[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3568":3,"related-3568":96},{"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":71,"slug":74,"view_count":75,"doi":76,"paper":77,"created_at":95},3568,"Interpretable multi-index framework for extracting dry-season built-up areas: benchmarking machine learning with Sentinel-2","https:\u002F\u002Fdoi.org\u002F10.4995\u002Fraet.2027.25885","Spectral similarity between built-up surfaces and exposed dry soil significantly constrains built-up area extraction during dry seasons. Although machine-learning techniques can address this challenge by modeling complex spectral relationships, they generally require sufficiently large and representative labeled training datasets. This study therefore investigates a less data-demanding, rule-based multi-index approach for dry-season built-up mapping, aiming to reduce reliance on large labeled training datasets while maintaining effective classification performance. Sentinel-2A imagery acquired in April 2024 was analyzed, and temporal validation was conducted for 2020, 2022, and 2024. Among the tested combinations, the Built-up Area Extraction Index (BAEI), Dry BareSoil Index (DBSI), and Normalized Difference Vegetation Index (NDVI) achieved the highest performance, yielding 95 % overall accuracy and a Kappa coefficient of 0.89. This represents a substantial improvement over singleindex BAEI (80 % overall accuracy; Kappa 0.55), increasing the built-up user’s accuracy from 61 % to 92 %. Multitemporal validation confirmed that the optimized BAEI–DBSI–NDVI decision rules remained stable across the 2020–2024 dry-season images without recalibration, achieving 93–96 % overall accuracy and a built-up F1-score of 84–93 %. Spatial analysis demonstrated robust performance across peripheral and agricultural zones, with moderate variability in dense urban cores due to spectral heterogeneity. To evaluate the operational robustness of the proposed framework, it was benchmarked against machine-learning classifiers, specifically Support Vector Machine and Random Forest (RF). Under the specific conditions of this study, the proposed rule-based method (95 % accuracy) marginally outperformed both SVM (93 %) and RF (92 %), while offering greater transparency and reducing dependency on large, labeled training datasets. Furthermore, feature importance analysis confirmed the critical role of these selected indices in resolving spectral confusion. These findings suggest that the proposed framework offers a transparent and computationally efficient approach for dry-season urban monitoring in tropical coastal environments similar to Visakhapatnam.","建成表面与裸露干土之间的光谱相似性显著制约了旱季建成区提取。尽管机器学习技术可通过建模复杂光谱关系来应对这一挑战，但其通常需要足够大且具有代表性的标记训练数据集。因此，本研究探讨了一种对数据需求较低、基于规则的多指数方法用于旱季建成区制图，旨在减少对大规模标记训练数据集的依赖，同时保持有效的分类性能。研究分析了2024年4月获取的Sentinel-2A影像，并对2020年、2022年和2024年进行了时间验证。在测试的组合中，建成区提取指数（BAEI）、干裸土指数（DBSI）和归一化差异植被指数（NDVI）表现最佳，总体精度达95%，Kappa系数为0.89。相较于单一指数BAEI（总体精度80%；Kappa 0.55），这一结果有显著提升，建成区用户精度从61%提高至92%。多时相验证证实，优化后的BAEI–DBSI–NDVI决策规则在2020—2024年旱季影像上无需重新校准即可保持稳定，总体精度达93%—96%，建成区F1分数为84%—93%。空间分析表明，该方法在外围和农业区域表现稳健，而在密集城市核心区由于光谱异质性存在中等程度变异。为评估所提框架的业务化稳健性，将其与机器学习分类器进行了基准比较，具体为支持向量机（SVM）和随机森林（RF）。在本研究的特定条件下，所提出的基于规则的方法（95%精度）略优于SVM（93%）和RF（92%），同时具有更高的透明性并减少了对大规模标记训练数据集的依赖。此外，特征重要性分析证实了所选指数在解决光谱混淆方面的关键作用。这些发现表明，所提框架为类似维沙卡帕特南的热带沿海环境旱季城市监测提供了一种透明且计算高效的方法。",null,"Revista de teledetección: Revista de la Asociación Española de Teledetección","2026-09-25T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,9,1,"该研究提出可解释的多指数规则框架，在旱季建成区提取上以更少标注数据达到95%精度并优于SVM\u002FRF，方法新颖、验证充分，对农业遥感与乡村土地利用监测有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"Sentinel-2","机器学习","NDVI","遥感监测","建成区提取",{"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":46},[45,51,59,66],{"code":33,"score":17,"supported":46,"title":47,"abstract":49,"tags":50},true,[48],"machine learning",[28],[28],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":58},"I4",6,[],[56,57],"空间分析","spatial analysis",[],{"code":60,"score":61,"supported":46,"title":62,"abstract":63,"tags":65},"H2",3,[],[64],"soil",[],{"code":67,"score":61,"supported":14,"title":68,"abstract":69,"tags":70},"I3",[],[],[29,27,30],[72,73],"Sentinel-2 建成区提取","BAEI DBSI NDVI 旱季","Sentinel-2建成区提取-3568",2,"10.4995\u002Fraet.2027.25885",{"doi":76,"openalex_id":78,"authors":79,"venue":10,"cited_by_count":86,"oa_url":6,"card":87,"direction":93,"ingested_from":94},"W7214385607",[80,83],{"name":81,"orcid":82},"Sarah Sejari","https:\u002F\u002Forcid.org\u002F0009-0002-7300-9084",{"name":84,"orcid":85},"Vazeer Mahammood","https:\u002F\u002Forcid.org\u002F0000-0003-1667-5232",0,{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"提出可解释多指数规则框架，用Sentinel-2提取旱季建成区，性能优于机器学习。","Sentinel-2A影像，BAEI、DBSI、NDVI组合规则，对比SVM与随","BAEI-DBSI-NDVI规则达95%精度、Kappa 0.89，优于单指数及SVM\u002FRF且无需重","农业遥感与作物表型","可探索该规则框架向其他气候带与传感器迁移，并结合少量样本的半监督学习提升城市核心区精度。","农业人工智能与决策模型","openalex","2026-09-26T23:30:53.200163Z",{"total":53,"page":22,"page_size":53,"items":97},[98,191,257,358,431,510],{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":9,"source_name":104,"source_url":101,"published_at":105,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":106,"score_detail":107,"sources":110,"tags":112,"primary_topic":116,"secondary_topics":122,"classification":128,"search_phrases":167,"slug":170,"view_count":86,"doi":171,"paper":172,"created_at":190},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","2026-10-21T00:00:00Z",77,{"impact":108,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":109},16,"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。",[111],{"name":104,"url":101},[113,28,30,114,115],"智慧农业","土壤墒情","冬小麦",{"code":117,"slug":118,"name":119,"path":120,"parent_code":121},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[123,127],{"code":60,"slug":64,"name":124,"path":125,"parent_code":126},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":129},{"candidates":130,"agricultural_evidence":46},[131,137,143,147,154,161],{"code":117,"score":132,"supported":46,"title":133,"abstract":135,"tags":136},26,[134,118],"winter wheat",[119,134,118],[115],{"code":60,"score":138,"supported":46,"title":139,"abstract":140,"tags":142},15,[64],[141,64],"土壤",[114],{"code":33,"score":138,"supported":46,"title":144,"abstract":145,"tags":146},[48],[28,48],[28],{"code":67,"score":13,"supported":46,"title":148,"abstract":149,"tags":153},[],[150,151,152],"遥感","remote sensing","multispectral",[30],{"code":155,"score":53,"supported":46,"title":156,"abstract":157,"tags":160},"H3",[],[158,159],"灌溉","irrigation",[],{"code":162,"score":163,"supported":46,"title":164,"abstract":165,"tags":166},"I1",4,[],[113],[113],[168,169],"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":171,"openalex_id":173,"authors":174,"venue":104,"cited_by_count":86,"oa_url":101,"card":184,"direction":189,"ingested_from":94},"W7223156204",[175,178,180,182],{"name":176,"orcid":177},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":179,"orcid":9},"Yi Zhang",{"name":181,"orcid":9},"WEI Lei",{"name":183,"orcid":9},"Chengcai Zhang",{"tldr":185,"method":186,"finding":187,"direction":91,"opportunity":188},"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。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.887301Z",{"id":192,"title":193,"url":194,"summary":195,"summary_zh":196,"content":9,"source_name":197,"source_url":194,"published_at":198,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":86,"score_detail":199,"sources":201,"tags":203,"primary_topic":206,"secondary_topics":207,"classification":212,"search_phrases":234,"slug":237,"view_count":86,"doi":238,"paper":239,"created_at":256},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE","2026-10-20T00:00:00Z",{"impact":86,"substance":86,"depth":86,"authority":86,"freshness":86,"relevant":86,"comment":200},"研究聚焦埃塞俄比亚城市扩张与土地利用变化，属城市地理与遥感应用，与三农、农业信息化主题无直接关联，不建议进入每日精选。",[202],{"name":197,"url":194},[28,30,204,205],"土地利用","城市扩张",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[208],{"code":52,"slug":209,"name":210,"path":211,"parent_code":37},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":40,"locked":14,"version":41,"method":42,"evidence":213},{"candidates":214,"agricultural_evidence":46},[215,219,224,229],{"code":33,"score":138,"supported":46,"title":216,"abstract":217,"tags":218},[48],[28,48],[28],{"code":52,"score":21,"supported":46,"title":220,"abstract":221,"tags":223},[],[56,222,57],"GIS",[],{"code":67,"score":225,"supported":46,"title":226,"abstract":227,"tags":228},7,[],[150,151],[30],{"code":230,"score":22,"supported":14,"title":231,"abstract":232,"tags":233},"H6",[],[],[204],[235,236],"Gambella Town 城市扩张","Random Forest 土地利用分类","GambellaTown城市扩张-5485","10.1371\u002Fjournal.pone.0360419",{"doi":238,"openalex_id":240,"authors":241,"venue":197,"cited_by_count":86,"oa_url":194,"card":251,"direction":91,"ingested_from":94},"W7223022155",[242,245,247,249],{"name":243,"orcid":244},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":246,"orcid":9},"Goanar Jal Dup",{"name":248,"orcid":9},"Weyesa Merga Chemeda",{"name":250,"orcid":9},"Shankar Karuppannan",{"tldr":252,"method":253,"finding":254,"direction":91,"opportunity":255},"用机器学习与遥感分析埃塞俄比亚甘贝拉镇2005-2025年城市扩张与土地利用变化。","Landsat多时相影像、随机森林分类与GIS空间分析，结合277份问卷。","建成区从93公顷增至1956公顷，灌草地大幅转为建成区和农地。","可将随机森林时序分类与驱动力调查结合，扩展到更多非洲小城镇并耦合城市增长模拟。","2026-10-21T23:30:23.271293Z",{"id":258,"title":259,"url":260,"summary":261,"summary_zh":262,"content":9,"source_name":263,"source_url":260,"published_at":264,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":265,"score_detail":266,"sources":273,"tags":275,"primary_topic":278,"secondary_topics":282,"classification":289,"search_phrases":334,"slug":337,"view_count":75,"doi":338,"paper":339,"created_at":357},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":267,"substance":268,"depth":269,"authority":270,"freshness":271,"relevant":22,"comment":272},18,22,19,14,8,"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。",[274],{"name":263,"url":260},[113,276,28,30,277],"粮食安全","农业干旱",{"code":67,"slug":279,"name":280,"path":281,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[283,288],{"code":284,"slug":285,"name":286,"path":287,"parent_code":126},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":290},{"candidates":291,"agricultural_evidence":46},[292,296,302,309,315,319,325,330],{"code":67,"score":138,"supported":46,"title":293,"abstract":294,"tags":295},[151],[150,151],[30],{"code":284,"score":270,"supported":46,"title":297,"abstract":299,"tags":301},[298],"drought",[300,298],"干旱",[],{"code":33,"score":13,"supported":46,"title":303,"abstract":304,"tags":308},[],[305,28,48,306,307],"深度学习","deep learning","artificial intelligence",[28],{"code":310,"score":225,"supported":46,"title":311,"abstract":312,"tags":314},"A8",[],[276,313],"food security",[276],{"code":60,"score":53,"supported":46,"title":316,"abstract":317,"tags":318},[],[141,64],[],{"code":320,"score":61,"supported":46,"title":321,"abstract":322,"tags":324},"D",[],[323],"水产",[],{"code":230,"score":61,"supported":46,"title":326,"abstract":327,"tags":329},[],[328],"农业生态",[],{"code":162,"score":22,"supported":14,"title":331,"abstract":332,"tags":333},[],[],[113],[335,336],"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":338,"openalex_id":340,"authors":341,"venue":263,"cited_by_count":86,"oa_url":351,"card":352,"direction":189,"ingested_from":94},"W7221081979",[342,345,348],{"name":343,"orcid":344},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":346,"orcid":347},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":349,"orcid":350},"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":353,"method":354,"finding":355,"direction":91,"opportunity":356},"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。","2026-10-10T23:30:14.933078Z",{"id":359,"title":360,"url":361,"summary":362,"summary_zh":363,"content":9,"source_name":364,"source_url":361,"published_at":365,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":366,"score_detail":367,"sources":369,"tags":371,"primary_topic":375,"secondary_topics":376,"classification":380,"search_phrases":407,"slug":410,"view_count":22,"doi":411,"paper":412,"created_at":430},4527,"Socio‐Economic Impact of Sustainable Development of Water Resources in the Semi‐Arid Regions of Village Raleras, Maharashtra: A Case Study Using Machine Learning Techniques","https:\u002F\u002Fdoi.org\u002F10.1111\u002F1477-8947.70100","ABSTRACT From a socio‐economic perspective, it is of interest to find out whether sustainable water resources can truly sustain rural livelihoods. Based on historical research, it is known that an improvement on resilient economic indicators such as ground water is often correlated with socio‐economic improvements and other aspects of rural livelihoods. The extent of factors affecting livelihood sustenance and improvement in the village of Raleras, Maharashtra, aids in creating a definite understanding of the facets correlating with groundwater restoration. Subsistence channels, arising as an outcome of the created impoundment structures, are tested for their impact on income gains and livelihood improvement. Examination on collected opinion was carried out to provide insights into the ‘before’ and ‘after’ (2015–2023) conditions. A five‐capitals framework has been employed for the interpretation of these results, along with a multivariate analysis to predict a causal framework for the improvement of livelihoods in agrarian economies through creation of check dams and watershed management. Time series study shows that there is greater variance in the NDVI progressively from 2021 to 2023 with values ranging from 0.25 to 0.50 in 2015, to 0.50–0.75 by 2021. Further increase in green patches in 2023 indicates enhanced vegetation health especially in the northern regions. Alternately, NDWI values elaborate initial severe water stress (−0.25 to 0.00) with progressive improvements in localized areas. The spatial variation map shows that eventually there is a notable improvement with more land supporting shrubbery along with more water bodies. The prominent increase in vegetation particularly in the later years can be attributed to water management initiatives undertaken in Raleras. The distributions of the annual average revenue from agricultural operations in 2015 (M = 146,907, SD = 95445.8) and 2023 (M = 706860.5, SD = 655748.3) showed a statistically significant improvement [ t (214) = 13.06, p \u003C 0.001]. The low p ‐value of 0.001205 ( p \u003C 0.05) of the educational facilities utilised indicates a statistically significant result, proving the association between educational quality and improved water facilities. All of the variables under evaluation, including the respondents' vehicle type, landholding type, and house type, have large and positive chi‐squared values, suggesting that the ‘before’ and ‘after’ distributions for each of these variables differ. Principal Component Analysis is used to summarise correlated livelihood indicators and reveals that income growth and livelihood improvement is multi‐dimensional. Multiple pathways to income change are identified through Partial Least Squares Regression, including agriculture and market‐linked channels. A Random Forest Regression Model is utilised to assess the relative importance of livelihood channels and baseline characteristics. Results indicate that improvement in irrigation access, crop diversification, housing quality, livestock ownership and female literacy are strongly associated with higher income gains. Hence, it may be said that the groundwater recharge as a result of construction of check dams must have improved agricultural activity or agricultural output in a material manner, and accordingly all capitals for Raleras have improved.","从社会经济视角来看，探究可持续水资源能否真正维持农村生计具有重要意义。基于历史研究可知，地下水等韧性经济指标的改善往往与社会经济进步及农村生计的其他方面相关联。马哈拉施特拉邦拉莱拉斯村影响生计维持与改善的因素程度，有助于明确理解与地下水恢复相关的各个维度。作为所建蓄水设施产物的生计渠道，其对面收入增长和生计改善的影响得到了检验。对收集到的意见进行了考察，以深入了解“之前”和“之后”（2015—2023年）的状况。研究采用了五资本框架来解读这些结果，并结合多变量分析，预测通过修建拦水坝和流域管理来改善农业经济生计的因果框架。时间序列研究表明，从2021年到2023年，NDVI的变异性逐渐增大，2015年数值范围为0.25至0.50，到2021年升至0.50至0.75。2023年绿色斑块进一步增加，表明植被健康状况改善，尤其是在北部地区。另一方面，NDWI值显示初期存在严重的水分胁迫（−0.25至0.00），后在局部地区逐步改善。空间变异图显示，最终出现了显著改善，更多土地能够支持灌木生长，同时水体也有所增加。植被的显著增加，尤其是在后期，可归因于拉莱拉斯村所实施的水资源管理举措。2015年（M = 146,907，SD = 95445.8）和2023年（M = 706860.5，SD = 655748.3）农业经营年均收入的分布显示出统计学上显著的改善[t(214) = 13.06，p \u003C 0.001]。所利用教育设施的p值为0.001205（p \u003C 0.05），表明结果具有统计学显著性，证明了教育质量与改善的供水设施之间的关联。所有受评估变量，包括受访者的车辆类型、土地持有类型和房屋类型，均具有较大且正向的卡方值，表明这些变量各自的“之前”和“之后”分布存在差异。","Natural Resources Forum","2026-10-07T00:00:00Z",73,{"impact":17,"substance":268,"depth":267,"authority":20,"freshness":271,"relevant":22,"comment":368},"以机器学习与遥感数据量化印度半干旱村庄水资源可持续性对生计的影响，方法扎实、结论具体，但属境外案例研究，对国内三农信息化仅有方法借鉴价值。",[370],{"name":364,"url":361},[372,28,373,30,374],"乡村振兴","水资源管理","灌溉农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[377],{"code":155,"slug":159,"name":378,"path":379,"parent_code":126},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":40,"locked":14,"version":41,"method":42,"evidence":381},{"candidates":382,"agricultural_evidence":46},[383,387,392,398,403],{"code":33,"score":21,"supported":46,"title":384,"abstract":385,"tags":386},[48],[],[28],{"code":155,"score":271,"supported":46,"title":388,"abstract":389,"tags":391},[],[390,159],"水资源",[373,374],{"code":393,"score":61,"supported":46,"title":394,"abstract":395,"tags":397},"C",[],[396],"livestock",[],{"code":399,"score":22,"supported":14,"title":400,"abstract":401,"tags":402},"G",[],[],[372],{"code":67,"score":22,"supported":14,"title":404,"abstract":405,"tags":406},[],[],[30],[408,409],"Raleras 马哈拉施特拉 水资源","check dam 地下水 生计","Raleras马哈拉施特拉水资源-4527","10.1111\u002F1477-8947.70100",{"doi":411,"openalex_id":413,"authors":414,"venue":364,"cited_by_count":86,"oa_url":9,"card":424,"direction":93,"ingested_from":94},"W7220922038",[415,418,420,422],{"name":416,"orcid":417},"Papiya Deb","https:\u002F\u002Forcid.org\u002F0000-0002-6554-5111",{"name":419,"orcid":9},"Roopsha Deb",{"name":421,"orcid":9},"Udaysinh Bhosale",{"name":423,"orcid":9},"Vemula Sai Vikas",{"tldr":425,"method":426,"finding":427,"direction":428,"opportunity":429},"用机器学习评估印度半干旱村庄水资源可持续开发对农村生计的社会经济影响。","NDVI\u002FNDWI遥感、五资本框架、多元分析与卡方检验，2015–2023数据。","水管理举措显著提升植被覆盖与农业收入，教育设施改善与水资源改善显著相关。","数字乡村与农业信息化","可结合遥感时序与农户调查，量化水利工程对生计的因果效应并推广至其他半干旱区。","2026-10-09T23:30:50.079298Z",{"id":432,"title":433,"url":434,"summary":435,"summary_zh":436,"content":9,"source_name":437,"source_url":434,"published_at":438,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":439,"score_detail":440,"sources":442,"tags":444,"primary_topic":448,"secondary_topics":449,"classification":452,"search_phrases":477,"slug":480,"view_count":75,"doi":481,"paper":482,"created_at":509},4172,"Machine learning reveals distinct drivers of soil organic carbon across the mountain-oasis-desert system of northwest China","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13021-026-00522-5","The mountain-oasis-desert system (MODS) is a distinctive landscape pattern shaped by interactions between geomorphological configurations and hydrological processes in arid regions. However, most soil organic carbon (SOC) mapping studies treat arid landscapes as homogeneous units, leaving limited quantitative evidence on how SOC patterns and environmental associations differ among MODS subsystems. As a critical ecosystem highly sensitive to climate change and human activities, understanding these responses is essential for regional soil carbon management and ecological restoration. In this study, we developed an ecosystem-based modeling framework in the Hexi Region (HXR) to construct separate SOC prediction models for different subsystems, aiming to investigate the spatiotemporal dynamics of SOC stocks and identify their key driving factors across MODS. The results show that incorporating remote sensing (RS) variables significantly improved model performance, increasing the mean R 2 from 0.51 to 0.58. Among the tested models, XGBoost achieved the best performance, providing an effective tool for SOC mapping in arid regions. Shapley additive explanations (SHAP) analysis of the best-performing XGBoost models identified distinct leading predictors across the MODS, including surface shortwave radiation (SRAD) in mountain and desert ecosystems and the difference vegetation index (DVI) in oasis ecosystems. Furthermore, the partial least squares path model (PLS-PM) revealed contrasting influences of climate change and human activities across ecological subsystems. Climatic factors exerted the strongest influence in the mountainous subsystem, whereas in the oasis subsystem intensive human management became the dominant driver of soil carbon cycling. From 2001 to 2023, SOC stocks in oasis regions increased significantly at a rate of 0.17 t C ha⁻ 1 year⁻ 1 , potentially associated with land-use change and agricultural management. Approximately 19.3% of the study area experienced significant SOC stocks declines. By resolving subsystem-specific SOC patterns and environmental associations, this study advances ecosystem-aware SOC assessment in arid regions. The resulting framework also provides a basis for differentiated carbon management across MODS.","山地-绿洲-荒漠系统（MODS）是干旱区地貌格局与水文过程相互作用形成的独特景观格局。然而，大多数土壤有机碳（SOC）制图研究将干旱区景观视为均质单元，关于MODS各子系统间SOC格局及其环境关联差异的定量证据仍然有限。作为对气候变化和人类活动高度敏感的关键生态系统，理解这些响应对于区域土壤碳管理和生态恢复至关重要。本研究在河西地区（HXR）构建了基于生态系统的建模框架，针对不同子系统分别建立SOC预测模型，旨在探究MODS中SOC储量的时空动态并识别其关键驱动因子。结果表明，引入遥感（RS）变量显著提升了模型性能，平均R²从0.51提高至0.58。在测试的模型中，XGBoost表现最佳，为干旱区SOC制图提供了有效工具。对最优XGBoost模型的SHAP（Shapley加法解释）分析识别出MODS各子系统的不同主导预测因子，其中山地和荒漠生态系统为地表短波辐射（SRAD），绿洲生态系统为差值植被指数（DVI）。此外，偏最小二乘路径模型（PLS-PM）揭示了气候变化和人类活动在各生态子系统中的差异化影响。气候因子在山地子系统中影响最强，而在绿洲子系统中，集约化人类管理成为土壤碳循环的主导驱动因素。2001年至2023年，绿洲区域SOC储量以0.17 t C ha⁻¹ year⁻¹的速率显著增加，这可能与土地利用变化和农业管理有关。约19.3%的研究区域经历了SOC储量的显著下降。通过解析子系统特定的SOC格局及其环境关联，本研究推进了干旱区生态系统感知型SOC评估，所构建的框架也为MODS差异化碳管理提供了依据。","Carbon Balance and Management","2026-10-03T00:00:00Z",79,{"impact":108,"substance":268,"depth":267,"authority":270,"freshness":21,"relevant":22,"comment":441},"基于XGBoost与SHAP的干旱区SOC分异驱动研究，方法新颖、数据翔实，对区域碳管理与生态修复有参考价值。",[443],{"name":437,"url":434},[28,445,446,30,447],"土壤有机碳","生态修复","干旱区农业",{"code":60,"slug":64,"name":124,"path":125,"parent_code":126},[450,451],{"code":284,"slug":285,"name":286,"path":287,"parent_code":126},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":453},{"candidates":454,"agricultural_evidence":46},[455,459,465,469,473],{"code":60,"score":138,"supported":46,"title":456,"abstract":457,"tags":458},[64],[141,64],[445],{"code":284,"score":13,"supported":46,"title":460,"abstract":461,"tags":464},[],[462,300,463],"气候变化","climate change",[447],{"code":33,"score":21,"supported":46,"title":466,"abstract":467,"tags":468},[48],[],[28],{"code":67,"score":225,"supported":46,"title":470,"abstract":471,"tags":472},[],[150,151],[30],{"code":230,"score":22,"supported":14,"title":474,"abstract":475,"tags":476},[],[],[446],[478,479],"河西地区 土壤有机碳 机器学习","山地-绿洲-荒漠 遥感 碳储量","河西地区土壤有机碳机器学习-4172","10.1186\u002Fs13021-026-00522-5",{"doi":481,"openalex_id":483,"authors":484,"venue":437,"cited_by_count":86,"oa_url":434,"card":504,"direction":91,"ingested_from":94},"W7219075005",[485,487,490,492,495,497,500,502],{"name":486,"orcid":9},"Y. Chen",{"name":488,"orcid":489},"Haiyang Xi","https:\u002F\u002Forcid.org\u002F0000-0003-3158-8161",{"name":491,"orcid":9},"Meng Zhu",{"name":493,"orcid":494},"Wenju Cheng","https:\u002F\u002Forcid.org\u002F0000-0002-1822-5668",{"name":496,"orcid":9},"Yulian Hao",{"name":498,"orcid":499},"Linbo Qu","https:\u002F\u002Forcid.org\u002F0009-0007-4669-8407",{"name":501,"orcid":9},"Zhehui Wang",{"name":503,"orcid":9},"Bin Wang",{"tldr":505,"method":506,"finding":507,"direction":91,"opportunity":508},"构建基于生态系统的机器学习框架，预测西北荒漠-绿洲-山地系统土壤有机碳并识别各子系统驱动因子。","XGBoost、SHAP、PLS-PM，结合遥感变量与2001-2023年多源数","遥感变量提升模型R²至0.58；山地和沙漠由辐射主导，绿洲由人类管理主导，绿洲碳储量年增0.17 t","可延伸研究绿洲农业管理措施对土壤碳的长期影响，并开发子系统自适应的碳监测与决策模型。","2026-10-04T23:30:24.631460Z",{"id":511,"title":512,"url":513,"summary":514,"summary_zh":515,"content":9,"source_name":516,"source_url":513,"published_at":517,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":518,"sources":520,"tags":522,"primary_topic":525,"secondary_topics":526,"classification":527,"search_phrases":542,"slug":545,"view_count":163,"doi":546,"paper":547,"created_at":571},4092,"Integrating multi-sensor optical and SAR data with machine learning ensembles for chickpea area estimation in the Krishna zone of Andhra Pradesh","https:\u002F\u002Fdoi.org\u002F10.33545\u002F2618060x.2026.v9.i10sa.6760","This study estimated chickpea (Cicer arietinum L.) cultivation area using geospatial technologies by combining optical and microwave remote sensing data in the Krishna zone of Andhra Pradesh during the rabi season of 2025-2026. The study employed 143 georeferenced ground truth observations for crop classification using Landsat-9 OLI, Sentinel-2 MSI, and Sentinel-1 SAR data. Two different workflows were used to assess chickpea area estimation, a Maximum Likelihood Classification (MLC) in ENVI software and an advanced machine learning ensemble classifier that combined Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM) algorithms applied to a 21 band multitemporal feature stack created in Google Earth Engine (GEE) and processed in R software. MLC technique estimated a total chickpea area of 87,004 ha with an overall accuracy of 88.23% ( κ=0.74 κ=0.74), the machine learning ensemble classifier significantly outperformed MLC, identifying a total area of 1,33,216 ha with a higher overall accuracy of 94.11% ( κ=0.88 κ=0.88). The multi-sensor ensemble approach significantly improved the spatial representation of chickpea distribution across the six districts of the Krishna Zone, according to a comparative analysis with Department of Agriculture (DoA) statistics. This highlights the potential of machine learning ensemble classification frameworks for reliable regional crop monitoring.","本研究利用地理空间技术，结合光学与微波遥感数据，对安得拉邦克里希纳地区2025—2026年rabi季鹰嘴豆（Cicer arietinum L.）种植面积进行了估算。研究采用143个地理参考地面真值观测数据，基于Landsat-9 OLI、Sentinel-2 MSI和Sentinel-1 SAR数据进行作物分类。采用两种不同工作流程评估鹰嘴豆面积估算：一种是在ENVI软件中进行的最大似然分类（MLC），另一种是先进的机器学习集成分类器，该分类器将随机森林（RF）、支持向量机（SVM）和梯度提升机（GBM）算法相结合，应用于在Google Earth Engine（GEE）中创建的21波段多时相特征堆栈，并在R软件中进行处理。MLC技术估算鹰嘴豆总面积为87,004 ha，总体精度为88.23%（κ=0.74）；机器学习集成分类器显著优于MLC，识别总面积为1,33,216 ha，总体精度更高，达94.11%（κ=0.88）。与农业部（DoA）统计数据相比，多传感器集成方法显著改善了克里希纳地区六个区鹰嘴豆分布的空间表征。这凸显了机器学习集成分类框架在可靠区域作物监测方面的潜力。","International Journal of Research in Agronomy","2026-10-01T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":519},"多源光学与SAR遥感结合机器学习集成分类估算鹰嘴豆面积，方法新颖、精度提升明显，对区域作物监测有参考价值，但属区域性研究，公共影响有限。",[521],{"name":516,"url":513},[113,28,30,523,524],"作物面积估算","鹰嘴豆",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":528},{"candidates":529,"agricultural_evidence":46},[530,534,538],{"code":33,"score":138,"supported":46,"title":531,"abstract":532,"tags":533},[48],[28,48],[28],{"code":67,"score":225,"supported":46,"title":535,"abstract":536,"tags":537},[],[150,151],[30],{"code":162,"score":22,"supported":14,"title":539,"abstract":540,"tags":541},[],[],[113],[543,544],"作物面积估算 智慧农业 机器学习 遥感监测","作物面积估算 智慧农业","作物面积估算智慧农业机器学习遥感监测-4092","10.33545\u002F2618060x.2026.v9.i10sa.6760",{"doi":546,"openalex_id":548,"authors":549,"venue":516,"cited_by_count":86,"oa_url":9,"card":566,"direction":91,"ingested_from":94},"W7215997699",[550,552,554,556,558,560,562,564],{"name":551,"orcid":9},"A Harshitha",{"name":553,"orcid":9},"Sunil M Kumar",{"name":555,"orcid":9},"AS Bharathi",{"name":557,"orcid":9},"TV Sridhar",{"name":559,"orcid":9},"IB Ramarao",{"name":561,"orcid":9},"Y Srilatha Ch. Vani",{"name":563,"orcid":9},"AK Mrudula",{"name":565,"orcid":9},"AD Grace",{"tldr":567,"method":568,"finding":569,"direction":91,"opportunity":570},"融合光学与SAR多时相数据，用机器学习集成分类器估算安得拉邦鹰嘴豆种植面积。","Landsat-9、Sentinel-2与Sentinel-1数据，GEE构建2","集成分类器精度94.11%（κ=0.88），优于最大似然分类的88.23%，面积估算更接近农业部门统","可探索多源遥感与集成学习在更多小宗作物、跨区域和跨年份的迁移能力与不确定性评估。","2026-10-03T23:30:35.319788Z"]