Influence of thin cirrus clouds on land surface temperture retrieval using the generalized split-window algorithm from thermal infrared data

Land surface temperature (LST) is a critical parameter for numerical weather forecasting, drought monitoring, water resources management and global climate change studies. Because of the supercooled temperature, the cirrus cloud can significantly reduce the LST retrieved from thermal infrared data. This paper focused on analyzing and reducing the influence of thin cirrus cloud on the accuracy of LST retrieved using the generalized split-window (GSW) algorithm. A correction method was proposed with the LST retrieval error expressed as linear functions of cirrus optical depth (COD). The slopes of the linear functions were further written as the combination of the difference and mean of two used channels emissivities and cirrus cloud top height (CTH). The results showed that the LST retrieval accuracy could be significantly improved with root mean square error (RMSE) of LST changing from 14.4 K before LST error correction to 1.8 K after LST error correction for COD equivalent to 0.3.