[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3799":3,"related-3799":98},{"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":9,"secondary_topics":32,"classification":33,"search_phrases":72,"slug":75,"view_count":76,"doi":77,"paper":78,"created_at":97},3799,"How land retirement programs affect crop production: Evidence from China's Grain for Green Program","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fajae.70097","Abstract This paper evaluates the impact of one of the world's largest land conservation programs—China's Grain for Green Program—on crop production, and examines whether land retirement policies can balance ecological goals with food security objectives. By combining detailed county‐level crop production data with remote sensing data, our difference‐in‐differences estimates suggest that the program significantly reduced total cropland area but increased total crop production. This unexpected increase in production can be explained by the program's significant increase in labor input, fertilizer use, and multiple cropping on the remaining cropland. More importantly, we find that the program improved the local microclimate and substantially alleviated the damage of weather stress on crop production. Our findings suggest that, under certain institutional conditions, it is possible to adopt land‐use policy to protect the ecosystem without compromising food security.","摘要 本文评估了世界上最大的土地保护项目之一——中国退耕还林工程——对作物生产的影响，并考察休耕政策能否在生态目标与粮食安全目标之间取得平衡。通过将详细的县级作物生产数据与遥感数据相结合，我们的双重差分估计表明，该工程显著减少了耕地总面积，但提高了作物总产量。这一出乎意料的产量增长可以由该工程显著增加了剩余耕地上的劳动投入、化肥使用和复种来解释。更重要的是，我们发现该工程改善了当地微气候，并大幅缓解了天气胁迫对作物生产的损害。我们的研究结果表明，在某些制度条件下，可以采取土地利用政策来保护生态系统而不损害粮食安全。",null,"American Journal of Agricultural Economics","2026-09-27T00:00:00Z","论文",10,true,91,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},26,23,19,15,8,1,"顶刊论文，用县级遥感与双重差分证据揭示退耕还林减地不减产、微气候改善缓解气象胁迫，对生态与粮食安全协同政策有重要参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"粮食安全","耕地保护","遥感监测","生态补偿","退耕还林",[],{"status":34,"locked":35,"version":36,"method":37,"evidence":38},"needs_review",false,"2026-10-22.2","rules",{"candidates":39,"agricultural_evidence":14,"reason":71},[40,47,54,61,66],{"code":41,"score":42,"supported":14,"title":43,"abstract":44,"tags":46},"A8",7,[],[27,45],"food security",[27],{"code":48,"score":42,"supported":14,"title":49,"abstract":50,"tags":53},"I3",[],[51,52],"遥感","remote sensing",[29],{"code":55,"score":56,"supported":14,"title":57,"abstract":58,"tags":60},"F3",3,[],[59],"fertilizer",[],{"code":62,"score":22,"supported":35,"title":63,"abstract":64,"tags":65},"H1",[],[],[28],{"code":67,"score":22,"supported":35,"title":68,"abstract":69,"tags":70},"H6",[],[],[30],"多个产业证据相当，需要确认主归属",[73,74],"中国 退耕还林 粮食安全","Grain for Green 遥感 作物产量","中国退耕还林粮食安全-3799",12,"10.1002\u002Fajae.70097",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":88,"oa_url":89,"card":90,"direction":94,"ingested_from":96},"W7214551215",[81,83,86],{"name":82,"orcid":9},"Xiaoxue Zong",{"name":84,"orcid":85},"Kaixing Huang","https:\u002F\u002Forcid.org\u002F0000-0002-7761-4153",{"name":87,"orcid":9},"Xi Ji",0,"https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fajae.70097",{"tldr":91,"method":92,"finding":93,"direction":94,"opportunity":95},"评估中国退耕还林工程对作物生产的影响，发现耕地减少但产量反增。","县级作物产量数据与遥感数据结合，采用双重差分法。","退耕还林减少耕地却提高产量，因增加劳动、化肥、复种并改善微气候。","农业遥感与作物表型","可探究退耕还林在不同区域和作物类型下的异质性效应及微气候机制。","openalex","2026-09-29T23:30:30.512194Z",{"total":99,"page":22,"page_size":99,"items":100},6,[101,182,269,333,406,452],{"id":102,"title":103,"url":104,"summary":105,"summary_zh":106,"content":9,"source_name":107,"source_url":104,"published_at":108,"category":12,"cover_url":9,"hotness":13,"is_selected":35,"score":109,"score_detail":110,"sources":116,"tags":118,"primary_topic":121,"secondary_topics":127,"classification":128,"search_phrases":162,"slug":165,"view_count":22,"doi":166,"paper":167,"created_at":181},5005,"Geospatial Dynamics of Urban Sprawl and Agricultural Land Loss in the Faiyum, Egypt: Scenario Modeling for Sustainable Land Management","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010459","Rapid and uncontrolled urban growth poses a severe threat to arable agricultural oases by jeopardizing vital ecosystem services and regional food security. This study investigates the spatiotemporal dynamics of urban sprawl over a 16-year period (2007–2023) in Faiyum City (Egypt), an ancient oasis surrounded by prime agricultural soils, and projects future spatial trends under unconstrained development scenarios. Utilizing high-resolution multi-temporal satellite imagery integrated with Remote Sensing (RS) and Geographic Information Systems (GIS) techniques, built-up area expansion was quantitatively measured, sectoral decomposition shows expansion on every flank of the historic core but at markedly uneven rates, converting between 13.8% and 55.7% of the land available in each direction and an area-based growth model was applied to simulate short- and long-term land transformation trends. Empirical findings reveal that between 2007 and 2023 the urban built-up area expanded by 55.7%, from 861.7 ha to 1341.6 ha, at an average annual compound growth rate of 2.8%, raising its share of the enclosed study area from 37.7% to 58.6%. Sampling of the converted area shows that 80.6% of it had been agricultural land at the baseline date, giving 386.9 ha of farmland lost over the period. The projections indicate that, without intervention, built-up cover would occupy 67.3% of the 2287 ha enclosed by the Ring Road by 2028 and 77.3% by 2033, leaving 518.7 ha of the unbuilt land that remained in 2023. This trajectory bears directly on SDG 11 and SDG 15 targets. Containment instruments such as binding Urban Growth Boundaries and compact infill policies are discussed as directions indicated by the spatial evidence, although their feasibility, cost and enforcement requirements lie outside the scope of this analysis.","快速且不受控制的城市增长对赖以维持关键生态系统服务和区域粮食安全的农业绿洲构成了严重威胁。本研究考察了埃及法尤姆城（Faiyum City）——一座被优质农业土壤环绕的古老绿洲——在16年期间（2007–2023年）城市蔓延的时空动态，并预测了无约束开发情景下的未来空间趋势。利用高分辨率多时相卫星影像，结合遥感（RS）与地理信息系统（GIS）技术，对建成区扩张进行了定量测度，部门分解显示历史核心区各方向均发生扩张，但速率明显不均，各方向所转化土地占可用土地的比例在13.8%至55.7%之间；同时应用基于面积的增长模型模拟了短期和长期土地转化趋势。实证结果表明，2007年至2023年间，城市建成区面积扩张了55.7%，从861.7公顷增至1341.6公顷，年均复合增长率为2.8%，使其在封闭研究区内的占比从37.7%升至58.6%。对转化区域的抽样显示，其中80.6%在基准日期为农业用地，即该期间损失农田386.9公顷。预测表明，若无干预，到2028年建成区将占环路所围2287公顷的67.3%，到2033年将占77.3%，仅余2023年尚存的518.7公顷未建设用地。这一轨迹直接关系到可持续发展目标11（SDG 11）和目标15（SDG 15）。空间证据所指示的方向包括具有约束力的城市增长边界和紧凑型填充式政策等遏制工具，但其可行性、成本及执行要求不在本分析范围之内。","Sustainability","2026-10-14T00:00:00Z",75,{"impact":76,"substance":111,"depth":112,"authority":113,"freshness":114,"relevant":22,"comment":115},22,18,14,9,"基于16年遥感与GIS数据量化埃及法尤姆绿洲城市扩张导致的农地流失并模拟未来情景，方法扎实、数据具体，对耕地保护与可持续土地管理有参考价值，但属区域案例研究，公共影响层级有限。",[117],{"name":107,"url":104},[27,28,29,119,120],"城市扩张","可持续土地管理",{"code":122,"slug":123,"name":124,"path":125,"parent_code":126},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":129,"locked":35,"version":36,"method":37,"evidence":130},"classified",{"candidates":131,"agricultural_evidence":14},[132,139,143,147,153,158],{"code":122,"score":114,"supported":14,"title":133,"abstract":134,"tags":138},[],[135,136,137],"地理信息","GIS","geographic information",[],{"code":41,"score":42,"supported":14,"title":140,"abstract":141,"tags":142},[],[27,45],[27],{"code":48,"score":42,"supported":14,"title":144,"abstract":145,"tags":146},[],[51,52],[29],{"code":148,"score":56,"supported":14,"title":149,"abstract":150,"tags":152},"H2",[],[151],"土壤",[],{"code":67,"score":56,"supported":14,"title":154,"abstract":155,"tags":157},[],[156],"生态系统服务",[],{"code":62,"score":22,"supported":35,"title":159,"abstract":160,"tags":161},[],[],[28],[163,164],"Faiyum 城市扩张 农地流失","遥感 GIS 耕地损失 埃及","Faiyum城市扩张农地流失-5005","10.3390\u002Fsu182010459",{"doi":166,"openalex_id":168,"authors":169,"venue":107,"cited_by_count":88,"oa_url":104,"card":176,"direction":94,"ingested_from":96},"W7221785333",[170,173],{"name":171,"orcid":172},"Mahmut Dingil","https:\u002F\u002Forcid.org\u002F0000-0002-4901-7554",{"name":174,"orcid":175},"Levent Atatanır","https:\u002F\u002Forcid.org\u002F0000-0001-5638-6676",{"tldr":177,"method":178,"finding":179,"direction":94,"opportunity":180},"基于遥感与GIS分析埃及法尤姆市2007-2023年城市扩张，并模拟未来农地流失情景。","多时相高分辨率卫星影像、RS\u002FGIS空间分析、面积增长模型情景模拟。","建成区16年增55.7%，80.6%来自农地，2033年将占环城路内77.3%。","可结合城市增长边界与紧凑填充政策，量化农地保护情景下的粮食安全与生态服务权衡。","2026-10-15T23:30:29.550250Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":187,"content":9,"source_name":188,"source_url":185,"published_at":189,"category":12,"cover_url":9,"hotness":13,"is_selected":35,"score":190,"score_detail":191,"sources":194,"tags":196,"primary_topic":199,"secondary_topics":204,"classification":205,"search_phrases":242,"slug":245,"view_count":22,"doi":246,"paper":247,"created_at":268},3844,"Assessing Rice Food Availability Resilience Under Paddy Field Land Use Change in Sukabumi Regency","https:\u002F\u002Fdoi.org\u002F10.59188\u002Feduvest.v6i9.53513","Rice food availability is increasingly challenged by paddy field conversion driven by socioeconomic development and land-use transformation. However, reductions in paddy field extent do not necessarily indicate decreased food availability, as agricultural systems may respond through intensification and productivity improvement. This study evaluated rice food availability resilience under paddy field land-use change in Sukabumi Regency, West Java, Indonesia, during 2000–2020 by integrating Landsat-based spatial analysis with agricultural production indicators. Paddy field dynamics were analyzed using multi-temporal Landsat imagery through Object-Based Image Analysis (OBIA) and Random Forest classification. Agricultural responses were assessed using harvested area, cropping intensity, productivity, rice production, and the Production Efficiency Index (PEI).The results showed that paddy field area declined from 71,641.44 ha in 2000 to 65,982.15 ha in 2020, representing a 7.9% reduction, with the largest decline occurring in rainfed paddy fields. Despite this reduction, rice production increased from 622,189 tons to 931,328 tons, supported by increases in harvested area, cropping intensity, and productivity, particularly within irrigated paddy field systems. The PEI increased from 8.68 to 14.12 ton ha⁻¹, indicating improved production efficiency relative to available land resources. These findings indicated that rice food availability resilience in Sukabumi Regency was supported by agricultural adaptation strategies, particularly through intensification and productivity enhancement rather than land expansion.Nevertheless, productivity improvements may not indefinitely compensate for continued paddy field conversion due to ecological and biophysical limitations. Integrating remote sensing-based land-use change analysis with agricultural performance indicators provides a useful framework for evaluating food availability resilience and supporting sustainable paddy field management.","水稻粮食供给日益受到社会经济发展和土地利用转型所驱动的稻田转换的挑战。然而，稻田面积的减少并不必然意味着粮食供给下降，因为农业系统可能通过集约化和生产力提升作出响应。本研究通过将基于Landsat的空间分析与农业生产指标相结合，评估了印度尼西亚西爪哇省苏加武眉县2000—2020年稻田土地利用变化下的水稻粮食供给韧性。稻田动态采用多时相Landsat影像，通过面向对象影像分析（OBIA）和随机森林分类进行分析。农业响应则通过收获面积、种植强度、生产力、水稻产量和生产效率指数（PEI）进行评估。结果表明，稻田面积从2000年的71,641.44公顷下降至2020年的65,982.15公顷，减少了7.9%，其中雨养稻田的降幅最大。尽管稻田面积减少，水稻产量却从622,189吨增加至931,328吨，这得益于收获面积、种植强度和生产力的提高，尤其是在灌溉稻田系统中。生产效率指数从8.68吨\u002F公顷上升至14.12吨\u002F公顷，表明相对于可用土地资源的生产效率有所提高。这些发现表明，苏加武眉县的水稻粮食供给韧性得益于农业适应策略，特别是通过集约化和生产力提升而非土地扩张来实现。然而，由于生态和生物物理限制，生产力的提高可能无法无限期地弥补稻田的持续转换。将基于遥感的土地利用变化分析与农业绩效指标相结合，为评估粮食供给韧性和支持可持续稻田管理提供了一个有用的框架。","Eduvest - Journal Of Universal Studies","2026-09-29T00:00:00Z",60,{"impact":21,"substance":112,"depth":192,"authority":114,"freshness":114,"relevant":22,"comment":193},16,"印尼区域案例，方法（Landsat+OBIA+随机森林）与结论有参考价值，但影响范围限于地方，适合作为遥感与耕地变化主题的补充素材。",[195],{"name":188,"url":185},[197,27,28,29,198],"水稻","土地利用",{"code":200,"slug":201,"name":197,"path":202,"parent_code":203},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":129,"locked":35,"version":36,"method":37,"evidence":206},{"candidates":207,"agricultural_evidence":14},[208,214,218,224,230,234,238],{"code":200,"score":17,"supported":14,"title":209,"abstract":211,"tags":213},[201,210],"paddy",[197,212,201,210],"稻田",[197],{"code":48,"score":42,"supported":14,"title":215,"abstract":216,"tags":217},[],[51,52],[29],{"code":122,"score":99,"supported":14,"title":219,"abstract":220,"tags":223},[],[221,222],"空间分析","spatial analysis",[],{"code":225,"score":56,"supported":14,"title":226,"abstract":227,"tags":229},"H3",[],[228],"灌溉",[],{"code":41,"score":22,"supported":35,"title":231,"abstract":232,"tags":233},[],[],[27],{"code":62,"score":22,"supported":35,"title":235,"abstract":236,"tags":237},[],[],[28],{"code":67,"score":22,"supported":35,"title":239,"abstract":240,"tags":241},[],[],[198],[243,244],"Sukabumi 稻田 遥感","水稻 耕地转换 粮食供给","Sukabumi稻田遥感-3844","10.59188\u002Feduvest.v6i9.53513",{"doi":246,"openalex_id":248,"authors":249,"venue":188,"cited_by_count":88,"oa_url":185,"card":263,"direction":94,"ingested_from":96},"W7214788951",[250,252,255,257,260],{"name":251,"orcid":9},"Tatang Kurniawan",{"name":253,"orcid":254},"Widiatmaka Widiatmaka","https:\u002F\u002Forcid.org\u002F0000-0001-8613-9561",{"name":256,"orcid":9},"M. Yanuar J. Purwanto",{"name":258,"orcid":259},"Yudi Setiawan","https:\u002F\u002Forcid.org\u002F0000-0002-1482-1905",{"name":261,"orcid":262},"Iman Firmansyah","https:\u002F\u002Forcid.org\u002F0000-0001-5180-8964",{"tldr":264,"method":265,"finding":266,"direction":94,"opportunity":267},"评估印尼苏卡布米稻田2000-2020年土地利用变化下稻米供应韧性。","Landsat多时相影像、OBIA与随机森林分类，结合农业生产指标。","稻田减少7.9%但稻米产量增49.7%，靠集约化与生产力提升维持韧性。","可研究集约化补偿的生态阈值及多区域遥感-生产指标耦合的韧性评估框架。","2026-09-30T23:30:29.307801Z",{"id":270,"title":271,"url":272,"summary":273,"summary_zh":9,"content":9,"source_name":274,"source_url":9,"published_at":275,"category":276,"cover_url":9,"hotness":13,"is_selected":14,"score":277,"score_detail":278,"sources":281,"tags":283,"primary_topic":9,"secondary_topics":287,"classification":288,"search_phrases":328,"slug":331,"view_count":22,"doi":9,"paper":9,"created_at":332},4838,"新修订的农业法开启'三农'法治建设新篇章","https:\u002F\u002Fwww.farmer.com.cn\u002F2026\u002F10\u002F13\u002F991030629.html","新修订的农业法对粮食安全、乡村振兴、耕地保护、绿色发展和农民增收等核心领域作出系统部署。明确提出推进乡村全面振兴、加快建设农业强国,坚持党对'三农'工作的全面领导,统筹发展科技农业、绿色农业、质量农业、品牌农业。支持智慧农业发展,鼓励物联网、人工智能等新技术与农业生产、经营、管理相融合,为农业科技创新扫清制度障碍。实行最严格的耕地保护制度,将大农业观、大食物观写入法律。","中国农网 农民日报 2026 年 10 月 13 日","2026-10-13T00:00:00Z","政策",92,{"impact":279,"substance":18,"depth":112,"authority":113,"freshness":114,"relevant":22,"comment":280},28,"全国性法律修订，条款细节扎实且明确支持智慧农业与AI融合，权威央媒首发，时效性强，值得进入每日精选。",[282],{"name":274,"url":272},[284,285,27,28,286],"智慧农业","乡村振兴","农业法修订",[],{"status":34,"locked":35,"version":36,"method":37,"evidence":289},{"candidates":290,"agricultural_evidence":14,"reason":71},[291,296,301,305,310,316,322],{"code":41,"score":292,"supported":14,"title":293,"abstract":294,"tags":295},4,[],[27],[27],{"code":297,"score":292,"supported":14,"title":298,"abstract":299,"tags":300},"G",[],[285],[285],{"code":62,"score":292,"supported":14,"title":302,"abstract":303,"tags":304},[],[28],[28],{"code":306,"score":292,"supported":14,"title":307,"abstract":308,"tags":309},"I1",[],[284],[284],{"code":311,"score":56,"supported":14,"title":312,"abstract":313,"tags":315},"G6",[],[314],"农民增收",[],{"code":317,"score":56,"supported":14,"title":318,"abstract":319,"tags":321},"I5",[],[320],"物联网",[],{"code":323,"score":56,"supported":14,"title":324,"abstract":325,"tags":327},"J2",[],[326],"农业强国",[],[329,330],"农业法 修订 法治","农业法 智慧农业 物联网","农业法修订法治-4838","2026-10-14T00:04:01.960161Z",{"id":334,"title":335,"url":336,"summary":337,"summary_zh":338,"content":9,"source_name":339,"source_url":336,"published_at":340,"category":12,"cover_url":9,"hotness":13,"is_selected":35,"score":341,"score_detail":342,"sources":347,"tags":349,"primary_topic":353,"secondary_topics":359,"classification":364,"search_phrases":387,"slug":390,"view_count":88,"doi":391,"paper":392,"created_at":405},4747,"Spatiotemporal mapping of agricultural drought using geospatial approach in the East Bale Zone, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fagg2.70475","Abstract Agricultural drought significantly threatens food security, livelihoods, and ecosystem stability, especially in regions prone to drought, such as the East Bale zone. This study investigates the spatial and temporal patterns of agricultural drought over 30 years (1993–2024) by combining meteorological station data with satellite‐derived vegetation and thermal indices. To assess meteorological drought, the standardized precipitation index (SPI) was calculated at 3‐, 6‐, and 12‐month intervals. For agricultural and thermal stress analysis, remote sensing‐based indices including the vegetation condition index (VCI), temperature condition index, and vegetation health index (VHI) were utilized. The results indicate that the years 2003, 2008, and 2018 experienced the most severe droughts, as shown consistently across all indices. Spatial analysis revealed that the central, southern, and eastern lowland areas faced the highest frequency and severity of drought, while the northwestern highlands remained comparatively resilient. Cross‐index correlation revealed strong agreement between SPI and VCI ( R 2 = 0.77), and between VHI and VCI ( R 2 = 0.83), supporting the effectiveness of integrated drought monitoring approaches. This research advances the field of drought assessment by enhancing spatial and temporal resolution and validating cross‐index relationships at the subregional level. The results provide critical insights for localized drought early warning systems, climate adaptation strategies, and future research to improve agricultural resilience under changing climatic conditions. Hence, agricultural drought risk mapping serves as a valuable tool for guiding decision‐making in drought monitoring and for mitigating its impacts on agricultural productivity.","摘要 农业干旱严重威胁粮食安全、生计和生态系统稳定性，尤其是在东巴莱区等易旱地区。本研究结合气象站数据与卫星反演的植被和热力指数，探讨了30年（1993—2024年）间农业干旱的时空格局。为评估气象干旱，计算了3个月、6个月和12个月尺度的标准化降水指数（SPI）。在农业与热胁迫分析方面，采用了基于遥感的植被状态指数（VCI）、温度状态指数和植被健康指数（VHI）。结果表明，2003年、2008年和2018年经历了最严重的干旱，各指数均一致反映了这一情况。空间分析显示，中部、南部和东部低地地区干旱发生频率最高、程度最严重，而西北部高地则相对具有较强恢复力。跨指数相关性分析表明，SPI与VCI之间（R²=0.77）以及VHI与VCI之间（R²=0.83）具有高度一致性，支持了综合干旱监测方法的有效性。本研究通过提高时空分辨率并在次区域尺度上验证跨指数关系，推进了干旱评估领域的发展。研究结果为地方性干旱预警系统、气候适应策略以及未来研究提供了重要见解，有助于在气候变化条件下提升农业韧性。因此，农业干旱风险制图为干旱监测决策及其对农业生产影响的减缓提供了有价值的工具。","Agrosystems Geosciences & Environment","2026-10-11T00:00:00Z",68,{"impact":21,"substance":343,"depth":344,"authority":345,"freshness":114,"relevant":22,"comment":346},21,17,13,"基于30年气象与遥感指数交叉验证的农业干旱时空制图研究，方法扎实、结论可靠，但属区域性案例，对国内三农信息化仅有方法借鉴价值。",[348],{"name":339,"url":336},[27,350,29,351,352],"防灾减灾","植被指数","农业干旱",{"code":354,"slug":355,"name":356,"path":357,"parent_code":358},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[360],{"code":48,"slug":361,"name":362,"path":363,"parent_code":126},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":129,"locked":35,"version":36,"method":37,"evidence":365},{"candidates":366,"agricultural_evidence":14},[367,375,379,383],{"code":354,"score":112,"supported":14,"title":368,"abstract":370,"tags":374},[369],"drought",[371,372,373,369],"气候变化","气候适应","干旱",[350],{"code":48,"score":21,"supported":14,"title":376,"abstract":377,"tags":378},[],[51,52],[351,29],{"code":41,"score":42,"supported":14,"title":380,"abstract":381,"tags":382},[],[27,45],[27],{"code":122,"score":99,"supported":14,"title":384,"abstract":385,"tags":386},[],[221,222],[],[388,389],"埃塞俄比亚 East Bale 农业干旱","SPI VCI 干旱监测","埃塞俄比亚EastBale农业干旱-4747","10.1002\u002Fagg2.70475",{"doi":391,"openalex_id":393,"authors":394,"venue":339,"cited_by_count":88,"oa_url":336,"card":400,"direction":94,"ingested_from":96},"W7221455809",[395,397],{"name":396,"orcid":9},"Murad Hedo Haji",{"name":398,"orcid":399},"Karuturi Venkata Suryabhagavan","https:\u002F\u002Forcid.org\u002F0000-0003-2528-9106",{"tldr":401,"method":402,"finding":403,"direction":94,"opportunity":404},"结合气象站与卫星植被热指数，绘制埃塞俄比亚East Bale区30年农业干旱时空图。","用SPI（3\u002F6\u002F12月）与遥感VCI、TCI、VHI指数做交叉相关分析。","2003、2008、2018年干旱最重，中南部和东部低地最脆弱，SPI与VCI相关性R²=0.77。","可在小区域尺度融合多源指数构建干旱预警模型，并验证指数在不同作物区的适用性。","2026-10-12T23:30:24.342375Z",{"id":407,"title":408,"url":409,"summary":410,"summary_zh":9,"content":411,"source_name":412,"source_url":9,"published_at":413,"category":414,"cover_url":9,"hotness":13,"is_selected":35,"score":415,"score_detail":416,"sources":418,"tags":420,"primary_topic":425,"secondary_topics":429,"classification":430,"search_phrases":447,"slug":450,"view_count":88,"doi":9,"paper":9,"created_at":451},4613,"重庆445万块耕地实现赋码管理:45项信息一图统揽、一码查地","https:\u002F\u002Fnyncw.cq.gov.cn\u002Fzwxx_161\u002Fzwdt\u002F202610\u002Ft20261009_16204300.html","重庆市依托巴渝良田应用系统,整合农业农村、自然资源、水利、林业等部门123个图层数据,将全市445万块耕地纳入农地一张图,每块地赋唯一编码、关联地类、质量、坡度、权属、高标准农田建设情况等45项信息。AI识别+无人机巡查可半小时巡完上千亩耕地、秒级预警隐蔽问题;建设资金专户管理与预算一体化平台互联互通,实现资金流、项目流、业务流三流贯通全程可追溯。","本报讯  10月8日，记者从市农业农村委获悉，我市依托“巴渝良田”应用系统，全市445万块耕地实现“赋码管理”，通过整合123个图层数据，构建起“农地一张图”，实现地类、质量、坡度等45项信息“一图统揽、一码查地”。\n\n10月8日，在垫江县桂东街道龙田村，种粮大户李伦明站在田埂上，掏出手机对着高标准农田建设项目标识牌上的二维码轻轻一扫，屏幕上立刻弹出地块信息：面积326亩，坡度小于15度，土壤类型为紫色土，属于2025年高标准农田改造提升项目区，施工单位、监理单位、建设工期清清楚楚。“有了这个‘电子身份证’，哪块地什么情况，一目了然。”李伦明说。\n\n李伦明口中的“电子身份证”，正是我市全面启动的“巴渝良田”应用系统。该系统通过整合农业农村、自然资源、水利、林业等部门的123个图层数据，将全市445万块耕地纳入“农地一张图”，每块地都被赋予唯一编码，关联地类、质量、坡度、权属、高标准农田建设情况等45项信息。管理人员和群众扫码即可查地，彻底告别了过去翻图纸、跑现场、层层问询的传统模式。\n\n垫江县农业农村委工作人员王强打开“巴渝良田”系统，轻点鼠标，一架无人机沿着预设航线对在建的高标准农田项目进行空中巡查。高清画面实时回传，AI很快识别出某处沟渠铺设厚度不达标。系统立即生成整改工单，同步推送给施工方、监理方和项目负责人。“以前发现这种隐蔽问题，得靠人下到田里一处处量，现在‘空中慧眼’半小时就能巡完上千亩耕地，问题秒级预警、闭环整改。”王强说。\n\n除了“管地”，数字监管还“管钱”。我市将所有高标准农田项目建设资金纳入专户管理，依托“巴渝良田”系统与预算管理一体化平台互联互通，让资金流、项目流、业务流“三流贯通”，每一笔资金拥有唯一“身份证号”，实现全程可追溯、可核验。","重庆市农业农村委员会 2026-10-09","2026-10-09T00:00:00Z","报道",81,{"impact":111,"substance":111,"depth":344,"authority":76,"freshness":21,"relevant":22,"comment":417},"省级耕地数字化管理落地案例，445万块耕地赋码、123图层整合与AI无人机巡查等细节扎实，具备主题聚合与检索价值。",[419],{"name":412,"url":409},[421,422,28,29,423,424],"数字乡村","高标准农田","农地一张图","赋码管理",{"code":62,"slug":426,"name":427,"path":428,"parent_code":358},"farmland","耕地保护与高标准农田","\u002Ftopic\u002Fenvironment\u002Ffarmland",[],{"status":129,"locked":35,"version":36,"method":37,"evidence":431},{"candidates":432,"agricultural_evidence":14},[433,438,442],{"code":62,"score":21,"supported":14,"title":434,"abstract":435,"tags":437},[],[422,436],"农田建设",[28,422],{"code":48,"score":22,"supported":35,"title":439,"abstract":440,"tags":441},[],[],[29],{"code":443,"score":22,"supported":35,"title":444,"abstract":445,"tags":446},"I6",[],[],[421],[448,449],"重庆 巴渝良田 赋码管理","重庆 高标准农田 一码查地","重庆巴渝良田赋码管理-4613","2026-10-11T00:09:35.623381Z",{"id":453,"title":454,"url":455,"summary":456,"summary_zh":457,"content":9,"source_name":458,"source_url":455,"published_at":459,"category":12,"cover_url":9,"hotness":13,"is_selected":35,"score":415,"score_detail":460,"sources":462,"tags":464,"primary_topic":466,"secondary_topics":467,"classification":474,"search_phrases":517,"slug":520,"view_count":521,"doi":522,"paper":523,"created_at":542},4575,"A review of remote sensing for agricultural drought: technologies, indices, modelling advances and future prospects","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15481603.2026.2738981","Agricultural drought is a complex hazard that jeopardises food security and progress toward the Sustainable Development Goals. We systematically reviewed peer-reviewed English-language literature published between 2015 and 2025, focusing on Remote Sensing (RS)-driven agricultural drought monitoring and prediction. Eligible studies were identified from the Web of Science and Scopus databases (last searched in August 2025); review articles, duplicates, and irrelevant studies were excluded, resulting in 181 articles. This review critically examined the evolution of sensors, drought indices, modelling approaches, and emerging technologies used to assess agricultural drought across diverse agro-ecological regions. Specifically, we (1) systematically evaluated the use of optical, thermal, and microwave RS platforms, along with satellite-based, model-based, and reanalysis products for agricultural drought monitoring; (2) reviewed widely used RS-based drought indices, including single indices grouped into nine categories, as well as composite indices; (3) reviewed software or programming languages applied; (4) assessed the progression of modelling approaches from traditional statistical and time-series methods to machine learning (ML) and deep learning (DL) frameworks; and (5) examined validation strategies applied to agricultural drought assessment. The analysis reveals a growing research interest in this topic, with a strong geographic concentration in Asia, particularly China. Optical and thermal platforms (e.g. MODIS and Landsat) dominate the literature (~54%) due to their accessibility, followed by satellite-derived precipitation products (17%), while microwave soil-moisture missions remain underutilized (~7%). Regarding RS-based indices, most studies employed more than one index: 44% analysed multiple individual indices, 51% used composite indices (either alone or in combination with individual indices), and only 5% relied on a single index. Vegetation indices, such as NDVI, remain workhorses; however, their sensitivity to soil background effects and canopy saturation has driven increased adoption of thermal composites (e.g. VHI), as well as adaptive weighting schemes and principal component analysis-based approaches. Moreover, computational tools used in drought research can be broadly grouped into geospatial and RS platforms for large-scale data processing and analysis (e.g. QGIS, GEE), and general-purpose programming languages (e.g. R, Python) for model development and advanced analytics. Regarding the methodology, statistical and time-series approaches account for 64% of the reviewed studies, followed by ML models (20%) and DL methods (15%), while explainable artificial intelligence (XAI) remains nascent. Techniques, such as random forests, support vector machines, and CNN and LSTM architectures, generally outperform traditional models but require large training datasets and raise concerns related to interpretability and ethics. In terms of validation, hydro-climatic indices such as SPI and SPEI are most commonly used (32%), followed by crop yield and yield anomaly data (19%). This review further identifies key research gaps across sensors, indices, and modelling approaches and proposes a roadmap for developing equitable, interpretable, and resilient agricultural drought monitoring systems. Emerging opportunities include context-aware and interpretable AI, multi-sensor and multi-scale data fusion across optical, thermal, and microwave domains, integration of physiologically meaningful indicators such as sun-induced chlorophyll fluorescence (SIF), robust uncertainty quantification, and the development of hybrid and physics-informed modelling frameworks.","农业干旱是一种复杂的灾害，威胁粮食安全和可持续发展目标的实现。我们系统综述了2015年至2025年间发表的同行评议英文文献，聚焦于遥感（RS）驱动的农业干旱监测与预测。符合条件的研究通过Web of Science和Scopus数据库检索确定（最后检索时间为2025年8月）；综述文章、重复文献及不相关研究被排除，最终纳入181篇文献。本综述批判性地审视了用于评估不同农业生态区农业干旱的传感器、干旱指数、建模方法及新兴技术的演变。具体而言，我们（1）系统评估了光学、热红外和微波遥感平台，以及基于卫星、基于模型和再分析产品在农业干旱监测中的应用；（2）综述了广泛使用的基于遥感的干旱指数，包括归为九类的单一指数以及复合指数；（3）综述了所应用的软件或编程语言；（4）评估了建模方法从传统统计和时间序列方法向机器学习（ML）和深度学习（DL）框架的演进；（5）考察了应用于农业干旱评估的验证策略。分析显示，该主题的研究兴趣日益增长，地理上高度集中于亚洲，尤其是中国。光学和热红外平台（如MODIS和Landsat）因其可获取性在文献中占主导地位（约54%），其次是卫星降水产品（17%），而微波土壤水分任务仍未得到充分利用（约7%）。在基于遥感的指数方面，大多数研究采用了不止一种指数：44%分析了多个单一指数，51%使用了复合指数（单独使用或与单一指数结合使用），仅5%依赖单一指数。植被指数（如NDVI）仍是主力工具；然而，其对土壤背景效应和冠层饱和的敏感性推动了热红外复合指数（如VHI）以及自适应权重方案和基于主成分分析的方法得到更多采用。此外，干旱研究中使用的计算工具可大致分为用于大规模数据处理和分析的地理空间与遥感平台（如QGIS、GEE），以及用于模型开发的通用编程语言（如R、Python）。","GIScience & Remote Sensing","2026-10-08T00:00:00Z",{"impact":112,"substance":111,"depth":19,"authority":113,"freshness":21,"relevant":22,"comment":461},"系统综述181篇文献，梳理遥感干旱监测的传感器、指数与建模进展，方法学价值高，但属学术综述，产业落地影响有限。",[463],{"name":458,"url":455},[284,27,465,29,352],"机器学习",{"code":48,"slug":361,"name":362,"path":363,"parent_code":126},[468,469],{"code":354,"slug":355,"name":356,"path":357,"parent_code":358},{"code":470,"slug":471,"name":472,"path":473,"parent_code":126},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":129,"locked":35,"version":36,"method":37,"evidence":475},{"candidates":476,"agricultural_evidence":14},[477,481,485,493,497,502,508,513],{"code":48,"score":20,"supported":14,"title":478,"abstract":479,"tags":480},[52],[51,52],[29],{"code":354,"score":113,"supported":14,"title":482,"abstract":483,"tags":484},[369],[373,369],[],{"code":470,"score":13,"supported":14,"title":486,"abstract":487,"tags":492},[],[488,465,489,490,491],"深度学习","machine learning","deep learning","artificial intelligence",[465],{"code":41,"score":42,"supported":14,"title":494,"abstract":495,"tags":496},[],[27,45],[27],{"code":148,"score":99,"supported":14,"title":498,"abstract":499,"tags":501},[],[151,500],"soil",[],{"code":503,"score":56,"supported":14,"title":504,"abstract":505,"tags":507},"D",[],[506],"水产",[],{"code":67,"score":56,"supported":14,"title":509,"abstract":510,"tags":512},[],[511],"农业生态",[],{"code":306,"score":22,"supported":35,"title":514,"abstract":515,"tags":516},[],[],[284],[518,519],"遥感 农业干旱 监测","GIScience Remote Sensing 干旱指数","遥感农业干旱监测-4575",2,"10.1080\u002F15481603.2026.2738981",{"doi":522,"openalex_id":524,"authors":525,"venue":458,"cited_by_count":88,"oa_url":535,"card":536,"direction":541,"ingested_from":96},"W7221081979",[526,529,532],{"name":527,"orcid":528},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":530,"orcid":531},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":533,"orcid":534},"Olena Dubovyk","https:\u002F\u002Forcid.org\u002F0000-0002-7338-3167","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15481603.2026.2738981?needAccess=true",{"tldr":537,"method":538,"finding":539,"direction":94,"opportunity":540},"系统综述2015-2025年遥感农业干旱监测与预测的传感器、指数、建模及验证进展。","基于Web of Science与Scopus的181篇文献系统综述，涵盖光学\u002F","光学热红外平台占主导，微波土壤水分利用不足；复合指数与机器学习建模成为趋势。","微波土壤水分数据融合与深度学习干旱预测模型在数据稀缺区验证是潜在研究空白。","智慧农业 \u002F 农业物联网","2026-10-10T23:30:14.933078Z"]