A spatial regression approach to modeling urban land surface temperature

Land surface temperature (LST) is the instantaneous radiative skin temperature of land obtained from satellite sensors. Measured by visible, infrared or microwave sensors, the LST is useful in determining thermal comfort for urban planning. It also serves as a precursor to many underlying impacts such as health, climate change and the likelihood of rainfall. Due to the paucity of observed data because of cloud cover or rain-bearing clouds in the case of microwave sensors, it is necessary to model LST for the purpose of forecasting. Two spatial regression models were employed: the spatial lag model and the spatial error model. Using Landsat 8 and Shuttle Radar Topography Mission (SRTM), these models can be studied and compared in terms of their robustness in reproducing LST. Whereas LST is to be the independent variable, built-up area, water surface, albedo, elevation, and vegetation are to be considered as dependent variables and their relative contributions to LST examined. • Modeling LST based on spatial regression models with calculated LST as independent variable. • Dependent variables to be considered are normalised difference Built-up index (NDBI), normalised difference vegetation index (NDVI), modified normalised difference water index (MNDWI), albedo and elevation. • The models were validated using k-fold cross validation method, mean square error and standard deviation.