Freeze–Thaw Detection Over High-Latitude Regions by Means of GNSS-R Data
Kimmo Rautiainena, Davide Comiteb c d, Juval Cohena, Estel Cardellache f g, Martin J. Unwinh, Nazzareno Pierdiccab c d
- aFinnish Meteorological Institute (FMI), Helsinki, Finland
- bDepartment of Information Engineering, Electronics and Telecommunications (DIET), “
- cSapienza”
- dUniversity, Rome, Italy
- eInstitut de Ciencies del’
- fEspai, Consejo Superior de Investigaciones Cientificas (ICE-CSIC), Barcelona, Spain
- gInstitut d'Estudis Espacials de Catalunya (IEEC), Barcelona, Spain
- hSurrey Satellite Technology Ltd., Guildford, GU2 7YE, U.K
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
Abstract
Monitoring freeze/thaw variations of the Earth surfaces is of paramount importance for the study of biogeochemical processes and climate change. At present, the use of passive sensors is well established, but, very recently, some studies demonstrated the potentialities of observations exploiting signals of opportunity. We propose here an advanced study to demonstrate the capability of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) to provide accurate and systematic information about the Earth-surface freeze/thaw state. Reflectivity values derived from TechDemoSat-1 (TDS-1) data are elaborated and compared against the Soil Moisture and Ocean Salinity (SMOS) freeze/thaw product, while state-of-the-art land cover data are used to select GNSS-R data within an estimated footprint. In spite of the limited data availability due to sparse spatial coverage and calibration issues of TDS-1 observations, the proposed analysis demonstrates the possibility of monitoring the freeze/thaw state by analyzing the calibrated reflectivity, including also the possibility of detecting the transition state between frozen and thawed conditions across seasonal variations. This feature makes the design of next-generation GNSS-R satellite missions a unique opportunity to achieve high-resolution freeze/thaw monitoring with small and low-cost platforms.

