[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3175":3,"related-3175":51},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":50},3175,"Assessing the reliability of remote sensing-based evapotranspiration models for enhanced water management under center-pivot irrigation system in arid region. Case study: East Oweinat, Egypt","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs40899-026-01401-0","Assessing the reliability of remote sensing-based evapotranspiration models for enhanced water management under center-pivot irrigation system in arid region. Case study: East Oweinat, Egypt。Sustainable Water Resources Management",null,"Sustainable Water Resources Management","2026-09-21T00:00:00Z","论文",10,false,71,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,20,17,13,9,1,"埃及干旱区中心支轴灌溉下遥感蒸散发模型可靠性评估，方法扎实但属区域案例研究，对国内智慧灌溉有参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"遥感","水资源管理","精准灌溉","蒸散发","干旱区农业",[32,33],"East Oweinat 中心支轴灌溉","遥感 蒸散发 模型 灌溉管理","EastOweinat中心支轴灌溉-3175",0,"10.1007\u002Fs40899-026-01401-0",{"doi":36,"openalex_id":38,"authors":39,"venue":9,"cited_by_count":35,"oa_url":8,"card":8,"direction":48,"ingested_from":49},"W7213900869",[40,42,44,46],{"name":41,"orcid":8},"Hadeel A. Ibrahim",{"name":43,"orcid":8},"Mohamed H. Elgamal",{"name":45,"orcid":8},"Ashraf M. Ghanem",{"name":47,"orcid":8},"Mohamed H. Nour","农业遥感与作物表型","openalex","2026-09-22T23:30:23.383931Z",{"total":52,"page":21,"page_size":52,"items":53},6,[54,105,143,180,226,264],{"id":55,"title":56,"url":57,"summary":58,"summary_zh":59,"content":8,"source_name":60,"source_url":57,"published_at":61,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":62,"score_detail":63,"sources":68,"tags":70,"search_phrases":73,"slug":76,"view_count":35,"doi":77,"paper":78,"created_at":104},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":64,"substance":65,"depth":64,"authority":66,"freshness":20,"relevant":21,"comment":67},18,22,14,"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。",[69],{"name":60,"url":57},[71,72,26,28,29],"智慧农业","无人机",[74,75],"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":77,"openalex_id":79,"authors":80,"venue":60,"cited_by_count":35,"oa_url":57,"card":99,"direction":48,"ingested_from":49},"W7212242584",[81,83,85,88,91,93,96],{"name":82,"orcid":8},"Ruiqi Du",{"name":84,"orcid":8},"Yonghong Zhang",{"name":86,"orcid":87},"Youzhen Xiang","https:\u002F\u002Forcid.org\u002F0000-0002-8268-1609",{"name":89,"orcid":90},"Fucang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6659-3262",{"name":92,"orcid":8},"Jian Gao",{"name":94,"orcid":95},"Qiliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-3274-2119",{"name":97,"orcid":98},"Xianghui Lu","https:\u002F\u002Forcid.org\u002F0000-0003-0638-3068",{"tldr":100,"method":101,"finding":102,"direction":48,"opportunity":103},"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":106,"title":107,"url":108,"summary":109,"summary_zh":110,"content":8,"source_name":111,"source_url":108,"published_at":112,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":113,"score_detail":114,"sources":116,"tags":118,"search_phrases":121,"slug":124,"view_count":35,"doi":125,"paper":126,"created_at":142},3190,"Comparing Irrigation Identification Methods in Colorado: Limitations of Downscaled SMAP Soil Moisture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183223","With limited freshwater resources and growing water demands, it is imperative to identify and monitor water needs. Agricultural irrigation is the world’s largest water user, comprising 45–90% of freshwater withdrawals. Identifying and tracking changes in irrigated land is necessary for sustainable water management and forecasting agricultural water needs and patterns; however, the low resolution of available remote sensing observations hinders field-scale analysis. Downscaling soil moisture observations has been offered as a solution to this problem. Our study compares the performance of five irrigation identification methods using a newly developed downscaled deep soil moisture extrapolation method used to estimate Soil Moisture Active Passive (SMAP) soil moisture (SM) at a spatial resolution of 400 m for 5 cm, 20 cm, and 50 cm depths. Using this data along with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation and Landsat Normalized Difference Vegetation Index (NDVI), we evaluate these methods with respect to crop type, irrigation type, and observation depth on agricultural fields in Colorado using the irrigation maps provided by the Colorado Decision Support System from 2015 through 2024. With no crop\u002Firrigation type–observation depth combination exceeding an F1 score of 0.149 or MCC value of 0.163, we find that none of the methods can accurately identify irrigated land regardless of crop type, irrigation type, and observation depth. Because these methods succeeded in earlier small-area studies, and because the classified maps resolved into large, spatially uniform blocks, we interpret this as a limitation specific to field-scale detection over large, heterogeneous regions rather than a defect in the dataset. These findings highlight a limitation of this downscaled SM dataset and raise the question of whether other downscaled soil moisture products share this limitation.","在淡水资源有限且用水需求不断增长的背景下，识别和监测水资源需求势在必行。农业灌溉是全球最大的用水部门，占淡水取水量的45%–90%。识别和追踪灌溉土地的变化对于可持续水资源管理以及预测农业用水需求和模式至关重要；然而，现有遥感观测的低分辨率阻碍了田块尺度的分析。土壤湿度观测数据的降尺度被提出作为解决这一问题的方法。本研究比较了五种灌溉识别方法的性能，所用数据基于新开发的降尺度深层土壤湿度外推方法，用于估算土壤湿度主动被动（SMAP）卫星在400 m空间分辨率下5 cm、20 cm和50 cm深度的土壤湿度（SM）。利用该数据以及气候灾害组红外降水与站点数据（CHIRPS）降水和Landsat归一化植被指数（NDVI），我们结合科罗拉多决策支持系统提供的2015年至2024年灌溉地图，在科罗拉多州的农田上就作物类型、灌溉类型和观测深度对这些方法进行了评估。在没有任何作物\u002F灌溉类型–观测深度组合的F1分数超过0.149或MCC值超过0.163的情况下，我们发现无论作物类型、灌溉类型和观测深度如何，这些方法均无法准确识别灌溉土地。由于这些方法在早期小区域研究中取得了成功，且分类地图呈现为大的、空间均一的斑块，我们将此解释为大规模异质区域上田块尺度检测所特有的局限性，而非数据集本身的缺陷。这些发现凸显了该降尺度SM数据集的局限性，并提出其他降尺度土壤湿度产品是否也存在这一局限性的问题。","Remote Sensing","2026-09-19T00:00:00Z",75,{"impact":16,"substance":65,"depth":64,"authority":66,"freshness":20,"relevant":21,"comment":115},"该研究通过大区域对比实验揭示降尺度SMAP土壤水分在田块尺度灌溉识别上的局限，方法严谨、结论明确，对农业遥感与水资源管理有参考价值，但属细分领域学术进展，公共影响有限。",[117],{"name":111,"url":108},[71,26,27,119,120],"土壤水分","灌溉识别",[122,123],"SMAP 土壤水分 灌溉识别","科罗拉多 灌溉制图 遥感","SMAP土壤水分灌溉识别-3190","10.3390\u002Frs18183223",{"doi":125,"openalex_id":127,"authors":128,"venue":111,"cited_by_count":35,"oa_url":108,"card":137,"direction":48,"ingested_from":49},"W7213934204",[129,132,134],{"name":130,"orcid":131},"Annelise M. Turman","https:\u002F\u002Forcid.org\u002F0009-0000-2392-3631",{"name":133,"orcid":8},"Bin Fang",{"name":135,"orcid":136},"V. Vijaya Lakshmi","https:\u002F\u002Forcid.org\u002F0000-0001-9522-7897",{"tldr":138,"method":139,"finding":140,"direction":48,"opportunity":141},"比较五种灌溉识别方法，发现降尺度SMAP土壤湿度在科罗拉多田间尺度无法准确识别灌溉。","用400米降尺度SMAP土壤湿度、CHIRPS降水和Landsat NDVI，对","所有方法F1最高仅0.149，无法准确识别灌溉地，归因于大区域异质性而非数据缺陷。","可检验其他降尺度土壤湿度产品是否同样受大区域异质性限制，并探索融合多源数据提升田间尺度灌溉识别。","2026-09-22T23:30:26.627420Z",{"id":144,"title":145,"url":146,"summary":147,"summary_zh":148,"content":8,"source_name":149,"source_url":146,"published_at":150,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":35,"score_detail":151,"sources":153,"tags":155,"search_phrases":157,"slug":160,"view_count":35,"doi":161,"paper":162,"created_at":179},2793,"Delineation of groundwater potential zones using analytical hierarchy process and GIS in Bhilangana Block of Tehri Garhwal District, Uttarakhand, India","https:\u002F\u002Fdoi.org\u002F10.33765\u002Fthate.16.4.4","Groundwater is one of the most reliable freshwater resources globally, but overexploitation has led to declining water tables, deteriorating water quality, and increased water scarcity. These problems have raised serious questions about how sustainable groundwater management can be achieved. In recent times, geospatial techniques and the Analytical Hierarchy Process (AHP) have been increasingly applied to assess groundwater availability. This research aims to delineate groundwater potential zones in the Bhilangana Block using the above-mentioned methods. Initially, remote sensing data were employed to generate thematic layers, including geomorphology, geology, slope, elevation, drainage density, lineament density, soil, rainfall, and land use\u002Fland cover. Subsequently, these layers were integrated using a multi-criteria evaluation approach, and weights were assigned using AHP-based weighted overlay analysis. The resulting groundwater potential zones were classified into five categories: very low and low (over 45 %), moderate (35.05 %), high (16.66 %), and very high (3.81 %). The findings of this research offer insights into groundwater occurrence in the study area, which could be helpful for sustainable groundwater management and long-term planning of water resources.","地下水是全球最可靠的淡水资源之一，但过度开采已导致地下水位下降、水质恶化以及水资源短缺加剧。这些问题引发了关于如何实现可持续地下水管理的严肃思考。近年来，地理空间技术和层次分析法（Analytical Hierarchy Process, AHP）越来越多地被应用于地下水可获得性评估。本研究旨在利用上述方法圈定Bhilangana区块的地下水潜力区。首先，利用遥感数据生成专题图层，包括地貌、地质、坡度、高程、河网密度、线性构造密度、土壤、降雨以及土地利用\u002F土地覆盖。随后，采用多准则评价方法整合这些图层，并利用基于AHP的加权叠加分析分配权重。所得到的地下水潜力区被划分为五类：极低和低（超过45 %）、中等（35.05 %）、高（16.66 %）以及极高（3.81 %）。本研究结果有助于认识研究区地下水的赋存情况，可为可持续地下水管理和水资源长期规划提供参考。","The holistic approach to environment","2026-09-16T00:00:00Z",{"impact":35,"substance":35,"depth":35,"authority":35,"freshness":35,"relevant":35,"comment":152},"该论文研究印度北阿坎德邦地下水潜力区划，属水文地质与遥感应用领域，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[154],{"name":149,"url":146},[26,27,156],"GIS",[158,159],"水资源管理 遥感 GIS","水资源管理 遥感","水资源管理遥感GIS-2793","10.33765\u002Fthate.16.4.4",{"doi":161,"openalex_id":163,"authors":164,"venue":149,"cited_by_count":35,"oa_url":173,"card":174,"direction":48,"ingested_from":49},"W7213317885",[165,168,171],{"name":166,"orcid":167},"Siddharth Rana","https:\u002F\u002Forcid.org\u002F0009-0006-5304-3233",{"name":169,"orcid":170},"Gaurav Gaurav","https:\u002F\u002Forcid.org\u002F0000-0001-8857-5797",{"name":172,"orcid":8},"Mohan Singh Panwar","https:\u002F\u002Fcasopis.hrcpo.com\u002Fwp-content\u002Fuploads\u002F2026\u002F09\u002FRana-et-al_HAE_16_2026_4.pdf",{"tldr":175,"method":176,"finding":177,"direction":48,"opportunity":178},"用AHP与GIS遥感数据划分印度Bhilangana区块地下水潜力区。","遥感专题图层+AHP加权叠加多准则评价。","潜力区以低和极低为主（超45%），极高仅3.81%。","可结合灌溉需求与作物分布，将地下水潜力图用于农业用水优化与井位选址。","2026-09-17T23:30:36.203631Z",{"id":181,"title":182,"url":183,"summary":184,"summary_zh":185,"content":8,"source_name":111,"source_url":183,"published_at":186,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":187,"score_detail":188,"sources":191,"tags":193,"search_phrases":196,"slug":199,"view_count":35,"doi":200,"paper":201,"created_at":225},2666,"Automated Machine Learning-Driven UAV Remote Sensing for Accurate Winter Wheat Water Content Prediction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183161","Crop water content is a critical indicator of crop growth status, and its efficient and accurate monitoring is essential for agricultural water resource management. Conventional methods for monitoring winter wheat water content, however, rely mainly on destructive sampling and are labor-intensive and time-consuming. To address these limitations, this study explored the potential of unmanned aerial vehicle (UAV) remote sensing for the rapid and accurate assessment of winter wheat water content. High-resolution canopy remote sensing images were acquired using UAVs equipped with multispectral (MS), RGB, and thermal infrared (TIR) cameras during the flowering and filling stages under six irrigation treatments. Ground-truth sampling data were integrated with the UAV-derived remote sensing data, and an automated machine learning (AutoML) framework—which automatically searches over a range of candidate algorithms and hyperparameters to select the optimal model—was employed to establish regression models for predicting winter wheat moisture content (MC). All models were evaluated using five-fold cross-validation. The results demonstrated that MC prediction performed best during the filling stage, with the TIR sensor achieving the highest accuracy (R2 = 0.812, MAE = 0.0204, RMSE = 0.0274). Compared with single-sensor approaches, multi-sensor fusion further improved predictive performance, achieving an R2 of 0.876, an MAE of 0.0191, and an RMSE of 0.0259 for MC prediction. These findings indicate that UAV-based multi-sensor remote sensing provides an effective means of monitoring winter wheat water content, facilitating timely assessment of crop growth status and optimized irrigation management. Moreover, the use of AutoML enables high-accuracy prediction with minimal human intervention, enhancing the precision of crop water monitoring and advancing precision agriculture.","作物含水量是反映作物生长状况的关键指标，对其进行高效、准确的监测对农业水资源管理至关重要。然而，传统冬小麦含水量监测方法主要依赖破坏性采样，费时费力。为解决这些局限，本研究探索了无人机（UAV）遥感在快速准确评估冬小麦含水量方面的潜力。在六种灌溉处理下，利用搭载多光谱（MS）、RGB和热红外（TIR）相机的无人机在开花期和灌浆期获取了高分辨率冠层遥感图像。将地面实测采样数据与无人机遥感数据相结合，采用自动化机器学习（AutoML）框架——该框架可在一系列候选算法和超参数中自动搜索以选择最优模型——建立预测冬小麦含水量（MC）的回归模型。所有模型均采用五折交叉验证进行评估。结果表明，灌浆期MC预测表现最佳，其中TIR传感器精度最高（R2 = 0.812，MAE = 0.0204，RMSE = 0.0274）。与单传感器方法相比，多传感器融合进一步提升了预测性能，MC预测的R2达到0.876，MAE为0.0191，RMSE为0.0259。这些发现表明，基于无人机的多传感器遥感为监测冬小麦含水量提供了有效手段，有助于及时评估作物生长状况并优化灌溉管理。此外，AutoML的使用使得在最少人工干预下实现高精度预测成为可能，提升了作物水分监测的精度，推动了精准农业发展。","2026-09-15T00:00:00Z",80,{"impact":64,"substance":65,"depth":64,"authority":66,"freshness":189,"relevant":21,"comment":190},8,"AutoML结合无人机多传感器遥感预测冬小麦含水量，方法新颖、数据扎实，对精准灌溉有实用价值，值得进入每日精选。",[192],{"name":111,"url":183},[71,194,195,26,28],"农业人工智能","小麦",[197,198],"农业人工智能 智慧农业 精准灌溉 小麦","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉小麦-2666","10.3390\u002Frs18183161",{"doi":200,"openalex_id":202,"authors":203,"venue":111,"cited_by_count":35,"oa_url":183,"card":220,"direction":48,"ingested_from":49},"W7213246708",[204,207,209,211,214,217],{"name":205,"orcid":206},"Fan Ding","https:\u002F\u002Forcid.org\u002F0000-0001-5482-8290",{"name":208,"orcid":8},"Qian Cheng",{"name":210,"orcid":8},"Fuyi Duan",{"name":212,"orcid":213},"Shuaipeng Fei","https:\u002F\u002Forcid.org\u002F0000-0002-8774-7929",{"name":215,"orcid":216},"Junjie Feng","https:\u002F\u002Forcid.org\u002F0000-0001-8900-2691",{"name":218,"orcid":219},"Zhen Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2847-0042",{"tldr":221,"method":222,"finding":223,"direction":48,"opportunity":224},"用无人机多光谱、RGB和热红外遥感结合AutoML预测冬小麦含水量。","无人机多传感器影像与地面采样，AutoML自动选模型，五折交叉验证。","灌浆期热红外精度最高R²=0.812，多传感器融合提升至R²=0.876。","可探索AutoML与多时相\u002F多源卫星遥感融合，实现区域尺度作物水分精准监测。","2026-09-16T23:30:29.163192Z",{"id":227,"title":228,"url":229,"summary":230,"summary_zh":231,"content":8,"source_name":232,"source_url":229,"published_at":233,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":234,"score_detail":235,"sources":238,"tags":240,"search_phrases":243,"slug":246,"view_count":35,"doi":247,"paper":248,"created_at":263},2437,"Machine Learning-Based Prediction of Soil Moisture in Sikkim's High-Rainfall Zones Using Multimodal Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.52151\u002Fjae2026634.2043","Soil moisture is an important variable influencing agricultural productivity, hydrological processes, and land management, particularly in high-rainfall regions such as the North Eastern Hill (NEH) States of India. Although conventional soil moisture measurement techniques provide reliable observations, they are time-consuming, labour-intensive and limited in spatial coverage, restricting their applicability for regional-scale monitoring. Remote sensing integrated with machine learning provides a promising alternative for generating spatially continuous and timely soil moisture estimates. This study aimed to predict surface soil moisture in the Ranipool-Rumtek administrative region of Sikkim, India, using multimodal remote sensing data and machine-learning techniques. Soil moisture was measured at 85 locations using the gravimetric method and these same locations were used as ground truthing sites. Multi-temporal imagery from Landsat-8 and Sentinel-2 was processed to derive vegetation and moisture-related indices, i.e., Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), Normalized Shortwave-infrared Difference Soil Moisture Index (NSDSI3), Land Surface Temperature (LST), Moisture Stress Index (MSI), and Vegetation Supply Water Index (VSWI). These indices were used as predictor variables to develop artificial neural network (ANN), support vector machine (SVM), and multiple linear regression (MLR) models. The results revealed that the ANN model developed using Sentinel-2-derived indices exhibited the highest predictive accuracy, achieving values of coefficient of determination (R2) as 0.82, Root Mean Square Error (RMSE) as 6% and Mean Absolute Error (MAE) of 4.6%. The performance of the Sentinel-2-derived ANN model was found to be better than that of the Landsat-8-based ANN model as well as the SVM and MLR models developed using Sentinel-2 data. The strong predictive performance demonstrated the effectiveness of integrating high-resolution Sentinel-2 imagery data with ANN for accurate, scalable, and efficient soil moisture estimation in high-relief, data-sparse environments. The proposed approach provides a robust framework to support precision agriculture, irrigation scheduling, hydrological modelling, and drought and flood monitoring.","土壤水分是影响农业生产力、水文过程和土地管理的重要变量，尤其是在印度东北丘陵邦（NEH）等高降雨地区。尽管传统土壤水分测量技术能够提供可靠的观测数据，但其耗时、费力且空间覆盖有限，限制了其在区域尺度监测中的适用性。遥感与机器学习相结合，为生成空间连续且及时的土壤水分估算提供了一种有前景的替代方案。本研究旨在利用多模态遥感数据和机器学习技术，预测印度锡金邦拉尼普尔-鲁姆特克行政区的表层土壤水分。采用重量法在85个位置测量了土壤水分，并将这些位置作为地面验证点。对Landsat-8和Sentinel-2的多时相影像进行处理，以提取植被和水分相关指数，即归一化差异植被指数（NDVI）、归一化差异水体指数（NDWI）、归一化差异水分指数（NDMI）、归一化短波红外差异土壤水分指数（NSDSI3）、地表温度（LST）、水分胁迫指数（MSI）和植被供水指数（VSWI）。这些指数被用作预测变量，以构建人工神经网络（ANN）、支持向量机（SVM）和多元线性回归（MLR）模型。结果表明，使用Sentinel-2衍生指数构建的ANN模型预测精度最高，决定系数（R²）达到0.82，均方根误差（RMSE）为6%，平均绝对误差（MAE）为4.6%。Sentinel-2衍生的ANN模型性能优于基于Landsat-8的ANN模型以及使用Sentinel-2数据构建的SVM和MLR模型。较强的预测性能表明，将高分辨率Sentinel-2影像数据与ANN相结合，能够在地形起伏大、数据稀疏的环境中实现准确、可扩展且高效的土壤水分估算。所提出的方法为支持精准农业、灌溉调度、水文建模以及旱涝监测提供了一个稳健的框架。","Journal of Agricultural Engineering (India)","2026-09-11T00:00:00Z",70,{"impact":236,"substance":17,"depth":18,"authority":16,"freshness":52,"relevant":21,"comment":237},15,"基于多模态遥感与人工神经网络的土壤墒情预测研究，方法新颖、精度可靠，对高降雨山区精准农业与灌溉调度有参考价值，但属区域性案例，影响范围有限。",[239],{"name":232,"url":229},[71,241,26,28,242],"机器学习","土壤墒情",[244,245],"土壤墒情 智慧农业 机器学习 精准灌溉","土壤墒情 智慧农业","土壤墒情智慧农业机器学习精准灌溉-2437","10.52151\u002Fjae2026634.2043",{"doi":247,"openalex_id":249,"authors":250,"venue":232,"cited_by_count":35,"oa_url":8,"card":258,"direction":48,"ingested_from":49},"W7212454193",[251,253,256],{"name":252,"orcid":8},"Pranjal Dubey",{"name":254,"orcid":255},"G. T. Patle","https:\u002F\u002Forcid.org\u002F0000-0002-9175-8567",{"name":257,"orcid":8},"Vinay Kumar Gautam",{"tldr":259,"method":260,"finding":261,"direction":48,"opportunity":262},"用多模态遥感与机器学习预测印度锡金高降雨区表层土壤水分。","Landsat-8与Sentinel-2植被\u002F水分指数，ANN、SVM、MLR建","Sentinel-2指数驱动的ANN精度最高，R²=0.82、RMSE=6%，优于Landsat-8","可探索多源时序遥感与深度学习融合，提升高降雨山区土壤水分时空连续估算与业务化监测能力。","2026-09-14T23:30:27.771449Z",{"id":265,"title":266,"url":267,"summary":268,"summary_zh":269,"content":8,"source_name":270,"source_url":267,"published_at":233,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":234,"score_detail":271,"sources":273,"tags":275,"search_phrases":278,"slug":281,"view_count":35,"doi":282,"paper":283,"created_at":320},2427,"Machine learning based precipitation modeling using multi satellite data for climate resilient water resource management in Bundelkhand India","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01047-x","Precipitation modeling can be improved by using spatially continuous climate data from satellite remote sensing; nevertheless, incorporating heterogeneous sensor-derived variables and understanding machine learning results continue to be significant hurdles. In this work, a multi-source climatic parameter-based satellite-driven machine learning system for precipitation prediction is presented. The dependent variable was precipitation, and the model inputs were satellite-derived predictors such as land surface temperature, atmospheric moisture, surface pressure, wind speed, relative humidity, soil wetness, and temporal indicators. Convolutional Neural Networks (CNN) and Extreme Gradient Boosting (XGBoost), two sophisticated machine learning models, were used to capture multiscale and nonlinear interactions between precipitation and climate factors. Standard statistical measures were used to evaluate the model's performance, and explicable machine learning methods were used to determine the relative significance of the input variables. The findings suggest that both models make good use of satellite-derived climate data, with CNN demonstrating a great capacity to learn intricate feature interactions and XGBoost demonstrating strong predictive ability. With R2 = 0.77, RMSE = 88.79, as well as MAE = 42.06, XGBoost scored far superior than CNN (R2 = 0.60, RMSE = 120.82, MAE = 73.46). According to the interpretability analysis, the main factors influencing precipitation variability are soil wetness, land surface temperature, and atmospheric moisture. The suggested system supports enhanced hydrological forecasting as well as sustainable water resource management by providing a transparent and scalable method for precipitation prediction, especially in areas with limited data. Overall, the findings show that a scalable and efficient framework for precipitation prediction in semi-arid, data-poor areas may be created by combining interpretable machine learning with multi-source Earth observation data. Graphical Abstract","利用卫星遥感提供的空间连续气候数据可以改进降水建模；然而，整合来自不同传感器的异构变量以及理解机器学习结果仍然是重大难题。本研究提出了一种基于多源气候参数的卫星驱动机器学习降水预测系统。因变量为降水，模型输入为卫星衍生的预测因子，包括地表温度、大气湿度、地表气压、风速、相对湿度、土壤湿度和时间指标。研究采用了两种先进的机器学习模型——卷积神经网络（CNN）和极端梯度提升（XGBoost），以捕捉降水与气候因子之间的多尺度和非线性交互作用。使用标准统计指标评估模型性能，并采用可解释机器学习方法确定输入变量的相对重要性。研究结果表明，两种模型均能有效利用卫星衍生的气候数据，其中CNN展现出学习复杂特征交互的强大能力，XGBoost则表现出强劲的预测性能。XGBoost的R² = 0.77、RMSE = 88.79、MAE = 42.06，显著优于CNN（R² = 0.60、RMSE = 120.82、MAE = 73.46）。可解释性分析表明，影响降水变率的主要因素为土壤湿度、地表温度和大气湿度。所提出的系统为降水预测提供了一种透明且可扩展的方法，有助于增强水文预报和可持续水资源管理，尤其在数据稀缺地区。总体而言，研究结果表明，将可解释机器学习与多源地球观测数据相结合，可为半干旱、数据匮乏地区构建一个可扩展且高效的降水预测框架。图形摘要","Discover Environment",{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":189,"relevant":21,"comment":272},"基于多源卫星遥感与可解释机器学习构建降水预测框架，方法对比与结论可靠，对半干旱缺数据区水资源管理有参考价值，但属区域性学术成果，公共影响有限。",[274],{"name":270,"url":267},[194,26,276,27,277],"气候适应","降水预测",[279,280],"农业人工智能 水资源管理 气候适应 降水预测","农业人工智能 水资源管理","农业人工智能水资源管理气候适应降水预测-2427","10.1007\u002Fs44274-026-01047-x",{"doi":282,"openalex_id":284,"authors":285,"venue":270,"cited_by_count":35,"oa_url":314,"card":315,"direction":48,"ingested_from":49},"W7212224443",[286,289,291,294,296,299,301,304,306,309,311],{"name":287,"orcid":288},"Pavan Kumar","https:\u002F\u002Forcid.org\u002F0000-0003-3653-8163",{"name":290,"orcid":8},"Megha Paul",{"name":292,"orcid":293},"Prashant K. Srivastava","https:\u002F\u002Forcid.org\u002F0000-0002-4155-630X",{"name":295,"orcid":8},"Manmohan Dobriyal",{"name":297,"orcid":298},"Yogeshwar Singh","https:\u002F\u002Forcid.org\u002F0000-0002-3324-9289",{"name":300,"orcid":8},"Manish Srivastav",{"name":302,"orcid":303},"Ajay Singh","https:\u002F\u002Forcid.org\u002F0000-0003-2933-4058",{"name":305,"orcid":8},"Abu Salim",{"name":307,"orcid":308},"Shams Tabrez Siddiqui","https:\u002F\u002Forcid.org\u002F0000-0002-6567-3383",{"name":310,"orcid":8},"Aasif Aftab",{"name":312,"orcid":313},"Benson Turyasingura","https:\u002F\u002Forcid.org\u002F0000-0003-1325-4483","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01047-x.pdf",{"tldr":316,"method":317,"finding":318,"direction":48,"opportunity":319},"用多源卫星数据和机器学习预测印度半干旱区降水，XGBoost优于CNN。","CNN与XGBoost，输入LST、湿度、风速等卫星变量，可解释性分析。","XGBoost预测最佳（R²=0.77），土壤湿度、地表温度、大气水汽最关键。","可迁移至其他数据稀缺区，融合多源遥感与可解释AI提升水文预报。","2026-09-14T23:30:25.935081Z"]