[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2437":3},{"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,"view_count":32,"doi":33,"paper":34,"created_at":51},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。",null,"Journal of Agricultural Engineering (India)","2026-09-11T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,20,17,12,6,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","机器学习","遥感","精准灌溉","土壤墒情",0,"10.52151\u002Fjae2026634.2043",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":9,"card":44,"direction":48,"ingested_from":50},"W7212454193",[37,39,42],{"name":38,"orcid":9},"Pranjal Dubey",{"name":40,"orcid":41},"G. T. Patle","https:\u002F\u002Forcid.org\u002F0000-0002-9175-8567",{"name":43,"orcid":9},"Vinay Kumar Gautam",{"tldr":45,"method":46,"finding":47,"direction":48,"opportunity":49},"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","农业遥感与作物表型","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。","openalex","2026-09-14T23:30:27.771449Z"]