[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1431":3,"related-1431":111},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":43,"search_phrases":88,"slug":91,"view_count":21,"doi":92,"paper":93,"created_at":110},1431,"S-SEBI based Evapotranspiration Estimation Using Landsat 8 Data in Kendrapara District of Odisha, India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijecc\u002F2026\u002Fv16i95656","Background: Accurate estimation of evapotranspiration (ET) is essential for agricultural water management, irrigation planning, and regional water-resource assessment, particularly in areas where ground-based meteorological observations are limited. Aim: This study aimed to assess the spatial and temporal variability of actual evapotranspiration (ETa) and reference evapotranspiration (ETo) over Kendrapara district, Odisha, India, during 2016 and 2021 using the Simplified Surface Energy Balance Index (S-SEBI) model, and to compare satellite-derived ETa with FAO Penman–Monteith and Hargreaves–Samani ETo estimates. Study Design: A remote sensing and GIS-based comparative study was conducted using the S-SEBI surface energy balance model to estimate ETa from Landsat 8 imagery and evaluate its performance against conventional ETo methods. Place and Duration of Study: The study was carried out in Kendrapara district, Odisha, India, using satellite and climate data for the years 2016 and 2021. Two dates were selected primarily based on the availability of suitable Landsat 8 images with adequate quality, spatial coverage, and minimal cloud contamination over the study area. Methodology: Landsat 8 (WRS Path\u002FRow 139\u002F46) Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data were obtained from the USGS Earth Explorer platform to derive surface reflectance and land surface temperature. ETa was estimated using the S-SEBI model, while ETo values based on the FAO Penman–Monteith and Hargreaves–Samani methods were obtained from the Climate Engine platform. Statistical error analysis was performed to assess the agreement between satellite-derived ETa and reference ETo estimates. Results: ETa exhibited significant spatial variability associated with vegetation condition and surface moisture. Mean ETa increased from 3.27 mm day⁻¹ in 2016 to 3.87 mm day⁻¹ in 2021, corresponding to higher net radiation and soil heat flux. S-SEBI-derived ETa closely matched FAO Penman–Monteith ETo, whereas the Hargreaves–Samani method consistently overestimated evapotranspiration. Error analysis indicated lower bias and uncertainty for the S-SEBI estimates. Conclusion: The S-SEBI model reliably estimated spatially explicit ETa with minimal ground data, demonstrating strong potential for drought monitoring, crop water requirement assessment, irrigation planning, and sustainable water resource management in data-scarce regions.","背景：准确估算蒸散量（ET）对于农业用水管理、灌溉规划及区域水资源评估至关重要，尤其是在地面气象观测资料有限的地区。目的：本研究旨在利用简化地表能量平衡指数（S-SEBI）模型，评估2016年和2021年印度奥里萨邦肯德拉帕拉县实际蒸散量（ETa）和参考蒸散量（ETo）的时空变化，并将卫星反演的ETa与FAO Penman–Monteith和Hargreaves–Samani方法估算的ETo进行比较。研究设计：采用基于遥感和GIS的对比研究，利用S-SEBI地表能量平衡模型从Landsat 8影像估算ETa，并评估其与传统ETo方法的性能差异。研究地点与时间：研究在印度奥里萨邦肯德拉帕拉县开展，使用2016年和2021年的卫星及气候数据。主要根据研究区域内可用Landsat 8影像的质量、空间覆盖范围和云污染程度，选取了两个日期。方法：从USGS Earth Explorer平台获取Landsat 8（WRS Path\u002FRow 139\u002F46）操作陆地成像仪（OLI）和热红外传感器（TIRS）数据，用于反演地表反射率和地表温度。采用S-SEBI模型估算ETa，而基于FAO Penman–Monteith和Hargreaves–Samani方法的ETo值则从Climate Engine平台获取。通过统计误差分析评估卫星反演ETa与参考ETo估算值之间的一致性。结果：ETa表现出与植被状况和地表湿度相关的显著空间变异性。平均ETa从2016年的3.27毫米\u002F天增加到2021年的3.87毫米\u002F天，这与较高的净辐射和土壤热通量相对应。S-SEBI反演的ETa与FAO Penman–Monteith ETo高度吻合，而Hargreaves–Samani方法则持续高估蒸散量。误差分析表明，S-SEBI估算的偏差和不确定性较低。结论：S-SEBI模型能够在极少地面数据条件下可靠地估算具有空间显式特征的ETa，显示出在数据稀缺地区进行干旱监测、作物需水量评估、灌溉规划和可持续水资源管理的巨大潜力。",null,"International Journal of Environment and Climate Change","2026-09-02T00:00:00Z","论文",10,false,58,{"impact":13,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},18,16,12,2,1,"论文利用Landsat 8数据评估印度奥里萨邦蒸散发，方法可靠，对数据稀缺地区农业水资源管理有参考价值，但地域性强，时效性低。",[24],{"name":10,"url":6},[26,27,28,29],"农业遥感","水资源管理","蒸散发","Landsat",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[37],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":44,"locked":14,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,59,66,74,81],{"code":31,"score":50,"supported":51,"title":52,"abstract":54,"tags":58},19,true,[53],"evapotranspiration",[55,56,32,57,53],"灌溉","水资源","water resource",[27,28],{"code":38,"score":60,"supported":51,"title":61,"abstract":62,"tags":65},8,[],[63,64],"遥感","remote sensing",[29,26],{"code":67,"score":68,"supported":51,"title":69,"abstract":70,"tags":73},"H2",6,[],[71,72],"土壤","soil",[],{"code":75,"score":68,"supported":51,"title":76,"abstract":77,"tags":80},"H7",[],[78,79],"干旱","drought",[],{"code":82,"score":83,"supported":51,"title":84,"abstract":85,"tags":87},"I4",3,[],[86],"GIS",[],[89,90],"水资源管理 农业遥感 蒸散发 Landsat","水资源管理 农业遥感","水资源管理农业遥感蒸散发Landsat-1431","10.9734\u002Fijecc\u002F2026\u002Fv16i95656",{"doi":92,"openalex_id":94,"authors":95,"venue":10,"cited_by_count":102,"oa_url":6,"card":103,"direction":107,"ingested_from":109},"W7205007124",[96,98,100],{"name":97,"orcid":9},"Ritoban Pandit",{"name":99,"orcid":9},"Bama Shankar Rath",{"name":101,"orcid":9},"Dwarika Mohan Das",0,{"tldr":104,"method":105,"finding":106,"direction":107,"opportunity":108},"利用Landsat 8和S-SEBI模型估算印度奥里萨邦Kendrapara地区的蒸散发，验证其精度","S-SEBI模型、Landsat 8遥感数据、FAO Penman-Montei","S-SEBI估算的ETa与FAO PM方法接近，Hargreaves-Samani高估；ETa从20","农业遥感与作物表型","可探索S-SEBI模型在不同气候区及高分辨率数据下的适用性，结合机器学习改进地表参数反演，提升数据稀缺区蒸散发估算精度。","openalex","2026-09-02T23:30:51.113113Z",{"total":68,"page":21,"page_size":68,"items":112},[113,187,263,334,402,491],{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":9,"source_name":119,"source_url":116,"published_at":120,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":122,"sources":128,"tags":130,"primary_topic":133,"secondary_topics":137,"classification":140,"search_phrases":166,"slug":169,"view_count":102,"doi":170,"paper":171,"created_at":186},4753,"Mapping groundwater potential zones using GIS, remote sensing and analytical hierarchy process (AHP) in Pader and Kitgum Districts, Uganda","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04943-7","Groundwater is a crucial source of freshwater in semi-arid regions, particularly in water-stressed areas, such as northern Uganda, where the surface water is scarce and unreliable precipitation patterns changes. This province lacks sufficient clean water for human consumption and only 20% of the water for agriculture extracted from the ground due to inaccessibility and insufficient information regarding the groundwater. With regard to these difficulties in water that are faced by the local communities, it has not been yet studied the distribution of groundwater in Kitgum and Pader districts. Therefore, the result of this research contributes preliminary groundwater investigation for contacting this indispensable source of water. The study employed a geospatial approach integrated with multi-criteria analysis to identify groundwater potential zones in Kitgum and Pader districts of Acholi subregion. The Utilization of Geographic Information Systems (GIS), remote sensing, and the Analytic Hierarchy Process (AHP) and six thematic layers; rainfall, drainage density, slope, soil type, geology, and land use\u002Fland cover were selected based on their hydrogeological influence and the accessibility of the data. After standardizing, and assigning normalized weights,a weighted overlay analysis in ArcGIS Pro version 3.1.0 was used to synthesize these layers into a comprehensive groundwater potential zonation map. The result categorized the study area into five zones; very low, low, medium, high, and very high potential. The analysis revealed that Kitgum District is predominantly characterized by very low to low groundwater potential zones of 67.24% when it is combined both, constrained by factors such as higher drainage density, and steeper slopes. Conversely, Pader District showed favorable conditions, with 85.5% of its area classified as high to very high groundwater potential, attributed to gentle slopes, and higher rainfall, that enhance infiltration capacity. Validation against yield data from 18 existing boreholes demonstrated a 75.0% alignment between the model and actual well performance, confirming the model’s reliability at acceptable result. This study demonstrated the effectiveness of GIS-based and AHP as a cost-effective tool for preliminary groundwater exploration in data-scarce environments. The resulting groundwater potential map provides a vital scientific foundation for sustainable water resource management, enabling targeted borehole siting, informed aquifer development, and resilience-building strategies to enhance water security for communities and livelihoods in the Acholi sub-region.","地下水是半干旱地区重要的淡水来源，尤其是在乌干达北部等水资源紧张地区，那里地表水稀缺且降水模式变化不可靠。该地区缺乏足够的清洁饮用水供人类消费，由于地下水难以获取且信息不足，仅有20%的农业用水来自地下开采。鉴于当地社区面临这些水资源困难，基特古姆和帕德尔地区的地下水分布尚未得到研究。因此，本研究结果为接触这一不可或缺的水源提供了初步的地下水调查。本研究采用地理空间方法与多准则分析相结合，识别阿乔利次区域基特古姆和帕德尔地区的地下水潜力区。利用地理信息系统（GIS）、遥感技术和层次分析法（AHP），基于其水文地质影响和数据可获取性，选取了六个主题图层：降雨量、河网密度、坡度、土壤类型、地质和土地利用\u002F土地覆盖。经过标准化和归一化权重赋值后，使用ArcGIS Pro 3.1.0版本中的加权叠加分析将这些图层综合成一张全面的地下水潜力分区图。结果将研究区划分为五个区域：极低、低、中等、高和极高潜力。分析表明，基特古姆区主要以极低至低地下水潜力区为主，两者合计占67.24%，受较高河网密度和较陡坡度等因素制约。相反，帕德尔区表现出有利条件，其85.5%的面积被归类为高至极高地下水潜力，这归因于平缓的坡度和较高的降雨量，增强了入渗能力。利用18口现有钻孔的出水量数据进行验证，模型与实际井表现之间的一致率达到75.0%，证实了模型在可接受结果范围内的可靠性。本研究证明了基于GIS和AHP的方法作为数据稀缺环境中初步地下水勘探的成本效益工具的有效性。所生成的地下水潜力图为可持续水资源管理提供了重要的科学基础，有助于有针对性地选址钻孔，为知情决策提供依据。","Discover Sustainability","2026-10-10T00:00:00Z",67,{"impact":60,"substance":123,"depth":124,"authority":125,"freshness":126,"relevant":21,"comment":127},20,17,13,9,"基于GIS、遥感与AHP的乌干达北部地下水潜力制图研究，方法成熟、验证可靠，对缺水地区农业水资源规划有参考价值，但属区域性初步研究，公共影响有限。",[129],{"name":119,"url":116},[63,27,86,131,132],"地下水潜力","乌干达农业",{"code":82,"slug":134,"name":135,"path":136,"parent_code":42},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[138,139],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":44,"locked":14,"version":45,"method":46,"evidence":141},{"candidates":142,"agricultural_evidence":51},[143,149,154,158,162],{"code":82,"score":17,"supported":51,"title":144,"abstract":145,"tags":148},[86],[146,86,147],"地理信息","geographic information",[86],{"code":38,"score":150,"supported":51,"title":151,"abstract":152,"tags":153},15,[64],[63,64],[63],{"code":31,"score":60,"supported":51,"title":155,"abstract":156,"tags":157},[],[56,57],[131,27],{"code":67,"score":68,"supported":51,"title":159,"abstract":160,"tags":161},[],[71,72],[],{"code":75,"score":83,"supported":51,"title":163,"abstract":164,"tags":165},[],[78],[],[167,168],"乌干达 Kitgum Pader 地下水","GIS AHP 地下水潜力","乌干达KitgumPader地下水-4753","10.1007\u002Fs43621-026-04943-7",{"doi":170,"openalex_id":172,"authors":173,"venue":119,"cited_by_count":102,"oa_url":116,"card":181,"direction":107,"ingested_from":109},"W7221339030",[174,177,179],{"name":175,"orcid":176},"Ahmed Abdirizak Dirie","https:\u002F\u002Forcid.org\u002F0009-0002-8109-0724",{"name":178,"orcid":9},"Abdulkadir Ahmed Mohamed",{"name":180,"orcid":9},"Ambiga Kannapiran",{"tldr":182,"method":183,"finding":184,"direction":107,"opportunity":185},"用GIS、遥感和AHP制图评估乌干达两区地下水潜力并验证。","GIS与遥感结合AHP，加权叠加6个主题图层，用18口井数据验证。","Kitgum区67.24%为低潜力，Pader区85.5%为高潜力，模型与井数据吻合75%。","可将该地下水潜力制图方法迁移到其他数据稀缺区，并结合农业灌溉需求优化井位。","2026-10-12T23:30:24.761508Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":192,"content":9,"source_name":193,"source_url":190,"published_at":194,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":195,"score_detail":196,"sources":200,"tags":202,"primary_topic":207,"secondary_topics":211,"classification":214,"search_phrases":239,"slug":242,"view_count":21,"doi":243,"paper":244,"created_at":262},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":197,"substance":198,"depth":17,"authority":125,"freshness":60,"relevant":21,"comment":199},14,21,"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。",[201],{"name":193,"url":190},[203,27,204,205,206],"农业气象","遥感监测","Google Earth Engine","干旱预警",{"code":75,"slug":208,"name":209,"path":210,"parent_code":35},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[212,213],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":44,"locked":14,"version":45,"method":46,"evidence":215},{"candidates":216,"agricultural_evidence":51},[217,221,225,229,233],{"code":75,"score":18,"supported":51,"title":218,"abstract":219,"tags":220},[79],[78,79],[203,206],{"code":38,"score":125,"supported":51,"title":222,"abstract":223,"tags":224},[64],[63],[205,204],{"code":31,"score":13,"supported":51,"title":226,"abstract":227,"tags":228},[],[56,57,53],[27],{"code":67,"score":68,"supported":51,"title":230,"abstract":231,"tags":232},[],[71,72],[],{"code":234,"score":83,"supported":51,"title":235,"abstract":236,"tags":238},"J1",[],[237],"农业规划",[],[240,241],"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":243,"openalex_id":245,"authors":246,"venue":193,"cited_by_count":102,"oa_url":190,"card":257,"direction":107,"ingested_from":109},"W7215092640",[247,250,252,254],{"name":248,"orcid":249},"Hadi Siasar","https:\u002F\u002Forcid.org\u002F0000-0002-9323-4345",{"name":251,"orcid":9},"Mahboube Ebrahimi",{"name":253,"orcid":9},"Mehdi Bahrami",{"name":255,"orcid":256},"Hossein Hamidifar","https:\u002F\u002Forcid.org\u002F0000-0001-9054-0120",{"tldr":258,"method":259,"finding":260,"direction":107,"opportunity":261},"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":264,"title":265,"url":266,"summary":267,"summary_zh":268,"content":9,"source_name":269,"source_url":266,"published_at":270,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":195,"score_detail":271,"sources":274,"tags":276,"primary_topic":280,"secondary_topics":285,"classification":288,"search_phrases":305,"slug":308,"view_count":20,"doi":309,"paper":310,"created_at":333},3504,"WaterFormer-MS: A multi-stream deep learning framework for surface water segmentation from satellite imagery","https:\u002F\u002Fdoi.org\u002F10.24850\u002Fj-tyca-18-03-08","Accurate mapping of surface water bodies is essential for dam monitoring, irrigation planning, and sustainable water resource management. Conventional methods based on spectral indices, such as NDWI, can face challenges in complex environments due to spectral ambiguity, seasonal variability, and urban or vegetated surfaces. Deep learning approaches such as U-Net and DeepLabv3+ have improved water-body segmentation; however, accurately delineating fine boundaries and detecting small water bodies remain challenging. To address these limitations, this study proposes WaterFormer-MS, a multi-stream deep learning framework for surface water segmentation from Sentinel-2 imagery. The architecture integrates spectral, geometric, and contextual information through dedicated processing streams and combines the extracted features using a convolutional fusion module enhanced with CBAM attention. This design aims to improve feature representation and support accurate water-body delineation. The proposed model was trained and evaluated using Sentinel-2 imagery from a publicly available Kaggle dataset containing diverse land-cover conditions, including urban, vegetated, and agricultural environments. At 300 epochs, WaterFormer-MS achieved an overall accuracy of 97.4%, a precision of 93.2%, a recall of 94.2%, an F1-score of 93.7%, and an IoU of 89.7%. Within the experimental framework, WaterFormer-MS achieved higher values than the other proposed variants. Comparisons with conventional NDWI-based approaches and published deep learning methods suggest competitive performance; however, these comparisons should be interpreted cautiously because datasets, preprocessing procedures, training protocols, and evaluation settings may differ. The results support the potential of the proposed framework for surface water segmentation and environmental monitoring applications in diverse remote sensing scenarios.","准确绘制地表水体分布对于大坝监测、灌溉规划和水资源可持续管理至关重要。基于NDWI等光谱指数的传统方法在复杂环境中可能因光谱模糊性、季节性变化以及城市或植被覆盖地表而面临挑战。U-Net和DeepLabv3+等深度学习方法改善了水体分割效果，但精确描绘精细边界和检测小型水体仍然具有挑战性。为解决这些局限，本研究提出WaterFormer-MS，一种用于Sentinel-2影像地表水分割的多流深度学习框架。该架构通过专门的处理流整合光谱、几何和上下文信息，并利用经CBAM注意力增强的卷积融合模块将提取的特征进行融合。该设计旨在改善特征表征并支持精确的水体描绘。所提模型使用来自公开Kaggle数据集的Sentinel-2影像进行训练和评估，该数据集包含城市、植被和农业环境等多种土地覆盖条件。在300个训练轮次时，WaterFormer-MS取得了97.4%的总体精度、93.2%的精确率、94.2%的召回率、93.7%的F1分数和89.7%的IoU。在实验框架内，WaterFormer-MS取得了高于其他所提变体的数值。与传统基于NDWI的方法及已发表的深度学习方法进行比较，结果表明其性能具有竞争力；然而，这些比较应谨慎解读，因为数据集、预处理流程、训练方案和评估设置可能存在差异。研究结果支持所提框架在多样化遥感场景中用于地表水分割和环境监测应用的潜力。","Tecnología y Ciencias del Agua","2026-09-24T00:00:00Z",{"impact":150,"substance":272,"depth":17,"authority":13,"freshness":126,"relevant":21,"comment":273},22,"提出多流深度学习框架用于卫星影像地表水提取，精度指标明确，对灌溉规划与水资源监测有参考价值，但属方法类论文，产业影响有限。",[275],{"name":269,"url":266},[277,278,63,27,279],"农业人工智能","Sentinel-2","地表水体提取",{"code":281,"slug":282,"name":283,"path":284,"parent_code":42},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[286,287],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":14,"version":45,"method":46,"evidence":289},{"candidates":290,"agricultural_evidence":51},[291,297,301],{"code":281,"score":150,"supported":51,"title":292,"abstract":294,"tags":296},[293],"deep learning",[295,293],"深度学习",[277],{"code":31,"score":13,"supported":51,"title":298,"abstract":299,"tags":300},[],[55,56,32,57],[27],{"code":38,"score":60,"supported":51,"title":302,"abstract":303,"tags":304},[],[63,64],[278,63],[306,307],"WaterFormer-MS 地表水 分割","Sentinel-2 水体提取 深度学习","WaterFormer-MS地表水分割-3504","10.24850\u002Fj-tyca-18-03-08",{"doi":309,"openalex_id":311,"authors":312,"venue":269,"cited_by_count":102,"oa_url":266,"card":328,"direction":107,"ingested_from":109},"W7214228825",[313,316,319,322,325],{"name":314,"orcid":315},"Ayoub Benali","https:\u002F\u002Forcid.org\u002F0000-0002-8989-755X",{"name":317,"orcid":318},"Yesma Bendaha","https:\u002F\u002Forcid.org\u002F0000-0002-1347-856X",{"name":320,"orcid":321},"Malika Seddik Bouchouicha","https:\u002F\u002Forcid.org\u002F0009-0001-6092-784X",{"name":323,"orcid":324},"Zakaria Bellahcene","https:\u002F\u002Forcid.org\u002F0000-0003-4353-7871",{"name":326,"orcid":327},"Rachid Rimani","https:\u002F\u002Forcid.org\u002F0000-0002-1923-4312",{"tldr":329,"method":330,"finding":331,"direction":107,"opportunity":332},"提出多流深度学习框架WaterFormer-MS，从Sentinel-2影像中精准分割地表水体。","多流架构融合光谱、几何与上下文特征，CBAM注意力卷积融合，Sentinel-2","300轮训练下总体精度97.4%、IoU 89.7%，优于NDWI及对比深度学习方法。","可探索多流框架在农业灌溉水体与小型水塘精细提取中的迁移，并引入时序Sentinel-2提升季节鲁棒性。","2026-09-25T23:30:31.338437Z",{"id":335,"title":336,"url":337,"summary":338,"summary_zh":339,"content":9,"source_name":340,"source_url":337,"published_at":341,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":342,"score_detail":343,"sources":345,"tags":347,"primary_topic":351,"secondary_topics":352,"classification":354,"search_phrases":370,"slug":373,"view_count":102,"doi":374,"paper":375,"created_at":401},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",79,{"impact":17,"substance":272,"depth":17,"authority":125,"freshness":60,"relevant":21,"comment":344},"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。",[346],{"name":340,"url":337},[27,204,348,349,350],"智慧灌溉","地下水超采","干旱农业",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[353],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":44,"locked":14,"version":45,"method":46,"evidence":355},{"candidates":356,"agricultural_evidence":51},[357,361,366],{"code":38,"score":150,"supported":51,"title":358,"abstract":359,"tags":360},[64],[63,64],[204],{"code":31,"score":362,"supported":51,"title":363,"abstract":364,"tags":365},11,[],[55,56,28,32,53],[348,27],{"code":75,"score":83,"supported":51,"title":367,"abstract":368,"tags":369},[],[78],[],[371,372],"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":374,"openalex_id":376,"authors":377,"venue":340,"cited_by_count":102,"oa_url":337,"card":396,"direction":107,"ingested_from":109},"W7212247745",[378,381,384,387,390,393],{"name":379,"orcid":380},"Rachid Amiha","https:\u002F\u002Forcid.org\u002F0000-0002-9863-6685",{"name":382,"orcid":383},"Belkacem Kabbachi","https:\u002F\u002Forcid.org\u002F0000-0003-3802-1256",{"name":385,"orcid":386},"Mohamed Ait Haddou","https:\u002F\u002Forcid.org\u002F0000-0002-0587-1230",{"name":388,"orcid":389},"Youssef Bouchriti","https:\u002F\u002Forcid.org\u002F0000-0002-8566-1451",{"name":391,"orcid":392},"Hicham Gougueni","https:\u002F\u002Forcid.org\u002F0000-0001-5340-7566",{"name":394,"orcid":395},"Mustapha Ikirri","https:\u002F\u002Forcid.org\u002F0000-0002-9971-6249",{"tldr":397,"method":398,"finding":399,"direction":107,"opportunity":400},"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",{"id":403,"title":404,"url":405,"summary":406,"summary_zh":407,"content":9,"source_name":408,"source_url":405,"published_at":409,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":410,"score_detail":411,"sources":413,"tags":415,"primary_topic":419,"secondary_topics":420,"classification":428,"search_phrases":459,"slug":462,"view_count":83,"doi":463,"paper":464,"created_at":490},2318,"Estimation of field-scale crop evapotranspiration from the mechanistic SIF-ET model using the UAV hyperspectral imagery","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128332","Accurate estimation of evapotranspiration (ET) is critical for agricultural water management and understanding land-atmosphere interactions. Conventional thermal and optical remote sensing is a powerful tool for ET estimation, but existing methods remain constrained by empirical parameterization, calibration complexity, and limited applicability. As a direct indicator of vegetation photosynthesis, solar-induced chlorophyll fluorescence (SIF) enables consistent and accurate ET estimation through a mechanistically grounded framework that does not require site-specific empirical calibration of the SIF-GPP relationship. However, current SIF-based ET estimation mainly rely on coarse-resolution satellite data and its capacity to resolve high-resolution ET dynamics over farmland remains unexplored. To address this, the mechanistic light response model and water-carbon relationship was integrated to construct a SIF-ET model at field-scale using UAV narrow-band hyperspectra imagery. The principal conclusions are: (1) High-resolution crop (canola,soybean and wheat) canopy SIF 740 at 1-nm SR was retrieved from UAV hyperspectra imagery using the Photochemistry and Energy Fluxes (SCOPE) model; (2)The daily ET estimates from SIF-ET model were validated against the in-situ measurements across multiple growth stages and water-nitrogen treatments, with wheat exhibiting the highest precision (R²=0.72–0.94, RMSE = 0.04–0.11 mm\u002Fd, MAPE = 1.70–5.89%), followed by canola and soybean; (3) Cumulative ET errors across repeated UAV observations within each growth stage remained within acceptable bounds, indicating the stability of SIF-ET model in capturing long-term ET dynamics. Overall, the SIF-ET model from UAV hyperspectra imagery revealed fine-scale cropland ET spatial-temporal heterogeneity. These findings provide support for precision irrigation, rational water-nitrogen management, and stress diagnostics under changing environmental conditions.","准确估算蒸散发（ET）对农业水资源管理和理解陆气相互作用至关重要。传统的热红外与光学遥感是估算ET的有力工具，但现有方法仍受限于经验参数化、校准复杂性和适用性有限等问题。作为植被光合作用的直接指示因子，日光诱导叶绿素荧光（SIF）能够通过机理明确的框架实现一致且准确的ET估算，且无需对SIF-GPP关系进行站点特定的经验校准。然而，当前基于SIF的ET估算主要依赖粗分辨率卫星数据，其解析农田高分辨率ET动态的能力尚未得到探索。为此，本研究整合机理光响应模型与水碳关系，利用无人机窄波段高光谱影像构建了田块尺度的SIF-ET模型。主要结论如下：（1）利用光化学与能量通量（SCOPE）模型，从无人机高光谱影像中反演了1 nm光谱分辨率下油菜、大豆和小麦的高分辨率冠层SIF₇₄₀；（2）基于SIF-ET模型的日ET估算值在多个生育期和水氮处理下与原位观测进行了验证，其中小麦精度最高（R²=0.72–0.94，RMSE=0.04–0.11 mm\u002Fd，MAPE=1.70–5.89%），油菜和大豆次之；（3）各生育期内重复无人机观测的累积ET误差均在可接受范围内，表明SIF-ET模型在捕捉长期ET动态方面具有稳定性。总体而言，基于无人机高光谱影像的SIF-ET模型揭示了精细尺度的农田ET时空异质性。这些发现为精准灌溉、合理水氮管理以及变化环境条件下的胁迫诊断提供了支持。","European Journal of Agronomy","2026-09-12T00:00:00Z",81,{"impact":17,"substance":272,"depth":17,"authority":197,"freshness":126,"relevant":21,"comment":412},"该研究基于无人机高光谱与SIF-ET机理模型实现田块尺度作物蒸散发高精度估算，方法新颖、验证充分，对精准灌溉与水氮管理有实质参考价值，值得进入每日精选。",[414],{"name":408,"url":405},[416,417,63,418,28],"智慧农业","无人机","精准灌溉",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[421,422],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":423,"slug":424,"name":425,"path":426,"parent_code":427},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",{"status":44,"locked":14,"version":45,"method":46,"evidence":429},{"candidates":430,"agricultural_evidence":51},[431,435,440,447,454],{"code":31,"score":50,"supported":51,"title":432,"abstract":433,"tags":434},[53],[55,56,28,32,53],[418,28],{"code":38,"score":150,"supported":51,"title":436,"abstract":438,"tags":439},[437],"hyperspectral",[63,64],[63],{"code":423,"score":126,"supported":51,"title":441,"abstract":442,"tags":446},[],[443,444,445],"大豆","油菜","soybean",[],{"code":448,"score":68,"supported":51,"title":449,"abstract":450,"tags":453},"A2",[],[451,452],"小麦","wheat",[],{"code":455,"score":21,"supported":14,"title":456,"abstract":457,"tags":458},"I1",[],[],[416],[460,461],"智慧农业 精准灌溉 无人机 蒸散发","智慧农业 精准灌溉","智慧农业精准灌溉无人机蒸散发-2318","10.1016\u002Fj.eja.2026.128332",{"doi":463,"openalex_id":465,"authors":466,"venue":408,"cited_by_count":102,"oa_url":405,"card":485,"direction":107,"ingested_from":109},"W7212242584",[467,469,471,474,477,479,482],{"name":468,"orcid":9},"Ruiqi Du",{"name":470,"orcid":9},"Yonghong Zhang",{"name":472,"orcid":473},"Youzhen Xiang","https:\u002F\u002Forcid.org\u002F0000-0002-8268-1609",{"name":475,"orcid":476},"Fucang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6659-3262",{"name":478,"orcid":9},"Jian Gao",{"name":480,"orcid":481},"Qiliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-3274-2119",{"name":483,"orcid":484},"Xianghui Lu","https:\u002F\u002Forcid.org\u002F0000-0003-0638-3068",{"tldr":486,"method":487,"finding":488,"direction":107,"opportunity":489},"利用无人机高光谱影像构建田间尺度SIF-ET机理模型，实现作物蒸散发高精度估算。","无人机窄波段高光谱结合SCOPE模型反演SIF，耦合光响应与水碳关系构建SIF-","小麦估算精度最高（R²=0.72–0.94），模型能稳定捕捉多生育期蒸散发动态与空间异质性。","可探索多作物多环境下的SIF-ET普适性，并融合热红外与机器学习提升胁迫诊断能力。","2026-09-13T23:30:22.677041Z",{"id":492,"title":493,"url":494,"summary":495,"summary_zh":496,"content":9,"source_name":497,"source_url":494,"published_at":498,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":499,"score_detail":500,"sources":502,"tags":504,"primary_topic":507,"secondary_topics":508,"classification":515,"search_phrases":545,"slug":548,"view_count":102,"doi":549,"paper":550,"created_at":561},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",75,{"impact":17,"substance":123,"depth":18,"authority":19,"freshness":126,"relevant":21,"comment":501},"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[503],{"name":497,"url":494},[277,505,63,348,506],"农业物联网","节水农业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[509,514],{"code":510,"slug":511,"name":512,"path":513,"parent_code":42},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":14,"version":45,"method":46,"evidence":516},{"candidates":517,"agricultural_evidence":51},[518,522,529,533,538],{"code":31,"score":50,"supported":51,"title":519,"abstract":520,"tags":521},[32],[55,56,32],[348,506],{"code":510,"score":17,"supported":51,"title":523,"abstract":525,"tags":528},[524],"IoT",[526,527,524],"物联网","internet of things",[505],{"code":38,"score":150,"supported":51,"title":530,"abstract":531,"tags":532},[64],[63,64],[63],{"code":281,"score":19,"supported":51,"title":534,"abstract":536,"tags":537},[535],"artificial intelligence",[535],[277],{"code":539,"score":68,"supported":51,"title":540,"abstract":541,"tags":544},"H8",[],[542,543],"可持续农业","sustainable agriculture",[],[546,547],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":549,"openalex_id":551,"authors":552,"venue":497,"cited_by_count":102,"oa_url":494,"card":555,"direction":559,"ingested_from":109},"W7223090389",[553],{"name":554,"orcid":9},"Patil",{"tldr":556,"method":557,"finding":558,"direction":559,"opportunity":560},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","智慧农业 \u002F 农业物联网","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z"]