Abstract
Ground surface temperature (GST), measured at approximately 5 cm into the ground is a key parameter controlling all the subsurface biophysical processes at the land-atmosphere boundary. Despite the GST significant importance, the current observational network for GST is sparse, particularly in mountain regions. This work exploits the relationship between the GST and satellite-based land surface temperature (LST) derived from MODerate resolution Imaging Spectroradiometer (MODIS). The GST and LST were compared at 14 weather stations in Mazia Valley, North-eastern Italian Alps. The 1-km MODIS LST was downscaled to a spatial resolution of 250-m using the random forest algorithm. The LST dataset covers the years 2014-2017 during the phenological cycle, between April and October. The in-situ GST measurements were recorded using Campbell Scientific CS655 data loggers. LSTs were usually larger than GSTs with temperature differences ranging from 0.1 to 22 °C and an average of 7.9 °C. The lowest and largest average difference was 4.49 °C (1823 m, pasture, south slope) and 10.27 °C (1778 m, forest, north slope), respectively. GST was positively correlated with LST with an R2 ranging from 0.24 to 0.52 and was above 0.45 for 57 % of the stations. The RMSE ranged between 6.05 and 11.05 °C, while for 71 % of the stations was below 9.3 °C. The statistics were influenced by the number of available pairwise for comparison that were ranging from 110 to 377 due to cloud contamination or logger malfunction. Although the RMSE was relatively high, the LST closely followed the pattern of the GST variability suggesting the possibility of linking GST to LST products.