Downscaling Geostationary Land Surface Temperature Imagery for Urban Analysis

Abstract

Although Earth observation data have been used in urban thermal applications extensively, these studies are often limited by the choices made in data selection, i.e., either using data with high spatial and low temporal resolution, or data with high temporal and low spatial resolution. The challenge of advancing the low spatial (3-5 km) resolution of geostationary land surface temperature (LST) images to 1 km-while maintaining the excellent temporal resolution of 15 min-is approached in this letter. The downscaling was performed using different advanced regression algorithms, such as support vector regression machines, neural networks, and regression trees, and its performance was improved using gradient boosting. The methodologies were tested on Meteosat Second Generation (MSG) SEVIRI LST images over an area of 19 600 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> centered in Athens, Greece. The output 1-km downscaled LST images were assessed against coincident LST maps derived from the thermal infrared imagery of the Moderate Resolution Imaging Spectroradiometer, the Advanced Very High Resolution Radiometer, and the Advanced Along Track Scanning Radiometer. The results showed that support vector machines coupled with gradient boosting proved to be a robust high-performance methodology reaching correlation coefficients from 0.69 to 0.81 when compared with the other satellite-derived LST maps.