[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4432":3,"related-4432":101},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":70,"slug":73,"view_count":55,"doi":74,"paper":75,"created_at":100},4432,"Horticulture acreage estimation: remote sensing vis-a-vis sample surveys","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74697-9","Timely and spatially explicit estimates of horticultural crop acreage are required to support agricultural statistics, resource allocation, and production planning. This study assessed the agreement between satellite-derived acreage estimates and house-to-house survey data for selected villages in Erode, Chittoor, and Nashik districts of India. Multispectral imagery from Resourcesat-2 LISS-IV and Sentinel-2 was subjected to radiometric and geometric correction, band stacking, mosaicking, and normalized difference vegetation index-based vegetation masking. Maximum Likelihood Classification (MLC), Spectral Angle Mapper (SAM), and Support Vector Machine (SVM) algorithms were evaluated for mapping banana, tapioca, mango, and onion. Classification performance varied among crops. MLC achieved overall accuracies of 84.83% for banana and 76.81% for mango, with kappa coefficients of 0.767 and 0.917, respectively. SAM produced an overall accuracy of 78.70% and a kappa coefficient of 0.797 for tapioca, whereas SVM performed best for onion, attaining an overall accuracy of 76.41% and a kappa coefficient of 0.713. Satellite-derived acreage estimates were strongly associated with survey estimates for banana (R 2 = 0.94), tapioca (R 2 = 0.93), and mango (R 2 = 0.94). Compared with the survey estimates, remote sensing underestimated the total areas of banana, tapioca, mango, and onion by 9.04%, 5.35%, 5.88%, and 26.08%, respectively. The results indicate that classification performance depends on crop structure, field size, spatial fragmentation, and classifier selection. Satellite imagery provided more reliable estimates for relatively large and contiguous orchards than for small and fragmented crop fields. Integrating satellite-based mapping with targeted field verification can therefore provide a scalable and spatially explicit framework for operational horticultural acreage estimation.","及时且具有空间明确性的园艺作物种植面积估算，是支撑农业统计、资源分配和生产规划的必要条件。本研究评估了卫星遥感估算面积与印度埃罗德、奇图尔和纳西克地区部分村庄逐户调查数据之间的一致性。对来自Resourcesat-2 LISS-IV和Sentinel-2的多光谱影像进行了辐射校正和几何校正、波段叠加、镶嵌以及基于归一化植被指数（NDVI）的植被掩膜处理。评估了最大似然分类（MLC）、光谱角制图（SAM）和支持向量机（SVM）算法在香蕉、木薯、芒果和洋葱制图中的应用效果。不同作物的分类性能存在差异。MLC对香蕉和芒果的总体精度分别为84.83%和76.81%，Kappa系数分别为0.767和0.917。SAM对木薯的总体精度为78.70%，Kappa系数为0.797；而SVM对洋葱的表现最佳，总体精度为76.41%，Kappa系数为0.713。卫星估算面积与调查估算值在香蕉（R²=0.94）、木薯（R²=0.93）和芒果（R²=0.94）上呈强相关。与调查估算值相比，遥感分别低估了香蕉、木薯、芒果和洋葱的总面积，低估幅度为9.04%、5.35%、5.88%和26.08%。结果表明，分类性能取决于作物结构、田块大小、空间破碎化程度和分类器选择。对于相对较大且连片的果园，卫星影像提供的估算值比小而破碎的农田更为可靠。因此，将基于卫星的制图与有针对性的实地验证相结合，可为业务化园艺种植面积估算提供一个可扩展且具有空间明确性的框架。",null,"Scientific Reports","2026-10-07T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,14,9,1,"该研究以多光谱遥感与入户调查对比估算印度园艺作物面积，方法扎实、数据具体，对农业遥感统计具有参考价值，但属区域性案例，影响力有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感监测","印度农业","园艺作物","作物面积估算","农业统计",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":46},[45,53,60,65],{"code":33,"score":19,"supported":46,"title":47,"abstract":49,"tags":52},true,[48],"remote sensing",[50,48,51],"遥感","multispectral",[27],{"code":54,"score":55,"supported":46,"title":56,"abstract":57,"tags":59},"B2",3,[],[58],"芒果",[],{"code":61,"score":22,"supported":14,"title":62,"abstract":63,"tags":64},"B",[],[],[29],{"code":66,"score":22,"supported":14,"title":67,"abstract":68,"tags":69},"J3",[],[],[28],[71,72],"Resourcesat-2 Sentinel-2 园艺作物 面积估算","Erode Chittoor Nashik 遥感 抽样调查","Resourcesat-2Sentinel-2园艺作物面积估算-4432","10.1038\u002Fs41598-026-74697-9",{"doi":74,"openalex_id":76,"authors":77,"venue":10,"cited_by_count":92,"oa_url":6,"card":93,"direction":97,"ingested_from":99},"W7220842723",[78,81,83,86,89],{"name":79,"orcid":80},"Tauqueer Ahmad","https:\u002F\u002Forcid.org\u002F0000-0002-2573-9667",{"name":82,"orcid":9},"Prachi Misra Sahoo",{"name":84,"orcid":85},"A.K. Biswas","https:\u002F\u002Forcid.org\u002F0000-0002-1708-7363",{"name":87,"orcid":88},"Gopal Krishna","https:\u002F\u002Forcid.org\u002F0000-0002-1070-9916",{"name":90,"orcid":91},"Karunesh K. Shukla","https:\u002F\u002Forcid.org\u002F0000-0001-8537-2433",0,{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"对比卫星遥感与入户调查的园艺作物面积估算，评估分类算法精度与面积一致性。","用Resourcesat-2 LISS-IV和Sentinel-2影像，比较ML","遥感对连片大果园估算可靠（R²约0.94），但普遍低估面积，洋葱低估达26%。","农业遥感与作物表型","可研究碎片化小农户地块的遥感面积校正方法，结合抽样验证提升园艺作物统计精度。","openalex","2026-10-08T23:30:29.318866Z",{"total":102,"page":22,"page_size":102,"items":103},6,[104,183,256,336,395,465],{"id":105,"title":106,"url":107,"summary":108,"summary_zh":109,"content":9,"source_name":110,"source_url":111,"published_at":112,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":113,"score_detail":114,"sources":119,"tags":121,"primary_topic":125,"secondary_topics":126,"classification":132,"search_phrases":163,"slug":166,"view_count":167,"doi":168,"paper":169,"created_at":182},3187,"Spatio-temporal analysis of Land Use and Land Cover (LULC) change using high resolution satellite data: A case study of Nagaon, Assam","https:\u002F\u002Fdoi.org\u002F10.31018\u002Fjans.v18i3.7774","Geographic information systems (GIS) coupled with satellite remote sensing (RS) have had a significant impact on the assessment and mapping of land surface dynamics, specifically the analysis of land-use land-cover (LULC) change. This study uses high-resolution multispectral LISS-IV (Linear Imaging Self-Scanning Sensor-IV) imagery with a 5.8-meter resolution to evaluate LULC trends in the Nagaon district of Assam between 2015 and 2024. Land use classes in a variety of categories, including vegetation, water bodies, agricultural land, built-up areas, scrubland, sandbars, tea plantations, and trees outside forests (TOF), were identified applying the maximum likelihood classification algorithm. Overall, The accuracy was 85.7% in 2015 and 90.3% in 2024, with respective Kappa Coefficients of 0.836 and 0.889, respectively. The results showed that between 2015 and 2024, the areas of tea plantations, natural vegetation, scrubland, and agricultural land fell by 0.26%, 6.14%, 0.26%, and 9.83%, respectively. Conversely, the waterbody, sandbar, built-up, and TOF have all increased by 0.29%, 0.91%, 0.82%, and 14.46%, respectively. This noticeable shift from conventional agricultural and natural vegetation landscapes toward tree-based systems and urban expansion on the study area, emphasize on matters concerning climate stress, declining soil fertility, economic benefits, policy support, and the need for resilient livelihoods.","地理信息系统（GIS）与卫星遥感（RS）相结合，对地表动态的评估与制图产生了显著影响，尤其是土地利用与土地覆盖（LULC）变化分析。本研究利用分辨率为5.8米的高分辨率多光谱LISS-IV（线性成像自扫描传感器-IV）影像，评估了阿萨姆邦纳冈县2015年至2024年间的LULC变化趋势。采用最大似然分类算法，识别了植被、水体、农地、建设用地、灌丛地、沙洲、茶园及林外树木（TOF）等多种土地利用类别。总体而言，2015年分类精度为85.7%，2024年为90.3%，Kappa系数分别为0.836和0.889。结果表明，2015年至2024年间，茶园、自然植被、灌丛地和农地面积分别减少了0.26%、6.14%、0.26%和9.83%。相反，水体、沙洲、建设用地和TOF分别增加了0.29%、0.91%、0.82%和14.46%。研究区从传统农业和自然植被景观向林基系统和城市扩张的显著转变，凸显了气候胁迫、土壤肥力下降、经济效益、政策支持以及韧性生计需求等相关问题。","Journal of Applied and Natural Science","https:\u002F\u002Fjournals.ansfoundation.org\u002Findex.php\u002Fjans\u002Farticle\u002Fview\u002F7774","2026-09-20T00:00:00Z",62,{"impact":115,"substance":19,"depth":116,"authority":117,"freshness":115,"relevant":22,"comment":118},8,15,13,"基于高分辨率遥感的区域土地利用变化实证研究，方法规范、数据翔实，对农业遥感监测有参考价值，但属地方性案例，公共影响有限。",[120],{"name":110,"url":111},[122,27,123,28,124],"农业遥感","土地利用","植被覆盖",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[127],{"code":128,"slug":129,"name":130,"path":131,"parent_code":37},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":40,"locked":14,"version":41,"method":42,"evidence":133},{"candidates":134,"agricultural_evidence":46},[135,140,147,154,159],{"code":33,"score":136,"supported":46,"title":137,"abstract":138,"tags":139},12,[],[50,48,51],[122,124,27],{"code":128,"score":21,"supported":46,"title":141,"abstract":142,"tags":146},[],[143,144,145],"地理信息","GIS","geographic information",[],{"code":148,"score":102,"supported":46,"title":149,"abstract":150,"tags":153},"H2",[],[151,152],"土壤","soil",[],{"code":155,"score":22,"supported":14,"title":156,"abstract":157,"tags":158},"H6",[],[],[123],{"code":66,"score":22,"supported":14,"title":160,"abstract":161,"tags":162},[],[],[28],[164,165],"Nagaon Assam LULC 遥感","LISS-IV 土地利用变化","NagaonAssamLULC遥感-3187",5,"10.31018\u002Fjans.v18i3.7774",{"doi":168,"openalex_id":170,"authors":171,"venue":110,"cited_by_count":92,"oa_url":111,"card":177,"direction":97,"ingested_from":99},"W7213942371",[172,174],{"name":173,"orcid":9},"J. C. Das",{"name":175,"orcid":176},"Prodyut Bhattacharya","https:\u002F\u002Forcid.org\u002F0000-0002-4294-5585",{"tldr":178,"method":179,"finding":180,"direction":97,"opportunity":181},"利用高分辨率卫星影像分析印度阿萨姆邦纳冈地区2015-2024年土地利用\u002F覆盖变化。","LISS-IV 5.8米多光谱影像，最大似然分类，精度与Kappa系数评估。","农业用地和自然植被分别减少9.83%和6.14%，而林外树木和建设用地分别增加14.46%和0.82","可结合时序高分辨率影像与农户调查，探究林外树木扩张对农业韧性和碳汇的驱动机制。","2026-09-22T23:30:25.539323Z",{"id":184,"title":185,"url":186,"summary":187,"summary_zh":188,"content":9,"source_name":189,"source_url":186,"published_at":190,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":92,"score_detail":191,"sources":193,"tags":195,"primary_topic":198,"secondary_topics":199,"classification":200,"search_phrases":222,"slug":225,"view_count":92,"doi":226,"paper":227,"created_at":255},5390,"Land use and socio-economic changes caused by riverbank erosion - case study: Obrenovac municipality, Serbia","https:\u002F\u002Fdoi.org\u002F10.59954\u002Fstnv.797","Lateral bank erosion of meandering rivers represents a significant geomorphological process in the lower part of the Kolubara River Basin in Serbia. This process directly impacts land-use and agricultural productivity and lead to broader environmental, socioeconomic, and demographic changes. Historically, the Kolubara riverbanks have been intensively used for agriculture. Today, lateral channel migration and riverbank erosion, driven by the interaction between natural process and human activities, directly threaten traditionally active land plots along the river. This study investigates the long-term dynamics of riverbank erosion along the four active meanders of the Kolubara River in Obrenovac Municipality over a 97-year period (1930–2026). Main tasks include quantification of lateral channel migration and land loss rates, analysis of land-use changes within the endangered plots, and an investigation of socioeconomic consequences and demographic dynamics in affected settlements. The methodological framework integrates cadastral maps analysis, remote sensing techniques (UAVs), and GIS software, with analysis of demographic and land-use data. The results indicate that maximum lateral channel migration reached 259.3 m during the observed period, while the total land loss amounted to 17.6 ha, consisting mostly of arable land. This reduction has directly affected the local agricultural sector, resulting in an estimated land loss value of approximately 24,122 USD. Demographic analysis reveals population decline exceeding 40% in the analysed settlements (Draževac, Konatice and Poljane) between 1961 and 2022, from over 90% of the economically active population in 1961 to less than 2% in 2022. Ultimately, by highlighting the socioeconomic impacts of riverbank erosion such as irreversible agricultural land loss and rural livelihood disruption, these findings offer valuable insights for spatial planning and sustainable land management.","蜿蜒河流的侧向河岸侵蚀是塞尔维亚科卢巴拉河（Kolubara River）流域下游一个重要的地貌过程。该过程直接影响土地利用和农业生产力，并引发更广泛的环境、社会经济和人口变化。历史上，科卢巴拉河岸曾被密集用于农业生产。如今，在自然过程与人类活动相互作用驱动下的侧向河道迁移和河岸侵蚀，直接威胁着沿河传统上活跃的地块。本研究考察了奥布雷诺瓦茨市（Obrenovac Municipality）科卢巴拉河四个活跃曲流段在97年期间（1930—2026年）河岸侵蚀的长期动态。主要任务包括：量化侧向河道迁移和土地流失速率，分析受威胁地块内的土地利用变化，以及调查受影响居民点的社会经济后果和人口动态。方法框架整合了地籍图分析、遥感技术（无人机，UAVs）和GIS软件，并结合人口与土地利用数据分析。结果表明，在观测期内侧向河道迁移最大距离达259.3 m，土地流失总量为17.6 ha，其中大部分为耕地。这一减少直接影响了当地农业部门，估计土地流失价值约为24,122美元。人口分析显示，1961年至2022年间，所分析居民点（Draževac、Konatice和Poljane）的人口下降幅度超过40%，经济活动人口比例从1961年的超过90%降至2022年的不足2%。最终，通过突出河岸侵蚀的社会经济影响，如不可逆的农业土地流失和农村生计破坏，这些发现为空间规划和可持续土地管理提供了有价值的见解。","Stanovnistvo","2026-10-19T00:00:00Z",{"impact":92,"substance":92,"depth":92,"authority":92,"freshness":92,"relevant":92,"comment":192},"塞尔维亚河岸侵蚀案例研究，属地理与土地利用领域，与三农及农业信息化主题关联弱，不建议进入每日精选。",[194],{"name":189,"url":186},[27,123,196,197],"土壤侵蚀","乡村人口",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":201},{"candidates":202,"agricultural_evidence":46},[203,208,212,218],{"code":33,"score":204,"supported":46,"title":205,"abstract":206,"tags":207},7,[],[50,48],[27],{"code":128,"score":55,"supported":46,"title":209,"abstract":210,"tags":211},[],[144],[],{"code":213,"score":55,"supported":46,"title":214,"abstract":215,"tags":217},"I7",[],[216],"methodological framework",[],{"code":155,"score":22,"supported":14,"title":219,"abstract":220,"tags":221},[],[],[123],[223,224],"Kolubara 河岸侵蚀","Obrenovac 土地利用","Kolubara河岸侵蚀-5390","10.59954\u002Fstnv.797",{"doi":226,"openalex_id":228,"authors":229,"venue":189,"cited_by_count":92,"oa_url":186,"card":249,"direction":97,"ingested_from":99},"W7222945959",[230,233,236,238,241,244,247],{"name":231,"orcid":232},"Slavoljub Dragićević","https:\u002F\u002Forcid.org\u002F0000-0002-1417-5405",{"name":234,"orcid":235},"Marko Langović","https:\u002F\u002Forcid.org\u002F0000-0002-4614-7265",{"name":237,"orcid":9},"Милан Радовић",{"name":239,"orcid":240},"Radislav Tošić","https:\u002F\u002Forcid.org\u002F0000-0002-3994-125X",{"name":242,"orcid":243},"Milica Langović","https:\u002F\u002Forcid.org\u002F0000-0002-6979-377X",{"name":245,"orcid":246},"Novica Lovrić","https:\u002F\u002Forcid.org\u002F0009-0006-0838-4370",{"name":248,"orcid":9},"Manojlović Stefan",{"tldr":250,"method":251,"finding":252,"direction":253,"opportunity":254},"研究塞尔维亚Kolubara河岸侵蚀97年间的河道迁移、耕地损失及社会经济影响。","结合地籍图、UAV遥感、GIS与人口土地利用数据分析四条曲流。","最大侧向迁移259.3米，损失17.6公顷耕地约2.4万美元，村落人口降超40%。","其他","可将河岸侵蚀遥感监测与农户生计、土地价值评估耦合，构建侵蚀风险预警与补偿决策模型。","2026-10-20T23:30:18.982943Z",{"id":257,"title":258,"url":259,"summary":260,"summary_zh":261,"content":9,"source_name":262,"source_url":259,"published_at":263,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":264,"score_detail":265,"sources":267,"tags":269,"primary_topic":274,"secondary_topics":280,"classification":282,"search_phrases":307,"slug":310,"view_count":92,"doi":311,"paper":312,"created_at":335},5367,"Integration of multi-source UAV remote sensing features and derived features for winter wheat aboveground biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102656","Accurate estimation of winter wheat ( Triticum aestivum L.) aboveground biomass (AGB) is important for crop growth monitoring and field management. However, unmanned aerial vehicle (UAV)-based AGB estimation remains challenging due to variations in canopy conditions across growth stages, and different remote sensing features can capture complementary characteristics related to AGB estimation. This study integrated multi-source UAV-derived features, including vegetation indices (VIs), texture features (TFs), light detection and ranging (LiDAR)-derived features, and derived features (DFs) generated from the original feature sets, with four machine learning algorithms for winter wheat AGB estimation. Results showed that TFs provided better prediction performance than other single-source feature sets. Compared with single-source feature sets, the integration of VIs, TFs, LiDAR-derived features, and DFs further improved AGB estimation, and the random forest (RF) model achieved the best performance among the evaluated models (R² = 0.923, RMSE = 1.731 Mg ha⁻¹). Incremental R² analysis based on a stage-only baseline model showed that the RF model explained additional variation in AGB beyond growth-stage information, with an incremental R² of 0.479. Stage-specific evaluation indicated that model performance varied among growth stages, with the highest accuracy observed at the booting stage (R² = 0.632, RMSE = 1.425 Mg ha⁻¹). Feature contribution analysis based on the full-feature fusion model using all growth-stage data identified canopy coverage and several DFs as important predictors, while stage-specific analyses showed differences in dominant predictors across growth stages. The integration of multi-source UAV-derived features and DFs with machine learning provides an effective approach for winter wheat AGB estimation, while model performance remains strongly dependent on growth stage.","准确估算冬小麦（Triticum aestivum L.）地上生物量（aboveground biomass, AGB）对作物生长监测和田间管理具有重要意义。然而，由于不同生育期冠层条件存在差异，且不同遥感特征能够捕捉与AGB估算相关的互补信息，基于无人机（unmanned aerial vehicle, UAV）的AGB估算仍面临挑战。本研究将多源无人机衍生特征，包括植被指数（vegetation indices, VIs）、纹理特征（texture features, TFs）、激光雷达（light detection and ranging, LiDAR）衍生特征以及由原始特征集生成的衍生特征（derived features, DFs），与四种机器学习算法相结合，用于冬小麦AGB估算。结果表明，TFs的预测性能优于其他单一来源特征集。与单一来源特征集相比，融合VIs、TFs、LiDAR衍生特征和DFs进一步提高了AGB估算精度，其中随机森林（random forest, RF）模型在所评估的模型中表现最佳（R² = 0.923，RMSE = 1.731 Mg ha⁻¹）。基于仅含生育期信息的基线模型进行的增量R²分析表明，RF模型在生育期信息之外解释了AGB的额外变异，增量R²为0.479。分生育期评估表明，模型性能在不同生育期之间存在差异，其中在孕穗期观察到最高精度（R² = 0.632，RMSE = 1.425 Mg ha⁻¹）。基于使用全部生育期数据的全特征融合模型进行的特征贡献分析识别出冠层覆盖度和若干DFs为重要预测因子，而分生育期分析则表明不同生育期的主导预测因子存在差异。将多源无人机衍生特征和DFs与机器学习相结合，为冬小麦AGB估算提供了一种有效方法，但模型性能仍强烈依赖于生育期。","Smart Agricultural Technology","2026-10-17T00:00:00Z",77,{"impact":17,"substance":18,"depth":19,"authority":117,"freshness":115,"relevant":22,"comment":266},"该研究融合无人机多源遥感特征与衍生特征，用随机森林估算冬小麦地上生物量，方法新颖、结论可靠，对作物长势监测有参考价值。",[268],{"name":262,"url":259},[270,271,27,272,273],"智慧农业","无人机","冬小麦","生物量估算",{"code":275,"slug":276,"name":277,"path":278,"parent_code":279},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[281],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":283},{"candidates":284,"agricultural_evidence":46},[285,291,295,302],{"code":275,"score":286,"supported":46,"title":287,"abstract":289,"tags":290},26,[288,276],"winter wheat",[277,288,276],[272],{"code":33,"score":17,"supported":46,"title":292,"abstract":293,"tags":294},[48],[50,48],[273,27],{"code":296,"score":102,"supported":46,"title":297,"abstract":298,"tags":301},"I2",[],[299,300],"机器学习","machine learning",[],{"code":303,"score":22,"supported":14,"title":304,"abstract":305,"tags":306},"I1",[],[],[270],[308,309],"无人机 多源遥感 冬小麦 生物量","冬小麦 地上生物量 随机森林","无人机多源遥感冬小麦生物量-5367","10.1016\u002Fj.atech.2026.102656",{"doi":311,"openalex_id":313,"authors":314,"venue":262,"cited_by_count":92,"oa_url":259,"card":330,"direction":97,"ingested_from":99},"W7222806066",[315,317,319,321,324,326,328],{"name":316,"orcid":9},"Guangtao Wang",{"name":318,"orcid":9},"Sumei Yao",{"name":320,"orcid":9},"Yanzheng Xie",{"name":322,"orcid":323},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":325,"orcid":9},"Xiangdong Chen",{"name":327,"orcid":9},"Yuhao Zhang",{"name":329,"orcid":9},"Lina Jiang",{"tldr":331,"method":332,"finding":333,"direction":97,"opportunity":334},"融合无人机多源遥感特征与衍生特征，用机器学习估算冬小麦地上生物量。","无人机植被指数、纹理、LiDAR特征及衍生特征结合四种机器学习算法建模。","多源特征融合优于单一特征，随机森林最优（R²=0.923），精度随生育期变化。","可探索生育期自适应建模与特征选择，提升跨期生物量估算的稳健性与可迁移性。","2026-10-20T23:30:03.106267Z",{"id":337,"title":338,"url":339,"summary":340,"summary_zh":341,"content":9,"source_name":342,"source_url":339,"published_at":263,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":92,"score_detail":343,"sources":345,"tags":347,"primary_topic":351,"secondary_topics":352,"classification":360,"search_phrases":377,"slug":380,"view_count":92,"doi":381,"paper":382,"created_at":394},5326,"Flood Hazard and Vulnerability Assessment of Chitrakoot District, Uttar Pradesh: An Integrated Geographical, GIS and Remote Sensing Approach","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.89708","Flood is one of the most important hydro-meteorological hazards affecting human settlements, agriculture, infrastructure and regional economies. Chitrakoot district of Uttar Pradesh presents a distinctive case of hydro-climatic vulnerability because it belongs to the drought-prone Bundelkhand region but simultaneously experiences recurrent riverine and flash-flood conditions during intense monsoon rainfall. The present study assesses the spatial characteristics and determinants of flood hazard and vulnerability in Chitrakoot district using an integrated geographical, GIS and remote sensing approach. The analysis incorporates the Digital Elevation Model (DEM), drainage network, physiography, river proximity, historical flood information, satellite-based flood observations and socio-economic exposure. The district's DEM indicates a marked elevation gradient, approximately from 66 m to 451 m, with relatively higher terrain in the southern part and lower terrain towards the northern Yamuna valley. This topographic configuration facilitates rapid surface runoff from elevated southern terrain towards comparatively low-lying riverine areas. The drainage system is dominated by the Yamuna and Mandakini\u002FPaisuni systems along with Baghain, Gunta, Bardaha and smaller tributaries. The Uttar Pradesh Flood Hazard Atlas identifies 5,114 ha of flood-hazard area in Chitrakoot for 1998–2020, of which 4,129 ha (81%) falls in the very-low hazard category and 985 ha (19%) in the low-hazard category. However, the historical classification does not fully represent the potential consequences of extreme rainfall events. The August 2026 flood provides compelling evidence: the Mandakini at Chitrakoot reached a reported new high flood level of 145.60 m on 29 August 2026, approximately 2.95 m above the previous reported high level, while the water level rose by about 7.5 m within six hours. The study concludes that flood risk in Chitrakoot is spatially concentrated around river corridors, low-elevation areas, floodplains, agricultural land, settlements and tourism infrastructure. A shift from conventional relief-oriented flood management towards integrated floodplain regulation, watershed management, GIS-based risk zonation, real-time monitoring and community-based preparedness is therefore essential.","洪水是影响人类住区、农业、基础设施和区域经济的最重要的水文气象灾害之一。北方邦吉德古德（Chitrakoot）县在水文气候脆弱性方面呈现出独特案例，因为它属于易受干旱影响的本德尔坎德（Bundelkhand）地区，但同时在强季风降雨期间反复遭遇河流洪水和山洪状况。本研究采用地理学、地理信息系统（GIS）和遥感相结合的综合方法，评估吉德古德县洪水灾害与脆弱性的空间特征及其决定因素。分析纳入了数字高程模型（DEM）、河网、地貌、距河流远近、历史洪水信息、基于卫星的洪水观测以及社会经济暴露度。该县的DEM显示出明显的高程梯度，大约从66米到451米，南部地形相对较高，向北部的亚穆纳（Yamuna）河谷地势降低。这种地形格局促使地表径流从较高的南部地形快速流向相对低洼的河流区域。排水系统以亚穆纳河和曼达基尼\u002F派苏尼（Mandakini\u002FPaisuni）水系为主，另有巴盖恩（Baghain）、贡塔（Gunta）、巴尔达哈（Bardaha）及较小支流。北方邦洪水灾害图集确认，1998—2020年吉德古德县洪水灾害面积为5，114公顷，其中4，129公顷（81%）属于极低灾害类别，985公顷（19%）属于低灾害类别。然而，历史分类并不能充分反映极端降雨事件的潜在后果。2026年8月的洪水提供了有力证据：据报道，2026年8月29日吉德古德的曼达基尼河达到145.60米的新高洪水位，比此前报告的高水位高出约2.95米，而水位在六小时内上升了约7.5米。研究得出结论，吉德古德的洪水风险在空间上集中于河流廊道、低海拔地区、洪泛平原、农业用地、住区和旅游基础设施周围。因此，必须从传统的以救济为导向的洪水管理转向洪泛平原综合调控、流域管理、基于GIS的风险区划、实时监测和社区为基础的备灾。","International Journal For Multidisciplinary Research",{"impact":92,"substance":92,"depth":92,"authority":92,"freshness":92,"relevant":92,"comment":344},"该研究聚焦印度北方邦的洪水灾害评估，虽涉及GIS与遥感方法，但与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[346],{"name":342,"url":339},[348,27,349,350],"防灾减灾","洪水风险评估","GIS应用",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[353,354],{"code":128,"slug":129,"name":130,"path":131,"parent_code":37},{"code":355,"slug":356,"name":357,"path":358,"parent_code":359},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":361},{"candidates":362,"agricultural_evidence":46},[363,367,371],{"code":33,"score":116,"supported":46,"title":364,"abstract":365,"tags":366},[48],[50,48],[27],{"code":128,"score":20,"supported":46,"title":368,"abstract":369,"tags":370},[144],[143,144],[],{"code":355,"score":115,"supported":46,"title":372,"abstract":373,"tags":376},[],[374,375],"干旱","drought",[349,348],[378,379],"Chitrakoot 洪水风险 GIS","Yamuna 洪水脆弱性 遥感","Chitrakoot洪水风险GIS-5326","10.36948\u002Fijfmr.2026.v08i05.89708",{"doi":381,"openalex_id":383,"authors":384,"venue":342,"cited_by_count":92,"oa_url":388,"card":389,"direction":97,"ingested_from":99},"W7222885988",[385],{"name":386,"orcid":387},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":390,"method":391,"finding":392,"direction":97,"opportunity":393},"用GIS与遥感综合评估Chitrakoot区洪水灾害与脆弱性空间分布。","DEM、河网、历史洪水与卫星观测及社会经济数据叠加分析。","洪水风险集中于河谷低地、农田与居民点，历史分级低估极端降雨风险。","可结合时序遥感与水文模型，构建极端降雨情景下的农田洪涝风险动态制图与预警。","2026-10-19T23:30:24.356743Z",{"id":396,"title":397,"url":398,"summary":399,"summary_zh":400,"content":9,"source_name":401,"source_url":398,"published_at":190,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":402,"score_detail":403,"sources":406,"tags":408,"primary_topic":413,"secondary_topics":414,"classification":415,"search_phrases":427,"slug":430,"view_count":92,"doi":431,"paper":432,"created_at":464},5318,"Lagged Effects and Mechanisms of Central Environmental Inspection Policies on Agricultural Straw Burning: Evidence from Remote Sensing Data in China","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10668-026-08214-3","Lagged Effects and Mechanisms of Central Environmental Inspection Policies on Agricultural Straw Burning: Evidence from Remote Sensing Data in China。Environment Development and Sustainability","中央环保督察政策对农业秸秆焚烧的滞后效应与作用机制：来自中国遥感数据的证据。《环境、发展与可持续性》","Environment Development and Sustainability",84,{"impact":18,"substance":404,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":405},20,"基于全国遥感数据评估中央环保督察对秸秆焚烧的滞后效应，数据规模大、方法新颖，对农业环境政策有参考价值，值得进入每日精选。",[407],{"name":401,"url":398},[409,27,410,411,412],"农业政策","卫星遥感","秸秆焚烧","环保督察",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":416},{"candidates":417,"agricultural_evidence":46},[418,422],{"code":33,"score":17,"supported":46,"title":419,"abstract":420,"tags":421},[48],[50,48],[410,27],{"code":423,"score":22,"supported":14,"title":424,"abstract":425,"tags":426},"H5",[],[],[411],[428,429],"中央环保督察 秸秆焚烧","遥感数据 秸秆焚烧","中央环保督察秸秆焚烧-5318","10.1007\u002Fs10668-026-08214-3",{"doi":431,"openalex_id":433,"authors":434,"venue":401,"cited_by_count":92,"oa_url":9,"card":458,"direction":97,"ingested_from":99},"W7222877438",[435,437,439,441,443,445,447,450,452,454,456],{"name":436,"orcid":9},"Lu Dong",{"name":438,"orcid":9},"Linru Zhang",{"name":440,"orcid":9},"Na Liu",{"name":442,"orcid":9},"Xin Long",{"name":444,"orcid":9},"Ziyi Wang",{"name":446,"orcid":9},"Siyue Yang",{"name":448,"orcid":449},"Jie Tian","https:\u002F\u002Forcid.org\u002F0000-0001-6585-3643",{"name":451,"orcid":9},"Qiyuan Wang",{"name":453,"orcid":9},"Zhanfeng Dong",{"name":455,"orcid":9},"Long Wu",{"name":457,"orcid":9},"Yichen Wang",{"tldr":459,"method":460,"finding":461,"direction":462,"opportunity":463},"利用遥感数据评估中央环保督察政策对秸秆焚烧的滞后效应与作用机制。","基于中国遥感火点数据构建面板，采用滞后效应模型识别政策影响机制。","中央环保督察对秸秆焚烧具有显著但滞后的抑制效应，并存在区域异质性。","农业绿色发展与碳","可结合地块级遥感与农户调查，量化督察滞后期内秸秆离田利用与替代处置的路径。","2026-10-19T23:30:22.693590Z",{"id":466,"title":467,"url":468,"summary":469,"summary_zh":470,"content":9,"source_name":471,"source_url":468,"published_at":263,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":92,"score_detail":472,"sources":474,"tags":476,"primary_topic":480,"secondary_topics":481,"classification":482,"search_phrases":489,"slug":492,"view_count":92,"doi":493,"paper":494,"created_at":509},5244,"Diurnal and Seasonal Surface Urban Heat Island Dynamics and Thermal Field Variance in Faridabad District, India: A MODIS-Based Assessment (2001–2025)","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fajgr\u002F2026\u002Fv9i4489","Aims: To examine the spatial and temporal dynamics of land surface temperature (LST), urban–rural biophysical indices (BUI and NDVI), daily Surface Urban Heat Island Intensity (SUHII), and the Urban Thermal Field Variance Index (UTFVI) on a diurnal and seasonal basis in Faridabad District of northern India. Study Design: Multi-temporal geospatial remote sensing and statistical modelling study. Place and Duration of Study: Faridabad District (Urban Core and Rural Hinterland), Haryana, India; Summer and Winter Seasons Analysed through Six Benchmark Periods (2001, 2005, 2010, 2015, 2020, and 2025). Methodology: MODIS optical (MOD09A1) and thermal infrared (MOD11A2) composite data were analysed using Google Earth Engine at the daytime (10:30 am) and night-time (10:30 pm) overpasses. LST values were grouped into UTFVI (Urban Thermal Field Variance Index) classes. Multivariable OLS regressions and empirical biophysical sensitivities such as Vegetation Cooling Efficiency (VCE) and Impervious Heating Effect (IHE) were evaluated in Python using a stratified random grid (N = 2,500 sampling locations; 235 n 872 pixels). Results: Over the 25-year study period, the built-up gradient for winter (BUI) increased from +0.096 to +0.233 in association with agricultural changes and urban development in the region, while the vegetative deficit for winter (NDVI) increased from -0.061 to -0.136. In terms of temperature, Faridabad showed a SUHI regime during summer (ranging from to ), where the urban core nocturnal LST increased from in 2001 to in 2025, together with regional warming and compression of the urban DTR. The pre-monsoon diurnal regimes showed the SUCI phenomenon (as low as in 2005) owing to the effects of sensible heating in the desiccated fallow rural lands after harvest season. Urban Core thermal field variance showed that moderate thermal stress (UTFVI Class 3) expanded from to , while severe stress tiers (Classes 4–6) concentrated within industrial estates. Daytime vegetation provided consistent cooling (VCE up to per ), whereas nocturnal VCE exhibited temporal variability and diminished transpirational cooling following stomatal closure. Conclusion: Urban greening offers a strong mechanism for daytime heat mitigation but is insufficient to address independently nocturnal heat trapping resulting from the high thermal inertia of construction materials. The sustainable heat-mitigation strategy in industrial nodes entails a combination of urban greening and reflective cool coatings on industrial sheds and impervious surfaces.","目的：考察印度北部法里达巴德县地表温度（LST）、城乡生物物理指数（BUI和NDVI）、日间地表城市热岛强度（SUHII）以及城市热场方差指数（UTFVI）在昼夜和季节尺度上的时空动态。研究设计：多时相地理空间遥感与统计建模研究。研究地点与时长：印度哈里亚纳邦法里达巴德县（城市核心区与乡村腹地）；通过六个基准时段（2001、2005、2010、2015、2020和2025年）分析夏季与冬季。方法：利用Google Earth Engine分析MODIS光学（MOD09A1）和热红外（MOD11A2）合成数据，分别对应白天（上午10：30）和夜间（晚上10：30）过境时刻。LST值按UTFVI（城市热场方差指数）类别分组。在Python中采用分层随机网格（N=2，500个采样点；235 n 872像素）评估多变量OLS回归及经验生物物理敏感性指标，如植被冷却效率（VCE）和不透水面增温效应（IHE）。结果：在25年研究期内，冬季建成区梯度（BUI）从+0.096增至+0.233，与该地区农业变化和城市发展相关；冬季植被亏缺（NDVI）从-0.061增至-0.136。温度方面，法里达巴德在夏季呈现SUHI态势（范围为至），城市核心区夜间LST从2001年的增至2025年的，同时伴随区域增暖和城市日较差（DTR）压缩。季风前昼夜态势因收获季后干燥休耕乡村土地的感热加热效应而呈现SUCI现象（2005年低至）。城市核心区热场方差显示，中度热胁迫（UTFVI第3类）从扩大至，而严重胁迫层级（第4—6类）集中于工业园区。白天植被提供持续冷却（VCE最高达每），而夜间VCE呈现时间变异性，且气孔关闭后蒸腾冷却减弱。结论：城市绿化是白天热缓解的有力机制，但不足以独立应对建筑材料高热惯性导致的夜间热量滞留。工业节点的可持续热缓解策略需要将城市绿化与反射性","Asian Journal of Geographical Research",{"impact":92,"substance":92,"depth":92,"authority":92,"freshness":92,"relevant":92,"comment":473},"印度城市热岛遥感研究，属城市气候与环境领域，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。",[475],{"name":471,"url":468},[27,477,478,479],"地表温度","城市热岛","MODIS",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":483},{"candidates":484,"agricultural_evidence":46},[485],{"code":33,"score":115,"supported":46,"title":486,"abstract":487,"tags":488},[],[50,48],[477,27],[490,491],"Faridabad 城市热岛","MODIS 地表温度 遥感","Faridabad城市热岛-5244","10.9734\u002Fajgr\u002F2026\u002Fv9i4489",{"doi":493,"openalex_id":495,"authors":496,"venue":471,"cited_by_count":92,"oa_url":468,"card":504,"direction":97,"ingested_from":99},"W7222794736",[497,500,502],{"name":498,"orcid":499},"Abhishek Malik","https:\u002F\u002Forcid.org\u002F0009-0009-4878-0871",{"name":501,"orcid":9},"Shubham Mor",{"name":503,"orcid":9},"Gaurav Kalotra",{"tldr":505,"method":506,"finding":507,"direction":97,"opportunity":508},"基于MODIS分析印度Faridabad 2001–2025年昼夜与季节地表城市热岛及热场差异演变。","MODIS光学与热红外数据、Google Earth Engine、OLS回归及","白天绿化降温显著，但夜间建筑热惯性导致的热滞留难以单独缓解。","可结合城乡梯度与作物休耕期感热效应，研究农业用地变化对昼夜热岛的非对称贡献。","2026-10-18T23:30:31.211684Z"]