[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2953":3,"related-2953":50},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":9,"paper":37,"created_at":49},2953,"Seasonal agricultural vulnerability in semi-arid Morocco: combining remote sensing and farmer knowledge to inform climate adaptation","https:\u002F\u002Fmel.cgiar.org\u002Freporting\u002Fdownloadmelspace\u002Fhash\u002F5daf70ceb0d05067d3d316f855cfa4f0","Seasonal agricultural vulnerability in semi-arid Morocco: combining remote sensing and farmer knowledge to inform climate adaptation。MELSpace (ICARDA (The International Center for Agricultural Research in Dry Areas))","半干旱摩洛哥的季节性农业脆弱性：结合遥感与农民知识为气候适应提供依据。MELSpace（ICARDA（国际干旱地区农业研究中心））",null,"MELSpace (ICARDA (The International Center for Agricultural Research in Dry Areas))","2026-09-17T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,18,13,8,1,"国际干旱农业研究机构成果，遥感与农户知识结合评估季节性脆弱性，方法有参考价值但属区域案例，影响力有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感","气候适应","农户知识","干旱农业","摩洛哥",[33,34],"ICARDA 摩洛哥 遥感 气候适应","农户知识 干旱农业 气候适应 摩洛哥","ICARDA摩洛哥遥感气候适应-2953",0,{"doi":9,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":36,"oa_url":6,"card":42,"direction":46,"ingested_from":48},"W7213576262",[40],{"name":41,"orcid":9},"Cesar Ivan Alvarez",{"tldr":43,"method":44,"finding":45,"direction":46,"opportunity":47},"结合遥感与农户知识评估摩洛哥半干旱区季节性农业脆弱性，以支持气候适应。","遥感植被指数与农户访谈\u002F地方知识结合，分析季节性脆弱性。","遥感与农户知识互补，可更全面识别半干旱区季节性农业脆弱性。","农业遥感与作物表型","可探索遥感指标与农户感知的定量耦合模型，用于气候适应决策支持。","openalex","2026-09-19T23:30:34.812798Z",{"total":51,"page":22,"page_size":51,"items":52},6,[53,114,145,175,203,231],{"id":54,"title":55,"url":56,"summary":57,"summary_zh":58,"content":9,"source_name":59,"source_url":56,"published_at":60,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":61,"score_detail":62,"sources":65,"tags":67,"search_phrases":71,"slug":74,"view_count":36,"doi":75,"paper":76,"created_at":113},2427,"Machine learning based precipitation modeling using multi satellite data for climate resilient water resource management in Bundelkhand India","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01047-x","Precipitation modeling can be improved by using spatially continuous climate data from satellite remote sensing; nevertheless, incorporating heterogeneous sensor-derived variables and understanding machine learning results continue to be significant hurdles. In this work, a multi-source climatic parameter-based satellite-driven machine learning system for precipitation prediction is presented. The dependent variable was precipitation, and the model inputs were satellite-derived predictors such as land surface temperature, atmospheric moisture, surface pressure, wind speed, relative humidity, soil wetness, and temporal indicators. Convolutional Neural Networks (CNN) and Extreme Gradient Boosting (XGBoost), two sophisticated machine learning models, were used to capture multiscale and nonlinear interactions between precipitation and climate factors. Standard statistical measures were used to evaluate the model's performance, and explicable machine learning methods were used to determine the relative significance of the input variables. The findings suggest that both models make good use of satellite-derived climate data, with CNN demonstrating a great capacity to learn intricate feature interactions and XGBoost demonstrating strong predictive ability. With R2 = 0.77, RMSE = 88.79, as well as MAE = 42.06, XGBoost scored far superior than CNN (R2 = 0.60, RMSE = 120.82, MAE = 73.46). According to the interpretability analysis, the main factors influencing precipitation variability are soil wetness, land surface temperature, and atmospheric moisture. The suggested system supports enhanced hydrological forecasting as well as sustainable water resource management by providing a transparent and scalable method for precipitation prediction, especially in areas with limited data. Overall, the findings show that a scalable and efficient framework for precipitation prediction in semi-arid, data-poor areas may be created by combining interpretable machine learning with multi-source Earth observation data. Graphical Abstract","利用卫星遥感提供的空间连续气候数据可以改进降水建模；然而，整合来自不同传感器的异构变量以及理解机器学习结果仍然是重大难题。本研究提出了一种基于多源气候参数的卫星驱动机器学习降水预测系统。因变量为降水，模型输入为卫星衍生的预测因子，包括地表温度、大气湿度、地表气压、风速、相对湿度、土壤湿度和时间指标。研究采用了两种先进的机器学习模型——卷积神经网络（CNN）和极端梯度提升（XGBoost），以捕捉降水与气候因子之间的多尺度和非线性交互作用。使用标准统计指标评估模型性能，并采用可解释机器学习方法确定输入变量的相对重要性。研究结果表明，两种模型均能有效利用卫星衍生的气候数据，其中CNN展现出学习复杂特征交互的强大能力，XGBoost则表现出强劲的预测性能。XGBoost的R² = 0.77、RMSE = 88.79、MAE = 42.06，显著优于CNN（R² = 0.60、RMSE = 120.82、MAE = 73.46）。可解释性分析表明，影响降水变率的主要因素为土壤湿度、地表温度和大气湿度。所提出的系统为降水预测提供了一种透明且可扩展的方法，有助于增强水文预报和可持续水资源管理，尤其在数据稀缺地区。总体而言，研究结果表明，将可解释机器学习与多源地球观测数据相结合，可为半干旱、数据匮乏地区构建一个可扩展且高效的降水预测框架。图形摘要","Discover Environment","2026-09-11T00:00:00Z",70,{"impact":17,"substance":18,"depth":63,"authority":20,"freshness":21,"relevant":22,"comment":64},17,"基于多源卫星遥感与可解释机器学习构建降水预测框架，方法对比与结论可靠，对半干旱缺数据区水资源管理有参考价值，但属区域性学术成果，公共影响有限。",[66],{"name":59,"url":56},[68,27,28,69,70],"农业人工智能","水资源管理","降水预测",[72,73],"农业人工智能 水资源管理 气候适应 降水预测","农业人工智能 水资源管理","农业人工智能水资源管理气候适应降水预测-2427","10.1007\u002Fs44274-026-01047-x",{"doi":75,"openalex_id":77,"authors":78,"venue":59,"cited_by_count":36,"oa_url":107,"card":108,"direction":46,"ingested_from":48},"W7212224443",[79,82,84,87,89,92,94,97,99,102,104],{"name":80,"orcid":81},"Pavan Kumar","https:\u002F\u002Forcid.org\u002F0000-0003-3653-8163",{"name":83,"orcid":9},"Megha Paul",{"name":85,"orcid":86},"Prashant K. Srivastava","https:\u002F\u002Forcid.org\u002F0000-0002-4155-630X",{"name":88,"orcid":9},"Manmohan Dobriyal",{"name":90,"orcid":91},"Yogeshwar Singh","https:\u002F\u002Forcid.org\u002F0000-0002-3324-9289",{"name":93,"orcid":9},"Manish Srivastav",{"name":95,"orcid":96},"Ajay Singh","https:\u002F\u002Forcid.org\u002F0000-0003-2933-4058",{"name":98,"orcid":9},"Abu Salim",{"name":100,"orcid":101},"Shams Tabrez Siddiqui","https:\u002F\u002Forcid.org\u002F0000-0002-6567-3383",{"name":103,"orcid":9},"Aasif Aftab",{"name":105,"orcid":106},"Benson Turyasingura","https:\u002F\u002Forcid.org\u002F0000-0003-1325-4483","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01047-x.pdf",{"tldr":109,"method":110,"finding":111,"direction":46,"opportunity":112},"用多源卫星数据和机器学习预测印度半干旱区降水，XGBoost优于CNN。","CNN与XGBoost，输入LST、湿度、风速等卫星变量，可解释性分析。","XGBoost预测最佳（R²=0.77），土壤湿度、地表温度、大气水汽最关键。","可迁移至其他数据稀缺区，融合多源遥感与可解释AI提升水文预报。","2026-09-14T23:30:25.935081Z",{"id":115,"title":116,"url":117,"summary":118,"summary_zh":9,"content":9,"source_name":119,"source_url":117,"published_at":120,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":122,"sources":127,"tags":129,"search_phrases":132,"slug":135,"view_count":36,"doi":136,"paper":137,"created_at":144},2054,"Quantifying Thermoregulation in Niche Construction of Apis mellifera as an Extended Phenotype to Map the Selection Pressure of Volatile Climate Using Hybrid CNN Bi-LSTM Architecture and Remote Sensing","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10938207\u002Fv1","Quantifying Thermoregulation in Niche Construction of Apis mellifera as an Extended Phenotype to Map the Selection Pressure of Volatile Climate Using Hybrid CNN Bi-LSTM Architecture and Remote Sensing。Research Square","Research Square","2026-09-08T00:00:00Z",52,{"impact":21,"substance":123,"depth":124,"authority":51,"freshness":125,"relevant":22,"comment":126},14,15,9,"以混合CNN-BiLSTM与遥感量化蜜蜂巢域构建的热调节，方法新颖但属预印本、应用面窄，适合主题聚合而非每日精选。",[128],{"name":119,"url":117},[130,68,27,131,28],"智慧农业","蜜蜂",[133,134],"农业人工智能 智慧农业 气候适应 蜜蜂","农业人工智能 智慧农业","农业人工智能智慧农业气候适应蜜蜂-2054","10.21203\u002Frs.3.rs-10938207\u002Fv1",{"doi":136,"openalex_id":138,"authors":139,"venue":119,"cited_by_count":36,"oa_url":143,"card":9,"direction":46,"ingested_from":48},"W7211926390",[140],{"name":141,"orcid":142},"M. R. K. Pathan","https:\u002F\u002Forcid.org\u002F0009-0002-1943-7942","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10938207\u002Flatest.pdf","2026-09-10T23:30:24.223473Z",{"id":146,"title":147,"url":148,"summary":149,"summary_zh":9,"content":9,"source_name":150,"source_url":9,"published_at":151,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":152,"score_detail":153,"sources":156,"tags":158,"search_phrases":162,"slug":165,"view_count":36,"doi":9,"paper":166,"created_at":174},3001,"UAV多光谱不同空间分辨率匹配春小麦多性状监测","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1811","天津师范大学张程程等联合天津市农科院农业资源与环境研究所，从原生0.07 m四波段UAV多光谱影像通过像素聚合重采样生成14种空间分辨率（0.07-3.03 m），耦合PROSAIL辐射传输模型与随机森林评估尺度依赖反演性能。研究揭示了叶面积指数（LAI）、叶绿素含量（Cab）和冠层水分含量（Cw）反演精度对空间分辨率的非单调响应，提出物候阶段自适应分辨率策略并开发Heterogeneity-Scale Game Model（HSGM）刻画最优聚合尺度形成机制。","MDPI Agronomy 16(18):1811","2026-09-15T00:00:00Z",74,{"impact":124,"substance":154,"depth":19,"authority":20,"freshness":51,"relevant":22,"comment":155},22,"方法新颖、数据扎实的作物遥感反演研究，对精准农业变量施药与无人机监测有参考价值，但属细分领域学术进展，公共影响有限。",[157],{"name":150,"url":148},[130,159,27,160,161],"精准农业","作物表型","春小麦",[163,164],"天津师范大学 春小麦 多光谱","UAV 多光谱 空间分辨率","天津师范大学春小麦多光谱-3001",{"doi":9,"openalex_id":9,"authors":167,"venue":9,"cited_by_count":36,"oa_url":9,"card":168,"direction":46,"ingested_from":173},[],{"tldr":169,"method":170,"finding":171,"direction":46,"opportunity":172},"用无人机多光谱重采样14种分辨率，结合PROSAIL与随机森林，研究春小麦多性状反演的空间尺度效应。","UAV四波段多光谱像素聚合重采样，耦合PROSAIL模型与随机森林反演LAI、C","反演精度对空间分辨率呈非单调响应，提出物候自适应分辨率策略与HSGM模型。","可探索不同作物与物候下最优分辨率普适规律，并将尺度自适应策略嵌入实时无人机监测系统。","agent","2026-09-20T00:03:08.168753Z",{"id":176,"title":177,"url":178,"summary":179,"summary_zh":9,"content":9,"source_name":180,"source_url":9,"published_at":181,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":182,"score_detail":183,"sources":186,"tags":188,"search_phrases":191,"slug":194,"view_count":36,"doi":9,"paper":195,"created_at":202},2999,"基于改进DeepLabv3+的高标准农田田间道路提取与结构指标量化框架","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1986","沈阳农业大学刘永生等开发了基于MobileNetV2改进DeepLabv3+的高标准农田田间道路提取轻量化框架，集成Normalization-based Attention Module与Content-Aware ReAssembly of FEatures。三次随机种子训练平均mIoU 93.34%、mPA 96.75%、精度98.90%，模型参数6.14M、推理速度17.04 FPS；沥青、混凝土、砾石道路宽度预测R²分别为0.650、0.486、0.662，宽度MAE 0.130\u002F0.140\u002F0.100 m。第二验证区域连通性指数从0.4682提升至0.4795，支持高标准农田田间道路高效、可量化、可追溯的验收检查。","MDPI Agriculture 16(18):1986","2026-09-16T00:00:00Z",77,{"impact":184,"substance":154,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":185},16,"方法有创新、指标详实，对高标准农田道路验收有实用价值，但属细分技术论文，影响面有限。",[187],{"name":180,"url":178},[130,68,189,27,190],"高标准农田","田间道路",[192,193],"沈阳农业大学 高标准农田 道路提取","DeepLabv3 田间道路 遥感","沈阳农业大学高标准农田道路提取-2999",{"doi":9,"openalex_id":9,"authors":196,"venue":9,"cited_by_count":36,"oa_url":9,"card":197,"direction":46,"ingested_from":173},[],{"tldr":198,"method":199,"finding":200,"direction":46,"opportunity":201},"提出改进DeepLabv3+轻量框架，提取高标准农田田间道路并量化结构指标。","MobileNetV2+NAM+CARAFE改进DeepLabv3+，多区域遥感","mIoU 93.34%，道路宽度预测R²最高0.662，连通性指数提升至0.4795。","可拓展至多作物、多地形道路提取，并结合时序遥感实现道路损毁动态监测。","2026-09-20T00:03:08.023498Z",{"id":204,"title":205,"url":206,"summary":207,"summary_zh":9,"content":9,"source_name":208,"source_url":9,"published_at":209,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":210,"score_detail":211,"sources":213,"tags":215,"search_phrases":219,"slug":222,"view_count":36,"doi":9,"paper":223,"created_at":230},2996,"基于高光谱遥感和跨区域迁移学习的冬小麦叶绿素含量评估","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7686128266574430720","甘肃农业大学李怡悦等与中国农业科学院作物科学研究所肖永贵、孟亚雄研究员团队合作，针对高光谱遥感跨区域叶绿素评估中因\"域偏移\"导致的模型泛化性能下降问题，提出一种稳健自适应迁移学习框架（RATL），通过自适应特征选择、特征权重调整与域适应训练3模块协同实现跨区域知识有效迁移。基于2023—2024年冬小麦灌浆后期新乡与周口两地共1491份冠层高光谱与叶片SPAD数据，设置4种场景比较RATL与直接迁移、迁移成分分析和相关对齐之间的精度。","智慧农业(中英文)2026,8(4):47-59","2026-09-16T13:43:00Z",76,{"impact":184,"substance":154,"depth":19,"authority":123,"freshness":51,"relevant":22,"comment":212},"方法新颖、数据扎实的跨区域叶绿素遥感评估研究，对智慧农业遥感应用有参考价值。",[214],{"name":208,"url":206},[130,27,216,217,218],"冬小麦","迁移学习","叶绿素监测",[220,221],"冬小麦 叶绿素 高光谱遥感","跨区域迁移学习 冬小麦","冬小麦叶绿素高光谱遥感-2996",{"doi":9,"openalex_id":9,"authors":224,"venue":9,"cited_by_count":36,"oa_url":9,"card":225,"direction":46,"ingested_from":173},[],{"tldr":226,"method":227,"finding":228,"direction":46,"opportunity":229},"提出RATL迁移学习框架，解决高光谱遥感跨区域冬小麦叶绿素评估的域偏移问题。","基于新乡与周口1491份冠层高光谱与SPAD数据，对比RATL与直接迁移、TCA","RATL通过自适应特征选择、权重调整与域适应训练，有效提升跨区域叶绿素评估泛化精度。","可探索多作物、多生育期及不同传感器间的迁移学习，构建通用跨域叶绿素评估模型。","2026-09-20T00:03:07.685193Z",{"id":232,"title":233,"url":234,"summary":235,"summary_zh":236,"content":9,"source_name":237,"source_url":234,"published_at":238,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":239,"score_detail":240,"sources":242,"tags":244,"search_phrases":247,"slug":250,"view_count":36,"doi":251,"paper":252,"created_at":273},2956,"Integrating AI and Earth Observation Data for Disaster Risk Reduction","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02595-8","Integrating AI and Earth Observation Data for Disaster Risk Reduction。Journal of the Indian Society of Remote Sensing","将人工智能与地球观测数据相结合以降低灾害风险。《印度遥感学会杂志》","Journal of the Indian Society of Remote Sensing","2026-09-18T00:00:00Z",62,{"impact":17,"substance":123,"depth":124,"authority":20,"freshness":21,"relevant":22,"comment":241},"AI与地球观测融合用于灾害风险降低的学术论文，与农业信息化相关但偏通用防灾，产业落地价值有限。",[243],{"name":237,"url":234},[68,245,27,246],"防灾减灾","地球观测",[248,249],"AI 地球观测 灾害风险","农业人工智能 地球观测 防灾减灾 遥感","AI地球观测灾害风险-2956","10.1007\u002Fs12524-026-02595-8",{"doi":251,"openalex_id":253,"authors":254,"venue":237,"cited_by_count":36,"oa_url":9,"card":267,"direction":272,"ingested_from":48},"W7213552966",[255,258,261,264],{"name":256,"orcid":257},"Surajit Ghosh","https:\u002F\u002Forcid.org\u002F0000-0002-3928-2135",{"name":259,"orcid":260},"Md. Munsur Rahman","https:\u002F\u002Forcid.org\u002F0000-0002-9922-0374",{"name":262,"orcid":263},"Fasikaw A. Zimale","https:\u002F\u002Forcid.org\u002F0000-0001-9778-2712",{"name":265,"orcid":266},"Rajib Shaw","https:\u002F\u002Forcid.org\u002F0000-0003-3153-1800",{"tldr":268,"method":269,"finding":270,"direction":46,"opportunity":271},"综述AI与地球观测数据融合用于灾害风险减少的研究进展。","综述AI与地球观测数据融合方法。","AI与地球观测融合可提升灾害风险监测与评估能力。","可探索AI与遥感融合在农业灾害风险预警与保险中的具体应用。","数字乡村与农业信息化","2026-09-19T23:30:40.818538Z"]