[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4236":3,"related-4236":69},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":24,"classification":25,"search_phrases":38,"slug":41,"view_count":15,"doi":42,"paper":43,"created_at":68},4236,"Drivers of spatial and temporal heterogeneity in ecosystem functional diversity on the Yamal Peninsula, Siberia, Russia","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2752-664x\u002Faeaf5d","Abstract Combinations of seasonal Normalized Difference Vegetation Index (NDVI) metrics that serve as proxies for aboveground biomass, primary productivity, and phenology can be used to define Ecosystem Functional Types (EFTs), discrete areas with similar carbon gain dynamics. EFT richness, the number of unique EFTs within a prescribed area, represents the spatial heterogeneity in the seasonal dynamics of primary productivity across a landscape, and can be used as an index of functional diversity. EFTs and EFT richness can facilitate a more comprehensive understanding of how carbon cycling, climate feedbacks, and ecosystem resistance \u002F resilience will be altered under projected warming; however, the potential drivers of EFT distribution and EFT richness have not yet been evaluated for many biomes, including the Arctic. In this study, we used recently developed NDVI-based Arctic EFTs and a suite of climatic, geologic, biologic, and anthropogenic variables to determine the drivers of ecosystem functioning and functional diversity related to primary productivity dynamics on the Yamal Peninsula, Siberia, Russia between 2001 and 2018. EFTs represented spatially distinct functional niches on the Yamal Peninsula. The spatial and temporal dynamics of EFT richness were primarily defined by landscape heterogeneity (elevation, hydrology, substrate) and continentality, which promote distinctive vegetation responses. EFT richness values (i.e. the number of distinct ecosystem functional types at 250-m resolution within 2.25 km2 areas) ranged from 1 to 11 (maximum of 30 for the Yamal Peninsula) and increased over time across 30% of the Peninsula (7% significant trends, p-value \u003C 0.05). Changes in EFT were primarily driven by long-term climate patterns, including summer warming, earlier snowmelt, and greater moisture availability. These findings demonstrate how Arctic EFTs and EFT richness are useful tools for monitoring shifting carbon gain dynamics in response to multiple driving forces in complex Arctic ecosystems, and how Arctic regions with heterogeneous landscapes shaped by permafrost disturbance regimes could experience changes in functional diversity under dynamic climate conditions.","摘要 季节性归一化植被指数（NDVI）指标的组合可作为地上生物量、初级生产力和物候的代理变量，用于定义生态系统功能类型（EFTs），即在碳获取动态上相似的离散区域。EFT丰富度，即特定区域内独特EFT的数量，表征了景观尺度上初级生产力季节性动态的空间异质性，可作为功能多样性的指标。EFTs和EFT丰富度有助于更全面地理解碳循环、气候反馈以及生态系统抵抗力\u002F恢复力在预期变暖情景下将如何改变；然而，EFT分布和EFT丰富度的潜在驱动因素尚未在许多生物群区中得到评估，包括北极地区。在本研究中，我们利用近期开发的基于NDVI的北极EFTs以及一套气候、地质、生物和人为变量，确定了2001年至2018年间俄罗斯西伯利亚亚马尔半岛上与初级生产力动态相关的生态系统功能和功能多样性的驱动因素。EFTs代表了亚马尔半岛上空间上不同的功能生态位。EFT丰富度的时空动态主要由景观异质性（海拔、水文、基质）和大陆性所决定，这些因素促进了植被的差异化响应。EFT丰富度值（即在2.25 km²区域内250 m分辨率下不同生态系统功能类型的数量）范围为1至11（亚马尔半岛最大值为30），并在半岛30%的区域随时间增加（7%呈显著趋势，p值 \u003C 0.05）。EFT的变化主要受长期气候模式驱动，包括夏季变暖、融雪提前和水分有效性增加。这些发现表明，北极EFTs和EFT丰富度是监测复杂北极生态系统中响应多重驱动力的碳获取动态变化的有效工具，并且由永久冻土扰动机制塑造的景观异质性较强的北极地区，在动态气候条件下可能会经历功能多样性的变化。",null,"Environmental Research Ecology","2026-10-02T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文研究西伯利亚亚马尔半岛生态系统功能多样性的时空异质性，属于极地生态遥感领域，与三农、农业信息化、智慧农业等主题无关，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23],"遥感","植被指数","北极生态",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":29},"needs_review","2026-10-22.2","rules",{"candidates":30,"agricultural_evidence":14,"reason":37},[31],{"code":32,"score":33,"supported":14,"title":34,"abstract":35,"tags":36},"I3",2,[],[],[22,21],"农业相关性需要复核",[39,40],"北极生态 植被指数 遥感","北极生态 植被指数","北极生态植被指数遥感-4236","10.1088\u002F2752-664x\u002Faeaf5d",{"doi":42,"openalex_id":44,"authors":45,"venue":10,"cited_by_count":15,"oa_url":60,"card":61,"direction":65,"ingested_from":67},"W7216269526",[46,48,51,54,57],{"name":47,"orcid":9},"Morgan Tassone",{"name":49,"orcid":50},"Howard E. Epstein","https:\u002F\u002Forcid.org\u002F0000-0003-2817-4486",{"name":52,"orcid":53},"Amanda Armstrong","https:\u002F\u002Forcid.org\u002F0000-0002-9123-8924",{"name":55,"orcid":56},"Domingo Alcaraz‐Segura","https:\u002F\u002Forcid.org\u002F0000-0001-8988-4540",{"name":58,"orcid":59},"Martha K. Raynolds","https:\u002F\u002Forcid.org\u002F0000-0003-0506-1737","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2752-664X\u002Faeaf5d\u002Fpdf",{"tldr":62,"method":63,"finding":64,"direction":65,"opportunity":66},"基于NDVI定义生态系统功能型，分析俄罗斯亚马尔半岛2001-2018年功能多样性的时空异质性驱动因","使用NDVI季节指标划分北极生态系统功能型，结合气候、地质、生物和人为变量分析。","功能型丰富度受景观异质性和大陆性驱动，30%区域随时间增加，主要受夏季变暖、融雪提前和湿度增加影响。","农业遥感与作物表型","可借鉴该方法将生态系统功能型与功能多样性指数应用于农业景观，评估作物生产力动态对气候变化的响应。","openalex","2026-10-05T23:30:26.692874Z",{"total":70,"page":71,"page_size":70,"items":72},6,1,[73,144,222,305,382,430],{"id":74,"title":75,"url":76,"summary":77,"summary_zh":78,"content":9,"source_name":79,"source_url":76,"published_at":80,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":81,"score_detail":82,"sources":89,"tags":91,"primary_topic":95,"secondary_topics":100,"classification":101,"search_phrases":117,"slug":120,"view_count":15,"doi":121,"paper":122,"created_at":143},5243,"Identificación de índices de vegetación relacionados con componentes de rendimiento de frijol común (Phaseolus vulgaris L.) mediante el uso de sensores multiespectrales","https:\u002F\u002Fdoi.org\u002F10.18845\u002Ftm.v39i4.8378","Bean production in Costa Rica supplies only 20 % of domestic consumption, requiring the importation of the remaining amount; this situation highlights the need to know in advance the actual productive capacity of local farmers to improve planning, agronomic management, and import regulation. Therefore, the objective of this study was to evaluate the use of multispectral data and vegetation indices to analyze their relationship with yield components in bean varieties cultivated in different production areas of Costa Rica and to identify the yield component with the greatest spectral association. The research was conducted at three sites located in Alajuela, Costa Rica. Multispectral reflectance data were collected during the R5 phenological stage using an airborne multispectral sensor. Subsequently, yield components were quantified per meter of crop row, including 100-grain weight, number of pods per meter, number of plants, number of grains, and total grain weight. The number of pods showed the highest association with spectral variables and was therefore identified as the yield component with the greatest potential for analysis through remote sensing. The SR (Simple Ratio) and GCI (Green Chlorophyll Index) indices showed correlations greater than 0.7 with this variable, being identified as the vegetation indices with the highest spectral association during the R5 stage of the crop.","哥斯达黎加的菜豆（bean）产量仅能满足国内消费量的20%，其余部分需要进口；这一状况凸显了提前了解当地农民实际生产能力的必要性，以改进生产规划、农艺管理和进口调控。因此，本研究旨在评估多光谱数据和植被指数（vegetation indices）的应用，分析其与哥斯达黎加不同产区菜豆品种产量构成因素（yield components）之间的关系，并确定与光谱关联性最强的产量构成因素。研究在哥斯达黎加阿拉胡埃拉（Alajuela）的三个地点开展。在R5物候期使用机载多光谱传感器采集多光谱反射率数据。随后，按每米作物行量化产量构成因素，包括百粒重、每米豆荚数、植株数、籽粒数和籽粒总重。豆荚数与光谱变量的关联性最高，因此被确定为最具遥感分析潜力的产量构成因素。SR（简单比值，Simple Ratio）和GCI（绿色叶绿素指数，Green Chlorophyll Index）与该变量的相关系数大于0.7，被确定为作物R5期光谱关联性最高的植被指数。","Revista Tecnología en Marcha","2026-10-16T00:00:00Z",62,{"impact":83,"substance":84,"depth":85,"authority":86,"freshness":87,"relevant":71,"comment":88},8,18,15,12,9,"该研究利用机载多光谱传感器识别与菜豆产量组分相关的植被指数，方法清晰、结论具体，但属于哥斯达黎加的小区域研究，公共影响有限，可作为遥感作物监测领域的技术性资讯收录。",[90],{"name":79,"url":76},[92,93,21,22,94],"智慧农业","产量预测","菜豆",{"code":32,"slug":96,"name":97,"path":98,"parent_code":99},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":102,"locked":14,"version":27,"method":28,"evidence":103},"classified",{"candidates":104,"agricultural_evidence":106},[105,112],{"code":32,"score":86,"supported":106,"title":107,"abstract":108,"tags":111},true,[],[21,109,110],"remote sensing","multispectral",[93,22,21],{"code":113,"score":71,"supported":14,"title":114,"abstract":115,"tags":116},"I1",[],[],[92],[118,119],"哥斯达黎加 菜豆 多光谱 遥感","Phaseolus vulgaris 植被指数 产量","哥斯达黎加菜豆多光谱遥感-5243","10.18845\u002Ftm.v39i4.8378",{"doi":121,"openalex_id":123,"authors":124,"venue":79,"cited_by_count":15,"oa_url":137,"card":138,"direction":65,"ingested_from":67},"W7222720635",[125,128,131,134],{"name":126,"orcid":127},"Guillermo Vargas-Hernández","https:\u002F\u002Forcid.org\u002F0000-0003-1410-3898",{"name":129,"orcid":130},"Eddison José Araya-Morales","https:\u002F\u002Forcid.org\u002F0009-0001-0550-414X",{"name":132,"orcid":133},"Juan Carlos Hernández","https:\u002F\u002Forcid.org\u002F0000-0002-1915-3213",{"name":135,"orcid":136},"Ignacio Mora-Cordero","https:\u002F\u002Forcid.org\u002F0009-0007-0925-0099","https:\u002F\u002Frevistas.tec.ac.cr\u002Findex.php\u002Ftec_marcha\u002Farticle\u002Fdownload\u002F8378\u002F8641",{"tldr":139,"method":140,"finding":141,"direction":65,"opportunity":142},"用机载多光谱传感器评估菜豆产量构成与植被指数的关系。","机载多光谱遥感获取R5期反射率，结合地面产量构成测定与相关分析。","豆荚数与光谱变量关联最强，SR和GCI指数与其相关系数超过0.7。","可探索将SR\u002FGCI用于菜豆产量早期预测，并扩展到多品种、多生育期与无人机高光谱验证。","2026-10-18T23:30:20.259545Z",{"id":145,"title":146,"url":147,"summary":148,"summary_zh":149,"content":9,"source_name":150,"source_url":147,"published_at":151,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":152,"score_detail":153,"sources":157,"tags":159,"primary_topic":9,"secondary_topics":163,"classification":164,"search_phrases":202,"slug":205,"view_count":15,"doi":206,"paper":207,"created_at":221},4893,"Systematic vegetation index discovery for phenology-based crop mapping","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1911652","Introduction The Systematic Vegetation Index Discovery (SVID) framework generates and evaluates large numbers of vegetation indices (VIs) by combining spectral bands with basic arithmetic operations. We evaluated whether systematically discovered indices could improve agricultural land-cover classification compared with conventional indices such as NDVI while reducing the dimensionality of multitemporal spectral data. Methods We applied SVID to a 323.9 ha agricultural landscape in Andong, South Korea, containing rice paddies, pepper fields, and plastic-covered greenhouses, using 32 cloud‐free Sentinel‐2A images acquired during 2022. Of 1,328 candidate expressions, 1,144 produced numerically valid rasters. A separate Random Forest model was trained for each index using its 32‐date temporal profile and evaluated against 500 independent reference points withheld from training. Before ranking, 388 expressions were identified as algebraically identical to another expression, and a further 355 were strictly monotone transformations and therefore indistinguishable to a rank‐based classifier. Results The 1,144 indices consequently represented 401 distinct features. Several near‐infrared‐to‐visible ratio families outperformed NDVI. The best‐performing index, (NIR – Red)\u002F(Green × Blue), achieved 97.0% overall accuracy and a Kappa coefficient of 0.873, compared with 94.4% and 0.734 for NDVI; this difference was significant by McNemar's test (p = 0.002). The improvement was concentrated in the greenhouse class, whose producer's accuracy increased from 58.6% to 84.5%. A model using all four raw bands across all dates (128 features) achieved 96.0% accuracy and was statistically indistinguishable from the best single index, demonstrating a four‐fold reduction in dimensionality without a detectable loss in accuracy. Rank positions were nevertheless unstable: ten of the 401 features were statistically tied with the best, and mathematically identical formulations differed by up to 3.4 percentage points because of finite‐precision effects. Discussion SVID can identify compact spectral representations that perform as well as or better than conventional indices and full multiband temporal models. However, the instability among top‐ranked indices suggests that results should be reported as index families with confidence intervals rather than as a single winner. Limitations include the use of one site, one year, and four spectral bands; broader testing across sites, years, and spectral configurations is needed.","引言 系统性植被指数发现（Systematic Vegetation Index Discovery, SVID）框架通过将光谱波段与基本算术运算相结合，生成并评估大量植被指数（vegetation indices, VIs）。我们评估了系统性发现的指数能否在降低多时相光谱数据维度的同时，相比NDVI等传统指数提升农业土地覆盖分类性能。方法 我们将SVID应用于韩国安东一处323.9公顷的农业景观，该区域包含稻田、辣椒田和覆膜温室，使用2022年获取的32景无云Sentinel-2A影像。在1,328个候选表达式中，1,144个生成了数值有效栅格。针对每个指数，利用其32个时相的时间剖面分别训练随机森林模型，并在500个未参与训练的独立参考点上进行评估。在排序之前，388个表达式被识别为与另一表达式代数等价，另有355个为严格单调变换，因而对基于秩的分类器不可区分。结果 这1,144个指数因此代表401个不同特征。若干近红外与可见光比值族的表现优于NDVI。表现最佳的指数(NIR – Red)\u002F(Green × Blue)总体精度达到97.0%，Kappa系数为0.873，而NDVI分别为94.4%和0.734；该差异经McNemar检验显著（p = 0.002）。提升主要集中在温室类别，其生产者精度从58.6%提高至84.5%。使用所有时相全部四个原始波段（128个特征）的模型精度为96.0%，与最佳单一指数在统计上不可区分，表明在未检测到精度损失的情况下实现了四倍的维度缩减。然而，排名位置并不稳定：401个特征中有10个与最佳特征在统计上并列，且由于有限精度效应，数学上等价的公式之间差异可达3.4个百分点。讨论 SVID能够识别出性能与常规指数及全多波段时间模型相当或更优的紧凑光谱表示。然而，排名靠前指数之间的不稳定性表明，结果应以指数族及置信区间的形式报告，而非单一最优指数。局限性包括仅使用一个站点、一年数据和四个光谱波段；需要在不同站点、年份和s","Frontiers in Remote Sensing","2026-10-13T00:00:00Z",74,{"impact":86,"substance":154,"depth":84,"authority":155,"freshness":87,"relevant":71,"comment":156},22,13,"提出系统性植被指数发现框架，在韩国安洞农田验证优于NDVI，方法新颖但仅单点单年，属细分领域方法学进展。",[158],{"name":150,"url":147},[160,161,21,162,22],"农业人工智能","精准农业","作物识别",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":165},{"candidates":166,"agricultural_evidence":106,"reason":201},[167,174,181,188,192,196],{"code":168,"score":70,"supported":106,"title":169,"abstract":170,"tags":173},"A1",[],[171,172],"稻田","rice",[],{"code":175,"score":70,"supported":106,"title":176,"abstract":177,"tags":180},"B7",[],[178,179],"温室","greenhouse",[],{"code":182,"score":183,"supported":106,"title":184,"abstract":185,"tags":187},"B1",3,[],[186],"辣椒",[],{"code":32,"score":33,"supported":14,"title":189,"abstract":190,"tags":191},[],[],[22,21],{"code":113,"score":71,"supported":14,"title":193,"abstract":194,"tags":195},[],[],[161],{"code":197,"score":71,"supported":14,"title":198,"abstract":199,"tags":200},"I2",[],[],[160],"多个产业证据相当，需要确认主归属",[203,204],"Sentinel-2 作物分类 植被指数","SVID 植被指数 水稻 辣椒","Sentinel-2作物分类植被指数-4893","10.3389\u002Ffrsen.2026.1911652",{"doi":206,"openalex_id":208,"authors":209,"venue":150,"cited_by_count":15,"oa_url":215,"card":216,"direction":65,"ingested_from":67},"W7221623694",[210,213],{"name":211,"orcid":212},"Ehsan Rahimi","https:\u002F\u002Forcid.org\u002F0000-0002-8401-472X",{"name":214,"orcid":9},"Chuleui Jung","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fremote-sensing\u002Farticles\u002F10.3389\u002Ffrsen.2026.1911652\u002Fpdf",{"tldr":217,"method":218,"finding":219,"direction":65,"opportunity":220},"系统生成并评估大量植被指数，用于基于物候的农作物分类，发现优于NDVI的指数。","Sentinel-2A时序影像，组合光谱波段生成1328个指数，随机森林分类。","最佳指数(NIR-Red)\u002F(Green×Blue)精度97.0%，优于NDVI的94.4%，且维度","可探索更多波段组合与多站点验证，并研究指数排名不稳定的置信区间报告方法。","2026-10-14T23:30:20.231693Z",{"id":223,"title":224,"url":225,"summary":226,"summary_zh":227,"content":9,"source_name":228,"source_url":225,"published_at":229,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":230,"score_detail":231,"sources":235,"tags":237,"primary_topic":240,"secondary_topics":246,"classification":248,"search_phrases":275,"slug":278,"view_count":71,"doi":279,"paper":280,"created_at":304},4598,"Seasonal compatibility of RGB and multispectral vegetation indices in Amazon Cacao","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12518-026-00815-x","Abstract Precision agriculture in cacao cultivation is constrained by multispectral sensor costs, limiting accessibility for smallholder farmers. This study evaluated the seasonal compatibility between RGB-derived visible reflectance and multispectral vegetation indices (NDVI, GNDVI, NDRE) to establish a low-cost monitoring approach for Eastern Amazon cacao plantations. We collected spectral data from 1,431 random sampling points across a 0.9-ha commercial cacao plantation during four seasonal periods (February, May, August, and November 2025) using UAV-mounted RGB and multispectral sensors. Pearson correlations and linear regression models assessed relationships between mean visible reflectance [(R + G+B)\u002F3] and multispectral indices. Geostatistical analysis characterized spatial dependence structure across seasons. GNDVI demonstrated superior compatibility across all seasons (mean R² = 59.3%; range: 52.5–62.9%), outperforming NDRE (44.2%) and NDVI (40.8%) by 15–19% points. Temporal stability was 2.9× higher for GNDVI (SD = 4.7 pp) than NDRE (14.1 pp) and NDVI (12.7 pp). Peak compatibility occurred in May (R² = 62.9%) during wet-to-dry transition. The conversion equation (GNDVI = 0.509–0.000012 × Visible Reflectance; RMSE = 0.077) enables reliable estimation, retaining 59% of multispectral information. Strong spatial dependence (Co\u002F(Co + C) \u003C 10%) was confirmed across all periods, with August providing optimal structure (range = 11.4 m) for management zone delineation. GNDVI is the most reliable RGB proxy for cacao monitoring, democratizing precision agriculture access through affordable technology while maintaining robust spatial and temporal performance year-round.","摘要 可可种植中的精准农业受到多光谱传感器成本的制约，限制了小农户的可及性。本研究评估了RGB衍生的可见光反射率与多光谱植被指数（NDVI、GNDVI、NDRE）之间的季节性兼容性，以建立一种面向东亚马逊可可种植园的低成本监测方法。我们使用无人机搭载的RGB和多光谱传感器，在四个季节时段（2025年2月、5月、8月和11月）从一片0.9公顷商业可可种植园的1，431个随机采样点采集了光谱数据。采用Pearson相关分析和线性回归模型评估平均可见光反射率[（R + G + B）\u002F3]与多光谱指数之间的关系。地统计学分析刻画了各季节的空间依赖结构。GNDVI在所有季节均表现出最优兼容性（平均R² = 59.3%；范围：52.5–62.9%），较NDRE（44.2%）和NDVI（40.8%）高出15–19个百分点。GNDVI的时间稳定性（SD = 4.7个百分点）是NDRE（14.1个百分点）和NDVI（12.7个百分点）的2.9倍。最高兼容性出现在5月（R² = 62.9%），即湿季向干季过渡期。转换方程（GNDVI = 0.509–0.000012 × 可见光反射率；RMSE = 0.077）可实现可靠估算，保留了59%的多光谱信息。所有时期均确认存在强空间依赖性（Co\u002F(Co + C) \u003C 10%），其中8月提供了最优结构（变程 = 11.4 m），适合用于管理分区划分。GNDVI是可可监测中最可靠的RGB替代指标，通过可负担的技术使精准农业更广泛可及，同时全年保持稳健的空间和时间性能。","Applied Geomatics","2026-10-08T00:00:00Z",68,{"impact":83,"substance":232,"depth":233,"authority":155,"freshness":87,"relevant":71,"comment":234},21,17,"以低成本RGB替代多光谱监测亚马逊可可，数据扎实、结论明确，对精准农业降本有参考价值，但属细分作物研究，公共影响有限。",[236],{"name":228,"url":225},[92,21,22,238,239],"可可种植","无人机监测",{"code":241,"slug":242,"name":243,"path":244,"parent_code":245},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[247],{"code":32,"slug":96,"name":97,"path":98,"parent_code":99},{"status":102,"locked":14,"version":27,"method":28,"evidence":249},{"candidates":250,"agricultural_evidence":106},[251,258,262,268],{"code":241,"score":252,"supported":106,"title":253,"abstract":255,"tags":257},14,[254],"cacao",[256,254],"可可",[],{"code":32,"score":155,"supported":106,"title":259,"abstract":260,"tags":261},[110],[110],[22,21],{"code":113,"score":263,"supported":106,"title":264,"abstract":265,"tags":267},7,[],[161,266],"precision agriculture",[92],{"code":269,"score":70,"supported":106,"title":270,"abstract":271,"tags":274},"G2",[],[272,273],"小农户","smallholder",[],[276,277],"亚马逊 可可 无人机 遥感","GNDVI RGB 植被指数 可可","亚马逊可可无人机遥感-4598","10.1007\u002Fs12518-026-00815-x",{"doi":279,"openalex_id":281,"authors":282,"venue":228,"cited_by_count":15,"oa_url":298,"card":299,"direction":65,"ingested_from":67},"W7221039911",[283,286,289,292,295],{"name":284,"orcid":285},"Charlyngton da Silva e Silva","https:\u002F\u002Forcid.org\u002F0009-0003-9332-4302",{"name":287,"orcid":288},"Luiz Antônio Soares Cardoso","https:\u002F\u002Forcid.org\u002F0000-0001-6868-7847",{"name":290,"orcid":291},"Ronilson de Souza Santos","https:\u002F\u002Forcid.org\u002F0000-0002-2683-456X",{"name":293,"orcid":294},"Carlos Rodrigo Tanajura Caldeira","https:\u002F\u002Forcid.org\u002F0000-0001-7102-7735",{"name":296,"orcid":297},"Norberto Cornejo Noronha","https:\u002F\u002Forcid.org\u002F0000-0001-7216-8687","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs12518-026-00815-x.pdf",{"tldr":300,"method":301,"finding":302,"direction":65,"opportunity":303},"评估RGB与多光谱植被指数在亚马逊可可园的季节性兼容性，以低成本监测替代多光谱传感器。","无人机RGB与多光谱传感器采集1431个样点，用相关、回归和地统计分析四季数据。","GNDVI兼容性最佳（R²=59.3%），时空稳定性最高，5月表现最优，可保留59%多光谱信息。","可探索RGB代理指数在其他热带作物和不同冠层结构下的普适性，并优化低成本管理分区方法。","2026-10-10T23:30:31.142887Z",{"id":306,"title":307,"url":308,"summary":309,"summary_zh":310,"content":9,"source_name":311,"source_url":308,"published_at":229,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":312,"score_detail":313,"sources":315,"tags":317,"primary_topic":9,"secondary_topics":321,"classification":322,"search_phrases":360,"slug":363,"view_count":15,"doi":364,"paper":365,"created_at":381},4592,"Desertification Assessment in the Hodna Basin, Algeria using Desertification Degree Index and Geospatial Techniques","https:\u002F\u002Fdoi.org\u002F10.56093\u002Faaz.v65i3.177561","This study assesses desertification in the Hodna Basin, Algeria, using remote sensing and Geographic Information Systems (GIS) through the development of a Desertification Degree Index (DDI) based on feature-space analysis. Landsat 9 imagery was used to derive four spectral indices: the Normalized Difference Vegetation Index (NDVI), surface albedo, the Modified Soil-Adjusted Vegetation Index (MSAVI), and the Topsoil Grain Size Index (TGSI). Five feature-space combinations were evaluated, with the Albedo-NDVI and TGSI-NDVI models exhibiting the strongest statistically significant negative correlations. These two models were subsequently used for DDI generation and comparison. The Albedo-NDVI model achieved the highest performance, with an Overall Accuracy of 91.9% and a Kappa coefficient of 0.89, substantially outperforming the TGSI-NDVI model. The resulting desertification map indicates that approximately 65% of the Hodna Basin is affected by high to severe desertification, particularly in the central and southern regions, where prolonged aridity and intensive human activities have accelerated land degradation. In contrast, the northern part of the basin exhibits relatively low desertification levels owing to denser vegetation cover and more favorable environmental conditions. These findings demonstrate the robustness of the Albedo-NDVI feature-space model for desertification assessment in arid and semi-arid environments and highlight the value of integrating spectral indices with statistical feature-space analysis. The proposed approach provides a reliable and objective framework for large-scale desertification mapping and offers valuable decision support for soil conservation, ecosystem restoration, and sustainable land management in vulnerable dryland regions.","本研究利用遥感与地理信息系统（GIS），通过构建基于特征空间的荒漠化程度指数（DDI），对阿尔及利亚霍德纳盆地的荒漠化进行了评估。研究采用Landsat 9影像提取了四个光谱指数：归一化差异植被指数（NDVI）、地表反照率、修正土壤调整植被指数（MSAVI）和表土粒度指数（TGSI）。对五种特征空间组合进行了评价，其中反照率-NDVI和TGSI-NDVI模型表现出最强的统计显著负相关。随后，这两个模型被用于DDI生成与比较。反照率-NDVI模型取得了最佳性能，总体精度为91.9%，Kappa系数为0.89，显著优于TGSI-NDVI模型。生成的荒漠化图表明，霍德纳盆地约65%的区域受到高度至严重荒漠化的影响，尤其是在中部和南部地区，长期的干旱和密集的人类活动加速了土地退化。相比之下，盆地北部由于植被覆盖较密、环境条件较为有利，荒漠化程度相对较低。这些发现证明了反照率-NDVI特征空间模型在干旱和半干旱环境荒漠化评估中的稳健性，并凸显了将光谱指数与统计特征空间分析相结合的价值。所提出的方法为大规模荒漠化制图提供了可靠且客观的框架，并为脆弱旱地地区的土壤保持、生态系统恢复和可持续土地管理提供了有价值的决策支持。","Annals of Arid Zone",73,{"impact":86,"substance":154,"depth":84,"authority":155,"freshness":83,"relevant":71,"comment":314},"该研究提出基于Landsat 9与特征空间分析的荒漠化程度指数，方法新颖、精度高，对干旱区土地退化监测与农业可持续发展有参考价值，但属区域性案例研究，影响范围有限。",[316],{"name":311,"url":308},[21,318,319,22,320],"土地退化","GIS","荒漠化监测",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":323},{"candidates":324,"agricultural_evidence":14,"reason":37},[325,333,337,344,350,355],{"code":326,"score":13,"supported":106,"title":327,"abstract":328,"tags":332},"I4",[],[329,330,319,331],"地理信息","空间分析","geographic information",[319],{"code":32,"score":83,"supported":106,"title":334,"abstract":335,"tags":336},[],[21,109],[22,21],{"code":338,"score":70,"supported":106,"title":339,"abstract":340,"tags":343},"H2",[],[341,342],"土壤","soil",[],{"code":345,"score":183,"supported":106,"title":346,"abstract":347,"tags":349},"H7",[],[348],"干旱",[],{"code":197,"score":183,"supported":106,"title":351,"abstract":352,"tags":354},[],[353],"decision support",[],{"code":356,"score":71,"supported":14,"title":357,"abstract":358,"tags":359},"H1",[],[],[318],[361,362],"Albedo-NDVI 荒漠化","荒漠化监测 土地退化 植被指数 遥感","Albedo-NDVI荒漠化-4592","10.56093\u002Faaz.v65i3.177561",{"doi":364,"openalex_id":366,"authors":367,"venue":311,"cited_by_count":15,"oa_url":375,"card":376,"direction":65,"ingested_from":67},"W7220932821",[368,370,372],{"name":369,"orcid":9},"Sami Mekentichi",{"name":371,"orcid":9},"Salah Abdesselam",{"name":373,"orcid":374},"Razika Sahnouni","https:\u002F\u002Forcid.org\u002F0000-0003-4914-6804","http:\u002F\u002Fepubs.icar.org.in\u002Findex.php\u002FAAZ\u002Farticle\u002Fdownload\u002F177561\u002F68055",{"tldr":377,"method":378,"finding":379,"direction":65,"opportunity":380},"基于Landsat 9与特征空间分析构建荒漠化程度指数，评估阿尔及利亚Hodna盆地荒漠化。","Landsat 9影像提取NDVI、反照率、MSAVI、TGSI，用Albedo","Albedo-NDVI模型精度最高（总体精度91.9%，Kappa 0.89），约65%区域处于高至","可将Albedo-NDVI特征空间模型迁移至其他干旱区，并结合时序影像与人类活动因子做驱动力归因。","2026-10-10T23:30:29.517004Z",{"id":383,"title":384,"url":385,"summary":386,"summary_zh":387,"content":9,"source_name":388,"source_url":385,"published_at":229,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":312,"score_detail":389,"sources":391,"tags":393,"primary_topic":9,"secondary_topics":397,"classification":398,"search_phrases":409,"slug":412,"view_count":71,"doi":413,"paper":414,"created_at":429},4591,"Uydu görüntüleri ile yanan orman alanının değerlendirilmesi: Çandır orman yangını örneği","https:\u002F\u002Fdoi.org\u002F10.18182\u002Ftjf.1721381","Bu çalışmada, 2021 yılında Çandır Orman İşletme Şefliğinde meydana gelen orman yangınında zarar gören orman alanlarının uzaktan algılama veri ve yöntemleriyle belirlenmesi ve değerlendirilmesi amaçlanmıştır. Bu kapsamda, yangın öncesi ve yangın sonrası Landsat-8 ve Sentinel-2 uydu görüntülerinden Normalleştirilmiş Fark Vejetasyon İndeksi (NDVI) ve Normalleştirilmiş Yanma Oranı (NBR) spektral indeksleri üretilmiştir. Yangının etkisini nicel olarak ortaya koymak amacıyla yangın öncesi ve sonrası indeks görüntüleri arasındaki fark alınarak dNDVI ve dNBR görüntüleri elde edilmiştir. Bu fark görüntülerine eşikleme yöntemi uygulanarak yanan orman alanları belirlenmiştir. Ayrıca Landsat-8 ve Sentinel-2 görüntüleri üzerinde kontrollü sınıflandırma yöntemi uygulanmış ve Maximum Likelihood algoritmasıyla tematik haritalar üretilmiştir. Elde edilen sonuçlara göre en yüksek sınıflandırma doğrulukları Sentinel-2 verilerinden üretilen dNBR ve dNDVI görüntülerinde elde edilmiş olup, genel doğruluk değerleri sırasıyla 0.92 ve 0.88, kappa değerleri ise 0.83 ve 0.77 olarak hesaplanmıştır. Landsat-8 verilerinden üretilen dNBR ve dNDVI görüntülerinde ise genel doğruluk değerleri sırasıyla 0.87 ve 0.85, kappa değerleri ise 0.73 ve 0.7 olarak belirlenmiştir. Kontrollü sınıflandırma sonucunda üretilen tematik haritalarda Sentinel-2 ve Landsat-8 için genel doğruluk değerleri sırasıyla 0.88 ve 0.85, kappa değerleri ise 0.85 ve 0.82 olarak bulunmuştur. Farklı yöntemlerden elde edilen sonuçların ortalamasına göre yanan orman alanının büyüklüğü 760 hektar hesaplanmıştır. Sonuç olarak, Sentinel-2 ve Landsat-8 uydu verilerinden elde edilen dNDVI ve dNBR görüntülerinin, yanan orman alanlarının belirlenmesi ve yangın etkisinin değerlendirilmesinde verimli, güvenilir ve uygulanabilir sonuçlar üretme potansiyeline sahip olduğu ortaya konulmuştur.","本研究旨在利用遥感数据和方法，确定并评估2021年昌德尔森林经营管理局辖区内发生的森林火灾中受损的森林面积。在此范围内，利用火灾前后的Landsat-8和Sentinel-2卫星影像生成了归一化差异植被指数（NDVI）和归一化燃烧比率（NBR）光谱指数。为了定量揭示火灾的影响，通过计算火灾前后指数影像之间的差值，获得了dNDVI和dNBR影像。对这些差值影像应用阈值法，确定了过火森林区域。此外，对Landsat-8和Sentinel-2影像应用了监督分类方法，并利用最大似然法（Maximum Likelihood）生成了专题地图。根据所得结果，最高的分类精度来自Sentinel-2数据生成的dNBR和dNDVI影像，其总体精度分别为0.92和0.88，Kappa系数分别为0.83和0.77。在Landsat-8数据生成的dNBR和dNDVI影像中，总体精度分别为0.87和0.85，Kappa系数分别为0.73和0.7。在监督分类生成的专题地图中，Sentinel-2和Landsat-8的总体精度分别为0.88和0.85，Kappa系数分别为0.85和0.82。根据不同方法所得结果的平均值，过火森林面积计算为760公顷。综上所述，由Sentinel-2和Landsat-8卫星数据获得的dNDVI和dNBR影像在确定过火森林区域和评估火灾影响方面具有产生高效、可靠且可应用结果的潜力。","Turkish Journal of Forestry | Türkiye Ormancılık Dergisi",{"impact":86,"substance":154,"depth":84,"authority":155,"freshness":83,"relevant":71,"comment":390},"该论文利用Landsat-8与Sentinel-2遥感数据评估土耳其Çandır森林火灾过火面积，方法可靠、结论具体，对农业信息化中的遥感监测有参考价值，但属区域性案例研究，影响范围有限。",[392],{"name":388,"url":385},[394,21,395,22,396],"Sentinel-2","森林火灾","Landsat-8",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":399},{"candidates":400,"agricultural_evidence":14,"reason":37},[401,405],{"code":32,"score":70,"supported":106,"title":402,"abstract":403,"tags":404},[],[21],[394,22,21],{"code":345,"score":71,"supported":14,"title":406,"abstract":407,"tags":408},[],[],[395],[410,411],"Çandır orman yangını","森林火灾 植被指数 遥感 Sentinel-2","Çandırormanyangını-4591","10.18182\u002Ftjf.1721381",{"doi":413,"openalex_id":415,"authors":416,"venue":388,"cited_by_count":15,"oa_url":423,"card":424,"direction":65,"ingested_from":67},"W7220904781",[417,420],{"name":418,"orcid":419},"Abdurrahman Cankut Göz","https:\u002F\u002Forcid.org\u002F0000-0003-1786-7658",{"name":421,"orcid":422},"HÜSEYİN OĞUZ ÇOBAN","https:\u002F\u002Forcid.org\u002F0000-0002-4037-4811","https:\u002F\u002Fdergipark.org.tr\u002Ftr\u002Fdownload\u002Farticle-file\u002F4966779",{"tldr":425,"method":426,"finding":427,"direction":65,"opportunity":428},"利用Landsat-8和Sentinel-2影像评估2021年Çandır森林火灾过火面积。","计算火灾前后NDVI、NBR及dNDVI、dNBR，结合阈值法和最大似然监督分类","Sentinel-2的dNBR精度最高（总体精度0.92，Kappa 0.83），过火面积约760公","可探索多源卫星时序数据融合与深度学习，提升火烧区制图精度并拓展至植被恢复监测。","2026-10-10T23:30:29.424999Z",{"id":431,"title":432,"url":433,"summary":434,"summary_zh":435,"content":9,"source_name":436,"source_url":433,"published_at":437,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":438,"score_detail":439,"sources":443,"tags":445,"primary_topic":448,"secondary_topics":454,"classification":455,"search_phrases":472,"slug":475,"view_count":183,"doi":476,"paper":477,"created_at":490},3850,"Изследване на приложимостта на вегетационни индекси за диагностика на азотния статус при тютюн","https:\u002F\u002Fdoi.org\u002F10.61308\u002Fpxjs7160","This study evaluates the applicability of seven vegetation indices (CI Green, CI Red, CI Red Edge, LCI, DCNI, NDCI, and CNDVI) for diagnosing nitrogen status in oriental and Virginia tobacco. Leaf level spectral measurements obtained with a CI-710s SpectraVue spectrometer were compared with laboratory determined total nitrogen content.In the oriental tobacco cultivar Krumovgrad 58, all indices exhibited strong positive correlations with leaf nitrogen concentration (r = 0.772-0.965), with LCI, CI Green, and CI Red Edge showing the highest sensitivity. Similar results were observed in the Virginia 842 cultivar (r = 0.797-0.916), where four indices (CI Green, CI Red Edge, CNDVI, and NDCI) demonstrated statistically significant relationships.Linear regression models developed for both cultivars showed high predictive performance (R2 = 0.596-0.931 for oriental tobacco and R2 = 0.635-0.839 for Virginia tobacco), confirming the potential of vegetation indices for indirect nitrogen estimation. The differences between the cultivars of the two tobacco types can be explained by specific features of leaf morphology and mesophyll structure, which influence the spectral signal.The results indicate that spectral vegetation indices are a reliable tool for monitoring nitrogen nutrition in tobacco. Further calibration and expanded studies across cultivars, growth stages, and environmental conditions are required to enhance model robustness and practical applicability.","本研究评估了七种植被指数（CI Green、CI Red、CI Red Edge、LCI、DCNI、NDCI和CNDVI）在诊断东方型烟草和弗吉尼亚型烟草氮素状况方面的适用性。利用CI-710s SpectraVue光谱仪获取的叶片尺度光谱测量数据与实验室测定的全氮含量进行了比较。在东方型烟草品种Krumovgrad 58中，所有植被指数均与叶片氮浓度呈强正相关（r = 0.772–0.965），其中LCI、CI Green和CI Red Edge表现出最高的敏感性。在弗吉尼亚型品种Virginia 842中观察到了类似的结果（r = 0.797–0.916），其中四个植被指数（CI Green、CI Red Edge、CNDVI和NDCI）表现出统计学显著的关系。为两个品种建立的线性回归模型显示出较高的预测性能（东方型烟草R² = 0.596–0.931，弗吉尼亚型烟草R² = 0.635–0.839），证实了植被指数在间接估算氮素方面的潜力。两种烟草类型品种间的差异可归因于叶片形态和叶肉结构的特异性，这些特性影响了光谱信号。结果表明，光谱植被指数是监测烟草氮素营养的可靠工具。仍需进一步校准并在品种、生育期和环境条件方面开展扩展研究，以增强模型的稳健性和实际适用性。","Bulgarian Journal of Soil Science Agrochemistry and Ecology","2026-09-28T00:00:00Z",66,{"impact":83,"substance":440,"depth":441,"authority":155,"freshness":87,"relevant":71,"comment":442},20,16,"该研究验证了七种植被指数在烟草氮素诊断中的适用性，方法扎实、数据可靠，对智慧农业遥感监测有参考价值，但属细分作物基础研究，影响范围有限。",[444],{"name":436,"url":433},[92,21,22,446,447],"烟草种植","氮素诊断",{"code":449,"slug":450,"name":451,"path":452,"parent_code":453},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[],{"status":102,"locked":14,"version":27,"method":28,"evidence":456},{"candidates":457,"agricultural_evidence":106},[458,464,468],{"code":449,"score":70,"supported":106,"title":459,"abstract":460,"tags":463},[],[461,462],"烟草","tobacco",[],{"code":32,"score":33,"supported":14,"title":465,"abstract":466,"tags":467},[],[],[22,21],{"code":113,"score":71,"supported":14,"title":469,"abstract":470,"tags":471},[],[],[92],[473,474],"烟草 氮素 植被指数","Krumovgrad 58 烟草","烟草氮素植被指数-3850","10.61308\u002Fpxjs7160",{"doi":476,"openalex_id":478,"authors":479,"venue":436,"cited_by_count":15,"oa_url":484,"card":485,"direction":65,"ingested_from":67},"W7214629574",[480,482],{"name":481,"orcid":9},"Радка Божинова",{"name":483,"orcid":9},"Yovko Dyulgerski","https:\u002F\u002Fagriacad.eu\u002Fojs\u002Findex.php\u002Fbjssae\u002Farticle\u002Fdownload\u002F6863\u002F5937",{"tldr":486,"method":487,"finding":488,"direction":65,"opportunity":489},"评估七种植被指数诊断两种烟草氮素状况的适用性并建立回归模型。","CI-710s光谱仪叶片光谱与实验室全氮含量对比，线性回归建模。","多指数与叶片氮浓度强相关，LCI、CI Green、CI Red Edge最敏感，模型R²达0.93","可扩展至冠层尺度、多品种多生育期及环境条件，提升模型鲁棒性与田间实用性。","2026-09-30T23:30:31.090531Z"]