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 reflectivity collected by the TechDemoSat-1 (TDS-1) experimental satellite, although, to a limited extent, the Cyclone GNSS (CyGNSS) constellation was considered as well. The encouraging 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. Topography effects and sensitivity to moisture and roughness of a rough soil were included as well as the effect of vegetation cover. The project was carried out by a large team involving different research groups in Europe. It has led to main conclusions and recommendations derived from a beneficial collaboration and fertilization of ideas. The primary approaches and outcomes are summarized here, including comparisons to the recent literature.

The Potential of Spaceborne GNSS Reflectometry for Soil Moisture, Biomass, and Freeze-Thaw Monitoring: Summary of a European Space Agency-Funded Study

Paloscia Simonetta;Santi Emanuele;
2021

Abstract

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 reflectivity collected by the TechDemoSat-1 (TDS-1) experimental satellite, although, to a limited extent, the Cyclone GNSS (CyGNSS) constellation was considered as well. The encouraging 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. Topography effects and sensitivity to moisture and roughness of a rough soil were included as well as the effect of vegetation cover. The project was carried out by a large team involving different research groups in Europe. It has led to main conclusions and recommendations derived from a beneficial collaboration and fertilization of ideas. The primary approaches and outcomes are summarized here, including comparisons to the recent literature.
2021
Vegetation mapping
Sensitivity
Biomass
Global navigation satellite system
Monitoring
Spaceborne radar
Satellite broadcasting
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/441905
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