[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1616":3,"related-1616":106},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":50,"search_phrases":79,"slug":82,"view_count":83,"doi":84,"paper":85,"created_at":105},1616,"Climate change, vegetation dynamics, and maize production in Mozambique: evidence from method of moments quantile regression and machine learning","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1889991","This study examines the impacts of climate variability and vegetation dynamics on maize production across 10 provinces in Mozambique using a balanced panel dataset covering the period 2002–2023. Following confirmation of cross-sectional dependence, second-generation panel techniques are employed. The Kao and Westerlund cointegration tests reveal a stable long-run relationship among the variables. Results from the Method of Moments Quantile Regression (MMQREG) indicate significant distributional heterogeneity in climate–maize relationships. Rainfall and the Normalized Difference Vegetation Index (NDVI) positively influence maize output, with stronger effects observed in lower-producing provinces, whereas temperature exerts a negative and statistically significant impact, particularly at the median and upper quantiles. Mediation analysis further confirms that NDVI partially transmits the effects of climatic variables to maize production, providing empirical support for the proposed climate–vegetation–production pathway. Interaction analysis shows that adequate rainfall and healthier vegetation conditions help mitigate temperature-induced stress. In addition, regional analysis across Northern, Central, and Southern Mozambique reveals substantial spatial heterogeneity, with semi-arid provinces exhibiting greater climate sensitivity. Complementary machine learning analysis identifies the K-Nearest Neighbors (KNN) model as the most accurate predictor of maize production, while temperature, NDVI, and rainfall emerge as the most influential predictors. The findings underscore the importance of accounting for both distributional and spatial heterogeneity in climate–agriculture linkages and highlight the need for climate-smart, region-specific interventions and data-driven forecasting tools to strengthen maize productivity and resilience in Mozambique.","本研究利用2002—2023年间的平衡面板数据，考察了气候变率与植被动态对莫桑比克10个省份玉米产量的影响。在确认截面相依性后，采用第二代面板技术进行分析。Kao和Westerlund协整检验显示变量间存在稳定的长期关系。矩分位数回归（MMQREG）结果表明，气候—玉米关系存在显著的分位数异质性。降雨量和归一化植被指数（NDVI）对玉米产量具有正向影响，且在低产省份中效应更强；而温度则产生显著负向影响，尤其在中位数及上分位数处表现明显。中介分析进一步证实，NDVI部分传导了气候变量对玉米产量的影响，为所提出的“气候—植被—产量”路径提供了实证支持。交互作用分析表明，充足的降雨和良好的植被状况有助于缓解温度胁迫。此外，对莫桑比克北部、中部和南部的区域分析揭示了显著的空间异质性，半干旱省份表现出更高的气候敏感性。补充的机器学习分析识别出K近邻（KNN）模型为玉米产量预测精度最高的模型，而温度、NDVI和降雨量则是最具影响力的预测因子。研究结果强调了在气候—农业关联中考虑分布异质性与空间异质性的重要性，并凸显了采取气候智慧型、区域针对性干预措施及数据驱动的预测工具以增强莫桑比克玉米生产力和韧性的必要性。",null,"Frontiers in Sustainable Food Systems","2026-09-03T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,12,7,1,"论文运用多种计量与机器学习方法，揭示气候与植被对莫桑比克玉米产量的异质性影响，数据详实，结论具区域政策参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业遥感","机器学习","气候变化","玉米生产","莫桑比克",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[38,44],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,63,68,74],{"code":45,"score":57,"supported":58,"title":59,"abstract":61,"tags":62},15,true,[60],"machine learning",[27,60],[27],{"code":32,"score":64,"supported":58,"title":65,"abstract":66,"tags":67},14,[33],[34,33],[],{"code":39,"score":19,"supported":58,"title":69,"abstract":71,"tags":73},[70],"climate change",[72],"干旱",[28],{"code":75,"score":21,"supported":14,"title":76,"abstract":77,"tags":78},"I3",[],[],[26],[80,81],"农业遥感 机器学习 气候变化 玉米生产","农业遥感 机器学习","农业遥感机器学习气候变化玉米生产-1616",0,"10.3389\u002Ffsufs.2026.1889991",{"doi":84,"openalex_id":86,"authors":87,"venue":10,"cited_by_count":83,"oa_url":96,"card":97,"direction":103,"ingested_from":104},"W7207867677",[88,91,93],{"name":89,"orcid":90},"Faustino Dinis","https:\u002F\u002Forcid.org\u002F0000-0002-5192-0144",{"name":92,"orcid":9},"Jianbo Hu",{"name":94,"orcid":95},"Nazir Muhammad Abdullahi","https:\u002F\u002Forcid.org\u002F0000-0003-2149-0638","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1889991\u002Fpdf",{"tldr":98,"method":99,"finding":100,"direction":101,"opportunity":102},"研究气候变化与植被动态对莫桑比克玉米生产的影响，采用分位数回归和机器学习方法。","使用2002-2023年面板数据，矩分位数回归（MMQREG）和KNN机器学习模","降雨和NDVI正向影响玉米产量，温度负向影响，且存在分布和空间异质性。","农业人工智能与决策模型","可延伸研究气候变化下作物产量预测的机器学习模型优化，或区域适应性农业管理策略。","智慧农业 \u002F 农业物联网","openalex","2026-09-04T23:30:08.552197Z",{"total":107,"page":21,"page_size":107,"items":108},6,[109,197,253,319,409,488],{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":9,"source_name":115,"source_url":112,"published_at":116,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":117,"score_detail":118,"sources":122,"tags":124,"primary_topic":129,"secondary_topics":130,"classification":132,"search_phrases":180,"slug":183,"view_count":21,"doi":184,"paper":185,"created_at":196},5086,"Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23399627","Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.","摘要 气候变化和环境退化已成为二十一世纪最紧迫的全球性挑战之一，威胁着生态系统、生物多样性、人类健康和可持续发展。人工智能（AI）通过实现先进的数据分析、实时监测、预测建模和智能决策支持系统，改变了环境研究与管理的方式。本文综述批判性地考察了AI在应对环境和气候相关挑战中的应用，包括气候预测、空气和水质监测、生物多样性保护、精准农业、可再生能源优化、碳排放评估、废物管理和灾害风险降低。此外，本综述还指出了当前的研究空白，并强调了未来开发透明、节能且可解释的AI系统的机遇，以支持基于证据的环境决策。文章得出结论，AI已成为加速气候变化减缓、环境可持续发展以及实现联合国可持续发展目标（SDGs）的变革性工具。加强跨学科合作、负责任的AI治理和政策支持，对于最大化AI的环境效益并同时最小化其生态足迹至关重要。关键词：人工智能；气候变化；环境可持续性；机器学习；深度学习；遥感；物联网；可再生能源；生物多样性保护；可持续发展目标。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-15T00:00:00Z",70,{"impact":17,"substance":119,"depth":57,"authority":19,"freshness":120,"relevant":21,"comment":121},16,9,"综述性论文，覆盖AI在气候预测、精准农业、遥感监测等方向的应用，与农业信息化相关但偏宏观综述，增量有限。",[123],{"name":115,"url":112},[125,126,127,28,128],"智慧农业","农业人工智能","可持续发展","遥感监测",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[131],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":51,"locked":14,"version":52,"method":53,"evidence":133},{"candidates":134,"agricultural_evidence":58},[135,142,146,153,159,166,173],{"code":45,"score":17,"supported":58,"title":136,"abstract":138,"tags":141},[137],"artificial intelligence",[139,27,60,140,137],"深度学习","deep learning",[126],{"code":39,"score":57,"supported":58,"title":143,"abstract":144,"tags":145},[70],[28,70],[28],{"code":147,"score":20,"supported":58,"title":148,"abstract":149,"tags":152},"I1",[],[150,151],"精准农业","precision agriculture",[125],{"code":75,"score":20,"supported":58,"title":154,"abstract":155,"tags":158},[],[156,157],"遥感","remote sensing",[128],{"code":160,"score":107,"supported":58,"title":161,"abstract":162,"tags":165},"H4",[],[163,164],"碳排放","carbon emission",[],{"code":167,"score":107,"supported":58,"title":168,"abstract":169,"tags":172},"H6",[],[170,171],"生物多样性","biodiversity",[],{"code":174,"score":107,"supported":58,"title":175,"abstract":176,"tags":179},"I5",[],[177,178],"物联网","internet of things",[],[181,182],"AI 气候变化","人工智能 环境可持续","AI气候变化-5086","10.5281\u002Fzenodo.23399627",{"doi":184,"openalex_id":186,"authors":187,"venue":115,"cited_by_count":83,"oa_url":112,"card":190,"direction":103,"ingested_from":104},"W7222092855",[188],{"name":189,"orcid":9},"Deepmala Suryavanshi",{"tldr":191,"method":192,"finding":193,"direction":194,"opportunity":195},"综述AI在气候变化缓解与环境可持续领域的应用进展、挑战与未来方向。","文献综述，涵盖机器学习、深度学习、遥感、物联网等技术应用。","AI已成为推动气候减缓与可持续发展目标实现的变革性工具。","农业绿色发展与碳","可聚焦可解释、低能耗AI在农业碳减排与精准环境决策中的落地研究。","2026-10-16T23:30:15.059867Z",{"id":198,"title":199,"url":200,"summary":201,"summary_zh":202,"content":9,"source_name":203,"source_url":200,"published_at":204,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":205,"score_detail":206,"sources":209,"tags":211,"primary_topic":213,"secondary_topics":214,"classification":216,"search_phrases":233,"slug":236,"view_count":83,"doi":237,"paper":238,"created_at":252},4759,"Machine Learning-based Prediction of SVI Parameter in Maize: A Comparative Study and Feature Importance Analysis","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i10.2870-2879.9043","Background: Accurate estimation of seedling vigor indices (SVI) is critical for monitoring plant health and predicting yields in precision agriculture. This study comparatively evaluates the performance of different machine learning algorithms in estimating the SVI parameter. Methods: Germination data were collected from 39 maize genotypes under three different salinity conditions (0 mM, 100 mM, and 200 mM NaCl), including the parameters Gmax, MGT, GI, SL, RL, SdL, SMR, RMR, and SVI. The dataset was preprocessed through normalization to the [0–1] range. The SVI parameter was estimated using four different machine learning methods: Random Forest, CatBoost, LightGBM, and Support Vector Regression (SVR). To evaluate the performance of the models, both 10-fold cross-validation and 80%–20% hold-out validation techniques were employed. The model performance was examined using RMSE, MSE, MAE, and R² metrics. Results: The findings show that all models have a high explanatory power in the training dataset. Specifically, the CatBoost model showed the highest success in the training phase (R²=0.9962), whereas the SVR model achieved the highest generalization performance on the test dataset (R²=0.9712, RMSE=0.0582). Feature importance analysis results revealed that the 'App' parameter had the highest impact on the target variable in most models, but different structural interactions were prominent in the LightGBM model. The results demonstrate that machine learning techniques provide high accuracy in SVI estimation and that the tendency towards overfitting is limited. Conclusions: In conclusion, this study confirms that machine learning techniques are powerful and reliable tools for estimating spectral indices in precision agriculture applications. The integration of SVR and ensemble-based models into agricultural decision support systems is expected to significantly contribute to optimizing resource use and improving yield predictions.","背景：准确估计幼苗活力指数（SVI）对于精准农业中监测植物健康和预测产量至关重要。本研究比较评估了不同机器学习算法在估计SVI参数方面的性能。方法：从39个玉米基因型在三种不同盐度条件（0 mM、100 mM和200 mM NaCl）下收集发芽数据，包括Gmax、MGT、GI、SL、RL、SdL、SMR、RMR和SVI等参数。数据集通过归一化至[0–1]范围进行预处理。采用四种不同的机器学习方法估计SVI参数：随机森林、CatBoost、LightGBM和支持向量回归（SVR）。为评估模型性能，采用了10折交叉验证和80%–20%留出验证技术。使用RMSE、MSE、MAE和R²指标检验模型性能。结果：研究结果表明，所有模型在训练数据集中均具有较高的解释力。具体而言，CatBoost模型在训练阶段表现出最高的成功率（R²=0.9962），而SVR模型在测试数据集上取得了最高的泛化性能（R²=0.9712，RMSE=0.0582）。特征重要性分析结果显示，在大多数模型中，“App”参数对目标变量的影响最大，但在LightGBM模型中不同的结构交互作用更为突出。结果表明，机器学习技术在SVI估计中提供了高精度，且过拟合倾向有限。结论：总之，本研究证实机器学习技术是精准农业应用中估计光谱指数的强大而可靠的工具。将SVR和基于集成的模型整合到农业决策支持系统中，预计将显著有助于优化资源利用和改善产量预测。","Turkish Journal of Agriculture - Food Science and Technology","2026-10-11T00:00:00Z",63,{"impact":207,"substance":17,"depth":119,"authority":19,"freshness":120,"relevant":21,"comment":208},8,"方法对比扎实、结论可靠，但属细分领域论文，产业影响有限，适合主题聚合而非头条精选。",[210],{"name":203,"url":200},[125,126,27,34,212],"耐盐育种",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[215],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":217},{"candidates":218,"agricultural_evidence":58},[219,225,229],{"code":45,"score":220,"supported":58,"title":221,"abstract":222,"tags":224},19,[60],[27,60,223],"decision support",[126,27],{"code":32,"score":57,"supported":58,"title":226,"abstract":227,"tags":228},[33],[34,33],[34],{"code":147,"score":20,"supported":58,"title":230,"abstract":231,"tags":232},[],[150,151],[125],[234,235],"玉米 SVI 机器学习","玉米 耐盐 幼苗活力指数","玉米SVI机器学习-4759","10.24925\u002Fturjaf.v14i10.2870-2879.9043",{"doi":237,"openalex_id":239,"authors":240,"venue":203,"cited_by_count":83,"oa_url":200,"card":247,"direction":101,"ingested_from":104},"W7221462188",[241,244],{"name":242,"orcid":243},"Mustafa Turan Arslan","https:\u002F\u002Forcid.org\u002F0000-0001-5498-6571",{"name":245,"orcid":246},"Gülay Zülkadir","https:\u002F\u002Forcid.org\u002F0000-0003-3488-4011",{"tldr":248,"method":249,"finding":250,"direction":101,"opportunity":251},"用四种机器学习模型基于39个玉米基因型盐胁迫发芽数据预测幼苗活力指数SVI。","39个玉米基因型在三种盐浓度下的发芽数据，归一化后用RF、CatBoost、Li","SVR泛化最优（R²=0.9712），CatBoost训练拟合最高；App参数对SVI影响最大。","可扩展到多作物多胁迫环境，结合光谱或图像表型数据提升SVI预测的田间适用性。","2026-10-12T23:30:44.762497Z",{"id":254,"title":255,"url":256,"summary":257,"summary_zh":9,"content":9,"source_name":258,"source_url":256,"published_at":204,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":259,"score_detail":260,"sources":263,"tags":265,"primary_topic":269,"secondary_topics":270,"classification":272,"search_phrases":293,"slug":296,"view_count":297,"doi":298,"paper":299,"created_at":318},4650,"A process-oriented framework for daily dynamic monitoring of summer maize drought: From meteorological stress to agricultural impact","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105011","A process-oriented framework for daily dynamic monitoring of summer maize drought: From meteorological stress to agricultural impact。Agricultural Systems","Agricultural Systems",81,{"impact":17,"substance":261,"depth":17,"authority":64,"freshness":13,"relevant":21,"comment":262},21,"核心期刊提出的面向过程的夏玉米干旱逐日动态监测框架，方法新颖、面向农业信息化应用，时效性强，值得进入每日精选。",[264],{"name":258,"url":256},[125,26,266,267,268],"夏玉米","农业气象","干旱监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[271],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":51,"locked":14,"version":52,"method":53,"evidence":273},{"candidates":274,"agricultural_evidence":58},[275,281,285,289],{"code":39,"score":276,"supported":58,"title":277,"abstract":279,"tags":280},13,[278],"drought",[278],[267,268],{"code":32,"score":19,"supported":58,"title":282,"abstract":283,"tags":284},[33],[33],[266],{"code":147,"score":21,"supported":14,"title":286,"abstract":287,"tags":288},[],[],[125],{"code":75,"score":21,"supported":14,"title":290,"abstract":291,"tags":292},[],[],[26],[294,295],"夏玉米 干旱 动态监测","气象胁迫 农业影响 监测框架","夏玉米干旱动态监测-4650",2,"10.1016\u002Fj.agsy.2026.105011",{"doi":298,"openalex_id":300,"authors":301,"venue":258,"cited_by_count":83,"oa_url":256,"card":9,"direction":317,"ingested_from":104},"W7221308782",[302,304,306,308,311,313,315],{"name":303,"orcid":9},"Yuanda Zhang",{"name":305,"orcid":9},"Peijuan Wang",{"name":307,"orcid":9},"Jun Zhu",{"name":309,"orcid":310},"Yang Li","https:\u002F\u002Forcid.org\u002F0000-0001-9021-0201",{"name":312,"orcid":9},"Dianchen Han",{"name":314,"orcid":9},"Dingrong Wu",{"name":316,"orcid":9},"Jianping Guo","农业遥感与作物表型","2026-10-11T23:30:03.734429Z",{"id":320,"title":321,"url":322,"summary":323,"summary_zh":324,"content":9,"source_name":325,"source_url":322,"published_at":326,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":259,"score_detail":327,"sources":329,"tags":331,"primary_topic":334,"secondary_topics":338,"classification":341,"search_phrases":385,"slug":388,"view_count":297,"doi":389,"paper":390,"created_at":408},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":17,"substance":18,"depth":220,"authority":64,"freshness":207,"relevant":21,"comment":328},"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。",[330],{"name":325,"url":322},[125,332,27,128,333],"粮食安全","农业干旱",{"code":75,"slug":335,"name":336,"path":337,"parent_code":49},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[339,340],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":342},{"candidates":343,"agricultural_evidence":58},[344,348,352,356,362,369,376,381],{"code":75,"score":57,"supported":58,"title":345,"abstract":346,"tags":347},[157],[156,157],[128],{"code":39,"score":64,"supported":58,"title":349,"abstract":350,"tags":351},[278],[72,278],[],{"code":45,"score":13,"supported":58,"title":353,"abstract":354,"tags":355},[],[139,27,60,140,137],[27],{"code":357,"score":20,"supported":58,"title":358,"abstract":359,"tags":361},"A8",[],[332,360],"food security",[332],{"code":363,"score":107,"supported":58,"title":364,"abstract":365,"tags":368},"H2",[],[366,367],"土壤","soil",[],{"code":370,"score":371,"supported":58,"title":372,"abstract":373,"tags":375},"D",3,[],[374],"水产",[],{"code":167,"score":371,"supported":58,"title":377,"abstract":378,"tags":380},[],[379],"农业生态",[],{"code":147,"score":21,"supported":14,"title":382,"abstract":383,"tags":384},[],[],[125],[386,387],"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":389,"openalex_id":391,"authors":392,"venue":325,"cited_by_count":83,"oa_url":402,"card":403,"direction":103,"ingested_from":104},"W7221081979",[393,396,399],{"name":394,"orcid":395},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":397,"orcid":398},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":400,"orcid":401},"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":404,"method":405,"finding":406,"direction":317,"opportunity":407},"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":410,"title":411,"url":412,"summary":413,"summary_zh":414,"content":9,"source_name":415,"source_url":412,"published_at":416,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":417,"score_detail":418,"sources":420,"tags":422,"primary_topic":426,"secondary_topics":429,"classification":432,"search_phrases":455,"slug":458,"view_count":297,"doi":459,"paper":460,"created_at":487},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":119,"substance":18,"depth":17,"authority":64,"freshness":120,"relevant":21,"comment":419},"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。",[421],{"name":415,"url":412},[27,423,424,128,425],"土壤有机碳","生态修复","干旱区农业",{"code":363,"slug":367,"name":427,"path":428,"parent_code":43},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[430,431],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":433},{"candidates":434,"agricultural_evidence":58},[435,439,443,447,451],{"code":363,"score":57,"supported":58,"title":436,"abstract":437,"tags":438},[367],[366,367],[423],{"code":39,"score":13,"supported":58,"title":440,"abstract":441,"tags":442},[],[28,72,70],[425],{"code":45,"score":120,"supported":58,"title":444,"abstract":445,"tags":446},[60],[],[27],{"code":75,"score":20,"supported":58,"title":448,"abstract":449,"tags":450},[],[156,157],[128],{"code":167,"score":21,"supported":14,"title":452,"abstract":453,"tags":454},[],[],[424],[456,457],"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":459,"openalex_id":461,"authors":462,"venue":415,"cited_by_count":83,"oa_url":412,"card":482,"direction":317,"ingested_from":104},"W7219075005",[463,465,468,470,473,475,478,480],{"name":464,"orcid":9},"Y. Chen",{"name":466,"orcid":467},"Haiyang Xi","https:\u002F\u002Forcid.org\u002F0000-0003-3158-8161",{"name":469,"orcid":9},"Meng Zhu",{"name":471,"orcid":472},"Wenju Cheng","https:\u002F\u002Forcid.org\u002F0000-0002-1822-5668",{"name":474,"orcid":9},"Yulian Hao",{"name":476,"orcid":477},"Linbo Qu","https:\u002F\u002Forcid.org\u002F0009-0007-4669-8407",{"name":479,"orcid":9},"Zhehui Wang",{"name":481,"orcid":9},"Bin Wang",{"tldr":483,"method":484,"finding":485,"direction":317,"opportunity":486},"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":489,"title":490,"url":491,"summary":492,"summary_zh":493,"content":9,"source_name":494,"source_url":491,"published_at":495,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":496,"score_detail":497,"sources":499,"tags":501,"primary_topic":504,"secondary_topics":505,"classification":507,"search_phrases":545,"slug":548,"view_count":297,"doi":549,"paper":550,"created_at":578},3878,"Climate Change and Sustainable Maize Production: Risks, Response and Resilience Pathways","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16192123","Climate change significantly threatens maize (Zea mays L.) production through rising temperatures, erratic precipitation, and extreme weather events, thereby jeopardising global food security. This review synthesises current knowledge on the impacts of climate change, abiotic stresses, crop simulation models, and adaptation strategies for maize. A bibliometric analysis of 485 articles published between 2015 and 2025 and indexed in the Web of Science was conducted using VOSviewer. The results showed a significant increase in publications (Mann–Kendall test, p = 0.0001; R2 = 0.8207), with dominant research themes shifting toward climate-smart agriculture and sustainable intensification. Heat stress, drought, and nutrient deficiencies were identified as the primary abiotic constraints. DSSAT-CERES-Maize and APSIM emerged as the most widely used models for assessing climate impacts and the effectiveness of adaptation strategies. Effective adaptation measures include the use of resilient varieties, optimised water management, conservation agriculture, and diversified cropping systems. Future research should focus on integrated adaptation approaches, improved model validation, and long-term field studies to enhance maize resilience. This study provides a comprehensive overview of the research landscape and highlights critical pathways for ensuring sustainable maize production under changing climate scenarios.","气候变化通过气温升高、降水异常和极端天气事件，严重威胁玉米（Zea mays L.）生产，进而危及全球粮食安全。本文综述了气候变化影响、非生物胁迫、作物模拟模型及玉米适应策略的现有知识。利用VOSviewer对2015至2025年间Web of Science收录的485篇文献进行了文献计量分析。结果表明，发文量显著增加（Mann–Kendall检验，p = 0.0001；R2 = 0.8207），主导研究主题转向气候智慧型农业和可持续集约化。热胁迫、干旱和养分缺乏被确定为主要的非生物限制因素。DSSAT-CERES-Maize和APSIM是评估气候影响及适应策略效果最广泛使用的模型。有效的适应措施包括使用抗逆品种、优化水分管理、保护性农业和多样化种植制度。未来研究应聚焦于综合适应方法、改进模型验证和长期田间试验，以增强玉米的恢复力。本研究全面概述了该领域的研究格局，并指出了在气候变化情景下确保玉米可持续生产的关键路径。","Agriculture","2026-09-30T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":64,"freshness":207,"relevant":21,"comment":498},"基于485篇文献的计量综述，系统梳理气候变化下玉米生产的风险与适应路径，方法规范、结论可靠，对农业科技与智慧农业领域有较高参考价值。",[500],{"name":494,"url":491},[125,34,28,502,503],"作物模型","适应策略",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[506],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":51,"locked":14,"version":52,"method":53,"evidence":508},{"candidates":509,"agricultural_evidence":58},[510,514,518,522,529,535,541],{"code":39,"score":17,"supported":58,"title":511,"abstract":512,"tags":513},[70],[28,72,70,278],[28],{"code":32,"score":57,"supported":58,"title":515,"abstract":516,"tags":517},[33],[34,33],[34],{"code":357,"score":107,"supported":58,"title":519,"abstract":520,"tags":521},[],[332,360],[],{"code":523,"score":107,"supported":58,"title":524,"abstract":525,"tags":528},"I7",[],[526,527],"文献计量","bibliometric",[],{"code":147,"score":530,"supported":58,"title":531,"abstract":532,"tags":534},4,[],[533],"smart agriculture",[125],{"code":536,"score":371,"supported":58,"title":537,"abstract":538,"tags":540},"H8",[],[539],"conservation agriculture",[],{"code":45,"score":21,"supported":14,"title":542,"abstract":543,"tags":544},[],[],[502],[546,547],"玉米 气候变化 适应策略","DSSAT APSIM 玉米","玉米气候变化适应策略-3878","10.3390\u002Fagriculture16192123",{"doi":549,"openalex_id":551,"authors":552,"venue":494,"cited_by_count":83,"oa_url":491,"card":573,"direction":103,"ingested_from":104},"W7214894179",[553,556,559,561,564,567,570],{"name":554,"orcid":555},"Magdoline Mustafa Ahmed Osman","https:\u002F\u002Forcid.org\u002F0009-0003-5743-9161",{"name":557,"orcid":558},"Ronald Kuunya","https:\u002F\u002Forcid.org\u002F0009-0007-3502-0297",{"name":560,"orcid":9},"Erastus Wasikoyo",{"name":562,"orcid":563},"András Tamás","https:\u002F\u002Forcid.org\u002F0000-0001-8175-3300",{"name":565,"orcid":566},"Árpád Illés","https:\u002F\u002Forcid.org\u002F0000-0001-6548-2926",{"name":568,"orcid":569},"Adrienn Széles","https:\u002F\u002Forcid.org\u002F0000-0003-4101-3177",{"name":571,"orcid":572},"Tamás Rátonyi","https:\u002F\u002Forcid.org\u002F0000-0001-9436-5446",{"tldr":574,"method":575,"finding":576,"direction":194,"opportunity":577},"综述气候变化对玉米生产的影响，通过文献计量分析揭示研究趋势与适应策略。","文献计量分析485篇文献，使用VOSviewer和DSSAT、APSIM模型。","热胁迫、干旱和营养缺乏是主要限制，气候智慧型农业和可持续集约化成为研究热点。","可探索综合适应策略的长期田间验证及模型改进，以增强玉米气候韧性。","2026-10-01T23:30:09.232711Z"]