Downscaling MODIS nighttime land surface temperatures in urban areas using ASTER thermal data through local linear forest

Abstract

Spatial downscaling effectively produces high spatiotemporal resolution land surface temperature (LST) in urban areas. Although nighttime LST is an essential indicator in urban thermal research, few LST downscaling studies have focused on nighttime in fine resolution. This study proposed a novel approach using local linear forest (LLF) to downscale 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) nighttime LSTs to 250 m spatial resolution in three cities: Rome, Madrid, and Seoul. First, we used Least Absolute Shrinkage and Selection Operator (LASSO) to select a set of past clear-sky ASTER LSTs (ALST) which showed a high spatial correlation with the target MODIS LST. Downscaling models were then developed using input kernels of the selected ALSTs and eight auxiliary variables: normalized difference vegetation index (NDVI), elevation, slope, built-up area percentage, road density, population density, wind speed, and distance from the built-up weighted center of the study area. Three schemes were evaluated: scheme 1 (S1) using only auxiliary variables as input kernels with a random forest (RF) model; scheme 2 (S2) using selected ALSTs and auxiliary variables as input kernels with an RF model; and scheme 3 (S3) using input kernels as in S2 but with the LLF model. Validation was performed using bias-corrected ALSTs for seven reference dates in the three cities. LLF-based S3 showed the highest accuracy with an average correlation coefficient (R) -0.94 and Root Mean Square Error (RMSE) -0.64 K while maintaining the dynamic range of the original LST at the finer resolution. The downscaled LST (DLST) based on S3 effectively depicted the nocturnal thermal spatial pattern in greater detail than the other two schemes did. The S3-based DLST also showed a relatively high spatial correlation with the in-situ nighttime air temperature within the cities. When compared to the original 1 km LST, S3-based DLST showed larger surface urban heat island intensity for the urban-type surfaces and a higher temporal correlation with nighttime air temperature.