{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:44:19Z","timestamp":1776444259666,"version":"3.51.2"},"reference-count":36,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2017,9,20]],"date-time":"2017-09-20T00:00:00Z","timestamp":1505865600000},"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>In a perspective to develop an inversion approach for estimating surface soil moisture of crop fields from Sentinel-1\/2 data (radar and optical sensors), the Water Cloud Model (WCM) was calibrated from C-band Synthetic Aperture Radar (SAR) data and Normalized Difference Vegetation Index (NDVI) values collected over crops fields and grasslands. The soil contribution that depends on soil moisture and surface roughness (in addition to SAR instrumental parameters) was simulated using the physical backscattering model IEM (Integral Equation Model). The vegetation descriptor used in the WCM is the NDVI because it can be directly calculated from optical images. A large dataset consisting of radar backscattered signal in Vertical transmit and Vertical receive (VV) and Vertical transmit and Horizontal receive (VH) polarizations with wide range of incidence angle, soil moisture, surface roughness, and NDVI-values was used. It was collected over two agricultural study sites. Results show that the soil contribution to the total radar backscattered signal is lower in VH than in VV because VH is more sensitive to vegetation cover. Thus, the use of VH alone or in addition to VV for retrieving the soil moisture is not advantageous in presence of well-developed vegetation cover.<\/jats:p>","DOI":"10.3390\/rs9090969","type":"journal-article","created":{"date-parts":[[2017,9,20]],"date-time":"2017-09-20T10:57:41Z","timestamp":1505905061000},"page":"969","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":173,"title":["Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9461-4120","authenticated-orcid":false,"given":"Nicolas","family":"Baghdadi","sequence":"first","affiliation":[{"name":"Institut National de Recherche en Sciences et Technologies Pour l\u2019Environnement et l\u2019Agriculture (IRSTEA), UMR TETIS, 500 rue Fran\u00e7ois Breton, 34093 Montpellier CEDEX 5, France"}]},{"given":"Mohammad","family":"El Hajj","sequence":"additional","affiliation":[{"name":"Institut National de Recherche en Sciences et Technologies Pour l\u2019Environnement et l\u2019Agriculture (IRSTEA), UMR TETIS, 500 rue Fran\u00e7ois Breton, 34093 Montpellier CEDEX 5, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6141-8222","authenticated-orcid":false,"given":"Mehrez","family":"Zribi","sequence":"additional","affiliation":[{"name":"French National Centre for Scientific Research (CESBIO), 18 av. Edouard Belin, bpi 2801, 31401 Toulouse CEDEX 9, France"}]},{"given":"Safa","family":"Bousbih","sequence":"additional","affiliation":[{"name":"French National Centre for Scientific Research (CESBIO), 18 av. Edouard Belin, bpi 2801, 31401 Toulouse CEDEX 9, France"},{"name":"Institut National Agronomique de Tunis, Universit\u00e9 de Carthage, Tunis, Tunisia"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1109\/JSTARS.2012.2220124","article-title":"Toward an Operational Bare Soil Moisture Mapping Using TerraSAR-X Data Acquired Over Agricultural Areas","volume":"6","author":"Aubert","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. 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