{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T19:56:52Z","timestamp":1775937412010,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T00:00:00Z","timestamp":1649289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFA0605002"],"award-info":[{"award-number":["2017YFA0605002"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41961124007"],"award-info":[{"award-number":["41961124007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51779145"],"award-info":[{"award-number":["51779145"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41830863"],"award-info":[{"award-number":["41830863"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"\u201cSix top talents\u201d in Jiangsu province","award":["RJFW-031"],"award-info":[{"award-number":["RJFW-031"]}]},{"name":"the Belt and Road Fund on Water and Sustainability of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering","award":["2019nkzd02"],"award-info":[{"award-number":["2019nkzd02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Modern smart agriculture initiative presents more requests for soil moisture (SM) monitoring over large agricultural areas. Remote sensing techniques facilitate high-resolution surface SM (SSM) estimation at a large scale but lack root zone SM (RZSM) information. Establishing the deduction method of RZSM from the SSM has long been the focus of most attention. Data assimilation methods are promising techniques for RZSM estimation, developing numerous assimilated reanalysis datasets, e.g., ERA5 and the latest Soil Moisture Active and Passive (SMAP) L4 SM product. However, data latency and large computation during data collecting and processing often inhibits further applications. This work proposes a rapid estimation scheme for estimating RZSM with short latency and small computations, based on the Exponential Filter (EF) method. The EF model with single parameter T was firstly calibrated and validated using the SSM and RZSM of ERA5 reanalysis dataset, obtaining the optimum parameter T map for each grid. Then, the fast-updating SMAP L3 SSM product together with the scale-matched optimum T were adopted as inputs into the EF model to retrieve RZSM estimation of each grid. Specifically, such estimation scheme was tested over the central and eastern agricultural areas of China, using a dense monitoring network of 796 SM observation sites, which contains various land uses, as well as meteorological and hydrological conditions. The calibrated optimum parameter T presented an increasing trend with good physical explanations. Furthermore, all the estimated RZSMs were found to have good performances on capturing the temporal-spatial variations of RZSM and well reflecting seasonal RZSM changes. Overall, such an estimation scheme was proven to be a desirable alternative for estimating RZSM over large agricultural areas.<\/jats:p>","DOI":"10.3390\/rs14081785","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T21:08:22Z","timestamp":1649365702000},"page":"1785","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["An Exponential Filter Model-Based Root-Zone Soil Moisture Estimation Methodology from Multiple Datasets"],"prefix":"10.3390","volume":"14","author":[{"given":"Yanqing","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China"},{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China"},{"name":"Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5821-4755","authenticated-orcid":false,"given":"Zhenxin","family":"Bao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China"},{"name":"Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China"}]},{"given":"Houfa","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China"},{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China"},{"name":"Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9121-9571","authenticated-orcid":false,"given":"Guoqing","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China"},{"name":"Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China"}]},{"given":"Cuishan","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China"},{"name":"Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China"},{"name":"Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China"},{"name":"School of Civil Engineering, Tianjin University, Tianjin 300350, China"}]},{"given":"Jianyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China"},{"name":"Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.jhydrol.2006.09.004","article-title":"Soil moisture spatial variability in experimental areas of central Italy","volume":"333","author":"Brocca","year":"2007","journal-title":"J. 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