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Human-induced global warming has significantly increased the importance ofsatellite monitoring of land surface temperature (LST) on a global scale. The MODerate-resolution Imaging Spectroradiometer (MODIS) provides a 1-km resolution LST productwith almost daily coverage of the Earth, invaluable to both local and global change studies.The Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) provides aLST product with a high spatial resolution of 90-m and a 16-day recurrent cycle,simultaneously acquired at the same height and nadir view as MODIS. ASTER andMODIS are complementary in resolution, offering a unique opportunity for scale-relatedstudies. ASTER and MODIS LST have been widely used but the errors in LST were mostlydisregarded. Correction of ASTER-to-MODIS LST discrepancies is essential for studiesreliant upon the joint use of these sensors. In this study, we compared three correctionapproaches: the Wan et al.'s approach, the refined Wan et al.'s approach, and thegeneralized split window (GSW) algorithm based approach. The Wan et al.'s approachcorrects the MODIS 1-km LST using MODIS 5-km LST. The refined approach modifiesthe Wan et al.'s approach through incorporating ASTER emissivity and MODIS 5-km data.The GSW algorithm approach does not use MODIS 5-km but only ASTER emissivity data. We examined the case over a semi-arid terrain area for the part of the Loess Plateau of China. All the approaches reduced the ASTER-to-MODIS LST discrepancy effectively. With terrain correction, the original ASTER-to-MODIS LST difference reduced from 2.7±1.28 K to -0.1±1.87 K for the Wan et al.'s approach, 0.2±1.57 K for the refined approach, and 0.1±1.33 K for the GSW algorithm based approach. Among all the approaches, the GSW algorithm based approach performed best in terms of mean, standard deviation, root mean square root, and correlation coefficient.

Reducing the Discrepancy Between ASTER and MODIS Land Surface Temperature Products