Abstract
In this study, we evaluated three different downscaling approaches to enhance spatial
resolution of thermal imagery over Alpine vegetated areas. Due to the topographical and land-cover
complexity and to the sparse distribution of meteorological stations in the region, the remotely-sensed
land surface temperature (LST) at regional scale is of major area of interest for environmental
applications. Even though the Moderate Resolution Imaging Spectroradiometer (MODIS) LST fills
the gap regarding high temporal resolution and length of the time-series, its spatial resolution is not
adequate for mountainous areas. Given this limitation, random forest algorithm for downscaling
LST to 250 m spatial resolution was evaluated. This study exploits daily MODIS LST with a
spatial resolution of 1 km to obtain sub-pixel information at 250 m spatial resolution. The nonlinear
relationship between coarse resolution MODIS LST (CR) and fine resolution (FR) explanatory variables
was performed by building three different models including: (i) all pixels (BM), (ii) only pixels with
more than 90% of vegetation content (EM1) and (iii) only pixels with 75% threshold of homogeneity
for vegetated land-cover classes (EM2). We considered normalized difference vegetation index (NDVI)
and digital elevation model (DEM) as predictors. The performances of the thermal downscaling
methods were evaluated by the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE)
between the downscaled dataset and Landsat LST. Validation indicated that the error values for
vegetation fraction (EM1, EM2) were smaller than for basic modelling (BM). BM model determined
averaged RMSE of 2.3 K and MAE of 1.8 K. Enhanced methods (EM1 and EM2) gave slightly better
results yielding 2.2 K and 1.7 K for RMSE and MAE, respectively. In contrast to the EMs, BM showed
a reduction of 22% and 18% of RMSE and MAE respectively with regard to Landsat and the original
MODIS LST. Despite some limitations, mainly due to cloud contamination effect and coarse resolution
pixel heterogeneity, random forest downscaling exhibits a large potential for producing improved
LST maps.