{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T19:26:31Z","timestamp":1780773991561,"version":"3.54.1"},"reference-count":171,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T00:00:00Z","timestamp":1644192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The correction of Soil Moisture (SM) estimates in Land Surface Models (LSMs) is considered essential for improving the performance of numerical weather forecasting and hydrologic models used in weather and climate studies. Along with surface screen-level variables, the satellite data, including Brightness Temperature (BT) from passive microwave sensors, and retrieved SM from active, passive, or combined active\u2013passive sensor products have been used as two critical inputs in improvements of the LSM. The present study reviewed the current status in correcting LSM SM estimates, evaluating the results with in situ measurements. Based on findings from previous studies, a detailed analysis of related issues in the assimilation of SM in LSM, including bias correction of satellite data, applied LSMs and in situ observations, input data from various satellite sensors, sources of errors, calibration (both LSM and radiative transfer model), are discussed. Moreover, assimilation approaches are compared, and considerations for assimilation implementation are presented. A quantitative representation of results from the literature review, including ranges and variability of improvements in LSMs due to assimilation, are analyzed for both surface and root zone SM. A direction for future studies is then presented.<\/jats:p>","DOI":"10.3390\/rs14030770","type":"journal-article","created":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T08:38:48Z","timestamp":1644223128000},"page":"770","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review"],"prefix":"10.3390","volume":"14","author":[{"given":"Reza","family":"Khandan","sequence":"first","affiliation":[{"name":"Department of GIS and Remote Sensing, Faculty of Geography, University of Tehran, Teheran 14155-6619, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Pierre","family":"Wigneron","sequence":"additional","affiliation":[{"name":"INRAE, nUMR1391 ISPA, F-33140 Villenave d\u2019Ornon, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6485-393X","authenticated-orcid":false,"given":"Stefania","family":"Bonafoni","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3261-7043","authenticated-orcid":false,"given":"Arastoo Pour","family":"Biazar","sequence":"additional","affiliation":[{"name":"Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, AL 35805, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2881-0024","authenticated-orcid":false,"given":"Mehdi","family":"Gholamnia","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Islamic Azad University, Sanandaj Branch, Sanandaj P.O. 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