[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1738":3,"related-1738":117},{"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":23,"tags":25,"primary_topic":9,"secondary_topics":30,"classification":31,"search_phrases":79,"slug":82,"view_count":21,"doi":83,"paper":84,"created_at":116},1738,"Integrated Remote Sensing and GIS-Based Agricultural Drought Vulnerability Assessment in Sivagangai District, Tamil Nadu, India","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fbiosphere2030009","Agricultural drought is an important issue for food security, water availability and rural livelihoods, particularly in the semi-arid regions of the Indian state of Tamil Nadu, especially in Sivagangai District. This study combines multi-temporal remote sensing data and GIS techniques to evaluate the agricultural drought vulnerability during a 30-year time frame (1994–2024). Landsat satellite imagery and climate data were used to derive some key bio-physical indicators such as NDVI, NDWI, VCI, SMI, LST, LULC and SPI. An AHP was used to assign weights to each parameter and was created. The spatiotemporal analysis shows that there is a substantial reduction in vegetated and moist areas as well as a high growth of high and very high temperature areas and built-up land. The percentage of areas classified as ‘Very High’ drought vulnerability has increased significantly from 1.08% (1994) to 40.04% (2024), highlighting a growing threat from drought conditions. NDVI and NDWI were chosen as the most significant indices that affect drought. The findings reflect the deteriorating environmental condition and stress the need for specific mitigation measures, including afforestation, sustainable land-use planning and management of water resources. This study is both spatially explicit and scalable; it provides key information to local planners, policymakers and stakeholders to support agricultural resilience in drought-prone regions.","农业干旱是粮食安全、水资源供应和农村生计面临的重要问题，尤其是在印度泰米尔纳德邦半干旱地区，特别是西瓦冈盖县。本研究结合多时相遥感数据和GIS技术，评估了1994年至2024年30年间的农业干旱脆弱性。利用Landsat卫星影像和气候数据，提取了若干关键生物物理指标，如NDVI、NDWI、VCI、SMI、LST、LULC和SPI。采用层次分析法（AHP）为各参数赋予权重并构建模型。时空分析表明，植被覆盖区和湿润区面积显著减少，而高温和极高温度区域以及建设用地面积大幅增加。被归类为“极高”干旱脆弱性的区域比例从1994年的1.08%显著上升至2024年的40.04%，凸显了干旱状况日益加剧的威胁。NDVI和NDWI被确定为影响干旱的最重要指标。研究结果反映了环境状况的恶化，并强调了采取具体缓解措施的必要性，包括植树造林、可持续土地利用规划和水资源管理。本研究兼具空间明确性和可扩展性，为当地规划者、政策制定者和利益相关者提供了关键信息，以支持干旱易发地区的农业韧性建设。",null,"Biosphere","2026-09-04T00:00:00Z","论文",10,false,58,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},12,18,16,2,1,"基于遥感和GIS的农业干旱脆弱性评估，方法成熟，数据详实，对区域农业防灾有参考价值。",[24],{"name":10,"url":6},[26,27,28,29],"农业遥感","农业信息化","GIS","干旱监测",[],{"status":32,"locked":14,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":40,"reason":78},[37,46,53,58,66,73],{"code":38,"score":39,"supported":40,"title":41,"abstract":43,"tags":45},"H7",15,true,[42],"drought",[44,42],"干旱",[29],{"code":47,"score":39,"supported":40,"title":48,"abstract":50,"tags":52},"I3",[49],"remote sensing",[51,49],"遥感",[26],{"code":54,"score":17,"supported":40,"title":55,"abstract":56,"tags":57},"I4",[28],[28],[28],{"code":59,"score":60,"supported":40,"title":61,"abstract":62,"tags":65},"A8",6,[],[63,64],"粮食安全","food security",[],{"code":67,"score":68,"supported":40,"title":69,"abstract":70,"tags":72},"H3",3,[],[71],"水资源",[],{"code":74,"score":21,"supported":14,"title":75,"abstract":76,"tags":77},"I1",[],[],[27],"多个产业证据相当，需要确认主归属",[80,81],"农业信息化 农业遥感 干旱监测 GIS","农业信息化 农业遥感","农业信息化农业遥感干旱监测GIS-1738","10.3390\u002Fbiosphere2030009",{"doi":83,"openalex_id":85,"authors":86,"venue":10,"cited_by_count":108,"oa_url":6,"card":109,"direction":113,"ingested_from":115},"W7208763077",[87,90,93,96,99,102,105],{"name":88,"orcid":89},"Kongeswaran Thangaraj","https:\u002F\u002Forcid.org\u002F0000-0002-0752-8727",{"name":91,"orcid":92},"M. Rajendran","https:\u002F\u002Forcid.org\u002F0000-0001-7944-7259",{"name":94,"orcid":95},"Perumal Velmayil","https:\u002F\u002Forcid.org\u002F0000-0002-5744-0870",{"name":97,"orcid":98},"Venkatramanan Senapathi","https:\u002F\u002Forcid.org\u002F0000-0002-1698-1101",{"name":100,"orcid":101},"Prabakaran Kulandaisamy","https:\u002F\u002Forcid.org\u002F0000-0002-3393-8033",{"name":103,"orcid":104},"Raghu R. Krishnamoorthy","https:\u002F\u002Forcid.org\u002F0000-0001-7278-4324",{"name":106,"orcid":107},"Sakthi Sivakumar","https:\u002F\u002Forcid.org\u002F0009-0001-5839-8096",0,{"tldr":110,"method":111,"finding":112,"direction":113,"opportunity":114},"利用遥感和GIS评估印度Sivagangai地区农业干旱脆弱性，发现极高脆弱区从1.08%增至40.","结合Landsat影像和气候数据，计算NDVI、NDWI等指标，采用AHP加权和","植被和湿润区减少，高温和建设用地增加，干旱脆弱性显著上升，NDVI和NDWI是关键指标。","农业遥感与作物表型","可扩展至其他半干旱区，结合高分辨率影像和机器学习优化权重，并纳入社会经济因素以增强适应性策略。","openalex","2026-09-05T23:30:27.688056Z",{"total":60,"page":21,"page_size":60,"items":118},[119,179,250,318,416,478],{"id":120,"title":121,"url":122,"summary":123,"summary_zh":124,"content":9,"source_name":125,"source_url":122,"published_at":126,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":127,"score_detail":128,"sources":131,"tags":133,"primary_topic":136,"secondary_topics":141,"classification":142,"search_phrases":164,"slug":167,"view_count":21,"doi":168,"paper":169,"created_at":178},1626,"Assessing and Mapping Drought Vulnerability Areas in Homa Bay County, Kenya","https:\u002F\u002Fdoi.org\u002F10.59765\u002Fkfw6y","Drought is a recurring environmental hazard in Homa Bay County, Kenya, affecting agriculture, water resources, and community livelihoods.Despite its impacts, there is limited spatially explicit information on drought-prone areas within the county.This study aimed to map drought vulnerability in Homa Bay County using remote sensing and GIS techniques.The study was anchored on two theories; Disaster risk reduction theory and Vulnerability theory.The study used MODIS NDVI data (2020-2023) to map drought severity, while ESA World Cover land use and CHIRPS rainfall data were integrated as vulnerability factors.A weighted overlay analysis was run in QGIS to combine three factors: drought severity (50%), land use (30%), and rainfall (20%).The final vulnerability map was classified into four classes: low, moderate, mild, and severe.The results show that 0.91% of the county falls under severe vulnerability, concentrated in Mbita and Karachuonyo sub-counties.Moderate vulnerability dominates 53.69% of the county, followed by mild (31.22%) and low (14.18%).The study provides spatially explicit information to support county-level drought planning and intervention strategies.The paper recommends development in severe vulnerable counties; Mbita and Karachuonyo with carefully drought planning and intervention strategies.","干旱是肯尼亚霍马贝县反复出现的环境灾害，影响农业、水资源和社区生计。尽管其影响重大，但该县内关于干旱易发区域的空间明确信息仍然有限。本研究旨在利用遥感和GIS技术绘制霍马贝县的干旱脆弱性地图。研究基于两大理论：灾害风险减轻理论和脆弱性理论。研究使用MODIS NDVI数据（2020-2023年）绘制干旱严重程度图，同时整合ESA World Cover土地利用数据和CHIRPS降雨数据作为脆弱性因素。在QGIS中进行了加权叠加分析，以综合三个因素：干旱严重程度（50%）、土地利用（30%）和降雨量（20%）。最终脆弱性地图分为四个等级：低、中、轻度和严重。结果显示，该县0.91%的面积属于严重脆弱性，集中在姆比塔和卡拉丘奥尼分区。中度脆弱性占全县面积的53.69%，其次是轻度（31.22%）和低度（14.18%）。本研究提供了空间明确的信息，以支持县级干旱规划和干预策略。论文建议在严重脆弱的分区——姆比塔和卡拉丘奥尼，制定周密的干旱规划和干预策略。","Journal of Research Innovation and Implications in Education","2026-09-02T00:00:00Z",55,{"impact":129,"substance":18,"depth":19,"authority":13,"freshness":68,"relevant":21,"comment":130},8,"研究利用遥感与GIS技术绘制肯尼亚Homa Bay县干旱脆弱性地图，为当地农业规划提供空间信息，但影响范围局限于县级，时效性较低。",[132],{"name":125,"url":122},[26,134,28,29,135],"肯尼亚","灾害评估",{"code":38,"slug":137,"name":138,"path":139,"parent_code":140},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":143,"locked":14,"version":33,"method":34,"evidence":144},"classified",{"candidates":145,"agricultural_evidence":40},[146,150,155,160],{"code":38,"score":19,"supported":40,"title":147,"abstract":148,"tags":149},[42],[44,42],[29,135],{"code":47,"score":151,"supported":40,"title":152,"abstract":153,"tags":154},7,[],[51,49],[26],{"code":54,"score":156,"supported":40,"title":157,"abstract":158,"tags":159},4,[],[28],[28],{"code":67,"score":68,"supported":40,"title":161,"abstract":162,"tags":163},[],[71],[],[165,166],"农业遥感 干旱监测 灾害评估 肯尼亚","农业遥感 干旱监测","农业遥感干旱监测灾害评估肯尼亚-1626","10.59765\u002Fkfw6y",{"doi":168,"openalex_id":170,"authors":171,"venue":125,"cited_by_count":108,"oa_url":122,"card":172,"direction":177,"ingested_from":115},"W7206217503",[],{"tldr":173,"method":174,"finding":175,"direction":113,"opportunity":176},"利用遥感和GIS技术绘制肯尼亚霍马贝县干旱脆弱性地图，识别脆弱区域。","MODIS NDVI、ESA土地覆盖和CHIRPS降雨数据，加权叠加分析。","严重脆弱区占0.91%，集中在Mbita和Karachuonyo；中度脆弱占53.69%。","可扩展至其他地区，结合社会经济数据，提高干旱脆弱性评估精度，并制定针对性适应策略。","智慧农业 \u002F 农业物联网","2026-09-04T23:30:13.864272Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":184,"content":9,"source_name":185,"source_url":182,"published_at":186,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":187,"score_detail":188,"sources":194,"tags":196,"primary_topic":9,"secondary_topics":200,"classification":201,"search_phrases":220,"slug":223,"view_count":21,"doi":224,"paper":225,"created_at":249},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":129,"substance":189,"depth":190,"authority":191,"freshness":192,"relevant":21,"comment":193},20,17,13,9,"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。",[195],{"name":185,"url":182},[26,197,198,29,199],"NDVI","遥感监测","森林生态",[],{"status":32,"locked":14,"version":33,"method":34,"evidence":202},{"candidates":203,"agricultural_evidence":14,"reason":219},[204,208,213],{"code":38,"score":39,"supported":40,"title":205,"abstract":206,"tags":207},[42],[44,42],[29],{"code":47,"score":209,"supported":40,"title":210,"abstract":211,"tags":212},14,[49],[51],[197,26,198],{"code":67,"score":60,"supported":40,"title":214,"abstract":215,"tags":218},[],[216,217],"蒸散发","evapotranspiration",[],"农业相关性需要复核",[221,222],"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":224,"openalex_id":226,"authors":227,"venue":185,"cited_by_count":108,"oa_url":243,"card":244,"direction":113,"ingested_from":115},"W7221717828",[228,231,234,236,238,240],{"name":229,"orcid":230},"Mai Son Le","https:\u002F\u002Forcid.org\u002F0000-0002-5360-7119",{"name":232,"orcid":233},"Ha Thu Nguyen","https:\u002F\u002Forcid.org\u002F0000-0002-4490-308X",{"name":235,"orcid":9},"Hung Manh Nguyen",{"name":237,"orcid":9},"Huy Xuan Chu",{"name":239,"orcid":9},"Hoa Thanh Nguyen",{"name":241,"orcid":242},"Duy-Toan Dao","https:\u002F\u002Forcid.org\u002F0009-0003-6057-4436","https:\u002F\u002Ferem.ktu.lt\u002Findex.php\u002Ferem\u002Farticle\u002Fdownload\u002F44241\u002F23724",{"tldr":245,"method":246,"finding":247,"direction":113,"opportunity":248},"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":251,"title":252,"url":253,"summary":254,"summary_zh":9,"content":9,"source_name":255,"source_url":253,"published_at":256,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":257,"score_detail":258,"sources":261,"tags":263,"primary_topic":267,"secondary_topics":273,"classification":275,"search_phrases":294,"slug":297,"view_count":20,"doi":298,"paper":299,"created_at":317},4650,"A process-oriented framework for daily dynamic monitoring of summer maize drought: From meteorological stress to agricultural impact","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105011","A process-oriented framework for daily dynamic monitoring of summer maize drought: From meteorological stress to agricultural impact。Agricultural Systems","Agricultural Systems","2026-10-11T00:00:00Z",81,{"impact":18,"substance":259,"depth":18,"authority":209,"freshness":13,"relevant":21,"comment":260},21,"核心期刊提出的面向过程的夏玉米干旱逐日动态监测框架，方法新颖、面向农业信息化应用，时效性强，值得进入每日精选。",[262],{"name":255,"url":253},[264,26,265,266,29],"智慧农业","夏玉米","农业气象",{"code":268,"slug":269,"name":270,"path":271,"parent_code":272},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[274],{"code":38,"slug":137,"name":138,"path":139,"parent_code":140},{"status":143,"locked":14,"version":33,"method":34,"evidence":276},{"candidates":277,"agricultural_evidence":40},[278,282,286,290],{"code":38,"score":191,"supported":40,"title":279,"abstract":280,"tags":281},[42],[42],[266,29],{"code":268,"score":17,"supported":40,"title":283,"abstract":284,"tags":285},[269],[269],[265],{"code":74,"score":21,"supported":14,"title":287,"abstract":288,"tags":289},[],[],[264],{"code":47,"score":21,"supported":14,"title":291,"abstract":292,"tags":293},[],[],[26],[295,296],"夏玉米 干旱 动态监测","气象胁迫 农业影响 监测框架","夏玉米干旱动态监测-4650","10.1016\u002Fj.agsy.2026.105011",{"doi":298,"openalex_id":300,"authors":301,"venue":255,"cited_by_count":108,"oa_url":253,"card":9,"direction":113,"ingested_from":115},"W7221308782",[302,304,306,308,311,313,315],{"name":303,"orcid":9},"Yuanda Zhang",{"name":305,"orcid":9},"Peijuan Wang",{"name":307,"orcid":9},"Jun Zhu",{"name":309,"orcid":310},"Yang Li","https:\u002F\u002Forcid.org\u002F0000-0001-9021-0201",{"name":312,"orcid":9},"Dianchen Han",{"name":314,"orcid":9},"Dingrong Wu",{"name":316,"orcid":9},"Jianping Guo","2026-10-11T23:30:03.734429Z",{"id":319,"title":320,"url":321,"summary":322,"summary_zh":323,"content":9,"source_name":324,"source_url":321,"published_at":325,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":326,"score_detail":327,"sources":330,"tags":332,"primary_topic":9,"secondary_topics":336,"classification":337,"search_phrases":375,"slug":378,"view_count":108,"doi":379,"paper":380,"created_at":415},4232,"Integrated machine learning and AHP-based framework for spatial delineation of rainwater harvesting potential","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72943-8","Groundwater scarcity and its declining situation in the semi-arid area of Jaipur district, Rajasthan make it imperative to adopt scientific solutions to locate suitable rainwater harvesting sites. This study combines Geographic Information Systems (GIS), Analytical Hierarchy Process (AHP) and machine learning (ML) algorithms for delineating the RWH suitability zones. Eleven biophysical and anthropogenic factors, namely elevation (280–647 m), slope (0–35°), topographic wetness index (TWI), drainage density, soil profile, land use\u002Fland cover (LULC), rainfall (350–700 mm yr⁻ 1 ), hillshade, aspect, lineament density, and proximity to roads, were standardized and incorporated into a spatial database. A georeferenced inventory of 150 locations was divided into training (70%; n = 105) and testing (30%; n = 45) datasets and analyzed using XGBoost, Support Vector Machine (SVM), Gradient Boosting Machine (GBM), CatBoost, and AHP models. Among the evaluated algorithms, XGBoost exhibited the highest predictive performance with a ROC-AUC of 0.78, followed by SVM (0.76), AHP (0.73), CatBoost (0.69), and GBM (0.68). The XGBoost model identified 38.1% (3902.6 km 2 ) of the study area as highly suitable to very highly suitable for RWH implementation, whereas 34.6% (3533.7 km 2 ) and 27.3% (2783.7 km 2 ) were classified as moderate and low-to-very low suitability classes, respectively. Variable importance analysis revealed that rainfall (15.7%), drainage density (15.6%), elevation (11.5%), and TWI (8.8%) were the dominant controls on RWH suitability. The AHP framework showed acceptable consistency (CR \u003C 0.10) and model validation showed an overall accuracy of 88% and a Kappa coefficient of 0.64. The integrated framework successfully identified priority recharge areas in the southeastern lowlands and offers a scalable decision support tool for sustainable groundwater management and water resource planning that is resilient to climate change in semi-arid areas.","拉贾斯坦邦斋浦尔地区半干旱区的地下水稀缺及其持续下降的态势，使得采用科学方法确定适宜的雨水收集地点势在必行。本研究结合地理信息系统（Geographic Information Systems, GIS）、层次分析法（Analytical Hierarchy Process, AHP）和机器学习（Machine Learning, ML）算法，划定雨水收集适宜性分区。研究将11项生物物理和人为因素，即高程（280–647 m）、坡度（0–35°）、地形湿度指数（Topographic Wetness Index, TWI）、河网密度、土壤剖面、土地利用\u002F土地覆被（Land Use\u002FLand Cover, LULC）、降雨量（350–700 mm yr⁻¹）、山体阴影、坡向、线理密度和道路邻近度，进行标准化处理并纳入空间数据库。将150个地理参考位置清单划分为训练集（70%；n = 105）和测试集（30%；n = 45），并采用XGBoost、支持向量机（Support Vector Machine, SVM）、梯度提升机（Gradient Boosting Machine, GBM）、CatBoost和AHP模型进行分析。在评估的算法中，XGBoost表现出最高的预测性能，ROC-AUC为0.78，其次是SVM（0.76）、AHP（0.73）、CatBoost（0.69）和GBM（0.68）。XGBoost模型识别出研究区38.1%（3902.6 km²）的区域为雨水收集高度适宜至极高适宜区，而34.6%（3533.7 km²）和27.3%（2783.7 km²）分别被归类为中度适宜和低至极低适宜等级。变量重要性分析表明，降雨量（15.7%）、河网密度（15.6%）、高程（11.5%）和TWI（8.8%）是雨水收集适宜性的主导控制因素。AHP框架显示出可接受的一致性（CR \u003C 0.10），模型验证显示总体精度为88%，Kappa系数为0.64。该集成框架成功识别了东南部低地的优先补给区，并为半干旱地区可持续地下水管理和应对气候变化的淡水资源规划提供了一种可扩展的决策支持工具。","Scientific Reports","2026-10-03T00:00:00Z",70,{"impact":129,"substance":328,"depth":190,"authority":209,"freshness":192,"relevant":21,"comment":329},22,"方法扎实、结论可复用的半干旱区雨水收集选址研究，但属区域案例，公共影响有限。",[331],{"name":324,"url":321},[26,333,28,334,335],"机器学习","雨水收集","地下水管理",[],{"status":32,"locked":14,"version":33,"method":34,"evidence":338},{"candidates":339,"agricultural_evidence":14,"reason":219},[340,347,353,359,366,371],{"code":341,"score":18,"supported":40,"title":342,"abstract":344,"tags":346},"I2",[343],"machine learning",[333,343,345],"decision support",[333],{"code":54,"score":13,"supported":40,"title":348,"abstract":349,"tags":352},[],[350,28,351],"地理信息","geographic information",[28],{"code":38,"score":192,"supported":40,"title":354,"abstract":355,"tags":358},[],[356,44,357],"气候变化","climate change",[],{"code":360,"score":60,"supported":40,"title":361,"abstract":362,"tags":365},"H2",[],[363,364],"土壤","soil",[],{"code":67,"score":60,"supported":40,"title":367,"abstract":368,"tags":370},[],[71,369],"water resource",[],{"code":47,"score":21,"supported":14,"title":372,"abstract":373,"tags":374},[],[],[26],[376,377],"Jaipur 雨水收集 选址","XGBoost AHP 地下水补给","Jaipur雨水收集选址-4232","10.1038\u002Fs41598-026-72943-8",{"doi":379,"openalex_id":381,"authors":382,"venue":324,"cited_by_count":108,"oa_url":409,"card":410,"direction":113,"ingested_from":115},"W7218709806",[383,386,388,391,393,395,397,399,401,403,406],{"name":384,"orcid":385},"Agradeep Mohanta","https:\u002F\u002Forcid.org\u002F0000-0002-9369-3445",{"name":387,"orcid":9},"Ameet Yadav",{"name":389,"orcid":390},"Dharmendra Singh","https:\u002F\u002Forcid.org\u002F0000-0001-8528-8637",{"name":392,"orcid":9},"Sultan Singh",{"name":394,"orcid":9},"Shuchi Mishra",{"name":396,"orcid":9},"Kanwarpreet Singh",{"name":398,"orcid":9},"Abhishek Sharma",{"name":400,"orcid":9},"Amenjor Senagah",{"name":402,"orcid":9},"Aditya Kumar Tiwary",{"name":404,"orcid":405},"Chandni Thakur","https:\u002F\u002Forcid.org\u002F0000-0001-8976-2738",{"name":407,"orcid":408},"Mohamed A. Mattar","https:\u002F\u002Forcid.org\u002F0000-0002-7506-3036","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-72943-8_reference.pdf",{"tldr":411,"method":412,"finding":413,"direction":113,"opportunity":414},"结合GIS、AHP与多种机器学习算法，划分印度斋浦尔半干旱区雨水收集适宜性分区。","11个生物物理与人为因子，150个样本，XGBoost、SVM、GBM、CatB","XGBoost表现最佳（AUC=0.78），38.1%区域高度适宜，降雨与排水密度为主导因子。","可将该集成框架迁移至其他半干旱农业区，并结合遥感时序数据动态更新适宜性。","2026-10-05T23:30:25.173949Z",{"id":417,"title":418,"url":419,"summary":420,"summary_zh":421,"content":9,"source_name":422,"source_url":419,"published_at":423,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":424,"score_detail":425,"sources":427,"tags":429,"primary_topic":9,"secondary_topics":432,"classification":433,"search_phrases":457,"slug":460,"view_count":68,"doi":461,"paper":462,"created_at":477},3561,"GIS and Machine Learning Framework for Landslide Susceptibility Mapping: A Raster-Based Approach","https:\u002F\u002Fdoi.org\u002F10.48123\u002Frsgis.1864203","Traditional landslide susceptibility models often rely on statistical methods applied to limited point-based samples, which can limit the spatial continuity of predictions. This study proposes a GIS-integrated, pixel-based machine learning approach that operates directly on raster data to generate continuous landslide susceptibility maps. Implemented in Rwanda, the workflow uses variables derived from a 30 m-resolution digital elevation model (DEM), including slope, aspect, elevation, and curvature, together with auxiliary environmental data such as NDVI, soil type, and distances to hydrological and infrastructural features. Several machine learning algorithms were evaluated, including Random Forest, Support Vector Machine, Logistic Regression, and ensemble approaches. Using only topographic variables, the Stacked Ensemble achieved the highest performance (F1-score = 0.8515; ROC-AUC = 0.9200). When all predictor variables were included, CatBoost achieved the highest performance (F1-score = 0.8860; ROC-AUC = 0.9564). This scalable approach is particularly suitable for landslide susceptibility assessment in data-scarce regions and can contribute to spatial planning and risk reduction efforts in Rwanda and similar regions.","传统的滑坡易发性模型通常依赖统计方法，并应用于有限的点位样本，这可能限制预测的空间连续性。本研究提出了一种集成GIS、基于像素的机器学习方法，直接对栅格数据进行操作，以生成连续的滑坡易发性图。该方法在卢旺达实施，工作流程使用源自30 m分辨率数字高程模型（DEM）的变量，包括坡度、坡向、高程和曲率，并结合NDVI、土壤类型以及距水文和基础设施要素的距离等辅助环境数据。研究评估了多种机器学习算法，包括随机森林、支持向量机、逻辑回归和集成方法。仅使用地形变量时，堆叠集成（Stacked Ensemble）取得了最高性能（F1分数 = 0.8515；ROC-AUC = 0.9200）。当纳入所有预测变量时，CatBoost取得了最高性能（F1分数 = 0.8860；ROC-AUC = 0.9564）。这种可扩展的方法特别适用于数据稀缺地区的滑坡易发性评估，并可为卢旺达及类似地区的空间规划和风险降低工作提供支持。","Turkish Journal of Remote Sensing and GIS","2026-09-24T00:00:00Z",65,{"impact":129,"substance":189,"depth":190,"authority":17,"freshness":129,"relevant":21,"comment":426},"方法新颖、指标扎实的遥感机器学习论文，可为丘陵山区农业风险与空间规划提供参考，但属境外案例、应用面偏窄。",[428],{"name":422,"url":419},[430,26,333,28,431],"数字乡村","滑坡风险",[],{"status":32,"locked":14,"version":33,"method":34,"evidence":434},{"candidates":435,"agricultural_evidence":14,"reason":219},[436,440,444,448,452],{"code":341,"score":39,"supported":40,"title":437,"abstract":438,"tags":439},[343],[333,343],[333],{"code":54,"score":17,"supported":40,"title":441,"abstract":442,"tags":443},[28],[28],[28],{"code":360,"score":60,"supported":40,"title":445,"abstract":446,"tags":447},[],[363,364],[],{"code":47,"score":21,"supported":14,"title":449,"abstract":450,"tags":451},[],[],[26],{"code":453,"score":21,"supported":14,"title":454,"abstract":455,"tags":456},"I6",[],[],[430],[458,459],"卢旺达 滑坡易发性 机器学习","DEM NDVI 滑坡风险制图","卢旺达滑坡易发性机器学习-3561","10.48123\u002Frsgis.1864203",{"doi":461,"openalex_id":463,"authors":464,"venue":422,"cited_by_count":108,"oa_url":471,"card":472,"direction":113,"ingested_from":115},"W7214227863",[465,468],{"name":466,"orcid":467},"Albert Iradukunda","https:\u002F\u002Forcid.org\u002F0009-0001-6899-7615",{"name":469,"orcid":470},"Kaan Ozgun","https:\u002F\u002Forcid.org\u002F0000-0002-3853-1400","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5606819",{"tldr":473,"method":474,"finding":475,"direction":113,"opportunity":476},"提出GIS集成、基于栅格像元的机器学习框架，生成连续滑坡易发性图。","用30米DEM地形变量、NDVI、土壤等，比较RF、SVM、CatBoost等模","加入全部变量后CatBoost最优，F1=0.886，AUC=0.956，优于仅地形变量。","可迁移到农业区滑坡风险评估，结合多时相遥感与作物分布，服务山区耕地安全与规划。","2026-09-26T23:30:32.763631Z",{"id":479,"title":480,"url":481,"summary":482,"summary_zh":483,"content":9,"source_name":484,"source_url":481,"published_at":423,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":485,"score_detail":486,"sources":488,"tags":490,"primary_topic":492,"secondary_topics":497,"classification":503,"search_phrases":539,"slug":542,"view_count":543,"doi":544,"paper":545,"created_at":567},3495,"Remote Sensing and GIS in Modern Drought Assessment: Bridging Conventional Methods and Emerging Technologies","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123","Drought is a complex and recurring hydroclimatic hazard that affects agricultural production, water resources, ecosystems and socioeconomic development. Effective drought assessment requires approaches capable of capturing its spatial and temporal variability and its multiple dimensions. This review examines the evolution of drought assessment from conventional drought indices to integrated approaches based on remote sensing and Geographic Information Systems (GIS), with an emphasis on their applications, strengths, limitations and emerging developments. Conventional indices, including the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Reconnaissance Drought Index (RDI) and Percent of Normal Precipitation Index (PNPI), remain widely used because of their established methodologies and long-term applicability. However, their dependence on meteorological observations can limit spatial characterisation and the representation of vegetation, soil moisture and other land-surface responses. Remote sensing provides spatially extensive and repeated observations of vegetation condition, land surface temperature, soil moisture, evapotranspiration and water-related conditions, enabling the development of satellite-derived drought indicators and indices. GIS further facilitates the integration, spatial analysis, visualisation, and mapping of drought-related information from multiple sources. The review also discusses hybrid approaches that combine climate-based indices with satellite-derived indicators, as well as drought monitoring platforms and multi-source assessment frameworks. Despite substantial advances, challenges remain regarding cloud contamination, differences in spatial and temporal resolution, data continuity, ground-based validation and uncertainty associated with multi-source datasets. Emerging machine learning, deep learning and artificial intelligence approaches offer opportunities for integrating heterogeneous datasets and improving drought characterisation and early warning. Overall, the integration of conventional observations, remote sensing, GIS and advanced analytical approaches provides a promising framework for more comprehensive drought monitoring and risk assessment under increasing climate variability and change.","干旱是一种复杂且反复出现的水文气候灾害，影响农业生产、水资源、生态系统和社会经济发展。有效的干旱评估需要能够捕捉其时空变异性和多维特征的方法。本文综述了干旱评估从传统干旱指数到基于遥感与地理信息系统（GIS）的综合方法的演变，重点探讨其应用、优势、局限性和新兴发展。传统指数，包括标准化降水指数（SPI）、标准化降水蒸散指数（SPEI）、帕尔默干旱强度指数（PDSI）、侦察干旱指数（RDI）和降水距平百分率指数（PNPI），因其方法成熟且具有长期适用性而仍被广泛使用。然而，这些指数对气象观测的依赖可能限制其空间表征能力以及对植被、土壤水分和其他陆面响应的刻画。遥感提供了对植被状况、地表温度、土壤水分、蒸散量及与水相关状况的大范围重复观测，使得卫星衍生的干旱指标和指数得以发展。GIS进一步促进了多来源干旱相关信息的整合、空间分析、可视化和制图。本文还讨论了将基于气候的指数与卫星衍生指标相结合的混合方法，以及干旱监测平台和多源评估框架。尽管取得了实质性进展，但在云污染、时空分辨率差异、数据连续性、地面验证以及多源数据集相关的不确定性方面仍存在挑战。新兴的机器学习、深度学习和人工智能方法为整合异质数据集、改进干旱表征和预警提供了机遇。总体而言，在气候变异性和变化日益加剧的背景下，传统观测、遥感、GIS和先进分析方法的整合为更全面的干旱监测和风险评估提供了一个有前景的框架。","Journal of Geography Environment and Earth Science International",66,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":129,"relevant":21,"comment":487},"综述系统梳理遥感与GIS在干旱评估中的应用演进，方法学价值明确，但属综述类论文、非国内落地事件，影响力有限。",[489],{"name":484,"url":481},[264,491,51,28,29],"农业人工智能",{"code":54,"slug":493,"name":494,"path":495,"parent_code":496},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[498,499],{"code":38,"slug":137,"name":138,"path":139,"parent_code":140},{"code":47,"slug":500,"name":501,"path":502,"parent_code":496},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":143,"locked":14,"version":33,"method":34,"evidence":504},{"candidates":505,"agricultural_evidence":40},[506,512,516,520,527,531,535],{"code":54,"score":18,"supported":40,"title":507,"abstract":508,"tags":511},[28],[350,509,28,510,351],"空间分析","spatial analysis",[28],{"code":38,"score":39,"supported":40,"title":513,"abstract":514,"tags":515},[42],[44,42],[29],{"code":47,"score":39,"supported":40,"title":517,"abstract":518,"tags":519},[49],[51,49],[51],{"code":341,"score":13,"supported":40,"title":521,"abstract":522,"tags":526},[],[523,333,343,524,525],"深度学习","deep learning","artificial intelligence",[491],{"code":360,"score":60,"supported":40,"title":528,"abstract":529,"tags":530},[],[363,364],[],{"code":67,"score":60,"supported":40,"title":532,"abstract":533,"tags":534},[],[71,217],[],{"code":74,"score":21,"supported":14,"title":536,"abstract":537,"tags":538},[],[],[264],[540,541],"遥感 GIS 干旱评估","卫星遥感 干旱指数","遥感GIS干旱评估-3495",5,"10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123",{"doi":544,"openalex_id":546,"authors":547,"venue":484,"cited_by_count":108,"oa_url":481,"card":562,"direction":113,"ingested_from":115},"W7214156857",[548,550,552,554,556,558,560],{"name":549,"orcid":9},"V. Dhanalakshmi",{"name":551,"orcid":9},"N. Manikandan",{"name":553,"orcid":9},"V. S. Jinsy",{"name":555,"orcid":9},"K. V. Sumesh",{"name":557,"orcid":9},"P. Nideesh",{"name":559,"orcid":9},"P. S. Manju",{"name":561,"orcid":9},"N. Gopika",{"tldr":563,"method":564,"finding":565,"direction":113,"opportunity":566},"综述了从传统干旱指数到遥感、GIS及AI集成的现代干旱评估方法演进。","文献综述，对比SPI、SPEI等传统指数与遥感、GIS及混合方法。","遥感与GIS弥补传统指数空间局限，但云污染、分辨率差异和验证仍是挑战。","可探索多源遥感与机器学习融合的干旱早期预警，重点解决数据不确定性与地面验证。","2026-09-25T23:30:30.576065Z"]