{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T18:04:35Z","timestamp":1776449075218,"version":"3.51.2"},"reference-count":47,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2019,10,22]],"date-time":"2019-10-22T00:00:00Z","timestamp":1571702400000},"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>This research aimed at exploiting the joint use of machine learning and polarimetry for improving the retrieval of surface soil moisture content (SMC) from synthetic aperture radar (SAR) acquisitions at C-band. The study was conducted on two agricultural areas in Canada, for which a series of RADARSAT-2 (RS2) images were available along with direct measurements of SMC from in situ stations. The analysis confirmed the sensitivity of RS2 backscattering (\u03c3\u00b0) to SMC. The comparison of SMC with the compact polarimetry (CP) parameters, computed from the RS2 acquisitions by the CP data simulator, pointed out that some CP parameters had a sensitivity to SMC equal or better than \u03c3\u00b0, with correlation coefficients up to R \u2243 0.4. Based on these results, the potential of machine learning (ML) for SMC retrieval was exploited by implementing and testing on the available data an artificial neural network (ANN) algorithm. The algorithm was implemented using several combinations of \u03c3\u00b0 and CP parameters. Validation results of the algorithm with in situ observations confirmed the promising capabilities of the ML techniques for SMC monitoring. Furthermore, results pointed out the potential of CP in improving the SMC retrieval accuracy, especially when used in combination with linearly polarized \u03c3\u00b0. Depending on the considered input combination, the ANN algorithm was able to estimate SMC with Root Mean Square Error (RMSE) between 3% and 7% of SMC and R between 0.7 and 0.9.<\/jats:p>","DOI":"10.3390\/rs11202451","type":"journal-article","created":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T11:46:59Z","timestamp":1571831219000},"page":"2451","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Combining Machine Learning and Compact Polarimetry for Estimating Soil Moisture from C-Band SAR Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1882-6321","authenticated-orcid":false,"given":"Emanuele","family":"Santi","sequence":"first","affiliation":[{"name":"CNR-IFAC, via Madonna del Piano, 10\u201350019 Firenze, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3486-9890","authenticated-orcid":false,"given":"Mohammed","family":"Dabboor","sequence":"additional","affiliation":[{"name":"Science and Technology Branch, Environment and Climate Change Canada, Government of Canada, Dorval, QC H9P 1J3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3155-8918","authenticated-orcid":false,"given":"Simone","family":"Pettinato","sequence":"additional","affiliation":[{"name":"CNR-IFAC, via Madonna del Piano, 10\u201350019 Firenze, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3414-4531","authenticated-orcid":false,"given":"Simonetta","family":"Paloscia","sequence":"additional","affiliation":[{"name":"CNR-IFAC, via Madonna del Piano, 10\u201350019 Firenze, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3492","DOI":"10.1109\/TGRS.2014.2377714","article-title":"Soil Moisture Retrieval Using L-Band Radar Observations","volume":"53","author":"Narvekar","year":"2015","journal-title":"IEEE Trans. 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