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Students","award":["2572021BA08"],"award-info":[{"award-number":["2572021BA08"]}]},{"name":"Innovation and Entrepreneurship Training Program for College Students","award":["2572019CP12"],"award-info":[{"award-number":["2572019CP12"]}]},{"name":"Innovation and Entrepreneurship Training Program for College Students","award":["2019M661239"],"award-info":[{"award-number":["2019M661239"]}]},{"name":"Innovation and Entrepreneurship Training Program for College Students","award":["202110225089"],"award-info":[{"award-number":["202110225089"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil moisture plays a significant role in the global hydrological cycle, which is an important component of soil parameterization. Remote sensing is one of the most important methods used to estimate soil moisture. In this study, we developed a new nonlinear Erf-BP neural network method to establish a soil-moisture-content-estimation model with integrated multiple-resource remote-sensing data from high-resolution, hyperspectral and microwave sensors. Next, we compared the result with the single-resource remote-sensing data for SMC (soil-moisture content) estimation models by using the linear-fitting method. The results showed that the soil-moisture estimation model offers better accuracy by using multiple-resource remote-sensing data. Furthermore, the SMC predicted the results by using the new Erf-BP neural network with multiple-resource remote-sensing data and a good overall correlation coefficient of 0.6838. Compared with the linear model\u2019s estimation results, the accuracy of the SMC estimation using the Erf-BP method was increased, and the RMSE decreased from 0.017 g\/g to 0.0146 g\/g, a decrease of 16.44%. These results also indicate that the improved algorithm of the Erf-BP artificial neural network has better fitting results and precision. This research provides a reference for multiple-resource remote-sensing data for soil-moisture estimation.<\/jats:p>","DOI":"10.3390\/rs15010139","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T02:53:11Z","timestamp":1672109591000},"page":"139","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Soil-Moisture Estimation Based on Multiple-Source Remote-Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Tianhao","family":"Mu","sequence":"first","affiliation":[{"name":"School of Forestry, Northeast Forestry University, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guiwei","family":"Liu","sequence":"additional","affiliation":[{"name":"China Railway Design Corporation, Tianjin 300251, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6441-6836","authenticated-orcid":false,"given":"Xiguang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Forestry, Northeast Forestry University, Harbin 150040, China"},{"name":"Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, Northeast Forestry University, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4305-3271","authenticated-orcid":false,"given":"Ying","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Forestry, Northeast Forestry University, Harbin 150040, China"},{"name":"Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, Northeast Forestry University, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1029\/2018RG000618","article-title":"Ground, Proximal, and Satellite Remote Sensing of Soil Moisture","volume":"57","author":"Babaeian","year":"2019","journal-title":"Rev. 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