{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:33:04Z","timestamp":1765233184080,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T00:00:00Z","timestamp":1633564800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31971781"],"award-info":[{"award-number":["31971781"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Basic Public Welfare Research Project Foundation of China","award":["LGN19D040001"],"award-info":[{"award-number":["LGN19D040001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This article aims to attempt to increase the number of satellites that can be used for monitoring soil moisture to obtain more precise results using GNSS-IR (Global Navigation Satellite System-Interferometric Reflectometry) technology to estimate soil moisture. We introduce a soil moisture inversion model by using GPS SNR (Signal-to-Noise Ratio) data and propose a novel Robust Kalman Filter soil moisture inversion model based on that. We validate our models on a data set collected at Lamasqu\u00e8re, France. This paper also compares the precision of the Robust Kalman Filter model with the conventional linear regression method and robust regression model in three different scenarios: (1) single-band univariate regression, by using only one observable feature such as frequency, amplitude, or phase; (2) dual-band data fusion univariate regression; and (3) dual-band data fusion multivariate regression. First, the proposed models achieve higher accuracy than the conventional method for single-band univariate regression, especially by using the phase as the input feature. Second, dual-band univariate data fusion achieves higher accuracy than single-band and the result of the Robust Kalman Filter model correlates better to the in situ measurement. Third, multivariate variable fusion improves the accuracy for both models, but the Robust Kalman Filter model achieves better improvement. Overall, the Robust Kalman Filter model shows better results in all the scenarios.<\/jats:p>","DOI":"10.3390\/rs13194013","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"4013","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Robust Kalman Filter Soil Moisture Inversion Model Using GPS SNR Data\u2014A Dual-Band Data Fusion Approach"],"prefix":"10.3390","volume":"13","author":[{"given":"Lili","family":"Jing","sequence":"first","affiliation":[{"name":"Institute of Space Science, Shandong University, Weihai 264209, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5702-5474","authenticated-orcid":false,"given":"Lei","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"},{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Wentao","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geological Engineering and Surveying and Mapping, Chang\u2019an University, Xi\u2019an 710054, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5818-6264","authenticated-orcid":false,"given":"Tianhe","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Space Science, Shandong University, Weihai 264209, China"}]},{"given":"Fan","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute of Space Science, Shandong University, Weihai 264209, China"}]},{"given":"Yilin","family":"Lu","sequence":"additional","affiliation":[{"name":"China Association of Remote Sensing Application, Beijing 100094, China"}]},{"given":"Bo","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}]},{"given":"Dongkai","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Xuebao","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Nazi","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Space Science, Shandong University, Weihai 264209, China"}]},{"given":"Hongliang","family":"Ruan","sequence":"additional","affiliation":[{"name":"Business School, Jinhua Polytechnic, Jinhua 321000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1750-2819","authenticated-orcid":false,"given":"Jos\u00e9","family":"Darrozes","sequence":"additional","affiliation":[{"name":"Laboratoire G\u00e9osciences Environnement Toulouse, Universit\u00e9 Paul Sabatier, 31400 Toulouse, France"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Munoz-Martin, J., Onrubia, R., Pascual, D., Park, H., Pablos, M., Camps, A., R\u00fcdiger, C., Walker, J., and Monerris, A. 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