GPS snow surface thermometer: Surface thermal transmission and estimation

The Global Positioning System (GPS) reflected signals are able to remotely sense the Earth surface characteristics, such as soil moisture, snow depth, vegetation growth and ocean wave height. Since the snow depth is coupling with surface temperature, it is possible to estimate the snow depth and surface air temperature by incorporating GPS-Reflectometry (GPS-R) in Greenland. In this paper, the ionosphere geometric free linear combination of GPS signal variability (GPS-L4) is employed to estimate the snow surface temperature (SST) by considering the physical thermal transmission which causing snow height (SH) variations thermodynamically. The results show that the estimated SST values extracted from the inversion of bootstrapping model have a good agreement to the in-situ meteorological observations in MARG site in Greenland. The mean bias for the estimated SST values during the 286 days from March 21 to December 31, 2010 is about 3.8 C with correlation coefficient of 0.69 between SST values from the inversion of bootstrapping model and observations at MARG station. Moreover, some uncertainties are further discussed.