2026-04-14T05:49:05Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/3302292026-01-26T02:16:23Zcom_2072_1033col_2072_452950

Single-pass soil moisture retrievals using GNSS-R: lessons learned Camps Carmona, Adriano José Hyuk, Park Castellví Esturi, Jordi Corbera, Jordi Ascaso, Emili Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Satèl·lits i ràdioenllaços Remote sensing Artificial satellites in remote sensing GNSS-R Reflectivity Surface roughness Vegetation Soil moisture Teledetecció Satèl·lits artificials en teledetecció In this paper, an algorithm to retrieve surface soil moisture from GNSS-R (Global Navigaton Satellite System Reflectometry) observations is presented. Surface roughness and vegetation effects are found to be the most critical ones to be corrected. On one side, the NASA SMAP (Soil Moisture Active and Passive) correction for vegetation opacity (multiplied by two to account for the descending and ascending passes) seems too high. Surface roughness effects cannot be compensated using in situ measurements, as they are not representative. An ad hoc correction for surface roughness, including the dependence with the incidence angle, and the actual reflectivity value is needed. With this correction, reasonable surface soil moisture values are obtained up to approximately a 30° incidence angle, beyond which the GNSS-R retrieved surface soil moisture spreads significantly. This work has been funded by the Spanish MCIU and EU ERDF project (RTI2018-099008-B-C21) “Sensing with pioneering opportunistic techniques” and grant to ”CommSensLab-UPC” Excellence Research Unit Maria de Maeztu (MINECO grant MDM-2016-600), and by a Doctorat Industrial grant from ICGC. Peer Reviewed Postprint (published version) 2020-06-01 Article https://www.mdpi.com/2072-4292/12/12/2064 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-099008-B-C21/ES/SENSING WITH PIONEERING OPPORTUNISTIC TECHNIQUES/ info:eu-repo/grantAgreement/MINECO/1PE/MDM-2016-0600 http://creativecommons.org/licenses/by-nc-nd/3.0/es/ Open Access Attribution-NonCommercial-NoDerivs 3.0 Spain Multidisciplinary Digital Publishing Institute (MDPI)