Production and Evaluation of GOES-16 Land Surface Temperature

Land surface temperature (LST) is defined as the radiative skin temperature of the land. It is one of baseline products for the GOES-R satellite mission measured from the Advanced Baseline Imager (ABI). LST data is widely used to many aspects of the geosciences, e.g., studies of net radiation budget at the Earth surface, monitoring state of crops and vegetation, as well as an important indicator of both the greenhouse effect and the physics of land-surface processes at local through global scales. It has been listed as an Essential Climate Variable (ECV) in the Global Climate Observation System (GCOS).

Algorithm of the GOES-R LST derivation was developed at NOAA/NESDIS center for SaTellite Applications and Research (STAR), based on a traditional split-window technique. In which, the LST is primarily estimated by a top of atmosphere brightness temperature (BT) at a ABI thermal infrared channel, and corrected by the BT difference to the near-by thermal infrared channel. Quality of the LST estimation may vary depending on cloudy fraction, water vapor, view zenith angle, etc. Such quality information, recorded as quality flags and metadata, is provided with the LST estimates for user reference, product monitoring and evaluation analysis. GOES-R satellite provides LST products with three coverage mode: CONUS, hemisphere, and mesoscale.

Evaluation of the GOES-R LST product has been conducted using radiative transfer simulation datasets and proxy ABI data. After the launch of the first GOES-R series satellite, i.e. GOES-16, in November 2016, we have performed the ABI LST evaluation using 5 months of the ABI SDR and LST dataset, for the beta version release. Quality flags and metadata of the LST product are tested and compared with local independent computation, LST estimates were compared to some in-situ LST data derived from the SURFRAD station measurements. Since June 2017 we have conducted further evaluation of the GOES-16 LST products for the provisional version release schedule in Dec. 2017. This presentation shows our evaluation results, as well as the ABI LST derivation details, which are helpful in user’s product applications.