The GNSS reflectometry (GNSS-R) potential for the monitoring of hydrological parameters as soil moisture (SM) and forest aboveground biomass (AGB) has been largely proved in recent years. In this study, algorithms based on Artificial Neural Networks (ANN) have been developed for the retrieval of both SM and AGB from GNSS-R observations. This activity has been carried out in view of the ESA's HydroGNSS mission. Waiting for HydroGNSS data, the algorithms have been implemented and validated by using the NASA's Cyclone GNSS (CyGNSS) land observations, confirming a promising potential of GNSS-R for the monitoring of both SM and AGB.
Combining Cygnss and Machine Learning for Soil Moisture and Forest Biomass Retrieval in View of the ESA Scout Hydrognss Mission / Santi, E.; Clarizia, M. P.; Comite, D.; Dente, L.; Guerriero, L.; Pierdicca, N.; Floury, N.. - 2022-:(2022), pp. 7433-7436. (Intervento presentato al convegno 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 tenutosi a Malesia) [10.1109/IGARSS46834.2022.9884738].
Combining Cygnss and Machine Learning for Soil Moisture and Forest Biomass Retrieval in View of the ESA Scout Hydrognss Mission
Comite D.;Dente L.;Guerriero L.;Pierdicca N.;
2022
Abstract
The GNSS reflectometry (GNSS-R) potential for the monitoring of hydrological parameters as soil moisture (SM) and forest aboveground biomass (AGB) has been largely proved in recent years. In this study, algorithms based on Artificial Neural Networks (ANN) have been developed for the retrieval of both SM and AGB from GNSS-R observations. This activity has been carried out in view of the ESA's HydroGNSS mission. Waiting for HydroGNSS data, the algorithms have been implemented and validated by using the NASA's Cyclone GNSS (CyGNSS) land observations, confirming a promising potential of GNSS-R for the monitoring of both SM and AGB.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
