Reducing the Discrepancy Between ASTER and MODIS Land Surface Temperature Products

Sensors

Yuanbo Liu¹  Yasushi Yamaguchi¹ 

[1] Graduate School of Environmental Studies, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan; E-mail:

关键词: land surface temperature;    surface emissivity;    retrieval algorithm;    scale effects;    terrain effects;    remote sensing;    ASTER;    MODIS;   

DOI  :  10.3390/s7123043

来源: mdpi

【 摘 要 】

Human-induced global warming has significantly increased the importance of satellite monitoring of land surface temperature (LST) on a global scale. The MODerate-resolution Imaging Spectroradiometer (MODIS) provides a 1-km resolution LST product with almost daily coverage of the Earth, invaluable to both local and global change studies. The Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) provides a LST 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 and MODIS are complementary in resolution, offering a unique opportunity for scale-related studies. ASTER and MODIS LST have been widely used but the errors in LST were mostly disregarded. Correction of ASTER-to-MODIS LST discrepancies is essential for studies reliant upon the joint use of these sensors. In this study, we compared three correction approaches: the Wan et al.'s approach, the refined Wan et al.'s approach, and the generalized split window (GSW) algorithm based approach. The Wan et al.'s approach corrects the MODIS 1-km LST using MODIS 5-km LST. The refined approach modifies the 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.

【 授权许可】

Unknown   
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