[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-Sentinel":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":7,"page_size":8,"items":9},"Sentinel","tag",[4],1,100,[10],{"id":11,"title":12,"url":13,"summary":14,"summary_zh":15,"content":16,"source_name":17,"source_url":13,"published_at":18,"category":19,"cover_url":16,"hotness":20,"is_selected":21,"score":22,"score_detail":23,"sources":30,"tags":32,"primary_topic":36,"secondary_topics":41,"classification":42,"search_phrases":76,"slug":79,"view_count":80,"doi":81,"paper":82,"created_at":117},1433,"Field-scale irrigation estimation using Sentinel-1 and Sentinel-2 data within a soil water balance model coupled to the Water Cloud Model","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115639","Irrigation is a significant component of the water cycle in agricultural regions: however, information about how much and where irrigation is applied is scarce and uncertain. Remote sensing-based products have been developed to retrieve irrigation across scales, but they exhibit limitations due to spatial uncertainties and confounding factors, particularly in highly fragmented agricultural landscapes. This paper presents a novel method to estimate irrigation at the field scale by integrating Sentinel-1 C-band Synthetic Aperture Radar (SAR) backscatter data and Sentinel-2 NDVI data within a modeling framework. The proposed approach involves the joint calibration of the Water Cloud Model (WCM) and a two-layer Soil Water Balance (SWB) model to simultaneously simulate backscatter, irrigation, surface and root-zone soil moisture, and other water balance components. The model is driven by reanalysis meteorological data, and selected parameters in the WCM and SWB are calibrated exclusively using Sentinel-1 backscatter data, thus making the framework independent of in situ ground measurements. The framework is tested on 38 fields within an irrigation district in the Po Valley, Italy, during 2018, for which irrigation data are available as reference. It is shown that the capability to estimate irrigation events depends on the treatment of the SAR data: after bias-correcting Sentinel-1 observations, average time series correlations of 0.43 to 0.95 could be obtained for daily to bi-weekly irrigation estimates, respectively. The associated bias amounts to 0.01 mm\u002Fday and 0.04 mm\u002F15 days, and less than 1% for the annual estimates.","灌溉是农业区域水循环的重要组成部分，然而关于灌溉量及其分布的信息却稀缺且不确定。基于遥感的产品已被开发用于跨尺度反演灌溉信息，但由于空间不确定性和混杂因素，特别是在高度破碎化的农业景观中，这些产品表现出局限性。本文提出了一种新方法，通过将Sentinel-1 C波段合成孔径雷达（SAR）后向散射数据与Sentinel-2 NDVI数据整合在一个建模框架内，实现田块尺度上的灌溉估算。该方法涉及水云模型（WCM）与两层土壤水平衡（SWB）模型的联合校准，以同时模拟后向散射、灌溉量、地表及根区土壤湿度以及其他水平衡组分。模型由再分析气象数据驱动，且WCM和SWB中的选定参数仅使用Sentinel-1后向散射数据进行校准，从而使该框架不依赖于实地地面测量。该框架在意大利波河流域一个灌区的38块田地上进行了测试，时间为2018年，并提供了相应的灌溉数据作为参考。研究表明，估算灌溉事件的能力取决于对SAR数据的处理方式：在对Sentinel-1观测值进行偏差校正后，日尺度至双周尺度的灌溉估算平均时间序列相关系数分别可达0.43至0.95。相应的偏差分别为0.01毫米\u002F天和0.04毫米\u002F15天，年度估算的偏差则小于1%。",null,"Remote Sensing of Environment","2026-09-01T00:00:00Z","论文",10,false,82,{"impact":24,"substance":25,"depth":26,"authority":27,"freshness":28,"relevant":7,"comment":29},18,22,20,15,7,"提出结合Sentinel-1\u002F2与土壤水平衡模型的田间尺度灌溉估算新方法，无需地面实测，精度高，对农业水资源管理有重要价值。",[31],{"name":17,"url":13},[33,34,35,4],"智慧农业","遥感","灌溉估算",{"code":37,"slug":38,"name":39,"path":40,"parent_code":16},"H","environment","资源环境与气候变化","\u002Ftopic\u002Fenvironment",[],{"status":43,"locked":21,"version":44,"method":45,"evidence":46},"partial","2026-10-22.2","rules",{"candidates":47,"agricultural_evidence":51,"reason":75},[48,57,64,70],{"code":49,"score":50,"supported":51,"title":52,"abstract":54,"tags":56},"H2",14,true,[53],"soil",[55,53],"土壤",[],{"code":58,"score":50,"supported":51,"title":59,"abstract":61,"tags":63},"H3",[60],"irrigation",[62,60],"灌溉",[],{"code":65,"score":28,"supported":51,"title":66,"abstract":67,"tags":69},"I3",[],[34,68],"remote sensing",[34],{"code":71,"score":7,"supported":21,"title":72,"abstract":73,"tags":74},"I1",[],[],[33],"一级领域明确，二级方向需要细分",[77,78],"智慧农业 灌溉估算 遥感 Sentinel","智慧农业 灌溉估算","智慧农业灌溉估算遥感Sentinel-1433",2,"10.1016\u002Fj.rse.2026.115639",{"doi":81,"openalex_id":83,"authors":84,"venue":17,"cited_by_count":109,"oa_url":13,"card":110,"direction":114,"ingested_from":116},"W4415587276",[85,88,91,94,97,100,103,106],{"name":86,"orcid":87},"Martina Natali","https:\u002F\u002Forcid.org\u002F0009-0007-5466-686X",{"name":89,"orcid":90},"Sara Modanesi","https:\u002F\u002Forcid.org\u002F0000-0003-4720-5233",{"name":92,"orcid":93},"Domenico De Santis","https:\u002F\u002Forcid.org\u002F0000-0003-0267-0078",{"name":95,"orcid":96},"Gabriëlle De Lannoy","https:\u002F\u002Forcid.org\u002F0000-0002-6743-7122",{"name":98,"orcid":99},"Susan Steele‐Dunne","https:\u002F\u002Forcid.org\u002F0000-0002-8644-3077",{"name":101,"orcid":102},"Alessia Flammini","https:\u002F\u002Forcid.org\u002F0000-0002-9823-3413",{"name":104,"orcid":105},"Alessio Domeneghetti","https:\u002F\u002Forcid.org\u002F0000-0003-4726-5316",{"name":107,"orcid":108},"Christian Massari","https:\u002F\u002Forcid.org\u002F0000-0003-0983-1276",0,{"tldr":111,"method":112,"finding":113,"direction":114,"opportunity":115},"提出一种仅用Sentinel-1和Sentinel-2数据估算田间灌溉量的方法，无需地面实测。","耦合水云模型和两层土壤水平衡模型，联合校准，用Sentinel-1后向散射数据驱","偏差校正后，日到双周灌溉估算相关系数0.43-0.95，年估算偏差小于1%。","农业遥感与作物表型","可探索在更破碎景观或不同作物下扩展该方法，或结合高分辨率土壤湿度产品提高估算精度。","openalex","2026-09-02T23:30:51.202761Z"]