A Strategy to Improve the GOES-R Land Surface Temperature Product with
All Weather Information in Near Real Time
LST is routinely retrieved from the GOES-R Advanced Baseline Imager
(ABI) long wave spectral channels. Since the product is available only
under clear sky conditions, large gaps exist in the data stream which
correspond to contamination by clouds. However, continuous estimates of
LST data are still vitally needed for several applications such as
drought monitoring ,vegetation growth, and crop yield estimation etc.
Studies have shown that LST tracks with corresponding changes in
incident solar radiation or more specifically changes in surface
absorbed solar radiation with good correlation irrespective of sky
conditions (clear or cloudy). In the present study, a scheme is
developed to fill in the large spatio-temporal gaps in the LST time
series using surface solar absorption parameter (SSA) retrieved in near
real time from other satellites. Validation of retrieved LST values over
all of the SURFRAD stations reveal RMS errors of less than 1 K.