[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1541":3,"related-1541":82},{"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":18,"tags":20,"primary_topic":22,"secondary_topics":28,"classification":29,"search_phrases":48,"slug":49,"view_count":44,"doi":50,"paper":51,"created_at":81},1541,"Spatiotemporal Variations of NDVI and Their Responses to Climate Change and Human Activities in Guizhou, China (2000-2020)","https:\u002F\u002Fdoi.org\u002F10.15244\u002Fpjoes\u002F220950","The low spatial resolution of vegetation index data has limited comprehensive studies on the impacts of human activities on vegetation cover. In this context, the spatiotemporal variations of the Landsat-derived Normalized Difference Vegetation Index (NDVI) and its responses to air temperature, precipitation, and land cover changes in Guizhou Province, China, were analyzed from 2000 to 2020 at a spatial resolution of 30 m. The results showed: (1) NDVI exhibited a significant linear increasing trend, with a rate of change of 0.0033 per year (P\u003C0.05). (2) NDVI values were positively correlated with mean annual air temperatures and annual precipitation across 79.12% and 80.44% of the total provincial area, respectively. (3) The decreasing rate of NDVI values in constructed areas was more than twice the increasing rate of NDVI values in areas where mutual conversion among cultivated land, forest, and grassland was observed. Moreover, the decreasing rate of the difference between the mean annual NDVI of areas where cultivated land, forest, and grassland were converted to constructed area, and the initial mean annual NDVI was over ten times higher than the decreasing rate in areas where forest and grassland were converted to cultivated land. This research suggested that controlling the conversion of forests and grasslands to cultivated land and strengthening the regulation of human activities (especially urban expansion) are of great importance for improving the provincial vegetation coverage level and the stability of the vegetation ecosystem.","植被指数数据的低空间分辨率限制了人类活动对植被覆盖影响的全方位研究。在此背景下，本研究以30米空间分辨率分析了2000年至2020年间贵州省基于Landsat数据的归一化植被指数（NDVI）的时空变化及其对气温、降水和土地覆盖变化的响应。结果表明：（1）NDVI呈显著线性增加趋势，变化速率为每年0.0033（P\u003C0.05）。（2）全省79.12%和80.44%的面积上，NDVI值分别与年均气温和年降水量呈正相关。（3）建设用地中NDVI值的下降速率是耕地、林地和草地相互转换区域NDVI值上升速率的两倍以上。此外，耕地、林地和草地转为建设用地区域的平均年NDVI与初始平均年NDVI之差的下降速率，是林地和草地转为耕地区域下降速率的十倍以上。研究表明，控制林地和草地转为耕地，并加强对人类活动（尤其是城市扩张）的调控，对于提升省级植被覆盖水平和植被生态系统的稳定性具有重要意义。",null,"Polish Journal of Environmental Studies","2026-08-31T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究区域为贵州省，主题为植被覆盖变化，与农业信息化、智慧农业等核心领域不直接相关，故不入选。",[19],{"name":10,"url":6},[21],"遥感",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":30,"locked":14,"version":31,"method":32,"evidence":33},"classified","2026-10-22.2","rules",{"candidates":34,"agricultural_evidence":37},[35,42],{"code":23,"score":36,"supported":37,"title":38,"abstract":40,"tags":41},8,true,[39],"climate change",[],[],{"code":43,"score":44,"supported":14,"title":45,"abstract":46,"tags":47},"I3",1,[],[],[21],[],"SpatiotemporalVariationsofNDVIandTheirRe-1541","10.15244\u002Fpjoes\u002F220950",{"doi":50,"openalex_id":52,"authors":53,"venue":10,"cited_by_count":15,"oa_url":72,"card":73,"direction":79,"ingested_from":80},"W7204831490",[54,57,59,61,64,66,68,70],{"name":55,"orcid":56},"Tao Chen","https:\u002F\u002Forcid.org\u002F0009-0007-6224-2821",{"name":58,"orcid":9},"Zhonghua He",{"name":60,"orcid":9},"Jun Luo",{"name":62,"orcid":63},"Man Li","https:\u002F\u002Forcid.org\u002F0000-0003-4296-4097",{"name":65,"orcid":9},"Kun Yang",{"name":67,"orcid":9},"Ying Zeng",{"name":69,"orcid":9},"郭站群",{"name":71,"orcid":9},"Yuhua Yang","https:\u002F\u002Fwww.pjoes.com\u002Fpdf-220950-143796?filename=Spatiotemporal-Variations.pdf",{"tldr":74,"method":75,"finding":76,"direction":77,"opportunity":78},"利用30米分辨率Landsat NDVI数据，分析贵州2000-2020年植被时空变化及其对气候和人","基于Landsat NDVI数据，结合气温、降水和土地覆盖变化，采用趋势分析和相","贵州NDVI显著增加，与气温降水正相关；建设用地扩张导致植被下降速率远高于其他转换。","农业遥感与作物表型","可进一步研究高分辨率遥感在精细尺度上区分人类活动与气候变化对植被影响的定量方法。","农业绿色发展与碳","openalex","2026-09-03T23:31:08.761784Z",{"total":83,"page":44,"page_size":83,"items":84},6,[85,162,240,323,415,489],{"id":86,"title":87,"url":88,"summary":89,"summary_zh":90,"content":9,"source_name":91,"source_url":88,"published_at":92,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":93,"score_detail":94,"sources":100,"tags":102,"primary_topic":107,"secondary_topics":113,"classification":115,"search_phrases":138,"slug":141,"view_count":15,"doi":142,"paper":143,"created_at":161},5320,"Machine Learning for Drought Analysis and Forecasting: A Critical Review of Data Fusion, Extreme-Event Sensitivity, Explainability and Generalization","https:\u002F\u002Fdoi.org\u002F10.17485\u002Fijst\u002Fv19i36.1263","Background: Machine learning is increasingly applied to drought monitoring and forecasting, but studies differ widely in how drought is defined, what data are used, and how models are validated, which makes reported performance difficult to compare directly. Objectives: To explain why reported machine-learning drought-model performance varies across definitions, data sources and validation schemes, and to assess whether recent studies adequately address rare severe events and cross-domain transferability. Method: Journal articles published January 2018 to August 2026 were reviewed, starting from a 70-record high-relevance subset of a 359-record discovery export and extended through citation chaining. Publisher and DOI verification produced a final evidence base of 55 articles: 48 primary studies and 7 reviews. Target definition, input data, model family, validation design, metrics, class-imbalance treatment, explainability and external testing were compared across studies. Findings: Reported performance varied with the modelling task rather than with any single algorithm: Random Forest reached 97.7% AUC-ROC for agricultural-drought mapping in Queensland, SVM reached average R²=0.90 for hydrological drought in Algeria, and a CatBoost stacking model reached R²=0.9065 for remote-sensing-based drought monitoring. These scores are not directly comparable because targets, data and validation protocols differ. Recent studies increasingly test cross-product and cross-region transfer, use SHAP\u002FLIME explainability, and adopt extreme-aware losses, yet class-wise reporting of severe and extreme drought, ordinal-aware evaluation, and leakage-resistant temporal validation remain inconsistently applied across the 48 primary studies. Novelty: The review jointly evaluates predictive accuracy alongside rare-event sensitivity, ordinal drought severity, leakage-resistant validation, explainability, uncertainty and cross-domain generalization, criteria largely absent from earlier machine-learning drought reviews. Keywords: Drought Forecasting, Machine Learning, Remote Sensing, Explainable AI, Extreme Drought","背景：机器学习正日益应用于干旱监测与预报，但各研究在干旱定义、所用数据和模型验证方式上差异很大，导致所报告的模型性能难以直接比较。目标：解释为何已报告的机器学习干旱模型性能会因定义、数据来源和验证方案的不同而变化，并评估近期研究是否充分处理了罕见的严重事件和跨域可迁移性问题。方法：综述了2018年1月至2026年8月发表的期刊论文，起始于359条发现导出记录中70条高相关性记录的子集，并通过引文追溯加以扩展。经出版商和DOI核验，最终证据基础为55篇文章：48篇原始研究和7篇综述。比较了各研究的目标定义、输入数据、模型族、验证设计、指标、类别不平衡处理、可解释性和外部测试。发现：所报告的性能随建模任务而非任何单一算法而变化：随机森林在昆士兰农业干旱制图中达到97.7%的AUC-ROC，SVM在阿尔及利亚水文干旱中达到平均R²=0.90，CatBoost堆叠模型在基于遥感的干旱监测中达到R²=0.9065。这些分数不能直接比较，因为目标、数据和验证方案不同。近期研究越来越多地测试跨产品和跨区域迁移，使用SHAP\u002FLIME可解释性，并采用极端事件感知损失函数，然而在48篇原始研究中，严重和极端干旱的分类别报告、序数感知评估以及防泄漏的时间验证仍应用不一致。新颖性：本综述将预测准确性与罕见事件敏感性、序数干旱严重程度、防泄漏验证、可解释性、不确定性和跨域泛化等标准共同评估，这些标准在以往的机器学习干旱综述中大多缺失。关键词：干旱预报，机器学习，遥感，可解释人工智能，极端干旱","Indian Journal of Science and Technology","2026-10-18T00:00:00Z",71,{"impact":95,"substance":96,"depth":97,"authority":95,"freshness":98,"relevant":44,"comment":99},12,21,17,9,"系统综述机器学习干旱分析与预测，指出评估口径不一、极端事件敏感性与可解释性不足，对农业旱情监测信息化有参考价值，但属学术综述、影响面偏细分领域。",[101],{"name":91,"url":88},[103,104,105,21,106],"农业人工智能","可解释AI","极端天气","干旱监测",{"code":108,"slug":109,"name":110,"path":111,"parent_code":112},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[114],{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},{"status":30,"locked":14,"version":31,"method":32,"evidence":116},{"candidates":117,"agricultural_evidence":37},[118,125,132],{"code":108,"score":119,"supported":37,"title":120,"abstract":122,"tags":124},16,[121],"machine learning",[123,121],"机器学习",[103,104],{"code":23,"score":126,"supported":37,"title":127,"abstract":129,"tags":131},15,[128],"drought",[130,128],"干旱",[106],{"code":43,"score":133,"supported":37,"title":134,"abstract":135,"tags":137},7,[],[21,136],"remote sensing",[21],[139,140],"机器学习 干旱预测","遥感 干旱监测","机器学习干旱预测-5320","10.17485\u002Fijst\u002Fv19i36.1263",{"doi":142,"openalex_id":144,"authors":145,"venue":91,"cited_by_count":15,"oa_url":88,"card":155,"direction":77,"ingested_from":80},"W7222882764",[146,149,152],{"name":147,"orcid":148},"Ritik Mallik","https:\u002F\u002Forcid.org\u002F0009-0000-2785-0265",{"name":150,"orcid":151},"Dayal Kumar Behera","https:\u002F\u002Forcid.org\u002F0000-0002-9740-8066",{"name":153,"orcid":154},"Satarupa Mohanty","https:\u002F\u002Forcid.org\u002F0000-0002-1434-7158",{"tldr":156,"method":157,"finding":158,"direction":159,"opportunity":160},"综述55篇文献，比较机器学习干旱监测预报在定义、数据与验证上的差异。","系统综述2018-2026年55篇文献，比较目标定义、数据、模型、验证与可解释性","模型性能差异主要源于任务与验证设计而非算法，极端事件与泄漏防护评估仍不足。","农业人工智能与决策模型","可构建面向极端干旱的序数感知评估与抗泄漏时序验证框架，提升跨区域泛化。","2026-10-19T23:30:22.840943Z",{"id":163,"title":164,"url":165,"summary":166,"summary_zh":167,"content":9,"source_name":168,"source_url":165,"published_at":169,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":170,"score_detail":171,"sources":175,"tags":177,"primary_topic":183,"secondary_topics":184,"classification":189,"search_phrases":207,"slug":210,"view_count":211,"doi":212,"paper":213,"created_at":239},3784,"Analysis of the Growth Responses and Multispectral Reflectance Characteristics of Rehmannia glutinosa Plug Seedlings under Drought Stress","https:\u002F\u002Fdoi.org\u002F10.7235\u002Fhort.20260033","Rehmannia glutinosa is a high-value medicinal crop with extreme sensitivity to drought stress.However, non-destructive methods by which to assess the quality of plug seedlings remain limited.This study evaluated the spectral reflectance and vegetation index responses of R. glutinosa plug seedlings to drought stress using multispectral imaging.Drought stress significantly increased the leaf temperature and decreased shoot and root growth parameters compared to the control.NIR (near-infrared) band reflectance declined initially in the early stage of drought stress, with this followed by a significant decrease in visible bands (blue, green, red) as stress intensified.Among the five vegetation indices, the NGRDI (normalized green-red difference index) responded earliest at the early stage of drought stress, while the GNDVI (green normalized difference vegetation index), PSRI (plant senescence reflectance index), and NDRE (normalized difference red edge index) showed significant changes as stress intensified.The NDVI (normalized difference vegetation index) showed a delayed response and was significantly lower in the drought-stressed seedlings than in the control only after rewatering.These results suggest that multispectral imaging is a promising nondestructive tool for early drought stress detection and seedling quality assessments in medicinal plug seedling production systems.","地黄是一种高价值药用作物，对干旱胁迫极为敏感。然而，目前用于评估穴盘苗质量的无损方法仍然有限。本研究利用多光谱成像技术，评估了地黄穴盘苗对干旱胁迫的光谱反射率和植被指数响应。与对照相比，干旱胁迫显著提高了叶片温度，并降低了地上部和根系的生长参数。近红外（NIR）波段反射率在干旱胁迫早期首先下降，随后随着胁迫加剧，可见光波段（蓝、绿、红）反射率显著降低。在5个植被指数中，归一化绿红差异指数（NGRDI）在干旱胁迫早期响应最早，而绿归一化差异植被指数（GNDVI）、植物衰老反射指数（PSRI）和归一化红边差异指数（NDRE）随着胁迫加剧表现出显著变化。归一化差异植被指数（NDVI）响应滞后，仅在复水后干旱胁迫幼苗才显著低于对照。这些结果表明，多光谱成像在药用穴盘苗生产系统中用于干旱胁迫早期检测和种苗质量评估具有良好的无损应用前景。","Horticultural Science and Technology","2026-09-28T00:00:00Z",66,{"impact":36,"substance":172,"depth":119,"authority":173,"freshness":98,"relevant":44,"comment":174},20,13,"多光谱成像用于药用作物穴盘苗干旱胁迫早期无损检测，方法新颖、结论可靠，但属细分领域研究，产业影响有限。",[176],{"name":168,"url":165},[178,21,179,180,181,182],"智慧农业","干旱胁迫","多光谱成像","地黄","种苗质量",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[185],{"code":43,"slug":186,"name":187,"path":188,"parent_code":112},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":30,"locked":14,"version":31,"method":32,"evidence":190},{"candidates":191,"agricultural_evidence":37},[192,197,202],{"code":23,"score":193,"supported":37,"title":194,"abstract":195,"tags":196},14,[128],[130,128],[],{"code":43,"score":173,"supported":37,"title":198,"abstract":200,"tags":201},[199],"multispectral",[199],[180,21],{"code":203,"score":44,"supported":14,"title":204,"abstract":205,"tags":206},"I1",[],[],[178],[208,209],"地黄 穴盘苗 多光谱","地黄 干旱胁迫 植被指数","地黄穴盘苗多光谱-3784",5,"10.7235\u002Fhort.20260033",{"doi":212,"openalex_id":214,"authors":215,"venue":168,"cited_by_count":15,"oa_url":233,"card":234,"direction":77,"ingested_from":80},"W7214608276",[216,219,221,222,224,227,230,232],{"name":217,"orcid":218},"Ji Hye Yun","https:\u002F\u002Forcid.org\u002F0009-0004-9551-5353",{"name":220,"orcid":9},"So Yeong Hwang",{"name":220,"orcid":9},{"name":223,"orcid":9},"Jeong Kil Koo",{"name":225,"orcid":226},"Hyeong Eun Choi","https:\u002F\u002Forcid.org\u002F0009-0009-9318-9845",{"name":228,"orcid":229},"Seong Ha Park","https:\u002F\u002Forcid.org\u002F0009-0005-9189-8972",{"name":231,"orcid":9},"Seung Jae Hwang",{"name":231,"orcid":9},"https:\u002F\u002Fwww.hst-j.org\u002Farticles\u002Fpdf\u002Fbz4M\u002Fkshs-2026-044-00-15.pdf",{"tldr":235,"method":236,"finding":237,"direction":77,"opportunity":238},"用多光谱成像无损检测地黄穴盘苗干旱胁迫下的生长与光谱响应。","多光谱成像测反射率与5种植被指数，结合生长参数和叶温。","NGRDI最早响应早期干旱，GNDVI、PSRI、NDRE随胁迫加剧变化，NDVI复水后才显著。","可构建基于早期敏感植被指数的药用植物穴盘苗干旱预警与质量分级模型。","2026-09-29T23:30:21.147972Z",{"id":241,"title":242,"url":243,"summary":244,"summary_zh":245,"content":9,"source_name":246,"source_url":243,"published_at":247,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":248,"score_detail":249,"sources":253,"tags":255,"primary_topic":259,"secondary_topics":265,"classification":272,"search_phrases":299,"slug":302,"view_count":303,"doi":304,"paper":305,"created_at":322},3607,"Analysis of drought carry-over effect on apple trees using the Leafiness-LiDAR index","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104603","Apple trees ( Malus × domestica Borkh.) are widely grown in Mediterranean regions, where drought-induced irrigation restrictions can impair canopy development and productivity. This study assessed the Leafiness-LiDAR Index (LLI), derived from terrestrial Light Detection and Ranging (LiDAR) point clouds, as a proxy for Leaf Area Index (LAI) and as a non-destructive indicator of canopy response and recovery following water deficit. An ‘Opal®’ apple orchard was monitored over three growing seasons (2023–2025), encompassing one drought year and two recovery years. In 2023, full irrigation (FI; 480 mm) was compared with deficit irrigation (DI; 300 mm; 37.5% less water) under two planting densities (0.5 and 1.0 m tree spacing). Mixed-effects models revealed significant Year × Irrigation interactions for LAI (p = 0.006) and LLI (p \u003C 0.001), but not for yield (p = 0.654), suggesting a partial decoupling between structural and productive recovery. During the drought year, DI reduced LLI by 37% and yield by 47%. Following irrigation restoration, LLI differences persisted, particularly in 2024, indicating lasting structural effects of water deficit. Higher planting density promoted canopy recovery and increased yield. LLI proved to be a valuable, non-destructive estimator of LAI for detecting drought legacy effects and tracking canopy recovery, supporting precision orchard management under Mediterranean water scarcity.","苹果树（Malus × domestica Borkh.）广泛种植于地中海地区，该地区因干旱导致的灌溉限制会损害树冠发育和生产力。本研究评估了基于地面激光雷达（LiDAR）点云衍生的叶量-激光雷达指数（Leafiness-LiDAR Index, LLI）作为叶面积指数（Leaf Area Index, LAI）的替代指标，以及作为水分亏缺后树冠响应与恢复的非破坏性指标的可行性。对一个‘Opal®’苹果园进行了三个生长季（2023—2025年）的监测，涵盖一个干旱年和两个恢复年。2023年，在两种种植密度（株距0.5 m和1.0 m）下，比较了充分灌溉（FI；480 mm）与亏缺灌溉（DI；300 mm；减少37.5%水量）。混合效应模型显示，LAI（p = 0.006）和LLI（p \u003C 0.001）存在显著的年×灌溉交互作用，而产量（p = 0.654）则无此交互作用，表明结构与产量恢复之间存在部分脱耦。在干旱年，DI使LLI降低37%，产量降低47%。灌溉恢复后，LLI差异持续存在，尤其在2024年，表明水分亏缺具有持久的结构性影响。较高的种植密度促进了树冠恢复并提高了产量。LLI被证明是一种有价值的、非破坏性的LAI估算指标，可用于检测干旱遗留效应并追踪树冠恢复，为地中海水资源短缺条件下的精准果园管理提供支持。","Biosystems Engineering","2026-09-26T00:00:00Z",77,{"impact":126,"substance":250,"depth":251,"authority":193,"freshness":36,"relevant":44,"comment":252},22,18,"地中海苹果园三年试验证实LiDAR叶量指数可无损监测干旱遗留效应与冠层恢复，方法新颖、数据扎实，对果园精准灌溉有参考价值。",[254],{"name":246,"url":243},[178,256,21,257,258],"苹果","果园管理","精准灌溉",{"code":260,"slug":261,"name":262,"path":263,"parent_code":264},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[266,267],{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},{"code":268,"slug":269,"name":270,"path":271,"parent_code":27},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":30,"locked":14,"version":31,"method":32,"evidence":273},{"candidates":274,"agricultural_evidence":37},[275,281,285,291,295],{"code":260,"score":251,"supported":37,"title":276,"abstract":278,"tags":280},[277],"apple",[262,256,279,277],"orchard",[256],{"code":23,"score":193,"supported":37,"title":282,"abstract":283,"tags":284},[128],[130,128],[],{"code":268,"score":13,"supported":37,"title":286,"abstract":287,"tags":290},[],[288,289,269],"灌溉","水资源",[258],{"code":203,"score":44,"supported":14,"title":292,"abstract":293,"tags":294},[],[],[178],{"code":43,"score":44,"supported":14,"title":296,"abstract":297,"tags":298},[],[],[21],[300,301],"LiDAR 苹果园 干旱","Leafiness-LiDAR Index 苹果","LiDAR苹果园干旱-3607",4,"10.1016\u002Fj.biosystemseng.2026.104603",{"doi":304,"openalex_id":306,"authors":307,"venue":246,"cited_by_count":15,"oa_url":243,"card":317,"direction":77,"ingested_from":80},"W7214458453",[308,311,314],{"name":309,"orcid":310},"Leire Sandonís-Pozo","https:\u002F\u002Forcid.org\u002F0000-0003-2472-0259",{"name":312,"orcid":313},"José Antonio Martínez-Casasnovas","https:\u002F\u002Forcid.org\u002F0000-0003-1480-3632",{"name":315,"orcid":316},"Miquel Pascual","https:\u002F\u002Forcid.org\u002F0000-0002-3329-4207",{"tldr":318,"method":319,"finding":320,"direction":77,"opportunity":321},"用LiDAR叶量指数追踪苹果园干旱遗留效应与冠层恢复。","三年果园试验，地面LiDAR点云提取LLI，混合效应模型分析。","亏缺灌溉使LLI降37%、产量降47%，复水后结构差异仍持续。","可探索LLI与多源遥感融合，构建果园干旱遗留效应早期预警与精准补灌决策模型。","2026-09-27T23:30:09.907348Z",{"id":324,"title":325,"url":326,"summary":327,"summary_zh":328,"content":9,"source_name":329,"source_url":326,"published_at":330,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":170,"score_detail":331,"sources":333,"tags":335,"primary_topic":337,"secondary_topics":342,"classification":345,"search_phrases":387,"slug":390,"view_count":211,"doi":391,"paper":392,"created_at":414},3495,"Remote Sensing and GIS in Modern Drought Assessment: Bridging Conventional Methods and Emerging Technologies","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123","Drought is a complex and recurring hydroclimatic hazard that affects agricultural production, water resources, ecosystems and socioeconomic development. Effective drought assessment requires approaches capable of capturing its spatial and temporal variability and its multiple dimensions. This review examines the evolution of drought assessment from conventional drought indices to integrated approaches based on remote sensing and Geographic Information Systems (GIS), with an emphasis on their applications, strengths, limitations and emerging developments. Conventional indices, including the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Reconnaissance Drought Index (RDI) and Percent of Normal Precipitation Index (PNPI), remain widely used because of their established methodologies and long-term applicability. However, their dependence on meteorological observations can limit spatial characterisation and the representation of vegetation, soil moisture and other land-surface responses. Remote sensing provides spatially extensive and repeated observations of vegetation condition, land surface temperature, soil moisture, evapotranspiration and water-related conditions, enabling the development of satellite-derived drought indicators and indices. GIS further facilitates the integration, spatial analysis, visualisation, and mapping of drought-related information from multiple sources. The review also discusses hybrid approaches that combine climate-based indices with satellite-derived indicators, as well as drought monitoring platforms and multi-source assessment frameworks. Despite substantial advances, challenges remain regarding cloud contamination, differences in spatial and temporal resolution, data continuity, ground-based validation and uncertainty associated with multi-source datasets. Emerging machine learning, deep learning and artificial intelligence approaches offer opportunities for integrating heterogeneous datasets and improving drought characterisation and early warning. Overall, the integration of conventional observations, remote sensing, GIS and advanced analytical approaches provides a promising framework for more comprehensive drought monitoring and risk assessment under increasing climate variability and change.","干旱是一种复杂且反复出现的水文气候灾害，影响农业生产、水资源、生态系统和社会经济发展。有效的干旱评估需要能够捕捉其时空变异性和多维特征的方法。本文综述了干旱评估从传统干旱指数到基于遥感与地理信息系统（GIS）的综合方法的演变，重点探讨其应用、优势、局限性和新兴发展。传统指数，包括标准化降水指数（SPI）、标准化降水蒸散指数（SPEI）、帕尔默干旱强度指数（PDSI）、侦察干旱指数（RDI）和降水距平百分率指数（PNPI），因其方法成熟且具有长期适用性而仍被广泛使用。然而，这些指数对气象观测的依赖可能限制其空间表征能力以及对植被、土壤水分和其他陆面响应的刻画。遥感提供了对植被状况、地表温度、土壤水分、蒸散量及与水相关状况的大范围重复观测，使得卫星衍生的干旱指标和指数得以发展。GIS进一步促进了多来源干旱相关信息的整合、空间分析、可视化和制图。本文还讨论了将基于气候的指数与卫星衍生指标相结合的混合方法，以及干旱监测平台和多源评估框架。尽管取得了实质性进展，但在云污染、时空分辨率差异、数据连续性、地面验证以及多源数据集相关的不确定性方面仍存在挑战。新兴的机器学习、深度学习和人工智能方法为整合异质数据集、改进干旱表征和预警提供了机遇。总体而言，在气候变异性和变化日益加剧的背景下，传统观测、遥感、GIS和先进分析方法的整合为更全面的干旱监测和风险评估提供了一个有前景的框架。","Journal of Geography Environment and Earth Science International","2026-09-24T00:00:00Z",{"impact":95,"substance":251,"depth":119,"authority":95,"freshness":36,"relevant":44,"comment":332},"综述系统梳理遥感与GIS在干旱评估中的应用演进，方法学价值明确，但属综述类论文、非国内落地事件，影响力有限。",[334],{"name":329,"url":326},[178,103,21,336,106],"GIS",{"code":338,"slug":339,"name":340,"path":341,"parent_code":112},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[343,344],{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},{"code":43,"slug":186,"name":187,"path":188,"parent_code":112},{"status":30,"locked":14,"version":31,"method":32,"evidence":346},{"candidates":347,"agricultural_evidence":37},[348,356,360,364,371,378,383],{"code":338,"score":251,"supported":37,"title":349,"abstract":350,"tags":355},[336],[351,352,336,353,354],"地理信息","空间分析","spatial analysis","geographic information",[336],{"code":23,"score":126,"supported":37,"title":357,"abstract":358,"tags":359},[128],[130,128],[106],{"code":43,"score":126,"supported":37,"title":361,"abstract":362,"tags":363},[136],[21,136],[21],{"code":108,"score":13,"supported":37,"title":365,"abstract":366,"tags":370},[],[367,123,121,368,369],"深度学习","deep learning","artificial intelligence",[103],{"code":372,"score":83,"supported":37,"title":373,"abstract":374,"tags":377},"H2",[],[375,376],"土壤","soil",[],{"code":268,"score":83,"supported":37,"title":379,"abstract":380,"tags":382},[],[289,381],"evapotranspiration",[],{"code":203,"score":44,"supported":14,"title":384,"abstract":385,"tags":386},[],[],[178],[388,389],"遥感 GIS 干旱评估","卫星遥感 干旱指数","遥感GIS干旱评估-3495","10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123",{"doi":391,"openalex_id":393,"authors":394,"venue":329,"cited_by_count":15,"oa_url":326,"card":409,"direction":77,"ingested_from":80},"W7214156857",[395,397,399,401,403,405,407],{"name":396,"orcid":9},"V. Dhanalakshmi",{"name":398,"orcid":9},"N. Manikandan",{"name":400,"orcid":9},"V. S. Jinsy",{"name":402,"orcid":9},"K. V. Sumesh",{"name":404,"orcid":9},"P. Nideesh",{"name":406,"orcid":9},"P. S. Manju",{"name":408,"orcid":9},"N. Gopika",{"tldr":410,"method":411,"finding":412,"direction":77,"opportunity":413},"综述了从传统干旱指数到遥感、GIS及AI集成的现代干旱评估方法演进。","文献综述，对比SPI、SPEI等传统指数与遥感、GIS及混合方法。","遥感与GIS弥补传统指数空间局限，但云污染、分辨率差异和验证仍是挑战。","可探索多源遥感与机器学习融合的干旱早期预警，重点解决数据不确定性与地面验证。","2026-09-25T23:30:30.576065Z",{"id":416,"title":417,"url":418,"summary":419,"summary_zh":420,"content":9,"source_name":421,"source_url":418,"published_at":422,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":423,"score_detail":424,"sources":426,"tags":428,"primary_topic":433,"secondary_topics":436,"classification":438,"search_phrases":466,"slug":469,"view_count":211,"doi":470,"paper":471,"created_at":488},3191,"Spatial prediction of soil organic carbon stocks in Sudanese clay soils using regression kriging","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fsjss.2026.16733","Soil organic carbon (SOC) stocks are a critical component of terrestrial carbon pools, influencing soil quality, agricultural productivity, and climate change mitigation. This study aimed to map and improve spatial estimation of SOC stocks in Sudan’s Blue Nile clay soils using regression kriging (RK). The model integrated 554 spatially unique soil profiles with nine environmental covariates: precipitation, temperature, relative humidity, normalized difference vegetation index (NDVI), land use\u002Fcover, bare soil index (BSI), digital elevation model (DEM), LS-factor, and aspect. Spectral indices were derived from Landsat 9 imagery (April 2024), while climate and terrain data were obtained from CHIRPS\u002FWorldClim and SRTM (30 m). RK performance was robust, with spatial cross-validation R 2 = 0.72, RMSE = 8.4 Mg C ha −1 (29% of mean observed stock), and mean bias = −0.8 Mg C ha −1 . Predicted SOC stocks (0–30 cm) ranged from 12.4 to 51.2 Mg C ha −1 (mean 28.6 Mg C ha −1 ). NDVI, clay content, and topographic wetness index were the most influential predictors. Agricultural lands exhibited the highest stocks (51.2 Mg C ha −1 ), while bare lands had the lowest (14.2 Mg C ha −1 ). This study (1) applies spatially explicit validation for SOC mapping in Sudan’s Blue Nile region, (2) harmonizes legacy and contemporary soil data using equivalent soil mass correction, and (3) provides high-resolution SOC maps for climate-resilient agricultural planning. Findings support soil carbon management and climate mitigation in semi-arid regions.","土壤有机碳（SOC）储量是陆地碳库的重要组成部分，影响土壤质量、农业生产力及气候变化减缓。本研究旨在利用回归克里金（RK）方法对苏丹青尼罗河黏土区SOC储量进行制图并改进其空间估算。该模型整合了554个空间独立土壤剖面与9个环境协变量：降水、温度、相对湿度、归一化植被指数（NDVI）、土地利用\u002F覆盖、裸土指数（BSI）、数字高程模型（DEM）、LS因子和坡向。光谱指数源自Landsat 9影像（2024年4月），气候与地形数据分别来自CHIRPS\u002FWorldClim和SRTM（30 m）。RK表现稳健，空间交叉验证R²=0.72，RMSE=8.4 Mg C ha⁻¹（为实测储量均值的29%），平均偏差=−0.8 Mg C ha⁻¹。预测SOC储量（0–30 cm）范围为12.4–51.2 Mg C ha⁻¹（均值28.6 Mg C ha⁻¹）。NDVI、黏粒含量和地形湿度指数是最具影响力的预测因子。农地储量最高（51.2 Mg C ha⁻¹），裸地最低（14.2 Mg C ha⁻¹）。本研究（1）对苏丹青尼罗河地区SOC制图采用空间显式验证，（2）利用等效土壤质量校正协调历史与当代土壤数据，（3）为气候韧性农业规划提供高分辨率SOC图。研究结果支持半干旱地区的土壤碳管理与气候减缓。","Spanish Journal of Soil Science","2026-09-22T00:00:00Z",68,{"impact":36,"substance":96,"depth":97,"authority":173,"freshness":98,"relevant":44,"comment":425},"基于554个土壤剖面与多源遥感协变量的回归克里金制图研究，方法规范、验证充分，对半干旱区土壤碳管理与气候适应型农业规划有参考价值，但属区域性学术成果，公共影响有限。",[427],{"name":421,"url":418},[429,430,21,431,432],"农业遥感","气候变化","土壤碳汇","数字土壤制图",{"code":372,"slug":376,"name":434,"path":435,"parent_code":27},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[437],{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},{"status":30,"locked":14,"version":31,"method":32,"evidence":439},{"candidates":440,"agricultural_evidence":37},[441,445,449,456,461],{"code":372,"score":193,"supported":37,"title":442,"abstract":443,"tags":444},[376],[375,376],[],{"code":23,"score":13,"supported":37,"title":446,"abstract":447,"tags":448},[],[430,130,39],[430],{"code":450,"score":451,"supported":37,"title":452,"abstract":453,"tags":455},"J1",3,[],[454],"农业规划",[],{"code":43,"score":457,"supported":14,"title":458,"abstract":459,"tags":460},2,[],[],[429,21],{"code":462,"score":44,"supported":14,"title":463,"abstract":464,"tags":465},"H4",[],[],[431],[467,468],"苏丹青尼罗河 土壤有机碳 回归克里金","Landsat 9 土壤有机碳 空间预测","苏丹青尼罗河土壤有机碳回归克里金-3191","10.3389\u002Fsjss.2026.16733",{"doi":470,"openalex_id":472,"authors":473,"venue":421,"cited_by_count":15,"oa_url":418,"card":482,"direction":487,"ingested_from":80},"W7213971196",[474,476,478,480],{"name":475,"orcid":9},"Faroug A.H. Jadalla",{"name":477,"orcid":9},"Kolapo O. Oluwasemire",{"name":479,"orcid":9},"Abd Elmagid A. Elmobarak",{"name":481,"orcid":9},"Mohammed A. M. Mohammed Zein",{"tldr":483,"method":484,"finding":485,"direction":77,"opportunity":486},"用回归克里金结合多源环境协变量预测苏丹青尼罗河粘土区土壤有机碳储量。","554个土壤剖面与9个环境协变量，Landsat 9、CHIRPS\u002FWorldC","模型R²=0.72，NDVI、粘土含量和地形湿度指数影响最大，农地碳储量最高。","可引入时序遥感与机器学习提升半干旱区SOC动态预测，并耦合农业管理措施评估固碳潜力。","数字乡村与农业信息化","2026-09-22T23:30:31.028178Z",{"id":490,"title":491,"url":492,"summary":493,"summary_zh":9,"content":9,"source_name":494,"source_url":9,"published_at":495,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":496,"score_detail":497,"sources":499,"tags":501,"primary_topic":505,"secondary_topics":506,"classification":508,"search_phrases":534,"slug":537,"view_count":457,"doi":9,"paper":538,"created_at":546},3121,"Optimization of Farmland Management Zoning in the Black Soil Region: A Climate Adaptability Assessment Considering Crop Growth Response and Topographic Characteristics（黑土区农田管理分区优化：考虑作物生长响应与地形特征的气候适应性评估）","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3260","吉林农业大学 Han Yongqi 等联合中科院东北地理与农业生态研究所、东北农业大学在《Remote Sensing》18(18): 3260 发表论文（2026-09-21 发表）。针对精准农业管理分区对单日期影像依赖难以捕捉年际作物环境变化问题，研究评估 29 个特征组合（融合 Sentinel-2 多光谱、PCA、NDVI 和 DEM 数据）在黑土区友谊农场干旱、湿润和融合场景下的气候适应性。实施异构空间注意力网络（HSAN）和 K-means 聚类，以变异系数（CV）评估稳定性与适应性。结果显示 HSAN 在多源融合下优于 K-means，CV 分别为 11.303-14.774% 与 14.823-16.011%；多期 NDVI 数据是主导因素，相对 CV 减少 34.850-53.701%；DEM 贡献有限；PCA 增强稳定性；多期融合在极端气候年份提升分区生态一致性与适用性。","MDPI Remote Sensing","2026-09-21T00:00:00Z",79,{"impact":126,"substance":250,"depth":251,"authority":193,"freshness":13,"relevant":44,"comment":498},"黑土区精准农业管理分区研究，方法新颖、数据扎实，对农业遥感应用有参考价值。",[500],{"name":494,"url":492},[178,502,503,21,504],"精准农业","黑土区","管理分区",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[507],{"code":372,"slug":376,"name":434,"path":435,"parent_code":27},{"status":30,"locked":14,"version":31,"method":32,"evidence":509},{"candidates":510,"agricultural_evidence":37},[511,516,520,524,528],{"code":23,"score":193,"supported":37,"title":512,"abstract":514,"tags":515},[513],"气候适应",[513,130],[],{"code":372,"score":36,"supported":37,"title":517,"abstract":518,"tags":519},[376],[],[],{"code":203,"score":211,"supported":37,"title":521,"abstract":522,"tags":523},[],[502],[178,502],{"code":43,"score":303,"supported":37,"title":525,"abstract":526,"tags":527},[],[136],[21],{"code":529,"score":451,"supported":37,"title":530,"abstract":531,"tags":533},"H6",[],[532],"农业生态",[],[535,536],"黑土区 管理分区 遥感","Sentinel-2 黑土区 气候适应性","黑土区管理分区遥感-3121",{"doi":9,"openalex_id":9,"authors":539,"venue":9,"cited_by_count":15,"oa_url":9,"card":540,"direction":77,"ingested_from":545},[],{"tldr":541,"method":542,"finding":543,"direction":77,"opportunity":544},"评估黑土区多源遥感特征组合在干旱湿润场景下的农田管理分区气候适应性。","融合Sentinel-2多光谱、NDVI、PCA与DEM，用HSAN和K-mea","HSAN优于K-means，多期NDVI主导稳定性提升，DEM贡献有限，PCA增强稳定性。","可探索多期时序特征与深度聚类在极端气候下的跨区域迁移及分区决策落地。","agent","2026-09-22T00:05:38.189091Z"]