{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T06:49:53Z","timestamp":1774334993680,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the R&amp;D+I Program of the Universidad Polit\u00e9cnica de Madrid (Programa Propio UPM 2019)","award":["1411041901010"],"award-info":[{"award-number":["1411041901010"]}]},{"name":"the R&amp;D+I Program of the Universidad Polit\u00e9cnica de Madrid (Programa Propio UPM 2019)","award":["JSZRHYKJ202002"],"award-info":[{"award-number":["JSZRHYKJ202002"]}]},{"name":"the R&amp;D+I Program of the Universidad Polit\u00e9cnica de Madrid (Programa Propio UPM 2019)","award":["XDA23040100"],"award-info":[{"award-number":["XDA23040100"]}]},{"name":"the Talent Start-Up Funding project of NUIST","award":["1411041901010"],"award-info":[{"award-number":["1411041901010"]}]},{"name":"the Talent Start-Up Funding project of NUIST","award":["JSZRHYKJ202002"],"award-info":[{"award-number":["JSZRHYKJ202002"]}]},{"name":"the Talent Start-Up Funding project of NUIST","award":["XDA23040100"],"award-info":[{"award-number":["XDA23040100"]}]},{"name":"the Jiangsu Natural Resources Development Special Project","award":["1411041901010"],"award-info":[{"award-number":["1411041901010"]}]},{"name":"the Jiangsu Natural Resources Development Special Project","award":["JSZRHYKJ202002"],"award-info":[{"award-number":["JSZRHYKJ202002"]}]},{"name":"the Jiangsu Natural Resources Development Special Project","award":["XDA23040100"],"award-info":[{"award-number":["XDA23040100"]}]},{"name":"the Strategic Priority Research Program Project of the Chinese Academy of Sciences","award":["1411041901010"],"award-info":[{"award-number":["1411041901010"]}]},{"name":"the Strategic Priority Research Program Project of the Chinese Academy of Sciences","award":["JSZRHYKJ202002"],"award-info":[{"award-number":["JSZRHYKJ202002"]}]},{"name":"the Strategic Priority Research Program Project of the Chinese Academy of Sciences","award":["XDA23040100"],"award-info":[{"award-number":["XDA23040100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The reflection of Global Navigation Satellite Systems (GNSS) signals, namely GNSS-Reflectometry (GNSS-R), has recently proven to be able to monitor land surface properties in the microwave spectrum, at a global scale, and with very low revisiting time. Moreover, this new technique has numerous additional advantages, including low cost, low power consumption, lightweight and small payloads, and near real-time massive data availability, as compared to conventional monostatic microwave remote sensing. However, the GNSS-R surface reflectivity values estimated through the bistatic radar equation, and the Fresnel coefficients have shown a lack of coincidence with real surface reflectivity data, mostly due to calibration issues. Previous studies have attempted to avoid this matter with direct regression methods between uncalibrated GNSS-R reflectivity data and external soil moisture content (SMC) products. However, calibration of GNSS-R reflectivity used in traditional inversion models is still a challenge, such as those to estimate SMC, freeze\/thaw, or biomass. In this paper, a successful procedure for GNSS-R reflectivity calibration is established using data from the CYGNSS (Cyclone GNSS) constellation. The scale and bias parameters are estimated from the theoretical dielectric properties of water and dry sand, which are well-known and empirically validated values. We employ four calibration areas that provide maximum range limits of reflectivity, such as deserts and wetlands. The CYGNSS scale factor and the bias parameter resulted in a = 3.77 and b = 0.018, respectively. The derived scale and bias parameters are applied to the CYGNSS dataset, and the retrieved SMC values through the Fresnel reflection coefficients are in excellent agreement with the Soil Moisture Active Passive (SMAP) SMC product. Then, the SMAP SMC is used as a reference true value, and provides a standard linear regression with an R-square coefficient of 0.803, a root mean square error (RMSE) of 0.084, and a Pearson\u2019s correlation coefficient of 0.896.<\/jats:p>","DOI":"10.3390\/rs14143262","type":"journal-article","created":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T21:15:52Z","timestamp":1657142152000},"page":"3262","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Calibration and Validation of CYGNSS Reflectivity through Wetlands\u2019 and Deserts\u2019 Dielectric Permittivity"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6223-6874","authenticated-orcid":false,"given":"I\u00f1igo","family":"Molina","sequence":"first","affiliation":[{"name":"School of Land Surveying, Geodesy and Mapping Engineering, Universidad Polit\u00e9cnica de Madrid, 28031 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6779-4341","authenticated-orcid":false,"given":"Andr\u00e9s","family":"Calabia","sequence":"additional","affiliation":[{"name":"School of Land Surveying, Geodesy and Mapping Engineering, Universidad Polit\u00e9cnica de Madrid, 28031 Madrid, Spain"},{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5108-4828","authenticated-orcid":false,"given":"Shuanggen","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China"},{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0142-9864","authenticated-orcid":false,"given":"Komi","family":"Edokossi","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2707-024X","authenticated-orcid":false,"given":"Xuerui","family":"Wu","sequence":"additional","affiliation":[{"name":"Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8272","DOI":"10.1029\/2018GL078923","article-title":"Use of Cyclone Global Navigation Satellite System (CyGNSS) Observations for Estimation of Soil Moisture","volume":"45","author":"Kim","year":"2018","journal-title":"Geophys. 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