16:00 〜 16:15
[ACG36-09] LST and Albedo Algorithms Developed for GOES-R ABI Observations
★Invited Papers
キーワード：satellite, algorithm, land surface temperature, albedo
As essential climate variables defined by the Global Climate Observing System of the World Meteorological Organization, land surface temperature (LST) and land surface albedo (LSA) are fundamental parameters in the physics of land surface processes from regional to global scales. LST and LSA products have been widely applied in estimating radiative, latent and sensible heat fluxes at the surface-atmosphere interface. Satellite platforms provide an excellent opportunity of measuring LSTs and LSAs continuously at such scales. Among satellites with different types of orbits, geostationary satellites, uniquely, provide LST/LSA measurements with high temporal resolution, which are critical in many of the LST/LSA applications.
Enterprise LST/LSA algorithms have been developed and applied for the U.S. Geostationary Environmental Observation Satellite (GOES) R series (GOES-R) at the U.S. National Environmental Satellite Data Information Service (NESDIS), currently including GOES-east (GOES-16) and GOES-west (GOES-18, which replaced GOES-17 on January 4, 2023) satellites. The LST algorithm is based on a traditional linear regression split-window LST retrieval technique in the thermal infrared spectrum, while the LSA algorithm is based on a daily BRDF optimization approach using visible and near-infrared data. Those high-resolution shortwave and thermal observations are all available from the Advanced Baseline Imager onboard the GOES-R satellites. Both the LST and LSA data have been produced operationally through the NOAA GOES-R ground system.
In this study, we present some sample ABI LST and LSA datasets for analytics and evaluation. We also demonstrate and evaluate how the ABI LSA can be applied to Advanced Himawari Imager (AHI) data, considering that the AHI sensor specifications are mostly similar to the ABI sensor. It implies that the LSA algorithm can be applied for AHI LSA retrieval.
Enterprise LST/LSA algorithms have been developed and applied for the U.S. Geostationary Environmental Observation Satellite (GOES) R series (GOES-R) at the U.S. National Environmental Satellite Data Information Service (NESDIS), currently including GOES-east (GOES-16) and GOES-west (GOES-18, which replaced GOES-17 on January 4, 2023) satellites. The LST algorithm is based on a traditional linear regression split-window LST retrieval technique in the thermal infrared spectrum, while the LSA algorithm is based on a daily BRDF optimization approach using visible and near-infrared data. Those high-resolution shortwave and thermal observations are all available from the Advanced Baseline Imager onboard the GOES-R satellites. Both the LST and LSA data have been produced operationally through the NOAA GOES-R ground system.
In this study, we present some sample ABI LST and LSA datasets for analytics and evaluation. We also demonstrate and evaluate how the ABI LSA can be applied to Advanced Himawari Imager (AHI) data, considering that the AHI sensor specifications are mostly similar to the ABI sensor. It implies that the LSA algorithm can be applied for AHI LSA retrieval.