Reducing the Discrepancy Between ASTER and MODIS Land Surface Temperature Products

• Main Authors: Changqing Ke, Yasushi Yamaguchi, Yuanbo Liu
• Format: Article
• Language: English
• Published: MDPI AG 2007-12-01
• Series: Sensors
• Subjects: land surface temperature; surface emissivity; retrieval algorithm; scale effects; terrain effects; remote sensing; ASTER; MODIS
• Online Access: http://www.mdpi.com/1424-8220/7/12/3043/

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.