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Combining Machine Learning and Compact Polarimetry for Estimating Soil Moisture from C-Band SAR Data.

, , , and . Remote Sensing, 11 (20): 2451 (2019)

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Model analysis and experimental investigations of X-band backscattering sensitivity to snowpack characteristics., , , , , and . IGARSS, page 1254-1257. IEEE, (2012)The Potential of COSMO-SkyMed SAR Images in Monitoring Snow Cover Characteristics., , , , , and . IEEE Geosci. Remote. Sens. Lett., 10 (1): 9-13 (2013)The Intercomparison of X-Band SAR Images from COSMO-SkyMed and TerraSAR-X Satellites: Case Studies., , , , and . Remote. Sens., 5 (6): 2928-2942 (2013)COSMO-SkyMed Image Investigation of Snow Features in Alpine Environment., , , and . Sensors, 17 (1): 84 (2017)Soil Moisture and Forest Biomass retrieval on a global scale by using CyGNSS data and Artificial Neural Networks., , , , , , , and . IGARSS, page 5905-5908. IEEE, (2020)Microwave Emission from Forested Areas by Using Microwave AMSR-E Data., , , , , and . IGARSS (1), page 41-44. IEEE, (2008)An Operational Algorithm for Snow Cover Mapping in Hydrological Applications., , , , and . IGARSS (4), page 964-967. IEEE, (2009)The use of COSMO-SkyMed images for retrieving snow depth and soil moisture in mountainous areas., , , , , , and . IGARSS, page 3654-3657. IEEE, (2014)The retrieval and monitoring of vegetation parameters from COSMO-SkyMed images., , , , , , , and . IGARSS, page 7031-7034. IEEE, (2012)A merged SMAP - Sentinel-1 soil moisture product using Artificial Neural Networks: a case study in Central Italy., , , , , , , and . IGARSS, page 7077-7080. IEEE, (2019)