Angular Normalization of Land Surface Temperature Using Feature-Space Method

Land surface temperature (LST) is a crucial parameter in the energy and material balance of land surface system. The angle effect of LST makes the accuracy of LST restricted and limits the application of remote sensing LST product. In order to eliminate the influence of viewing angle, this study proposed a novel method to perform angular normalization by constructing a feature space of surface emission radiance and fractional vegetation coverage (Radiance-FVC space). The proposed approach is applied in Hetao Plain as an example. It is found that the Root Mean Square Error (RMSE) can reach 5.1K, and the angular normalization effect is more significant for pixels with larger viewing zenith angle.