This study aims at assessing the potential of the NASA's Cyclone GNSS (CyGNSS) data for observing SM and forest biomass. As reference values for the comparison, global datasets of Vegetation Optical Depth (VOD) and SM derived from NASA's Soil Moisture Active and Passive mission SMAP have been considered. The results of the sensitivity analysis suggested exploiting the CyGNSS capabilities in estimating VOD and SM by setting-up prototype retrieval algorithms based on Artificial Neural Networks (ANN).
Soil moisture and forest biomass retrieval on a global scale by using CyGNSS data and artificial neural networks / Santi, E.; Pettinato, S.; Paloscia, S.; Clarizia, M. P.; Dente, L.; Guerriero, L.; Comite, D.; Pierdicca, N.. - (2020), pp. 5905-5908. (Intervento presentato al convegno 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 tenutosi a usa) [10.1109/IGARSS39084.2020.9323896].
Soil moisture and forest biomass retrieval on a global scale by using CyGNSS data and artificial neural networks
Comite D.;Pierdicca N.
2020
Abstract
This study aims at assessing the potential of the NASA's Cyclone GNSS (CyGNSS) data for observing SM and forest biomass. As reference values for the comparison, global datasets of Vegetation Optical Depth (VOD) and SM derived from NASA's Soil Moisture Active and Passive mission SMAP have been considered. The results of the sensitivity analysis suggested exploiting the CyGNSS capabilities in estimating VOD and SM by setting-up prototype retrieval algorithms based on Artificial Neural Networks (ANN).| File | Dimensione | Formato | |
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