Surface Temperature Estimation from Aatsr Data

A series of dual-angle and split-window algorithms is presented for estimating sea (SST) and land surface temperature (LST) from the Advanced Along-Track Scanning Radiometer (AATSR). The numerical values of the coefficients have been obtained from statistical regression method using synthetic data. The algorithms have been tested with simulated and real AATSR data. Comprehensive sensitivity and error analysis has been made to evaluate the performance of the proposed algorithms. A comparison using synthetic data suggests better results from the dual-angle algorithms than from the split-window ones and that the algorithms with water vapour dependence give an improvement of the accuracy of the results. A validation of split-window algorithms using a classification method shows a rmse better than 1.6 K. However, one of the conditions for precise dual-angle algorithms is an accurate knowledge of the angular variation of surface emissivity in the thermal infrared region. We provide angular emissivity measurements for representative samples (water, sand, clay, loam and gravel). The measurements have been made with a thermal infrared radiometer at angles from 0 to 60 degrees. The results show a general decrease of the emissivity with increasing viewing angles.