[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4313":3,"related-4313":132},{"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":84,"slug":87,"view_count":88,"doi":89,"paper":90,"created_at":131},4313,"A semi-supervised multi-sensor remote sensing pipeline for mapping irrigation dynamics in ground-truth deficient conflict zones: a case study of the Kakhovka Dam failure","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1920951","The ongoing war in Ukraine has drawn international attention to the impact of conflict on global agricultural production and food security. On 6 June 2023, the Kakhovka Dam was destroyed. The loss of this critical agricultural and energy infrastructure has left most farmers on the East bank of the Dnipro River without irrigation in Kherson and Zaporizhzhia oblasts. This study develops a multi-sensor Earth Observation framework combining high resolution satellite imagery (Sentinel-2, Landsat) with meteorological data (CHIRPS) and water productivity data (FAO WaPOR), using a semi-supervised machine learning pipeline. Using a rigorous stratified random-sampling approach with photointerpretation, we found pre-war irrigated areas between 2019 to 2021 to be on average 187.94 ± 14.43 kHa, approximately 4% of pre-war baseline cropland and 8% of summer season cropland in Kherson and Zaporizhzhia oblasts. In 2022, these numbers contracted to 140.04 ± 17.44 kHa with the expanded occupation of these territories by Russia, seeing an additional decline in 2023 to 74.97 ± 17.84 kHa. Fields in 2023 also exhibited variability in irrigation, ranging from interrupted irrigation owing to the destruction of the Kakhovka reservoir, to those that were sufficiently irrigated with water remaining in the channels. Irrigated areas diminished further in 2024 to 15.95 ± 9.97 kHa, a ∼91% decline from pre-war averages. Based on inspection, formerly irrigated fields before 2023 were either systematically abandoned or converted to winter cropland with lower input requirements by 2024–a pattern that continued into 2025 (total irrigated area 5.76 ± 0.87 kHa). This study demonstrates the power of advanced EO workflows to precisely quantify agricultural transformation and infrastructural failure in ground-truth deficient, high-risk environments.","乌克兰持续不断的战争使国际社会关注冲突对全球农业生产和粮食安全的影响。2023年6月6日，卡霍夫卡大坝被毁。这一关键农业和能源基础设施的丧失，使第聂伯河（Dnipro River）东岸赫尔松州和扎波罗热州的大多数农民失去了灌溉条件。本研究开发了一个多传感器地球观测（Earth Observation）框架，将高分辨率卫星影像（Sentinel-2、Landsat）与气象数据（CHIRPS）和水生产力数据（FAO WaPOR）相结合，采用半监督机器学习流程。通过严格的 stratified random-sampling 方法并结合照片判读，我们发现2019年至2021年战前灌溉面积平均为187.94 ± 14.43 kHa，约占赫尔松州和扎波罗热州战前基线耕地面积的4%，以及夏季作物耕地面积的8%。2022年，随着俄罗斯对这些领土占领的扩大，上述数字缩减至140.04 ± 17.44 kHa，2023年进一步下降至74.97 ± 17.84 kHa。2023年的田块在灌溉方面也表现出差异性，从因卡霍夫卡水库被毁而灌溉中断，到因渠道中仍有存水而得到充分灌溉的情况不等。2024年，灌溉面积进一步减少至15.95 ± 9.97 kHa，较战前平均值下降约91%。根据核查，2023年之前曾受灌溉的田块到2024年要么被系统性弃耕，要么转为投入需求较低的冬季作物用地——这一模式延续至2025年（总灌溉面积为5.76 ± 0.87 kHa）。本研究表明，先进的地球观测工作流程能够在缺乏地面真值、高风险的环境中精确量化农业转型和基础设施失效。",null,"Frontiers in Remote Sensing","2026-10-05T00:00:00Z","论文",10,false,87,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,24,19,13,9,1,"用半监督多源遥感量化战区灌溉萎缩，方法新颖、数据扎实，对农业遥感与粮食安全研究有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"粮食安全","遥感监测","冲突农业","多源遥感","灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","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":53},[51,57,62,69,77],{"code":33,"score":52,"supported":53,"title":54,"abstract":55,"tags":56},14,true,[34],[31,34],[],{"code":40,"score":13,"supported":53,"title":58,"abstract":60,"tags":61},[59],"remote sensing",[],[30,28],{"code":63,"score":64,"supported":53,"title":65,"abstract":66,"tags":68},"A8",7,[],[27,67],"food security",[27],{"code":70,"score":71,"supported":53,"title":72,"abstract":73,"tags":76},"I2",6,[],[74,75],"机器学习","machine learning",[],{"code":78,"score":79,"supported":53,"title":80,"abstract":81,"tags":83},"J3",3,[],[82],"全球农业",[],[85,86],"冲突农业 多源遥感 粮食安全 遥感监测","冲突农业 多源遥感","冲突农业多源遥感粮食安全遥感监测-4313",0,"10.3389\u002Ffrsen.2026.1920951",{"doi":89,"openalex_id":91,"authors":92,"venue":10,"cited_by_count":88,"oa_url":123,"card":124,"direction":128,"ingested_from":130},"W7220438608",[93,96,99,102,105,108,111,113,115,117,120],{"name":94,"orcid":95},"Sheila Baber","https:\u002F\u002Forcid.org\u002F0000-0002-5596-8970",{"name":97,"orcid":98},"Sergii V. Skakun","https:\u002F\u002Forcid.org\u002F0000-0002-9039-0174",{"name":100,"orcid":101},"Yuval Sadeh","https:\u002F\u002Forcid.org\u002F0000-0002-4346-0432",{"name":103,"orcid":104},"Леонід Леонідович Шуміло","https:\u002F\u002Forcid.org\u002F0000-0002-7395-7933",{"name":106,"orcid":107},"Oleksandra Oliinyk","https:\u002F\u002Forcid.org\u002F0000-0002-2392-3938",{"name":109,"orcid":110},"Mehdi Hosseini","https:\u002F\u002Forcid.org\u002F0000-0003-3242-613X",{"name":112,"orcid":9},"Josef Wagner",{"name":114,"orcid":9},"Abdul Qadir Khan",{"name":116,"orcid":9},"Shabarinath Nair",{"name":118,"orcid":119},"Abhishek Kotcharlakota","https:\u002F\u002Forcid.org\u002F0009-0001-1832-4631",{"name":121,"orcid":122},"Inbal Becker‐Reshef","https:\u002F\u002Forcid.org\u002F0000-0002-2160-5151","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fremote-sensing\u002Farticles\u002F10.3389\u002Ffrsen.2026.1920951\u002Fpdf",{"tldr":125,"method":126,"finding":127,"direction":128,"opportunity":129},"用半监督多源遥感监测卡霍夫卡大坝被毁后灌溉面积从战前18.8万公顷降至2025年0.58万公顷。","Sentinel-2、Landsat、CHIRPS与FAO WaPOR数据结合半","灌溉面积由2019-2021年均187.94 kHa降至2024年15.95 kHa，降幅约91%，","农业遥感与作物表型","可将该半监督多源框架迁移至其他缺地面真值冲突区，并耦合作物产量与粮食安全评估。","openalex","2026-10-06T23:30:20.495894Z",{"total":71,"page":22,"page_size":71,"items":133},[134,214,287,368,435,508],{"id":135,"title":136,"url":137,"summary":138,"summary_zh":139,"content":9,"source_name":140,"source_url":137,"published_at":141,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":142,"score_detail":143,"sources":147,"tags":149,"primary_topic":153,"secondary_topics":158,"classification":161,"search_phrases":194,"slug":197,"view_count":22,"doi":198,"paper":199,"created_at":213},4504,"Development of the Potential Evapotranspiration-Vegetation-Precipitation Drought Index (PETVPDI) and Its Application in West Africa Using Remote Sensing Datasets","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193435","Drought remains one of the most severe climate-related hazards affecting water resources, ecosystems, and agricultural productivity in West Africa (WA). Traditional drought indices often rely on individual hydroclimatic or vegetation variables and may not fully represent the coupled effects of precipitation supply, atmospheric evaporative demand, and vegetation response. To address this limitation, this study develops and applies a composite drought indicator, the Potential Evapotranspiration–Vegetation–Precipitation Drought Index (PETVPDI), using multi-source remote sensing datasets over WA for 2001–2022. PETVPDI integrates precipitation (P), potential evapotranspiration (PET), and the normalized difference vegetation index (NDVI) within a three-dimensional Euclidean-distance framework. The index was evaluated against GLDAS soil moisture (SM), ESA CCI SM, gross primary productivity (GPP), and net primary productivity (NPP), and compared with four conventional remote-sensing drought indices (cRSDI): VCI, VHI, TVDI, and TVPDI. Deseasonalized anomaly correlations, used as the primary validation evidence to reduce the influence of the shared monsoon seasonal cycle, show that PETVPDI achieved the highest numerical correlations among the evaluated indices with GLDAS SM (R = 0.556, p \u003C 0.001), ESA CCI SM (R = 0.513, p \u003C 0.001), and GPP (R = 0.483, p \u003C 0.001). Steiger’s tests for dependent overlapping correlations show that the PETVPDI–TVPDI difference was statistically significant for raw GLDAS SM and deseasonalized ESA CCI SM, but not for the other evaluated comparisons. Spatial PETVPDI patterns represent each pixel’s relative position within its own 2001–2022 hydroclimatic and vegetation range and therefore should not be interpreted as direct differences in absolute climatic aridity among locations. Long-term trends are spatially heterogeneous across WA. Overall, PETVPDI provides an integrated framework for monitoring relative drought–wetness variability and offers complementary information for drought assessment and early-warning applications in data-scarce regions.","干旱仍然是影响西非（WA）水资源、生态系统和农业生产力最严重的气候相关灾害之一。传统干旱指数通常依赖单一的水文气候或植被变量，可能无法充分反映降水供给、大气蒸发需求和植被响应之间的耦合效应。为解决这一局限，本研究利用2001—2022年西非多源遥感数据集，开发并应用了一种复合干旱指标——潜在蒸散–植被–降水干旱指数（PETVPDI）。PETVPDI在三维欧氏距离框架内整合了降水（P）、潜在蒸散（PET）和归一化差异植被指数（NDVI）。该指数以GLDAS土壤湿度（SM）、ESA CCI SM、总初级生产力（GPP）和净初级生产力（NPP）进行评估，并与四种传统遥感干旱指数（cRSDI）：VCI、VHI、TVDI和TVPDI进行比较。以去季节化异常相关性作为主要验证依据，以减少共同季风季节循环的影响，结果表明PETVPDI在所评估的指数中与GLDAS SM（R = 0.556，p \u003C 0.001）、ESA CCI SM（R = 0.513，p \u003C 0.001）和GPP（R = 0.483，p \u003C 0.001）的数值相关性最高。针对相依重叠相关性的Steiger检验表明，PETVPDI–TVPDI差异在原始GLDAS SM和去季节化ESA CCI SM上具有统计学显著性，但在其他评估比较中不显著。PETVPDI的空间格局表示每个像元在其自身2001—2022年水文气候和植被范围内的相对位置，因此不应被解释为地点之间绝对气候干燥度的直接差异。长期趋势在西非呈现空间异质性。总体而言，PETVPDI为监测相对干旱–湿润变率提供了一个集成框架，并为数据稀缺地区的干旱评估和预警应用提供了补充信息。","Remote Sensing","2026-10-08T00:00:00Z",75,{"impact":144,"substance":17,"depth":145,"authority":52,"freshness":21,"relevant":22,"comment":146},12,18,"提出融合降水、蒸散与植被的复合干旱指数，方法新颖、验证扎实，对数据稀缺区农业干旱预警有参考价值，但区域局限于西非，国内应用关联度有限。",[148],{"name":140,"url":137},[27,150,28,151,152],"农业气象","西非农业","干旱指数",{"code":154,"slug":155,"name":156,"path":157,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[159,160],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":162},{"candidates":163,"agricultural_evidence":53},[164,171,177,183,190],{"code":154,"score":165,"supported":53,"title":166,"abstract":168,"tags":170},16,[167],"drought",[169,167],"干旱",[150,152],{"code":40,"score":172,"supported":53,"title":173,"abstract":174,"tags":176},15,[59],[175,59],"遥感",[28],{"code":33,"score":52,"supported":53,"title":178,"abstract":180,"tags":182},[179],"evapotranspiration",[181,179],"水资源",[],{"code":184,"score":71,"supported":53,"title":185,"abstract":186,"tags":189},"H2",[],[187,188],"土壤","soil",[],{"code":63,"score":22,"supported":14,"title":191,"abstract":192,"tags":193},[],[],[27],[195,196],"PETVPDI 干旱指数 西非","遥感 干旱监测 NDVI","PETVPDI干旱指数西非-4504","10.3390\u002Frs18193435",{"doi":198,"openalex_id":200,"authors":201,"venue":140,"cited_by_count":88,"oa_url":137,"card":208,"direction":128,"ingested_from":130},"W7220963372",[202,205],{"name":203,"orcid":204},"Abdoul-Aziz Bio Sidi D. Bouko","https:\u002F\u002Forcid.org\u002F0009-0003-1179-8046",{"name":206,"orcid":207},"Bing Gao","https:\u002F\u002Forcid.org\u002F0000-0003-0782-8858",{"tldr":209,"method":210,"finding":211,"direction":128,"opportunity":212},"构建融合蒸散、植被与降水的复合干旱指数PETVPDI，并用于西非2001—2022年干旱监测。","多源遥感数据，三维欧氏距离整合P、PET、NDVI，与土壤湿度、GPP\u002FNPP及","PETVPDI与土壤湿度和GPP的相关性最高，能更好反映干旱-湿润相对变化，但趋势空间异质。","可将该复合指数迁移至其他数据稀缺区，并结合作物产量或灌溉需水做农业干旱预警与适应性研究。","2026-10-09T23:30:25.838340Z",{"id":215,"title":216,"url":217,"summary":218,"summary_zh":219,"content":9,"source_name":220,"source_url":217,"published_at":221,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":222,"score_detail":223,"sources":227,"tags":229,"primary_topic":233,"secondary_topics":234,"classification":237,"search_phrases":263,"slug":266,"view_count":22,"doi":267,"paper":268,"created_at":286},4030,"Enhancing Drought Monitoring Using Integrated Remote Sensing and Ground-Based Indices","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jastp.2026.106987","ABSTRACT Accurate and timely drought monitoring is essential for sustainable water resource management, particularly in semi-arid regions where meteorological observations are sparse and spatially heterogeneous. This study presents an integrated evaluation of drought conditions by applying established individual and composite drought indices within a unified multi-source framework that combines ground-based meteorological data with satellite-derived datasets, including MODIS land surface products and GPM\u002FTRMM precipitation estimates, and is implemented on the Google Earth Engine platform. A set of drought indicators representing vegetation condition, thermal stress, precipitation deficit, and soil moisture variability was analyzed alongside five composite indices to provide a multi-dimensional assessment of drought dynamics. The analysis was conducted across monthly, seasonal, and annual timescales for Tehran, Iran, over the period 2000–2022. In addition, trend significance was assessed using the Mann–Kendall test and Sen’s slope estimator to quantify long-term changes in drought behavior. The results indicate that composite indices provide a more coherent representation of drought evolution and are more effective in capturing the interaction among key climatic drivers. In particular, the analysis reveals a transition from precipitation-driven droughts (e.g., 2008 and 2016) to increasingly temperature-driven drought conditions (e.g., 2022), associated with elevated potential evapotranspiration and thermal stress. This highlights the growing importance of atmospheric evaporative demand in intensifying drought severity under warming conditions. From an applied perspective, the proposed framework supports drought early warning, spatial risk assessment, and informed decision-making in water resource management and agricultural planning. However, the reliability of the results depends on data quality, spatial resolution, and the representation of key drought drivers and should therefore be interpreted with these limitations in mind. The study demonstrates that the integrated application of multi-source data and multi-scale drought indices provides a robust basis for understanding and monitoring drought dynamics in semi-arid urban environments.","准确的干旱监测对于可持续水资源管理至关重要，尤其是在气象观测稀疏且空间异质性强的半干旱地区。本研究在统一的多源框架内，应用已有的单一和综合干旱指数对干旱状况进行综合评估，该框架将地面气象数据与卫星遥感数据集（包括MODIS地表产品和GPM\u002FTRMM降水估算）相结合，并在Google Earth Engine平台上实现。研究分析了表征植被状况、热胁迫、降水亏缺和土壤湿度变率的干旱指标集以及五种综合指数，以对干旱动态进行多维评估。分析在月、季和年尺度上针对伊朗德黑兰2000—2022年期间开展。此外，采用Mann–Kendall检验和Sen斜率估计器评估趋势显著性，以量化干旱行为的长期变化。结果表明，综合指数能更一致地表征干旱演变，且更能有效捕捉关键气候驱动因子之间的相互作用。具体而言，分析揭示了从降水驱动型干旱（如2008年和2016年）向日益增强的温度驱动型干旱（如2022年）的转变，这与潜在蒸散和热胁迫升高有关。这凸显了在变暖条件下大气蒸发需求在加剧干旱严重程度方面日益重要的作用。从应用角度看，所提出的框架可支持干旱预警、空间风险评估以及水资源管理和农业规划中的知情决策。然而，结果的可靠性取决于数据质量、空间分辨率以及关键干旱驱动因子的表征，因此在解读时应考虑这些局限性。本研究表明，多源数据与多尺度干旱指数的综合应用为理解和监测半干旱城市环境中的干旱动态提供了稳健基础。","Journal of Atmospheric and Solar-Terrestrial Physics","2026-10-01T00:00:00Z",74,{"impact":52,"substance":224,"depth":145,"authority":20,"freshness":225,"relevant":22,"comment":226},21,8,"多源遥感与地面指数融合的干旱监测框架，方法扎实、结论有新意，但属区域案例研究，产业影响有限。",[228],{"name":220,"url":217},[150,230,28,231,232],"水资源管理","Google Earth Engine","干旱预警",{"code":154,"slug":155,"name":156,"path":157,"parent_code":37},[235,236],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":238},{"candidates":239,"agricultural_evidence":53},[240,244,248,253,257],{"code":154,"score":165,"supported":53,"title":241,"abstract":242,"tags":243},[167],[169,167],[150,232],{"code":40,"score":20,"supported":53,"title":245,"abstract":246,"tags":247},[59],[175],[231,28],{"code":33,"score":13,"supported":53,"title":249,"abstract":250,"tags":252},[],[181,251,179],"water resource",[230],{"code":184,"score":71,"supported":53,"title":254,"abstract":255,"tags":256},[],[187,188],[],{"code":258,"score":79,"supported":53,"title":259,"abstract":260,"tags":262},"J1",[],[261],"农业规划",[],[264,265],"MODIS GPM 干旱指数","德黑兰 干旱监测 遥感","MODISGPM干旱指数-4030","10.1016\u002Fj.jastp.2026.106987",{"doi":267,"openalex_id":269,"authors":270,"venue":220,"cited_by_count":88,"oa_url":217,"card":281,"direction":128,"ingested_from":130},"W7215092640",[271,274,276,278],{"name":272,"orcid":273},"Hadi Siasar","https:\u002F\u002Forcid.org\u002F0000-0002-9323-4345",{"name":275,"orcid":9},"Mahboube Ebrahimi",{"name":277,"orcid":9},"Mehdi Bahrami",{"name":279,"orcid":280},"Hossein Hamidifar","https:\u002F\u002Forcid.org\u002F0000-0001-9054-0120",{"tldr":282,"method":283,"finding":284,"direction":128,"opportunity":285},"整合遥感与地面数据，在GEE上评估德黑兰2000–2022年多尺度干旱动态。","MODIS、GPM\u002FTRMM与气象数据，构建5种复合干旱指数，Mann-Kend","复合指数更一致；干旱由降水驱动转向温度驱动，蒸散需求加剧干旱。","可引入机器学习融合多源指数，提升半干旱区干旱预警的时空精度与驱动归因。","2026-10-02T23:30:24.756475Z",{"id":288,"title":289,"url":290,"summary":291,"summary_zh":292,"content":9,"source_name":293,"source_url":290,"published_at":294,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":295,"score_detail":296,"sources":299,"tags":301,"primary_topic":305,"secondary_topics":306,"classification":313,"search_phrases":351,"slug":354,"view_count":22,"doi":355,"paper":356,"created_at":367},3901,"Geospatial analysis of irrigation suitability in Nigeria using remote sensing and GIS","https:\u002F\u002Fdoi.org\u002F10.53030\u002Ftjags.1937471","This research offers a comprehensive geospatial evaluation of land suitability for irrigation across Nigeria. It leverages 2020 Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, processed within the Google Earth Engine (GEE) cloud-based computational environment. Given growing concerns about food security and climate variability, ascertaining areas amenable to irrigation is paramount for fostering sustainable agricultural development within Nigeria. This investigation integrated multi-temporal remote sensing observations, including the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Normalized Difference Water Index - NDWI, to construct an Irrigation Suitability Index (ISI). These datasets underwent analysis at a 500-meter spatial resolution, encompassing the entirety of the nation. Weighted overlay analysis, performed within the GEE environment, generated a nationwide map delineating irrigation potential across five distinct categories: Very Low, Low, Moderate, High, and Very High suitability. The findings reveal suitability scores ranging from a minimum of 0.11 (very low) to a maximum of 1 (very high). Specifically, northern Nigerian states such as Sokoto, Kebbi, Yobe, and Borno demonstrated high to very high suitability. In contrast, southern states, including Lagos, Rivers, Cross River, and Delta, were characterized by very low suitability. The study underscores significant geographical disparities, particularly in arid northern regions which experience greater constraints due to lower precipitation and elevated evapotranspiration. These findings offer actionable directives for agricultural planners, policymakers, and stakeholders seeking to enhance irrigation capacity and strengthen food security resilience amidst evolving climatic conditions.","本研究对尼日利亚全国灌溉土地适宜性进行了综合地理空间评估。研究利用2020年中分辨率成像光谱仪（MODIS）卫星数据，在谷歌地球引擎（GEE）云端计算环境中进行处理。鉴于粮食安全和气候变率日益受到关注，确定适宜灌溉的区域对于促进尼日利亚可持续农业发展至关重要。本研究整合了多时相遥感观测数据，包括归一化植被指数（NDVI）、地表温度（LST）和归一化水体指数（NDWI），以构建灌溉适宜性指数（ISI）。这些数据集以500米空间分辨率进行分析，覆盖全国范围。在GEE环境中进行的加权叠加分析生成了全国灌溉潜力分布图，划分为五个类别：极低、低、中等、高和极高适宜性。研究结果显示，适宜性评分从最低0.11（极低）到最高1（极高）不等。具体而言，尼日利亚北部各州如索科托、凯比、约贝和博尔诺表现出高至极高适宜性。相比之下，南部各州包括拉各斯、河流州、十字河州和三角洲州则以极低适宜性为特征。研究强调了显著的地理差异，特别是干旱的北部地区因降水量较低和蒸散量较高而面临更大限制。这些发现为农业规划者、政策制定者和利益相关者提供了可操作的指导，以在气候条件不断变化的情况下增强灌溉能力并提升粮食安全韧性。","OpenAlex","2026-09-30T00:00:00Z",78,{"impact":145,"substance":17,"depth":297,"authority":20,"freshness":225,"relevant":22,"comment":298},17,"基于MODIS与GEE的全国尺度灌溉适宜性制图，方法规范、结论具体，对农业规划有参考价值，但属他国案例，国内落地性有限。",[300],{"name":293,"url":290},[302,27,175,303,304],"数字农业","GIS","灌溉适宜性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[307,308],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":309,"slug":310,"name":311,"path":312,"parent_code":44},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":46,"locked":14,"version":47,"method":48,"evidence":314},{"candidates":315,"agricultural_evidence":53},[316,320,324,328,332,336,342,346],{"code":33,"score":297,"supported":53,"title":317,"abstract":318,"tags":319},[34],[31,34,179],[],{"code":40,"score":172,"supported":53,"title":321,"abstract":322,"tags":323},[59],[175,59],[175],{"code":309,"score":21,"supported":53,"title":325,"abstract":326,"tags":327},[303],[],[303],{"code":63,"score":64,"supported":53,"title":329,"abstract":330,"tags":331},[],[27,67],[27],{"code":154,"score":79,"supported":53,"title":333,"abstract":334,"tags":335},[],[169],[],{"code":337,"score":79,"supported":53,"title":338,"abstract":339,"tags":341},"H8",[],[340],"可持续农业",[],{"code":258,"score":79,"supported":53,"title":343,"abstract":344,"tags":345},[],[261],[],{"code":347,"score":22,"supported":14,"title":348,"abstract":349,"tags":350},"I1",[],[],[302],[352,353],"尼日利亚 灌溉适宜性 遥感","Google Earth Engine 灌溉","尼日利亚灌溉适宜性遥感-3901","10.53030\u002Ftjags.1937471",{"doi":355,"openalex_id":357,"authors":358,"venue":9,"cited_by_count":88,"oa_url":9,"card":362,"direction":128,"ingested_from":130},"W7214955379",[359],{"name":360,"orcid":361},"Emmanuel M. Menegbo","https:\u002F\u002Forcid.org\u002F0000-0002-6694-3226",{"tldr":363,"method":364,"finding":365,"direction":128,"opportunity":366},"用遥感与GIS评估尼日利亚全国灌溉适宜性并绘制五级分区图。","2020年MODIS数据在GEE中计算NDVI、LST、NDWI并加权叠加。","北部各州适宜性高，南部低，全国适宜性得分0.11至1。","可结合土壤、地下水与作物需水数据，将静态适宜性图升级为动态灌溉决策支持系统。","2026-10-01T23:30:29.159371Z",{"id":369,"title":370,"url":371,"summary":372,"summary_zh":373,"content":9,"source_name":374,"source_url":371,"published_at":375,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":376,"score_detail":377,"sources":379,"tags":381,"primary_topic":386,"secondary_topics":389,"classification":392,"search_phrases":412,"slug":415,"view_count":79,"doi":416,"paper":417,"created_at":434},3845,"Problems of soil degradation assessment using spectral indices from space images","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71433-1","This article presents an integrated method of spectral indices for assessing soil degradation using Sentinel-2 Earth Remote Sensing (ERS) data. Unlike approaches based on individual indices, the method combines indicators of vegetation, humidity, thermal state, and salinity, increasing the accuracy of mapping saline and degraded lands while minimizing errors caused by variations in natural conditions. The application of the method in the Tashkent region of Uzbekistan (41.1465°N, 69.3009°E) showed that the category of “extremely high” and “high” degradation accounts for 3.29% and 8.28% of pixels, “medium” and “low” for 9.4% and 9.52%, while the remaining 69.51% belong to other category (class) of land cover, confirming the concentration of degradation foci in lowland areas. Comparison with 2020 showed a notable increase in the share of critical degradation (growth from 10.46% to 11.57%), while the share of the “extremely high” category increased from 2.80% to 3.29%, indicating a shift toward more severe conditions that aligns with official regional salinization statistics. Analysis of meteorological data (2013–2025) revealed an inverse relationship between temperature and precipitation, confirming the climatic nature of salinization; four stages of salinization were identified—latent, progressive, severe, and complete degradation and the main components method (PCA) was applied to reduce the size of the data. These results correspond to the expansion of irrigated lands in Uzbekistan from 3.5 to 8.5 million hectares and the associated increase in groundwater levels. Consequently, this method is not the final means of delimiting degraded lands, but represents a quick, inexpensive preliminary check before field surveys: it marks potentially degraded areas for priority ground surveys, supplementing official surveys and government statistics rather than replacing them. The method is applicable in agriculture, land cadastre, and environmental monitoring as an auxiliary tool for climate adaptation and water resource management.","本文提出了一种利用Sentinel-2地球遥感（ERS）数据评估土壤退化的光谱指数集成方法。与基于单一指数的方法不同，该方法综合了植被、湿度、热状态和盐分指标，提高了盐碱化和退化土地制图的精度，同时最大限度地减少了自然条件变化引起的误差。该方法在乌兹别克斯坦塔什干地区（北纬41.1465°，东经69.3009°）的应用表明，“极高”和“高”退化类别分别占像元的3.29%和8.28%，“中等”和“低”分别占9.4%和9.52%，其余69.51%属于其他土地覆盖类别（等级），证实了退化热点集中在低地地区。与2020年相比，临界退化比例显著增加（从10.46%增至11.57%），而“极高”类别比例从2.80%增至3.29%，表明退化状况趋于严重，这与官方区域盐渍化统计数据一致。气象数据（2013—2025年）分析揭示了气温与降水之间的反比关系，证实了盐渍化的气候成因；识别出盐渍化的四个阶段——潜伏期、进展期、严重期和完全退化期，并应用主成分分析方法（PCA）对数据进行了降维处理。这些结果与乌兹别克斯坦灌溉面积从350万公顷扩展至850万公顷以及相关的地下水位上升相吻合。因此，该方法并非划定退化土地的最终手段，而是实地调查前一种快速、低成本的初步筛查：它标记出潜在退化区域以供优先开展地面调查，是对官方调查和政府统计的补充而非替代。该方法适用于农业、土地地籍和环境监测领域，可作为气候适应和水资源管理的辅助工具。","Scientific Reports","2026-09-29T00:00:00Z",79,{"impact":165,"substance":17,"depth":145,"authority":52,"freshness":21,"relevant":22,"comment":378},"基于Sentinel-2多光谱指数的土壤退化综合评估方法，方法新颖、数据翔实，对盐渍化遥感监测与耕地保护有参考价值。",[380],{"name":374,"url":371},[382,28,383,384,385],"Sentinel-2","土壤退化","盐渍化","土地退化评估",{"code":184,"slug":188,"name":387,"path":388,"parent_code":37},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[390,391],{"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":393},{"candidates":394,"agricultural_evidence":53},[395,399,403,407],{"code":184,"score":172,"supported":53,"title":396,"abstract":397,"tags":398},[188],[187,188],[384],{"code":33,"score":21,"supported":53,"title":400,"abstract":401,"tags":402},[],[31,181,251],[],{"code":40,"score":225,"supported":53,"title":404,"abstract":405,"tags":406},[],[175,59],[382,28],{"code":154,"score":79,"supported":53,"title":408,"abstract":409,"tags":411},[],[410],"气候适应",[],[413,414],"乌兹别克斯坦 塔什干 土壤盐渍化","Sentinel-2 光谱指数 土壤退化","乌兹别克斯坦塔什干土壤盐渍化-3845","10.1038\u002Fs41598-026-71433-1",{"doi":416,"openalex_id":418,"authors":419,"venue":374,"cited_by_count":88,"oa_url":428,"card":429,"direction":128,"ingested_from":130},"W7214801255",[420,423,426],{"name":421,"orcid":422},"Z. Z. Shamsiev","https:\u002F\u002Forcid.org\u002F0000-0002-0323-9741",{"name":424,"orcid":425},"L. F. Sulyukova","https:\u002F\u002Forcid.org\u002F0000-0002-7150-4359",{"name":427,"orcid":9},"Rasul Shamsiev","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71433-1_reference.pdf",{"tldr":430,"method":431,"finding":432,"direction":128,"opportunity":433},"提出融合多光谱指数的Sentinel-2土壤退化评估方法，在乌兹别克斯坦验证并识别盐渍化阶段。","Sentinel-2影像多指数融合（植被、湿度、热、盐分）结合PCA与气象数据分","退化集中于低地，2020年以来极重度退化占比由2.80%升至3.29%，与官方盐渍化统计一致。","可将该快速预筛方法与地面调查、机器学习结合，构建多区域可迁移的退化早期预警与制图框架。","2026-09-30T23:30:29.386707Z",{"id":436,"title":437,"url":438,"summary":439,"summary_zh":440,"content":9,"source_name":441,"source_url":438,"published_at":442,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":443,"score_detail":444,"sources":447,"tags":449,"primary_topic":454,"secondary_topics":455,"classification":458,"search_phrases":481,"slug":484,"view_count":485,"doi":486,"paper":487,"created_at":507},3551,"Diachronic Assessment of Water Springs in Ribat El Kheir Plateau (Morocco), under Climatic and Anthropogenic Pressure, Using Remote Sensing (1985-2025)","https:\u002F\u002Fdoi.org\u002F10.4028\u002Fp-9o0ojj","The Ribat El Kheir plateau, located in the northern part of the Tabular Middle Atlas (Morocco), is characterized by carbonate formations (Liasic dolomitic limestones) and a dense network of tectonic faults. This study conducts a diachronic assessment of water springs and their interaction with climatic conditions and anthropogenic factors related to the expansion of irrigated agriculture (farms) from 1985 to 2025. A multi-source methodological approach was adopted, combining the inventory and mapping of springs, the application of remote sensing, using Landsat and Sentinel satellite imagery, and the calculation of standard indices, including the Normalized Difference Water Index (NDWI), the Normalized Difference Vegetation Index (NDVI), and the Standardized Precipitation Index (SPI). The results indicate that a combination of geomorphological context primarily influences the spatial distribution of the 101 inventoried springs, the lithological nature of the formations (permeable dolomites), structural features, and climatic factors. However, a significant regression in both the number and flow of these springs has been observed. Concurrently, irrigated fruit agriculture has expanded remarkably over the study period. The SPI analysis further reveals a climatic trend towards increased aridity. These findings highlight the critical and synergistic pressure of climate change and intensive agriculture on water resources in this vulnerable semi-arid region.","位于摩洛哥中阿特拉斯板状山脉北部的里巴特·埃尔·海尔（Ribat El Kheir）高原，以碳酸盐岩地层（里阿斯统白云质灰岩）和密集的构造断裂网络为特征。本研究对1985年至2025年间泉水动态及其与气候条件和灌溉农业（农场）扩张相关人为因素的相互作用进行了历时性评估。研究采用多源方法学途径，结合泉水编录与制图、遥感技术应用（利用Landsat和Sentinel卫星影像）以及标准指数的计算，包括归一化差异水体指数（NDWI）、归一化差异植被指数（NDVI）和标准化降水指数（SPI）。结果表明，101处编录泉水的空间分布主要受地貌背景、地层岩性特征（渗透性白云岩）、构造特征及气候因素的综合影响。然而，观测到这些泉水的数量和流量均出现显著衰退。与此同时，灌溉果树农业在研究期内显著扩张。SPI分析进一步揭示了气候趋于干旱化的趋势。这些发现凸显了气候变化与集约化农业对这一脆弱半干旱地区水资源的临界协同压力。","International journal of engineering research in Africa","2026-09-25T00:00:00Z",67,{"impact":225,"substance":445,"depth":297,"authority":20,"freshness":21,"relevant":22,"comment":446},20,"基于Landsat与Sentinel遥感的40年泉水退化评估，方法扎实、数据翔实，但属区域案例研究，对国内三农实践的直接参考价值有限。",[448],{"name":441,"url":438},[450,451,28,452,453],"农业水资源","NDVI","灌溉农业","干旱化",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[456,457],{"code":154,"slug":155,"name":156,"path":157,"parent_code":37},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":459},{"candidates":460,"agricultural_evidence":53},[461,465,471,475],{"code":40,"score":165,"supported":53,"title":462,"abstract":463,"tags":464},[59],[175,59],[451,28],{"code":154,"score":21,"supported":53,"title":466,"abstract":467,"tags":470},[],[468,169,469],"气候变化","climate change",[],{"code":33,"score":225,"supported":53,"title":472,"abstract":473,"tags":474},[],[31,181],[450,452],{"code":476,"score":79,"supported":53,"title":477,"abstract":478,"tags":480},"B2",[],[479],"果树",[],[482,483],"Ribat El Kheir 泉水 遥感","摩洛哥 灌溉农业 水资源","RibatElKheir泉水遥感-3551",2,"10.4028\u002Fp-9o0ojj",{"doi":486,"openalex_id":488,"authors":489,"venue":441,"cited_by_count":88,"oa_url":9,"card":502,"direction":128,"ingested_from":130},"W7214294130",[490,492,493,496,498,499],{"name":491,"orcid":9},"Hamid Achiban",{"name":491,"orcid":9},{"name":494,"orcid":495},"Miloud Afenzar","https:\u002F\u002Forcid.org\u002F0009-0008-0839-0230",{"name":497,"orcid":9},"Hassan Achiban",{"name":497,"orcid":9},{"name":500,"orcid":501},"Nourddine El Gali","https:\u002F\u002Forcid.org\u002F0009-0001-7025-4765",{"tldr":503,"method":504,"finding":505,"direction":128,"opportunity":506},"基于遥感和指数分析，评估摩洛哥Ribat El Kheir高原1985-2025年泉水数量与流量的退","Landsat\u002FSentinel影像、NDWI\u002FNDVI\u002FSPI指数、泉水编目与","泉水数量与流量显著减少，灌溉农业扩张与气候干旱化协同加剧水资源压力。","可结合多源遥感与机器学习，量化灌溉扩张对泉水流量的贡献，并预测未来情景。","2026-09-26T23:30:29.568284Z",{"id":509,"title":510,"url":511,"summary":512,"summary_zh":513,"content":9,"source_name":514,"source_url":511,"published_at":515,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":376,"score_detail":516,"sources":518,"tags":520,"primary_topic":524,"secondary_topics":525,"classification":527,"search_phrases":544,"slug":547,"view_count":88,"doi":548,"paper":549,"created_at":575},2324,"Inferring groundwater overdraft in data-scarce arid agro-ecosystems: a semi-empirical remote sensing framework applied to the Elfeija watershed, Morocco","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffeart.2026.1914670","Introduction Small-scale irrigated agriculture in arid regions relies heavily on unmetered groundwater, creating an “invisible pumping” threat to aquifer sustainability. Monitoring these diffuse withdrawals remains a critical challenge for water governance. This study proposes a synergistic multi-sensor remote sensing workflow to infer groundwater abstraction and assess its impact on local water stress using a semi-empirical thermal-phenological model. Methods We leveraged high-resolution vegetation phenology (NDVI) from Sentinel-2 and thermal data (LST) from Landsat 8\u002F9 over the Elfeija watershed, Morocco (2020–2024). By isolating the thermal-phenological anomaly (ΔLST) between irrigated plots and natural reference areas, we translated the surface cooling effect into evapotranspiration fluxes and pumping volumes. To ensure physical plausibility and avoid circularity, the approach was constrained by independent bottom-up agronomic benchmarks alongside a multi-tier cross-comparison against global models (MOD16, ERA5-Land, GLEAM), and a rigorous Monte Carlo uncertainty propagation integrating both parametric and recharge uncertainties. Results The analysis reveals a distinct seasonal signature of irrigation. Probabilistic modeling indicates that the inferred annual pumping for 2022 (central estimates: 5.85–6.79 Mm 3 ) strongly suggests a state of severe overdraft, with a high probability (P > 80%) of exceeding the estimated renewable aquifer recharge (4.70 Mm 3 ). Furthermore, the framework successfully disentangled climatic triggers from anthropogenic forcing, capturing an anomalous, water-intensive late-season agricultural cycle in 2024. Discussion While the lack of in-situ piezometric data limits absolute ground-truthing, the multi-source convergence (global models and local infrastructure data) robustly brackets the overdraft scenario. The proposed semi-empirical inference workflow provides a scalable, cost-effective framework for data-scarce regions, offering river basin agencies a proactive tool to identify over-extraction hotspots and negotiate sustainable water quotas.","引言 干旱地区的小规模灌溉农业严重依赖未计量的地下水，对含水层可持续性构成了“隐形抽水”威胁。监测这些分散取水仍是水治理面临的关键挑战。本研究提出一种多传感器协同遥感工作流程，利用半经验热-物候模型推断地下水开采量并评估其对局部水资源压力的影响。方法 我们利用Sentinel-2的高分辨率植被物候（NDVI）和Landsat 8\u002F9的热数据（LST），覆盖摩洛哥Elfeija流域（2020—2024年）。通过分离灌溉地块与自然参照区之间的热-物候异常（ΔLST），我们将地表冷却效应转化为蒸散发通量和抽水量。为确保物理合理性和避免循环论证，该方法受到独立自下而上的农学基准约束，并与全球模型（MOD16、ERA5-Land、GLEAM）进行多层级交叉比较，同时通过严格的蒙特卡洛不确定性传播整合参数不确定性和补给不确定性。结果 分析揭示了灌溉明显的季节性特征。概率建模表明，2022年推断的年抽水量（中心估计值：5.85—6.79 Mm³）强烈提示存在严重超采状态，超过估计可再生含水层补给量（4.70 Mm³）的概率较高（P > 80%）。此外，该框架成功地将气候触发因素与人为强迫区分开来，捕捉到2024年异常的、高耗水的晚季农业周期。讨论 尽管缺乏原位测压数据限制了绝对的地面验证，但多源汇聚（全球模型和本地基础设施数据）稳健地界定了超采情景。所提出的半经验推断工作流程为数据稀缺地区提供了可扩展、成本效益高的框架，为流域机构提供了主动识别过度开采热点并协商可持续用水配额的工具。","Frontiers in Earth Science","2026-09-11T00:00:00Z",{"impact":145,"substance":17,"depth":145,"authority":20,"freshness":225,"relevant":22,"comment":517},"提出多源遥感半经验框架推断数据稀缺区地下水超采，方法新颖、结论可靠，对农业水资源治理有参考价值。",[519],{"name":514,"url":511},[230,28,521,522,523],"智慧灌溉","地下水超采","干旱农业",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[526],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":528},{"candidates":529,"agricultural_evidence":53},[530,534,540],{"code":40,"score":172,"supported":53,"title":531,"abstract":532,"tags":533},[59],[175,59],[28],{"code":33,"score":535,"supported":53,"title":536,"abstract":537,"tags":539},11,[],[31,181,538,34,179],"蒸散发",[521,230],{"code":154,"score":79,"supported":53,"title":541,"abstract":542,"tags":543},[],[169],[],[545,546],"地下水超采 水资源管理 干旱农业 智慧灌溉","地下水超采 水资源管理","地下水超采水资源管理干旱农业智慧灌溉-2324","10.3389\u002Ffeart.2026.1914670",{"doi":548,"openalex_id":550,"authors":551,"venue":514,"cited_by_count":88,"oa_url":511,"card":570,"direction":128,"ingested_from":130},"W7212247745",[552,555,558,561,564,567],{"name":553,"orcid":554},"Rachid Amiha","https:\u002F\u002Forcid.org\u002F0000-0002-9863-6685",{"name":556,"orcid":557},"Belkacem Kabbachi","https:\u002F\u002Forcid.org\u002F0000-0003-3802-1256",{"name":559,"orcid":560},"Mohamed Ait Haddou","https:\u002F\u002Forcid.org\u002F0000-0002-0587-1230",{"name":562,"orcid":563},"Youssef Bouchriti","https:\u002F\u002Forcid.org\u002F0000-0002-8566-1451",{"name":565,"orcid":566},"Hicham Gougueni","https:\u002F\u002Forcid.org\u002F0000-0001-5340-7566",{"name":568,"orcid":569},"Mustapha Ikirri","https:\u002F\u002Forcid.org\u002F0000-0002-9971-6249",{"tldr":571,"method":572,"finding":573,"direction":128,"opportunity":574},"提出半经验遥感框架，在摩洛哥缺水灌区推断地下水超采量。","Sentinel-2 NDVI与Landsat 8\u002F9 LST构建热-物候异常，","2022年抽水量5.85–6.79 Mm³，超补给量概率超80%，呈严重超采。","可迁移至其他数据稀缺干旱区，融合多源遥感与用水配额决策支持。","2026-09-13T23:30:23.143553Z"]