A Combined Algorithm for Soil and Vegetation Temperatures with SLSTR Dual-Angle Data

Because of noises from land surface temperature (LST) inversion and different spatial resolution between nadir and oblique views, we proposed a combined algorithm for soil and vegetation temperatures with Sea and Land Surface Temperature Radiometer (SLSTR) data, in which a Bayesian strategy was adopted and retrieval results from a multi-pixel algorithm were selected as a priori information in a multi-angle algorithm. The SLSTR LST that inverted by a general split-window algorithm was evaluated first to understand inversion noise. Then, the combined algorithm was evaluated using a synthetic dataset, which displayed a robust performance than the multiangle and the multi-pixel algorithm.