A Semi-Empirical Split-Window Algorithm for Retrieving near Surface Air Temperature from MODIS Data

摘要

This study attempts to develop an effective algorithm to directly derive near surface air temperaturefrom EOS/MODIS data. From a theoretical viewpoint, the split-window algorithmfor retrieving near surface air temperature was developed based on the radiative transferequation, which includes the calculation of atmospheric thermal radiance, the linearizationof Planck functions, the transformation from effective atmospheric mean temperature tonear surface air temperature and other derivation processes. Considering that the coefficientsof the theoretical algorithm are highly dependent on the atmospheric profile, whichis difficult to acquire in practical applications, a semi-empirical split-window algorithm isgenerated on the basis of the theoretical algorithm to improve the practicality. The semiempiricalalgorithm was applied and validated in the Jing-Jin-Ji (JJJ) Region and the Jiang-Zhe-Hu-Wan (JZHW) Region in China. Results indicate that the algorithm achieves an MAEof 2.11 °C in the JJJ Region and an MAE of 2.22 °C in the JZHW Region. The semi-empiricalsplit-window algorithm also shows better stability than linear regression and machine learningmethods when being applied to other data periods. Due to its accuracy and simplicity,the semi-empirical split-window algorithm is a novel method for retrieving near surface airtemperature from MODIS thermal bands.