Comparative Analysis of two Land Surface Temperature Retrieval Algorithms based on Multi-source Remote Sensing Data

摘要

The main disadvantage of Land surface temperature (LST) retrieval methods from Landsat TM thermal channel images is that atmospheric profile parameters are needed, and MODIS has several near infrared bands that can be used to estimate atmospheric profile parameters. Two methods that could be used to retrieve the LST from Landsat TM and MODIS data were compared in this paper, the first of them is the mono-window algorithm developed by Qin et al. and the second is the single-channel algorithm developed by Jimenez-Munoz and Sobrino. Atmospheric profile parameters such as atmospheric moisture content, atmospheric transmittance and average atmospheric temperature have been estimated from MODIS data, and the land surface emissivity values have been estimated from a methodology based on spectral mixture analysis. Finally, a comparison between the LST measured in situ and retrieved by the algorithms over urban area of Changsha city in China is present. Result indicates that the two LST retrieval algorithms can get high-precision results in support of atmospheric parameters from MODIS images, the average deviation of mono-window algorithm is 0.76K, and the deviation of generalized single-channel algorithm is 1.23k.