[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3081":3,"related-3081":117},{"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":45,"search_phrases":77,"slug":80,"view_count":81,"doi":82,"paper":83,"created_at":116},3081,"Evaluating vegetation indices for monitoring drought and post-drought declines in European forest productivity","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fbg-23-6557-2026","Drought is causing increasingly severe and widespread negative impacts on forest gross primary productivity (GPP), but modelling these impacts over large spatial scales with remote sensing data is challenging. It is especially problematic in forests that have lower spectral sensitivity to drought compared to other ecosystems and where the timing of vegetation index (VI) response may lag GPP. However, the length of time lags between drought start, GPP and VI response in forests have not been quantified or compared among VIs. We tested the ability of 12 MODIS variables (land surface temperature, leaf area index, fraction absorbed photosynthetic active radiation and nine VIs) to capture drought-induced reductions in GPP from ICOS and FLUXNET eddy covariance data at 18 forest sites across Europe. Our analysis quantified the time lags between the Standardized Precipitation Evapotranspiration Index, GPP and VI response to drought as well as legacy effects in the first year post-drought. We found that land surface temperature was the only MODIS variable that showed significant change between drought and non-drought reference periods at both deciduous broadleaf and evergreen coniferous forests. At deciduous sites, the Chlorophyll\u002FCarotenoid Index, Normalized Difference Water Index and Normalized Difference Vegetation Index (NDVI) were also significantly reduced during drought while the Near Infrared Reflectance Index (NIRv) was significantly reduced at coniferous sites. There were substantial variations in the magnitude and timing of drought response among the VIs which we relate to drought-induced changes in tree physiology and their differences between the five tree genera represented at the study sites. VIs related to canopy structure (NDVI, Plant Phenology Index and NIRv) remained low in the first year following drought at both broadleaf and coniferous sites (although not statistically significant), while GPP often recovered to long-term mean values, implying possible post-drought decoupling between GPP and these VIs.","干旱正对森林总初级生产力（GPP）造成日益严重且广泛的负面影响，但利用遥感数据在大空间尺度上模拟这些影响仍具挑战性。对于光谱对干旱敏感性低于其他生态系统的森林，以及植被指数（VI）响应时间可能滞后于GPP的森林，这一问题尤为突出。然而，森林中干旱开始、GPP与VI响应之间的时间滞后长度尚未被量化，也未在不同VI之间进行比较。我们测试了12个MODIS变量（地表温度、叶面积指数、吸收光合有效辐射分数及9个植被指数）在捕捉欧洲18个森林站点由干旱引起的GPP下降方面的能力，所用GPP数据来自ICOS和FLUXNET涡度相关观测。我们的分析量化了标准化降水蒸散指数、GPP和VI对干旱响应之间的时间滞后，以及干旱后第一年的遗留效应。我们发现，地表温度是唯一在落叶阔叶林和常绿针叶林中均显示干旱期与非干旱参考期之间存在显著差异的MODIS变量。在落叶站点，叶绿素\u002F类胡萝卜素指数、归一化差异水分指数和归一化差异植被指数（NDVI）在干旱期间也显著降低，而在针叶站点，近红外反射率指数（NIRv）显著降低。各VI在干旱响应的幅度和时间上存在显著差异，我们将其与干旱引起的树木生理变化以及研究站点所代表的五个树属之间的差异联系起来。与冠层结构相关的VI（NDVI、植物物候指数和NIRv）在干旱后第一年在阔叶和针叶站点均保持较低水平（尽管未达统计显著性），而GPP通常恢复至长期均值，暗示干旱后GPP与这些VI之间可能出现解耦。",null,"Biogeosciences","2026-09-18T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,14,6,1,"基于18个欧洲森林站点与12种MODIS变量的对比研究，量化了干旱及灾后GPP与植被指数的时滞与解耦，方法新颖、数据扎实，对遥感监测森林生产力具有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感","植被指数","森林干旱","GPP监测","欧洲森林",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":52},[51,58,64,71],{"code":33,"score":20,"supported":52,"title":53,"abstract":55,"tags":57},true,[54],"drought",[56,54],"干旱",[],{"code":40,"score":59,"supported":52,"title":60,"abstract":61,"tags":63},8,[],[27,62],"remote sensing",[28,27],{"code":65,"score":66,"supported":52,"title":67,"abstract":68,"tags":70},"B1",3,[],[69],"萝卜",[],{"code":72,"score":66,"supported":52,"title":73,"abstract":74,"tags":76},"H3",[],[75],"evapotranspiration",[],[78,79],"MODIS 植被指数 干旱","欧洲森林 GPP 遥感监测","MODIS植被指数干旱-3081",2,"10.5194\u002Fbg-23-6557-2026",{"doi":82,"openalex_id":84,"authors":85,"venue":10,"cited_by_count":107,"oa_url":108,"card":109,"direction":113,"ingested_from":115},"W7213558520",[86,89,92,95,98,101,104],{"name":87,"orcid":88},"Julia Kelly","https:\u002F\u002Forcid.org\u002F0000-0002-7370-1401",{"name":90,"orcid":91},"Tim Schacherl","https:\u002F\u002Forcid.org\u002F0009-0005-6778-0763",{"name":93,"orcid":94},"Lars Eklundh","https:\u002F\u002Forcid.org\u002F0000-0001-7644-6517",{"name":96,"orcid":97},"Hongxiao Jin","https:\u002F\u002Forcid.org\u002F0000-0003-3100-7814",{"name":99,"orcid":100},"Anne Klosterhalfen","https:\u002F\u002Forcid.org\u002F0000-0001-7999-8966",{"name":102,"orcid":103},"Alexander Knohl","https:\u002F\u002Forcid.org\u002F0000-0002-7615-8870",{"name":105,"orcid":106},"Natascha Kljun","https:\u002F\u002Forcid.org\u002F0000-0001-9650-2184",0,"https:\u002F\u002Fbg.copernicus.org\u002Farticles\u002F23\u002F6557\u002F2026\u002Fbg-23-6557-2026.pdf",{"tldr":110,"method":111,"finding":112,"direction":113,"opportunity":114},"评估12种MODIS植被指数监测欧洲森林干旱及灾后生产力下降的能力。","利用18个欧洲森林站点的涡度协方差GPP与MODIS数据，量化干旱响应时滞。","地表温度是唯一在两类森林均显著变化的指标，灾后GPP恢复但结构类植被指数仍偏低。","农业遥感与作物表型","可探究灾后GPP与植被指数解耦机制，并开发融合时滞与遗留效应的干旱监测模型。","openalex","2026-09-21T23:30:27.481162Z",{"total":21,"page":22,"page_size":21,"items":118},[119,184,252,325,420,498],{"id":120,"title":121,"url":122,"summary":123,"summary_zh":124,"content":9,"source_name":125,"source_url":122,"published_at":126,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":127,"score_detail":128,"sources":132,"tags":134,"primary_topic":139,"secondary_topics":140,"classification":142,"search_phrases":155,"slug":158,"view_count":107,"doi":159,"paper":160,"created_at":183},4826,"Multi-Index Remote Sensing Assessment of Agro-Climatic Change and Agricultural Responses in Temperate and Semi-Arid Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18203496","Diverse climatic regions exhibit unique agro-climatic vulnerabilities that are significantly shaped by climate stress. Understanding these distinct vulnerabilities is essential for developing effective policies to mitigate climate change impacts and ensuring sustainable environmental management. This study examines agro-climatic vulnerabilities in Nottinghamshire County (United Kingdom) and Udaipur District (India) from 2000 to 2024, comparing climate change impacts across temperate and semi-arid agricultural systems that represent fundamentally different system behaviours under contrasting climatic conditions. Using multi-temporal Landsat imagery and WorldClim climate data, nine environmental indices were analysed: the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Normalized Difference Water Index (NDWI), Moisture Stress Index (MSI), Land Surface Temperature (LST), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Drought Risk Index (DRI), and Crop Health Index (CHI). The results reveal contrasting regional trajectories that challenge conventional assumptions about climate vulnerability. Udaipur exhibited improved vegetation and moisture conditions across the observation period, whereas Nottinghamshire showed intensifying moisture stress and declining precipitation. Warming was more pronounced in Nottinghamshire, while strong inter-index correlations validated the monitoring approach. These findings indicate that Udaipur benefits from intensified monsoon patterns, requiring flood mitigation and water-storage infrastructure, whereas Nottinghamshire’s rising moisture stress underscores the need for sustained monitoring and adaptive management. This comparative framework provides evidence-based insights for region-specific adaptation strategies essential to sustainable agricultural development across diverse agro-climatic zones.","不同气候区域表现出独特的农业气候脆弱性，而这些脆弱性在很大程度上受到气候胁迫的影响。理解这些不同的脆弱性对于制定有效政策以减缓气候变化影响、确保可持续环境管理至关重要。本研究考察了2000年至2024年间英国诺丁汉郡与印度乌代布尔县的农业气候脆弱性，比较了温带与半干旱农业系统在气候变化影响下的差异，这两类系统在截然不同的气候条件下代表了根本不同的系统行为。研究利用多时相Landsat影像和WorldClim气候数据，分析了九个环境指数：归一化植被指数（NDVI）、植被状态指数（VCI）、归一化水体指数（NDWI）、水分胁迫指数（MSI）、地表温度（LST）、温度状态指数（TCI）、植被健康指数（VHI）、干旱风险指数（DRI）和作物健康指数（CHI）。结果揭示了截然不同的区域变化轨迹，挑战了关于气候脆弱性的传统假设。乌代布尔在观测期内表现出植被和水分条件的改善，而诺丁汉郡则显示出加剧的水分胁迫和降水减少。诺丁汉郡的增温更为显著，而指数间较强的相关性验证了该监测方法。这些发现表明，乌代布尔受益于季风模式的增强，需要防洪和水储存基础设施，而诺丁汉郡不断上升的水分胁迫则凸显了持续监测和适应性管理的必要性。这一比较框架为因地制宜的适应策略提供了循证依据，对跨不同农业气候区的可持续发展至关重要。","Remote Sensing","2026-10-12T00:00:00Z",75,{"impact":129,"substance":18,"depth":19,"authority":20,"freshness":130,"relevant":22,"comment":131},12,9,"基于多时相遥感与九项环境指数的跨区域对比研究，方法扎实、结论具政策参考价值，但属学术论文，产业影响有限。",[133],{"name":125,"url":122},[135,28,136,137,138],"遥感监测","干旱风险","适应策略","农业气候",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[141],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":143},{"candidates":144,"agricultural_evidence":52},[145,149],{"code":40,"score":13,"supported":52,"title":146,"abstract":147,"tags":148},[62],[],[28,135],{"code":33,"score":130,"supported":52,"title":150,"abstract":151,"tags":154},[],[152,56,153,54],"气候变化","climate change",[],[156,157],"Landsat 遥感 农业气候","诺丁汉郡 乌代布尔","Landsat遥感农业气候-4826","10.3390\u002Frs18203496",{"doi":159,"openalex_id":161,"authors":162,"venue":125,"cited_by_count":107,"oa_url":122,"card":178,"direction":113,"ingested_from":115},"W7221570035",[163,166,169,172,175],{"name":164,"orcid":165},"Bhanwar Vishvendra Raj Singh","https:\u002F\u002Forcid.org\u002F0000-0001-7750-5240",{"name":167,"orcid":168},"Vivek Agarwal","https:\u002F\u002Forcid.org\u002F0000-0003-1185-3802",{"name":170,"orcid":171},"Stuart Marsh","https:\u002F\u002Forcid.org\u002F0000-0002-1168-6760",{"name":173,"orcid":174},"Gajendra Singh Sisodiya","https:\u002F\u002Forcid.org\u002F0009-0006-4118-7491",{"name":176,"orcid":177},"Ajay Sharma","https:\u002F\u002Forcid.org\u002F0009-0001-6077-9875",{"tldr":179,"method":180,"finding":181,"direction":113,"opportunity":182},"用多时相遥感和九项指数对比英国诺丁汉郡与印度乌代浦2000—2024年农业气候脆弱性。","Landsat多时相影像与WorldClim气候数据，计算NDVI、VCI、ND","乌代浦植被与水分条件改善、季风增强，诺丁汉郡水分胁迫加剧、降水下降且升温更明显。","可拓展多区域长时序遥感指数耦合机器学习，构建跨气候带农业气候脆弱性预警与适应决策模型。","2026-10-13T23:30:24.062929Z",{"id":185,"title":186,"url":187,"summary":188,"summary_zh":189,"content":9,"source_name":190,"source_url":187,"published_at":191,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":192,"score_detail":193,"sources":198,"tags":200,"primary_topic":204,"secondary_topics":205,"classification":207,"search_phrases":233,"slug":236,"view_count":107,"doi":237,"paper":238,"created_at":251},4747,"Spatiotemporal mapping of agricultural drought using geospatial approach in the East Bale Zone, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fagg2.70475","Abstract Agricultural drought significantly threatens food security, livelihoods, and ecosystem stability, especially in regions prone to drought, such as the East Bale zone. This study investigates the spatial and temporal patterns of agricultural drought over 30 years (1993–2024) by combining meteorological station data with satellite‐derived vegetation and thermal indices. To assess meteorological drought, the standardized precipitation index (SPI) was calculated at 3‐, 6‐, and 12‐month intervals. For agricultural and thermal stress analysis, remote sensing‐based indices including the vegetation condition index (VCI), temperature condition index, and vegetation health index (VHI) were utilized. The results indicate that the years 2003, 2008, and 2018 experienced the most severe droughts, as shown consistently across all indices. Spatial analysis revealed that the central, southern, and eastern lowland areas faced the highest frequency and severity of drought, while the northwestern highlands remained comparatively resilient. Cross‐index correlation revealed strong agreement between SPI and VCI ( R 2 = 0.77), and between VHI and VCI ( R 2 = 0.83), supporting the effectiveness of integrated drought monitoring approaches. This research advances the field of drought assessment by enhancing spatial and temporal resolution and validating cross‐index relationships at the subregional level. The results provide critical insights for localized drought early warning systems, climate adaptation strategies, and future research to improve agricultural resilience under changing climatic conditions. Hence, agricultural drought risk mapping serves as a valuable tool for guiding decision‐making in drought monitoring and for mitigating its impacts on agricultural productivity.","摘要 农业干旱严重威胁粮食安全、生计和生态系统稳定性，尤其是在东巴莱区等易旱地区。本研究结合气象站数据与卫星反演的植被和热力指数，探讨了30年（1993—2024年）间农业干旱的时空格局。为评估气象干旱，计算了3个月、6个月和12个月尺度的标准化降水指数（SPI）。在农业与热胁迫分析方面，采用了基于遥感的植被状态指数（VCI）、温度状态指数和植被健康指数（VHI）。结果表明，2003年、2008年和2018年经历了最严重的干旱，各指数均一致反映了这一情况。空间分析显示，中部、南部和东部低地地区干旱发生频率最高、程度最严重，而西北部高地则相对具有较强恢复力。跨指数相关性分析表明，SPI与VCI之间（R²=0.77）以及VHI与VCI之间（R²=0.83）具有高度一致性，支持了综合干旱监测方法的有效性。本研究通过提高时空分辨率并在次区域尺度上验证跨指数关系，推进了干旱评估领域的发展。研究结果为地方性干旱预警系统、气候适应策略以及未来研究提供了重要见解，有助于在气候变化条件下提升农业韧性。因此，农业干旱风险制图为干旱监测决策及其对农业生产影响的减缓提供了有价值的工具。","Agrosystems Geosciences & Environment","2026-10-11T00:00:00Z",68,{"impact":59,"substance":194,"depth":195,"authority":196,"freshness":130,"relevant":22,"comment":197},21,17,13,"基于30年气象与遥感指数交叉验证的农业干旱时空制图研究，方法扎实、结论可靠，但属区域性案例，对国内三农信息化仅有方法借鉴价值。",[199],{"name":190,"url":187},[201,202,135,28,203],"粮食安全","防灾减灾","农业干旱",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[206],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":208},{"candidates":209,"agricultural_evidence":52},[210,215,219,226],{"code":33,"score":19,"supported":52,"title":211,"abstract":212,"tags":214},[54],[152,213,56,54],"气候适应",[202],{"code":40,"score":59,"supported":52,"title":216,"abstract":217,"tags":218},[],[27,62],[28,135],{"code":220,"score":221,"supported":52,"title":222,"abstract":223,"tags":225},"A8",7,[],[201,224],"food security",[201],{"code":227,"score":21,"supported":52,"title":228,"abstract":229,"tags":232},"I4",[],[230,231],"空间分析","spatial analysis",[],[234,235],"埃塞俄比亚 East Bale 农业干旱","SPI VCI 干旱监测","埃塞俄比亚EastBale农业干旱-4747","10.1002\u002Fagg2.70475",{"doi":237,"openalex_id":239,"authors":240,"venue":190,"cited_by_count":107,"oa_url":187,"card":246,"direction":113,"ingested_from":115},"W7221455809",[241,243],{"name":242,"orcid":9},"Murad Hedo Haji",{"name":244,"orcid":245},"Karuturi Venkata Suryabhagavan","https:\u002F\u002Forcid.org\u002F0000-0003-2528-9106",{"tldr":247,"method":248,"finding":249,"direction":113,"opportunity":250},"结合气象站与卫星植被热指数，绘制埃塞俄比亚East Bale区30年农业干旱时空图。","用SPI（3\u002F6\u002F12月）与遥感VCI、TCI、VHI指数做交叉相关分析。","2003、2008、2018年干旱最重，中南部和东部低地最脆弱，SPI与VCI相关性R²=0.77。","可在小区域尺度融合多源指数构建干旱预警模型，并验证指数在不同作物区的适用性。","2026-10-12T23:30:24.342375Z",{"id":253,"title":254,"url":255,"summary":256,"summary_zh":257,"content":9,"source_name":258,"source_url":255,"published_at":259,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":260,"score_detail":261,"sources":264,"tags":266,"primary_topic":272,"secondary_topics":273,"classification":275,"search_phrases":292,"slug":295,"view_count":296,"doi":297,"paper":298,"created_at":324},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":59,"substance":262,"depth":17,"authority":196,"freshness":130,"relevant":22,"comment":263},20,"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。",[265],{"name":258,"url":255},[267,27,268,269,270,271],"智慧农业","干旱胁迫","多光谱成像","地黄","种苗质量",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[274],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":276},{"candidates":277,"agricultural_evidence":52},[278,282,287],{"code":33,"score":20,"supported":52,"title":279,"abstract":280,"tags":281},[54],[56,54],[],{"code":40,"score":196,"supported":52,"title":283,"abstract":285,"tags":286},[284],"multispectral",[284],[269,27],{"code":288,"score":22,"supported":14,"title":289,"abstract":290,"tags":291},"I1",[],[],[267],[293,294],"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":297,"openalex_id":299,"authors":300,"venue":258,"cited_by_count":107,"oa_url":318,"card":319,"direction":113,"ingested_from":115},"W7214608276",[301,304,306,307,309,312,315,317],{"name":302,"orcid":303},"Ji Hye Yun","https:\u002F\u002Forcid.org\u002F0009-0004-9551-5353",{"name":305,"orcid":9},"So Yeong Hwang",{"name":305,"orcid":9},{"name":308,"orcid":9},"Jeong Kil Koo",{"name":310,"orcid":311},"Hyeong Eun Choi","https:\u002F\u002Forcid.org\u002F0009-0009-9318-9845",{"name":313,"orcid":314},"Seong Ha Park","https:\u002F\u002Forcid.org\u002F0009-0005-9189-8972",{"name":316,"orcid":9},"Seung Jae Hwang",{"name":316,"orcid":9},"https:\u002F\u002Fwww.hst-j.org\u002Farticles\u002Fpdf\u002Fbz4M\u002Fkshs-2026-044-00-15.pdf",{"tldr":320,"method":321,"finding":322,"direction":113,"opportunity":323},"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":326,"title":327,"url":328,"summary":329,"summary_zh":330,"content":9,"source_name":331,"source_url":328,"published_at":332,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":260,"score_detail":333,"sources":335,"tags":337,"primary_topic":341,"secondary_topics":345,"classification":348,"search_phrases":392,"slug":395,"view_count":296,"doi":396,"paper":397,"created_at":419},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":129,"substance":19,"depth":17,"authority":129,"freshness":59,"relevant":22,"comment":334},"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。",[336],{"name":331,"url":328},[267,338,27,339,340],"农业人工智能","GIS","干旱监测",{"code":227,"slug":342,"name":343,"path":344,"parent_code":44},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[346,347],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":349},{"candidates":350,"agricultural_evidence":52},[351,357,362,366,376,383,388],{"code":227,"score":19,"supported":52,"title":352,"abstract":353,"tags":356},[339],[354,230,339,231,355],"地理信息","geographic information",[339],{"code":33,"score":358,"supported":52,"title":359,"abstract":360,"tags":361},15,[54],[56,54],[340],{"code":40,"score":358,"supported":52,"title":363,"abstract":364,"tags":365},[62],[27,62],[27],{"code":367,"score":13,"supported":52,"title":368,"abstract":369,"tags":375},"I2",[],[370,371,372,373,374],"深度学习","机器学习","machine learning","deep learning","artificial intelligence",[338],{"code":377,"score":21,"supported":52,"title":378,"abstract":379,"tags":382},"H2",[],[380,381],"土壤","soil",[],{"code":72,"score":21,"supported":52,"title":384,"abstract":385,"tags":387},[],[386,75],"水资源",[],{"code":288,"score":22,"supported":14,"title":389,"abstract":390,"tags":391},[],[],[267],[393,394],"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":396,"openalex_id":398,"authors":399,"venue":331,"cited_by_count":107,"oa_url":328,"card":414,"direction":113,"ingested_from":115},"W7214156857",[400,402,404,406,408,410,412],{"name":401,"orcid":9},"V. Dhanalakshmi",{"name":403,"orcid":9},"N. Manikandan",{"name":405,"orcid":9},"V. S. Jinsy",{"name":407,"orcid":9},"K. V. Sumesh",{"name":409,"orcid":9},"P. Nideesh",{"name":411,"orcid":9},"P. S. Manju",{"name":413,"orcid":9},"N. Gopika",{"tldr":415,"method":416,"finding":417,"direction":113,"opportunity":418},"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":421,"title":422,"url":423,"summary":424,"summary_zh":425,"content":9,"source_name":426,"source_url":423,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":427,"score_detail":428,"sources":430,"tags":432,"primary_topic":436,"secondary_topics":437,"classification":440,"search_phrases":475,"slug":478,"view_count":22,"doi":479,"paper":480,"created_at":497},2945,"Assessing spatial suitability and climate resilience of agroforestry systems in the Indian Himalaya of Uttarakhand using remote sensing and artificial neural networks","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44415-026-00113-9","Agroforestry systems are widely recognized for their capacity to enhance ecological sustainability, livelihood security, and climate resilience in mountain environments. However, their spatial suitability and long-term resilience under changing climatic conditions remain poorly quantified in the Indian Himalaya. This study assesses the spatial suitability and climate resilience of agroforestry systems in Uttarakhand using an integrated framework of remote sensing, geographic information systems (GIS), and artificial neural networks (ANNs). Landsat 8 OLI imagery (30 m resolution) was classified using a supervised Gaussian Maximum Likelihood Classifier (MLC), while land-use categories were defined based on a modified Anderson Level I\u002FII classification scheme to represent the heterogeneous Himalayan landscape. Field surveys conducted across representative agroecological zones documented the composition of tree and crop species within prevailing agroforestry systems. A multi-criteria land suitability analysis, guided by FAO (Food and Agriculture Organization of the United Nations) principles, was implemented using key biophysical variables including altitude, slope, aspect, Normalized Difference Vegetation Index (NDVI), soil properties, temperature, and precipitation. Future agroforestry patterns were simulated using an ANN model under RCP 4.5 climate scenarios. The model demonstrated strong predictive performance, indicating reliable simulation of agroforestry distribution. Results show that mid-altitudinal zones offer the highest suitability for agroforestry expansion, whereas small and fragmented systems exhibit reduced climate resilience under future projections. The findings provide a spatially explicit framework to support climate-resilient agroforestry planning and policy formulation in the Himalayan region.","农林复合系统因其在山区环境中提升生态可持续性、生计安全及气候适应能力方面的作用而受到广泛认可。然而，在印度喜马拉雅地区，其空间适宜性及气候变化条件下的长期适应能力仍缺乏充分的量化评估。本研究采用遥感、地理信息系统（GIS）与人工神经网络（ANN）相结合的综合框架，评估了北阿坎德邦农林复合系统的空间适宜性与气候适应能力。利用监督式高斯最大似然分类器（MLC）对Landsat 8 OLI影像（30 m分辨率）进行分类，同时基于改进的Anderson一级\u002F二级分类方案界定土地利用类别，以表征喜马拉雅地区异质性景观。在代表性农业生态区开展实地调查，记录了现有农林复合系统中树种和作物物种的组成。依据联合国粮食及农业组织（FAO）原则，采用多准则土地适宜性分析，纳入海拔、坡度、坡向、归一化植被指数（NDVI）、土壤属性、温度和降水等关键生物物理变量。利用ANN模型在RCP 4.5气候情景下模拟未来农林复合系统格局。模型表现出较强的预测性能，表明对农林复合系统分布的模拟可靠。结果表明，中海拔区域为农林复合系统扩展提供了最高的适宜性，而小型和破碎化系统在未来预测情景下表现出较低的气候适应能力。研究结果为喜马拉雅地区气候适应型农林复合系统规划与政策制定提供了空间明确的框架支持。","Discover Forests",72,{"impact":129,"substance":194,"depth":195,"authority":196,"freshness":130,"relevant":22,"comment":429},"遥感与人工神经网络结合评估喜马拉雅地区农林复合系统空间适宜性与气候韧性，方法新颖、结论有区域政策参考价值，但属境外区域研究，公共影响有限。",[431],{"name":426,"url":423},[338,27,433,434,435],"气候韧性","土地适宜性","农林复合经营",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[438,439],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":227,"slug":342,"name":343,"path":344,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":441},{"candidates":442,"agricultural_evidence":52},[443,448,452,456,460,466,471],{"code":33,"score":19,"supported":52,"title":444,"abstract":446,"tags":447},[445],"climate resilience",[152,213,445],[433],{"code":40,"score":358,"supported":52,"title":449,"abstract":450,"tags":451},[62],[27,62],[27],{"code":227,"score":130,"supported":52,"title":453,"abstract":454,"tags":455},[],[354,339,355],[],{"code":377,"score":21,"supported":52,"title":457,"abstract":458,"tags":459},[],[380,381],[],{"code":461,"score":66,"supported":52,"title":462,"abstract":463,"tags":465},"H6",[],[464],"农业生态",[],{"code":467,"score":22,"supported":14,"title":468,"abstract":469,"tags":470},"H1",[],[],[434],{"code":367,"score":22,"supported":14,"title":472,"abstract":473,"tags":474},[],[],[338],[476,477],"Uttarakhand 农林复合系统 遥感","印度喜马拉雅 人工神经网络 土地适宜性","Uttarakhand农林复合系统遥感-2945","10.1007\u002Fs44415-026-00113-9",{"doi":479,"openalex_id":481,"authors":482,"venue":426,"cited_by_count":107,"oa_url":423,"card":492,"direction":113,"ingested_from":115},"W7213553408",[483,485,487,490],{"name":484,"orcid":9},"Deepak Kumar Mishra",{"name":486,"orcid":9},"Ujjwal Kumar",{"name":488,"orcid":489},"A. Arunachalam","https:\u002F\u002Forcid.org\u002F0000-0001-6590-4113",{"name":491,"orcid":9},"Ayyanadar Arunachalam",{"tldr":493,"method":494,"finding":495,"direction":113,"opportunity":496},"结合遥感、GIS与人工神经网络评估印度喜马拉雅地区农林复合系统的空间适宜性与气候韧性。","Landsat 8影像监督分类、FAO多准则适宜性分析、ANN在RCP4.5情景","中海拔区最适宜农林复合扩展，小而破碎系统在未来气候下韧性较低。","可将该遥感-ANN框架迁移至中国山区，耦合多情景气候与农户数据优化农林复合布局。","2026-09-19T23:30:33.082917Z",{"id":499,"title":500,"url":501,"summary":502,"summary_zh":503,"content":9,"source_name":504,"source_url":501,"published_at":505,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":506,"score_detail":507,"sources":509,"tags":511,"primary_topic":515,"secondary_topics":516,"classification":518,"search_phrases":534,"slug":537,"view_count":81,"doi":538,"paper":539,"created_at":563},2674,"Bi-decadal drought assessment in Northwestern Algeria: Integrating meteorological and remote sensing indices","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00704-026-06526-y","Abstract Drought is an escalating hazard in arid and semi‑arid regions with significant implications for agriculture, ecosystems, and water resources. This study presents a 2003–2023 integrated assessment of meteorological and vegetation-based drought across northwest Algeria, using the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset and Moderate Resolution Imaging Spectroradiometer (MODIS) remotely‑sensed products. Meteorological drought was quantified with the Standardized Precipitation Index (SPI) approximated using standardized precipitation anomalies, at 3‑, 6‑, and 12‑month timescales, whereas vegetation and thermal stress were assessed with MODIS‑derived Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). Temporal trends were evaluated using the Mann–Kendall test and Sen’s slope estimator, and relationships between precipitation and vegetation were examined with Pearson correlation. We identified recurrent drought episodes in 2007–2009, 2011–2012, and a pronounced dry phase from 2020–2023. Mann–Kendall results indicated widespread drying across all SPI timescales, with 56% of the study area showing significant negative trends at SPI‑3 (mean Sen’s slope = − 0.10 yr⁻ 1 ). Vegetation indices mirrored these changes, with VHI showing substantially more degraded area than improvement (4.89% vs 0.51% of the domain), while VCI and TCI responses were spatially heterogeneous. The correlation analysis showed moderate but statistically significant relationships between SPI and VHI at the short-term scale (SPI-3 vs. VHI, r = 0.567, p = 0.007), followed by SPI-6 ( r = 0.509, p = 0.019), indicating that vegetation response is more strongly associated with short- to medium-term precipitation variability. These results demonstrate the value of combining meteorological and satellite vegetation indices for regional drought monitoring and early warning, and underscore an ongoing shift toward increased aridity with implications for water management and agricultural adaptation.","摘要 干旱是干旱和半干旱地区日益加剧的灾害，对农业、生态系统和水资源具有重大影响。本研究利用气候灾害组红外降水与站点数据（CHIRPS）数据集和中分辨率成像光谱仪（MODIS）遥感产品，对阿尔及利亚西北部2003—2023年气象干旱和基于植被的干旱进行了综合评估。气象干旱采用标准化降水指数（SPI）量化，该指数通过标准化降水距平在3个月、6个月和12个月时间尺度上近似计算；植被和热胁迫则通过MODIS衍生的植被状态指数（VCI）、温度状态指数（TCI）和植被健康指数（VHI）进行评估。采用Mann–Kendall检验和Sen斜率估计器评估时间趋势，并使用Pearson相关分析检验降水与植被之间的关系。我们识别出2007—2009年、2011—2012年的反复干旱事件，以及2020—2023年显著的干旱阶段。Mann–Kendall结果表明，所有SPI时间尺度上均呈现大范围干旱化趋势，研究区56%的面积在SPI-3上表现出显著负趋势（Sen斜率均值 = −0.10 yr⁻¹）。植被指数反映了这些变化，VHI显示退化面积远大于改善面积（分别占研究区的4.89%和0.51%），而VCI和TCI的响应在空间上具有异质性。相关分析表明，SPI与VHI在短期尺度上存在中等但统计显著的关系（SPI-3与VHI，r = 0.567，p = 0.007），其次为SPI-6（r = 0.509，p = 0.019），表明植被响应与短期至中期降水变率的关系更为密切。这些结果证明了结合气象指标和卫星植被指数进行区域干旱监测与预警的价值，并凸显了干旱化持续加剧的趋势及其对水资源管理和农业适应的影响。","Theoretical and Applied Climatology","2026-09-15T00:00:00Z",71,{"impact":129,"substance":194,"depth":195,"authority":196,"freshness":59,"relevant":22,"comment":508},"基于CHIRPS与MODIS的阿尔及利亚西北部20年干旱综合评估，方法整合气象与遥感指标，结论对区域干旱监测与农业适应有参考价值，但属境外区域研究，国内产业影响有限。",[510],{"name":504,"url":501},[512,513,135,28,514],"农业气象","水资源管理","干旱预警",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[517],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":519},{"candidates":520,"agricultural_evidence":52},[521,525,529],{"code":33,"score":17,"supported":52,"title":522,"abstract":523,"tags":524},[54],[56,54],[512,514],{"code":40,"score":196,"supported":52,"title":526,"abstract":527,"tags":528},[62],[27],[28,135],{"code":72,"score":530,"supported":52,"title":531,"abstract":532,"tags":533},4,[],[386],[513],[535,536],"水资源管理 农业气象 干旱预警 植被指数","水资源管理 农业气象","水资源管理农业气象干旱预警植被指数-2674","10.1007\u002Fs00704-026-06526-y",{"doi":538,"openalex_id":540,"authors":541,"venue":504,"cited_by_count":107,"oa_url":501,"card":558,"direction":113,"ingested_from":115},"W7213346236",[542,545,548,551,553,556],{"name":543,"orcid":544},"Ramzi Benhizia","https:\u002F\u002Forcid.org\u002F0000-0002-8967-2051",{"name":546,"orcid":547},"Brahim Abdelkebir","https:\u002F\u002Forcid.org\u002F0000-0002-8761-8537",{"name":549,"orcid":550},"Behnam Ata","https:\u002F\u002Forcid.org\u002F0000-0002-9690-7269",{"name":552,"orcid":9},"Mukovhe Vele Singo",{"name":554,"orcid":555},"Kwanele Phinzi","https:\u002F\u002Forcid.org\u002F0000-0003-1865-7011",{"name":557,"orcid":9},"György Szabó",{"tldr":559,"method":560,"finding":561,"direction":113,"opportunity":562},"整合气象与遥感指数评估阿尔及利亚西北部2003–2023年干旱演变。","CHIRPS降水与MODIS植被指数，SPI、VCI、TCI、VHI，Mann-","全区普遍变干，SPI-3显著负趋势占56%，VHI退化面积远大于改善，短期降水与植被相关性最强。","可引入机器学习融合多源遥感与气象数据，构建区域干旱预警与作物适应性决策模型。","2026-09-16T23:30:30.795285Z"]