Assessment of the TsHARP method for spatial downscaling of land surface temperature over urban regions

Abstract

Nowadays, acquiring high spatial and temporal land surface temperature (LST) is one of the significant factors in urban climate studies. This study aims to assess the sharpening thermal imagery (TsHARP) method, which is developed for determining the LST in urban regions. Whereas the original TsHARP method correlates the Normalized Difference Vegetation Index (NDVI) with the LST, the current study seeks to evaluate the performance of the TsHARP method by correlating between the LST and NDVI over urban areas. Additionally, it is intended to investigate the relationship between the impervious surfaces (IS) and LST. At this point, the Advanced Spaceborne Thermal Emission and Reflection Radiometer ASTER image of the year 2003 was used to retrieve the IS as well as the LST of the Kuala Lumpur city, Malaysia. To retrieve the main LST, IS and NDVI, ASTER Level1B data were used. On the other hand, ASTER land surface temperature products (AST08) was utilized to evaluate the downscaled LST (DLST) image. With selection of the ~1/4 of the pixels with lowest coefficient of variation (CV), the LST has more correlation with the IS in comparison with the NDVI, while the correlation between the IS and LST was increased in this study. The IS index illustrates the most optimal correlation for the urban LST (R2 = 0.59). Furthermore, the TsHARPIS-based method depicted a good correlation between the DLST and the ASTER LST product (RMSE = 1.5 °C, R2 = 0.83) compared to the NDVI (RMSE = 4 °C, R2 = 0.73). Consequently, it is found that the application of TsHARPIS-based method to ASTER remote sensing data is a robust/low-cost approach for spatial downscaling of the LST over urban regions.