[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3191":3,"related-3191":58},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":57},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。",null,"Spanish Journal of Soil Science","2026-09-22T00:00:00Z","论文",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,21,17,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业遥感","气候变化","遥感","土壤碳汇","数字土壤制图",[33,34],"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",0,"10.3389\u002Fsjss.2026.16733",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":49,"direction":55,"ingested_from":56},"W7213971196",[41,43,45,47],{"name":42,"orcid":9},"Faroug A.H. Jadalla",{"name":44,"orcid":9},"Kolapo O. Oluwasemire",{"name":46,"orcid":9},"Abd Elmagid A. Elmobarak",{"name":48,"orcid":9},"Mohammed A. M. Mohammed Zein",{"tldr":50,"method":51,"finding":52,"direction":53,"opportunity":54},"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。","农业遥感与作物表型","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","openalex","2026-09-22T23:30:31.028178Z",{"total":59,"page":22,"page_size":59,"items":60},6,[61,113,155,208,254,324],{"id":62,"title":63,"url":64,"summary":65,"summary_zh":66,"content":9,"source_name":67,"source_url":64,"published_at":68,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":69,"score_detail":70,"sources":75,"tags":77,"search_phrases":81,"slug":84,"view_count":36,"doi":85,"paper":86,"created_at":112},3166,"Case Studies of Environmental Monitoring Based on the Integrated Use of Bioindicators and Remote Sensing","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183227","This narrative review examines how plant bioindication can be integrated with remote sensing to support the preliminary screening of soil contamination, framed as an environmental situational awareness problem addressed through multi-scale imagery and field validation. Seven case studies—illegal waste deposits, pipeline leaks, landfill impacts, industrial areas, agrochemical drift, oil-spills in coastal wetlands, and acid mine drainage—were selected based on joint documentation of a contamination source or exposure condition, a measurable plant response, and a remotely detected signal related to vegetation stress. This synthesis clarifies how vegetation response can serve as a proxy for large-scale contamination screening. A common contamination–vegetation–remote sensing logic emerges across the cases examined, linking contamination drivers and exposure pathways to canopy responses detectable as spectral, thermal, spatial, or temporal anomalies. Recurring spatial patterns of vegetation anomalies are identified as first-order interpretive cues rather than diagnostic signatures. Vegetation stress responses lack spectral specificity, making spectral equifinality the primary operational challenge for vegetation-based biomonitoring. Future workflows should integrate spectral, spatial, temporal, multi-sensor, and ancillary data validated against ground-truth. Future research should expand the evidence base through formal literature searches, multi-site validation, standardized protocols, and infrastructures integrating plant phenotyping and remote sensing to support quantitatively validated screening approaches.","本叙述性综述探讨了如何将植物生物指示与遥感相结合，以支持土壤污染的初步筛查，并将其构建为一个通过多尺度影像和实地验证来解决的环境态势感知问题。基于对污染源或暴露条件的联合记录、可测量的植物响应以及与植被胁迫相关的遥感信号，选取了七个案例研究——非法废物堆放、管道泄漏、垃圾填埋场影响、工业区、农用化学品飘移、沿海湿地溢油和酸性矿山排水。本综述阐明了植被响应如何能够作为大规模污染筛查的替代指标。在所考察的案例中，浮现出一种共同的污染—植被—遥感逻辑，将污染驱动因素和暴露途径与可被检测为光谱、热、空间或时间异常的冠层响应联系起来。植被异常的重复性空间格局被识别为一级解释线索，而非诊断性特征。植被胁迫响应缺乏光谱特异性，使得光谱等终性成为基于植被的生物监测面临的首要操作挑战。未来的工作流程应整合光谱、空间、时间、多传感器和辅助数据，并以地面真值进行验证。未来研究应通过正式文献检索、多站点验证、标准化协议以及整合植物表型分析与遥感的基础设施来扩展证据基础，以支持经过定量验证的筛查方法。","Remote Sensing","2026-09-19T00:00:00Z",77,{"impact":71,"substance":18,"depth":72,"authority":73,"freshness":17,"relevant":22,"comment":74},16,18,14,"综述性论文，系统梳理植被生物指示与遥感融合筛查土壤污染的七个案例，方法学与结论对农业环境遥感监测有参考价值，但非全国性政策或突破性成果。",[76],{"name":67,"url":64},[27,29,78,79,80],"环境监测","植被监测","土壤污染",[82,83],"植被生物指示 遥感 土壤污染","Remote Sensing 植被胁迫 遥感监测","植被生物指示遥感土壤污染-3166","10.3390\u002Frs18183227",{"doi":85,"openalex_id":87,"authors":88,"venue":67,"cited_by_count":36,"oa_url":64,"card":107,"direction":53,"ingested_from":56},"W7213930509",[89,92,95,98,101,104],{"name":90,"orcid":91},"Marco De Mizio","https:\u002F\u002Forcid.org\u002F0009-0009-6556-2606",{"name":93,"orcid":94},"Donato Amitrano","https:\u002F\u002Forcid.org\u002F0000-0002-2355-4503",{"name":96,"orcid":97},"Massimiliano Gargiulo","https:\u002F\u002Forcid.org\u002F0000-0002-6783-366X",{"name":99,"orcid":100},"Sara Parrilli","https:\u002F\u002Forcid.org\u002F0000-0002-7225-1269",{"name":102,"orcid":103},"Claudia Savarese","https:\u002F\u002Forcid.org\u002F0000-0003-0161-2407",{"name":105,"orcid":106},"Massimiliano Lega","https:\u002F\u002Forcid.org\u002F0000-0002-4842-6049",{"tldr":108,"method":109,"finding":110,"direction":53,"opportunity":111},"综述植物生物指示与遥感结合用于土壤污染初步筛查的七个案例，提出污染-植被-遥感逻辑。","叙事性综述，选取七类污染案例，整合多尺度影像与地面验证。","植被胁迫光谱缺乏特异性，光谱等终性是植被生物监测的主要操作挑战。","可构建标准化植物表型-遥感集成平台，开展多站点验证以量化污染筛查。","2026-09-22T23:30:20.917796Z",{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":9,"source_name":119,"source_url":116,"published_at":68,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":120,"score_detail":121,"sources":125,"tags":127,"search_phrases":131,"slug":134,"view_count":36,"doi":135,"paper":136,"created_at":154},3060,"Geospatial Intelligence for Peri-Urban Land-Use Conflicts: Evaluating Agricultural Suitability against Rapid Urbanisation using the Analytical Hierarchy Process and Cloud Computing","https:\u002F\u002Fdoi.org\u002F10.59543\u002F6mpcwr41","This paper presents a geospatial multi-criteria evaluation of agricultural potential in the suburban region of Bapatla using eight physical and land-use characteristics: elevation, slope, road accessibility, proximity to water bodies, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), Land Use\u002FLand Cover (LULC), and soil texture, processed using Google Earth Engine. The Analytic Hierarchy Process (AHP) was used to determine the relative weights of each criterion. NDVI received the highest weight (26.33%), followed by LST, slope, and proximity to water bodies (14.96% each), while elevation received the lowest weight (4.6%) due to the region's flat terrain. Weighted overlay analysis classified the 142.25 km² study area into Suitable (108.90 km²; 76.55%), Not Suitable (32.60 km²; 22.92%), and Highly Suitable (0.75 km²; 0.53%) categories. Suitable areas are mainly distributed across the southern and peripheral agricultural zones, whereas unsuitable areas are concentrated within Bapatla Urban and its surroundings. The limited extent of highly suitable land highlights the scarcity of optimal agricultural sites. The results reveal land-use conflicts driven primarily by urbanisation rather than environmental constraints. The AHP-weighted suitability map provides an evidence-based tool for agricultural land conservation, water-resource management, and sustainable urban expansion.","本文基于八项自然与土地利用特征，对巴帕特拉（Bapatla）郊区农业潜力进行了地理空间多准则评价，这些特征包括：海拔、坡度、道路可达性、距水体远近、地表温度（LST）、归一化植被指数（NDVI）、土地利用\u002F土地覆盖（LULC）以及土壤质地，并利用Google Earth Engine进行处理。采用层次分析法（AHP）确定各准则的相对权重。NDVI权重最高（26.33%），其次为LST、坡度和距水体远近（均为14.96%），而海拔因该地区地形平坦权重最低（4.6%）。加权叠加分析将142.25 km²的研究区划分为适宜（108.90 km²；76.55%）、不适宜（32.60 km²；22.92%）和高适宜（0.75 km²；0.53%）三类。适宜区主要分布于南部及外围农业区，而不适宜区集中于巴帕特拉城区及其周边。高适宜土地面积有限，凸显了优质农业用地的稀缺性。结果表明，土地利用冲突主要由城市化驱动，而非环境限制。基于AHP的适宜性地图为农业用地保护、水资源管理和可持续城市扩张提供了循证工具。","Journal of Urban Intelligence and Smart Systems",70,{"impact":122,"substance":123,"depth":19,"authority":20,"freshness":17,"relevant":22,"comment":124},12,20,"该论文利用遥感与AHP方法评估城郊农业用地冲突，方法新颖、数据详实，对农业土地保护有参考价值，但属细分领域研究，影响范围有限。",[126],{"name":119,"url":116},[128,27,129,29,130],"智慧农业","农业信息化","土地利用",[132,133],"Bapatla 农业用地 城市化","Google Earth Engine 农业适宜性","Bapatla农业用地城市化-3060","10.59543\u002F6mpcwr41",{"doi":135,"openalex_id":137,"authors":138,"venue":119,"cited_by_count":36,"oa_url":147,"card":148,"direction":153,"ingested_from":56},"W7213644047",[139,141,143,145],{"name":140,"orcid":9},"Sreerama Naik Naik S R",{"name":142,"orcid":9},"T K Prasad",{"name":144,"orcid":9},"Feba Jose Jasmine",{"name":146,"orcid":9},"Jayapal G","https:\u002F\u002Fjuiss.org\u002Findex.php\u002Fjuiss\u002Farticle\u002Fdownload\u002F366\u002F231",{"tldr":149,"method":150,"finding":151,"direction":53,"opportunity":152},"用AHP与云平台评估印度Bapatla城郊农业适宜性，揭示城市化引发的土地利用冲突。","Google Earth Engine处理8个因子，AHP加权叠加分析142.2","76.55%区域适宜农业，但高度适宜仅0.53%，冲突主因是城市化而非环境限制。","可引入时序遥感与动态城市扩张模拟，构建城郊农业保护与城市增长协同优化模型。","智慧农业 \u002F 农业物联网","2026-09-21T23:30:09.448414Z",{"id":156,"title":157,"url":158,"summary":159,"summary_zh":160,"content":9,"source_name":161,"source_url":158,"published_at":162,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":163,"score_detail":164,"sources":166,"tags":168,"search_phrases":171,"slug":174,"view_count":36,"doi":175,"paper":176,"created_at":207},2947,"AI-enabled UAV-based Soil Organic Carbon Mapping in Arid Environments: A Pilot Study Protocol","https:\u002F\u002Fdoi.org\u002F10.2174\u002F0118743315495282260915110324","Introduction Soil organic carbon (SOC) is an important indicator of soil health, agricultural productivity, and carbon sequestration potential. However, accurate and scalable SOC mapping in arid environments is constrained by high spatial heterogeneity and the limitations of conventional soil sampling. This study aims to develop a standardized UAV-enabled framework for high-resolution SOC mapping in arid agricultural environments. Methods A pilot-study protocol integrating UAV-based hyperspectral remote sensing with artificial intelligence and machine learning was developed. The workflow encompasses study-site selection, ground-reference sampling, UAV hyperspectral data acquisition, radiometric and geometric preprocessing, spectral feature extraction and selection, machine-learning model development, validation, uncertainty assessment, and performance evaluation using R 2 , RMSE, and MAE. The protocol also incorporates assessment of environmental confounders, including soil moisture, surface roughness, and crop residues. Results The resulting framework provides a systematic and reproducible workflow for UAV-based SOC estimation, integrating field observations, hyperspectral features, predictive modelling, and uncertainty assessment. It establishes defined procedures for evaluating model robustness and transferability across varying field conditions. Discussion The framework addresses an important methodological gap in UAV-enabled SOC mapping by integrating remote sensing and AI within a standardized pilot-study design. Its emphasis on environmental confounders and uncertainty assessment can improve the reliability and comparability of SOC mapping studies. However, field validation across diverse arid environments remains necessary. Conclusion The proposed protocol provides a practical foundation for reproducible SOC mapping and subsequent field validation, supporting precision agriculture, sustainable soil management, and carbon monitoring, reporting, and verification (MRV) in arid regions.","引言 土壤有机碳（SOC）是衡量土壤健康、农业生产力及碳固存潜力的重要指标。然而，干旱环境中高空间异质性和传统土壤采样的局限性制约了准确且可扩展的SOC制图。本研究旨在开发一个标准化的无人机（UAV）框架，用于干旱农业环境中的高分辨率SOC制图。方法 开发了一套整合无人机高光谱遥感与人工智能及机器学习的试点研究方案。该工作流程涵盖研究地点选择、地面参考采样、无人机高光谱数据采集、辐射与几何预处理、光谱特征提取与选择、机器学习模型开发、验证、不确定性评估，以及使用R²、RMSE和MAE进行的性能评价。该方案还包括对环境混杂因素的评估，包括土壤水分、地表粗糙度和作物残茬。结果 所构建的框架为基于无人机的SOC估算提供了系统且可重复的工作流程，整合了野外观测、高光谱特征、预测建模和不确定性评估。它建立了明确的程序，用于评估模型在不同田间条件下的稳健性和可迁移性。讨论 该框架通过将遥感与人工智能整合于标准化的试点研究设计中，填补了无人机SOC制图领域的重要方法学空白。其对环境混杂因素和不确定性评估的重视，可提高SOC制图研究的可靠性和可比性。然而，仍需在不同干旱环境中进行田间验证。结论 所提出的方案为可重复的SOC制图及后续田间验证提供了实用基础，支持干旱地区的精准农业、可持续土壤管理以及碳监测、报告与核查（MRV）。","The Open Agriculture Journal","2026-09-18T00:00:00Z",67,{"impact":122,"substance":72,"depth":71,"authority":20,"freshness":17,"relevant":22,"comment":165},"提出无人机高光谱结合AI的干旱区土壤有机碳制图标准化方案，方法框架清晰但尚属试点协议、缺乏实地验证，具备一定参考价值。",[167],{"name":161,"url":158},[128,169,170,29,30],"农业人工智能","精准农业",[172,173],"无人机 土壤有机碳 制图","AI 高光谱 干旱农业","无人机土壤有机碳制图-2947","10.2174\u002F0118743315495282260915110324",{"doi":175,"openalex_id":177,"authors":178,"venue":161,"cited_by_count":36,"oa_url":158,"card":202,"direction":53,"ingested_from":56},"W7213561504",[179,182,185,188,191,194,196,198,200],{"name":180,"orcid":181},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":183,"orcid":184},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":186,"orcid":187},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":189,"orcid":190},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":192,"orcid":193},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":195,"orcid":9},"Almaha Jamal",{"name":197,"orcid":9},"Afra Rashed",{"name":199,"orcid":9},"Hamda Yousif",{"name":201,"orcid":9},"Sarah Sadeq",{"tldr":203,"method":204,"finding":205,"direction":53,"opportunity":206},"提出一套无人机高光谱结合AI的干旱区土壤有机碳制图标准化试点方案。","无人机高光谱遥感、地面采样、光谱特征选择与机器学习建模，用R²、RMSE、MAE","构建了可复现的SOC估算流程，并纳入环境混杂因素与不确定性评估。","可在多干旱区开展跨区域验证，探索模型迁移性与不确定性量化方法。","2026-09-19T23:30:33.273156Z",{"id":209,"title":210,"url":211,"summary":212,"summary_zh":213,"content":9,"source_name":214,"source_url":211,"published_at":162,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":215,"score_detail":216,"sources":219,"tags":221,"search_phrases":225,"slug":228,"view_count":36,"doi":229,"paper":230,"created_at":253},2941,"Drought dynamics and climatic drivers in the Tarim Basin using remote sensing indices and pixel-wise machine learning","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72142-5","Abstract Drought characterization in hyper-arid endorheic basins requires multi-index approaches that capture distinct hydrometeorological processes. This study investigates spatio-temporal drought dynamics in the Tarim Basin (TB)—China’s largest inland arid region—using two complementary remote sensing indices: the Temperature Vegetation Dryness Index (TVDI) for landscape-scale moisture and the Crop Water Stress Index (CWSI) for agricultural drought. Based on 2000–2024 remote sensing and meteorological data, we employed a pixel-wise Random Forest framework with spatial cross-validation and permutation importance analysis to quantify climatic drivers across the TB. Results reveal a fundamental “core-periphery” dichotomy: TVDI identifies persistent extreme drought in the Taklamakan Desert core, while CWSI reveals alleviating water stress in peripheral oasis farmlands (73.21% showing significant decrease, p \u003C 0.05). Despite regional warming-wetting trends, TVDI exhibited an insignificant decrease (54.72% of the basin), contrasting with CWSI's significant agricultural drought alleviation. Vapor Pressure Deficit (VPD)—a key atmospheric dryness indicator—exhibited high relative permutation importance for both drought indices (72–75%), considerably exceeding the values obtained for precipitation (6–8%) within the Tarim Basin. Secondary drivers diverge by land surface type: TVDI responds to Relative Humidity (8.2%) and Precipitation (6.1%), while CWSI is modulated by Land Surface Temperature (9.4%) and Sunshine Hours (7.8%). Partial correlation analyses controlling for topography and temperature confirm VPD’s independent effect on drought severity. Large-scale climate oscillations, particularly the Arctic Oscillation (AO) and ENSO-PDO interactions, significantly modulate interannual drought variability (r = 0.74–0.75, p \u003C 0.01). This study provides the first pixel-scale quantification of the relative dominance of atmospheric water demand over precipitation in driving drought evolution in the Tarim Basin, with VPD contributing 72–75% of the total permutation importance compared to 6–8% for precipitation. This quantitative benchmark offers actionable parameters for drought monitoring systems in arid regions and underscores the need to integrate VPD and large-scale climate signals into early warning frameworks.","摘要 极端干旱内流盆地的干旱特征刻画需要能够捕捉不同水文气象过程的多指标方法。本研究利用两个互补的遥感指数——用于景观尺度土壤湿度的温度植被干旱指数（TVDI）和用于农业干旱的作物水分胁迫指数（CWSI）——探讨了塔里木盆地（TB）——中国最大的内陆干旱区——干旱的时空动态。基于2000—2024年遥感与气象数据，我们采用逐像元随机森林框架，结合空间交叉验证和置换重要性分析，量化了塔里木盆地气候驱动因子的作用。结果揭示了一种根本性的“核心—边缘”二分格局：TVDI识别出塔克拉玛干沙漠核心区持续存在的极端干旱，而CWSI则显示外围绿洲农田的水分胁迫正在缓解（73.21%呈显著下降，p \u003C 0.05）。尽管区域呈现暖湿化趋势，TVDI却表现出不显著的下降（占流域面积的54.72%），这与CWSI所反映的农业干旱显著缓解形成对比。饱和水汽压差（VPD）——一个关键的大气干燥度指标——对两个干旱指数均表现出较高的相对置换重要性（72%—75%），远超塔里木盆地降水所对应的值（6%—8%）。次要驱动因子因地表类型而异：TVDI响应相对湿度（8.2%）和降水（6.1%），而CWSI受地表温度（9.4%）和日照时数（7.8%）调控。控制地形和温度后的偏相关分析证实了VPD对干旱严重程度的独立影响。大尺度气候振荡，尤其是北极涛动（AO）和ENSO-PDO相互作用，显著调控着年际干旱变率（r = 0.74—0.75，p \u003C 0.01）。本研究首次在像元尺度上量化了大气需水量相对于降水在驱动塔里木盆地干旱演变中的相对主导地位，其中VPD贡献了总置换重要性的72%—75%，而降水仅贡献6%—8%。这一定量基准为干旱区干旱监测系统提供了可操作的参数，并凸显了将VPD和大尺度气候信号纳入预警框架的必要性。","Scientific Reports",81,{"impact":72,"substance":217,"depth":72,"authority":73,"freshness":17,"relevant":22,"comment":218},23,"首次在像元尺度量化VPD对干旱的主导作用，方法新颖、数据跨度长，对干旱预警系统建设有实质参考价值。",[220],{"name":214,"url":211},[27,28,222,223,224],"遥感监测","干旱预警","塔里木盆地",[226,227],"塔里木盆地 遥感 干旱","TVDI CWSI 干旱监测","塔里木盆地遥感干旱-2941","10.1038\u002Fs41598-026-72142-5",{"doi":229,"openalex_id":231,"authors":232,"venue":214,"cited_by_count":36,"oa_url":211,"card":248,"direction":53,"ingested_from":56},"W7213539719",[233,235,237,239,242,244,246],{"name":234,"orcid":9},"Mutallip Sattar",{"name":236,"orcid":9},"Alim Abbas",{"name":238,"orcid":9},"Sardar Parhat",{"name":240,"orcid":241},"Alimujiang Yasen","https:\u002F\u002Forcid.org\u002F0000-0002-9860-7921",{"name":243,"orcid":9},"Muhemaiti Wahafu",{"name":245,"orcid":9},"Akida Salam",{"name":247,"orcid":9},"Batur Bake",{"tldr":249,"method":250,"finding":251,"direction":53,"opportunity":252},"基于遥感指数与逐像元机器学习，量化塔里木盆地2000—2024年干旱动态及气候驱动因子。","TVDI与CWSI双指数，逐像元随机森林、空间交叉验证与置换重要性分析。","干旱呈核心—边缘分异，VPD贡献72–75%远超降水的6–8%，主导干旱演变。","可将VPD与大尺度气候振荡纳入干旱预警，并拓展至其他干旱内陆盆地的逐像元归因研究。","2026-09-19T23:30:32.698746Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":9,"source_name":260,"source_url":257,"published_at":261,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":215,"score_detail":262,"sources":266,"tags":268,"search_phrases":273,"slug":276,"view_count":36,"doi":277,"paper":278,"created_at":323},2797,"Generation of representative datasets of future Copernicus Sentinel Expansion Mission Data (hyperspectral, thermal and L-band) as basis for innovative agricultural products","https:\u002F\u002Fdoi.org\u002F10.62880\u002Frars26005","The Copernicus Sentinel Expansion Missions will provide new and unique remote sensing data. To enable rapid use of real data as soon as it becomes available, it is essential to generate comparable synthetic data in advance. This study proposes a novel data set for three of the upcoming sensors. CHIME hyperspectral data are generated by inverting multispectral reflectance data from Sentinel-2 time series by radiative transfer modelling to retrieve land surface parameters and subsequently forward-simulating bottom-of-atmosphere reflectance using expected CHIME sensor characteristics. Future LSTM land surface temperature data are derived from Sentinel-3 and Sentinel-2 data using the Sen-ET workflow with spatial data mining sharpening. L-band backscatter and coherence data for ROSE-L are simulated using SAOCOM-1 data, which are transformed to match the expected spatial and radiometric characteristics. The novel data set is available for three areas of interest (AOIs) defined by Sentinel-2 tiles located in Germany, Belgium, and Estonia. A validation of simulated CHIME data using existing comparable sensor data from EnMAP showed a high spectral correlation with an average RMSE of 6.154 [%] and a correlation of 0.924 for the German AOI in 2024. This publicly available, unique and well validated dataset already enables the preparation and development of future products and services across a wide range of application areas based on data from the Sentinel Expansion Mission. Due to the high data availability resulting from extensive two-year time series, as well as the various AOIs, future products can already be tested for their temporal and spatial transferability.","哥白尼哨兵扩展任务将提供新的独特遥感数据。为了在真实数据可用时尽快加以利用，必须提前生成可比的合成数据。本研究为其中三个即将发射的传感器提出了一个新的数据集。CHIME高光谱数据通过辐射传输建模对来自Sentinel-2时间序列的多光谱反射率数据进行反演，以获取地表参数，随后利用预期的CHIME传感器特征正向模拟大气底层反射率来生成。未来的LSTM地表温度数据利用Sen-ET工作流结合空间数据挖掘锐化方法，从Sentinel-3和Sentinel-2数据中导出。ROSE-L的L波段后向散射和相干性数据使用SAOCOM-1数据进行模拟，并将其转换为符合预期空间和辐射特征的形式。该新数据集可用于三个感兴趣区域（AOIs），分别位于德国、比利时和爱沙尼亚的Sentinel-2瓦片范围内。利用EnMAP现有可比传感器数据对模拟CHIME数据进行的验证表明，2024年德国AOI的光谱相关性较高，平均RMSE为6.154 [%]，相关系数为0.924。这一公开可用、独特且经过充分验证的数据集，已经能够支持基于哨兵扩展任务数据在广泛的应用领域中准备和开发未来产品与服务。由于两年广泛时间序列所带来的高数据可用性以及多个AOIs，未来产品已经可以测试其时间和空间可迁移性。","Recent advances in remote sensing.","2026-09-16T00:00:00Z",{"impact":72,"substance":263,"depth":264,"authority":20,"freshness":21,"relevant":22,"comment":265},22,19,"面向未来Sentinel扩展任务的高光谱、热红外与L波段合成数据集研究，方法新颖、验证充分且公开可用，对农业遥感产品预研具有实质价值，值得进入每日精选。",[267],{"name":260,"url":257},[27,269,29,270,271,272],"高光谱","地表温度","哥白尼计划","合成数据",[274,275],"哥白尼计划 农业遥感 合成数据 地表温度","哥白尼计划 农业遥感","哥白尼计划农业遥感合成数据地表温度-2797","10.62880\u002Frars26005",{"doi":277,"openalex_id":279,"authors":280,"venue":260,"cited_by_count":36,"oa_url":257,"card":318,"direction":53,"ingested_from":56},"W7213413531",[281,283,285,288,291,294,297,300,302,305,307,309,311,313,316],{"name":282,"orcid":9},"Christian Miesgang",{"name":284,"orcid":9},"Sandra Dotzler",{"name":286,"orcid":287},"Anusha Sanmathi Sathyaniranjan","https:\u002F\u002Forcid.org\u002F0009-0009-8710-4622",{"name":289,"orcid":290},"Silke Migdall","https:\u002F\u002Forcid.org\u002F0000-0001-9089-6274",{"name":292,"orcid":293},"Heike Bach","https:\u002F\u002Forcid.org\u002F0000-0001-8060-2498",{"name":295,"orcid":296},"J. A. D. L. Blommaert","https:\u002F\u002Forcid.org\u002F0000-0002-5797-2439",{"name":298,"orcid":299},"Astrid Vannoppen","https:\u002F\u002Forcid.org\u002F0000-0001-5140-832X",{"name":301,"orcid":9},"Louis Snyders",{"name":303,"orcid":304},"Mihkel Veske","https:\u002F\u002Forcid.org\u002F0000-0003-2367-9215",{"name":306,"orcid":9},"Sven Kautlenbach",{"name":308,"orcid":9},"Catherine Odera",{"name":310,"orcid":9},"Tetiana Shtym",{"name":312,"orcid":9},"Tanel Tamm",{"name":314,"orcid":315},"Anke Schickling","https:\u002F\u002Forcid.org\u002F0000-0001-7446-7752",{"name":317,"orcid":9},"Melisa Soledad Heredia",{"tldr":319,"method":320,"finding":321,"direction":53,"opportunity":322},"生成CHIME高光谱、LSTM热红外和ROSE-L L波段模拟数据集，为未来Sentinel扩展任务","辐射传输模型反演、Sen-ET时空锐化、SAOCOM-1模拟，覆盖德比爱三区两年","模拟CHIME与EnMAP光谱相关性0.924，RMSE 6.154%，数据集公开且验证良好。","可基于该模拟数据集提前开发高光谱、热红外与L波段融合的作物监测和表型反演新算法。","2026-09-17T23:30:37.404847Z",{"id":325,"title":326,"url":327,"summary":328,"summary_zh":329,"content":9,"source_name":330,"source_url":327,"published_at":331,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":332,"score_detail":333,"sources":335,"tags":337,"search_phrases":339,"slug":342,"view_count":36,"doi":343,"paper":344,"created_at":356},2678,"Simulating the Impact of Land Use and Land Cover Change on Surface Air Temperature Trends in South-Central Vietnam","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2515-7620\u002Faea745","Abstract The decline of vegetation, especially due to deforestation and urbanization, has significantly increased the temperature at local and regional scales in many places. In contrast, the development of water resources, increased investment in fertilizers for agricultural production, and afforestation to cover bare land have contributed to reducing temperature increases in many areas. This study aims to quantify the impact of land use and land cover change (LUCC) on temperature trends in South Central Vietnam. The data used are monthly averages over the past 24 years, including the Normalized Difference Vegetation Index (NDVI) and air temperature at 2 meters above the earth's surface. The methods used in the study include statistical analysis and ANN simulation. The main influencing variables included in the analysis are NDVI and its slope in buffer zones around weather stations with diameters ranging from 1 to 20 km. The results of the study show that the closer NDVI changes to the weather station, especially within a range of less than 2 km, the greater the impact on temperature trends. The variables in the buffer zone around the weather station where NDVI changes the most also have the best relationship with temperature trends. Using NDVI-derived variables, the ANN model reliably reproduced the temperature trend and clarified the contribution of LUCC. The NDVI trend variables contribute 46% to the simulation accuracy and 50% to the temperature trend differences between weather stations. This research direction can be used to assess the environmental impacts of LUCC, and to separate temperature trends due to global climate change from local causes.","植被退化，尤其是森林砍伐和城市化导致的植被减少，在许多地区显著加剧了局地和区域尺度的升温。与之相对，水资源开发、农业生产中肥料投入的增加以及裸露土地造林等措施，在许多地区有助于减缓温度上升。本研究旨在量化土地利用与土地覆被变化（LUCC）对越南中南部温度趋势的影响。所用数据为过去24年的月平均值，包括归一化植被指数（NDVI）和距地表2米高度的气温。研究方法包括统计分析和人工神经网络（ANN）模拟。分析中纳入的主要影响变量为气象站周围缓冲区内（直径1至20公里）的NDVI及其变化斜率。研究结果表明，NDVI变化距气象站越近，尤其是在2公里以内的范围内，对温度趋势的影响越大。气象站周围缓冲区内NDVI变化最大的变量与温度趋势的关系也最为密切。利用NDVI衍生的变量，ANN模型可靠地再现了温度趋势，并阐明了LUCC的贡献。NDVI趋势变量对模拟精度的贡献率为46%，对气象站间温度趋势差异的贡献率为50%。这一研究方向可用于评估LUCC的环境影响，并将全球气候变化引起的温度趋势与局地成因区分开来。","Environmental Research Communications","2026-09-14T00:00:00Z",75,{"impact":71,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":22,"comment":334},"以NDVI与ANN量化越南中南部落LUCC对气温趋势的影响，方法新颖、结论可靠，对农业遥感与气候适应研究有参考价值。",[336],{"name":330,"url":327},[169,28,29,130,338],"植被覆盖",[340,341],"农业人工智能 土地利用 植被覆盖 气候变化","农业人工智能 土地利用","农业人工智能土地利用植被覆盖气候变化-2678","10.1088\u002F2515-7620\u002Faea745",{"doi":343,"openalex_id":345,"authors":346,"venue":330,"cited_by_count":36,"oa_url":350,"card":351,"direction":53,"ingested_from":56},"W7212835908",[347],{"name":348,"orcid":349},"Luong Van Viet","https:\u002F\u002Forcid.org\u002F0000-0003-4416-7200","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2515-7620\u002Faea745\u002Fpdf",{"tldr":352,"method":353,"finding":354,"direction":53,"opportunity":355},"量化越南中南部24年土地利用\u002F覆盖变化对气温趋势的影响。","用NDVI与2米气温月均数据，结合统计分析和ANN模拟。","站点2公里内NDVI变化对气温趋势影响最大，NDVI趋势变量贡献46%模拟精度。","可结合多源遥感与深度学习，分离全球变暖与局地LUCC对气温的贡献。","2026-09-16T23:30:32.318792Z"]