New Retrieval Algorithm for Deriving Land Surface Temperature From Geostationary Orbiting Satellite Observations

Accurate derivations of land surface temperature (LST) and land surface emissivity (LSE) from satellite measurements are difficult because the two variables are closely coupled. Features of significant/insignificant temporal variations in LST/LSE are recognized to de-couple both values using multiple-temporal satellite observations over the same geolocation. In this paper, a new approach is presented for deriving LST and LSE simultaneously by using multiple-temporal satellite observations. Two split-window regression formulas are carefully selected for the approach, and two satellite observations over the same geolocation within a certain time interval are utilized. The method is particularly applicable to geostationary satellite missions from which qualified multiple-temporal observations are available. The approach is designed and implemented for generating the LST and LSE values from the U.S. geostationary operational environmental satellite (GOES) eight imager data and the european meteosat second generation (MSG) mission spinning enhanced visible and infrared imager (SEVIRI) data. The performance of the algorithm is evaluated in terms of both accuracy and sensitivity. The retrieval results are compared against ground-truth observations from the U.S. Atmospheric radiation measurement facility and six surface radiation budget network (SURFRAD) stations. The validation results show the LST retrieval accuracy is around 1.95 K with good correlations of up to 0.9038. The method is applicable to the future U.S. GOES-R mission as well as the MSG mission considering that the advanced baseline imager (ABI) onboard the GOES-R satellites and the SEVIRI onboard the MSG satellite have similar split-window bands.