The potential of spaceborne GNSS reflectometry for soil moisture, biomass, and freeze–thaw monitoring: Summary of a European Space Agency-funded study

This work presents an overview of the activity developed in the frame of a project funded by the European Space Agency (ESA). The research was focused on the study of the potential applications of GNSS Reflectometry (GNSS- R) over land, with an emphasis on soil moisture (SM) and biomass. A study about the sensitivity with respect to the freeze–thaw dynamics was considered as well. The work started with an analysis of the sensitivity of GNSS-R reflec- tivity collected by the TechDemoSat-1 (TDS-1) experimental satellite, although, to a limited extent, the Cyclone GNSS (CyGNSS) constellation was considered as well. The en - couraging sensitivity outcomes led to the development of retrieval algorithms: three different approaches for SM and one for biomass based on neural networks. A more theoretical investigation was carried out to better understand and predict the signal from a satellite platform which required the updating of two different models. To - pography effects and sensitivity to moisture and roughness of a rough soil were included as well as the effect of vegeta- tion cover. The project was carried out by a large team involving different research groups in Europe. It has led to main con- clusions and recommendations derived from a beneficial collaboration and fertilization of ideas. The primary ap - proaches and outcomes are summarized here, including comparisons to the recent literature.This study has been conducted in the framework of the ESA contract 4000120299/17/NL/AF/hh, “Potential of Spaceborne GNSS-R for Land Applications.” The TDS-1 data have been freely collected from the MERRBYS portal at merrbys.co.uk. The CyGNSS and SMAP data were downloaded from the NASA Earthdata portal at podaac.jpl.nasa.gov and nsidc.org/data/smap, respectively. The SMOS data are made available from the ESA through the smos-diss.eo.esa.int website. This work has also been partially supported by the project of the Ministerio de Ciencia, Innovación y Universidades and EU ERDF ref. RTI2018-099008-B-C21/AEI/10.13089/E01100011083, “Sensing with Pioneering Opportunistic Techniques,” and grant MDM-2016-0600 to the CommSensLab-UPC “María de Maeztu” Excellent Research Unit. We thank the ISMN for the availability of soil moisture data.Peer ReviewedPostprint (author's final draft