{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T17:49:53Z","timestamp":1776448193292,"version":"3.51.2"},"reference-count":30,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T00:00:00Z","timestamp":1689120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000844","name":"European Space Agency","doi-asserted-by":"publisher","award":["4000126684\/19\/I-NB"],"award-info":[{"award-number":["4000126684\/19\/I-NB"]}],"id":[{"id":"10.13039\/501100000844","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil moisture maps are essential for hydrological, agricultural and risk assessment applications. To best meet these requirements, it is essential to develop soil moisture products at high spatial resolution, which is now made possible using the free Sentinel-1 (S1) SAR (Synthetic Aperture Radar) data. Some soil moisture retrieval techniques using S1 data relied on the use of a priori weather information in order to increase the precision of soil moisture estimates, which required access to a weather-forecasting framework. This paper presents an improved and fully autonomous solution for high-resolution soil moisture mapping in bare agricultural areas. The proposed solution derives a priori weather information directly from the original Sentinel images, thus bypassing the need for a weather forecasting framework. For soil moisture estimation, the neural network technique was implemented to ensure the optimum integration of radar information. The neural networks were trained using synthetic data generated by the modified Integral Equation Model (IEM) model and validated on real data from two study sites in France and Tunisia. The main findings showed that the use of a radar signal averaged over grids of a few km2 in addition to radar signal at plot scale instead of a priori weather information provides good soil moisture estimations. The accuracy is even slightly better compared to the accuracy obtained using a priori weather information.<\/jats:p>","DOI":"10.3390\/rs15143502","type":"journal-article","created":{"date-parts":[[2023,7,13]],"date-time":"2023-07-13T01:52:25Z","timestamp":1689213145000},"page":"3502","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Soil Moisture Retrieval in Bare Agricultural Areas Using Sentinel-1 Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4927-6863","authenticated-orcid":false,"given":"Mouad","family":"Ettalbi","sequence":"first","affiliation":[{"name":"INRAE, UMR TETIS, Universit\u00e9 de Montpellier, 34090 Montpellier, France"},{"name":"INRAE, UMR RECOVER, Aix-Marseille Universit\u00e9, 13182 Aix-en-Provence, CEDEX 5, France"},{"name":"AIWAY, Mercure A, 565 rue Marcellin Berthelot, 13851 Aix-en-Provence, CEDEX 3, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9461-4120","authenticated-orcid":false,"given":"Nicolas","family":"Baghdadi","sequence":"additional","affiliation":[{"name":"INRAE, UMR TETIS, Universit\u00e9 de Montpellier, 34090 Montpellier, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8350-6741","authenticated-orcid":false,"given":"Pierre-Andr\u00e9","family":"Garambois","sequence":"additional","affiliation":[{"name":"INRAE, UMR RECOVER, Aix-Marseille Universit\u00e9, 13182 Aix-en-Provence, CEDEX 5, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5510-1832","authenticated-orcid":false,"given":"Hassan","family":"Bazzi","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, AgroParisTech, INRAE, UMR 518 MIA Paris-Saclay, 91120 Palaiseau, France"},{"name":"Atos France, Technical Services, 80 Quai Voltaire, 95870 Bezons, France"}]},{"given":"Emmanuel","family":"Ferreira","sequence":"additional","affiliation":[{"name":"AIWAY, Mercure A, 565 rue Marcellin Berthelot, 13851 Aix-en-Provence, CEDEX 3, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6141-8222","authenticated-orcid":false,"given":"Mehrez","family":"Zribi","sequence":"additional","affiliation":[{"name":"CESBIO (CNES\/CNRS\/INRAE\/IRD\/UPS), 18 Av. Edouard Belin, bpi 2801, 31401 Toulouse, CEDEX 9, France"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.3390\/encyclopedia1040084","article-title":"Natural Disasters-Origins, Impacts, Management","volume":"1","author":"Chaudhary","year":"2021","journal-title":"Encyclopedia"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Baghdadi, N., and Zribi, M. (2016). Characterization of Soil Surface Properties Using Radar Remote Sensing. Land Surf. Remote Sens. Cont. Hydrol., 1\u201339.","DOI":"10.1016\/B978-1-78548-104-8.50001-2"},{"key":"ref_3","unstructured":"Ulaby, F.T., Moore, R.K., and Fung, A.K. (1981). 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