[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4006":3,"related-4006":94},{"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":72,"slug":75,"view_count":76,"doi":77,"paper":78,"created_at":93},4006,"The impact of rural population shrinkage on cultivated land non-grain conversion—dual causal inference based on machine learning","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1898293","Against the backdrop of rapid advances in new urbanization and continued rural population contraction, the expansion of cropland non-grain use poses a potential challenge to national food security. Based on 3,598 observations from 257 prefecture-level cities in China over the 2011–2024 period, this study introduces a double machine learning approach and combines two-way fixed-effects models, threshold models, and spatial Durbin models to systematically examine the mechanisms through which rural population contraction affects cropland non-grain use. The results show: (1) rural population contraction significantly raises the degree of cropland non-grain use: for every one-unit increase in rural population contraction, cropland non-grain use rises by 0.089 units. (2) The relationship exhibits nonlinear characteristics, with per-mu grain returns serving as the threshold variable. As per-mu grain returns increase, the effect follows a pattern of “strong promotion–weak promotion–significant inhibition.” (3) Heterogeneity results show that the promoting effect of rural population decline on cropland non-grainization is more pronounced in eastern regions and major grain-marketing areas. (4) Spatial econometric analysis confirms that rural population decline exhibits significant positive spatial spillover effects, with the magnitude of the indirect spillover effect substantially exceeding that of the local direct effect. These findings are expected to provide empirical evidence and decision-making references for advancing differentiated and tiered governance of cropland non-grainization and strengthening food security.","在新型城镇化快速推进与乡村人口持续收缩的背景下，耕地非粮化扩张对国家粮食安全构成潜在挑战。本研究基于2011—2024年中国257个地级市的3598个观测样本，引入双重机器学习方法，并结合双向固定效应模型、门槛模型和空间杜宾模型，系统考察乡村人口收缩影响耕地非粮化的作用机制。结果表明：(1)乡村人口收缩显著提高耕地非粮化程度：乡村人口收缩每增加一个单位，耕地非粮化程度上升0.089个单位。(2)二者关系呈现非线性特征，以亩均粮食收益为门槛变量，随着亩均粮食收益提高，其作用表现为“强促进—弱促进—显著抑制”的演变规律。(3)异质性结果显示，乡村人口减少对耕地非粮化的促进效应在东部地区与粮食主销区更为明显。(4)空间计量分析证实，乡村人口减少具有显著的正向空间溢出效应，其间接溢出效应强度明显超过本地直接效应。上述发现可为推进耕地非粮化差异化、分层化治理与强化粮食安全保障提供经验证据与决策参考。",null,"Frontiers in Sustainable Food Systems","2026-10-01T00:00:00Z","论文",10,false,85,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,23,18,14,8,1,"基于257个地级市面板数据与双重机器学习因果推断，量化乡村人口收缩对耕地非粮化的非线性与空间溢出影响，方法新颖、结论对差异化耕地治理有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"粮食安全","机器学习","空间溢出效应","耕地非粮化","乡村人口收缩",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,58,65],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},15,true,[49],"machine learning",[28,49],[28],{"code":53,"score":21,"supported":47,"title":54,"abstract":56,"tags":57},"I7",[55],"causal inference",[],[],{"code":59,"score":60,"supported":47,"title":61,"abstract":62,"tags":64},"A8",7,[],[27,63],"food security",[27],{"code":66,"score":67,"supported":47,"title":68,"abstract":69,"tags":71},"I4",4,[],[70],"空间计量",[29],[73,74],"耕地非粮化 乡村人口收缩","双机器学习 粮食安全","耕地非粮化乡村人口收缩-4006",6,"10.3389\u002Ffsufs.2026.1898293",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":85,"oa_url":6,"card":86,"direction":90,"ingested_from":92},"W7215130187",[81,83],{"name":82,"orcid":9},"Wuwei Zhang",{"name":84,"orcid":9},"Shuo Zang",0,{"tldr":87,"method":88,"finding":89,"direction":90,"opportunity":91},"基于中国257个地级市数据，用双机器学习等方法研究农村人口收缩对耕地非粮化的影响。","双机器学习结合双向固定效应、门槛模型和空间杜宾模型，2011-2024年3598","农村人口收缩显著加剧耕地非粮化，存在以亩均粮食收益为门槛的非线性关系及正向空间溢出。","数字乡村与农业信息化","可探究数字技术或农业信息化能否调节人口收缩对非粮化的影响，并关注空间溢出的跨区治理机制。","openalex","2026-10-02T23:30:10.758271Z",{"total":76,"page":22,"page_size":76,"items":95},[96,168,276,339,391,449],{"id":97,"title":98,"url":99,"summary":100,"summary_zh":101,"content":9,"source_name":10,"source_url":99,"published_at":102,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":103,"score_detail":104,"sources":109,"tags":111,"primary_topic":115,"secondary_topics":116,"classification":117,"search_phrases":150,"slug":153,"view_count":154,"doi":155,"paper":156,"created_at":167},4867,"Climate policy uncertainty and domestic–international grain-market risk spillovers: a TVP-VAR-BK analysis with machine learning early warning","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1949662","Background Climate-policy transition uncertainty may affect grain-market risk transmission. Methods We use 2010–2025 daily data for Chinese and US corn and wheat, US soybean landed cost, and GCPU in a TVP-VAR-BK connectedness model and machine-learning warning exercise. Results TCI averages 15.29% and peaks at 57.32%; short-horizon transmission dominates; XGBoost achieves PR-AUC of 0.986\u002F0.949\u002F0.781 at 1\u002F5\u002F20-period horizons. Conclusion Climate-policy uncertainty is dynamically associated with grain-market risk and can support market-stability monitoring, but the evidence is associational rather than causal.","背景 气候政策转型不确定性可能影响粮食市场风险传导。方法 我们使用2010—2025年中国和美国玉米、小麦、美国大豆到岸成本以及GCPU的日度数据，构建TVP-VAR-BK关联性模型并进行机器学习预警演练。结果 TCI均值为15.29%，峰值为57.32%；短期传导占主导；XGBoost在1\u002F5\u002F20期预测期上的PR-AUC分别为0.986\u002F0.949\u002F0.781。结论 气候政策不确定性与粮食市场风险存在动态关联，可支持市场稳定性监测，但该证据属于关联性而非因果性。","2026-10-13T00:00:00Z",78,{"impact":105,"substance":17,"depth":19,"authority":106,"freshness":107,"relevant":22,"comment":108},16,13,9,"方法新颖、数据规模大且结论审慎，对粮食市场风险监测有参考价值，但属关联性研究而非因果证据，公共影响偏学术层面。",[110],{"name":10,"url":99},[27,28,112,113,114],"风险预警","农产品市场","气候政策",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":118},{"candidates":119,"agricultural_evidence":47},[120,125,132,139,146],{"code":33,"score":121,"supported":47,"title":122,"abstract":123,"tags":124},12,[49],[28],[28],{"code":126,"score":76,"supported":47,"title":127,"abstract":128,"tags":131},"A2",[],[129,130],"小麦","wheat",[],{"code":133,"score":76,"supported":47,"title":134,"abstract":135,"tags":138},"A3",[],[136,137],"玉米","corn",[],{"code":140,"score":76,"supported":47,"title":141,"abstract":142,"tags":145},"A4",[],[143,144],"大豆","soybean",[],{"code":59,"score":22,"supported":14,"title":147,"abstract":148,"tags":149},[],[],[27],[151,152],"气候政策不确定性 粮食市场 风险溢出","TVP-VAR-BK 粮食 机器学习 预警","气候政策不确定性粮食市场风险溢出-4867",2,"10.3389\u002Ffsufs.2026.1949662",{"doi":155,"openalex_id":157,"authors":158,"venue":10,"cited_by_count":85,"oa_url":99,"card":161,"direction":165,"ingested_from":92},"W7221588087",[159],{"name":160,"orcid":9},"Jia You",{"tldr":162,"method":163,"finding":164,"direction":165,"opportunity":166},"用TVP-VAR-BK与机器学习分析气候政策不确定性对中外粮食市场风险溢出的动态影响。","2010–2025日度玉米小麦大豆数据，TVP-VAR-BK溢出模型与XGBoo","总溢出指数均值15.29%、峰值57.32%，短期传导主导，XGBoost多期预警PR-AUC最高0","农业人工智能与决策模型","可引入因果推断或高频气候政策文本，检验气候政策不确定性对粮食市场的因果传导机制。","2026-10-14T23:30:06.565586Z",{"id":169,"title":170,"url":171,"summary":172,"summary_zh":173,"content":9,"source_name":174,"source_url":171,"published_at":175,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":176,"score_detail":177,"sources":180,"tags":182,"primary_topic":186,"secondary_topics":191,"classification":199,"search_phrases":250,"slug":253,"view_count":154,"doi":254,"paper":255,"created_at":275},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",81,{"impact":19,"substance":17,"depth":178,"authority":20,"freshness":21,"relevant":22,"comment":179},19,"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。",[181],{"name":174,"url":171},[183,27,28,184,185],"智慧农业","遥感监测","农业干旱",{"code":187,"slug":188,"name":189,"path":190,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[192,198],{"code":193,"slug":194,"name":195,"path":196,"parent_code":197},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":200},{"candidates":201,"agricultural_evidence":47},[202,208,214,221,225,232,239,245],{"code":187,"score":46,"supported":47,"title":203,"abstract":205,"tags":207},[204],"remote sensing",[206,204],"遥感",[184],{"code":193,"score":20,"supported":47,"title":209,"abstract":211,"tags":213},[210],"drought",[212,210],"干旱",[],{"code":33,"score":13,"supported":47,"title":215,"abstract":216,"tags":220},[],[217,28,49,218,219],"深度学习","deep learning","artificial intelligence",[28],{"code":59,"score":60,"supported":47,"title":222,"abstract":223,"tags":224},[],[27,63],[27],{"code":226,"score":76,"supported":47,"title":227,"abstract":228,"tags":231},"H2",[],[229,230],"土壤","soil",[],{"code":233,"score":234,"supported":47,"title":235,"abstract":236,"tags":238},"D",3,[],[237],"水产",[],{"code":240,"score":234,"supported":47,"title":241,"abstract":242,"tags":244},"H6",[],[243],"农业生态",[],{"code":246,"score":22,"supported":14,"title":247,"abstract":248,"tags":249},"I1",[],[],[183],[251,252],"遥感 农业干旱 监测","GIScience Remote Sensing 干旱指数","遥感农业干旱监测-4575","10.1080\u002F15481603.2026.2738981",{"doi":254,"openalex_id":256,"authors":257,"venue":174,"cited_by_count":85,"oa_url":267,"card":268,"direction":274,"ingested_from":92},"W7221081979",[258,261,264],{"name":259,"orcid":260},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":262,"orcid":263},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":265,"orcid":266},"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":269,"method":270,"finding":271,"direction":272,"opportunity":273},"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。","农业遥感与作物表型","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",{"id":277,"title":278,"url":279,"summary":280,"summary_zh":9,"content":9,"source_name":281,"source_url":279,"published_at":282,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":283,"score_detail":284,"sources":286,"tags":288,"primary_topic":292,"secondary_topics":295,"classification":297,"search_phrases":312,"slug":315,"view_count":234,"doi":316,"paper":317,"created_at":338},2290,"Optimizing biochar application strategies to improve soil properties and crop productivity in salt-affected croplands: Evidence from a global meta-analysis and machine learning approach","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104975","Optimizing biochar application strategies to improve soil properties and crop productivity in salt-affected croplands: Evidence from a global meta-analysis and machine learning approach。Agricultural Systems","Agricultural Systems","2026-09-13T00:00:00Z",82,{"impact":19,"substance":17,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":285},"全球荟萃分析与机器学习结合优化盐碱地生物炭施用策略，方法新颖、结论可靠，对盐碱地改良与产能提升有实质参考价值。",[287],{"name":281,"url":279},[27,28,289,290,291],"生物炭","土壤改良","盐碱地治理",{"code":226,"slug":230,"name":293,"path":294,"parent_code":197},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[296],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":298},{"candidates":299,"agricultural_evidence":47},[300,304,308],{"code":226,"score":20,"supported":47,"title":301,"abstract":302,"tags":303},[230],[230],[290,289,291],{"code":33,"score":121,"supported":47,"title":305,"abstract":306,"tags":307},[49],[49],[28],{"code":59,"score":22,"supported":14,"title":309,"abstract":310,"tags":311},[],[],[27],[313,314],"盐碱地治理 土壤改良 机器学习 粮食安全","盐碱地治理 土壤改良","盐碱地治理土壤改良机器学习粮食安全-2290","10.1016\u002Fj.agsy.2026.104975",{"doi":316,"openalex_id":318,"authors":319,"venue":281,"cited_by_count":85,"oa_url":279,"card":9,"direction":9,"ingested_from":92},"W7212358957",[320,322,324,326,328,330,333,336],{"name":321,"orcid":9},"Haoliang Deng",{"name":323,"orcid":9},"Xiaofan Pan",{"name":325,"orcid":9},"Guang Li",{"name":327,"orcid":9},"Bin Mu",{"name":329,"orcid":9},"Rang Xiao",{"name":331,"orcid":332},"Zheyuan Xiao","https:\u002F\u002Forcid.org\u002F0000-0003-0776-4508",{"name":334,"orcid":335},"Wei Pan","https:\u002F\u002Forcid.org\u002F0000-0003-4518-2082",{"name":337,"orcid":9},"Qinli Wang","2026-09-13T23:30:05.101959Z",{"id":340,"title":341,"url":342,"summary":343,"summary_zh":9,"content":9,"source_name":344,"source_url":9,"published_at":345,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":346,"score_detail":347,"sources":349,"tags":351,"primary_topic":354,"secondary_topics":355,"classification":357,"search_phrases":378,"slug":381,"view_count":67,"doi":9,"paper":382,"created_at":390},2272,"MDPI Remote Sensing 18(18):3107 MODIS NDVI与机器学习作物产量预测比较研究","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3107","Arai与Sanwal在MDPI Remote Sensing发表研究,基于印度2000-2026年州级数据,随机森林在保留时间顺序的走前验证中MAPE=11.4%、R²=0.982,优于梯度提升(MAPE=13.0%)。MODIS NDVI年最大值与粮食产量相关性r≈0.84,提供了可扩展的食品安全评估与农业决策支持实用路线。","MDPI Remote Sensing 18(18):3107","2026-09-09T16:00:00Z",77,{"impact":19,"substance":17,"depth":19,"authority":106,"freshness":76,"relevant":22,"comment":348},"基于印度2000-2026年州级数据的MODIS NDVI与机器学习产量预测对比研究，方法严谨、结论可靠，对农业遥感估产具有参考价值，但属境外案例、非国内政策或产业突破，适合作为专业精选而非头条。",[350],{"name":344,"url":342},[352,27,28,206,353],"农业人工智能","作物产量预测",{"code":187,"slug":188,"name":189,"path":190,"parent_code":37},[356],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":358},{"candidates":359,"agricultural_evidence":47},[360,364,368,374],{"code":187,"score":106,"supported":47,"title":361,"abstract":362,"tags":363},[204],[204],[353,206],{"code":33,"score":13,"supported":47,"title":365,"abstract":366,"tags":367},[28],[],[352,28],{"code":369,"score":234,"supported":47,"title":370,"abstract":371,"tags":373},"E4",[],[372],"食品安全",[],{"code":59,"score":22,"supported":14,"title":375,"abstract":376,"tags":377},[],[],[27],[379,380],"作物产量预测 农业人工智能 机器学习 粮食安全","作物产量预测 农业人工智能","作物产量预测农业人工智能机器学习粮食安全-2272",{"doi":9,"openalex_id":9,"authors":383,"venue":9,"cited_by_count":85,"oa_url":9,"card":384,"direction":272,"ingested_from":389},[],{"tldr":385,"method":386,"finding":387,"direction":272,"opportunity":388},"基于印度州级MODIS NDVI与机器学习比较作物产量预测，随机森林优于梯度提升。","用2000-2026年印度州级MODIS NDVI年最大值与随机森林、梯度提升走","随机森林MAPE=11.4%、R²=0.982，NDVI与产量相关性r≈0.84。","可在中国等区域验证NDVI-产量模型迁移性，并融合多源遥感与气象提升预测鲁棒性。","agent","2026-09-13T00:04:05.087805Z",{"id":392,"title":393,"url":394,"summary":395,"summary_zh":396,"content":9,"source_name":397,"source_url":394,"published_at":398,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":399,"score_detail":400,"sources":402,"tags":404,"primary_topic":406,"secondary_topics":407,"classification":408,"search_phrases":433,"slug":436,"view_count":85,"doi":437,"paper":438,"created_at":448},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",74,{"impact":121,"substance":17,"depth":19,"authority":121,"freshness":13,"relevant":22,"comment":401},"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[403],{"name":397,"url":394},[183,352,27,206,405],"气候分析",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":409},{"candidates":410,"agricultural_evidence":47},[411,415,421,425,429],{"code":33,"score":107,"supported":47,"title":412,"abstract":413,"tags":414},[219],[],[352],{"code":416,"score":234,"supported":47,"title":417,"abstract":418,"tags":420},"I5",[],[419],"农业传感",[],{"code":59,"score":22,"supported":14,"title":422,"abstract":423,"tags":424},[],[],[27],{"code":246,"score":22,"supported":14,"title":426,"abstract":427,"tags":428},[],[],[183],{"code":187,"score":22,"supported":14,"title":430,"abstract":431,"tags":432},[],[],[206],[434,435],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":437,"openalex_id":439,"authors":440,"venue":397,"cited_by_count":85,"oa_url":394,"card":443,"direction":165,"ingested_from":92},"W7223111766",[441],{"name":442,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":444,"method":445,"finding":446,"direction":272,"opportunity":447},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","2026-10-22T11:21:02.682497Z",{"id":450,"title":451,"url":452,"summary":453,"summary_zh":454,"content":9,"source_name":455,"source_url":452,"published_at":398,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":346,"score_detail":456,"sources":460,"tags":462,"primary_topic":465,"secondary_topics":468,"classification":471,"search_phrases":504,"slug":507,"view_count":85,"doi":508,"paper":509,"created_at":526},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy",{"impact":105,"substance":457,"depth":458,"authority":106,"freshness":13,"relevant":22,"comment":459},21,17,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[461],{"name":455,"url":452},[183,28,184,463,464],"土壤墒情","冬小麦",{"code":126,"slug":130,"name":129,"path":466,"parent_code":467},"\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[469,470],{"code":226,"slug":230,"name":293,"path":294,"parent_code":197},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":472},{"candidates":473,"agricultural_evidence":47},[474,480,484,488,493,500],{"code":126,"score":475,"supported":47,"title":476,"abstract":478,"tags":479},26,[477,130],"winter wheat",[129,477,130],[464],{"code":226,"score":46,"supported":47,"title":481,"abstract":482,"tags":483},[230],[229,230],[463],{"code":33,"score":46,"supported":47,"title":485,"abstract":486,"tags":487},[49],[28,49],[28],{"code":187,"score":13,"supported":47,"title":489,"abstract":490,"tags":492},[],[206,204,491],"multispectral",[184],{"code":494,"score":76,"supported":47,"title":495,"abstract":496,"tags":499},"H3",[],[497,498],"灌溉","irrigation",[],{"code":246,"score":67,"supported":47,"title":501,"abstract":502,"tags":503},[],[183],[183],[505,506],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":508,"openalex_id":510,"authors":511,"venue":455,"cited_by_count":85,"oa_url":452,"card":521,"direction":274,"ingested_from":92},"W7223156204",[512,515,517,519],{"name":513,"orcid":514},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":516,"orcid":9},"Yi Zhang",{"name":518,"orcid":9},"WEI Lei",{"name":520,"orcid":9},"Chengcai Zhang",{"tldr":522,"method":523,"finding":524,"direction":272,"opportunity":525},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","2026-10-22T11:20:18.887301Z"]