Assessment of multi-resolution and multi-sensor data for urban surface temperature retrieval

Abstract

Data from three thermal sensors with different spatial resolution were assessed for urban surface temperature retrieval over the Yokohama City, Japan. The sensors are Thermal Airborne Broadband Imager (TABI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODerate resolution Imaging Spectroradiometer (MODIS). Two algorithms were developed for land surface temperature (LST) retrieval from TABI image and ASTER thermal infrared (TIR) channels 13 and 14. In addition, ASTER LST and MODIS LST products were also collected. All the LST images were assessed by analyzing the relationship between LST and normalized difference vegetation index (NDVI) and by spatial distributions of LST profiles, derived from typical transects over the LST images. In this study, a strong negative relationship between LST and NDVI has been demonstrated although the degree of correlation between NDVI and LST varies slightly among the different LST images. Cross-validation among the LST images retrieved from the three thermal sensors of different spatial resolutions indicates that the LST images retrieved from the 2 channel ASTER data and a single band TABI thermal image using our developed algorithms are reliable. The LST images retrieved from the three sensors should have different potential to urban environmental studies. The MODIS thermal sensor can be used for the synoptic overview of an urban area and for studying urban thermal environment. The ASTER, with its TIR subsystem of 90-m resolution, allows for a more accurate determination of thermal patterns and properties of urban land use/land cover types, and hence, a more accurate determination of the LST. In consideration of the high heterogeneity of urban environment, the TABI thermal image, with a high spatial resolution of 2 m, can be used for rendering and assessing complex urban thermal patterns and detailed distribution of LST at the individual house level more accurately.