Abstract
Validation of the land surface temperature (LST) algorithm and product is a challenging task for future Geostationary Operational Environmental Satellite R-Series (GOES-R) applications. Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) full-disk data have been used as the key proxy data for the GOES-R LST algorithm and product development. A split window algorithm developed to generate GOES-R LST was applied to MSG SEVIRI data with the algorithm coefficients adjusted to the specific SEVIRI bands. The retrieved LST values were evaluated with in situ LST obtained from four validation stations with different surface features over various time periods. The results presented here clearly highlight the importance of accurate and seasonally representative site characterizations for the LST validation process. Furthermore, the study gives valuable insights into the limitations of the current version of the LST retrieval algorithm and on how to further refine it for the next generation of satellite sensors.
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