Land Surface Temperature Retrieval Based on Thermal Infrared Channel Decomposed Split-Window Algorithm

Abstract

Land surface temperature (LST) is an important parameter affecting ground-air energy exchange, and can be applied in many fields such as drought monitoring, evapotranspiration estimation, and crop yield assessment. The single-channel (SC) algorithm only needs thermal infrared (TIR) data with only one channel to retrieve the LST, but requires accurate atmospheric correction, which is difficult to guarantee in many cases. The split-window (SW) algorithm exploits the difference in atmospheric absorption of adjacent channels and can accurately obtain the LST without atmospheric parameters, but the input requirement of two TIR channels makes the application limited. Some previous studies found that there is a strong correlation between the top-of-atmosphere thermal radiance in adjacient TIR channels with similar wavelength ranges, which makes it possible to decompose a single TIR channel into two TIR channels with close wavelengths using empirical relationships with errors smaller than those caused by the uncertainty of atmospheric correction. In this article, Landsat-7 ETM+ TIR data was decomposed into two virtual TIR channels by fusing with the simultaneously observed Terra moderate resolution imaging spectroradiometer (MODIS) data, and the channel decomposed SW (TCD-SW) based on the generalized nonlinear SW algorithm was developed and applied to retrieve the LST to eliminate the dependence on atmospheric parameters. The validation results using the simulation dataset, ground-measured site data, and well-validated MODIS sea surface temperature product showed that the proposed TCD-SW algorithm achieved more accurate results than the SC algorithm, being more advantageous in the humid atmosphere. The TCD-SW algorithm can be used as a potential new method for LST retrieval from remote sensing data with only one TIR channel under complex atmospheric conditions.