9022",{"name":89,"orcid":90},"Belachew Getnet Eneyew","https:\u002F\u002Forcid.org\u002F0000-0003-1196-9004","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01136-x.pdf",{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"分析埃塞俄比亚Foket洪泛湿地植被动态及其气候、水文与土地利用驱动因素。","用Landsat影像做最大似然分类，结合全球地表水数据与NDVI及气候数据分析。","水体与湿地面积大幅减少，耕地、居民点和桉树种植扩张，温度显著上升。","农业遥感与作物表型","可结合多源遥感与水文连通性指标，量化湿地退化对农业灌溉与乡村生态服务的影响。","openalex","2026-10-22T11:20:42.476484Z",{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":17,"source_name":106,"source_url":103,"published_at":107,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":108,"score_detail":109,"sources":113,"tags":115,"primary_topic":17,"secondary_topics":120,"classification":121,"search_phrases":150,"slug":153,"view_count":80,"doi":154,"paper":155,"created_at":171},5481,"Comparison of Satellite-Based Drought Monitoring Indicators (NDVI, NDMI, and LST): The Case of the Northern Aegean Drainage Basin","https:\u002F\u002Fdoi.org\u002F10.30910\u002Fturkjans.2010067","Thanks to advancements in technology, satellite remote sensing provides a valuable means of monitoring the spatial and temporal responses of vegetation, surface moisture conditions, and land surface temperature to drought. Its capacity to monitor large areas with high temporal continuity significantly eliminates the spatial limitations of ground observations. This allows for the simultaneous and comparable analysis of key variables such as vegetation dynamics, soil moisture, and land surface temperature. Indicators such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST) provide complementary information on vegetation condition, surface moisture, and thermal stress, thereby supporting drought monitoring. This integrated monitoring framework supports the sustainable management of water resources while strengthening the scientific basis of decision-making processes, significantly contributing to disaster risk reduction. This study comparatively evaluates NDVI, NDMI, and LST values derived from Landsat 8 data obtained in March, April, and May between 2019 and 2022, and examines the relationships between these variables across the Northern Aegean Drainage Basin (NADB). The results show increasingly strong negative relationships between LST and the spectral indices from March to May, with the strongest correlations observed in May for NDMI–LST (r = −0.78) and NDVI–LST (r = −0.74). The NDMI-LST relationship can be said to be slightly better than the NDVI-LST relationship. The correlation coefficient between NDMI and NDVI was calculated to be lowest in March at r=0.54 and highest in May at r=0.90. It was determined that areas with high LST values had lower NDVI and NDMI values. Drought monitoring using remote sensing methods is important in terms of time and accuracy, but it is not sufficient on its own. In addition to problems arising from satellite imagery, land surfaces are affected by short-term climatic factors. Therefore, the indices obtained from satellite images must be evaluated together with meteorological and terrestrial data. However, it is thought that satellite-based drought monitoring will produce more reliable results under current technological and methodological conditions.","得益于技术进步，卫星遥感为监测植被、地表水分状况和地表温度对干旱的时空响应提供了重要手段。其大范围、高时间连续性的监测能力显著消除了地面观测的空间局限性，使得植被动态、土壤水分和地表温度等关键变量能够被同步、可比地分析。归一化差异植被指数（NDVI）、归一化差异水分指数（NDMI）和地表温度（LST）等指标提供了关于植被状况、地表水分和热胁迫的互补信息，从而支持干旱监测。这一综合监测框架既支持水资源的可持续管理，又增强了决策过程的科学基础，对减少灾害风险具有重要贡献。本研究对2019年至2022年3月、4月和5月获取的Landsat 8数据所衍生的NDVI、NDMI和LST值进行了比较评估，并考察了北爱琴海流域（NADB）内这些变量之间的关系。结果表明，从3月到5月，LST与光谱指数之间的负相关关系日益增强，5月观测到最强相关性，NDMI–LST（r = −0.78）和NDVI–LST（r = −0.74）。可以认为NDMI-LST关系略优于NDVI-LST关系。NDMI与NDVI之间的相关系数在3月最低（r=0.54），5月最高（r=0.90）。研究确定，LST值较高的区域其NDVI和NDMI值较低。利用遥感方法进行干旱监测在时间和精度方面具有重要意义，但仅靠其自身并不充分。除卫星影像带来的问题外，地表还受到短期气候因素的影响。因此，从卫星影像获得的指数必须与气象和地面数据结合评估。然而，可以认为在当前技术和方法条件下，基于卫星的干旱监测将产生更可靠的结果。","Türk Tarım ve Doğa Bilimleri Dergisi","2026-10-19T00:00:00Z",61,{"impact":25,"substance":110,"depth":111,"authority":28,"freshness":25,"relevant":8,"comment":112},18,15,"方法对比扎实、结论明确，但属区域案例研究，公共影响有限，适合作为遥感干旱监测主题的补充素材而非头条精选。",[114],{"name":106,"url":103},[4,116,117,118,119],"遥感监测","干旱监测","NDMI","LST",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":123},"needs_review",{"candidates":124,"agricultural_evidence":22,"reason":149},[125,131,137,144],{"code":60,"score":111,"supported":53,"title":126,"abstract":128,"tags":130},[127],"drought",[129,127],"干旱",[117],{"code":72,"score":25,"supported":53,"title":132,"abstract":133,"tags":136},[],[134,135],"遥感","remote sensing",[4,116],{"code":138,"score":52,"supported":53,"title":139,"abstract":140,"tags":143},"H2",[],[141,142],"土壤","soil",[],{"code":39,"score":61,"supported":53,"title":145,"abstract":146,"tags":148},[],[147],"水资源",[],"农业相关性需要复核",[151,152],"北爱琴海流域 干旱监测","干旱监测 遥感监测 NDMI NDVI","北爱琴海流域干旱监测-5481","10.30910\u002Fturkjans.2010067",{"doi":154,"openalex_id":156,"authors":157,"venue":106,"cited_by_count":80,"oa_url":164,"card":165,"direction":170,"ingested_from":98},"W7222954725",[158,161],{"name":159,"orcid":160},"Arman Monshizadeh Karimi","https:\u002F\u002Forcid.org\u002F0000-0002-2945-5346",{"name":162,"orcid":163},"İ̇smail Taş","https:\u002F\u002Forcid.org\u002F0000-0003-0872-2529","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F6296754",{"tldr":166,"method":167,"finding":168,"direction":96,"opportunity":169},"比较Landsat 8的NDVI、NDMI与LST三种遥感干旱指标在北爱琴海流域的相关性。","用2019-2022年3-5月Landsat 8数据计算NDVI、NDMI、LS","LST与NDMI、NDVI负相关随春季增强，5月最强（r=-0.78、-0.74），NDMI略优。","可融合气象与地面数据构建多源干旱指数，并验证NDMI-LST关系在其他气候区的普适性。","智慧农业 \u002F 农业物联网","2026-10-21T23:30:10.758669Z",{"id":173,"title":174,"url":175,"summary":176,"summary_zh":177,"content":17,"source_name":178,"source_url":175,"published_at":179,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":180,"score_detail":181,"sources":185,"tags":187,"primary_topic":17,"secondary_topics":190,"classification":191,"search_phrases":202,"slug":205,"view_count":8,"doi":206,"paper":207,"created_at":232},5246,"Spatial divergence of climate–vegetation relationships in Florida: A 43-year multi-method analysis (1982–2024)","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2515-7620\u002Faeb5af","Abstract Attributing long-term vegetation change to climate is difficult in subtropical ecosystems, where temperature and precipitation act simultaneously, are confounded by abrupt disturbances, and where statewide averages can conceal offsetting associations. Here we apply a multi-method convergence-of-evidence framework to a sensor-harmonized 43-year record (1982–2024) of NOAA CDR NDVI, temperature, and precipitation across Florida, which spans cool temperate forests to warm subtropical wetlands. Modified Mann–Kendall tests with false-discovery-rate correction detected monotonic trends; pixel-level correlation, spatial-error regression, and lagged cross-correlation evaluated association strength, direction, and timing; Empirical Orthogonal Function decomposition of deseasonalized anomalies separated persistent climatic signals from episodic disturbance, with the leading mode (58.4% of variance) tracking latitudinal warming and a lower-order mode (PC3) tracking hurricane impacts. Florida warmed by +0.033 °C yr⁻¹, significant in 99.6% of land pixels, while precipitation showed no field-significant trend. The temperature–greenness association was positive across essentially the entire state, marginally stronger in the south; localized negative responses were confined to specific southern wetland and urban-fringe pixels rather than forming a systematic north–south reversal. The state-mean precipitation–NDVI correlation was negative at zero lag and reversed from r = −0.263 to r = +0.102 when incoming shortwave radiation was partialed out, consistent with cloud-associated light suppression rather than water limitation—a phenomenon we term the Water Paradox. SPI\u002FSPEI–NDVI correlations were near zero, and Everglades water levels were largely decoupled from wetland greenness (r = 0.08, n.s.), indicating that inundation acts as a localized threshold modifier rather than a dominant suppressor. Climate–vegetation models for comparable high-rainfall subtropical systems may therefore need to account for spatially divergent temperature sensitivities and treat precipitation as an energy constraint rather than a uniform benefit.","摘要 在亚热带生态系统中，将长期植被变化归因于气候十分困难，因为温度和降水同时起作用，又受到突发干扰的混杂影响，而且全州尺度的平均值可能掩盖相互抵消的关联。本文采用多方法证据汇聚框架，对佛罗里达州1982—2024年共43年经传感器协调的NOAA CDR NDVI、温度和降水记录进行分析，该区域涵盖从冷温带森林到暖亚热带湿地。采用经错误发现率校正的改进Mann–Kendall检验检测单调趋势；通过像元级相关、空间误差回归和滞后互相关评估关联强度、方向和时间；对去季节化异常进行经验正交函数分解，以区分持续性气候信号与偶发干扰，其中主导模态（占总方差58.4%）追踪纬度变暖，低阶模态（PC3）追踪飓风影响。佛罗里达州以+0.033 °C yr⁻¹的速率变暖，在99.6%的陆地像元上显著，而降水未显示出区域显著趋势。温度—绿度关联在全州几乎均为正，南部略强；局地负响应仅限于特定的南部湿地和城市边缘像元，而非形成系统的南北反转。州平均降水—NDVI相关在零滞后时为负，当偏除入射短波辐射后由r = −0.263反转为r = +0.102，这与云相关的光抑制而非水分限制相一致——我们将这一现象称为“水悖论”。SPI\u002FSPEI—NDVI相关接近零，大沼泽地水位与湿地绿度基本解耦（r = 0.08，不显著），表明淹水作为局地阈值调节因子而非主导抑制因子起作用。因此，针对可比的高降雨亚热带系统的气候—植被模型可能需要考虑空间分异的温度敏感性，并将降水视为能量约束而非均一益处。","Environmental Research Communications","2026-10-16T00:00:00Z",70,{"impact":25,"substance":182,"depth":110,"authority":183,"freshness":29,"relevant":8,"comment":184},22,13,"基于43年NDVI遥感记录的多方法气候-植被关系研究，方法扎实、结论新颖，但属区域基础研究，对国内三农信息化仅有方法借鉴价值。",[186],{"name":178,"url":175},[64,134,4,188,189],"植被监测","佛罗里达",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":192},{"candidates":193,"agricultural_evidence":22,"reason":149},[194,198],{"code":72,"score":61,"supported":22,"title":195,"abstract":196,"tags":197},[],[],[4,188,134],{"code":60,"score":8,"supported":22,"title":199,"abstract":200,"tags":201},[],[],[64],[203,204],"Florida NDVI 气候变化","佛罗里达 植被 遥感","FloridaNDVI气候变化-5246","10.1088\u002F2515-7620\u002Faeb5af",{"doi":206,"openalex_id":208,"authors":209,"venue":178,"cited_by_count":80,"oa_url":226,"card":227,"direction":96,"ingested_from":98},"W7222444517",[210,213,216,219,222,224],{"name":211,"orcid":212},"A. Mandal","https:\u002F\u002Forcid.org\u002F0009-0004-0192-7497",{"name":214,"orcid":215},"Aman Kumar Thapa","https:\u002F\u002Forcid.org\u002F0009-0003-8956-6632",{"name":217,"orcid":218},"Madan Thapa Chhetri","https:\u002F\u002Forcid.org\u002F0009-0002-3567-7591",{"name":220,"orcid":221},"Hongbo Su","https:\u002F\u002Forcid.org\u002F0000-0002-2147-3646",{"name":223,"orcid":17},"Yan Yong",{"name":225,"orcid":17},"Fred Bloetscher","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2515-7620\u002Faeb5af\u002Fpdf",{"tldr":228,"method":229,"finding":230,"direction":96,"opportunity":231},"用多方法分析佛罗里达43年气候与植被关系，揭示空间分异。","NOAA CDR NDVI与气候数据，结合趋势检验、回归、EOF分解。","全州变暖显著，温度与绿度正相关；降水因云量抑制光照呈负相关。","高降雨亚热带区需将降水视为能量约束，并关注温度敏感性的空间分异。","2026-10-18T23:30:31.356484Z",{"id":234,"title":235,"url":236,"summary":237,"summary_zh":17,"content":17,"source_name":238,"source_url":236,"published_at":179,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":239,"score_detail":240,"sources":243,"tags":245,"primary_topic":17,"secondary_topics":249,"classification":250,"search_phrases":262,"slug":265,"view_count":80,"doi":266,"paper":267,"created_at":280},5241,"Assessing Nutritional Status in Super-High-Density Olive Orchards: Integrating Diagnostic Models and NDVI-Based Remote Sensing","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11156833\u002Fv1","Assessing Nutritional Status in Super-High-Density Olive Orchards: Integrating Diagnostic Models and NDVI-Based Remote Sensing。Research Square","Research Square",72,{"impact":27,"substance":110,"depth":241,"authority":28,"freshness":29,"relevant":8,"comment":242},17,"该研究将诊断模型与NDVI遥感结合用于超密植油橄榄园营养评估，方法有创新、对智慧果园管理有参考价值，但属预印本且应用面偏窄，可入精选但非头条。",[244],{"name":238,"url":236},[246,134,4,247,248],"智慧农业","果园管理","油橄榄",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":251},{"candidates":252,"agricultural_evidence":22,"reason":149},[253,257],{"code":72,"score":183,"supported":53,"title":254,"abstract":255,"tags":256},[135],[135],[4,134],{"code":258,"score":8,"supported":22,"title":259,"abstract":260,"tags":261},"I1",[],[],[246],[263,264],"超密植 油橄榄 营养诊断","NDVI 遥感 果园","超密植油橄榄营养诊断-5241","10.21203\u002Frs.3.rs-11156833\u002Fv1",{"doi":266,"openalex_id":268,"authors":269,"venue":238,"cited_by_count":80,"oa_url":279,"card":17,"direction":170,"ingested_from":98},"W7222710807",[270,273,276],{"name":271,"orcid":272},"Mahrokh Sharifmand","https:\u002F\u002Forcid.org\u002F0009-0006-8010-5348",{"name":274,"orcid":275},"Mehdi Taheri","https:\u002F\u002Forcid.org\u002F0000-0002-9228-763X",{"name":277,"orcid":278},"Ebrahim Sepehr","https:\u002F\u002Forcid.org\u002F0000-0001-5843-0669","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-11156833\u002Flatest.pdf","2026-10-18T23:30:11.167282Z",{"id":282,"title":283,"url":284,"summary":285,"summary_zh":286,"content":17,"source_name":287,"source_url":284,"published_at":288,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":289,"score_detail":290,"sources":292,"tags":294,"primary_topic":17,"secondary_topics":298,"classification":299,"search_phrases":312,"slug":315,"view_count":80,"doi":316,"paper":317,"created_at":328},5182,"Land Use Land Cover (LULC) Change Detection in Delhi NCT Using Multi-Sensor Satellite Data and Google Earth Engine","https:\u002F\u002Fdoi.org\u002F10.21275\u002Fsr26903183702","Land Use Land Cover (LULC) change detection is essential for understanding the dynamics of human modified landscapes and for supporting effective land management and urban planning. This study analyses LULC change across the Delhi National Capital Territory over a fifteen-year period, from 2010 to 2025, to assess urban sprawl and its environmental impact in one of India's fastest growing metropolitan regions. Multi temporal Landsat imagery was used as the primary data source, processed on Google Earth Engine, the cloud-based platform that enabled efficient handling and visualisation of large geospatial datasets. Five classes, built up area, agricultural land, vegetation, water, and barren land, were mapped and monitored, and classification accuracy was assessed for both dates. The Normalised Difference Vegetation Index was additionally employed to track vegetation vigour and ecological change. Results reveal a marked expansion of built-up areas alongside a substantial decline in agricultural and vegetated land, driven by peri urban growth along the northwestern and southwestern fringe. NDVI analysis shows rising greenness across the outer belt, reflecting more intensive cultivation on land that remains under crops, a pattern that complements rather than contradicts the measured loss of vegetated area. Combining classification based and an index-based approach provides a robust, scalable framework for LULC monitoring. By revealing where and how urban growth is reshaping the landscape, the study offers a transferable basis for environmental assessment, resource management, and sustainable urban planning in rapidly urbanising regions worldwide.","土地利用\u002F土地覆盖（LULC）变化检测对于理解人类改造景观的动态以及支撑有效的土地管理和城市规划至关重要。本研究分析了德里国家首都辖区在2010年至2025年十五年间的土地利用\u002F土地覆盖变化，以评估印度增长最快的大都市区之一的城市蔓延及其环境影响。研究以多时相Landsat影像作为主要数据源，在Google Earth Engine这一基于云的平台上进行处理，该平台能够高效处理和可视化大型地理空间数据集。研究对建成区、农业用地、植被、水体与裸地五个类别进行了制图和监测，并对两个时相的分类精度进行了评估。此外，研究采用归一化植被指数（NDVI）追踪植被长势与生态变化。结果表明，建成区显著扩张，农业用地和植被用地大幅减少，这一变化主要由西北部和西南部边缘的近郊增长所驱动。NDVI分析显示外围地带的绿度上升，反映出仍处于耕作状态的土地上集约化种植程度提高，这一格局与所测得的植被面积损失相辅相成而非相互矛盾。将基于分类的方法与基于指数的方法相结合，为土地利用\u002F土地覆盖监测提供了一个稳健且可扩展的框架。通过揭示城市增长在何处以及如何重塑景观，本研究为全球快速城市化地区的环境评估、资源管理和可持续城市规划提供了可迁移的基础。","International Journal of Science and Research (IJSR)","2026-10-15T00:00:00Z",62,{"impact":28,"substance":110,"depth":111,"authority":25,"freshness":29,"relevant":8,"comment":291},"方法可迁移的遥感LULC变化检测研究，对快速城市化地区土地管理有参考价值，但属区域案例、非三农核心议题。",[293],{"name":287,"url":284},[4,116,295,296,297],"土地利用","Google Earth Engine","城市化",[],{"status":300,"locked":22,"version":47,"method":48,"evidence":301},"unclassified",{"candidates":302,"agricultural_evidence":53,"reason":311},[303,307],{"code":72,"score":61,"supported":22,"title":304,"abstract":305,"tags":306},[],[],[296,4,116],{"code":67,"score":8,"supported":22,"title":308,"abstract":309,"tags":310},[],[],[295],"缺少足够的主题证据",[313,314],"德里 土地利用 遥感","Google Earth Engine 城市扩张","德里土地利用遥感-5182","10.21275\u002Fsr26903183702",{"doi":316,"openalex_id":318,"authors":319,"venue":287,"cited_by_count":80,"oa_url":322,"card":323,"direction":96,"ingested_from":98},"W7222341300",[320],{"name":321,"orcid":17},"Neil Rohatgi","https:\u002F\u002Fwww.ijsr.net\u002Farchive\u002Fv15i10\u002FSR26903183702.pdf",{"tldr":324,"method":325,"finding":326,"direction":96,"opportunity":327},"基于多时相Landsat与GEE分析德里国家首都辖区2010-2025年土地利用变化。","多时相Landsat影像、Google Earth Engine平台、五类分类与","建设用地显著扩张，农业与植被用地大幅减少，城郊西北和西南边缘增长驱动。","可结合分类与指数方法构建可迁移框架，拓展至城乡交错带耕地保护与可持续规划研究。","2026-10-17T23:30:31.767409Z",{"id":330,"title":331,"url":332,"summary":333,"summary_zh":334,"content":17,"source_name":335,"source_url":332,"published_at":288,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":336,"score_detail":337,"sources":340,"tags":342,"primary_topic":17,"secondary_topics":345,"classification":346,"search_phrases":362,"slug":365,"view_count":8,"doi":366,"paper":367,"created_at":393},5177,"Long-term associations between land-surface indicators and dust-storm occurrence along the margins of the Taklimakan Desert","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75251-3","Land-surface change and atmospheric variability jointly shape dust activity in arid regions. We examined land-cover transitions, station dust-storm records, a Sentinel-1 backscatter index, meteorological variables from the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) and dust diagnostics from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) along the margins of the Taklimakan Desert. Within the mapped domain, grassland increased from 9.45% in 1990 to 10.59% in 2023, while barren land decreased from 86.45% to 82.30%. Annual dust-storm-day trends were negative at 21 of 30 stations over available 1990–2024 records, and Sentinel-1 index trends were positive at 22 stations during 2015–2024. Mixed-effects path analysis of 3,804 station-month observations showed that the Sentinel-1 index was negatively associated with modelled dust emission (standardized coefficient = − 0.191; 95% confidence interval: −0.289 to − 0.043). The normalized difference vegetation index (NDVI) and friction velocity had the highest training-set feature importance in the random forest analysis.","地表变化与大气变率共同塑造了干旱地区的沙尘活动。我们沿塔克拉玛干沙漠边缘，研究了土地覆盖转变、台站沙尘暴记录、Sentinel-1后向散射指数、来自欧洲中期天气预报中心第五代再分析资料（ERA5）的气象变量，以及来自现代研究与应用回顾分析第2版（MERRA-2）的沙尘诊断量。在制图范围内，草地面积从1990年的9.45%增加到2023年的10.59%，而裸地面积从86.45%减少到82.30%。在1990—2024年现有记录中，30个台站中有21个台站的年沙尘暴日数呈下降趋势；2015—2024年间，22个台站的Sentinel-1指数呈上升趋势。对3804个台站—月观测值的混合效应路径分析表明，Sentinel-1指数与模拟沙尘排放呈负相关（标准化系数=−0.191；95%置信区间：−0.289至−0.043）。在随机森林分析中，归一化植被指数（NDVI）和摩擦速度在训练集中的特征重要性最高。","Scientific Reports",78,{"impact":27,"substance":182,"depth":110,"authority":338,"freshness":25,"relevant":8,"comment":339},14,"基于多源遥感与30站长期记录，量化地表指标与沙尘暴关联，方法扎实、数据规模可观，对干旱区生态监测有参考价值。",[341],{"name":335,"url":332},[4,343,116,295,344],"荒漠化","沙尘暴",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":347},{"candidates":348,"agricultural_evidence":22,"reason":149},[349,353,358],{"code":60,"score":61,"supported":53,"title":350,"abstract":351,"tags":352},[],[129],[],{"code":67,"score":354,"supported":22,"title":355,"abstract":356,"tags":357},2,[],[],[295,343],{"code":72,"score":354,"supported":22,"title":359,"abstract":360,"tags":361},[],[],[4,116],[363,364],"塔克拉玛干沙漠 沙尘暴 遥感","Sentinel-1 土地覆盖 沙尘","塔克拉玛干沙漠沙尘暴遥感-5177","10.1038\u002Fs41598-026-75251-3",{"doi":366,"openalex_id":368,"authors":369,"venue":335,"cited_by_count":80,"oa_url":387,"card":388,"direction":96,"ingested_from":98},"W7222086957",[370,372,375,378,380,382,384],{"name":371,"orcid":17},"Yifei Wang",{"name":373,"orcid":374},"Wen Huo","https:\u002F\u002Forcid.org\u002F0000-0002-7861-2424",{"name":376,"orcid":377},"Mayibaier Maihamuti","https:\u002F\u002Forcid.org\u002F0009-0007-4084-5891",{"name":379,"orcid":17},"Fan Yang",{"name":381,"orcid":17},"Chenglong Zhou",{"name":383,"orcid":17},"Xinghua Yang",{"name":385,"orcid":386},"Ali Mamtimin","https:\u002F\u002Forcid.org\u002F0000-0001-7335-801X","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75251-3_reference.pdf",{"tldr":389,"method":390,"finding":391,"direction":96,"opportunity":392},"分析塔克拉玛干沙漠边缘地表指标与沙尘暴发生的长期关联。","结合土地覆盖、站点沙尘记录、Sentinel-1后向散射、ERA5与MERRA-","站点沙尘日数多呈下降趋势，Sentinel-1指数与模拟沙尘排放显著负相关。","可引入多源遥感与机器学习量化地表变化对沙尘的因果贡献，并拓展至绿洲农业防护。","2026-10-17T23:30:30.235613Z",{"id":395,"title":396,"url":397,"summary":398,"summary_zh":399,"content":17,"source_name":400,"source_url":397,"published_at":288,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":7,"score_detail":401,"sources":403,"tags":405,"primary_topic":17,"secondary_topics":407,"classification":408,"search_phrases":419,"slug":422,"view_count":80,"doi":423,"paper":424,"created_at":435},5173,"Land-use patterns and plant community structure in the peri-urban environs of Lokoja, Kogi state, Nigeria: an integrated field and NDVI assessment, 2015 to 2025","https:\u002F\u002Fdoi.org\u002F10.61955\u002Fbplheb","Rapid urbanisation is transforming natural vegetation on the fringes of medium sized Nigerian cities, yet the flora of these zones is rarely documented. This study assessed plant community structure in relation to land use in the peri-urban fringe of Lokoja, Kogi State, by combining field surveys at three sites, namely Zango, Ganaja and Banda, with a dry season Landsat 8 NDVI record spanning 2015 to 2025. Along one transect of 2 km at each site, trees and shrubs were counted in twenty plots o","快速城市化正在改变尼日利亚中等城市边缘的自然植被，但这些区域的植物区系却鲜有记录。本研究通过将赞戈（Zango）、加纳贾（Ganaja）和班达（Banda）三个样地的野外调查与2015年至2025年旱季Landsat 8 NDVI记录相结合，评估了科吉州洛科贾城郊边缘植物群落结构与土地利用的关系。在每个样地沿一条2 km的样带，于二十个样方中对乔木和灌木进行了计数，","Journal of Interdisciplinary Postgraduate Research",{"impact":52,"substance":110,"depth":338,"authority":52,"freshness":25,"relevant":8,"comment":402},"该研究结合实地样方与十年NDVI遥感数据，方法有一定参考价值，但属尼日利亚地方性案例，对国内三农与农业信息化的公共价值有限，不宜进入每日精选。",[404],{"name":400,"url":397},[134,4,295,188,406],"城郊农业",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":409},{"candidates":410,"agricultural_evidence":22,"reason":149},[411,415],{"code":72,"score":61,"supported":22,"title":412,"abstract":413,"tags":414},[],[],[4,188,134],{"code":67,"score":8,"supported":22,"title":416,"abstract":417,"tags":418},[],[],[295],[420,421],"Lokoja 城郊 NDVI 植被","尼日利亚 土地利用 植物群落","Lokoja城郊NDVI植被-5173","10.61955\u002Fbplheb",{"doi":423,"openalex_id":425,"authors":426,"venue":400,"cited_by_count":80,"oa_url":429,"card":430,"direction":96,"ingested_from":98},"W7221946991",[427],{"name":428,"orcid":17},"Baba Baba","https:\u002F\u002Fboldscholar.com\u002Fimages\u002FJournal\u002FArticles\u002FI4_MS4_Baba_PeriUrban_Vegetation_NDVI_JIPR.pdf",{"tldr":431,"method":432,"finding":433,"direction":96,"opportunity":434},"结合野外样方与2015-2025年Landsat 8 NDVI，评估尼日利亚洛科贾城郊土地利用与植物","三站点各设2km样带20个样方调查乔灌木，结合旱季Landsat 8 NDVI时","城郊快速城市化改变自然植被，但该区域植物区系长期缺乏记录。","可延伸至城郊植被NDVI时序与地面样方耦合的长期监测，填补非洲中小城市城郊生态数据空白。","2026-10-17T23:30:29.810692Z",{"id":437,"title":438,"url":439,"summary":440,"summary_zh":441,"content":17,"source_name":442,"source_url":439,"published_at":443,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":444,"score_detail":445,"sources":447,"tags":449,"primary_topic":17,"secondary_topics":452,"classification":453,"search_phrases":468,"slug":471,"view_count":8,"doi":472,"paper":473,"created_at":497},5012,"Evaluating Multi-Index Remote Sensing Frameworks for Drought Monitoring in Seasonally Dry Dipterocarp Deciduous Forests: A Case Study of Yok Don National Park, Vietnam","https:\u002F\u002Fdoi.org\u002F10.5755\u002Fj01.erem.82.3.44241","This study examined the effectiveness of the normalized difference vegetation index (NDVI), land surface temperature (LST), and their integrated indices in capturing dry-season drought dynamics in the dipterocarp deciduous forest ecosystems of Yok Don National Park from 2014 to 2024. Results indicate that individual remote-sensing indices captured drought processes but reflected different dimensions of drought conditions, with NDVI indicating canopy greenness and LST detecting early thermal stress. Integrated indices partially mitigated the limitations of single indicators during confirmed drought years by showing stronger correlations with reference data. The effectiveness of the integrated indices varied significantly depending on the integration framework. The spatial-normalization–based integration of NDVI and LST showed consistently strong correlations with surface evapotranspiration (ET), with correlation coefficients of approximately 0.8 across years, and was less sensitive to land-cover heterogeneity. In contrast, the triangular NDVI–LST space integration exhibited variable relationships with ET due to its sensitivity to edge definition and phenological shifts in dipterocarp deciduous forests. Phase-dependent behavior was observed, with integrated indices maintaining more stable relationships with ET during drying periods, while NDVI showed greater sensitivity under wetter conditions. The results also indicate that VHI demonstrated a more robust and stable performance than TVDI, suggesting its suitability as a primary indicator in heterogeneous forest environments. These findings provide practical guidance for drought monitoring in heterogeneous forest landscapes by identifying adaptive integration strategies tailored to different hydroclimatic conditions.","本研究考察了归一化差异植被指数（NDVI）、地表温度（LST）及其综合指数在捕捉2014至2024年 Yok Don 国家公园龙脑香落叶林生态系统旱季干旱动态方面的有效性。结果表明，单一遥感指数能够捕捉干旱过程，但反映的是干旱状况的不同维度，其中NDVI指示冠层绿度，LST探测早期热胁迫。综合指数在已确认的干旱年份中通过与参考数据表现出更强的相关性，部分缓解了单一指标的局限性。综合指数的有效性因集成框架的不同而差异显著。基于空间标准化的NDVI与LST集成与地表蒸散发（ET）表现出持续较强的相关性，各年份相关系数约为0.8，且对土地覆盖异质性较不敏感。相比之下，三角NDVI–LST空间集成与ET的关系多变，这是由于其边缘定义及龙脑香落叶林物候变化较为敏感。研究观察到相位依赖行为，综合指数在干旱期与ET保持更稳定的关系，而NDVI在较湿润条件下表现出更高的敏感性。结果还表明，VHI比TVDI表现出更稳健和稳定的性能，表明其适合作为异质森林环境中的主要指标。这些发现通过识别适应不同水文气候条件的自适应集成策略，为异质森林景观的干旱监测提供了实践指导。","Environmental Research Engineering and Management","2026-10-13T00:00:00Z",67,{"impact":25,"substance":26,"depth":241,"authority":183,"freshness":29,"relevant":8,"comment":446},"越南干旱区遥感监测方法学研究，方法新颖数据扎实，对农业遥感干旱监测有参考价值，但属区域性案例，公共影响有限。",[448],{"name":442,"url":439},[450,4,116,117,451],"农业遥感","森林生态",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":454},{"candidates":455,"agricultural_evidence":22,"reason":149},[456,460,464],{"code":60,"score":111,"supported":53,"title":457,"abstract":458,"tags":459},[127],[129,127],[117],{"code":72,"score":338,"supported":53,"title":461,"abstract":462,"tags":463},[135],[134],[4,450,116],{"code":39,"score":52,"supported":53,"title":465,"abstract":466,"tags":467},[],[56,57],[],[469,470],"Yok Don 国家公园 遥感 干旱","NDVI LST 干旱监测 森林","YokDon国家公园遥感干旱-5012","10.5755\u002Fj01.erem.82.3.44241",{"doi":472,"openalex_id":474,"authors":475,"venue":442,"cited_by_count":80,"oa_url":491,"card":492,"direction":96,"ingested_from":98},"W7221717828",[476,479,482,484,486,488],{"name":477,"orcid":478},"Mai Son Le","https:\u002F\u002Forcid.org\u002F0000-0002-5360-7119",{"name":480,"orcid":481},"Ha Thu Nguyen","https:\u002F\u002Forcid.org\u002F0000-0002-4490-308X",{"name":483,"orcid":17},"Hung Manh Nguyen",{"name":485,"orcid":17},"Huy Xuan Chu",{"name":487,"orcid":17},"Hoa Thanh Nguyen",{"name":489,"orcid":490},"Duy-Toan Dao","https:\u002F\u002Forcid.org\u002F0009-0003-6057-4436","https:\u002F\u002Ferem.ktu.lt\u002Findex.php\u002Ferem\u002Farticle\u002Fdownload\u002F44241\u002F23724",{"tldr":493,"method":494,"finding":495,"direction":96,"opportunity":496},"评估NDVI、LST及其集成指数在越南干旱季风落叶林干旱监测中的效果。","利用2014-2024年NDVI、LST遥感数据，构建空间归一化和三角空间集成指","空间归一化集成指数与ET相关性稳定（约0.8），VHI比TVDI更稳健，集成效果依赖框架。","可探索自适应集成框架在不同水热条件和异质景观下的干旱监测普适性。","2026-10-15T23:30:32.336137Z",{"id":499,"title":500,"url":501,"summary":502,"summary_zh":503,"content":17,"source_name":504,"source_url":501,"published_at":505,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":506,"score_detail":507,"sources":509,"tags":511,"primary_topic":516,"secondary_topics":520,"classification":521,"search_phrases":547,"slug":550,"view_count":8,"doi":551,"paper":552,"created_at":579},5004,"Welfare effects of behavioral responses to basis risk in remote-sensing livestock insurance in northern Kenya","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrevc.2026.1750935","Introduction Livestock production in northern Kenya's arid and semi-arid rangelands is increasingly vulnerable to climate variability. The Index-Based Livestock Insurance (IBLI) program was introduced to mitigate drought-related losses by linking payouts to satellitederived Normalized Difference Vegetation Index (NDVI) thresholds. However, IBLI's efficacy may be hampered by basis risk -the discrepancy between actual losses and insurance payouts. This study examines how downside basis risk (losses without indemnity) is associated with pastoralist welfare and household consumption responses. Methods Using data from the IBLI program (2019-2021) across eight Kenyan counties (Garissa, Isiolo, Mandera, Marsabit, Samburu, Tana River, Turkana, and Wajir), this study employs a two-stage empirical approach. First, quantile regression assesses the relationship between precipitation and forage availability. Second, a modified variance-of-error model quantifies the magnitude of basis risk. Results The analysis reveals persistent downside basis risk across all counties and years, suggesting that households frequently incur losses without receiving insurance compensation. The findings demonstrate that repeated exposure to downside basis risk is associated with changes in pastoralists' consumption, which may ultimately lead to welfare losses. Discussion While IBLI is designed to stabilize consumption, persistent downside basis risk undermines its welfare benefits and may erode trust in the insurance mechanism. To strengthen IBLI's potential to deliver sustainable welfare outcomes for pastoralist communities, future policy interventions must enhance index precision, revise subsidy structures, and incorporate behavioral considerations into program design.","引言 肯尼亚北部干旱和半干旱牧区的畜牧业生产日益受到气候变率的威胁。基于指数的牲畜保险（Index-Based Livestock Insurance, IBLI）项目通过将赔付与卫星反演的归一化植被指数（Normalized Difference Vegetation Index, NDVI）阈值挂钩，旨在缓解干旱相关损失。然而，IBLI的有效性可能受到基差风险（basis risk）的制约，即实际损失与保险赔付之间的偏差。本研究探讨下行基差风险（即遭受损失却未获赔付）与牧民福利及家庭消费反应之间的关联。方法 本研究利用IBLI项目（2019—2021年）在肯尼亚八个县（加里萨、伊西奥洛、曼德拉、马萨比特、桑布鲁、塔纳河、图尔卡纳和瓦吉尔）的数据，采用两阶段实证方法。首先，运用分位数回归评估降水量与饲草可得性之间的关系。其次，采用修正的误差方差模型量化基差风险的幅度。结果 分析显示，所有县和所有年份均存在持续的下行基差风险，表明家庭经常遭受损失却未获得保险赔偿。研究结果表明，反复暴露于下行基差风险与牧民消费变化相关，最终可能导致福利损失。讨论 尽管IBLI旨在稳定消费，但持续的下行基差风险削弱了其福利效益，并可能侵蚀对保险机制的信任。为增强IBLI为牧民社区带来可持续福利成果的潜力，未来的政策干预必须提高指数精度、修订补贴结构，并将行为因素纳入项目设计。","Frontiers in Environmental Economics","2026-10-14T00:00:00Z",77,{"impact":27,"substance":182,"depth":110,"authority":183,"freshness":25,"relevant":8,"comment":508},"基于肯尼亚八县2019-2021年数据的实证研究，揭示遥感指数保险基差风险对牧民福利的侵蚀，方法扎实、结论有政策参考价值，但属境外区域研究，国内落地关联有限。",[510],{"name":504,"url":501},[4,512,513,514,515],"指数保险","气候风险","遥感保险","肯尼亚牧区",{"code":60,"slug":517,"name":518,"path":519,"parent_code":43},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[],{"status":46,"locked":22,"version":47,"method":48,"evidence":522},{"candidates":523,"agricultural_evidence":53},[524,531,536,543],{"code":525,"score":338,"supported":53,"title":526,"abstract":528,"tags":530},"C",[527],"livestock",[529,527],"畜牧",[],{"code":60,"score":532,"supported":53,"title":533,"abstract":534,"tags":535},7,[],[129,127],[513],{"code":537,"score":52,"supported":53,"title":538,"abstract":539,"tags":542},"C4",[],[540,541],"饲草","forage",[],{"code":72,"score":8,"supported":22,"title":544,"abstract":545,"tags":546},[],[],[4],[548,549],"肯尼亚 指数保险 NDVI","遥感 牲畜保险 基差风险","肯尼亚指数保险NDVI-5004","10.3389\u002Ffrevc.2026.1750935",{"doi":551,"openalex_id":553,"authors":554,"venue":504,"cited_by_count":80,"oa_url":573,"card":574,"direction":96,"ingested_from":98},"W7221780299",[555,558,561,564,567,570],{"name":556,"orcid":557},"Simon Maina","https:\u002F\u002Forcid.org\u002F0009-0008-6340-3890",{"name":559,"orcid":560},"Maryfrances Miller","https:\u002F\u002Forcid.org\u002F0000-0002-5405-3592",{"name":562,"orcid":563},"Gregory L. Torell","https:\u002F\u002Forcid.org\u002F0000-0002-3561-3291",{"name":565,"orcid":566},"Niall P. Hanan","https:\u002F\u002Forcid.org\u002F0000-0002-9130-5306",{"name":568,"orcid":569},"Julius Y. Anchang","https:\u002F\u002Forcid.org\u002F0000-0002-7500-1708",{"name":571,"orcid":572},"Njoki Kahiu","https:\u002F\u002Forcid.org\u002F0000-0002-7416-947X","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fenvironmental-economics\u002Farticles\u002F10.3389\u002Ffrevc.2026.1750935\u002Fpdf",{"tldr":575,"method":576,"finding":577,"direction":96,"opportunity":578},"研究肯尼亚北部遥感指数保险的基差风险如何影响牧民福利与消费。","用2019-2021年八县数据，分位数回归与误差方差模型量化基差风险。","各县各年持续存在下行基差风险，导致牧民消费变化与福利损失，削弱保险信任。","可探索融合多源遥感与行为数据改进指数精度，并设计含行为因素的保险机制。","2026-10-15T23:30:29.488616Z",{"id":581,"title":582,"url":583,"summary":584,"summary_zh":585,"content":17,"source_name":586,"source_url":583,"published_at":587,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":588,"score_detail":589,"sources":593,"tags":595,"primary_topic":17,"secondary_topics":600,"classification":601,"search_phrases":616,"slug":619,"view_count":8,"doi":620,"paper":621,"created_at":644},4828,"Reconstructed Evapotranspiration in Northern China: Trends, Drivers, and Ecohydrological Impacts","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fjoc.70627","ABSTRACT Attributing regional evapotranspiration (ET) and assessing its impacts are critical for understanding land‐atmosphere interactions in climate‐sensitive regions such as northern China. This study fuses six gridded ET products with in situ measurements using Random Forest after excluding poorly representative flux sites. The resulting monthly 0.25° dataset (1980 to 2021) outperforms all original products, reducing the cross‐validation RMSE from 15.15–22.03 mm month −1 to 11.54 mm month −1 . Based on reconstructed ET, significant increasing ET trends ( p \u003C 0.05) are observed on the Loess Plateau and North China Plain, with a mean trend of 13.3 mm (10a) −1 in humid regions. Using XGBoost with SHAP analysis reveals that precipitation dominates ET interannual variability in grasslands and bare lands, accounting for 35.4% to 40.6%. Yet, as a driver of the long‐term ET trend, precipitation is dominant only in about 5% of the region, specifically over bare land in western Inner Mongolia. Instead, NDVI drives ET trends over 52% of northern China, making vegetation greening a primary force enhancing ET under warming. Enhanced ET offsets precipitation‐driven water yield gains most markedly in semi‐arid regions. There, the ET‐induced reduction reaches −11.4 ± 7.7 mm (10a) −1 , exceeding the positive contribution from precipitation (2.3 ± 9.2 mm (10a) −1 ). Meanwhile, ET positively contributes to the Evaporative Stress Index (ET\u002FPET) over semi‐arid regions, generating a coexistence of ecological wetting but hydrological drying. This study provides ecological insights into ET changes and their contrasting hydrological impacts in climate‐sensitive regions.","摘要 归因区域蒸散发（ET）并评估其影响，对于理解中国北方等气候敏感地区的陆气相互作用至关重要。本研究在剔除代表性较差的通量站点后，利用随机森林将六种网格化ET产品与站点实测数据进行融合。所得1980—2021年月尺度0.25°数据集优于所有原始产品，将交叉验证的RMSE从15.15—22.03 mm month⁻¹降至11.54 mm month⁻¹。基于重建的ET，在黄土高原和华北平原观测到显著的ET增加趋势（p \u003C 0.05），湿润地区的平均趋势为13.3 mm (10a)⁻¹。利用XGBoost结合SHAP分析发现，降水主导了草地和裸地ET的年际变率，贡献率为35.4%至40.6%。然而，作为长期ET趋势的驱动因子，降水仅在大约5%的区域占主导，具体为内蒙古西部的裸地。相反，NDVI驱动了中国北方52%以上区域的ET趋势，使植被绿化成为增温背景下增强ET的主要力量。ET增强对降水驱动的水量增加的正向抵消在半干旱地区最为显著。在那里，ET引起的减少量达到−11.4 ± 7.7 mm (10a)⁻¹，超过了降水的正贡献（2.3 ± 9.2 mm (10a)⁻¹）。同时，ET对半干旱地区的蒸发胁迫指数（ET\u002FPET）有正贡献，导致生态变湿与水文变干并存。本研究为气候敏感地区ET变化及其截然不同的水文影响提供了生态学见解。","International Journal of Climatology","2026-10-11T00:00:00Z",81,{"impact":110,"substance":590,"depth":591,"authority":338,"freshness":532,"relevant":8,"comment":592},23,19,"融合六套蒸散发产品重建华北0.25°长序列数据集，揭示植被绿化主导蒸散发上升及半干旱区生态变湿、水文变干的分异，对区域农业水资源管理有参考价值。",[594],{"name":586,"url":583},[596,4,597,598,599],"农业水资源","黄河流域","华北平原","遥感蒸散发",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":602},{"candidates":603,"agricultural_evidence":22,"reason":149},[604,608,612],{"code":39,"score":111,"supported":53,"title":605,"abstract":606,"tags":607},[57],[56,57],[596],{"code":60,"score":61,"supported":53,"title":609,"abstract":610,"tags":611},[],[129],[],{"code":72,"score":8,"supported":22,"title":613,"abstract":614,"tags":615},[],[],[4],[617,618],"黄土高原 蒸散发 NDVI","华北平原 蒸散发 数据集","黄土高原蒸散发NDVI-4828","10.1002\u002Fjoc.70627",{"doi":620,"openalex_id":622,"authors":623,"venue":586,"cited_by_count":80,"oa_url":638,"card":639,"direction":96,"ingested_from":98},"W7221437954",[624,627,629,632,635],{"name":625,"orcid":626},"Hongyu Li","https:\u002F\u002Forcid.org\u002F0000-0001-6807-1874",{"name":628,"orcid":17},"Yubo Su",{"name":630,"orcid":631},"Qiang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-9067-4070",{"name":633,"orcid":634},"Ping Yue","https:\u002F\u002Forcid.org\u002F0000-0001-8736-949X",{"name":636,"orcid":637},"Liang Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-4013-3273","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fjoc.70627",{"tldr":640,"method":641,"finding":642,"direction":96,"opportunity":643},"融合六套蒸散发产品重建中国北方1980-2021年ET数据集，并归因趋势与生态水文影响。","随机森林融合六套ET产品与站点观测，XGBoost结合SHAP归因。","NDVI主导52%区域ET趋势，半干旱区ET增强抵消降水产水增益，生态变湿但水文变干。","可探究植被绿化与水资源约束的权衡，发展兼顾生态与水文安全的灌溉调控策略。","2026-10-13T23:30:25.195900Z",{"id":646,"title":647,"url":648,"summary":649,"summary_zh":650,"content":17,"source_name":651,"source_url":648,"published_at":587,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":289,"score_detail":652,"sources":654,"tags":656,"primary_topic":17,"secondary_topics":658,"classification":659,"search_phrases":672,"slug":675,"view_count":8,"doi":676,"paper":677,"created_at":688},4827,"تحليل العلاقة بين الجفاف الموسمي وتناقص الغطاء النباتي واثره في تكرار الظواهر الغبارية في محافظة المثنى للمدة (1980-2025)","https:\u002F\u002Fdoi.org\u002F10.66026\u002Fksfnen31","تعتبر الظواهر الغبارية من الظواهر المناخية التي لها العديد من التأثيرات السلبية على البيئة والاقتصاد والصحة. والغرض من هذه الدراسة التحقق من العلاقة بين تكرار العواصف الرملية والغبار المتطاير والغبار العالق والعوامل المناخية خاصة في المواسم الجافة والرطبة ، وتحليل ودراسة إمكانية حدوث الظواهر الغبارية في منطقة الدراسة وتأثير العوامل المناخية على تكرار حدوثها. اتبعت الدراسة المنهج الوصفي التحليلي، وذلك من خلال الاعتماد على بيانات محطة السماوة المناخية خلال مدة الدراسة من ( 1980- 2025 ) والمحطات الضابطة النجف والديوانية وايضا على بيانات الاستشعار عن بعد الخاصة بالغطاء النباتي الـ NDVI . حيث بينت نتائج الدراسة ان الظروف المناخية تؤثر على التوزيع المكاني للظواهر الغبارية وتكرارها، كما واستخدمت الدراسة معاملات الارتباط لمعالجة البيانات وبيان اتجاه وقوة العلاقة بين الظاهرة المدروسة وأيهما اكثر تأثير من خلال المتغيرات المستقلة والعوامل المناخية المؤثرة فيها. وتوصلت الدراسة إلى ان مصفوفة الارتباط ذات علاقة موجبة قوية بين سرعة الرياح و العاصفة الغبارية أكثر من ارتباطها بأي متغير آخر إذ بلغ معامل الارتباط ( r= 0.79 )، كما ارتبطت إيجابياً بالغبار المتصاعد (r=0.72) والغبار العالق( (r=0.61اما العلاقة الارتباطية بين الرطوبة النسبية والامطار علاقة عك تمثلت منطقة الدراسة بمحافظة المثنى التي تقع في الجزء الجنوبي من العراق يحدها من الشمال محافظة القادسية ومن الشرق محافظتي البصرة وذي قار ومن الغرب محافظة النجف ومن الجنوب المملكة العربية السعودية ، تقع فلكياً بين دائرتي عرض(¢5 29°) ( ¢42 31°) شمالاً وخطي طول (¢50- 43°) و (¢32 -46°)[i] شرقاً كما في الخريطة (1) ، في حين تمثلت المدة الزمنية للظواهر الغبارية من (1980-2025) بالاعتماد على البيانات المناخية لمحطة السماوة و محطة النجف والديوانية كمحطات ضابطة . سية قوية . [i] - اشواق حسن حميد ، الاتجاه الحديث في عناصر المناخ وظواهره لمحافظة المثنى واثره في الجفاف للمدة ( 1987- 2020) ، مجلة الجامعة العراقية ، المجلد 46 ، العدد 2 ، 2023 ، ص 313.","沙尘现象是一种气候现象，对环境、经济和健康具有诸多负面影响。本研究旨在探讨沙尘暴、扬尘和悬浮尘埃发生频率与气候因素之间的关系，尤其是在干旱季和湿润季，并分析和研究研究区域内沙尘现象发生的可能性以及气候因素对其发生频率的影响。本研究采用描述性分析方法，依据萨马沃气候站在研究期（1980—2025年）的数据以及纳杰夫和迪瓦尼耶对照站的数据，同时依据植被归一化植被指数（NDVI）的遥感数据。研究结果表明，气候条件影响沙尘现象的空间分布及其发生频率。本研究还使用相关系数处理数据，说明所研究现象与各独立变量及影响气候因素之间关系的方向和强度，并判断哪个因素影响更大。研究发现，相关矩阵显示风速与沙尘暴之间存在强正相关关系，其相关性高于与其他任何变量的相关性，相关系数达到（r=0.79）；风速与扬尘（r=0.72）和悬浮尘埃（r=0.61）也呈正相关。至于相对湿度与降雨之间的相关关系则呈强负相关。研究区域为穆萨纳省，位于伊拉克南部，北邻卡迪西亚省，东接巴士拉省和济加尔省，西连纳杰夫省，南与沙特阿拉伯王国接壤。该省在天文位置上位于北纬（29°5′）至（31°42′）之间，东经（43°50′）至（46°32′）之间，如图1所示。沙尘现象的时间跨度则为（1980—2025年），依据萨马沃站以及作为对照站的纳杰夫站和迪瓦尼耶站的气候数据。","Journal Of Babylon Center for Humanities Studies",{"impact":25,"substance":110,"depth":111,"authority":28,"freshness":29,"relevant":8,"comment":653},"基于1980-2025年气候与NDVI数据定量分析干旱、植被退化与沙尘频次关系，方法规范但属区域气候研究，与农业信息化关联偏弱。",[655],{"name":651,"url":648},[64,4,116,129,657],"沙尘天气",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":660},{"candidates":661,"agricultural_evidence":22,"reason":149},[662,667],{"code":72,"score":663,"supported":53,"title":664,"abstract":665,"tags":666},5,[],[134],[4,116],{"code":60,"score":668,"supported":53,"title":669,"abstract":670,"tags":671},4,[],[129],[64],[673,674],"穆萨纳省 沙尘 植被覆盖","NDVI 干旱 沙尘暴 伊拉克","穆萨纳省沙尘植被覆盖-4827","10.66026\u002Fksfnen31",{"doi":676,"openalex_id":678,"authors":679,"venue":651,"cited_by_count":80,"oa_url":682,"card":683,"direction":96,"ingested_from":98},"W7221436686",[680],{"name":681,"orcid":17},"حوراء راضي جاسم محمد","https:\u002F\u002Fbcchj.uobabylon.edu.iq\u002Findex.php\u002Fbcchj\u002Farticle\u002Fdownload\u002F4964\u002F5019",{"tldr":684,"method":685,"finding":686,"direction":96,"opportunity":687},"分析伊拉克穆萨纳省1980-2025年季节性干旱、植被减少与沙尘现象频率的关系。","利用气象站数据与NDVI遥感数据，采用描述性分析和相关系数法。","风速与沙尘暴相关性最强(r=0.79)，相对湿度和降雨与沙尘现象呈强负相关。","可结合多源遥感与机器学习，构建干旱-植被-沙尘耦合预警模型，并拓展到其他干旱区。","2026-10-13T23:30:25.135328Z",{"id":690,"title":691,"url":692,"summary":693,"summary_zh":694,"content":17,"source_name":695,"source_url":692,"published_at":696,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":239,"score_detail":697,"sources":700,"tags":702,"primary_topic":17,"secondary_topics":705,"classification":706,"search_phrases":749,"slug":752,"view_count":80,"doi":753,"paper":754,"created_at":770},4824,"Robust site-specific radiometric calibration of PlanetScope NDVI using UAS-based reference data for precision agriculture","https:\u002F\u002Fdoi.org\u002F10.3897\u002Fjbgs.e185817","Index images provide opportunities for monitoring phenotypic characteristics such as plant height and disease symptoms, as well as the detection of pest presence. This allows for targeted application of agrochemicals and the implementation of preventive measures, leading to increased yields and reduced environmental impact. The study focuses on the radiometric inconsistencies between satellite-based imagery and high-resolution unmanned aerial system (UAS) imagery. While PlanetScope provides high temporal resolution, its spectral response often deviates from ground-truth UAS flight missions due to varying atmospheric conditions and sensor specifications. A robust site-specific calibration workflow using Quantile-Based Piecewise Linear Normalization and residual-based outlier mitigation was used. The method was tested across three different crop types: barley, sunflower and maize. Initial results showed significant systematic offset. Following the application of the proposed robust normalization model, the root mean square error (RMSE) was reduced by up to 39%, while maintaining high coefficients of determination. The study demonstrates that site-specific non-linear calibration is essential for reliable crop health monitoring, particularly in heterogeneous fields where soil background and canopy shadows significantly affect the spectral signature satellite data, enabling accurate multi-temporal analysis and better-informed decisions for variable-rate fertilization and yield prediction.","指数影像为监测株高、病害症状等表型特征以及检测害虫存在提供了机会。这使得农药的精准施用和预防措施的实施成为可能，从而提高产量并减少环境影响。本研究聚焦于卫星影像与高分辨率无人机系统（UAS）影像之间的辐射不一致性问题。尽管PlanetScope具有高时间分辨率，但由于大气条件和传感器规格的差异，其光谱响应往往偏离地面实测的UAS飞行任务数据。本研究采用了一种稳健的站点特异性校准工作流程，使用基于分位数的分段线性归一化与基于残差的异常值抑制方法。该方法在三种不同作物类型上进行了测试：大麦、向日葵和玉米。初步结果显示出显著的系统性偏移。在应用所提出的稳健归一化模型后，均方根误差（RMSE）最多降低了39%，同时保持了较高的决定系数。研究表明，站点特异性非线性校准对于可靠的作物健康监测至关重要，尤其是在土壤背景和冠层阴影显著影响卫星数据光谱特征的异质田块中，该方法能够实现准确的多时相分析，并为变量施肥和产量预测提供更明智的决策依据。","Journal of the Bulgarian Geographical Society","2026-10-12T00:00:00Z",{"impact":28,"substance":698,"depth":241,"authority":183,"freshness":29,"relevant":8,"comment":699},21,"该论文提出基于UAS参考数据的PlanetScope NDVI站点特异性辐射定标方法，RMSE降低39%，对精准农业多时相监测有实用价值，但属细分方法研究，影响范围有限。",[701],{"name":695,"url":692},[246,703,134,4,704],"精准农业","作物监测",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":707},{"candidates":708,"agricultural_evidence":53,"reason":748},[709,714,721,728,732,738,744],{"code":258,"score":21,"supported":53,"title":710,"abstract":712,"tags":713},[711],"precision agriculture",[],[246,703],{"code":715,"score":52,"supported":53,"title":716,"abstract":717,"tags":720},"A3",[],[718,719],"玉米","maize",[],{"code":722,"score":52,"supported":53,"title":723,"abstract":724,"tags":727},"F3",[],[725,726],"施肥","fertilization",[],{"code":138,"score":52,"supported":53,"title":729,"abstract":730,"tags":731},[],[141,142],[],{"code":733,"score":61,"supported":53,"title":734,"abstract":735,"tags":737},"A4",[],[736],"向日葵",[],{"code":739,"score":61,"supported":53,"title":740,"abstract":741,"tags":743},"F4",[],[742],"农药",[],{"code":72,"score":61,"supported":22,"title":745,"abstract":746,"tags":747},[],[],[4,704,134],"多个产业证据相当，需要确认主归属",[750,751],"PlanetScope NDVI 辐射定标","UAS 无人机 遥感 精准农业","PlanetScopeNDVI辐射定标-4824","10.3897\u002Fjbgs.e185817",{"doi":753,"openalex_id":755,"authors":756,"venue":695,"cited_by_count":80,"oa_url":764,"card":765,"direction":96,"ingested_from":98},"W7221533152",[757,759,762],{"name":758,"orcid":17},"Yana Lipiyska",{"name":760,"orcid":761},"Tsvetelina Atansova-Evdenova","https:\u002F\u002Forcid.org\u002F0009-0000-5669-6439",{"name":763,"orcid":17},"A. S. Yordanov","https:\u002F\u002Fjbgs.arphahub.com\u002Farticle\u002F185817\u002Fdownload\u002Fpdf\u002F",{"tldr":766,"method":767,"finding":768,"direction":96,"opportunity":769},"提出基于无人机参考数据的站点特异性辐射校准方法，提升PlanetScope NDVI精度。","分位数分段线性归一化与残差异常值抑制，测试大麦、向日葵和玉米。","校准后RMSE降低达39%，保持高决定系数，证明站点非线性校准必要性。","可探索多时相、多作物通用校准模型，结合土壤背景与冠层阴影动态校正。","2026-10-13T23:30:23.916508Z",{"id":772,"title":773,"url":774,"summary":775,"summary_zh":776,"content":17,"source_name":651,"source_url":774,"published_at":777,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":180,"score_detail":778,"sources":780,"tags":782,"primary_topic":785,"secondary_topics":786,"classification":787,"search_phrases":803,"slug":806,"view_count":8,"doi":807,"paper":808,"created_at":823},4756,"ديناميكيات وحساسية المناخ والغطاء النباتي في العراق باستخدام بيانات رصد الأرض طويلة الأمد","https:\u002F\u002Fdoi.org\u002F10.66026\u002Fy7543b35","تؤدي تغيرات المناخ إلى تكثيف الإجهاد الحراري والمائي عبر المناطق الجافة، مما يزيد من الحاجة إلى رصد متسق مكانياً وطويل الأمد لاستجابات الغطاء النباتي. العراق معرض بشكل خاص لهذه التغيرات لأن التدرجات المناخية القوية، والجفاف المتكرر، ومحدودية توافر المياه تتزامن مع مساحات شاسعة من الأراضي المطرية والمروية. قامت هذه الدراسة بتقييم ديناميكيات المناخ والغطاء النباتي في العراق من عام 2001 إلى 2025 باستخدام مؤشر الاختلاف الغطائي النباتي الطبيعي (MODIS NDVI)، وبيانات هطول الأمطار (CHIRPS)، وبيانات درجة حرارة الهواء على ارتفاع مترين (ERA5-Land). تم تحليل متوسط هطول الأمطار للفترة من أكتوبر إلى مايو، واتجاهات مؤشر (NDVI) للفترة من يناير إلى مايو، وارتباطات المناخ مع مؤشر (NDVI) بعد إزالة الاتجاه (detrended) على شبكة مشتركة بدقة 0.1 درجة.تم بعد ذلك تصنيف البكسلات النباتية على أنها: حساسة لهطول الأمطار، أو حساسة للحرارة، أو حساسة لكليهما، أو لا تظهر علاقة متوقعة قوية، أو تظهر اتجاهاً قوياً آخر. أظهر هطول الأمطار في الموسم الرطب تدرجاً واضحاً من إجماليات عالية جداً في شمال وشمال شرق العراق إلى إجماليات منخفضة ومنخفضة جداً عبر الغرب والجنوب الغربي والجنوب. كان الاخضرار ذو الدلالة الإحصائية أكثر انتشاراً بشكل ملحوظ من الاسمرار، حيث تركزت أقوى اتجاهات مؤشر (NDVI) الإيجابية في المناطق الزراعية في الشمال والشمال الشرقي والوسط. بعد إزالة الاتجاهات الخطية، كانت ارتباطات هطول الأمطار مع مؤشر (NDVI) إيجابية في الغالب وواسعة النطاق مكانياً، في حين كانت ارتباطات درجة الحرارة مع مؤشر (NDVI) ذات دلالة سلبية بشكل رئيسي ومركّزة في شمال وشرق وجنوب شرق العراق. هيمنت حساسية هطول الأمطار على التصنيف المتكامل، بينما شكلت الحساسية المزدوجة لهطول الأمطار والحرارة مجموعات متميزة في شمال-وسط وجنوب شرق البلاد. توفر هذه النتائج خط أساس مكانياً وطنياً لتحديد مناطق الغطاء النباتي المرتبطة بتقلبات المياه والحرارة بين السنوات. وتمثل فئات الحساسية ارتباطات إحصائية وليست عزوًا سببيًا.","气候变化正在加剧干旱地区的高温与水分胁迫，从而增加了对植被响应进行空间一致、长期监测的需求。伊拉克尤其容易受到这些变化的影响，因为强烈的气候梯度、频繁的干旱以及有限的水资源可用性，与大片雨养和灌溉土地并存。本研究利用MODIS归一化植被指数（NDVI）、降雨数据（CHIRPS）以及2米高度气温数据（ERA5-Land），评估了2001年至2025年伊拉克的气候与植被动态。研究在0.1度分辨率的统一网格上，分析了10月至5月的平均降雨量、1月至5月的NDVI趋势，以及去趋势（detrended）后气候与NDVI的相关性。随后将植被像元分类为：对降雨敏感、对温度敏感、对两者均敏感、未表现出强预期关系，或表现出其他强趋势。湿季降雨呈现出从伊拉克北部和东北部极高总量向西、西南和南部低及极低总量的明显梯度。具有统计显著性的绿化现象明显比褐化更为广泛，其中最强的NDVI正趋势集中在北部、东北部和中部的农业区。去除线性趋势后，降雨与NDVI的相关性大多为正且在空间上广泛分布，而温度与NDVI的相关性主要为显著负相关，集中在伊拉克北部、东部和东南部。降雨敏感性在综合分类中占主导地位，而降雨与温度的双重敏感性在该国中北部和东南部形成了独特的集群。这些结果为识别与年际水热波动相关的植被区域提供了全国尺度的空间基线。敏感性类别代表统计关联，而非因果归因。","2026-10-09T00:00:00Z",{"impact":28,"substance":182,"depth":110,"authority":28,"freshness":52,"relevant":8,"comment":779},"基于MODIS、CHIRPS与ERA5-Land长时序数据评估伊拉克气候与植被敏感性，方法规范、结论有区域参考价值，但属境外基础研究，公共影响有限。",[781],{"name":651,"url":774},[64,4,116,783,784],"干旱区农业","植被覆盖",{"code":60,"slug":517,"name":518,"path":519,"parent_code":43},[],{"status":46,"locked":22,"version":47,"method":48,"evidence":788},{"candidates":789,"agricultural_evidence":53},[790,794,799],{"code":60,"score":25,"supported":53,"title":791,"abstract":792,"tags":793},[],[64,129],[783,64],{"code":39,"score":52,"supported":53,"title":795,"abstract":796,"tags":798},[],[797,147],"灌溉",[],{"code":72,"score":61,"supported":22,"title":800,"abstract":801,"tags":802},[],[],[4,784,116],[804,805],"伊拉克 NDVI 植被动态","MODIS CHIRPS ERA5-Land 遥感","伊拉克NDVI植被动态-4756","10.66026\u002Fy7543b35",{"doi":807,"openalex_id":809,"authors":810,"venue":651,"cited_by_count":80,"oa_url":817,"card":818,"direction":96,"ingested_from":98},"W7221306668",[811,813,815],{"name":812,"orcid":17},"خولة‌ حمه‌رشید عمر",{"name":814,"orcid":17},"سایە دلاوەر محمد",{"name":816,"orcid":17},"Daban Kadhim Omar","https:\u002F\u002Fbcchj.uobabylon.edu.iq\u002Findex.php\u002Fbcchj\u002Farticle\u002Fdownload\u002F4819\u002F4866",{"tldr":819,"method":820,"finding":821,"direction":96,"opportunity":822},"基于2001–2025年MODIS NDVI、CHIRPS降水与ERA5-Land温度数据，评估伊拉","MODIS NDVI、CHIRPS降水、ERA5-Land温度，0.1°网格去趋","植被绿化趋势多于褐化，降水敏感性主导，温度负相关集中于北部、东部和东南部。","可结合灌溉与土地覆盖数据，区分雨养与灌溉区植被对水热胁迫的差异化响应机制。","2026-10-12T23:30:27.995466Z",{"id":825,"title":826,"url":827,"summary":828,"summary_zh":829,"content":17,"source_name":830,"source_url":827,"published_at":831,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":832,"score_detail":833,"sources":835,"tags":837,"primary_topic":841,"secondary_topics":846,"classification":847,"search_phrases":861,"slug":864,"view_count":8,"doi":865,"paper":866,"created_at":881},4749,"Beyond site-specific archaeological prospection: Spectral-geometry reveals cross-site cropmark signals in satellite data","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fpnasnexus\u002Fpgag336","Abstract Remote sensing has long supported archaeological prospection, mainly through the detection of cropmarks, a key archaeological proxy expressed as spectral vegetation anomalies over subsurface features. Yet, cropmark detection is still assumed in the literature to be highly site-specific, governed by local agronomic and environmental conditions. Using multispectral Sentinel-2 data from three European archaeological sites, we show that models trained in one landscape can recover meaningful archaeological proxy signals in other locations. By relying strictly on satellite spectral signatures for training, our models achieve measurable cross-location transferability without incorporating ancillary environmental data. We find that cross-site performance arises not from geographic proximity but from the spectral-geometric similarity of vegetation responses, and that transferability is strongly directional. We quantify this relationship through a Directional Strength Score (DSS), derived from monthly subcluster distances under multiple norms, which captures directional geometric similarity and correlates strongly with machine-learning performance. To keep the analysis grounded in the underlying spectral signal, we relied on architecturally restrained nonlinear classifiers (SVM and constrained XGBoost) and used NDVI-identified phenophases to align training and evaluation with comparable seasonal windows. Taken together, these results demonstrate that partial, spectrally mediated cross-location cropmark detection is feasible and that the underlying vegetation responses exhibit stable spectral structure, with spectral geometry offering a clear interpretive lens on why these signals can transfer across sites. Leveraging globally harmonized satellite data, this approach offers a scalable complement to uneven historical aerial archives and supports systematic regional archaeological prioritization to be deployed within integrated archaeological workflows.","摘要 遥感长期支持考古勘探，主要通过检测作物痕迹（cropmarks）——一种关键的考古代用指标，表现为地下特征上方植被的光谱异常。然而，文献中仍普遍认为作物痕迹检测高度依赖于具体遗址，受当地农艺和环境条件制约。利用来自三个欧洲考古遗址的多光谱Sentinel-2数据，我们表明在一个景观中训练的模型可以在其他地点恢复有意义的考古代用信号。通过严格依赖卫星光谱特征进行训练，我们的模型在不纳入辅助环境数据的情况下实现了可测量的跨地点迁移能力。我们发现，跨遗址性能并非源于地理邻近性，而是源于植被响应的光谱-几何相似性，并且迁移能力具有强烈的方向性。我们通过方向强度评分（Directional Strength Score, DSS）量化这一关系，该评分基于多种范数下的逐月子簇距离推导而来，捕捉方向性几何相似性并与机器学习性能强烈相关。为使分析立足于底层光谱信号，我们采用架构受限的非线性分类器（SVM和约束XGBoost），并使用NDVI识别的物候期来对齐训练和评估，使其处于可比较的季节窗口内。综合来看，这些结果表明，部分、以光谱为中介的跨地点作物痕迹检测是可行的，且底层植被响应表现出稳定的光谱结构，光谱几何为理解这些信号为何能够跨遗址迁移提供了清晰的解释视角。利用全球协调的卫星数据，该方法为不均衡的历史航空档案提供了可扩展的补充，并支持在综合考古工作流程中部署系统性的区域考古优先级排序。","PNAS Nexus","2026-10-10T00:00:00Z",82,{"impact":110,"substance":182,"depth":591,"authority":111,"freshness":25,"relevant":8,"comment":834},"PNAS Nexus 论文，用 Sentinel-2 多光谱数据证明作物标记信号可跨遗址迁移，方法新颖、结论可靠，对农业遥感与考古信息化有参考价值，但属细分领域突破，未达全国性产业事件层级。",[836],{"name":830,"url":827},[838,134,4,839,840],"Sentinel-2","考古遥感","作物标记",{"code":72,"slug":842,"name":843,"path":844,"parent_code":845},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":46,"locked":22,"version":47,"method":48,"evidence":848},{"candidates":849,"agricultural_evidence":53},[850,855],{"code":72,"score":28,"supported":53,"title":851,"abstract":852,"tags":854},[],[134,135,853],"multispectral",[4,838,134],{"code":856,"score":61,"supported":53,"title":857,"abstract":858,"tags":860},"I2",[],[859],"机器学习",[],[862,863],"Sentinel-2 作物标记 考古","遥感 光谱几何 跨遗址","Sentinel-2作物标记考古-4749","10.1093\u002Fpnasnexus\u002Fpgag336",{"doi":865,"openalex_id":867,"authors":868,"venue":830,"cited_by_count":80,"oa_url":875,"card":876,"direction":96,"ingested_from":98},"W7221319130",[869,872],{"name":870,"orcid":871},"Elias Gravanis","https:\u002F\u002Forcid.org\u002F0000-0002-5331-6661",{"name":873,"orcid":874},"Άθως Αγαπίου","https:\u002F\u002Forcid.org\u002F0000-0001-9106-6766","https:\u002F\u002Facademic.oup.com\u002Fpnasnexus\u002Fadvance-article-pdf\u002Fdoi\u002F10.1093\u002Fpnasnexus\u002Fpgag336\u002F71838194\u002Fpgag336.pdf",{"tldr":877,"method":878,"finding":879,"direction":96,"opportunity":880},"用Sentinel-2多光谱数据证明作物标记模型可跨遗址迁移，且迁移性由光谱几何相似性决定。","Sentinel-2多光谱、SVM与约束XGBoost、NDVI物候对齐、方向强","跨遗址作物标记检测可行，迁移性源于植被光谱几何相似而非地理邻近，且具方向性。","可将光谱几何迁移性指标引入作物表型与病害遥感，构建跨区域可迁移的植被异常检测模型。","2026-10-12T23:30:24.489835Z",{"id":883,"title":884,"url":885,"summary":886,"summary_zh":887,"content":17,"source_name":335,"source_url":885,"published_at":777,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":80,"score_detail":888,"sources":890,"tags":892,"primary_topic":17,"secondary_topics":896,"classification":897,"search_phrases":908,"slug":911,"view_count":80,"doi":912,"paper":913,"created_at":931},4682,"Disentangling distance decay from vegetation-ground effects on traffic noise attenuation in highway-adjacent urban parks","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72076-y","Quantifying how vegetation and ground surfaces independently contribute to traffic noise attenuation, over and above geometric spreading, is a critical gap in urban green space planning. This study explicitly separates baseline geometric spreading from excess attenuation (additional noise reduction beyond distance decay) to quantify how vegetation and ground surfaces uniquely affect sound propagation. We measured equivalent continuous sound levels along transects extending 60 m from the highway edge in six road-aligned parks. A mixed-effects model showed that noise decreased by 8.48 dB per natural log unit increase in distance (β = − 8.48, p \u003C 0.001; marginal R 2 = 0.61, conditional R 2 = 0.73). Residual excess attenuation (observed minus distance-predicted noise) was then modeled against vegetation structure and ground surface characteristics (adjusted R 2 = 0.53). Vegetation height (β = − 0.38, p \u003C 0.001), canopy density (β = − 0.041, p \u003C 0.001), NDVI (β = − 3.21, p \u003C 0.001), and shrub density (β = − 0.19, p \u003C 0.001) were associated with greater noise reduction, whereas higher open soil proportion (β = 0.052, p \u003C 0.001) and ground hardness (β = 0.87, p = 0.003) were associated with higher residual noise levels (i.e., less attenuation). Scenario simulations indicated that increasing vegetation height by 8 m, canopy density by 60%, shrub density by 7 stems per 10 m 2 , and reducing open soil proportion by 40% could achieve up to 10.13 dBA of additional noise reduction beyond distance decay. These findings quantify the independent contributions of vegetation and ground surfaces to excess attenuation, supporting dense, vertically complex planting designs for noise mitigation in highway-adjacent urban green spaces.","量化植被与地表在几何扩散之外对交通噪声衰减的独立贡献，是城市绿地规划中的一个关键空白。本研究明确将基线几何扩散与超额衰减（超出距离衰减的额外降噪量）分离，以量化植被和地表对声音传播的独特影响。我们在六个沿路公园中，沿从公路边缘向外延伸60米的样线测量了等效连续声级。混合效应模型显示，距离每增加一个自然对数单位，噪声降低8.48分贝（β = − 8.48，p \u003C 0.001；边际R² = 0.61，条件R² = 0.73）。随后，将残差超额衰减（观测噪声减去距离预测噪声）对植被结构和地表特征进行建模（调整R² = 0.53）。植被高度（β = − 0.38，p \u003C 0.001）、冠层密度（β = − 0.041，p \u003C 0.001）、归一化植被指数（NDVI）（β = − 3.21，p \u003C 0.001）和灌木密度（β = − 0.19，p \u003C 0.001）与更大的降噪量相关，而较高的裸露土壤比例（β = 0.052，p \u003C 0.001）和地表硬度（β = 0.87，p = 0.003）与较高的残差噪声水平（即较少的衰减）相关。情景模拟表明，将植被高度增加8米、冠层密度增加60%、灌木密度增加每10平方米7株，并将裸露土壤比例减少40%，可在距离衰减之外额外实现高达10.13 dBA的降噪量。这些发现量化了植被和地表对超额衰减的独立贡献，支持在公路邻近的城市绿地中采用密集、垂直结构复杂的种植设计以缓解噪声。",{"impact":80,"substance":80,"depth":80,"authority":80,"freshness":80,"relevant":80,"comment":889},"研究城市公园植被与地面如何削减交通噪声，属城市声环境与绿地规划范畴，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[891],{"name":335,"url":885},[4,893,894,895],"城市绿地","交通噪声","植被降噪",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":898},{"candidates":899,"agricultural_evidence":22,"reason":149},[900,904],{"code":138,"score":52,"supported":53,"title":901,"abstract":902,"tags":903},[],[141,142],[],{"code":72,"score":8,"supported":22,"title":905,"abstract":906,"tags":907},[],[],[4],[909,910],"高速公路 城市公园 交通噪声 衰减","交通噪声 城市绿地 植被降噪 NDVI","高速公路城市公园交通噪声衰减-4682","10.1038\u002Fs41598-026-72076-y",{"doi":912,"openalex_id":914,"authors":915,"venue":335,"cited_by_count":80,"oa_url":924,"card":925,"direction":96,"ingested_from":98},"W7221106861",[916,919,922],{"name":917,"orcid":918},"Sarmad Mahdi Kadhum Alghanimi","https:\u002F\u002Forcid.org\u002F0000-0001-7518-4789",{"name":920,"orcid":921},"Atefeh Chamani","https:\u002F\u002Forcid.org\u002F0000-0002-9463-8579",{"name":923,"orcid":17},"Sawsan Samir Hadi","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-72076-y_reference.pdf",{"tldr":926,"method":927,"finding":928,"direction":929,"opportunity":930},"量化城市公园中植被与地面独立于距离衰减的交通噪声削减贡献。","六公园60米样带实测声级，混合效应模型分离距离与超额衰减。","植被高度、冠层密度、NDVI和灌木密度提升降噪，裸土与硬地面降低降噪。","农业绿色发展与碳","可探索不同植被配置与地面材质组合的降噪优化，并耦合碳汇与声景服务。","2026-10-11T23:30:31.548961Z",{"id":933,"title":934,"url":935,"summary":936,"summary_zh":937,"content":17,"source_name":938,"source_url":935,"published_at":939,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":940,"score_detail":941,"sources":943,"tags":945,"primary_topic":948,"secondary_topics":949,"classification":950,"search_phrases":969,"slug":972,"view_count":354,"doi":973,"paper":974,"created_at":989},4593,"Climate Resilience and the Hydro-Vegetal Paradox in El Bayadh Province (Algeria, 1984-2023)","https:\u002F\u002Fdoi.org\u002F10.56093\u002Faaz.v65i3.177509","The decade 2010 to 2020 was the warmest ever recorded in the Mediterranean basin (+1.5°C), exceeding the global average. North Africa, identified as a vulnerability hotspot by the IPCC AR6, experiences intensified droughts and ecosystem degradation. This study presents the first quantitative assessment of climate resilience in El Bayadh Province (Algeria), revealing an unexpected hydro-vegetal paradox. A comparative multi-index methodology (restricted climate index vs. global index integrating 7 variables) was applied to 22 municipalities over the period of 1984 to 2023. The results showed a universal degradation of water balance (-8.2 to -21.4 mm yr-1) and widespread warming (+0.8 to +2.1°C decade-1). Paradoxically, 73 to 77% of municipalities showed improvement in vegetation indices (NDVI, SAVI), suggesting ecological adaptation. Principal component analysis (PCA-99%) variance explained) identified water stress as the main differentiating factor, and clustering distinguishes 5 territorial profiles (silhouette = 0.521). The global index proves more discriminant than the restricted index for capturing the complexity of socio-ecological dynamics. This study highlights the need for differentiated adaptation policies addressing challenges related to water, food security, and migration.","2010年至2020年是有记录以来地中海盆地最暖的十年（升温+1.5°C），超过了全球平均水平。北非被IPCC第六次评估报告（AR6）确定为脆弱性热点地区，正经历着加剧的干旱和生态系统退化。本研究首次对阿尔及利亚埃尔贝伊德省（El Bayadh Province）的气候韧性进行了定量评估，揭示了一个意想不到的水文-植被悖论。研究采用比较多指标方法（限制性气候指数与整合7个变量的全局指数），对22个市镇在1984年至2023年期间进行了分析。结果表明，水平衡普遍退化（-8.2至-21.4 mm yr⁻¹），且普遍升温（+0.8至+2.1°C decade⁻¹）。悖论的是，73%至77%的市镇植被指数（NDVI、SAVI）有所改善，表明存在生态适应。主成分分析（PCA，方差解释率达99%）确定水分胁迫为主要区分因子，聚类分析区分出5种地域类型（轮廓系数=0.521）。在捕捉社会-生态动态复杂性方面，全局指数比限制性指数更具判别力。本研究强调了制定差异化适应政策的必要性，以应对与水、粮食安全和移民相关的挑战。","Annals of Arid Zone","2026-10-08T00:00:00Z",74,{"impact":28,"substance":182,"depth":110,"authority":183,"freshness":29,"relevant":8,"comment":942},"基于22个市镇40年多指标遥感数据的干旱区气候韧性定量研究，方法新颖、结论有反差价值，但属区域案例研究，公共影响层级有限。",[944],{"name":938,"url":935},[4,946,116,783,947],"气候适应","水资源安全",{"code":60,"slug":517,"name":518,"path":519,"parent_code":43},[],{"status":46,"locked":22,"version":47,"method":48,"evidence":951},{"candidates":952,"agricultural_evidence":53},[953,958,965],{"code":60,"score":27,"supported":53,"title":954,"abstract":956,"tags":957},[955],"climate resilience",[129,955],[783,946],{"code":959,"score":52,"supported":53,"title":960,"abstract":961,"tags":964},"A8",[],[962,963],"粮食安全","food security",[],{"code":72,"score":354,"supported":22,"title":966,"abstract":967,"tags":968},[],[],[4,116],[970,971],"El Bayadh 阿尔及利亚 气候韧性","NDVI 植被指数 干旱区","ElBayadh阿尔及利亚气候韧性-4593","10.56093\u002Faaz.v65i3.177509",{"doi":973,"openalex_id":975,"authors":976,"venue":938,"cited_by_count":80,"oa_url":983,"card":984,"direction":96,"ingested_from":98},"W7220949140",[977,980],{"name":978,"orcid":979},"Lakhdar Guérine","https:\u002F\u002Forcid.org\u002F0000-0003-0836-2703",{"name":981,"orcid":982},"Naîmi Bendouina","https:\u002F\u002Forcid.org\u002F0000-0002-9776-1660","http:\u002F\u002Fepubs.icar.org.in\u002Findex.php\u002FAAZ\u002Farticle\u002Fdownload\u002F177509\u002F68050",{"tldr":985,"method":986,"finding":987,"direction":96,"opportunity":988},"首次量化阿尔及利亚El Bayadh省1984-2023年气候韧性，揭示水-植被悖论。","多指标对比法（限制性气候指数与7变量全球指数），对22个市镇做PCA和聚类。","水均衡普遍恶化、升温显著，但73-77%市镇植被指数改善，呈生态适应悖论。","可探究干旱区NDVI改善的驱动机制（灌溉、物种更替或遥感尺度效应），并耦合社会经济数据。","2026-10-10T23:30:29.597640Z",{"id":991,"title":992,"url":993,"summary":994,"summary_zh":995,"content":17,"source_name":996,"source_url":993,"published_at":997,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":239,"score_detail":998,"sources":1000,"tags":1002,"primary_topic":17,"secondary_topics":1005,"classification":1006,"search_phrases":1017,"slug":1020,"view_count":8,"doi":1021,"paper":1022,"created_at":1036},4514,"Tree crown anomaly detection in UAV RGB-NIR imagery using an unsupervised cosine-distance-based approach in hemiboreal forests","https:\u002F\u002Fdoi.org\u002F10.1080\u002F22797254.2026.2740475","Automated UAV-based data processing can substantially enhance the temporal resolution and spatial accuracy of forest health monitoring. To facilitate practical applicability in operational forestry, there is a critical need for unsupervised workflows that minimise manual intervention. This study explored the efficacy of distance-based anomaly detection (AD) for identifying tree crown damage in RGB-NIR UAV orthophotos. We developed an unsupervised cosine-distance-based AD workflow and evaluated it against a standard NDVI thresholding baseline using both time-series analysis and a large spatial dataset comprising 31 hemiboreal forest stands. The results demonstrated that while both methods achieved comparable accuracy for mapping established deadwood, the AD workflow exhibited heightened sensitivity to subtle, early-stage canopy damage. However, AD requires uniform summer illumination, whereas NDVI—though simpler to execute—demands constant, scene-specific threshold recalibration. Despite the requirement for stable illumination, the AD workflow offers substantial operational value as an automated forest damage detection system that operates without continuous manual threshold recalibration or prior field data.","基于无人机的自动化数据处理能够显著提升森林健康监测的时间分辨率与空间精度。为促进其在业务化林业中的实际应用，亟需能够最大限度减少人工干预的无监督工作流。本研究探讨了基于距离的异常检测（anomaly detection, AD）在RGB-NIR无人机正射影像中识别树冠损伤的有效性。我们开发了一种无监督的余弦距离异常检测工作流，并利用时间序列分析及涵盖31个北方针叶林林分的大型空间数据集，将其与标准的NDVI阈值分割基线方法进行了对比评估。结果表明，尽管两种方法在制图已存在枯死木方面达到了相当的精度，但异常检测工作流对细微的早期冠层损伤表现出更高的敏感性。然而，异常检测需要均匀的夏季光照条件，而NDVI虽然操作更为简便，却需要持续进行针对特定场景的阈值重新校准。尽管对稳定光照有要求，异常检测工作流作为一种自动化森林损伤检测系统，无需持续的人工阈值重新校准或先验实地数据即可运行，具有重要的业务化应用价值。","European Journal of Remote Sensing","2026-10-07T00:00:00Z",{"impact":111,"substance":698,"depth":241,"authority":183,"freshness":52,"relevant":8,"comment":999},"提出无监督余弦距离异常检测方法，在31个林分上验证对早期冠层损伤更敏感，对林业遥感监测有实操价值。",[1001],{"name":996,"url":993},[246,1003,450,4,1004],"无人机","森林健康监测",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":1007},{"candidates":1008,"agricultural_evidence":22,"reason":149},[1009,1013],{"code":72,"score":354,"supported":22,"title":1010,"abstract":1011,"tags":1012},[],[],[4,450],{"code":258,"score":8,"supported":22,"title":1014,"abstract":1015,"tags":1016},[],[],[246],[1018,1019],"无人机 RGB-NIR 树冠异常检测","半寒带森林 森林健康监测","无人机RGB-NIR树冠异常检测-4514","10.1080\u002F22797254.2026.2740475",{"doi":1021,"openalex_id":1023,"authors":1024,"venue":996,"cited_by_count":80,"oa_url":1030,"card":1031,"direction":96,"ingested_from":98},"W7220861714",[1025,1027],{"name":1026,"orcid":17},"Linda Gulbe-Viluma",{"name":1028,"orcid":1029},"Grigorijs Goldbergs","https:\u002F\u002Forcid.org\u002F0000-0001-5092-4963","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F22797254.2026.2740475?needAccess=true",{"tldr":1032,"method":1033,"finding":1034,"direction":96,"opportunity":1035},"提出无监督余弦距离异常检测方法，从无人机RGB-NIR影像中识别树冠损伤。","无人机RGB-NIR正射影像，余弦距离异常检测，与NDVI阈值法对比。","两种方法对枯木制图精度相当，但异常检测对早期冠层损伤更敏感。","可探索多时相、多光照条件下无监督异常检测的鲁棒性及跨林型迁移能力。","2026-10-09T23:30:28.715413Z",{"id":1038,"title":1039,"url":1040,"summary":1041,"summary_zh":1042,"content":17,"source_name":335,"source_url":1040,"published_at":997,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":588,"score_detail":1043,"sources":1045,"tags":1047,"primary_topic":1049,"secondary_topics":1050,"classification":1051,"search_phrases":1072,"slug":1075,"view_count":354,"doi":1076,"paper":1077,"created_at":1093},4497,"A SwiGLU enhanced ConvNeXt framework for multispectral Sentinel-2 land use and land cover classification","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-67985-x","Land use and land cover (LCLU) classification from multispectral satellite imagery is important for applications such as urban planning, environmental monitoring, agriculture, and disaster management. Existing deep learning based approaches have improved LCLU; however, most of them utilize only the red, green, and blue (RGB) bands of multispectral sentinel-2 imagery, thereby underutilizing the complementary spectral information available in the near-infrared (NIR), short-wave infrared (SWIR), and derived spectral indices such as normalized difference built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). In this work, we propose a convolutional neural network (CNN), SwigNeXT, based on a modified ConvNeXt architecture for the classification of multispectral Sentinel-2 imagery using spectral band combinations which are RGB, NIR–Red–Green, SWIR–NIR–Red, and derived index combination NDBI–NDVI–NDWI rather than using only the RGB. The SwigNeXT incorporates SwiGLU-based gated feature modulation to improve spectral spatial feature representation. The final classification is obtained by ensembling the predictions of the three spectral band combinations and the derived index combination. Experimental results show that the proposed ensemble framework achieved an overall accuracy of 99.19% with a Kappa coefficient ( \\(\\kappa \\) ) of 0.9909, outperforming conventional CNNs, transfer learning based CNNs and transformer based approaches. A gradient based SHapley Additive exPlanations framework was further integrated to analyse spatial and spectral feature contributions. The proposed framework was further validated on Sentinel-2 imagery from the Delhi–NCR region, to examine its applicability under geographically distinct real world imaging conditions.","多光谱卫星影像的土地利用与土地覆盖（LCLU）分类对城市规划、环境监测、农业和灾害管理等应用具有重要意义。现有的基于深度学习的方法改进了LCLU分类；然而，大多数方法仅利用多光谱Sentinel-2影像的红、绿、蓝（RGB）波段，从而未能充分利用近红外（NIR）、短波红外（SWIR）以及归一化差异建成区指数（NDBI）、归一化差异植被指数（NDVI）和归一化差异水体指数（NDWI）等衍生光谱指数中可用的互补光谱信息。在本研究中，我们提出了一种基于改进ConvNeXt架构的卷积神经网络（CNN）——SwigNeXT，用于多光谱Sentinel-2影像分类，采用RGB、NIR–Red–Green、SWIR–NIR–Red等光谱波段组合以及NDBI–NDVI–NDWI衍生指数组合，而非仅使用RGB。SwigNeXT引入了基于SwiGLU的门控特征调制，以改进光谱空间特征表示。最终分类通过集成三种光谱波段组合和衍生指数组合的预测结果获得。实验结果表明，所提出的集成框架达到了99.19%的总体精度，Kappa系数（\\(\\kappa\\)）为0.9909，优于传统CNN、基于迁移学习的CNN以及基于Transformer的方法。进一步集成了基于梯度的SHapley加性解释框架，以分析空间和光谱特征贡献。所提出的框架在德里–NCR地区的Sentinel-2影像上进一步验证，以检验其在地理上不同的真实成像条件下的适用性。",{"impact":110,"substance":182,"depth":110,"authority":338,"freshness":29,"relevant":8,"comment":1044},"提出融合SwiGLU门控与多光谱波段组合的ConvNeXt集成框架，在Sentinel-2土地利用分类上达99.19%精度，方法新颖且对农业遥感监测有实用价值。",[1046],{"name":335,"url":1040},[246,1048,134,4,295],"农业人工智能",{"code":72,"slug":842,"name":843,"path":844,"parent_code":845},[],{"status":46,"locked":22,"version":47,"method":48,"evidence":1052},{"candidates":1053,"agricultural_evidence":53},[1054,1058,1064,1068],{"code":72,"score":183,"supported":53,"title":1055,"abstract":1056,"tags":1057},[853],[853],[4,134],{"code":856,"score":532,"supported":53,"title":1059,"abstract":1060,"tags":1063},[],[1061,1062],"深度学习","deep learning",[1048],{"code":67,"score":8,"supported":22,"title":1065,"abstract":1066,"tags":1067},[],[],[295],{"code":258,"score":8,"supported":22,"title":1069,"abstract":1070,"tags":1071},[],[],[246],[1073,1074],"Sentinel-2 多光谱 土地覆盖分类","ConvNeXt SwiGLU 遥感分类","Sentinel-2多光谱土地覆盖分类-4497","10.1038\u002Fs41598-026-67985-x",{"doi":1076,"openalex_id":1078,"authors":1079,"venue":335,"cited_by_count":80,"oa_url":1087,"card":1088,"direction":170,"ingested_from":98},"W7220869344",[1080,1082,1084],{"name":1081,"orcid":17},"Neeraj Kumar",{"name":1083,"orcid":17},"Navin Rajpal",{"name":1085,"orcid":1086},"Pramod Kumar Soni","https:\u002F\u002Forcid.org\u002F0000-0003-0632-5044","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-67985-x_reference.pdf",{"tldr":1089,"method":1090,"finding":1091,"direction":96,"opportunity":1092},"提出SwiGLU增强的ConvNeXt框架，融合多光谱波段与指数集成分类Sentinel-2土地利用","改进ConvNeXt加SwiGLU门控，用RGB、NIR、SWIR及NDVI\u002FN","集成框架总体精度99.19%、Kappa 0.9909，优于CNN、迁移学习和Transformer","可探索多光谱指数组合与门控机制在作物精细分类及跨区域迁移中的泛化能力。","2026-10-09T23:30:12.234854Z",{"id":1095,"title":1096,"url":1097,"summary":1098,"summary_zh":1099,"content":17,"source_name":335,"source_url":1097,"published_at":1100,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1101,"score_detail":1102,"sources":1104,"tags":1106,"primary_topic":17,"secondary_topics":1110,"classification":1111,"search_phrases":1123,"slug":1126,"view_count":8,"doi":1127,"paper":1128,"created_at":1152},4444,"Geostatistical modelling of the effect of vegetation on canine visceral leishmaniasis in Camaçari, Bahia State, Northeastern Brazil using remote-sensed data","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-73205-3","Abstract Visceral leishmaniasis (VL) is a vector-borne disease that can cause fever, hepatosplenomegaly, weight loss and pancytopenia. In Brazil, VL occurs when a female phlebotomine sand fly ( Lutzomyia longipalpis ) takes a blood meal from an infected reservoir. Domestic canines are likely the main source of the infectious agent since they live in close proximity to humans and can remain asymptomatic for several years. Consequently, sole treatment of human cases likely cannot prevent transmission. Current methods of control have been unsuccessful, and thus, a better understanding of canine transmission and the effect of the environment is required. Phlebotomine sand flies feed on plant sap and nectar, and lay eggs around tree roots, hence, we hypothesise that dogs are more likely to contract VL near vegetation. In this retrospective study, we investigate the effect of vegetation on canine visceral leishmaniasis (CVL) in the community of Camaçari, Bahia, Northeastern Brazil using geostatistical modelling. The normalised vegetation index (NDVI) is used to quantify vegetation. We find that, on average, an increase in NDVI of 0.1 led to a 1.21-fold increase in the odds of CVL. The odds of CVL were highest along the coast where plant species known to attract Lutzomyia longipalpis have been identified.","摘要 内脏利什曼病（VL）是一种媒介传播疾病，可引起发热、肝脾肿大、体重减轻和全血细胞减少。在巴西，当雌性白蛉（长须罗蛉）从受感染的储存宿主吸血时，便会传播VL。家犬很可能是传染源的主要来源，因为它们与人类居住地距离很近，并且可以多年保持无症状。因此，仅治疗人类病例可能无法阻止传播。目前的控制方法并不成功，因此需要更好地了解犬类传播以及环境的影响。白蛉以植物汁液和花蜜为食，并在树根周围产卵，因此我们假设犬类在植被附近更容易感染VL。在这项回顾性研究中，我们使用地统计模型研究了植被对巴西东北部巴伊亚州卡马萨里社区犬内脏利什曼病（CVL）的影响。采用归一化植被指数（NDVI）量化植被。我们发现，平均而言，NDVI每增加0.1，CVL的比值比增加1.21倍。CVL的比值比在沿海地区最高，那里已鉴定出已知会吸引长须罗蛉的植物物种。","2026-10-06T00:00:00Z",68,{"impact":25,"substance":698,"depth":241,"authority":338,"freshness":25,"relevant":8,"comment":1103},"基于遥感NDVI的地理统计建模揭示植被与犬内脏利什曼病传播关联，方法新颖、结论具体，对动物疫病遥感监测有参考价值，但属区域流行病学研究，与农业信息化关联偏弱。",[1105],{"name":335,"url":1097},[1107,134,4,1108,1109],"动物疫病防控","人兽共患病","公共卫生",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":1112},{"candidates":1113,"agricultural_evidence":22,"reason":149},[1114,1119],{"code":1115,"score":354,"supported":22,"title":1116,"abstract":1117,"tags":1118},"C5",[],[],[1108,1107],{"code":72,"score":354,"supported":22,"title":1120,"abstract":1121,"tags":1122},[],[],[4,134],[1124,1125],"Camaçari 犬内脏利什曼病 NDVI","巴西 植被 犬利什曼病 遥感","Camaçari犬内脏利什曼病NDVI-4444","10.1038\u002Fs41598-026-73205-3",{"doi":1127,"openalex_id":1129,"authors":1130,"venue":335,"cited_by_count":80,"oa_url":1146,"card":1147,"direction":96,"ingested_from":98},"W7220723172",[1131,1134,1137,1140,1143],{"name":1132,"orcid":1133},"F. Clark","https:\u002F\u002Forcid.org\u002F0000-0003-1083-0887",{"name":1135,"orcid":1136},"Manuela da Silva Solcà","https:\u002F\u002Forcid.org\u002F0000-0001-9014-8903",{"name":1138,"orcid":1139},"Deborah Bittencourt Mothé Fraga","https:\u002F\u002Forcid.org\u002F0000-0003-4788-0319",{"name":1141,"orcid":1142},"Cláudia Brodskyn","https:\u002F\u002Forcid.org\u002F0000-0003-3367-5365",{"name":1144,"orcid":1145},"Emanuele Giorgi","https:\u002F\u002Forcid.org\u002F0000-0003-0640-181X","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-73205-3_reference.pdf",{"tldr":1148,"method":1149,"finding":1150,"direction":96,"opportunity":1151},"用遥感NDVI和地统计模型分析巴西犬内脏利什曼病与植被的关系。","地统计建模，结合遥感NDVI数据与犬利什曼病病例。","NDVI每增加0.1，犬内脏利什曼病风险增加1.21倍，沿海植被区风险最高。","可拓展遥感植被指数至其他虫媒病或人畜共患病风险制图，结合多时相数据优化预警。","2026-10-08T23:30:32.248339Z",{"id":1154,"title":1155,"url":1156,"summary":1157,"summary_zh":1158,"content":17,"source_name":1159,"source_url":1156,"published_at":1160,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1161,"score_detail":1162,"sources":1164,"tags":1166,"primary_topic":17,"secondary_topics":1169,"classification":1170,"search_phrases":1181,"slug":1184,"view_count":354,"doi":1185,"paper":1186,"created_at":1204},4368,"Dinâmica de supressão e recuperação florestal por meio do índice NDVI: um estudo de caso em Realeza – PR (2016 - 2026)","https:\u002F\u002Fdoi.org\u002F10.4025\u002Fbolgeogr.v44.a2026.e83759","O constante avanço agropecuário sobre o bioma da Mata Atlântica exige um monitoramento contínuo da integridade ecológica dos remanescentes florestais locais. Este estudo avaliou a dinâmica espaço-temporal da cobertura florestal no município de Realeza-PR, entre os anos de 2016 e 2026, quantificando as taxas de supressão e ganho florestal analisando a influência do relevo e da fragmentação na paisagem. A metodologia aplicada baseou-se em séries temporais orbitais do sensor Sentinel-2 (MSI), com a calibração de limiares locais do índice NDVI ( e ) para diferenciar a floresta nativa estável da agricultura sazonal. A declividade foi extraída a partir do Modelo Digital de Elevação ALOS PALSAR, submetido à compatibilização matricial via software GDAL e ao cálculo subsequente de métricas de paisagem, realizados em ambiente R. Os resultados obtidos apontaram um ganho líquido total de 775,81 hectares na cobertura florestal durante a década analisada. Apesar do saldo quantitativo positivo, as métricas estruturais registraram um aumento de 8,79% no Número de Manchas (NP) e uma queda de 1,14 pontos percentuais no Índice de Agregação (AI). Desse modo, conclui-se que o ganho absoluto de área florestal mascarou um processo contínuo de retalhamento e fragmentação dos corredores ecológicos, comprometendo a qualidade e a resiliência ecológica da paisagem a longo prazo.","农业活动对马塔大西洋森林生物群落的持续扩张，要求对当地森林残余的生态完整性进行持续监测。本研究评估了雷阿莱扎市（巴拉那州）2016年至2026年间森林覆盖的时空动态，量化了森林抑制与增益速率，并分析了地形和破碎化对景观的影响。所采用的方法基于Sentinel-2（MSI）传感器的轨道时间序列，通过校准NDVI指数的局部阈值（和）以区分稳定原生林与季节性农业。坡度数据提取自ALOS PALSAR数字高程模型，经GDAL软件进行矩阵兼容化处理，随后在R环境中计算景观指标。结果表明，在分析的十年间，森林覆盖净增总面积达775.81公顷。尽管数量上呈正平衡，但结构指标显示斑块数量（NP）增加了8.79%，聚集度指数（AI）下降了1.14个百分点。因此，结论是森林面积的绝对增益掩盖了生态廊道持续被切割和破碎化的过程，损害了景观的长期质量和生态韧性。","Boletim de Geografia","2026-10-04T00:00:00Z",66,{"impact":25,"substance":26,"depth":241,"authority":28,"freshness":29,"relevant":8,"comment":1163},"基于Sentinel-2与NDVI的十年森林动态监测案例，方法扎实、数据具体，但属地方性研究，公共影响有限。",[1165],{"name":1159,"url":1156},[450,4,1167,116,1168],"生态保护","森林覆盖",[],{"status":300,"locked":22,"version":47,"method":48,"evidence":1171},{"candidates":1172,"agricultural_evidence":53,"reason":311},[1173,1177],{"code":72,"score":61,"supported":22,"title":1174,"abstract":1175,"tags":1176},[],[],[4,450,116],{"code":67,"score":8,"supported":22,"title":1178,"abstract":1179,"tags":1180},[],[],[1167],[1182,1183],"Realeza PR NDVI 森林","Sentinel-2 森林覆盖 监测","RealezaPRNDVI森林-4368","10.4025\u002Fbolgeogr.v44.a2026.e83759",{"doi":1185,"openalex_id":1187,"authors":1188,"venue":1159,"cited_by_count":80,"oa_url":1198,"card":1199,"direction":96,"ingested_from":98},"W7220453253",[1189,1192,1195],{"name":1190,"orcid":1191},"Marcos Henrique Tomazini Mikoanski","https:\u002F\u002Forcid.org\u002F0000-0003-0715-0581",{"name":1193,"orcid":1194},"Francieli Alves Teixeira","https:\u002F\u002Forcid.org\u002F0009-0000-0055-9718",{"name":1196,"orcid":1197},"Fernando César Manosso","https:\u002F\u002Forcid.org\u002F0000-0003-2568-7403","https:\u002F\u002Fperiodicos.uem.br\u002Fojs\u002Findex.php\u002FBolGeogr\u002Farticle\u002Fdownload\u002F83759\u002F751375163932",{"tldr":1200,"method":1201,"finding":1202,"direction":96,"opportunity":1203},"基于Sentinel-2 NDVI时序评估2016-2026年Realeza-PR森林消长与破碎化动","Sentinel-2 NDVI阈值校准、ALOS PALSAR坡度、R景观指标分","森林净增775.81公顷，但斑块数增8.79%、聚集度降1.14%，破碎化加剧。","可结合多源遥感与景观连通性模型，探究农业扩张下森林恢复的生态质量与廊道功能。","2026-10-07T23:30:30.363166Z",{"id":1206,"title":1207,"url":1208,"summary":1209,"summary_zh":1210,"content":17,"source_name":1211,"source_url":1208,"published_at":1212,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":180,"score_detail":1213,"sources":1215,"tags":1217,"primary_topic":1219,"secondary_topics":1220,"classification":1224,"search_phrases":1239,"slug":1242,"view_count":354,"doi":1243,"paper":1244,"created_at":1258},4365,"An Index-Based Algorithm for Detecting Water Soil Erosion Indicators from Sentinel-2 Multispectral Data and Its Verification on Field Data of Rostov Oblast and Krasnodar Krai","https:\u002F\u002Fdoi.org\u002F10.23947\u002F2587-8999-2026-10-3-49-57","Introduction . The paper presents the development and testing of an index-based algorithm for detecting indicators of soil erosion by water from Sentinel-2 remote sensing data, considered as the first practical stage in constructing a hybrid physics-based mass-transport model with neural-network parameter identification. Materials and Methods . The algorithm relies on joint analysis of time series of the Normalized Difference Vegetation Index (NDVI) and the Bare Soil Index (BSI), followed by spatial change detection (ΔBSI) with threshold filtering at the 90th percentile. The algorithm was first validated on synthetic data and then applied to real Sentinel-2 imagery for two test sites – the Kamensky District of Rostov Oblast and the vicinity of Moldovanskoye village, Krymsky District, Krasnodar Krai – covering 10 dates within 2024. Results. The algorithm is shown to consistently identify spatially localised zones of increasing bare-soil exposure. Discussion . A key limitation – the inability to distinguish erosion-related exposure from ordinary agrotechnical bare soil — is discussed, along with directions for resolving it (digital elevation model overlay, multi-year analysis). Conclusion . An index-based algorithm for detecting indicators of water-induced soil erosion from Sentinel-2 multispectral time series was developed and validated. Verification on synthetic data and application to two real test areas in southern Russia confirmed its operability while demonstrating the need to separate erosion-related changes from agricultural soil exposure in subsequent model development.","引言。本文介绍了一种基于指数的算法开发与测试，该算法用于从Sentinel-2遥感数据中检测水蚀土壤侵蚀指标，被视为构建基于物理的质量输运混合模型并结合神经网络参数辨识的第一个实践阶段。材料与方法。该算法依赖于对归一化植被指数（NDVI）和裸土指数（BSI）时间序列的联合分析，随后进行空间变化检测（ΔBSI），并在第90百分位数处进行阈值滤波。该算法首先在合成数据上进行了验证，然后应用于两个测试点的真实Sentinel-2影像——罗斯托夫州卡缅斯基区和克拉斯诺达尔边疆区克里姆斯克区摩尔多瓦诺夫斯科耶村附近——覆盖2024年内的10个日期。结果。结果表明，该算法能够一致地识别出裸土暴露增加的空间局部区域。讨论。讨论了关键局限性——无法区分与侵蚀相关的裸土暴露和普通农技作业造成的裸土——以及解决该问题的方向（叠加数字高程模型、多年分析）。结论。开发并验证了一种基于指数的算法，用于从Sentinel-2多光谱时间序列中检测水蚀土壤侵蚀指标。在合成数据上的验证以及在俄罗斯南部两个真实测试区域的应用证实了其可操作性，同时表明在后续模型开发中有必要将与侵蚀相关的变化与农业裸土暴露区分开来。","COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES","2026-10-05T00:00:00Z",{"impact":28,"substance":26,"depth":241,"authority":183,"freshness":25,"relevant":8,"comment":1214},"提出基于NDVI与BSI时序的土壤水蚀指标遥感识别算法，并在俄罗斯南部两个试验区验证，方法有新意但属区域尺度研究，对国内农业遥感监测有参考价值。",[1216],{"name":1211,"url":1208},[246,838,4,116,1218],"土壤侵蚀",{"code":72,"slug":842,"name":843,"path":844,"parent_code":845},[1221],{"code":138,"slug":142,"name":1222,"path":1223,"parent_code":43},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":46,"locked":22,"version":47,"method":48,"evidence":1225},{"candidates":1226,"agricultural_evidence":53},[1227,1231,1235],{"code":72,"score":26,"supported":53,"title":1228,"abstract":1229,"tags":1230},[853],[134,135,853],[4,838,116],{"code":138,"score":338,"supported":53,"title":1232,"abstract":1233,"tags":1234},[142],[141,142],[],{"code":258,"score":8,"supported":22,"title":1236,"abstract":1237,"tags":1238},[],[],[246],[1240,1241],"Sentinel-2 土壤侵蚀 遥感","罗斯托夫州 克拉斯诺达尔 土壤侵蚀","Sentinel-2土壤侵蚀遥感-4365","10.23947\u002F2587-8999-2026-10-3-49-57",{"doi":1243,"openalex_id":1245,"authors":1246,"venue":1211,"cited_by_count":80,"oa_url":1208,"card":1253,"direction":96,"ingested_from":98},"W7220651513",[1247,1250],{"name":1248,"orcid":1249},"V. A. Blagin","https:\u002F\u002Forcid.org\u002F0009-0003-3239-6150",{"name":1251,"orcid":1252},"V. A. Beraia","https:\u002F\u002Forcid.org\u002F0009-0006-2270-5011",{"tldr":1254,"method":1255,"finding":1256,"direction":96,"opportunity":1257},"开发基于Sentinel-2时序NDVI与BSI指数的水土流失指标检测算法，并在俄罗斯南部实地验证。","联合NDVI与BSI时序，用ΔBSI阈值（90百分位）检测裸土变化，合成与真实数","算法能稳定识别裸土暴露增加区域，但无法区分侵蚀与农事裸土，需DEM和多年度分析。","可融合DEM与多年度时序，构建区分侵蚀与农事裸土的分类模型，并接入神经网络参数识别。","2026-10-07T23:30:29.080156Z",{"id":1260,"title":1261,"url":1262,"summary":1263,"summary_zh":1264,"content":17,"source_name":1265,"source_url":1262,"published_at":1160,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1266,"score_detail":1267,"sources":1269,"tags":1271,"primary_topic":17,"secondary_topics":1274,"classification":1275,"search_phrases":1292,"slug":1295,"view_count":80,"doi":1296,"paper":1297,"created_at":1310},4315,"أثرتغير الحرارة والامطار على التدهور البيئي في ناحية الكوير","https:\u002F\u002Fdoi.org\u002F10.66359\u002Fitqan.v2i1.034","يتناول البحث أثر التغيرات المناخية في تدهور الغطاء النباتي بناحية الكوير خلال المدة (1990–2023)، من خلال تحليل بيانات الحرارة والأمطار، واستخدام تقنيات الاستشعار عن بعد ونظم المعلومات الجغرافية (GIS) ومؤشر الغطاء النباتي (NDVI). أظهرت النتائج ارتفاعاً في درجات الحرارة مقابل انخفاض وتذبذب كميات الأمطار، مما أدى إلى تراجع كثافة الغطاء النباتي واتساع مساحات الغطاء الضعيف، خاصة في عامي 2000 و2023 بفعل الجفاف وارتفاع التبخر. كما أكدت الدراسة وجود علاقة واضحة بين التغيرات المناخية وتدهور الغطاء النباتي، مما يبرز أهمية تبني إدارة بيئية مستدامة للحفاظ على الموارد الطبيعية في المنطقة","本研究探讨了1990年至2023年期间气候变化对科韦尔地区植被退化的影响，通过分析气温和降水数据，并运用遥感技术、地理信息系统（GIS）和植被指数（NDVI）进行研究。结果表明，气温上升而降水量减少且波动，导致植被密度下降、稀疏植被面积扩大，尤其是在2000年和2023年，干旱和蒸发加剧了这一趋势。研究还证实气候变化与植被退化之间存在明显关系，凸显了采取可持续环境管理以保护该地区自然资源的重要性。","مجلة الاتقان للعلوم الإنسانية",57,{"impact":25,"substance":110,"depth":111,"authority":21,"freshness":52,"relevant":8,"comment":1268},"基于遥感与GIS的伊拉克区域植被退化实证研究，方法规范但属地方性案例，公共影响有限。",[1270],{"name":1265,"url":1262},[64,4,116,1272,1273],"GIS","植被退化",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":1276},{"candidates":1277,"agricultural_evidence":22,"reason":149},[1278,1282,1288],{"code":60,"score":532,"supported":53,"title":1279,"abstract":1280,"tags":1281},[],[64,129],[64],{"code":1283,"score":532,"supported":53,"title":1284,"abstract":1285,"tags":1287},"I4",[],[1286,1272],"地理信息",[1272],{"code":72,"score":52,"supported":53,"title":1289,"abstract":1290,"tags":1291},[],[134],[4,1273,116],[1293,1294],"الكوير NDVI","تدهور الغطاء النباتي GIS","الكويرNDVI-4315","10.66359\u002Fitqan.v2i1.034",{"doi":1296,"openalex_id":1298,"authors":1299,"venue":1265,"cited_by_count":80,"oa_url":1304,"card":1305,"direction":96,"ingested_from":98},"W7219900093",[1300,1302],{"name":1301,"orcid":17},"معن جاسم محمد",{"name":1303,"orcid":17},"أ. م. د    خالد صطم عطية","https:\u002F\u002Falitqan.iraqiqsa.org\u002Findex.php\u002FAIJHS\u002Farticle\u002Fdownload\u002Fitqan-v2i1-034\u002F389",{"tldr":1306,"method":1307,"finding":1308,"direction":96,"opportunity":1309},"研究1990–2023年伊拉克Kوير地区气温降水变化对植被退化的影响。","用气温降水数据、遥感、GIS和NDVI分析植被覆盖变化。","气温升高、降水减少且波动导致植被密度下降、弱覆盖扩大，2000和2023年最严重。","可结合多源遥感与气候模型，量化干旱区植被退化驱动机制并预测未来趋势。","2026-10-06T23:30:21.747683Z",{"id":1312,"title":1313,"url":1314,"summary":1315,"summary_zh":1316,"content":17,"source_name":1317,"source_url":1314,"published_at":1318,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":23,"score_detail":1319,"sources":1321,"tags":1323,"primary_topic":17,"secondary_topics":1327,"classification":1328,"search_phrases":1335,"slug":1338,"view_count":80,"doi":1339,"paper":1340,"created_at":1357},4230,"Vegetation dynamics in Mizoram Northeast India from 2005 to 2025 using landsat imagery and image specific NDVI thresholding","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44415-026-00136-2","Vegetation monitoring is important for evaluating landscape change in ecologically sensitive mountain regions. This study assessed vegetation dynamics across Mizoram, India, an eastern Himalayan-extension landscape, between 2005 and 2025 using Landsat 5 Thematic Mapper and Landsat 9 Operational Land Imager\u002FThermal Infrared Sensor surface-reflectance imagery. Satellite images were processed in Google Earth Engine, and normalized difference vegetation index have been used to derive vegetation and non-vegetation areas using image-specific thresholds. Spatial overlay analysis was subsequently applied to identify vegetation persistence, gain, loss, and persistent non-vegetated land at the state and district levels.Vegetation remained the dominant mapped land-cover category in both years but declined from 20,558.8 km² (97.52%) in 2005 to 20,283.8 km² (96.20%) in 2025, representing a net reduction of 275.0 km². Gross vegetation loss is 602.0 km², exceeding gross vegetation gain of 326.9 km². Vegetation loss was spatially heterogeneous, with the largest net declines observed in Lawngtlai, Kolasib, Saiha, Aizawl, and Lunglei districts, whereas Mamit, Champhai, and Saitual recorded stable net gains. The findings provide baseline information on vegetation-extent dynamics at the state and district level in Mizoram and offer useful insights for future vegetation monitoring, forest conservation, sustainable landscape management, and land-use planning in the eastern Himalayan region.","植被监测对于评估生态敏感山区景观变化具有重要意义。本研究利用Landsat 5专题制图仪（Thematic Mapper）和Landsat 9操作陆地成像仪\u002F热红外传感器（Operational Land Imager\u002FThermal Infrared Sensor）地表反射率影像，评估了2005年至2025年间印度米佐拉姆邦——喜马拉雅东延景观区的植被动态。卫星影像在Google Earth Engine中处理，利用归一化植被指数（normalized difference vegetation index）并结合各影像特定阈值提取植被区与非植被区。随后应用空间叠加分析，在邦和县两级识别植被持续、增加、减少及持续非植被土地。两个年份中植被均为主要土地覆盖类型，但面积从2005年的20558.8 km²（97.52%）下降至2025年的20283.8 km²（96.20%），净减少275.0 km²。植被总减少面积为602.0 km²，超过总增加面积326.9 km²。植被减少在空间上呈异质性，净减少最大的县为Lawngtlai、Kolasib、Saiha、Aizawl和Lunglei，而Mamit、Champhai和Saitual则录得稳定的净增加。研究结果为米佐拉姆邦邦和县两级植被范围动态提供了基线信息，并为喜马拉雅东部地区未来的植被监测、森林保护、可持续景观管理和土地利用规划提供了有益参考。","Discover Forests","2026-10-03T00:00:00Z",{"impact":25,"substance":26,"depth":27,"authority":28,"freshness":29,"relevant":8,"comment":1320},"基于Landsat与GEE的20年植被动态监测，方法规范、数据扎实，但属区域生态研究，与国内农业信息化关联较弱，公共价值有限。",[1322],{"name":1317,"url":1314},[4,116,1324,1325,1326],"Landsat","森林保护","植被动态",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":1329},{"candidates":1330,"agricultural_evidence":22,"reason":149},[1331],{"code":72,"score":61,"supported":22,"title":1332,"abstract":1333,"tags":1334},[],[],[1324,4,1326,116],[1336,1337],"Mizoram Landsat NDVI 植被","印度东北部 植被变化 遥感","MizoramLandsatNDVI植被-4230","10.1007\u002Fs44415-026-00136-2",{"doi":1339,"openalex_id":1341,"authors":1342,"venue":1317,"cited_by_count":80,"oa_url":1351,"card":1352,"direction":96,"ingested_from":98},"W7218499716",[1343,1346,1349],{"name":1344,"orcid":1345},"K. Lalramngaizuala","https:\u002F\u002Forcid.org\u002F0009-0005-5754-3449",{"name":1347,"orcid":1348},"Vishwambhar Prasad Sati","https:\u002F\u002Forcid.org\u002F0000-0001-6423-3119",{"name":1350,"orcid":17},"F. C. Kypacharili","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44415-026-00136-2.pdf",{"tldr":1353,"method":1354,"finding":1355,"direction":96,"opportunity":1356},"基于Landsat影像与NDVI阈值法评估印度米佐拉姆2005—2025年植被动态。","Landsat 5\u002F9地表反射率影像，Google Earth Engine，影","植被占比由97.52%降至96.20%，净减275 km²，损失602 km²大于增益326.9 k","可引入时序NDVI与机器学习区分植被退化驱动因素，并耦合地形与政策数据做多尺度监测。","2026-10-05T23:30:25.009678Z",{"id":1359,"title":1360,"url":1361,"summary":1362,"summary_zh":1363,"content":17,"source_name":1364,"source_url":1361,"published_at":1365,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1366,"score_detail":1367,"sources":1369,"tags":1371,"primary_topic":17,"secondary_topics":1375,"classification":1376,"search_phrases":1393,"slug":1396,"view_count":354,"doi":1397,"paper":1398,"created_at":1418},4173,"The effect of fire regimes on above-ground biomass change in Baikiaea-dominated woodlands of the Chobe region, northern Botswana, 1991 – 2023","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jenvman.2026.131030","Accurate assessment of fire-vegetation dynamics is crucial for accounting for changes in carbon fluxes. We use a fire frequency index (FFI) against data collected from Permanent Sample Plots (PSPs) in two Forest Reserves, in 1991 and 2023, to assess the effects of the recent fire history (1991-2023) on tree basal area (BA—as a reliable proxy for biomass) changes at PSPs in Chobe Botswana. We explore the efficacy of applying remotely sensed Vegetation Indices (VIs) to map predicted BA on the basis of 1991 assessment data. Our results show a significant increase in plot BA between 1991 ( Mean = 3.73 m 2 \u002Fha) and 2023 ( Mean = 5.63 m 2 \u002Fha) between 1991 and 2023. Fire frequency alone (dominated by severe fires), explained 27% of variation in ΔBA ( p \u003C 0.001); infrequently burnt plots accrued BA, frequently burnt plots lost BA. For mapping predicted BA at landscape scale, we found that 2023 plot BA was most strongly correlated with NDVI from a March 2023 image and plot Canopy Cover; no significant relationships were observed between assessed 1991 VIs and 1991 plot BA, probably as a result of the quality of the available Landsat archive imagery. Our results suggest management of fire alone is key to maintaining and enhancing woody biomass and achieving woodland degradation mitigation and climate initiatives in regional Baikiaea miombo woodlands. The influence of fire on change in woodland structure supports the development of incentivised savanna burning approaches, including carbon sequestration methodologies, for sustainable management of woodland savannas under semi-arid rainfall conditions (ca. 600 mm MAR) in northern Botswana and regionally.","准确评估火-植被动态对于核算碳通量变化至关重要。我们利用火频指数（FFI），结合1991年和2023年在两个森林保护区永久样地（PSPs）采集的数据，评估近期火历史（1991-2023年）对博茨瓦纳乔贝地区永久样地树木断面积（BA——作为生物量的可靠代用指标）变化的影响。我们探讨了应用遥感植被指数（VIs）基于1991年评估数据绘制预测BA图的可行性。结果表明，1991年（均值 = 3.73 m²\u002Fha）至2023年（均值 = 5.63 m²\u002Fha）间样地BA显著增加。火频单独（以严重火灾为主）解释了ΔBA变异的27%（p \u003C 0.001）；低频火烧样地BA增加，高频火烧样地BA减少。在景观尺度预测BA制图方面，我们发现2023年样地BA与2023年3月影像的NDVI及样地冠层盖度相关性最强；1991年评估VIs与1991年样地BA之间未观察到显著关系，可能是由于可用Landsat存档影像的质量所致。我们的结果表明，仅火管理就是维持和增强木本生物量、实现林地退化缓解和气候倡议的关键，适用于区域Baikiaea miombo林地。火对林地结构变化的影响支持发展激励性稀树草原燃烧方法，包括碳封存方法，以在博茨瓦纳北部及区域半干旱降雨条件（约600 mm年平均降雨量）下实现林地稀树草原的可持续管理。","Journal of Environmental Management","2026-10-01T00:00:00Z",75,{"impact":28,"substance":182,"depth":110,"authority":338,"freshness":29,"relevant":8,"comment":1368},"基于31年永久样地与遥感指数的火烧制度对稀树草原生物量影响研究，方法扎实、结论对碳汇与火烧管理有参考价值，但属区域案例，公共影响有限。",[1370],{"name":1364,"url":1361},[4,116,1372,1373,1374],"碳汇核算","草原火管理","植被生物量",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":1377},{"candidates":1378,"agricultural_evidence":22,"reason":149},[1379,1383,1389],{"code":72,"score":663,"supported":53,"title":1380,"abstract":1381,"tags":1382},[],[134],[4,116],{"code":1384,"score":668,"supported":53,"title":1385,"abstract":1386,"tags":1388},"H4",[],[1387],"carbon sequestration",[1372],{"code":60,"score":61,"supported":53,"title":1390,"abstract":1391,"tags":1392},[],[129],[],[1394,1395],"Chobe 稀树草原 火烧频率 生物量","Baikiaea 林地 NDVI 生物量估算","Chobe稀树草原火烧频率生物量-4173","10.1016\u002Fj.jenvman.2026.131030",{"doi":1397,"openalex_id":1399,"authors":1400,"venue":1364,"cited_by_count":80,"oa_url":1412,"card":1413,"direction":96,"ingested_from":98},"W7215094148",[1401,1404,1407,1410],{"name":1402,"orcid":1403},"Othusitse Lekoko","https:\u002F\u002Forcid.org\u002F0000-0002-9269-7006",{"name":1405,"orcid":1406},"Jeremy Russell‐Smith","https:\u002F\u002Forcid.org\u002F0000-0003-1489-2866",{"name":1408,"orcid":1409},"Andrew Edwards","https:\u002F\u002Forcid.org\u002F0000-0003-2823-4565",{"name":1411,"orcid":17},"Cameron Yates","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0301479726024904\u002Fpdf",{"tldr":1414,"method":1415,"finding":1416,"direction":929,"opportunity":1417},"研究博茨瓦纳乔贝地区1991-2023年火 regime 对地上生物量变化的影响。","火频指数、永久样地调查数据与遥感植被指数（NDVI）结合分析。","火频解释27%生物量变化，频繁火烧样地生物量下降，稀火样地增加。","可探索将火管理纳入碳汇核算，发展稀树草原燃烧碳补偿方法学。","2026-10-04T23:30:27.108019Z",{"id":1420,"title":1421,"url":1422,"summary":1423,"summary_zh":1424,"content":17,"source_name":1425,"source_url":1422,"published_at":1318,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":444,"score_detail":1426,"sources":1428,"tags":1430,"primary_topic":1433,"secondary_topics":1436,"classification":1438,"search_phrases":1453,"slug":1456,"view_count":668,"doi":1457,"paper":1458,"created_at":1482},4168,"High-throughput phenotyping of maize genotype using drone-based multispectral imagery for NDVI estimation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12892-026-00392-w","High-throughput phenotyping of maize genotype using drone-based multispectral imagery for NDVI estimation。Journal of Crop Science and Biotechnology","利用基于无人机的多光谱影像对玉米基因型进行高通量表型分析以估算NDVI。《作物科学与生物技术杂志》","Journal of Crop Science and Biotechnology",{"impact":28,"substance":110,"depth":27,"authority":183,"freshness":25,"relevant":8,"comment":1427},"核心期刊论文，方法具体但属细分技术进展，影响范围有限，适合主题聚合而非每日精选。",[1429],{"name":1425,"url":1422},[246,718,4,1431,1432],"无人机遥感","高通量表型",{"code":715,"slug":719,"name":718,"path":1434,"parent_code":1435},"\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[1437],{"code":72,"slug":842,"name":843,"path":844,"parent_code":845},{"status":46,"locked":22,"version":47,"method":48,"evidence":1439},{"candidates":1440,"agricultural_evidence":53},[1441,1445,1449],{"code":715,"score":111,"supported":53,"title":1442,"abstract":1443,"tags":1444},[719],[718,719],[718],{"code":72,"score":183,"supported":53,"title":1446,"abstract":1447,"tags":1448},[853],[853],[4,1431],{"code":258,"score":8,"supported":22,"title":1450,"abstract":1451,"tags":1452},[],[],[246],[1454,1455],"玉米 无人机 多光谱 NDVI","高通量表型 玉米 基因型","玉米无人机多光谱NDVI-4168","10.1007\u002Fs12892-026-00392-w",{"doi":1457,"openalex_id":1459,"authors":1460,"venue":1425,"cited_by_count":80,"oa_url":17,"card":1477,"direction":96,"ingested_from":98},"W7218576117",[1461,1464,1467,1470,1473,1475],{"name":1462,"orcid":1463},"Nining Nurini Andayani","https:\u002F\u002Forcid.org\u002F0000-0002-4069-1307",{"name":1465,"orcid":1466},"Muhammad Azrai","https:\u002F\u002Forcid.org\u002F0000-0002-5546-9485",{"name":1468,"orcid":1469},"Roy Efendi","https:\u002F\u002Forcid.org\u002F0000-0002-6515-1198",{"name":1471,"orcid":1472},"Muhammad Aqil","https:\u002F\u002Forcid.org\u002F0000-0002-1842-9230",{"name":1474,"orcid":17},"Dwitari Lestary",{"name":1476,"orcid":17},"Haikal Akbar",{"tldr":1478,"method":1479,"finding":1480,"direction":96,"opportunity":1481},"利用无人机多光谱影像对玉米基因型进行高通量表型分析并估算NDVI。","无人机多光谱成像与NDVI植被指数估算。","无人机多光谱影像可高效获取玉米基因型NDVI表型数据。","可探索多光谱NDVI与产量基因型关联，构建高通量表型育种决策模型。","2026-10-04T23:30:18.734738Z",{"id":1484,"title":1485,"url":1486,"summary":1487,"summary_zh":1488,"content":17,"source_name":1489,"source_url":1486,"published_at":1490,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1491,"score_detail":1492,"sources":1494,"tags":1496,"primary_topic":1500,"secondary_topics":1501,"classification":1502,"search_phrases":1532,"slug":1535,"view_count":354,"doi":1536,"paper":1537,"created_at":1548},3894,"Assessment of Spatio-temporal Dynamics of the Remote Sensing Ecological Index (RSEI) based on Landsat 8 Data: A Case Study of Kherson Region, Ukraine (2013–2025)","https:\u002F\u002Fdoi.org\u002F10.36023\u002Fujrs.2026.13.3.310","This scientific paper presents a comprehensive assessment of the spatio-temporal dynamics of the ecological state of the Kherson region from 2013 to 2025, with a particular focus on the consequences of the full-scale military aggression by the Russian Federation. Amidst intensive hostilities, the destruction of critical infrastructure, and global climate challenges, objective geospatial monitoring of the natural environment is a crucial task for documenting damages and forecasting the region's future development.The study is based on the use of modern geographic information systems and remote sensing data. Using the Google Earth Engine cloud computing platform and an archive of multispectral satellite imagery from Landsat 8 OLI\u002FTIRS, the Remote Sensing Ecological Index (RSEI) was calculated. This index comprehensively integrates four key biophysical indicators: greenness (based on the Normalized Difference Vegetation Index, NDVI), wetness (Wet), dryness (based on the Normalized Difference Build-up and Soil Index, NDBSI), and land surface temperature (LST). The application of Principal Component Analysis (PCA) allowed these indicators to be mathematically combined into a single standardized index, where the first principal component (PC1) explains the highest proportion of the total spatial data variance. This indicates that the first principal component (PC1) successfully captures the vast majority of the variance among the four input indicators, which is a basic premise of the RSEI methodology.The results of the cartographic and statistical analyses revealed a sharp, unprecedented deterioration in the ecosystem health of the study region in 2025 compared to retrospective data from previous years. A critical drop in the average RSEI value was established – from 0.381 in the relatively stable year of 2021 to 0.205 in 2025. The spatial assessment demonstrated that the area of territories classified as being in a state of ecological crisis (the «Terrible» category, with an index ranking from 0 to 0.2) more than doubled: from 26% of the region's total area in 2021 to a critical 60% in 2025.The sharp drop in RSEI spatially aligns with the large-scale hydrological transformations caused by the destruction of the Kakhovka Dam; however, this indicator also reflects the synergistic impact of intense hostilities, fires, the cessation of irrigation, and climate anomalies. Kakhovka Dam destruction led to the catastrophic destruction of the hydrographic network, the disappearance of water supply sources for vital agricultural lands, and the disruption of life-support systems in urbanized areas. The only exception against the general backdrop of the ecological catastrophe is the recorded local natural restoration of the old Dnipro riverbed ecosystem, which is gradually returning to its historical state that preceded massive anthropogenic intervention and river regulation in the 1950s.In conclusion, the synergistic impact of continuous large-scale hostilities, the destruction of established ecosystem links, and the exacerbation of global climate change shape an extremely bleak forecast. Further degradation of the soil and vegetation cover and the depletion of water resources create a high risk of transforming a significant part of the Kherson region into a territory unfit for safe living, agriculture, and socio-economic functioning. Disclosure of AI use. In the process of conducting research and preparing the manuscript, the author used the large language model Google Gemini 3 Pro. The tool acted as a technical assistant and consultant at the stages:1. Software development: assistance in writing, debugging and optimizing JavaScript scripts for the Google Earth Engine environment, in particular the implementation of the PCA algorithm.2. Text preparation: providing recommendations on structuring the work, describing the methodology and improving the academic style of presentation.The author carried out a full check of the generated code and text, verified the results obtained and bears sole responsibility for the content of this publication. Data availability and integrity. The JavaScript code for Google Earth Engine developed for this study has been deposited in the Zenodo repository with a reserved DOI to ensure long-term accessibility and reproducibility of the results. The code will be publicly available upon acceptance of the manuscript for publication. The data can be provided by the author upon reasonable request. Conflicts of Interest. The author declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements. The author is grateful to the reviewers and editors for their valuable comments, recommendations, and attention to the work.","本文对2013年至2025年赫尔松地区生态状况的时空动态进行了综合评估，尤其关注俄罗斯联邦全面军事侵略所造成的后果。在激烈的敌对行动、关键基础设施的破坏以及全球气候挑战的背景下，对自然环境进行客观的地理空间监测是记录损害和预测该地区未来发展的关键任务。本研究基于现代地理信息系统和遥感数据的应用。利用Google Earth Engine云计算平台和Landsat 8 OLI\u002FTIRS多光谱卫星影像档案，计算了遥感生态指数（RSEI）。该指数综合集成了四个关键生物物理指标：绿度（基于归一化差异植被指数，NDVI）、湿度（Wet）、干度（基于归一化差异建筑与土壤指数，NDBSI）以及地表温度（LST）。主成分分析（PCA）的应用使这些指标能够在数学上组合为一个标准化的单一指数，其中第一主成分（PC1）解释了总空间数据方差的最大比例。这表明第一主成分（PC1）成功捕捉了四个输入指标之间的绝大部分方差，这是RSEI方法论的基本前提。制图与统计分析的结果揭示，与往年回顾性数据相比，2025年研究区域的生态系统健康出现了急剧且前所未有的恶化。研究确定了平均RSEI值的临界下降——从相对稳定的2021年的0.381降至2025年的0.205。空间评估表明，被归类为生态危机状态的领土面积（“极差”类别，指数排名从0到0.2）增加了一倍以上：从2021年占该地区总面积的26%增至2025年临界水平的60%。RSEI的急剧下降在空间上与卡霍夫卡大坝被毁所引发的大规模水文变化相吻合；然而，该指标也反映了激烈敌对行动、火灾、灌溉停止以及气候异常的协同影响。卡霍夫卡大坝被毁导致水文网络的灾难性破坏，","Ukrainian journal of remote sensing","2026-09-30T00:00:00Z",73,{"impact":28,"substance":182,"depth":110,"authority":28,"freshness":29,"relevant":8,"comment":1493},"基于Landsat 8与Google Earth Engine的RSEI时空动态研究，方法规范、数据翔实，对战争与气候双重压力下的农业生态退化具有实证价值，但属区域案例、与国内三农信息化关联偏弱。",[1495],{"name":1489,"url":1486},[4,116,1497,1498,1499],"生态遥感","Landsat 8","乌克兰农业",{"code":72,"slug":842,"name":843,"path":844,"parent_code":845},[],{"status":46,"locked":22,"version":47,"method":48,"evidence":1503},{"candidates":1504,"agricultural_evidence":53},[1505,1509,1513,1517,1522,1527],{"code":72,"score":26,"supported":53,"title":1506,"abstract":1507,"tags":1508},[135],[134,135,853],[4,1497,116],{"code":138,"score":52,"supported":53,"title":1510,"abstract":1511,"tags":1512},[],[141,142],[],{"code":39,"score":52,"supported":53,"title":1514,"abstract":1515,"tags":1516},[],[797,40],[],{"code":1283,"score":52,"supported":53,"title":1518,"abstract":1519,"tags":1521},[],[1286,1520],"geographic information",[],{"code":60,"score":61,"supported":53,"title":1523,"abstract":1524,"tags":1526},[],[1525],"climate change",[],{"code":856,"score":61,"supported":53,"title":1528,"abstract":1529,"tags":1531},[],[1530],"large language model",[],[1533,1534],"Kakhovka Dam 生态遥感","乌克兰农业 生态遥感 遥感监测 Landsat 8","KakhovkaDam生态遥感-3894","10.36023\u002Fujrs.2026.13.3.310",{"doi":1536,"openalex_id":1538,"authors":1539,"venue":1489,"cited_by_count":80,"oa_url":1486,"card":1543,"direction":96,"ingested_from":98},"W7214889788",[1540],{"name":1541,"orcid":1542},"Maksym Tymchyshyn","https:\u002F\u002Forcid.org\u002F0000-0003-2966-4794",{"tldr":1544,"method":1545,"finding":1546,"direction":96,"opportunity":1547},"基于Landsat 8与GEE计算RSEI，评估乌克兰赫尔松地区2013–2025年生态时空变化。","Google Earth Engine + Landsat 8 OLI\u002FTIRS","2025年RSEI均值由2021年0.381降至0.205，生态危机区由26%扩至60%。","可将RSEI框架迁移至战乱或灌溉中断农区，结合作物物候验证生态退化对农业生产的传导。","2026-10-01T23:30:28.551437Z",{"id":1550,"title":1551,"url":1552,"summary":1553,"summary_zh":1554,"content":17,"source_name":1555,"source_url":1552,"published_at":1556,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":80,"score_detail":1557,"sources":1559,"tags":1561,"primary_topic":17,"secondary_topics":1565,"classification":1566,"search_phrases":1587,"slug":1590,"view_count":668,"doi":1591,"paper":1592,"created_at":1619},3840,"Open-Source GIS Applications for Flood and Landslide Assessment: Comparative Cases from Poland and Malaysia","https:\u002F\u002Fdoi.org\u002F10.36777\u002Fjag2026.5.2.6","Research on natural disasters requires analytical procedures that are rapid, reproducible, and based on sound scientific principles. Such procedures should also be accessible to institutions responsible for environmental monitoring, land-use planning, and disaster risk reduction. This study evaluated the practical use of publicly available geospatial data and open-source GIS tools for the preliminary assessment of two contrasting natural hazards: the September 2024 flood in Lower Silesia, Poland, with particular focus on the urban floodplain of Wrocław, and the December 2022 landslide in Batang Kali, Selangor, Malaysia. The study utilised Copernicus Sentinel-2 MSI Level-2A satellite imagery, digital elevation model (DEM) data from the Copernicus programme, OpenStreetMap vector layers, and spatial processing in QGIS. The methodology employed a pre- and post-event change-detection approach based on spectral indices. The Normalized Difference Water Index (NDWI) was used to map the extent of flooding, while the Normalized Difference Vegetation Index (NDVI) was used to identify vegetation disturbances associated with the landslide. Raster classification, elevation analysis, vector-layer overlay, and thematic mapping were subsequently used to delineate disaster-affected areas and examine their spatial relationships with built-up areas, hydrographic features, and terrain morphology. The results show that the Copernicus Data Space Ecosystem and QGIS provide a transparent and cost-effective analytical environment for the preliminary assessment of natural disaster impacts. In the Wrocław case, the NDWI helped identify flooded areas and infrastructure potentially exposed to flooding. In the Batang Kali case, differences in NDVI helped identify patterns of vegetation disturbance associated with the landslide-affected area. The study demonstrates that open-source geospatial tools can support repeatable natural disaster analysis, geospatial education, and early-stage decision support for disaster risk reduction.","自然灾害研究需要快速、可重复且基于可靠科学原理的分析程序。此类程序还应能为负责环境监测、土地利用规划和灾害风险减少的机构所使用。本研究评估了公开地理空间数据和开源GIS工具在两类不同自然灾害初步评估中的实际应用：2024年9月波兰下西里西亚洪水，重点关注弗罗茨瓦夫的城市洪泛区；以及2022年12月马来西亚雪兰莪州巴当加里滑坡。研究使用了哥白尼Sentinel-2 MSI Level-2A卫星影像、哥白尼计划的数字高程模型（DEM）数据、OpenStreetMap矢量图层，并在QGIS中进行空间处理。方法采用基于光谱指数的灾前灾后变化检测方法。归一化差异水体指数（NDWI）用于制图洪水范围，归一化差异植被指数（NDVI）用于识别与滑坡相关的植被扰动。随后利用栅格分类、高程分析、矢量图层叠加和专题制图来圈定受灾区域，并考察其与建成区、水系特征和地形形态的空间关系。结果表明，哥白尼数据空间生态系统和QGIS为自然灾害影响的初步评估提供了透明且具有成本效益的分析环境。在弗罗茨瓦夫案例中，NDWI有助于识别淹没区和可能受洪水影响的基础设施。在巴当加里案例中，NDVI差异有助于识别与滑坡影响区相关的植被扰动格局。研究表明，开源地理空间工具能够支持可重复的自然灾害分析、地理空间教育以及灾害风险减少的早期决策支持。","Journal of Asian Geography","2026-09-29T00:00:00Z",{"impact":80,"substance":80,"depth":80,"authority":80,"freshness":80,"relevant":80,"comment":1558},"该论文聚焦开源GIS与遥感在洪涝滑坡灾害评估中的应用，属地理信息与灾害风险领域，与三农、农业信息化、智慧农业无直接关联，故不进入每日精选。",[1560],{"name":1555,"url":1552},[4,116,1562,1563,1564],"灾害评估","NDWI","开源GIS",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":1567},{"candidates":1568,"agricultural_evidence":22,"reason":149},[1569,1574,1579,1583],{"code":1283,"score":1570,"supported":53,"title":1571,"abstract":1572,"tags":1573},11,[1272],[1272],[],{"code":856,"score":61,"supported":53,"title":1575,"abstract":1576,"tags":1578},[],[1577],"decision support",[],{"code":72,"score":354,"supported":22,"title":1580,"abstract":1581,"tags":1582},[],[],[4,116],{"code":60,"score":8,"supported":22,"title":1584,"abstract":1585,"tags":1586},[],[],[1562],[1588,1589],"QGIS 洪水 滑坡 评估","Sentinel-2 灾害 遥感","QGIS洪水滑坡评估-3840","10.36777\u002Fjag2026.5.2.6",{"doi":1591,"openalex_id":1593,"authors":1594,"venue":1555,"cited_by_count":80,"oa_url":1613,"card":1614,"direction":96,"ingested_from":98},"W7214781133",[1595,1597,1599,1601,1603,1605,1608,1610],{"name":1596,"orcid":17},"Oliwia Szweda",{"name":1598,"orcid":17},"Wazif Akmal Bin Hassdi",{"name":1600,"orcid":17},"Muhammad Naqib Akmal Bin Hasbullah",{"name":1602,"orcid":17},"Kacper Kaczmarski",{"name":1604,"orcid":17},"Divbya A.P. Lachimanan",{"name":1606,"orcid":1607},"K. Tomiczek","https:\u002F\u002Forcid.org\u002F0000-0001-9227-310X",{"name":1609,"orcid":17},"Hareyani Zabidi",{"name":1611,"orcid":1612},"Monika Żogała","https:\u002F\u002Forcid.org\u002F0000-0002-2309-6493","https:\u002F\u002Fejournal.usm.my\u002Fjag\u002Farticle\u002Fdownload\u002F6257\u002F9385",{"tldr":1615,"method":1616,"finding":1617,"direction":96,"opportunity":1618},"用开源GIS与Sentinel-2数据对比评估波兰洪水与马来西亚滑坡灾害影响。","Sentinel-2影像、DEM、OpenStreetMap与QGIS，NDWI","开源工具可低成本、可重复地识别洪水范围与滑坡植被扰动，支持早期决策。","可将该开源遥感流程迁移至农田洪涝与滑坡风险监测，结合作物损失评估与预警。","2026-09-30T23:30:28.033994Z",{"id":1621,"title":1622,"url":1623,"summary":1624,"summary_zh":1625,"content":17,"source_name":1626,"source_url":1623,"published_at":1556,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1627,"score_detail":1628,"sources":1630,"tags":1632,"primary_topic":1635,"secondary_topics":1640,"classification":1642,"search_phrases":1658,"slug":1661,"view_count":61,"doi":1662,"paper":1663,"created_at":1673},3838,"Forecasting Cotton Productivity in the Mil-Mugan Economic Region using Climate Variables and Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.52171\u002Fherald.342","Satellite imagery is considered as one of the most valuable and functional data sources for evaluating and forecasting production in agricultural systems. This study aims to forecast productivity in cotton fields within the Mil-Mugan economic region by incorporating vegetation indices and climatic variables. NDVI indicators were derived from Sentinel-2 data, temperature (T) was obtained from ERA5-Land data, and precipitation (R) was collected from the CHIRPS database. The data were analyzed using Google Earth Engine, ArcGIS Pro, and Python environments, consolidating them into an integrated temporal-spatial dimension. The correlation and regression analysis assessed the link among NDVI, temperature, precipitation, and productivity, leading to the development of a multivariate model. The findings derived from the model indicate that NDVI is the primary factor influencing productivity. The model developed for one region was implemented in the other three locations, and the correlation between the actual and predicted indicators was assessed using R² and RMSE metrics. The results indicate that the model's forecast accuracy fluctuates within the 90% range, considered appropriate for predicting regional cotton yield. The study's findings are significant for enhancing operational monitoring and planning of agrotechnical decisions with satellite data.","卫星影像被认为是评估和预测农业系统产量最有价值且最实用的数据来源之一。本研究旨在通过整合植被指数与气候变量，预测米尔-穆甘经济区棉田的生产力。NDVI指标源自Sentinel-2数据，温度（T）取自ERA5-Land数据，降水（R）来自CHIRPS数据库。数据在Google Earth Engine、ArcGIS Pro和Python环境中进行分析，并将其整合为统一的时空维度。通过相关性与回归分析评估了NDVI、温度、降水与生产力之间的关系，进而构建了多变量模型。模型结果表明，NDVI是影响生产力的主要因素。将为某一区域开发的模型应用于其他三个地点，并使用R²和RMSE指标评估实际值与预测值之间的相关性。结果表明，模型的预测精度在90%范围内波动，被认为适用于预测区域棉花产量。本研究结果对于利用卫星数据加强农艺决策的业务化监测与规划具有重要意义。","Herald of the Azerbaijan Engineering Academy",63,{"impact":25,"substance":26,"depth":27,"authority":21,"freshness":29,"relevant":8,"comment":1629},"基于Sentinel-2与气候变量的棉花产量预测模型，方法扎实、精度达90%，但属区域案例研究，公共影响有限。",[1631],{"name":1626,"url":1623},[246,1633,1634,134,4],"产量预测","棉花",{"code":1636,"slug":1637,"name":1638,"path":1639,"parent_code":1435},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops",[1641],{"code":72,"slug":842,"name":843,"path":844,"parent_code":845},{"status":46,"locked":22,"version":47,"method":48,"evidence":1643},{"candidates":1644,"agricultural_evidence":53},[1645,1650,1654],{"code":1636,"score":111,"supported":53,"title":1646,"abstract":1648,"tags":1649},[1647],"cotton",[1634,1647],[1634],{"code":72,"score":1570,"supported":53,"title":1651,"abstract":1652,"tags":1653},[135],[],[4,1633,134],{"code":258,"score":8,"supported":22,"title":1655,"abstract":1656,"tags":1657},[],[],[246],[1659,1660],"Mil-Mugan 棉花 遥感","Sentinel-2 NDVI 棉花产量","Mil-Mugan棉花遥感-3838","10.52171\u002Fherald.342",{"doi":1662,"openalex_id":1664,"authors":1665,"venue":1626,"cited_by_count":80,"oa_url":1623,"card":1668,"direction":96,"ingested_from":98},"W7214748696",[1666],{"name":1667,"orcid":17},"B.H. Babashli",{"tldr":1669,"method":1670,"finding":1671,"direction":96,"opportunity":1672},"结合Sentinel-2 NDVI与气候变量，构建多元模型预测米尔-穆甘地区棉花产量。","Sentinel-2 NDVI、ERA5-Land温度、CHIRPS降水，GEE","NDVI是产量主控因子，模型跨区预测精度约90%，可用于区域估产。","可引入多时相NDVI与机器学习，提升跨区域迁移性和极端气候下的估产鲁棒性。","2026-09-30T23:30:27.786044Z",{"id":1675,"title":1676,"url":1677,"summary":1678,"summary_zh":1679,"content":17,"source_name":1680,"source_url":1677,"published_at":1681,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1682,"score_detail":1683,"sources":1685,"tags":1687,"primary_topic":17,"secondary_topics":1689,"classification":1690,"search_phrases":1707,"slug":1710,"view_count":354,"doi":1711,"paper":1712,"created_at":1729},3791,"Topographic heterogeneity regulates nonlinear vegetation responses to climate and human activities in China’s ecological transition zone","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fffgc.2026.1948232","Ecological transition zones, identified as climate-sensitive areas and biodiversity hotspots, are essential for maintaining regional ecological balance through their vegetation dynamics. However, the impact of topographic heterogeneity on the nonlinear responses of vegetation to climate change and human activities is not well understood. This gap limits our comprehension of ecosystem resilience. This study focuses on the ecological transition zone of the Qinling–Daba Mountains in China. We utilized 30-m resolution Landsat NDVI data from 2000 to 2022, along with climate, human activity, and topographic data. To characterize vegetation dynamics, we employed Theil–Sen trend analysis, the Mann–Kendall test, and the Hurst index. A Bayesian-optimized XGBoost model, combined with SHAP values, was employed to evaluate the relative contributions and response patterns of potential drivers across various vegetation trend zones and topographic gradients. The analysis revealed a significant greening trend, with the normalized difference vegetation index (NDVI) increasing at a rate of 0.0051 yr. −1 ( p \u003C 0.05). Approximately 90.13% of the region displayed an improving trend, while 89.83% indicated a propensity for continued enhancement. The most pronounced vegetation improvements were observed primarily at moderate elevations (1,750–2,250 m), on moderate slopes (15–20°), and on north-facing slopes. Land use and land cover (LULC) (42.5%), elevation (13.2%), population density (8.2%), and precipitation (7.0%) emerged as the most significant contributors to NDVI predictions across the study area. LULC consistently served as the most influential predictor in both regions experiencing significant increases and those facing declines. In contrast, the relative importance and nonlinear response patterns of other factors varied across different trend zones. These findings provide a scientific foundation for targeted ecological restoration and adaptive vegetation management in ecological transition zones.","生态过渡带被认为是气候敏感区和生物多样性热点区，其植被动态对维持区域生态平衡至关重要。然而，地形异质性如何影响植被对气候变化和人类活动的非线性响应，目前尚不明确。这一空白限制了我们对生态系统恢复力的理解。本研究以中国秦岭—大巴山生态过渡带为对象，利用2000—2022年30 m分辨率的Landsat NDVI数据，并结合气候、人类活动和地形数据。为刻画植被动态，我们采用了Theil–Sen趋势分析、Mann–Kendall检验和Hurst指数。采用贝叶斯优化的XGBoost模型，并结合SHAP值，评估了不同植被趋势区和地形梯度上潜在驱动因子的相对贡献及响应模式。分析揭示出显著的绿化趋势，归一化植被指数（NDVI）以0.0051 yr⁻¹的速率增加（p \u003C 0.05）。约90.13%的区域呈改善趋势，89.83%的区域表现出持续增强的倾向。植被改善最显著的区域主要出现在中等海拔（1,750–2,250 m）、中等坡度（15–20°）和北向坡。土地利用与土地覆盖（LULC）（42.5%）、海拔（13.2%）、人口密度（8.2%）和降水（7.0%）是整个研究区NDVI预测中最重要的贡献因子。在显著增加区和下降区，LULC始终是最具影响力的预测因子。相比之下，其他因素的相对重要性和非线性响应模式在不同趋势区存在差异。这些发现为生态过渡带的针对性生态恢复和适应性植被管理提供了科学依据。","Frontiers in Forests and Global Change","2026-09-28T00:00:00Z",76,{"impact":338,"substance":182,"depth":110,"authority":183,"freshness":29,"relevant":8,"comment":1684},"基于30米Landsat NDVI与XGBoost-SHAP量化地形对植被非线性响应的研究，方法新颖、数据扎实，对生态过渡带精准修复有参考价值，但属学术论文，公共传播价值有限。",[1686],{"name":1680,"url":1677},[64,1688,4,116,1326],"生态修复",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":1691},{"candidates":1692,"agricultural_evidence":22,"reason":149},[1693,1699,1703],{"code":67,"score":532,"supported":53,"title":1694,"abstract":1695,"tags":1698},[],[1696,1697],"生物多样性","biodiversity",[1688],{"code":60,"score":532,"supported":53,"title":1700,"abstract":1701,"tags":1702},[],[64,1525],[64],{"code":72,"score":61,"supported":22,"title":1704,"abstract":1705,"tags":1706},[],[],[4,1326,116],[1708,1709],"秦岭大巴山 生态过渡带 植被","NDVI 地形异质性 植被响应","秦岭大巴山生态过渡带植被-3791","10.3389\u002Fffgc.2026.1948232",{"doi":1711,"openalex_id":1713,"authors":1714,"venue":1680,"cited_by_count":80,"oa_url":1723,"card":1724,"direction":96,"ingested_from":98},"W7214562314",[1715,1717,1719,1721],{"name":1716,"orcid":17},"Xinwen Su",{"name":1718,"orcid":17},"Di Wen",{"name":1720,"orcid":17},"Jun Li",{"name":1722,"orcid":17},"Peng Dailiang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fforests-and-global-change\u002Farticles\u002F10.3389\u002Fffgc.2026.1948232\u002Fpdf",{"tldr":1725,"method":1726,"finding":1727,"direction":96,"opportunity":1728},"研究秦岭-大巴山生态过渡带植被非线性响应气候与人类活动，并揭示地形异质性的调控作用。","用2000-2022年30米Landsat NDVI、气候、人类活动和地形数据，","区域显著绿化，NDVI增速0.0051\u002F年；土地利用、海拔、人口密度和降水是主要驱动因子，地形梯度调","可探究其他生态过渡带中地形异质性对植被非线性响应的普适性，并耦合多源遥感与可解释机器学习优化生态修复。","2026-09-29T23:30:28.608664Z",{"id":1731,"title":1732,"url":1733,"summary":1734,"summary_zh":17,"content":17,"source_name":238,"source_url":1733,"published_at":1735,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1736,"score_detail":1737,"sources":1739,"tags":1741,"primary_topic":1743,"secondary_topics":1749,"classification":1751,"search_phrases":1767,"slug":1770,"view_count":663,"doi":1771,"paper":1772,"created_at":1799},3619,"Comparison of NDVI obtained from an active proximal sensor and UAV multispectral imagery in coffee","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10767247\u002Fv1","Comparison of NDVI obtained from an active proximal sensor and UAV multispectral imagery in coffee。Research Square","2026-09-24T00:00:00Z",51,{"impact":25,"substance":338,"depth":111,"authority":52,"freshness":25,"relevant":8,"comment":1738},"咖啡园NDVI主动传感器与无人机多光谱对比研究，方法有参考价值但属细分作物技术验证，影响面有限。",[1740],{"name":238,"url":1733},[246,1003,134,4,1742],"咖啡种植",{"code":1744,"slug":1745,"name":1746,"path":1747,"parent_code":1748},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[1750],{"code":72,"slug":842,"name":843,"path":844,"parent_code":845},{"status":46,"locked":22,"version":47,"method":48,"evidence":1752},{"candidates":1753,"agricultural_evidence":53},[1754,1758,1763],{"code":72,"score":183,"supported":53,"title":1755,"abstract":1756,"tags":1757},[853],[853],[4,134],{"code":1744,"score":28,"supported":53,"title":1759,"abstract":1761,"tags":1762},[1760],"coffee",[1760],[1742],{"code":258,"score":8,"supported":22,"title":1764,"abstract":1765,"tags":1766},[],[],[246],[1768,1769],"咖啡 NDVI 无人机 多光谱","Research Square 咖啡 遥感","咖啡NDVI无人机多光谱-3619","10.21203\u002Frs.3.rs-10767247\u002Fv1",{"doi":1771,"openalex_id":1773,"authors":1774,"venue":238,"cited_by_count":80,"oa_url":1798,"card":17,"direction":96,"ingested_from":98},"W7214220732",[1775,1777,1779,1781,1783,1786,1788,1790,1793,1796],{"name":1776,"orcid":17},"Gabriel de Morais Campos",{"name":1778,"orcid":17},"Aline Bhering Silva",{"name":1780,"orcid":17},"Cileimar Aparecida da Silva",{"name":1782,"orcid":17},"Vanda Maria Salles Andrade",{"name":1784,"orcid":1785},"Thaline M. Pimenta","https:\u002F\u002Forcid.org\u002F0000-0002-5002-177X",{"name":1787,"orcid":17},"Andressa Barcellos Silva",{"name":1789,"orcid":17},"Marco Thúlio Gonçalves Vieira",{"name":1791,"orcid":1792},"Daniel Marçal de Queiroz","https:\u002F\u002Forcid.org\u002F0000-0003-0987-3855",{"name":1794,"orcid":1795},"Fábio Daniel Tancredi","https:\u002F\u002Forcid.org\u002F0000-0002-7619-2200",{"name":1797,"orcid":17},"Flora Maria Melo Villar","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10767247\u002Flatest.pdf","2026-09-27T23:30:34.698079Z",{"id":1801,"title":1802,"url":1803,"summary":1804,"summary_zh":1805,"content":17,"source_name":1806,"source_url":1803,"published_at":1807,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":239,"score_detail":1808,"sources":1810,"tags":1812,"primary_topic":1814,"secondary_topics":1818,"classification":1819,"search_phrases":1841,"slug":1844,"view_count":354,"doi":1845,"paper":1846,"created_at":1861},3568,"Interpretable multi-index framework for extracting dry-season built-up areas: benchmarking machine learning with Sentinel-2","https:\u002F\u002Fdoi.org\u002F10.4995\u002Fraet.2027.25885","Spectral similarity between built-up surfaces and exposed dry soil significantly constrains built-up area extraction during dry seasons. Although machine-learning techniques can address this challenge by modeling complex spectral relationships, they generally require sufficiently large and representative labeled training datasets. This study therefore investigates a less data-demanding, rule-based multi-index approach for dry-season built-up mapping, aiming to reduce reliance on large labeled training datasets while maintaining effective classification performance. Sentinel-2A imagery acquired in April 2024 was analyzed, and temporal validation was conducted for 2020, 2022, and 2024. Among the tested combinations, the Built-up Area Extraction Index (BAEI), Dry BareSoil Index (DBSI), and Normalized Difference Vegetation Index (NDVI) achieved the highest performance, yielding 95 % overall accuracy and a Kappa coefficient of 0.89. This represents a substantial improvement over singleindex BAEI (80 % overall accuracy; Kappa 0.55), increasing the built-up user’s accuracy from 61 % to 92 %. Multitemporal validation confirmed that the optimized BAEI–DBSI–NDVI decision rules remained stable across the 2020–2024 dry-season images without recalibration, achieving 93–96 % overall accuracy and a built-up F1-score of 84–93 %. Spatial analysis demonstrated robust performance across peripheral and agricultural zones, with moderate variability in dense urban cores due to spectral heterogeneity. To evaluate the operational robustness of the proposed framework, it was benchmarked against machine-learning classifiers, specifically Support Vector Machine and Random Forest (RF). Under the specific conditions of this study, the proposed rule-based method (95 % accuracy) marginally outperformed both SVM (93 %) and RF (92 %), while offering greater transparency and reducing dependency on large, labeled training datasets. Furthermore, feature importance analysis confirmed the critical role of these selected indices in resolving spectral confusion. These findings suggest that the proposed framework offers a transparent and computationally efficient approach for dry-season urban monitoring in tropical coastal environments similar to Visakhapatnam.","建成表面与裸露干土之间的光谱相似性显著制约了旱季建成区提取。尽管机器学习技术可通过建模复杂光谱关系来应对这一挑战，但其通常需要足够大且具有代表性的标记训练数据集。因此，本研究探讨了一种对数据需求较低、基于规则的多指数方法用于旱季建成区制图，旨在减少对大规模标记训练数据集的依赖，同时保持有效的分类性能。研究分析了2024年4月获取的Sentinel-2A影像，并对2020年、2022年和2024年进行了时间验证。在测试的组合中，建成区提取指数（BAEI）、干裸土指数（DBSI）和归一化差异植被指数（NDVI）表现最佳，总体精度达95%，Kappa系数为0.89。相较于单一指数BAEI（总体精度80%；Kappa 0.55），这一结果有显著提升，建成区用户精度从61%提高至92%。多时相验证证实，优化后的BAEI–DBSI–NDVI决策规则在2020—2024年旱季影像上无需重新校准即可保持稳定，总体精度达93%—96%，建成区F1分数为84%—93%。空间分析表明，该方法在外围和农业区域表现稳健，而在密集城市核心区由于光谱异质性存在中等程度变异。为评估所提框架的业务化稳健性，将其与机器学习分类器进行了基准比较，具体为支持向量机（SVM）和随机森林（RF）。在本研究的特定条件下，所提出的基于规则的方法（95%精度）略优于SVM（93%）和RF（92%），同时具有更高的透明性并减少了对大规模标记训练数据集的依赖。此外，特征重要性分析证实了所选指数在解决光谱混淆方面的关键作用。这些发现表明，所提框架为类似维沙卡帕特南的热带沿海环境旱季城市监测提供了一种透明且计算高效的方法。","Revista de teledetección: Revista de la Asociación Española de Teledetección","2026-09-25T00:00:00Z",{"impact":28,"substance":698,"depth":241,"authority":183,"freshness":29,"relevant":8,"comment":1809},"该研究提出可解释的多指数规则框架，在旱季建成区提取上以更少标注数据达到95%精度并优于SVM\u002FRF，方法新颖、验证充分，对农业遥感与乡村土地利用监测有参考价值。",[1811],{"name":1806,"url":1803},[838,859,4,116,1813],"建成区提取",{"code":856,"slug":1815,"name":1816,"path":1817,"parent_code":845},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":46,"locked":22,"version":47,"method":48,"evidence":1820},{"candidates":1821,"agricultural_evidence":53},[1822,1827,1833,1837],{"code":856,"score":28,"supported":53,"title":1823,"abstract":1825,"tags":1826},[1824],"machine learning",[859],[859],{"code":1283,"score":52,"supported":53,"title":1828,"abstract":1829,"tags":1832},[],[1830,1831],"空间分析","spatial analysis",[],{"code":138,"score":61,"supported":53,"title":1834,"abstract":1835,"tags":1836},[],[142],[],{"code":72,"score":61,"supported":22,"title":1838,"abstract":1839,"tags":1840},[],[],[4,838,116],[1842,1843],"Sentinel-2 建成区提取","BAEI DBSI NDVI 旱季","Sentinel-2建成区提取-3568","10.4995\u002Fraet.2027.25885",{"doi":1845,"openalex_id":1847,"authors":1848,"venue":1806,"cited_by_count":80,"oa_url":1803,"card":1855,"direction":1860,"ingested_from":98},"W7214385607",[1849,1852],{"name":1850,"orcid":1851},"Sarah Sejari","https:\u002F\u002Forcid.org\u002F0009-0002-7300-9084",{"name":1853,"orcid":1854},"Vazeer Mahammood","https:\u002F\u002Forcid.org\u002F0000-0003-1667-5232",{"tldr":1856,"method":1857,"finding":1858,"direction":96,"opportunity":1859},"提出可解释多指数规则框架，用Sentinel-2提取旱季建成区，性能优于机器学习。","Sentinel-2A影像，BAEI、DBSI、NDVI组合规则，对比SVM与随","BAEI-DBSI-NDVI规则达95%精度、Kappa 0.89，优于单指数及SVM\u002FRF且无需重","可探索该规则框架向其他气候带与传感器迁移，并结合少量样本的半监督学习提升城市核心区精度。","农业人工智能与决策模型","2026-09-26T23:30:53.200163Z",{"id":1863,"title":1864,"url":1865,"summary":1866,"summary_zh":1867,"content":17,"source_name":1868,"source_url":1865,"published_at":1735,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1869,"score_detail":1870,"sources":1872,"tags":1874,"primary_topic":1877,"secondary_topics":1878,"classification":1879,"search_phrases":1894,"slug":1897,"view_count":61,"doi":1898,"paper":1899,"created_at":1914},3558,"Meteorolojik Kuraklığın Vejetasyon Sağlığı Üzerindeki Mekânsal ve Zamansal Etkilerinin SPI ve NDVI ile Analizi: Gediz Havzası Örneği","https:\u002F\u002Fdoi.org\u002F10.48123\u002Frsgis.1858019","Kuraklık, özellikle yarı kurak iklim koşullarına sahip havzalarda su kaynakları, tarımsal üretim ve ekosistem sürdürülebilirliği üzerinde belirleyici etkiler oluşturan başlıca doğal afetlerden biridir. Bu bağlamda, kuraklığın bitki örtüsü üzerindeki mekânsal ve zamansal etkilerinin bütüncül yaklaşımlarla değerlendirilmesi büyük önem taşımaktadır. Bu çalışmada, meteorolojik kuraklığın vejetasyon sağlığı üzerindeki mekânsal ve zamansal etkileri, Standartlaştırılmış Yağış İndeksi (SPI) ve Normalize Fark Bitki Örtüsü İndeksi (NDVI) kullanılarak Gediz Havzası örneğinde analiz edilmiştir. SPI analizleri 1970–2023 yılları arasındaki uzun dönem yağış verilerine dayanırken, NDVI verileri 2000–2023 dönemine ait MODIS uydu görüntülerinden elde edilmiştir. İki veri seti arasındaki ilişkiler, ortak dönem olan 2000–2023 yılları için incelenmiştir. Elde edilen bulgular, meteorolojik kuraklık ile vejetasyon sağlığı arasındaki ilişkinin mevsimsel olarak değişkenlik gösterdiğini ortaya koymaktadır. En güçlü ilişki yaz mevsiminde gözlenmiş (r ≈ 0.70) ve bu durum vejetasyonun yağış eksikliklerine en duyarlı olduğu dönemin yaz ayları olduğunu göstermiştir. Buna karşılık, kış (r ≈ 0.05) ve ilkbahar (r ≈ 0.02) mevsimlerinde ilişki oldukça zayıf bulunmuştur. Belirlenen kurak yıllarda (2004, 2008 ve 2022) NDVI değerlerinde belirgin düşüşler gözlenmiştir. Sonuç olarak, vejetasyonun kuraklığa verdiği tepkinin yıl boyunca homojen olmadığı, mevsimsel iklim koşulları ve diğer çevresel faktörlere bağlı olarak değiştiği belirlenmiştir.","干旱是主要自然灾害之一，尤其在具有半干旱气候条件的流域中，对水资源、农业生产和生态系统可持续性产生决定性影响。在此背景下，以整体性方法评估干旱对植被的时空影响具有重要意义。本研究以盖迪兹流域为例，利用标准化降水指数（SPI）和归一化植被指数（NDVI）分析了气象干旱对植被健康的时空影响。SPI分析基于1970—2023年的长期降水数据，而NDVI数据则来自2000—2023年期间的MODIS卫星影像。两个数据集之间的关系在共同时段2000—2023年进行了分析。研究结果表明，气象干旱与植被健康之间的关系呈现季节性变化。最显著的关系出现在夏季（r ≈ 0.70），这表明夏季是植被对降水亏缺最为敏感的时期。相比之下，冬季（r ≈ 0.05）和春季（r ≈ 0.02）的关系则非常微弱。在确定的干旱年份（2004年、2008年和2022年），NDVI值出现了明显下降。综上，植被对干旱的响应在全年并非均匀一致，而是随季节性气候条件及其他环境因素而变化。","Turkish Journal of Remote Sensing and GIS",71,{"impact":28,"substance":698,"depth":241,"authority":183,"freshness":25,"relevant":8,"comment":1871},"基于SPI与NDVI长时序数据揭示气象干旱对植被健康影响的季节性差异，方法规范、结论可靠，对农业干旱遥感监测有参考价值，但属区域案例研究，公共影响有限。",[1873],{"name":1868,"url":1865},[4,116,117,1875,1876],"植被指数","SPI",{"code":60,"slug":517,"name":518,"path":519,"parent_code":43},[],{"status":46,"locked":22,"version":47,"method":48,"evidence":1880},{"candidates":1881,"agricultural_evidence":53},[1882,1886,1890],{"code":60,"score":668,"supported":53,"title":1883,"abstract":1884,"tags":1885},[],[129],[117],{"code":39,"score":61,"supported":53,"title":1887,"abstract":1888,"tags":1889},[],[147],[],{"code":72,"score":61,"supported":22,"title":1891,"abstract":1892,"tags":1893},[],[],[4,1875,116],[1895,1896],"Gediz Havzası SPI NDVI","气象干旱 植被健康 遥感","GedizHavzasıSPINDVI-3558","10.48123\u002Frsgis.1858019",{"doi":1898,"openalex_id":1900,"authors":1901,"venue":1868,"cited_by_count":80,"oa_url":1908,"card":1909,"direction":96,"ingested_from":98},"W7214166880",[1902,1905],{"name":1903,"orcid":1904},"Kemal Yurddaş","https:\u002F\u002Forcid.org\u002F0000-0003-4691-4038",{"name":1906,"orcid":1907},"Murat Karabulut","https:\u002F\u002Forcid.org\u002F0000-0002-1456-6908","https:\u002F\u002Fdergipark.org.tr\u002Ftr\u002Fdownload\u002Farticle-file\u002F5578303",{"tldr":1910,"method":1911,"finding":1912,"direction":96,"opportunity":1913},"用SPI和NDVI分析土耳其盖迪兹流域气象干旱对植被健康的时空影响。","基于1970-2023年降水SPI与2000-2023年MODIS NDVI，做","干旱与植被关系随季节变化，夏季最强(r≈0.70)，冬春极弱；干旱年NDVI明显下降。","可引入滞后效应与多尺度SPI，结合土壤水分和灌溉数据，提升干旱对植被影响的预测能力。","2026-09-26T23:30:31.357310Z",{"id":1916,"title":1917,"url":1918,"summary":1919,"summary_zh":1920,"content":17,"source_name":1921,"source_url":1918,"published_at":1807,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":444,"score_detail":1922,"sources":1924,"tags":1926,"primary_topic":1929,"secondary_topics":1930,"classification":1933,"search_phrases":1954,"slug":1957,"view_count":354,"doi":1958,"paper":1959,"created_at":1979},3551,"Diachronic Assessment of Water Springs in Ribat El Kheir Plateau (Morocco), under Climatic and Anthropogenic Pressure, Using Remote Sensing (1985-2025)","https:\u002F\u002Fdoi.org\u002F10.4028\u002Fp-9o0ojj","The Ribat El Kheir plateau, located in the northern part of the Tabular Middle Atlas (Morocco), is characterized by carbonate formations (Liasic dolomitic limestones) and a dense network of tectonic faults. This study conducts a diachronic assessment of water springs and their interaction with climatic conditions and anthropogenic factors related to the expansion of irrigated agriculture (farms) from 1985 to 2025. A multi-source methodological approach was adopted, combining the inventory and mapping of springs, the application of remote sensing, using Landsat and Sentinel satellite imagery, and the calculation of standard indices, including the Normalized Difference Water Index (NDWI), the Normalized Difference Vegetation Index (NDVI), and the Standardized Precipitation Index (SPI). The results indicate that a combination of geomorphological context primarily influences the spatial distribution of the 101 inventoried springs, the lithological nature of the formations (permeable dolomites), structural features, and climatic factors. However, a significant regression in both the number and flow of these springs has been observed. Concurrently, irrigated fruit agriculture has expanded remarkably over the study period. The SPI analysis further reveals a climatic trend towards increased aridity. These findings highlight the critical and synergistic pressure of climate change and intensive agriculture on water resources in this vulnerable semi-arid region.","位于摩洛哥中阿特拉斯板状山脉北部的里巴特·埃尔·海尔（Ribat El Kheir）高原，以碳酸盐岩地层（里阿斯统白云质灰岩）和密集的构造断裂网络为特征。本研究对1985年至2025年间泉水动态及其与气候条件和灌溉农业（农场）扩张相关人为因素的相互作用进行了历时性评估。研究采用多源方法学途径，结合泉水编录与制图、遥感技术应用（利用Landsat和Sentinel卫星影像）以及标准指数的计算，包括归一化差异水体指数（NDWI）、归一化差异植被指数（NDVI）和标准化降水指数（SPI）。结果表明，101处编录泉水的空间分布主要受地貌背景、地层岩性特征（渗透性白云岩）、构造特征及气候因素的综合影响。然而，观测到这些泉水的数量和流量均出现显著衰退。与此同时，灌溉果树农业在研究期内显著扩张。SPI分析进一步揭示了气候趋于干旱化的趋势。这些发现凸显了气候变化与集约化农业对这一脆弱半干旱地区水资源的临界协同压力。","International journal of engineering research in Africa",{"impact":25,"substance":26,"depth":241,"authority":183,"freshness":29,"relevant":8,"comment":1923},"基于Landsat与Sentinel遥感的40年泉水退化评估，方法扎实、数据翔实，但属区域案例研究，对国内三农实践的直接参考价值有限。",[1925],{"name":1921,"url":1918},[596,4,116,1927,1928],"灌溉农业","干旱化",{"code":72,"slug":842,"name":843,"path":844,"parent_code":845},[1931,1932],{"code":60,"slug":517,"name":518,"path":519,"parent_code":43},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":46,"locked":22,"version":47,"method":48,"evidence":1934},{"candidates":1935,"agricultural_evidence":53},[1936,1940,1944,1948],{"code":72,"score":27,"supported":53,"title":1937,"abstract":1938,"tags":1939},[135],[134,135],[4,116],{"code":60,"score":29,"supported":53,"title":1941,"abstract":1942,"tags":1943},[],[64,129,1525],[],{"code":39,"score":25,"supported":53,"title":1945,"abstract":1946,"tags":1947},[],[797,147],[596,1927],{"code":1949,"score":61,"supported":53,"title":1950,"abstract":1951,"tags":1953},"B2",[],[1952],"果树",[],[1955,1956],"Ribat El Kheir 泉水 遥感","摩洛哥 灌溉农业 水资源","RibatElKheir泉水遥感-3551","10.4028\u002Fp-9o0ojj",{"doi":1958,"openalex_id":1960,"authors":1961,"venue":1921,"cited_by_count":80,"oa_url":17,"card":1974,"direction":96,"ingested_from":98},"W7214294130",[1962,1964,1965,1968,1970,1971],{"name":1963,"orcid":17},"Hamid Achiban",{"name":1963,"orcid":17},{"name":1966,"orcid":1967},"Miloud Afenzar","https:\u002F\u002Forcid.org\u002F0009-0008-0839-0230",{"name":1969,"orcid":17},"Hassan Achiban",{"name":1969,"orcid":17},{"name":1972,"orcid":1973},"Nourddine El Gali","https:\u002F\u002Forcid.org\u002F0009-0001-7025-4765",{"tldr":1975,"method":1976,"finding":1977,"direction":96,"opportunity":1978},"基于遥感和指数分析，评估摩洛哥Ribat El Kheir高原1985-2025年泉水数量与流量的退","Landsat\u002FSentinel影像、NDWI\u002FNDVI\u002FSPI指数、泉水编目与","泉水数量与流量显著减少，灌溉农业扩张与气候干旱化协同加剧水资源压力。","可结合多源遥感与机器学习，量化灌溉扩张对泉水流量的贡献，并预测未来情景。","2026-09-26T23:30:29.568284Z",{"id":1981,"title":1982,"url":1983,"summary":1984,"summary_zh":17,"content":17,"source_name":238,"source_url":1983,"published_at":1985,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1986,"score_detail":1987,"sources":1989,"tags":1991,"primary_topic":17,"secondary_topics":1994,"classification":1995,"search_phrases":2006,"slug":2009,"view_count":80,"doi":2010,"paper":2011,"created_at":2024},3180,"Satellite-Based Assessment of Vegetation and Hydrological Dynamics at the Garâa of Sejnane Ramsar Wetland (Tunisia): A Multi-Temporal NDVI and NDWI Analysis","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10690691\u002Fv1","Satellite-Based Assessment of Vegetation and Hydrological Dynamics at the Garâa of Sejnane Ramsar Wetland (Tunisia): A Multi-Temporal NDVI and NDWI Analysis。Research Square","2026-09-21T00:00:00Z",56,{"impact":25,"substance":110,"depth":111,"authority":52,"freshness":29,"relevant":8,"comment":1988},"基于多时相NDVI\u002FNDWI的湿地植被与水文动态遥感评估，方法规范但属区域性案例研究，公共影响有限。",[1990],{"name":238,"url":1983},[4,1992,116,1993,1563],"农业生态","湿地保护",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":1996},{"candidates":1997,"agricultural_evidence":22,"reason":149},[1998,2002],{"code":67,"score":354,"supported":22,"title":1999,"abstract":2000,"tags":2001},[],[],[1992,1993],{"code":72,"score":354,"supported":22,"title":2003,"abstract":2004,"tags":2005},[],[],[4,116],[2007,2008],"Sejnane 湿地 NDVI NDWI","突尼斯 拉姆萨尔湿地 遥感","Sejnane湿地NDVINDWI-3180","10.21203\u002Frs.3.rs-10690691\u002Fv1",{"doi":2010,"openalex_id":2012,"authors":2013,"venue":238,"cited_by_count":80,"oa_url":1983,"card":17,"direction":96,"ingested_from":98},"W7213955587",[2014,2016,2018,2021],{"name":2015,"orcid":17},"Imen Khemiri",{"name":2017,"orcid":17},"Chahida Chemingui",{"name":2019,"orcid":2020},"Alaeddine Rouissi","https:\u002F\u002Forcid.org\u002F0009-0000-9319-5182",{"name":2022,"orcid":2023},"Sahar Abidi","https:\u002F\u002Forcid.org\u002F0000-0003-3355-6876","2026-09-22T23:30:23.965045Z",{"id":2026,"title":2027,"url":2028,"summary":2029,"summary_zh":2030,"content":17,"source_name":2031,"source_url":2028,"published_at":1985,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":23,"score_detail":2032,"sources":2034,"tags":2036,"primary_topic":2037,"secondary_topics":2038,"classification":2040,"search_phrases":2060,"slug":2063,"view_count":61,"doi":2064,"paper":2065,"created_at":2083},3177,"A comparative analysis on maize yield prediction using sentinel 2A and Landsat 8 satellite image in Sundarganj, Gaibandha, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbjar.v51i1.92530","Maize is an important cereal crops in Bangladesh. Over the last two decades, its cultivation has increased promisingly, especially in the Northern part of Bangladesh. The effective estimation of crop yields at a regional scale holds significant importance in facilitating decision-making within the agricultural sector, thereby ensuring grain security. The traditional ground-based measurement techniques suffer from inefficiencies, and there exists a need for a reliable, precise, and effective method for estimating regional crop yields. This study used remote sensing (RS) techniques for forecasting pre-harvest maize yield to improve the management system. Currently, the normalized difference vegetation index (NDVI) is widely used to predict crop yield including maize. However, the present study used Landsat 8 (~ 30 m) and Sentinel 2A (~ 10 m) high resolution data for 2018-2019 and 2019-2020 to predict maize yield based on the year 2020-2021 at Sundarganj Upazila in Gaibandha district. The single cloud free image acquisition date based on maximum NDVI for both satellite images was used for each maize growing period to develop a yield prediction model. A regression model was performed between NDVI values and 20 farmers field-level maize yields. The absolute mean error of prediction was about 10.30% for Landsat 8 and 6.70% for Sentinel 2A compared to the actual maize yield during 2020-2021. The study revealed that NDVI data extracted from Sentinel 2A high resolution satellite images can be successfully used to predict the maize yield with appreciable accuracy. Finally, this study has demonstrated the efficacy of combining multi-temporal remote sensing data for accurate maize yield estimation, aiding agricultural authorities and production enterprises in the timely formulation and refinement of cropping strategies and management policies for the ongoing season. Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","玉米是孟加拉国重要的谷类作物。过去二十年间，其种植面积增长显著，尤其是在孟加拉国北部地区。在区域尺度上有效估算作物产量对于促进农业部门决策、进而保障粮食安全具有重要意义。传统的地面测量技术效率低下，亟需一种可靠、精确且有效的区域作物产量估算方法。本研究采用遥感（RS）技术预测收获前玉米产量，以改进管理体系。目前，归一化植被指数（NDVI）被广泛用于预测包括玉米在内的作物产量。然而，本研究利用Landsat 8（约30 m）和Sentinel 2A（约10 m）高分辨率数据，基于2018—2019年和2019—2020年的数据，对盖班达县孙达尔甘杰乌帕齐拉2020—2021年的玉米产量进行预测。在每个玉米生长期，选取两颗卫星影像中NDVI最大值对应的单幅无云影像获取日期，用于建立产量预测模型。对NDVI值与20户农民田块级玉米产量进行回归建模。与2020—2021年实际玉米产量相比，Landsat 8的绝对平均预测误差约为10.30%，Sentinel 2A约为6.70%。研究表明，利用Sentinel 2A高分辨率卫星影像提取的NDVI数据可成功用于预测玉米产量，且精度令人满意。最后，本研究证明了结合多时相遥感数据进行准确玉米产量估算的有效性，有助于农业主管部门和生产企业在当季及时制定和完善种植策略与管理政策。Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","Bangladesh Journal of Agricultural Research",{"impact":25,"substance":26,"depth":27,"authority":183,"freshness":25,"relevant":8,"comment":2033},"基于Sentinel 2A与Landsat 8的玉米遥感估产对比研究，方法清晰、误差数据具体，对遥感估产有参考价值，但属区域小尺度研究，公共影响有限。",[2035],{"name":2031,"url":2028},[246,1633,718,134,4],{"code":715,"slug":719,"name":718,"path":1434,"parent_code":1435},[2039],{"code":72,"slug":842,"name":843,"path":844,"parent_code":845},{"status":46,"locked":22,"version":47,"method":48,"evidence":2041},{"candidates":2042,"agricultural_evidence":53},[2043,2047,2051,2056],{"code":715,"score":111,"supported":53,"title":2044,"abstract":2045,"tags":2046},[719],[718,719],[718],{"code":72,"score":29,"supported":53,"title":2048,"abstract":2049,"tags":2050},[],[134,135],[4,1633,134],{"code":959,"score":52,"supported":53,"title":2052,"abstract":2053,"tags":2055},[],[962,2054],"grain security",[],{"code":258,"score":8,"supported":22,"title":2057,"abstract":2058,"tags":2059},[],[],[246],[2061,2062],"Sentinel 2A Landsat 8 玉米产量预测","孟加拉国 Sundarganj 玉米遥感估产","Sentinel2ALandsat8玉米产量预测-3177","10.3329\u002Fbjar.v51i1.92530",{"doi":2064,"openalex_id":2066,"authors":2067,"venue":2031,"cited_by_count":80,"oa_url":2028,"card":2078,"direction":96,"ingested_from":98},"W7213918280",[2068,2070,2072,2074,2076],{"name":2069,"orcid":17},"N Mohammad",{"name":2071,"orcid":17},"MA Islam",{"name":2073,"orcid":17},"MG Mahboob",{"name":2075,"orcid":17},"MM Rahman",{"name":2077,"orcid":17},"I Ahmed",{"tldr":2079,"method":2080,"finding":2081,"direction":96,"opportunity":2082},"用Sentinel 2A与Landsat 8的NDVI回归模型预测孟加拉国玉米产量并比较精度。","基于最大NDVI单期影像与20个农户地块产量做回归，比较两种卫星。","Sentinel 2A预测绝对平均误差6.70%，优于Landsat 8的10.30%。","可探索多时相NDVI与机器学习融合，提升小农户尺度玉米估产精度与迁移性。","2026-09-22T23:30:23.551878Z",{"id":2085,"title":2086,"url":2087,"summary":2088,"summary_zh":2089,"content":17,"source_name":2090,"source_url":2087,"published_at":2091,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":2092,"score_detail":2093,"sources":2095,"tags":2097,"primary_topic":17,"secondary_topics":2100,"classification":2101,"search_phrases":2116,"slug":2119,"view_count":354,"doi":2120,"paper":2121,"created_at":2139},3079,"Drought and Productivity Trends in Freshwater Flooded Forest Swamps in the North American Coastal Plain","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.09.16.752089","Climate change has resulted in an increase in drought conditions within the southeastern United States, impacting a variety of ecosystems and species, including the freshwater flooded forest swamps of the North American Coastal Plain (NACP). With the threat of drought leading to a decline in wetlands and shifts in seasonality patterns in the NACP, there is significant concern about the effects of a drying climate on the net primary productivity of bald cypress-tupelo swamps and the potential for ecosystem degradation or conversion. This study seeks to understand the relationship between the net primary productivity of wetlands dominated by bald cypress-tupelo swamps and drought conditions. We used Moderate Resolution Imaging Spectroradiometer (MODIS) imagery from 2000 to 2024, taken from the 500 largest flooded forest swamps in the southeastern USA using Google Earth Engine, to track the changes in productivity of these swamps over time. The trends in MODIS Fraction of Absorbed Photosynthetically Active Radiation (FPAR), Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) were compared against three common drought indices using seasonal decomposition and linear regression along with local bivariate spatial associations. We found that while there were small global correlations between drought and productivity metrics, the impact of droughts on these swamps is highly spatially variable, with specific geographical regions displaying differing impacts of drought and precipitation levels on the vegetation productivity. Coastal and southern regions, including the states of Texas, Florida, and Louisiana, are experiencing a decrease in precipitation but have mixed productivity trends. Meanwhile, more inland and coastal northern areas display an increase in precipitation and wetness. Overall, our results suggest that cypress-tupelo swamp productivity varies based on a variety of factors, including, but not limited to, precipitation and drought levels, but some areas may be at risk of further degradation due to drought under future climate change. Further research is encouraged to fully understand vegetation productivity in relation to environmental conditions in individual cypress-tupelo swamps.","气候变化导致美国东南部干旱状况加剧，影响了多种生态系统和物种，其中包括北美沿海平原（NACP）的淡水泛滥森林沼泽。随着干旱威胁导致湿地减少以及NACP季节性模式发生变化，人们对气候干化如何影响落羽杉-紫树沼泽的净初级生产力以及生态系统退化或转变的潜在风险深感担忧。本研究旨在理解以落羽杉-紫树沼泽为主的湿地净初级生产力与干旱状况之间的关系。我们利用Google Earth Engine获取了2000年至2024年期间美国东南部500个最大泛滥森林沼泽的中分辨率成像光谱仪（MODIS）影像，以追踪这些沼泽生产力随时间的变化。我们将MODIS光合有效辐射吸收比例（FPAR）、归一化差异植被指数（NDVI）和增强植被指数（EVI）的趋势与三种常用干旱指数进行比较，采用季节性分解和线性回归以及局部双变量空间关联方法。我们发现，尽管干旱与生产力指标之间存在较小的全局相关性，但干旱对这些沼泽的影响在空间上高度可变，特定地理区域在干旱和降水水平对植被生产力的影响方面表现出差异。沿海和南部地区，包括德克萨斯州、佛罗里达州和路易斯安那州，正在经历降水减少，但生产力趋势不一。与此同时，更内陆和沿海北部地区则显示降水和湿度增加。总体而言，我们的结果表明，落羽杉-紫树沼泽生产力受多种因素影响，包括但不限于降水和干旱水平，但在未来气候变化下，一些地区可能面临因干旱而进一步退化的风险。我们鼓励进一步研究，以充分理解单个落羽杉-紫树沼泽中植被生产力与环境条件的关系。","bioRxiv (Cold Spring Harbor Laboratory)","2026-09-18T00:00:00Z",69,{"impact":28,"substance":26,"depth":241,"authority":28,"freshness":25,"relevant":8,"comment":2094},"基于MODIS 2000-2024年长时序遥感数据评估干旱对北美柏树-图珀洛沼泽生产力的空间异质性影响，方法规范、数据规模大，对湿地生态遥感监测有参考价值，但属基础生态研究，与国内三农信息化直接关联有限。",[2096],{"name":2090,"url":2087},[64,4,116,2098,2099],"湿地生态","植被生产力",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":2102},{"candidates":2103,"agricultural_evidence":22,"reason":149},[2104,2108,2112],{"code":60,"score":110,"supported":53,"title":2105,"abstract":2106,"tags":2107},[127],[64,129,1525,127],[64],{"code":72,"score":354,"supported":22,"title":2109,"abstract":2110,"tags":2111},[],[],[4,116],{"code":67,"score":8,"supported":22,"title":2113,"abstract":2114,"tags":2115},[],[],[2098],[2117,2118],"北美沿海平原 柏树湿地 干旱","MODIS NDVI 湿地生产力","北美沿海平原柏树湿地干旱-3079","10.64898\u002F2026.09.16.752089",{"doi":2120,"openalex_id":2122,"authors":2123,"venue":2090,"cited_by_count":80,"oa_url":2133,"card":2134,"direction":96,"ingested_from":98},"W7213557702",[2124,2127,2130],{"name":2125,"orcid":2126},"Isabel Holland","https:\u002F\u002Forcid.org\u002F0000-0002-6416-7729",{"name":2128,"orcid":2129},"Kayla Stan","https:\u002F\u002Forcid.org\u002F0000-0002-2159-3090",{"name":2131,"orcid":2132},"Clay S. Tucker","https:\u002F\u002Forcid.org\u002F0000-0002-3283-171X","https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2026\u002F09\u002F18\u002F2026.09.16.752089.full.pdf",{"tldr":2135,"method":2136,"finding":2137,"direction":96,"opportunity":2138},"用MODIS遥感追踪北美沿海平原500个沼泽林2000-2024年生产力与干旱关系。","MODIS FPAR\u002FNDVI\u002FEVI与三种干旱指数，季节分解、线性回归及双变量","干旱对沼泽生产力影响空间高度异质，沿海南部降水减少但生产力趋势不一。","可结合高分辨率遥感与地面通量数据，解析单个沼泽林生产力对干旱的非线性响应机制。","2026-09-21T23:30:27.308588Z",{"id":2141,"title":2142,"url":2143,"summary":2144,"summary_zh":2145,"content":17,"source_name":2146,"source_url":2143,"published_at":2091,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":108,"score_detail":2147,"sources":2149,"tags":2151,"primary_topic":17,"secondary_topics":2155,"classification":2156,"search_phrases":2167,"slug":2170,"view_count":8,"doi":2171,"paper":2172,"created_at":2197},3078,"Carbon in the Canopy: An Aboveground Carbon Stock Assessment Using Remote Sensing and Forest Inventory Data of Reforested Sites in The City of Koronadal","https:\u002F\u002Fdoi.org\u002F10.54536\u002Fijfe.v2i1.7697","Reforestation is an important component to mitigate climate change because trees absorb carbon from the air and store it in their biomass. But even though people know that reforestation is good for the environment, there isn’t enough local data on how much carbon is stored in these areas, which makes it hard to tell how well they work to fight climate change. This study helped to fix the deficiency of local carbon stock data in reforested areas within the City of Koronadal by employing forest inventory data and remote sensing techniques. The study employed a quantitative research design that integrated field-based forest measurements with remote sensing analysis. NDVI results showed a positive relationship with field data, indicating that remote sensing is a useful tool in measuring NDVI. Measurements were done on tree species, diameter at breast height (DBH), height, and wood density in 200 m perimeter Barangay San Isidro, Barangay Assumption and Barangay San Jose. Allometric equations were used to determine aboveground biomass, which was further transformed into carbon stock according to IPCC. The Landsat 9 satellite images were provided in QGIS and used to compute the Normalized Difference Vegetation Index (NDVI) and to evaluate vegetation cover. The findings indicate a difference between the sites with Barangay San Jose (0.16 t C\u002Fha) and Barangay Assumption (NDVI 0.50) having higher biomass and carbon stock which are dominated mainly by Swietenia macrophylla (Mahogany) for monitoring forest biomass. The findings provide baseline information that can help local government units improve forest management such as the National Greening Program and support climate change mitigation efforts.","再造林是缓解气候变化的重要组成部分，因为树木能从空气中吸收碳并将其储存在生物量中。然而，尽管人们知道再造林对环境有益，但关于这些区域碳储量的本地数据仍然不足，这使得难以判断其在应对气候变化方面的实际效果。本研究通过采用森林清查数据和遥感技术，帮助弥补了科罗纳达尔市再造林区域本地碳储量数据的不足。本研究采用定量研究设计，将实地森林测量与遥感分析相结合。NDVI结果与实地数据呈正相关关系，表明遥感是测量NDVI的有效工具。在Barangay San Isidro、Barangay Assumption和Barangay San Jose周边200米范围内，对树种、胸径（DBH）、树高和木材密度进行了测量。采用异速生长方程确定地上生物量，并依据IPCC方法进一步转化为碳储量。在QGIS中提供Landsat 9卫星影像，用于计算归一化植被指数（NDVI）并评估植被覆盖状况。研究结果表明，各研究样地之间存在差异，其中Barangay San Jose（0.16 t C\u002Fha）和Barangay Assumption（NDVI 0.50）具有较高的生物量和碳储量，其主要优势树种为Swietenia macrophylla（桃花心木），可用于森林生物量监测。研究结果为地方政府单位改进森林管理（如国家绿化计划）和支持气候变化减缓工作提供了基线信息。","International Journal of Forestry and Ecosystem",{"impact":25,"substance":110,"depth":111,"authority":28,"freshness":25,"relevant":8,"comment":2148},"该研究结合遥感与森林清查数据评估再造林碳储量，方法扎实、数据有增量，但属地方性案例，影响范围有限，可作为农业遥感与林业碳汇主题的参考素材。",[2150],{"name":2146,"url":2143},[4,116,2152,2153,2154],"林业碳汇","森林碳储量","再造林",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":2157},{"candidates":2158,"agricultural_evidence":22,"reason":149},[2159,2163],{"code":72,"score":27,"supported":53,"title":2160,"abstract":2161,"tags":2162},[135],[134,135],[4,116],{"code":60,"score":52,"supported":53,"title":2164,"abstract":2165,"tags":2166},[],[64,1525],[],[2168,2169],"Koronadal 再造林 碳储量","Landsat 9 NDVI 森林生物量","Koronadal再造林碳储量-3078","10.54536\u002Fijfe.v2i1.7697",{"doi":2171,"openalex_id":2173,"authors":2174,"venue":2146,"cited_by_count":80,"oa_url":2191,"card":2192,"direction":96,"ingested_from":98},"W7213557390",[2175,2177,2179,2181,2183,2185,2187,2189],{"name":2176,"orcid":17},"Krizzel Jan A. Batislaon",{"name":2178,"orcid":17},"Keth Lorenz Corre",{"name":2180,"orcid":17},"Riz Jorich Franco",{"name":2182,"orcid":17},"Zamantha Janylle Funa",{"name":2184,"orcid":17},"Dannah Mae Pagod",{"name":2186,"orcid":17},"Janne Paulo Panzo",{"name":2188,"orcid":17},"Mia Joy A. Inocencio",{"name":2190,"orcid":17},"Carlos F. Gaygay","https:\u002F\u002Fjournals.e-palli.com\u002Fhome\u002Findex.php\u002Fijfe\u002Farticle\u002Fdownload\u002F7697\u002F4071",{"tldr":2193,"method":2194,"finding":2195,"direction":96,"opportunity":2196},"结合森林清查与遥感评估Koronadal市再造林地地上碳储量。","样地实测DBH、树高、木材密度，异速生长方程与Landsat 9 NDVI。","NDVI与实测数据正相关，不同样地碳储量差异明显，San Jose与Assumption较高。","可扩展多时相遥感与机器学习，建立区域再造林碳汇动态监测与不确定性评估。","2026-09-21T23:30:27.222233Z",{"id":2199,"title":2200,"url":2201,"summary":2202,"summary_zh":2203,"content":17,"source_name":335,"source_url":2201,"published_at":2091,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":506,"score_detail":2204,"sources":2206,"tags":2208,"primary_topic":2212,"secondary_topics":2215,"classification":2217,"search_phrases":2229,"slug":2232,"view_count":61,"doi":2233,"paper":2234,"created_at":2250},3075,"Mapping and monitoring land usage changes of gum Arabic belt in armed conflict-disturbed areas to support sustainable management of the belt","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-69562-8","Abstract The Gum Arabic Belt (GAB) in Sudan play a key role in sustaining rural livelihoods, supporting biodiversity, and delivering essential ecosystem services. However, this fragile dryland ecosystem is increasingly threatened by armed conflict, large-scale displacement, land use changes, and environmental degradation. Therefore, this study assessed the status of GAB and gum production under conflict-induced stressors using an integrated methodology that combines meteorological, remote sensing, and participatory field survey data. Multi-temporal MODIS NDVI data (2000–2023) were analyzed to detect spatiotemporal changes in vegetation cover, particularly during peak growing seasons. Normalized Difference Vegetation Index (NDVI) trends were evaluated using five-year intervals, except for the last period (2020–2023) to accommodate the eco-anthropogenic signs of the current war. The results reveal that vegetation recovery across large portions of the GAB is correlated with land abandonment due to ongoing conflict and locals’ displacement. Across all assessed years, moderate vegetation class consistently dominated the landscape, particularly in 2020 and 2023, indicating a notable improvement in vegetation cover in recent years. A significant variation was observed in Gum yield, with higher production in 2005. The study underscores the urgent need for target restoration strategies and sustainable management policies. It also demonstrates the value of integrating earth observation data with local knowledge systems for conflict-sensitive environmental monitoring and policy formation in dryland regions.","摘要 苏丹的阿拉伯胶带（Gum Arabic Belt, GAB）在维持农村生计、支持生物多样性和提供关键生态系统服务方面发挥着重要作用。然而，这一脆弱的旱地生态系统正日益受到武装冲突、大规模流离失所、土地利用变化和环境退化的威胁。因此，本研究采用综合气象、遥感和参与式实地调查数据的方法，评估了冲突引发的压力因素下GAB及阿拉伯胶生产的状况。研究分析了多时相MODIS NDVI数据（2000–2023年），以检测植被覆盖的时空变化，尤其是生长旺季期间的变化。归一化植被指数（NDVI）趋势按五年间隔进行评估，但最后一个时段（2020–2023年）除外，以适应当前战争带来的生态-人为影响迹象。结果表明，GAB大部分地区的植被恢复与持续冲突和当地居民流离失所导致的土地撂荒相关。在所有评估年份中，中等植被等级始终主导景观，尤其是在2020年和2023年，表明近年来植被覆盖显著改善。阿拉伯胶产量存在显著差异，2005年产量较高。该研究强调迫切需要有针对性的恢复策略和可持续管理政策。研究还表明，将地球观测数据与当地知识系统相结合，对于旱地地区冲突敏感型环境监测和政策制定具有重要价值。",{"impact":27,"substance":698,"depth":110,"authority":338,"freshness":25,"relevant":8,"comment":2205},"以MODIS NDVI长时序遥感结合实地调查评估冲突扰动下阿拉伯胶带植被与胶产量变化，方法扎实、结论有政策价值，但属区域案例研究，对国内三农信息化参考有限。",[2207],{"name":335,"url":2201},[4,116,2209,2210,2211],"荒漠化防治","可持续管理","苏丹",{"code":67,"slug":1697,"name":2213,"path":2214,"parent_code":43},"生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity",[2216],{"code":72,"slug":842,"name":843,"path":844,"parent_code":845},{"status":46,"locked":22,"version":47,"method":48,"evidence":2218},{"candidates":2219,"agricultural_evidence":53},[2220,2225],{"code":67,"score":21,"supported":53,"title":2221,"abstract":2222,"tags":2224},[],[1696,2223,1697],"生态系统服务",[2209],{"code":72,"score":25,"supported":53,"title":2226,"abstract":2227,"tags":2228},[],[134,135],[4,116],[2230,2231],"苏丹 阿拉伯胶带 NDVI","MODIS NDVI 植被覆盖 冲突","苏丹阿拉伯胶带NDVI-3075","10.1038\u002Fs41598-026-69562-8",{"doi":2233,"openalex_id":2235,"authors":2236,"venue":335,"cited_by_count":80,"oa_url":2244,"card":2245,"direction":96,"ingested_from":98},"W7213533975",[2237,2240,2242],{"name":2238,"orcid":2239},"Elmugheira M. I. Mohammed","https:\u002F\u002Forcid.org\u002F0000-0003-0977-9156",{"name":2241,"orcid":17},"Abdalrahman A. Ahmed",{"name":2243,"orcid":17},"Faisal I. Musa","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-69562-8_reference.pdf",{"tldr":2246,"method":2247,"finding":2248,"direction":96,"opportunity":2249},"结合遥感与实地调查，评估苏丹武装冲突干扰下阿拉伯胶带土地覆被变化及胶产量。","多时相MODIS NDVI（2000–2023）与气象、参与式实地调查数据集成分","冲突导致人口流离与土地撂荒，反而使大部分区域植被恢复，胶产量年际波动大。","可探究冲突驱动撂荒下植被恢复的生态-社会机制，并开发冲突敏感型遥感监测与可持续管理模型。","2026-09-21T23:30:25.775307Z",{"id":2252,"title":2253,"url":2254,"summary":2255,"summary_zh":2256,"content":17,"source_name":2257,"source_url":2254,"published_at":2258,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":108,"score_detail":2259,"sources":2261,"tags":2263,"primary_topic":2266,"secondary_topics":2267,"classification":2268,"search_phrases":2279,"slug":2282,"view_count":61,"doi":2283,"paper":2284,"created_at":2303},3072,"APPLICATION OF SENTINEL-2 DATA TO ASSESS SEASONAL VEGETATION DYNAMICS IN THE IALPUG RIVER BASIN, SOUTHERN PART OF THE REPUBLIC OF MOLDOVA","https:\u002F\u002Fdoi.org\u002F10.21163\u002Fgt_2027.221.06","This study uses Sentinel-2 imagery to characterize seasonal vegetation dynamics in the analysed part of the Ialpug River Basin from March to October 2025.Monthly NDVI, month-to-month NDVI differences (ΔNDVI) and mean NDVI were calculated to describe vegetation greenness, the direction and magnitude of change between observations, and average greenness across the study period.The results show spring greening followed by spatially contrasting changes in early summer.Moderate vegetation cover occupied its largest share in April, while dense vegetation reached its maximum extent in June.The most widespread decline occurred between June and July, when 54.18% of the study area recorded an NDVI decrease exceeding 0.1.Very sparse vegetation reached its greatest extent in September, followed by limited greening in October.The mean NDVI classification placed 69.20% of the study area in the sparse vegetation class and 24.49% in the very sparse vegetation class, indicating predominantly low to intermediate average greenness across the selected observations.These classes do not establish persistent vegetation conditions or directly identify land-cover types.The summer decline coincided with documented regional drought conditions, but the analysis did not separate climatic effects from crop development, harvesting or other seasonal processes.The combined indicators provide complementary information for identifying areas with pronounced seasonal decline or low average greenness.Multi-year observations and independent climatic, land-cover and field evidence are required to determine the causes of these patterns and assess their longer-term environmental significance.","本研究利用Sentinel-2影像刻画了雅尔普格河（Ialpug River）流域分析区2025年3月至10月的季节性植被动态。通过计算逐月NDVI、月际NDVI差值（ΔNDVI）及平均NDVI，以描述植被绿度、观测间变化的方向与幅度以及研究期内的平均绿度。结果表明，春季出现绿化，随后在初夏呈现空间分异的变化。中度植被覆盖在4月占比最大，而茂密植被在6月达到最大范围。最广泛的下降发生在6月至7月之间，届时研究区54.18%的面积NDVI下降超过0.1。极稀疏植被在9月达到最大范围，随后在10月出现有限绿化。平均NDVI分类将研究区69.20%的面积归为稀疏植被类，24.49%归为极稀疏植被类，表明所选观测期内平均绿度以低至中等为主。这些类别并不确立持续的植被状况，也不直接识别土地覆盖类型。夏季下降与已记录的区域干旱条件相吻合，但分析未将气候影响与作物发育、收割或其他季节性过程区分开来。综合指标为识别季节性下降显著或平均绿度较低的区域提供了互补信息。要确定这些格局的成因并评估其长期环境意义，尚需多年观测以及独立的气候、土地覆盖和野外证据。","Geographia Technica","2026-09-19T00:00:00Z",{"impact":25,"substance":110,"depth":111,"authority":28,"freshness":25,"relevant":8,"comment":2260},"基于Sentinel-2的流域植被季节性动态遥感研究，方法规范、数据翔实，但属区域性案例，对国内农业信息化仅有方法借鉴价值。",[2262],{"name":2257,"url":2254},[4,116,1326,2264,2265],"干旱影响","摩尔多瓦",{"code":60,"slug":517,"name":518,"path":519,"parent_code":43},[],{"status":46,"locked":22,"version":47,"method":48,"evidence":2269},{"candidates":2270,"agricultural_evidence":53},[2271,2275],{"code":60,"score":52,"supported":53,"title":2272,"abstract":2273,"tags":2274},[],[129,127],[],{"code":72,"score":61,"supported":22,"title":2276,"abstract":2277,"tags":2278},[],[],[4,1326,116],[2280,2281],"摩尔多瓦 NDVI 植被动态","干旱影响 摩尔多瓦 植被动态 遥感监测","摩尔多瓦NDVI植被动态-3072","10.21163\u002Fgt_2027.221.06",{"doi":2283,"openalex_id":2285,"authors":2286,"venue":2257,"cited_by_count":80,"oa_url":2297,"card":2298,"direction":96,"ingested_from":98},"W7213674461",[2287,2290,2292,2295],{"name":2288,"orcid":2289},"Ecaterina MELNICENCO","https:\u002F\u002Forcid.org\u002F0000-0002-2706-9417",{"name":2291,"orcid":17},"Viorica ANGHELUȚA",{"name":2293,"orcid":2294},"Iradion Jechiu","https:\u002F\u002Forcid.org\u002F0000-0001-9582-0615",{"name":2296,"orcid":17},"Iurie BEJAN","https:\u002F\u002Ftechnicalgeography.org\u002Fpdf\u002F1_2027\u002F06_melnicenco.pdf",{"tldr":2299,"method":2300,"finding":2301,"direction":96,"opportunity":2302},"用Sentinel-2影像分析摩尔多瓦Ialpug河流域2025年3-10月植被季节动态。","Sentinel-2月度NDVI、ΔNDVI与均值NDVI分类，覆盖2025年3","6-7月54.18%区域NDVI降幅超0.1，均值NDVI以稀疏植被为主（69.20%）。","可结合多年遥感与气候、物候及田间数据，分离干旱与作物管理对NDVI下降的贡献。","2026-09-21T23:30:25.567291Z",{"id":2305,"title":2306,"url":2307,"summary":2308,"summary_zh":2309,"content":17,"source_name":335,"source_url":2307,"published_at":2310,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":2311,"score_detail":2312,"sources":2314,"tags":2316,"primary_topic":2318,"secondary_topics":2319,"classification":2320,"search_phrases":2336,"slug":2339,"view_count":8,"doi":2340,"paper":2341,"created_at":2357},2681,"Forest encroachment prediction using multi-temporal satellite data and machine learning: a case study of Bandipur National Park, India","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-70597-0","Abstract Forest encroachment poses a significant threat to protected forest ecosystems due to increasing human activities, including infrastructure development, agricultural expansion, and settlement growth. Continuous monitoring is therefore essential for effective conservation planning and sustainable forest management. This study presents an artificial intelligence-based framework for forest encroachment prediction in Bandipur National Park, India, using multi-temporal Sentinel- 2 satellite imagery integrated with topographic, land-cover, and anthropogenic variables. Multi temporal Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) were combined with elevation, slope, aspect, forest and water masks, and distance-to-road and distance-to-settlement variables. Three predictive models, namely Random Forest (RF), MultiLayer Perceptron (MLP), and a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture, were comparatively evaluated. RF achieved the highest classification accuracy of 94.17%, followed by MLP at 91.50% and CNN–LSTM at 78.69%. Future projections for 2026 and 2030 indicated relatively limited encroachment under RF and MLP, with maximum projected areas of 1.27 km² (0.15%) and 0.64 km² (0.07%), respectively. In contrast, CNN–LSTM projected substantially larger encroached areas of 115.76 km² (13.24%) in 2026 and 71.94 km² (8.23%) in 2030. Overall, RF demonstrated the strongest and most consistent classification performance under the adopted experimental framework, indicating its potential for supporting forest monitoring, encroachment hotspot identification, and evidencebased conservation planning.","摘要 森林侵占对受保护森林生态系统构成重大威胁，其驱动因素包括基础设施建设、农业扩张和聚落增长等日益加剧的人类活动。因此，持续监测对于有效的保护规划和可持续森林管理至关重要。本研究提出了一种基于人工智能的森林侵占预测框架，以印度班迪普尔国家公园为研究区，利用多时相Sentinel-2卫星影像，并结合地形、土地覆盖和人为变量。研究将多时相归一化植被指数（NDVI）和归一化燃烧比（NBR）与高程、坡度、坡向、森林和水体掩膜以及距道路距离和距聚落距离等变量相结合。研究对比评估了三种预测模型，即随机森林（RF）、多层感知机（MLP）以及混合卷积神经网络—长短期记忆网络（CNN–LSTM）架构。RF取得了最高的分类精度，为94.17%，其次是MLP的91.50%和CNN–LSTM的78.69%。2026年和2030年的未来预测表明，在RF和MLP下森林侵占相对有限，最大预测面积分别为1.27 km²（0.15%）和0.64 km²（0.07%）。相比之下，CNN–LSTM预测的侵占面积要大得多，2026年为115.76 km²（13.24%），2030年为71.94 km²（8.23%）。总体而言，在所采用的实验框架下，RF表现出最强且最一致的分类性能，表明其具有支持森林监测、侵占热点识别和循证保护规划的潜力。","2026-09-16T00:00:00Z",79,{"impact":111,"substance":182,"depth":110,"authority":338,"freshness":21,"relevant":8,"comment":2313},"基于多时相Sentinel-2与机器学习预测森林侵占，方法对比扎实、结论明确，对农业遥感与生态监测有参考价值。",[2315],{"name":335,"url":2307},[450,859,4,2317,1325],"生态监测",{"code":856,"slug":1815,"name":1816,"path":1817,"parent_code":845},[],{"status":46,"locked":22,"version":47,"method":48,"evidence":2321},{"candidates":2322,"agricultural_evidence":53},[2323,2328,2332],{"code":856,"score":28,"supported":53,"title":2324,"abstract":2325,"tags":2327},[1824],[2326],"artificial intelligence",[859],{"code":72,"score":354,"supported":22,"title":2329,"abstract":2330,"tags":2331},[],[],[4,450],{"code":67,"score":8,"supported":22,"title":2333,"abstract":2334,"tags":2335},[],[],[2317],[2337,2338],"农业遥感 机器学习 森林保护 生态监测","农业遥感 机器学习","农业遥感机器学习森林保护生态监测-2681","10.1038\u002Fs41598-026-70597-0",{"doi":2340,"openalex_id":2342,"authors":2343,"venue":335,"cited_by_count":80,"oa_url":2307,"card":2352,"direction":1860,"ingested_from":98},"W7213307679",[2344,2346,2348,2350],{"name":2345,"orcid":17},"Pushpa B. R",{"name":2347,"orcid":17},"H. R. Chaitanya",{"name":2349,"orcid":17},"Chandhana U. Shankar",{"name":2351,"orcid":17},"R. Sudarshan",{"tldr":2353,"method":2354,"finding":2355,"direction":96,"opportunity":2356},"用多时相Sentinel-2影像和机器学习预测印度Bandipur国家公园的森林侵占。","Sentinel-2多时相NDVI\u002FNBR结合地形、土地覆盖和人为变量，比较RF","RF分类精度最高达94.17%，预测2026和2030年侵占面积有限，CNN-LSTM预测值显著偏大","可探索多源遥感与深度学习融合提升侵占预测精度，并推广到其他保护区或农业扩张监测。","2026-09-16T23:30:46.701925Z",{"id":2359,"title":2360,"url":2361,"summary":2362,"summary_zh":2363,"content":17,"source_name":2364,"source_url":2361,"published_at":2365,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":289,"score_detail":2366,"sources":2368,"tags":2370,"primary_topic":2371,"secondary_topics":2372,"classification":2373,"search_phrases":2396,"slug":2399,"view_count":8,"doi":2400,"paper":2401,"created_at":2416},2514,"Spatiotemporal Assessment of Land Use and Land Cover Change Effects on Eco- Hydroclimatic Conditions in Kiambu County, Kenya","https:\u002F\u002Fdoi.org\u002F10.54536\u002Fajgt.v5i1.8320","Increasing land-use transitions are major drivers of eco-hydroclimatic changes, particularly in rapidly growing urban areas. This study therefore sought to assess the spatiotemporal effects of land use changes on eco-hydroclimatic conditions in Kiambu County, utilizing remote sensing and Geographic Information Systems technologies. Landsat satellite imagery from 2004 and 2024 was used. Rainfall data, Land Surface Temperature (LST), and Normalized Difference Vegetation Index (NDVI) were used to assess variations in vegetation cover, thermal patterns, and precipitation trends. Land use and land cover maps were generated using supervised classification, and accuracy assessments were performed to validate the classified maps. Statistical analyses were conducted to examine the relationships between LULC dynamics and eco-hydroclimatic parameters. The findings showed a significant expansion of built-up areas, accompanied by declines in cultivated land, vegetation cover, and water bodies. These changes were associated with increased LST, reduced vegetation health, and alterations in rainfall patterns. The study further demonstrated that increased urbanization and substantial landscape changes have significantly affected the county’s eco-hydroclimatic conditions. These changes have significant implications for environmental sustainability, water resources, climate change adaptation and resilience, and land use planning at the county level. The findings are expected to provide evidence to support informed policy formulation and sustainable spatial planning aimed at enhancing ecological resilience in Kiambu County.","土地利用转变日益加剧是生态-水文气候发生变化的主要驱动因素，在快速扩张的城市地区尤为显著。因此，本研究旨在利用遥感与地理信息系统技术，评估基安布县土地利用变化对生态-水文气候条件的时空影响。研究使用了2004年和2024年的Landsat卫星影像，并采用降雨数据、地表温度（LST）和归一化植被指数（NDVI）来评估植被覆盖、热力格局和降水趋势的变化。通过监督分类生成土地利用与土地覆盖图，并进行精度评估以验证分类结果。研究还开展了统计分析，以检验土地利用与土地覆盖动态与生态-水文气候参数之间的关系。研究结果表明，建设用地显著扩张，同时耕地、植被覆盖和水体减少。这些变化与地表温度升高、植被健康状况下降以及降雨格局改变相关。研究进一步表明，城市化加剧和景观的显著变化已对该县的生态-水文气候条件产生了重大影响。这些变化对县域层面的环境可持续性、水资源、气候变化适应与韧性以及土地利用规划具有重要意义。研究结果有望为制定知情政策和可持续空间规划提供证据支持，以增强基安布县的生态韧性。","American Journal of Geospatial Technology","2026-09-14T00:00:00Z",{"impact":25,"substance":110,"depth":111,"authority":183,"freshness":25,"relevant":8,"comment":2367},"基于Landsat遥感与GIS的县域土地利用变化对生态水文气候影响研究，方法规范、数据扎实，但属肯尼亚地方案例，对国内三农信息化参考价值有限。",[2369],{"name":2364,"url":2361},[64,134,4,295,147],{"code":72,"slug":842,"name":843,"path":844,"parent_code":845},[],{"status":46,"locked":22,"version":47,"method":48,"evidence":2374},{"candidates":2375,"agricultural_evidence":53},[2376,2380,2384,2388,2392],{"code":72,"score":25,"supported":53,"title":2377,"abstract":2378,"tags":2379},[],[134,135],[4,134],{"code":60,"score":532,"supported":53,"title":2381,"abstract":2382,"tags":2383},[],[64,1525],[64],{"code":1283,"score":52,"supported":53,"title":2385,"abstract":2386,"tags":2387},[],[1286,1520],[],{"code":39,"score":668,"supported":53,"title":2389,"abstract":2390,"tags":2391},[],[147],[147],{"code":67,"score":8,"supported":22,"title":2393,"abstract":2394,"tags":2395},[],[],[295],[2397,2398],"土地利用 气候变化 水资源 遥感","土地利用 气候变化","土地利用气候变化水资源遥感-2514","10.54536\u002Fajgt.v5i1.8320",{"doi":2400,"openalex_id":2402,"authors":2403,"venue":2364,"cited_by_count":80,"oa_url":2410,"card":2411,"direction":170,"ingested_from":98},"W7212965144",[2404,2406,2408],{"name":2405,"orcid":17},"Pauline Angerlyline Wamalwa",{"name":2407,"orcid":17},"Samson Mabwoga",{"name":2409,"orcid":17},"Charity Konana","https:\u002F\u002Fjournals.e-palli.com\u002Fhome\u002Findex.php\u002Fajgt\u002Farticle\u002Fdownload\u002F8320\u002F4014",{"tldr":2412,"method":2413,"finding":2414,"direction":96,"opportunity":2415},"基于Landsat影像评估肯尼亚Kiambu县2004-2024年土地利用变化对生态水文气候的影响。","Landsat影像监督分类、NDVI、地表温度与降雨数据统计分析。","建设用地扩张导致耕地、植被和水体减少，地表温度升高、降雨格局改变。","可结合多源遥感与水文模型，量化城市化对农业区水热平衡的长期影响机制。","2026-09-15T23:30:08.755171Z",{"id":2418,"title":2419,"url":2420,"summary":2421,"summary_zh":2422,"content":17,"source_name":2423,"source_url":2420,"published_at":2424,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1101,"score_detail":2425,"sources":2427,"tags":2429,"primary_topic":2431,"secondary_topics":2432,"classification":2433,"search_phrases":2456,"slug":2459,"view_count":61,"doi":2460,"paper":2461,"created_at":2474},2436,"Oasis Expansion and its impacts on Ecosystem Services in Southern Tunisia","https:\u002F\u002Fdoi.org\u002F10.56027\u002Fjoasd.112026","Many ecosystems face multiple pressures that affect their ability to provide ecosystem services. These services are commonly grouped into four categories: Provisioning, Regulating, Cultural and Supporting services. These services can lead to develop business opportunities for local communities. However, human activities and natural hazards may conduct to ecosystem degradation and loss of biodiversity and consequently, to business risks. In arid regions, oasis ecosystems have a crucial economic and social impact. However, natural and anthropogenic factors may affect oases ecosystems and their services. Therefore, the main objective of this research is to assess changes in oasis ecosystems in southern Tunisia using multi-temporal satellite images and NDVI analysis. The methodology is based on Landsat satellite data acquired between 1995 and 2025 and the calculation of NDVI. The results shows that the oasis areas in the study area increased by 42.36%. While this expansion reflects agricultural intensification, it may also lead to higher water consumption, soil degradation and biodiversity loss. This research can help local stakeholders better understand environmental risks and support the development of appropriate strategies to preserve oasis ecosystems.","许多生态系统面临多重压力，影响其提供生态系统服务的能力。这些服务通常分为四类：供给服务、调节服务、文化服务和支持服务。这些服务可为当地社区带来商业机会。然而，人类活动和自然灾害可能导致生态系统退化和生物多样性丧失，进而带来商业风险。在干旱地区，绿洲生态系统具有重要的经济和社会影响。然而，自然和人为因素可能影响绿洲生态系统及其服务。因此，本研究的主要目标是利用多时相卫星影像和NDVI分析评估突尼斯南部绿洲生态系统的变化。该方法基于1995年至2025年间获取的Landsat卫星数据和NDVI计算。结果表明，研究区内的绿洲面积增加了42.36%。虽然这一扩张反映了农业集约化，但也可能导致更高的水资源消耗、土壤退化和生物多样性丧失。本研究可帮助当地利益相关者更好地理解环境风险，并支持制定适当的策略以保护绿洲生态系统。","JOURNAL OF OASIS AGRICULTURE AND SUSTAINABLE DEVELOPMENT","2026-09-11T00:00:00Z",{"impact":28,"substance":26,"depth":27,"authority":28,"freshness":25,"relevant":8,"comment":2426},"基于1995—2025年Landsat影像与NDVI分析突尼斯南部绿洲扩张及其生态代价，方法规范、数据跨度长，对干旱区农业遥感与生态风险研究有参考价值，但属区域案例，公共影响有限。",[2428],{"name":2423,"url":2420},[4,116,2223,2430,2209],"绿洲农业",{"code":67,"slug":1697,"name":2213,"path":2214,"parent_code":43},[],{"status":46,"locked":22,"version":47,"method":48,"evidence":2434},{"candidates":2435,"agricultural_evidence":53},[2436,2440,2444,2448,2452],{"code":67,"score":1570,"supported":53,"title":2437,"abstract":2438,"tags":2439},[],[1696,2223,1697],[2223,2209],{"code":138,"score":52,"supported":53,"title":2441,"abstract":2442,"tags":2443},[],[141,142],[],{"code":39,"score":61,"supported":53,"title":2445,"abstract":2446,"tags":2447},[],[147],[],{"code":60,"score":61,"supported":53,"title":2449,"abstract":2450,"tags":2451},[],[129],[],{"code":72,"score":354,"supported":22,"title":2453,"abstract":2454,"tags":2455},[],[],[4,116],[2457,2458],"生态系统服务 荒漠化防治 绿洲农业 遥感监测","生态系统服务 荒漠化防治","生态系统服务荒漠化防治绿洲农业遥感监测-2436","10.56027\u002Fjoasd.112026",{"doi":2460,"openalex_id":2462,"authors":2463,"venue":2423,"cited_by_count":80,"oa_url":2468,"card":2469,"direction":96,"ingested_from":98},"W7212394548",[2464,2466],{"name":2465,"orcid":17},"Abdelaziz Ben Abdallah",{"name":2467,"orcid":17},"Mohamed Kefi","https:\u002F\u002Fwww.joasdjournal.org\u002Findex.php\u002Fjoasd\u002Farticle\u002Fdownload\u002F347\u002F306",{"tldr":2470,"method":2471,"finding":2472,"direction":96,"opportunity":2473},"用Landsat多时相影像和NDVI分析突尼斯南部绿洲1995-2025年扩张及其对生态系统服务的影","Landsat多时相影像（1995-2025）与NDVI计算，评估绿洲面积变化。","绿洲面积增加42.36%，但扩张可能加剧水资源消耗、土壤退化和生物多样性丧失。","可结合多源遥感与生态系统服务模型，量化绿洲扩张对水资源和碳储的权衡关系。","2026-09-14T23:30:27.585327Z",{"id":2476,"title":2477,"url":2478,"summary":2479,"summary_zh":2480,"content":17,"source_name":2481,"source_url":2478,"published_at":2424,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":23,"score_detail":2482,"sources":2484,"tags":2486,"primary_topic":17,"secondary_topics":2488,"classification":2489,"search_phrases":2500,"slug":2503,"view_count":8,"doi":2504,"paper":2505,"created_at":2520},2429,"Türkiye’de durağan orman alanlarında 2001 ve 2025 yılları arasında yer yüzey fenolojilerindeki değişimlerin incelenmesi","https:\u002F\u002Fdoi.org\u002F10.53516\u002Fajfr.1970696","Giriş ve Hedefler Orman alanlarındaki yer yüzey fenolojilerinin (YYF) değişiminin izlenmesi iklim değişikliğine uyum ve sürdürülebilir doğal kaynak yönetimi açısından oldukça önemlidir. Bu çalışmada Türkiye’de bulunan durağan orman alanlarındaki 2001-2025 yılları arasında YYF parametreleri 250 m çözünürlüklü MODIS uydu görüntülerinden hesaplanan normalize fark bitki indeksi (NDVI) yardımıyla incelenmiştir. Yöntemler CORINE arazi örtüsü ve Hansen orman kaybı verileriyle belirlenen durağan geniş yapraklı (GY), iğne yapraklı (İY) ve karışık (KA) orman alanlarında yaklaşık iki milyon piksel analiz edilmiştir. Ham NDVI zaman serileri Whittaker filtresi ve çift lojistik regresyonla yeniden oluşturulmuştur. Büyüme mevsimi başlangıcı (BMB), büyüme mevsimi sonu (BMS), büyüme mevsimi uzunluğu (BMU), büyüme mevsimi zirve günü (BMZ), zirve NDVI (zNDVI) ve fenolojik eğri alanı (FEA) parametreleri dinamik mevsimsel genlik yöntemiyle hesaplanmıştır. Trendler Mann-Kendall ve Sen’s slope yöntemleriyle değerlendirilmiştir. YYF parametrelerinin iklim koşullarıyla ilişkisi mevsimsel ortalama sıcaklık ve toplam yağış değişkenleriyle korelasyon analizi yapılarak değerlendirilmiştir. Bulgular GY\u002FİY\u002FKA sınıflarında yılın günü biriminde (day of year), ortalama BMB 105\u002F94\u002F99, BMS 299\u002F298\u002F302, BMU 194\u002F204\u002F203 ve BMZ 186\u002F197\u002F193 olarak bulunmuştur. Büyüme mevsimi evrelerine ilişkin YYF parametrelerinde anlamlı trend oranları görece sınırlı kalırken, zNDVI ve FEA’da sırasıyla %52 ve %27 oranında anlamlı pozitif trend belirlenmiştir. İY ve KA ormanlarda BMS ilerleme eğilimi gösterirken, GY ormanlarda BMS’nin erkene kayması dikkat çekmektedir. YYF parametrelerinin özellikle mevsimsel sıcaklık koşullarıyla yakından ilişkili olduğu görülmüştür.","引言与目标：监测森林区域地表物候（YYF）变化，对于适应气候变化和可持续自然资源管理具有重要意义。本研究利用250 m分辨率的MODIS卫星影像计算归一化植被指数（NDVI），分析了土耳其稳定森林区域2001—2025年间的地表物候参数。方法：基于CORINE土地覆盖数据和Hansen森林损失数据确定的稳定阔叶林（GY）、针叶林（İY）和混交林（KA）区域，分析了约200万个像元。原始NDVI时间序列通过Whittaker滤波和双逻辑斯蒂回归重建。生长季始期（BMB）、生长季末期（BMS）、生长季长度（BMU）、生长季峰值日（BMZ）、峰值NDVI（zNDVI）和物候曲线面积（FEA）参数采用动态季节振幅法计算。趋势采用Mann-Kendall和Sen’s slope方法评估。地表物候参数与气候条件的关系通过其与季节平均气温和总降水量变量的相关分析进行评估。结果：在GY\u002FİY\u002FKA类别中，以年积日（day of year）为单位，平均BMB分别为105\u002F94\u002F99，BMS分别为299\u002F298\u002F302，BMU分别为194\u002F204\u002F203，BMZ分别为186\u002F197\u002F193。生长季各阶段的地表物候参数中显著趋势比例相对有限，而zNDVI和FEA中分别检测到52%和27%的显著正趋势。İY和KA森林中BMS呈推迟趋势，而GY森林中BMS提前值得关注。地表物候参数尤其与季节性温度条件密切相关。","Anadolu Orman Araştırmaları Dergisi",{"impact":25,"substance":26,"depth":241,"authority":28,"freshness":25,"relevant":8,"comment":2483},"基于MODIS NDVI长时序数据对土耳其稳定林地的植被物候变化进行量化分析，方法规范、数据规模大，属遥感生态监测领域，但与国内三农及农业信息化主题关联较弱，公共价值有限。",[2485],{"name":2481,"url":2478},[64,4,116,451,2487],"植被物候",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":2490},{"candidates":2491,"agricultural_evidence":22,"reason":149},[2492,2496],{"code":60,"score":668,"supported":53,"title":2493,"abstract":2494,"tags":2495},[],[64],[64],{"code":72,"score":354,"supported":22,"title":2497,"abstract":2498,"tags":2499},[],[],[4,116],[2501,2502],"森林生态 植被物候 气候变化 遥感监测","森林生态 植被物候","森林生态植被物候气候变化遥感监测-2429","10.53516\u002Fajfr.1970696",{"doi":2504,"openalex_id":2506,"authors":2507,"venue":2481,"cited_by_count":80,"oa_url":2514,"card":2515,"direction":96,"ingested_from":98},"W7212250137",[2508,2511],{"name":2509,"orcid":2510},"Ömer Faruk Atiz","https:\u002F\u002Forcid.org\u002F0000-0001-6180-7121",{"name":2512,"orcid":2513},"Süleyman Savaş DURDURAN","https:\u002F\u002Forcid.org\u002F0000-0003-0509-4037","https:\u002F\u002Fdergipark.org.tr\u002Ftr\u002Fdownload\u002Farticle-file\u002F6108437",{"tldr":2516,"method":2517,"finding":2518,"direction":96,"opportunity":2519},"分析土耳其稳定森林2001-2025年地表物候变化，基于MODIS NDVI数据。","MODIS NDVI、Whittaker滤波、双逻辑回归、Mann-Kendal","zNDVI和FEA显著正趋势，阔叶林生长季结束提前，针叶林和混交林推迟。","可结合多源遥感与气候数据，探究森林物候对极端气候事件的响应机制。","2026-09-14T23:30:26.111459Z",{"id":2522,"title":2523,"url":2524,"summary":2525,"summary_zh":2526,"content":17,"source_name":2527,"source_url":2524,"published_at":2528,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":108,"score_detail":2529,"sources":2531,"tags":2533,"primary_topic":2536,"secondary_topics":2540,"classification":2547,"search_phrases":2567,"slug":2570,"view_count":668,"doi":2571,"paper":2572,"created_at":2582},2332,"Wheat Nitrogen Fertilizer Management Using GreenSeeker Handheld Crop Canopy Sensor","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijaes.vol.11.no3.2025.pg12.22","One of the essential factors in increasing agricultural yields of cereal crops is increasing the grain yield without increasing production costs. Wheat (Triticum aestivum L.) is the main crop in the food basket of the entire world. Therefore, it is necessary to determine the appropriate nitrogen (N) requirements to obtain the optimal production of wheat crops by investigating the impact of different N levels on the wheat crop yield in the Al-Muthanna region, as well as determining the possibility of predicting grain yield using the GreenSeeker handheld proximal crop canopy sensorbased differences vegetative difference index (NDVI). Thus, there is a need to re-evaluate the previous recommendations using remote sensing techniques. The experimental treatments were five levels of N fertilizer including (0 Kg N ha-1 , 50 kg N ha-1 , 100 kg N ha-1 ,150 kg N ha-1, and 200 kg N ha-1, and each N level was divided into 100%, 70%, 50% percentages, respectively. This study was conducted at the Experiment Station and Agriculture Research of the College of AgricultureAl-Muthanna University. The NDVI measurements were obtained at FK5, FK7, and FK9 according to the Feekes scale growth stage. The results indicated significant differences in grain yield between different levels of N fertilizer and a 70% percentage outperformed on the 100% and 50% treatments for each of the two N levels of 200 and 150 kg ha-1 . The results also show significant differences between NDVI values for different N fertilizer levels. The NDVI readings and wheat yield values increased and followed a similar pattern with increasing N fertilizer levels. This suggests that NDVI can predict wheat grain yield when the NDVI is not saturated. This study showed the potential of using GreenSeeker proximal crop canopy sensor-based NDVI readings as a useful tool to predict wheat grain yield.","提高谷类作物农业产量的关键因素之一是在不增加生产成本的前提下提高谷物产量。小麦（Triticum aestivum L.）是全球粮食结构中的主要作物。因此，有必要通过研究不同氮水平对Al-Muthanna地区小麦作物产量的影响，确定获得小麦作物最佳产量所需的适宜氮（N）需求量，并确定利用基于GreenSeeker手持式近地作物冠层传感器的归一化植被差异指数（NDVI）预测谷物产量的可能性。因此，需要利用遥感技术重新评估以往的推荐方案。试验处理包括五个氮肥水平（0 kg N ha⁻¹、50 kg N ha⁻¹、100 kg N ha⁻¹、150 kg N ha⁻¹和200 kg N ha⁻¹），每个氮水平分别按100%、70%、50%的比例施用。本研究在Al-Muthanna大学农学院实验站与农业研究中心进行。NDVI测定根据Feekes尺度生育阶段在FK5、FK7和FK9时期进行。结果表明，不同氮肥水平间谷物产量存在显著差异，在200和150 kg ha⁻¹两个氮水平下，70%施用量处理均优于100%和50%处理。结果还显示，不同氮肥水平间NDVI值存在显著差异。随着氮肥水平的提高，NDVI读数和小麦产量值均增加并呈现相似的变化趋势。这表明在NDVI未饱和时，NDVI可以预测小麦谷物产量。本研究表明，利用基于GreenSeeker近地作物冠层传感器的NDVI读数作为预测小麦谷物产量的有用工具具有潜力。","INTERNATIONAL JOURNAL OF AGRICULTURE AND EARTH SCIENCE","2026-09-10T00:00:00Z",{"impact":28,"substance":110,"depth":111,"authority":21,"freshness":52,"relevant":8,"comment":2530},"基于GreenSeeker手持冠层传感器NDVI预测小麦产量并优化氮肥用量，方法实用但属区域性田间试验，产业影响有限。",[2532],{"name":2527,"url":2524},[246,2534,2535,134,4],"精准施肥","小麦",{"code":2537,"slug":2538,"name":2535,"path":2539,"parent_code":1435},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat",[2541,2546],{"code":722,"slug":2542,"name":2543,"path":2544,"parent_code":2545},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"code":72,"slug":842,"name":843,"path":844,"parent_code":845},{"status":46,"locked":22,"version":47,"method":48,"evidence":2548},{"candidates":2549,"agricultural_evidence":53},[2550,2554,2559,2563],{"code":2537,"score":111,"supported":53,"title":2551,"abstract":2552,"tags":2553},[2538],[2535,2538],[2535],{"code":722,"score":28,"supported":53,"title":2555,"abstract":2557,"tags":2558},[2556],"fertilizer",[2556],[2534],{"code":72,"score":25,"supported":53,"title":2560,"abstract":2561,"tags":2562},[],[134,135],[4,134],{"code":258,"score":8,"supported":22,"title":2564,"abstract":2565,"tags":2566},[],[],[246],[2568,2569],"智慧农业 精准施肥 小麦 遥感","智慧农业 精准施肥","智慧农业精准施肥小麦遥感-2332","10.56201\u002Fijaes.vol.11.no3.2025.pg12.22",{"doi":2571,"openalex_id":2573,"authors":2574,"venue":2527,"cited_by_count":80,"oa_url":17,"card":2577,"direction":96,"ingested_from":98},"W7212289620",[2575],{"name":2576,"orcid":17},"Mohammed A. Naser",{"tldr":2578,"method":2579,"finding":2580,"direction":96,"opportunity":2581},"用GreenSeeker手持冠层传感器NDVI预测小麦产量并优化氮肥管理。","伊拉克田间试验，5个氮水平，Feekes 5\u002F7\u002F9期测NDVI。","NDVI与产量随氮肥增加同步上升，未饱和时可预测产量，70%氮量表现更优。","可探索NDVI饱和条件下的替代植被指数及不同品种\u002F气候区的氮肥推荐模型迁移。","2026-09-13T23:30:26.260534Z",{"id":2584,"title":2585,"url":2586,"summary":2587,"summary_zh":2588,"content":17,"source_name":2589,"source_url":2586,"published_at":2528,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1161,"score_detail":2590,"sources":2592,"tags":2594,"primary_topic":17,"secondary_topics":2596,"classification":2597,"search_phrases":2612,"slug":2615,"view_count":663,"doi":2616,"paper":2617,"created_at":2640},2328,"Three decades of vegetation change in Jabel Marra: climate drivers and post-conflict vegetation dynamics","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12665-026-13109-7","Understanding long-term vegetation dynamics in semi-arid and conflict-affected environments is important for sustainable land management and climate adaptation. This study investigated vegetation change and climate variability in the Jabel Marra region of Central Darfur, Sudan, during 1994–2024 using Landsat-derived Normalized Difference Vegetation Index (NDVI), CHIRPS precipitation, MERRA-2 temperature, and the Standardized Precipitation Evapotranspiration Index (SPEI). Long-term vegetation trends were assessed using the Mann-Kendall trend test and Sen’s slope estimator, whereas climate–vegetation relationships were evaluated using correlation analysis. The annual NDVI showed a significant positive trend (Mann–Kendall Z = 4.78, p \u003C 0.001), with a Sen’s slope of 0.00143 NDVI units year⁻¹, indicating a gradual increase in vegetation greenness. Spatial trend analysis showed that positive vegetation trends were concentrated primarily in mid-to-high elevation vegetated areas. Annual precipitation exhibited substantial interannual variability (R² = 0.06), whereas temperature increased consistently (R² = 0.51). NDVI was positively correlated with precipitation ( r = 0.48) and negatively correlated with temperature ( r = − 0.57). The SPEI identified recurrent drought episodes before 2018, followed by predominantly wetter conditions thereafter. The findings indicate that vegetation dynamics in Jabel Marra were primarily associated with climatic variability, particularly precipitation and drought conditions, while conflict-related processes were considered contextual because they were not directly analyzed. This study demonstrates the value of integrating long-term satellite observations and climate datasets to monitor vegetation changes in data-scarce dryland environments.","理解半干旱和受冲突影响环境中的长期植被动态，对于可持续土地管理和气候适应具有重要意义。本研究利用基于Landsat的归一化植被指数（NDVI）、CHIRPS降水数据、MERRA-2温度数据以及标准化降水蒸散指数（SPEI），调查了苏丹中达尔富尔州杰贝尔马拉地区1994—2024年的植被变化与气候变率。长期植被趋势采用Mann-Kendall趋势检验和Sen斜率估计法进行评估，气候—植被关系则通过相关分析进行评价。年NDVI呈显著正趋势（Mann-Kendall Z = 4.78，p \u003C 0.001），Sen斜率为0.00143 NDVI单位·年⁻¹，表明植被绿度逐渐增加。空间趋势分析显示，植被正向趋势主要集中在中高海拔植被区。年降水量表现出较大的年际变率（R² = 0.06），而温度则持续上升（R² = 0.51）。NDVI与降水量呈正相关（r = 0.48），与温度呈负相关（r = −0.57）。SPEI识别出2018年之前反复出现的干旱事件，此后则以偏湿润条件为主。研究结果表明，杰贝尔马拉地区的植被动态主要与气候变率相关，尤其是降水量和干旱状况，而冲突相关过程因未被直接分析，仅作为背景因素加以考虑。本研究证明了整合长期卫星观测和气候数据集以监测数据稀缺干旱区环境植被变化的价值。","Environmental Earth Sciences",{"impact":25,"substance":26,"depth":241,"authority":183,"freshness":25,"relevant":8,"comment":2591},"基于30年Landsat与气候数据的半干旱冲突区植被动态研究，方法规范、结论可靠，对旱区土地管理与气候适应有参考价值，但属区域案例研究，公共影响有限。",[2593],{"name":2589,"url":2586},[4,946,116,783,2595],"植被变化",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":2598},{"candidates":2599,"agricultural_evidence":22,"reason":149},[2600,2604,2608],{"code":60,"score":1570,"supported":53,"title":2601,"abstract":2602,"tags":2603},[],[946,129,127],[783,946],{"code":39,"score":61,"supported":53,"title":2605,"abstract":2606,"tags":2607},[],[57],[],{"code":72,"score":354,"supported":22,"title":2609,"abstract":2610,"tags":2611},[],[],[4,116],[2613,2614],"干旱区农业 植被变化 气候适应 遥感监测","干旱区农业 植被变化","干旱区农业植被变化气候适应遥感监测-2328","10.1007\u002Fs12665-026-13109-7",{"doi":2616,"openalex_id":2618,"authors":2619,"venue":2589,"cited_by_count":80,"oa_url":2634,"card":2635,"direction":96,"ingested_from":98},"W7212149653",[2620,2623,2626,2628,2630,2632],{"name":2621,"orcid":2622},"Abdalrahman Ahmed","https:\u002F\u002Forcid.org\u002F0009-0000-0624-2615",{"name":2624,"orcid":2625},"Kornél Czimber","https:\u002F\u002Forcid.org\u002F0000-0002-3739-2461",{"name":2627,"orcid":17},"Saralees Nadarajah",{"name":2629,"orcid":17},"Elmugheira Mockarram Ibrahim Mohammed",{"name":2631,"orcid":17},"Ahmed Abdallah Adam Mohamed",{"name":2633,"orcid":17},"Faisal Ismail Musa","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs12665-026-13109-7.pdf",{"tldr":2636,"method":2637,"finding":2638,"direction":96,"opportunity":2639},"分析苏丹Jabel Marra地区1994–2024年植被变化及其气候驱动因素。","用Landsat NDVI、CHIRPS降水、MERRA-2温度和SPEI，结合","NDVI显著上升，与降水正相关、与温度负相关，2018年前多干旱后转湿。","冲突影响仅作背景，可结合社会经济与冲突数据量化其对干旱区植被恢复的独立贡献。","2026-09-13T23:30:24.709880Z",{"id":2642,"title":2643,"url":2644,"summary":2645,"summary_zh":2646,"content":17,"source_name":2647,"source_url":2644,"published_at":2424,"category":20,"cover_url":17,"hotness":2648,"is_selected":22,"score":23,"score_detail":2649,"sources":2651,"tags":2655,"primary_topic":2657,"secondary_topics":2658,"classification":2660,"search_phrases":2679,"slug":2682,"view_count":8,"doi":2683,"paper":2684,"created_at":2698},2321,"ASSESSMENT OF SOIL SALINITY USING THE NDVI VEGETATION INDEX BASED ON REMOTE SENSING DATA","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22702718","This article examines the assessment of soil salinity in the irrigated meadow-sierozem soils of the E. Qahhorov agricultural area in Khovos District, Syrdarya Region, based on remote sensing data. Sentinel-2 satellite data covering the period from 2016 to 2025 were used in the study, and the Normalized Difference Vegetation Index (NDVI) was calculated using geospatial technologies. The long-term and seasonal dynamics of vegetation cover in the study area were analyzed, and the relationship between NDVI values and salinity levels was assessed. Based on an NDVI classification adapted to the natural conditions of the study area, criteria for the indirect assessment of soil salinity levels were proposed. The results demonstrated that the NDVI can be effectively used as an indicator for determining the impact of soil salinity on vegetation cover, assessing salinity spatially, and conducting long-term monitoring of salinity in irrigated agricultural lands.","本文基于遥感数据，对锡尔河州霍沃斯区E. Qahhorov农业区灌溉草甸灰钙土的土壤盐渍化进行了评估。研究使用了2016年至2025年期间的Sentinel-2卫星数据，并利用地理空间技术计算了归一化植被指数（NDVI）。分析了研究区植被覆盖的长期和季节性动态，并评估了NDVI值与盐渍化程度之间的关系。基于适应研究区自然条件的NDVI分类，提出了土壤盐渍化程度间接评估的标准。结果表明，NDVI可有效用作确定土壤盐渍化对植被覆盖影响、空间评估盐渍化以及开展灌溉农田盐渍化长期监测的指标。","Zenodo (CERN European Organization for Nuclear Research)",25,{"impact":28,"substance":110,"depth":111,"authority":28,"freshness":25,"relevant":8,"comment":2650},"基于Sentinel-2长时序NDVI数据提出土壤盐渍化间接评估标准，方法可复用但属区域性案例研究，公共影响有限。",[2652,2653],{"name":2647,"url":2644},{"name":2647,"url":2654},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22702719",[246,4,116,2656,1927],"土壤盐渍化",{"code":72,"slug":842,"name":843,"path":844,"parent_code":845},[2659],{"code":138,"slug":142,"name":1222,"path":1223,"parent_code":43},{"status":46,"locked":22,"version":47,"method":48,"evidence":2661},{"candidates":2662,"agricultural_evidence":53},[2663,2667,2671,2675],{"code":72,"score":27,"supported":53,"title":2664,"abstract":2665,"tags":2666},[135],[134,135],[4,116],{"code":138,"score":338,"supported":53,"title":2668,"abstract":2669,"tags":2670},[142],[141,142],[],{"code":39,"score":668,"supported":53,"title":2672,"abstract":2673,"tags":2674},[],[797],[1927],{"code":258,"score":8,"supported":22,"title":2676,"abstract":2677,"tags":2678},[],[],[246],[2680,2681],"土壤盐渍化 智慧农业 灌溉农业 遥感监测","土壤盐渍化 智慧农业","土壤盐渍化智慧农业灌溉农业遥感监测-2321","10.5281\u002Fzenodo.22702718",{"doi":2683,"openalex_id":2685,"authors":2686,"venue":2647,"cited_by_count":80,"oa_url":2644,"card":2693,"direction":96,"ingested_from":98},"W7212227143",[2687,2689,2691],{"name":2688,"orcid":17},"U.K. Umirova",{"name":2690,"orcid":17},"D.A. Kodirova",{"name":2692,"orcid":17},"O.O. Davronov",{"tldr":2694,"method":2695,"finding":2696,"direction":96,"opportunity":2697},"利用Sentinel-2遥感数据计算NDVI，评估灌溉草甸灰钙土的土壤盐渍化程度。","Sentinel-2数据（2016-2025）与NDVI指数，结合地理空间技术分","NDVI可有效指示盐渍化对植被的影响，实现空间评估与长期监测。","可探索多指数融合与机器学习提升盐渍化反演精度，并推广至不同土壤类型区。","2026-09-13T23:30:22.820810Z",{"id":2700,"title":2701,"url":2702,"summary":2703,"summary_zh":2704,"content":17,"source_name":2705,"source_url":2702,"published_at":2706,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":80,"score_detail":2707,"sources":2709,"tags":2711,"primary_topic":17,"secondary_topics":2714,"classification":2715,"search_phrases":2722,"slug":2725,"view_count":80,"doi":2726,"paper":2727,"created_at":2743},2171,"Hydraulic geometry analyses of the Kohora River, Assam, India","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44288-026-00719-8","The Hydraulic geometry is crucial for understanding the hydrodynamic behavior in fluvial system. The present research analyzes the variation of hydraulic geometry parameters at-a-station and downstream sites along the Kohora River, Assam (India) aiming to quantify the exponential correlation between these parameters and the river’s flow discharge. For this research, six hydrological stations C1,C2,C3,C4,C5 and C6 are utilized to investigate the cross sectional hydraulic properties of Kohora River which are located at various sites along the river course. For each cross-section, a range of hydraulic geometry parameters including width, depth and velocity are recorded at different locations, and subsequent discharge is computed based on these parameters. To complement field-based observations, Landsat 8 satellite imagery (30 m spatial resolution) was processed using ArcGIS 10.7.1 to assess the river basin characteristics. The vegetation characteristics were examined by applying the Normalized Difference Vegetation Index (NDVI), while surface water extent and moisture variability were analyzed through the Normalized Difference Water Index (NDWI), providing supporting insights into catchment conditions influencing fluvial processes. Based on the present study, the mean values of exponents b, f, m which represent the scaling rates of change in width, depth and velocity were determined to be 0.334, 0.538 and 0.134 while the average values of coefficients a, c, k were noted to be 4.46, 0.56, 0.40 for at-a-station hydraulic geometry. For downstream hydraulic geometry, the exponents b, f, and m were 0.2, 0.4, and 0.4, while the coefficients a, c, and k were 5.19, 0.57, and 0.35. These values indicate a positive exponential relationship between the downstream hydraulic geometry parameters and flow discharge in Kohora River. The outcome of the present study is expected to be applicable to improving the watershed management of the river basin.","水力几何关系对于理解河流系统中的水动力行为至关重要。本研究分析了印度阿萨姆邦科霍拉河沿程站点和下游站点的水力几何参数变化，旨在量化这些参数与河流流量之间的指数相关关系。本研究利用C1、C2、C3、C4、C5和C6六个水文站，对科霍拉河沿河不同位置的断面水力特性进行了调查。对于每个断面，在不同位置记录了包括宽度、水深和流速在内的一系列水力几何参数，并据此计算了相应流量。为补充实地观测，利用ArcGIS 10.7.1处理了Landsat 8卫星影像（30 m空间分辨率），以评估流域特征。通过归一化植被指数（NDVI）考察植被特征，同时通过归一化水体指数（NDWI）分析地表水体范围和水分变异性，为影响河流过程的集水区条件提供了辅助认识。基于本研究，代表宽度、水深和流速变化缩放率的指数b、f、m的均值分别为0.334、0.538和0.134，而站点水力几何关系的系数a、c、k的平均值分别为4.46、0.56和0.40。对于下游水力几何关系，指数b、f和m分别为0.2、0.4和0.4，系数a、c和k分别为5.19、0.57和0.35。这些数值表明，科霍拉河下游水力几何参数与流量之间存在正指数关系。本研究成果有望应用于改善该流域的流域管理。","Discover Geoscience","2026-09-09T00:00:00Z",{"impact":80,"substance":80,"depth":80,"authority":80,"freshness":80,"relevant":80,"comment":2708},"印度河流水文几何论文，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[2710],{"name":2705,"url":2702},[4,116,2712,2713],"水文分析","流域管理",[],{"status":122,"locked":22,"version":47,"method":48,"evidence":2716},{"candidates":2717,"agricultural_evidence":22,"reason":149},[2718],{"code":72,"score":354,"supported":22,"title":2719,"abstract":2720,"tags":2721},[],[],[4,116],[2723,2724],"水文分析 流域管理 遥感监测 NDVI","水文分析 流域管理","水文分析流域管理遥感监测NDVI-2171","10.1007\u002Fs44288-026-00719-8",{"doi":2726,"openalex_id":2728,"authors":2729,"venue":2705,"cited_by_count":80,"oa_url":2737,"card":2738,"direction":96,"ingested_from":98},"W7212051368",[2730,2732,2735],{"name":2731,"orcid":17},"S. Rai Choudhury",{"name":2733,"orcid":2734},"Niranjan Bhattacharjee","https:\u002F\u002Forcid.org\u002F0000-0003-4205-0682",{"name":2736,"orcid":17},"Dharitri Choudhury","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44288-026-00719-8.pdf",{"tldr":2739,"method":2740,"finding":2741,"direction":96,"opportunity":2742},"分析印度科霍拉河的水力几何参数，量化其与流量的指数关系。","6个水文站实测断面参数，结合Landsat 8影像与NDVI\u002FNDWI分析。","站点与下游水力几何参数均与流量呈正指数关系，下游指数b=0.2、f=0.4、m=0.4。","可结合遥感与水文模型，研究河流水力几何对流域农业灌溉与洪泛区管理的影响。","2026-09-11T23:30:31.628460Z",{"id":2745,"title":2746,"url":2747,"summary":2748,"summary_zh":2749,"content":17,"source_name":2750,"source_url":2747,"published_at":2751,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":2752,"score_detail":2753,"sources":2755,"tags":2757,"primary_topic":2759,"secondary_topics":2760,"classification":2761,"search_phrases":2787,"slug":2790,"view_count":354,"doi":2791,"paper":2792,"created_at":2807},2001,"Accelerated Computation of Vegetation Indices on Heterogeneous Platforms Using OpenMP, CUDA, and OpenCL for Sustainable Agricultural Monitoring","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18189237","Timely computation of vegetation indices from remote sensing imagery would assist in sustainable agriculture monitoring through quick analysis of vegetation state and surface water. However, high-resolution multispectral imagery can be computationally expensive to process, especially on embedded systems. In this research work, we evaluate heterogeneous approaches that aim to enhance the computation of the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) through sequential C++, OpenMP, CUDA and OpenCL on desktop and embedded CPU–GPU platforms. We compare multicore and GPU-based computations while also exploring optimizations of the OpenCL kernels with respect to memory management, loop unrolling and work-group settings. OpenMP increased the image-processing throughput from 211.24 to 500.85 images\u002Fs on the desktop platform and from 55.30 to 145.11 images\u002Fs on the Odroid XU4. These results correspond to speedups of 2.37× and 2.62×, respectively. These results demonstrate that heterogeneous processing can accelerate vegetation index calculation and provide a computational basis for timely, locally available agricultural monitoring. This capability may support precision agriculture applications, including vegetation stress assessment and water management decisions. Nevertheless, the experiments used offline multispectral images; energy consumption, energy per image, water savings, and onboard UAV performance were not measured.","及时计算遥感影像中的植被指数，有助于通过快速分析植被状态和地表水来实现可持续农业监测。然而，高分辨率多光谱影像的处理计算成本可能很高，尤其是在嵌入式系统上。在本研究中，我们评估了异构方法，旨在通过顺序C++、OpenMP、CUDA和OpenCL在桌面及嵌入式CPU-GPU平台上提升归一化差异植被指数（NDVI）和归一化差异水体指数（NDWI）的计算效率。我们比较了基于多核和GPU的计算，同时探讨了OpenCL内核在内存管理、循环展开和工作组设置方面的优化。OpenMP将桌面平台上的图像处理吞吐量从211.24幅\u002F秒提升至500.85幅\u002F秒，在Odroid XU4上从55.30幅\u002F秒提升至145.11幅\u002F秒。这些结果分别对应2.37倍和2.62倍的加速比。这些结果表明，异构处理能够加速植被指数计算，为及时、本地化的农业监测提供计算基础。这一能力可能支持精准农业应用，包括植被胁迫评估和水资源管理决策。然而，实验使用的是离线多光谱影像；能耗、每幅图像能耗、节水量以及机载无人机性能未进行测量。","Sustainability","2026-09-08T00:00:00Z",60,{"impact":28,"substance":110,"depth":27,"authority":28,"freshness":354,"relevant":8,"comment":2754},"论文提出异构平台加速植被指数计算，对农业监测有实际应用价值，但时效性较低。",[2756],{"name":2750,"url":2747},[246,1048,134,4,2758],"高性能计算",{"code":72,"slug":842,"name":843,"path":844,"parent_code":845},[],{"status":46,"locked":22,"version":47,"method":48,"evidence":2762},{"candidates":2763,"agricultural_evidence":53},[2764,2768,2772,2779,2783],{"code":72,"score":1570,"supported":53,"title":2765,"abstract":2766,"tags":2767},[],[134,135,853],[4,134],{"code":258,"score":532,"supported":53,"title":2769,"abstract":2770,"tags":2771},[],[703,711],[246],{"code":2773,"score":52,"supported":53,"title":2774,"abstract":2775,"tags":2778},"H8",[],[2776,2777],"可持续农业","sustainable agriculture",[],{"code":39,"score":61,"supported":53,"title":2780,"abstract":2781,"tags":2782},[],[147],[],{"code":856,"score":8,"supported":22,"title":2784,"abstract":2785,"tags":2786},[],[],[1048],[2788,2789],"农业人工智能 高性能计算 智慧农业 遥感","农业人工智能 高性能计算","农业人工智能高性能计算智慧农业遥感-2001","10.3390\u002Fsu18189237",{"doi":2791,"openalex_id":2793,"authors":2794,"venue":2750,"cited_by_count":80,"oa_url":2747,"card":2802,"direction":96,"ingested_from":98},"W7211978646",[2795,2797,2800],{"name":2796,"orcid":17},"Khadija Jahid",{"name":2798,"orcid":2799},"Rachid Latif","https:\u002F\u002Forcid.org\u002F0000-0001-9833-1614",{"name":2801,"orcid":17},"Amine Saddik",{"tldr":2803,"method":2804,"finding":2805,"direction":170,"opportunity":2806},"评估异构平台加速NDVI和NDWI计算，实现可持续农业监测。","使用C++、OpenMP、CUDA和OpenCL在桌面和嵌入式平台优化植被指数计","OpenMP在桌面和Odroid XU4上分别提速2.37倍和2.62倍，吞吐量显著提升。","可研究嵌入式平台实时处理、能耗优化及无人机机载应用，填补离线图像与能耗评估空白。","2026-09-09T23:30:22.828261Z",{"id":2809,"title":2810,"url":2811,"summary":2812,"summary_zh":2813,"content":17,"source_name":2814,"source_url":2811,"published_at":2815,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1101,"score_detail":2816,"sources":2818,"tags":2820,"primary_topic":2823,"secondary_topics":2824,"classification":2825,"search_phrases":2846,"slug":2849,"view_count":8,"doi":2850,"paper":2851,"created_at":2883},1896,"Improving Selection Efficiency at the Head Rows Stage of Winter Wheat Breeding by Using the Normalized Difference Vegetation Index","https:\u002F\u002Fdoi.org\u002F10.17557\u002Ftjfc.1860713","Visual selection in early stage of wheat breeding is usually implemented method. This study was conducted to compare breeders’ visual selection with normalized difference vegetation index (NDVI) selection. Three different selection protocols were used at Head Rows (HR) stage: Breeder visual, NDVI+ (high mean NDVI), and NDVI- (low mean NDVI). Some HRs were selected by both Breeder and NDVI+ (Breeder & NDVI+). Performances of HRs selected by each protocol were studied for grain yield and relationship of NDVI with yield in yield trials. 403 HRs of total 1246 HRs derived from 38 populations were selected by protocols at varying rates: 13.2% by Breeder, 10% by NDVI+, 3% by NDVI-, and 6.1% by Breeder & NDVI+. NDVI values measured at the pre-heading stage in the HR and early grain filling in the Preliminary Yield Trial (PYT) showed strong correlations with grain yield (r= 0.55** and r= 0.73**, respectively; p","小麦育种早期阶段的视觉选择通常是常规实施的方法。本研究旨在比较育种者的视觉选择与归一化植被指数（NDVI）选择的效果。在穗行（HR）阶段采用了三种不同的选择方案：育种者视觉选择、NDVI+（高平均NDVI）和NDVI-（低平均NDVI）。部分穗行同时被育种者和NDVI+选中（育种者&NDVI+）。研究了各方案所选穗行在产量试验中的籽粒产量表现，以及NDVI与产量的相关性。在源自38个群体的总计1246个穗行中，有403个穗行按不同比例被各方案选中：育种者选择占13.2%，NDVI+占10%，NDVI-占3%，育种者&NDVI+占6.1%。在穗行阶段抽穗前及初级产量试验（PYT）灌浆早期测定的NDVI值与籽粒产量呈强相关性（r分别为0.55**和0.73**；p","Turkish Journal Of Field Crops","2026-09-06T00:00:00Z",{"impact":111,"substance":26,"depth":110,"authority":28,"freshness":61,"relevant":8,"comment":2817},"研究验证NDVI在小麦育种早期选择中的效率，对智慧农业与育种结合有参考价值。",[2819],{"name":2814,"url":2811},[246,2821,2822,134,4],"种业振兴","小麦育种",{"code":2537,"slug":2538,"name":2535,"path":2539,"parent_code":1435},[],{"status":46,"locked":22,"version":47,"method":48,"evidence":2826},{"candidates":2827,"agricultural_evidence":53},[2828,2833,2837,2842],{"code":2537,"score":590,"supported":53,"title":2829,"abstract":2831,"tags":2832},[2830,2538],"winter wheat",[2535,2538],[2822],{"code":72,"score":354,"supported":22,"title":2834,"abstract":2835,"tags":2836},[],[],[4,134],{"code":2838,"score":8,"supported":22,"title":2839,"abstract":2840,"tags":2841},"F1",[],[],[2821],{"code":258,"score":8,"supported":22,"title":2843,"abstract":2844,"tags":2845},[],[],[246],[2847,2848],"小麦育种 智慧农业 种业振兴 遥感","小麦育种 智慧农业","小麦育种智慧农业种业振兴遥感-1896","10.17557\u002Ftjfc.1860713",{"doi":2850,"openalex_id":2852,"authors":2853,"venue":2814,"cited_by_count":80,"oa_url":2877,"card":2878,"direction":96,"ingested_from":98},"W7210287349",[2854,2857,2860,2863,2866,2868,2871,2874],{"name":2855,"orcid":2856},"Emrah Koç","https:\u002F\u002Forcid.org\u002F0000-0003-3024-8691",{"name":2858,"orcid":2859},"Beyhan Akın","https:\u002F\u002Forcid.org\u002F0000-0002-2721-9577",{"name":2861,"orcid":2862},"Savaş Belen","https:\u002F\u002Forcid.org\u002F0000-0001-7357-8127",{"name":2864,"orcid":2865},"Emel ÖZER","https:\u002F\u002Forcid.org\u002F0000-0002-2320-3105",{"name":2867,"orcid":17},"Kemal Subaşi",{"name":2869,"orcid":2870},"Fatih Özdemir","https:\u002F\u002Forcid.org\u002F0000-0001-7934-2844",{"name":2872,"orcid":2873},"Mesut Keser","https:\u002F\u002Forcid.org\u002F0000-0001-6316-7981",{"name":2875,"orcid":2876},"Alexey Morgounov","https:\u002F\u002Forcid.org\u002F0000-0001-7082-5655","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5590561",{"tldr":2879,"method":2880,"finding":2881,"direction":170,"opportunity":2882},"比较NDVI与育种家目视选择在小麦育种早期穗行阶段的效果，发现NDVI选择可提高效率。","使用NDVI进行高、低均值选择，与育种家目视选择对比，并在产量试验中验证。","NDVI与籽粒产量强相关，NDVI+选择效率高，可辅助或替代目视选择。","可探索将NDVI与机器学习结合，实现高通量表型选择，提高育种效率。","2026-09-08T23:30:14.947877Z",{"id":2885,"title":2886,"url":2887,"summary":2888,"summary_zh":2889,"content":17,"source_name":2890,"source_url":2887,"published_at":2891,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":444,"score_detail":2892,"sources":2894,"tags":2896,"primary_topic":2900,"secondary_topics":2901,"classification":2902,"search_phrases":2913,"slug":2916,"view_count":8,"doi":2917,"paper":2918,"created_at":2929},1773,"The Cotton Line: Testing the Institutional Persistence of an Erased Colonial Land-Tenure Boundary Across Vidarbha's Agrarian and Vegetation Geography","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx51b86","Are there 150-year-old institutional legacies measurable in the 21st century as a spatial signature, in patterns of vegetation phenology (cotton signatures), agrarian labor structure, land use and land surface temperature? That is the core question this paper addresses and the design is not a 'before'-'after', it's a 'spatial discontinuity' test to see if there is still a measurable gap across an administrative line following a historical change. It was in 1853 when the British stormed the Berar region, consisting of the districts Amravati, Akola, Buldhana, Washim, and Yavatmal, and implemented a new land-tenure system in the region known as the \"Ryotwari\" system. The American Civil War brought an end to the export of cotton from America to Lancashire, and Lancashire pressured Berar to fill that gap, which led to Berar becoming a producer of cotton on a monoculture basis. The six districts in the East of Berar (Nagpur, Wardha, Chandrapur, Bhandara, Gondia and Gadchiroli), the heart of the Central Provinces, were administered on a somewhat different system of settlement which was known as \"Malguzari.\" The administrative boundary was officially renamed and discontinued in 1956, but it still corresponds, as would be the case today, to the current administrative division between Amravati Division and Nagpur Division. Two annual seasons of kharif-rabi NDVI reducing indicate a similar signature. The negative difference for Berar was 0.325 in its performance as compared with 0.255 of Central Provinces in year one, showing difference of 0.070 points across 68 talukas in Berar. Back in Year two, which was a new set of scenes, the picture was virtually the same: Berar scored 0.316 and Central Provinces 0.241, a difference of 0.075. I wasn't convinced by that repetition over a single unusual year, but by the repetition over two independent cycles. Before constructing the boundary was possible, it had to be built – a 384.7 km new line formed by joining 120 talukas in their two historical segregations and separating them by extracting the common frontier between the two – but only after double-checking that it did not assume that the frontier aligned with the old boundary because they shared geographical characteristics, but made sure also that it matched the documented old boundary from 1903–56. The headline null result was the failure to detect any significant level slope, collapse or shift along the historical trend line at any scale (immediate-boundary corridor of 32 talukas, or the extended scene footprint of 66-68 talukas) in the spatial-discontinuity regression (outcome ~ side + signed_dist_km + side×signed_dist_km + rainfall + elevation) of each of the outcomes. At the full footprint level, what the regression did reveal was a significant side×distance interaction for both EO outcomes (NDVI's decline: p = 0.0348; Land-Surface Temperature: p = 0.0105) — meaning the two sides follow different trends moving away from the old boundary, even without a sharp drop at it. The agricultural-laborer-to-cultivator ratio exhibited a similar slope difference at the corridor scale (p = 0.0361), with a similar direction for all 32 leave-one-out jackknife runs, but despite all this the slope difference at the full-footprint scale is offset by a level jump (p = 0.014) — not a contradiction to the underlying story but simply because the territory on the two sides of the corridor is lopsided within the satellite scene. Both these findings together imply no abrupt change of institution at the old Berar\u002FCentral Provinces border, but a rather subtle phenomenon: a strengthening gradient of the institution with increasing distance from the border. To reinforce the overall NDVI signal, a field-ground-truth effort was carried out against separate field-monitoring for the same field boundaries at Karanja Ghadge and Sawardoh villages of Wardha district, covering 7 boundary tests.","150年历史的制度遗产，在21世纪是否还能以空间印记的形式，在植被物候模式（棉花特征）、农业劳动力结构、土地利用和地表温度中得以衡量？这是本文探讨的核心问题。研究设计并非“前后”对比，而是“空间不连续性”检验，旨在考察在历史变革之后，行政界线两侧是否仍存在可测度的差距。1853年，英国人攻占贝拉尔地区（含阿姆拉瓦蒂、阿科拉、布尔达纳、瓦希姆和亚瓦特马尔等县），并在该地区推行名为“莱特瓦尔制”（Ryotwari）的新土地保有制度。美国内战导致美国棉花对兰开夏郡的出口中断，兰开夏郡施压贝拉尔填补这一缺口，促使贝拉尔成为单一作物制的棉花产区。贝拉尔以东的六个县（那格浦尔、瓦尔达、钱德拉普尔、班达拉、贡迪亚和加德奇罗利）——中央省的核心地带——则实行一种略有不同的定居制度，即“马尔古扎里制”（Malguzari）。该行政边界于1956年正式更名并废止，但其走向与当今阿姆拉瓦蒂专区与那格浦尔专区之间的行政分界仍然对应。两个年度的“卡里夫—拉比”（kharif-rabi）归一化植被指数（NDVI）下降均显示出相似的特征。第一年，贝拉尔的表现负差为0.325，而中央省为0.255，在贝拉尔68个塔卢卡（taluka）之间显示出0.070个百分点的差异。到了第二年——一组全新的影像场景——情况几乎相同：贝拉尔为0.316，中央省为0.241，差异为0.075。令我信服的并非单一异常年份中的这种重复，而是两个独立周期中的重复。在构建边界之前，必须先将其建立起来——一条384.7公里的新界线，由120个塔卢卡按其历史上的两种归属划分连接而成，并通过提取两者之间的共同边界将其分隔——但前提是经过双重核查，确保并未因两者共享地理特征而假定该界线与旧边界重合，同时确认其与1903年至1956年间有文献记载的旧边界相符。最核心的零结果在于：在任何尺度上（紧邻边界的走廊地带），均未能检测到沿历史趋势线存在显著的坡度变化、断裂或位移。","OpenAlex","2026-09-04T00:00:00Z",{"impact":28,"substance":182,"depth":110,"authority":28,"freshness":61,"relevant":8,"comment":2893},"论文利用空间断点回归检验150年前土地制度对当代农业与植被的持续影响，方法新颖，数据详实，但时效性较低。",[2895],{"name":2890,"url":2887},[450,4,2897,2898,2899],"历史制度","棉花种植","土地制度",{"code":1636,"slug":1637,"name":1638,"path":1639,"parent_code":1435},[],{"status":46,"locked":22,"version":47,"method":48,"evidence":2903},{"candidates":2904,"agricultural_evidence":53},[2905,2909],{"code":1636,"score":111,"supported":53,"title":2906,"abstract":2907,"tags":2908},[1647],[1634,1647],[2898],{"code":72,"score":354,"supported":22,"title":2910,"abstract":2911,"tags":2912},[],[],[4,450],[2914,2915],"农业遥感 历史制度 土地制度 棉花种植","农业遥感 历史制度","农业遥感历史制度土地制度棉花种植-1773","10.31223\u002Fx51b86",{"doi":2917,"openalex_id":2919,"authors":2920,"venue":17,"cited_by_count":80,"oa_url":2923,"card":2924,"direction":96,"ingested_from":98},"W7208761436",[2921],{"name":2922,"orcid":17},"Sakshi Maske","https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F14802\u002Fdownload\u002F25742\u002F",{"tldr":2925,"method":2926,"finding":2927,"direction":96,"opportunity":2928},"检验1853年英国殖民土地制度边界是否在21世纪仍留下空间印记。","空间断点回归，比较NDVI、土地利用、地表温度等指标。","历史边界两侧存在显著差异，且两年重复验证一致。","可探索其他历史制度边界对农业生态的长期影响，结合遥感大数据。","2026-09-06T23:30:28.937643Z",{"id":2931,"title":2932,"url":2933,"summary":2934,"summary_zh":2935,"content":17,"source_name":2936,"source_url":2933,"published_at":2937,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":1266,"score_detail":2938,"sources":2940,"tags":2942,"primary_topic":2943,"secondary_topics":2944,"classification":2945,"search_phrases":2960,"slug":2963,"view_count":354,"doi":2964,"paper":2965,"created_at":2980},1740,"Spatio-Temporal Dynamics of Vegetation Response to Climatic Variability in Akinyele Local Government Area, Oyo State, Nigeria","https:\u002F\u002Fdoi.org\u002F10.33003\u002Ffjs-2026-1016-5660","Although vegetation is very sensitive to climatic variability, there is limited evidence on a local scale in many tropical areas. This study has evaluated the spatio-temporal variability of vegetation response to climatic variability in Akinyele Local Government Area, Oyo State, Nigeria using NDVI, Rainfall, LST data of multi-temporal satellite image for the years 2015, 2020 and 2025. Google Earth Engine was used to process the sentinel-2 imagery, CHIRPS rainfall data, and MODIS LST products. NDVI was categorized into 3 classes (sparse, moderate and dense vegetation). The data set consisted of 5,991 sample points with which statistical analyses (Pearson correlation, multiple linear regression, ANOVA) were carried out. Results indicated that from 2015 to 2020 the density of vegetation decreased by 126.45 km² but increased by 158.55 km² until 2025, in accordance with the climatic changes. Mean NDVI decreased from 0.629 in 2015 to 0.611 in 2020, then increased to 0.654 in 2025. LST had a consistently high negative correlation (r = -0.650 to -0.669, p \u003C 0.001) with NDVI, whereas there was a relatively weak positive correlation with rainfall (r = 0.291–0.336). Regression models accounted for 43%–45% of the variability in NDVI, with LST being the most significant predictor (p \u003C 0.001). The results provide a basis for understanding how temperature is the dominant climatic factor controlling vegetation in the study area, and its implications for land use planning, climate change adaptation, sustainable agriculture, and urban green infrastructure in the urbanising landscapes of Nigeria.","尽管植被对气候变率非常敏感，但在许多热带地区，关于局部尺度的证据仍然有限。本研究利用2015年、2020年和2025年的多时相卫星影像数据，包括归一化植被指数（NDVI）、降雨量和地表温度（LST），评估了尼日利亚奥约州阿基涅尔地方政府区域植被对气候变率的时空响应。研究采用谷歌地球引擎（Google Earth Engine）处理哨兵二号（Sentinel-2）影像、CHIRPS降雨数据和MODIS地表温度产品。NDVI被划分为三个等级（稀疏植被、中等植被和茂密植被）。数据集包含5，991个样本点，并据此进行了统计分析（皮尔逊相关分析、多元线性回归、方差分析）。结果表明，从2015年到2020年，植被密度减少了126.45平方公里，但到2025年又增加了158.55平方公里，这与气候变化相一致。平均NDVI从2015年的0.629降至2020年的0.611，随后在2025年上升至0.654。地表温度与NDVI始终呈高度负相关（r = -0.650至-0.669，p \u003C 0.001），而降雨量与NDVI呈相对较弱的正相关（r = 0.291–0.336）。回归模型解释了NDVI变异的43%–45%，其中地表温度是最显著的预测因子（p \u003C 0.001）。研究结果为理解温度作为控制研究区植被的主导气候因子提供了基础，并揭示了其对尼日利亚城市化景观中土地利用规划、气候变化适应、可持续农业和城市绿色基础设施建设的启示意义。","FUDMA Journal of Sciences","2026-09-03T00:00:00Z",{"impact":25,"substance":26,"depth":110,"authority":25,"freshness":61,"relevant":8,"comment":2939},"地方尺度植被对气候响应的遥感研究，数据详实，但影响范围有限，时效性较低。",[2941],{"name":2936,"url":2933},[450,64,4,188],{"code":60,"slug":517,"name":518,"path":519,"parent_code":43},[],{"status":46,"locked":22,"version":47,"method":48,"evidence":2946},{"candidates":2947,"agricultural_evidence":53},[2948,2952,2956],{"code":60,"score":532,"supported":53,"title":2949,"abstract":2950,"tags":2951},[],[64,1525],[64],{"code":2773,"score":52,"supported":53,"title":2953,"abstract":2954,"tags":2955},[],[2776,2777],[],{"code":72,"score":61,"supported":22,"title":2957,"abstract":2958,"tags":2959},[],[],[4,450,188],[2961,2962],"农业遥感 植被监测 气候变化 NDVI","农业遥感 植被监测","农业遥感植被监测气候变化NDVI-1740","10.33003\u002Ffjs-2026-1016-5660",{"doi":2964,"openalex_id":2966,"authors":2967,"venue":2936,"cited_by_count":80,"oa_url":2974,"card":2975,"direction":96,"ingested_from":98},"W7207552979",[2968,2970,2972],{"name":2969,"orcid":17},"Monday Ghandi Daniel",{"name":2971,"orcid":17},"Ojodugbowa Shedrach Omachoko",{"name":2973,"orcid":17},"Iyam Ubi Effiom","https:\u002F\u002Ffjs.fudutsinma.edu.ng\u002Findex.php\u002Ffjs\u002Farticle\u002Fdownload\u002F5660\u002F3848",{"tldr":2976,"method":2977,"finding":2978,"direction":96,"opportunity":2979},"利用遥感数据评估尼日利亚某地植被对气候变化的时空响应。","GEE处理Sentinel-2、CHIRPS、MODIS数据，统计相关与回归分析","温度是主导因子，与NDVI强负相关，降雨弱正相关。","可延伸至热带城市化区域植被-气候耦合模型及适应性管理策略研究。","2026-09-05T23:30:27.920627Z",{"id":2982,"title":2983,"url":2984,"summary":2985,"summary_zh":17,"content":2986,"source_name":2987,"source_url":17,"published_at":2988,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":289,"score_detail":2989,"sources":2991,"tags":2993,"primary_topic":2998,"secondary_topics":3001,"classification":3002,"search_phrases":3024,"slug":3027,"view_count":668,"doi":17,"paper":17,"created_at":3028},742,"卫星—无人机两层组网葡萄园巡检与NDVI自适应路径规划(原标题:Satellite-Enabled Two-Tier UAV Vineyard Inspection with NDVI-Adaptive Path Planning)","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753","提出高空多光谱卫星+低空RGB无人机两层协同葡萄园巡检方案,基于NDVI兴趣点识别引导低空病检任务;引入LEO卫星通信模拟,带宽受限下路由优化可减少任务时间约15%,为丘陵果园精准植保提供端到端解决方案。","## 1. Introduction\n\nViticulture is a high-value agricultural sector with both economic and cultural significance in many regions. Vineyards are exposed to a range of pressures, including disease, water stress, and climatic variability. Efficient monitoring is essential for informing timely interventions and management decisions, while keeping labour costs and effort low. Automated and data-driven monitoring approaches offer an increasingly practical path to vineyard monitoring, combining sensing, communication, and analytics for decision support. Relevant technologies include satellite imagery and communications, drone mapping, multispectral vegetation indices, and computer vision.\n\nSatellite technologies can support crop monitoring both directly, through Earth observation, and indirectly, through communication. Satellite imagery can provide repeated, wide-area observations of vineyard condition and has been used to support precision viticulture by mapping vegetation vigour, water status, spatial variability, and other relevant indicators for improved sustainability and operational efficiency [[1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B1-agriculture-16-01753)]. As data-driven approaches gain traction in agriculture, satellite communication is also becoming increasingly relevant, especially for remote regions where terrestrial connectivity may be unreliable or unavailable [[2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B2-agriculture-16-01753)].\n\nDrones provide a flexible and high-resolution sensing platform for precision agriculture, complementing the wider coverage of satellite imagery with detailed observations for crop mapping, plant-health assessment, irrigation support, yield estimation, spraying, and other monitoring and management tasks [[3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B3-agriculture-16-01753)]. Drone-based multispectral mapping can provide vegetation indices, such as NDVI, which are widely used to quantify vegetation condition, growth, and stress [[4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B4-agriculture-16-01753)]. Previous vineyard-focused research has examined such data for specific applications including water-status variability mapping and disease progression monitoring [[5](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B5-agriculture-16-01753),[6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B6-agriculture-16-01753)]. Nevertheless, UAV monitoring involves an inherent trade-off between coverage and detail: high-altitude flights can map larger areas efficiently, while low-altitude inspection can provide more detailed visual information but requires additional flight time and produces larger data volumes. At a broader monitoring scale, drone imagery is therefore often considered alongside satellite imagery, combining wide-area observation with finer spatial detail [[7](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B7-agriculture-16-01753)].\n\nComputer vision and machine learning further expand the role of image-based crop monitoring by enabling automated interpretation of RGB, multispectral, and other data. In agriculture, these methods have been applied to a broad range of tasks, including plant and canopy segmentation, stress detection, disease identification, and yield estimation. For disease monitoring specifically, recent reviews highlight the growing use of UAV imagery, deep learning, and remote or proximal sensing tools for early detection and field-scale assessment [[8](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B8-agriculture-16-01753),[9](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B9-agriculture-16-01753),[10](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B10-agriculture-16-01753)]. In grapevines, recent work includes both leaf-level disease classification using convolutional neural networks and field-level disease monitoring using UAV multispectral data [[6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B6-agriculture-16-01753),[11](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B11-agriculture-16-01753)].\n\nBuilding on these developments, this work presents a satellite-enabled, two-tier UAV workflow for semi-automated vineyard inspection. The proposed approach combines high-altitude multispectral scanning, identification of points of interest from vegetation index analysis, live flight path adaptation, low-altitude RGB inspection, and vision-based disease detection, classification, and severity estimation. Processing tasks are offloaded to a remote server reached through an emulated satellite communication environment, allowing the UAV-side system to remain lightweight while receiving multispectral analysis results during the mission. These results are used to adapt the flight path and guide selective low-altitude inspection of areas that may indicate plant stress or disease.\n\nUnlike existing approaches that typically address multispectral crop monitoring, close-range disease inspection, communication constraints, or UAV path planning separately, this work integrates these elements into a single adaptive inspection workflow. The main contributions of this paper are as follows:\n\n*   A two-tier UAV inspection architecture combining high-altitude multispectral sensing, NDVI-based smart sampling, low-altitude RGB inspection, and remote image processing.\n\n*   On-the-fly path adaptation for inserting newly identified waypoints into an ongoing UAV mission, together with a comparative evaluation of alternative insertion and route-optimisation strategies.\n\n*   An experimental analysis of how satellite-link bandwidth affects image-transfer latency, waypoint availability, and total mission duration during adaptive inspection.\n\nThe experimental evaluation in [Section 3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#sec3-agriculture-16-01753) focuses on the effect of different path-adaptation strategies and communication bottlenecks on mission efficiency. A detailed evaluation of point-of-interest identification as a smart sampling mechanism for low-altitude image acquisition is beyond the scope of this work and will be addressed in future research. The disease-detection component is included as a downstream analysis module based primarily on established computer vision approaches, and is therefore not benchmarked as a separate contribution in this paper.\n\n## 2. Materials and Methods\n\n### 2.1. System Architecture\n\nThe overall system architecture and information flow are shown in [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#fig_body_display_agriculture-16-01753-f001). The drone captures the images from the vineyard and sends them to the remote server for processing. The system incorporates high-altitude multispectral scanning, dynamic mission adaptation, and computer vision-based disease assessment. It is deployed as two interacting parts:\n\n**Figure 1.** System Architecture and data flow.\n\n1.\nA locally deployed application that interfaces with—and controls—the drone: For missions with a real drone, this is an Android app leveraging an SDK to communicate with the drone and deployed on the drone remote controller; for the tests presented in this work, a simulator was used instead, to facilitate repeat testing, as described in [Section 2.6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#sec2dot6-agriculture-16-01753).\n\n2.\nA remote server that handles image processing and computer vision tasks: In this work, a server hosted in a VM within the satellite emulation testbed presented in [Section 2.2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#sec2dot2-agriculture-16-01753) was used.\n\nFurthermore, the system comprises three main software modules, each with a distinct input and output, as shown in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#table_body_display_agriculture-16-01753-t001).\n\n**Table 1.** System software modules.\n\nThe flow of information between the modules, during the course of a mission, is as follows:\n\nInitially, a mission is created according to user input, comprising only high-altitude waypoints, placed evenly on a grid so as to cover the whole vineyard. At each high-altitude waypoint, the drone captures a multispectral image. The local application sends the pair of Red and NIR bands to the remote server. There, the POI identification module ([Section 2.3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#sec2dot3-agriculture-16-01753)) receives and processes them, and sends back the coordinates of any POIs identified. The Path adaptation module ([Section 2.4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#sec2dot4-agriculture-16-01753)) inserts these POIs into the existing path, adapting and optimizing it to minimise its total length. When a POI is visited, the drone captures a close-up RGB photo of the grapevine. All RGB close-ups are sent to the remote server after the mission for Disease detection ([Section 2.5](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#sec2dot5-agriculture-16-01753)).\n\nThe satellite constellation is transparent and serves the purpose of providing connectivity between the drone and the ground node hosting the remote server.\n\n### 2.2. Satellite Emulation Testbed\n\nThe satellite emulation testbed is capable of emulating a configurable number of satellites in an orbital plane. In this work, it is used to reproduce the behaviour of a Low Earth Orbit (LEO) satellite constellation with Inter-Satellite Links (ISLs), providing the communication environment between the drone-side system and the remote processing server.\n\nThe testbed was developed by DLR for research purposes using Mininet [[12](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B12-agriculture-16-01753)], a network emulator that can create networks of virtual hosts, switches, controllers, and links. In contrast to purely analytical or discrete-event simulators, Mininet networks run real code, including standard Unix\u002FLinux network applications and the Linux kernel network stack. As a result, software developed and tested in Mininet can be transferred to real systems with minimal changes, making it suitable for network virtualisation, performance evaluation, and deployment-oriented testing.\n\nIn the emulated satellite network, ground nodes connect to the nearest visible satellite. Depending on the constellation configuration, communication between two ground nodes may traverse more than one satellite, making ISLs important for end-to-end connectivity. The performance of the communication path depends on the links between ground nodes and satellites, as well as on the ISLs between satellites. For this reason, the testbed supports multiple configurable parameters, including the following:\n\n*   The height of the orbital plane, which affects both the delay between a ground node and the nearest satellite and the duration of satellite visibility with respect to the ground node;\n\n*   The number of satellites between the source and destination ground nodes;\n\n*   Ground-to-satellite link parameters, such as bandwidth and packet loss rate;\n\n*   ISL parameters, including delay, bandwidth, and packet loss rate.\n\nAlthough the testbed can support two orbital planes, only one orbital plane was considered in this work for simplicity. The testbed can also emulate orbits other than LEO, provided that the relevant parameters are selected carefully to avoid unrealistic scenarios. Communication between nodes is based on Internet Protocol (IP) routing, and both Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) are supported.\n\nThe experimental setup is shown in [Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#fig_body_display_agriculture-16-01753-f002). The emulated satellite network runs inside a Virtual Machine (VM) on Ubuntu 22.04, which is accessed through a Virtual Private Network (VPN) connection. The drone-side system connects to this VPN and sends data to the VM. Upon receiving the data packets, the VM forwards them to the satellite emulation framework. Packets are then forwarded through the emulated satellite path, from the nearest satellite toward the gateway or destination ground node. In this work, end-to-end communication was carried out using TCP, and four satellites were modelled along the path between the drone and the ground node. The ground node hosts the Docker container running the remote processing service. After the images are processed, the results are returned through the same emulated communication path in the reverse direction.\n\n**Figure 2.** Experimental testbed configuration.\n\nSatellite emulation is considering a Starlink-like constellation. Hence, the ground nodes have continuous connectivity since at least one satellite is visible. This ensures that the ground nodes can always communicate without the need of a storage mechanism on the ground nodes. Considering that TCP is employed in the transport layer it is essential that the end-to-end connectivity is always present. Otherwise, the docker present on the other end of the constellation will not be readily reachable, which can adversely affect the overall performance.\n\nThe links between satellites and ground nodes, namely the drone and gateway, are modelled based on the orbital height of the satellite constellation and the relative movement between satellites and ground nodes. Link delay is highest when the satellite appears at the horizon of the ground node. It then decreases as the satellite moves toward the point above the ground node, before increasing again until the satellite is no longer visible. Satellite visibility duration also depends on orbital height, with higher orbital planes resulting in longer visibility duration. In the scenario considered in this work, the orbital height was set to 1221 km.\n\n### 2.3. POI Identification\n\n#### 2.3.1. POI Identification Rationale and Objective\n\nScanning an area, or a vineyard in the present case, is a trade-off between flight altitude and effective ground resolution. The ground area captured by a single photo (also known as the footprint) depends on the altitude of flight, as well as the camera’s field of view (FOV). For simplicity and uniformity, in this paper we are considering only photos taken at nadir orientation (i.e., the camera is pointing straight down).\n\nFor a camera with\n\n$F O V_{H} \\times F O V_{V}$\nfield of view and the drone flying at altitude A, the footprint is a rectangle of dimensions\n\n$X \\times Y$\n:\n\n$$\nX & = 2 A tan \\left(\\right. F O V_{H} \u002F 2 \\left.\\right) \\\\ Y & = 2 A tan \\left(\\right. F O V_{V} \u002F 2 \\left.\\right)\n$$\n\n(1)\n\nThe ground sampling distance (GSD) describes the distance between adjacent pixels and is equal to the footprint size divided by the camera resolution. For a camera with a\n\n$W \\times H$\nresolution:\n\n$$\nG S D_{x} & = 2 A \\frac{tan \\left(\\right. F O V_{H} \u002F 2 \\left.\\right)}{W} \\\\ G S D_{y} & = 2 A \\frac{tan \\left(\\right. F O V_{V} \u002F 2 \\left.\\right)}{H}\n$$\n\n(2)\n\nFor a given drone model, the camera specifications are constant and GSD is reduced to a function of altitude, which can be derived from either the horizontal or the vertical GSD and resolution, equivalently. For the camera of a modern commercial drone (in this work, experiments were conducted using the DJI Mavic 3M (DJI, Shenzhen, China)), with a vertical resolution of 3956 pixels and a corresponding vertical FOV of 48°, the altitude required to achieve a desired GSD is:\n\n$$\nA = \\frac{1}{2} \\frac{tan \\left(\\right. 48 \u002F 2 \\left.\\right)}{3956} G S D \\approx 4443 \\cdot G S D\n$$\n\n(3)\n\nGrapevine leaves average about 20 cm in size, while discolouration spots that can point to disease are often about 1 cm. In disease detection datasets such as [[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B13-agriculture-16-01753),[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B14-agriculture-16-01753),[15](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B15-agriculture-16-01753)], discolouration spots that signify disease are usually 7–20 pixels wide. Hence, targeting a similar resolution and assuming that spots would be clearly identifiable if they are at least 10 pixels wide, at a GSD of no more than 0.001 m per pixel would be required, pointing at a maximum altitude of about 4.4 m for disease detection. This GSD is also in line with relevant research on unhealthy leaf detection in the wild [[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B16-agriculture-16-01753)]. This corresponds to a footprint roughly 5.2 m × 3.9 m, or an area of 20 m 2, meaning that even a relatively small vineyard of 3–4 hectares would require thousands of photos to scan exhaustively.\n\nNon-exhaustive scans, or sampling, can provide insights regarding disease presence while keeping the number of images, the drone flight time, and the energy expenditure manageable. POI identification’s goal is to maximise the efficiency of low-altitude image sampling by identifying the most interesting, from a plant-health perspective, points in the vineyard.\n\n#### 2.3.2. Approach\n\nPrevious research has shown that NDVI bears a strong correlation with plant health and can be used to identify relevant risks [[6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B6-agriculture-16-01753),[17](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B17-agriculture-16-01753)]. Crucially, NDVI-based insights do not rely on specific details but on the average value of a plant or vineyard area, and hence can be acquired with a greater GSD (e.g., 0.02–0.05 m per pixel), at higher altitudes. Accounting for the comparatively lower resolution of multispectral cameras, this corresponds to an altitude of 45–110 m.\n\nHence, NDVI images constructed from high-altitude multispectral photos can provide insights regarding plant health. In this work, such photos are used to drive a smart sampling scheme, in which spots are identified as points of interest (POIs) based on their NDVI value, and are marked for low-altitude image acquisition.\n\n#### 2.3.3. NDVI Calculation and Correction\n\nThe NDVI is computed from the Red and Near Infrared (NIR) bands:\n\n$$\nN D V I = \\frac{N I R - R e d}{N I R + R e d}\n$$\n\n(4)\n\nWhile the NIR and Red bands are acquired as part of the multispectral photo, they each have slightly different fields of view, due to the placement of the sensors on the drone. In addition, both images have slight vignetting distortion. Before computing the NDVI, band images must be undistorted and aligned:\n\nTo compensate for vignetting distortion, the vignetting polynomial coefficients are extracted from each image’s metadata and used. For alignment, Oriented FAST and Rotated BRIEF (ORB) [[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B18-agriculture-16-01753)] is used to detect keypoints and compute compact binary descriptors in the Red and NIR images. ORB was selected because it combines low computational cost with robustness to the small rotational differences between the two bands, making it suitable for repeated processing of high-resolution multispectral images, while its binary descriptors can be matched efficiently using Hamming distance. A brute-force matcher compares the descriptors between the two bands, with cross-checking applied to retain only mutual best matches. The matches are sorted by distance, and the best 50 correspondences are used to estimate an affine transformation that aligns the NIR band to the Red band.\n\n#### 2.3.4. Grapevine Row Identification and Segmentation\n\nFrom each NDVI image depicting a patch of the vineyard, individual segments of plants, corresponding to potential POIs, must be identified and the mean NDVI of each must be calculated:\n\nFirstly, the NDVI images show rows of grapevines as well as soil. To separate the grapevines from the background, a binary mask is applied to NDVI with a threshold of 0.35. This threshold is in line with previous research, which suggests thresholds between 0.35 and 0.4, depending on plant development [[19](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B19-agriculture-16-01753)]. Grapevine rows are identified as continuous areas in the binary mask. Finally, each row is divided into smaller segments. Segmentation is performed along each row’s long axis, by lines perpendicular ","MDPI Agriculture 16(16):1753 2026-08-15","2026-08-15T00:00:00Z",{"impact":28,"substance":110,"depth":27,"authority":28,"freshness":668,"relevant":8,"comment":2990},"论文提出卫星-无人机两层组网巡检与NDVI自适应路径规划，方法新颖，数据详实，对智慧农业有参考价值，但时效性稍差。",[2992],{"name":2987,"url":2984},[2994,2995,2996,4,2997],"路径规划","智慧种植","葡萄园","卫星-UAV",{"code":1949,"slug":2999,"name":1952,"path":3000,"parent_code":1748},"fruit","\u002Ftopic\u002Fhorticulture\u002Ffruit",[],{"status":46,"locked":22,"version":47,"method":48,"evidence":3003},{"candidates":3004,"agricultural_evidence":53},[3005,3010,3015,3020],{"code":1949,"score":28,"supported":53,"title":3006,"abstract":3008,"tags":3009},[3007],"葡萄",[3007],[2996],{"code":739,"score":61,"supported":53,"title":3011,"abstract":3012,"tags":3014},[],[3013],"植保",[],{"code":3016,"score":8,"supported":22,"title":3017,"abstract":3018,"tags":3019},"F5",[],[],[2994],{"code":72,"score":8,"supported":22,"title":3021,"abstract":3022,"tags":3023},[],[],[4],[3025,3026],"智慧种植 路径规划 葡萄园 卫星-UAV","智慧种植 路径规划","智慧种植路径规划葡萄园卫星-UAV-742","2026-08-19T23:57:32.281962Z"]