Developing Land Surface Directional Reflectance and Albedo Products from Geostationary GOES-R and Himawari Data: Theoretical Basis, Operational Implementation, and Validation

Abstract

The new generation of geostationary satellite sensors is producing an unprecedented amount of Earth observations with high temporal, spatial and spectral resolutions, which enable us to detect and assess abrupt surface changes. In this study, we developed the land surface directional reflectance and albedo products from Geostationary Operational Environment Satellite-R (GOES-R) Advanced Baseline Imager (ABI) data using a method that was prototyped with the Moderate Resolution Imaging Spectroradiometer (MODIS) data in a previous study, and was also tested with data from the Advanced Himawari Imager (AHI) onboard Himawari-8. Surface reflectance is usually retrieved through atmospheric correction that requires the input of aerosol optical depth (AOD). We first estimated AOD and the surface bidirectional reflectance factor (BRF) model parameters simultaneously based on an atmospheric radiative transfer formulation with surface anisotropy, and then calculated the “blue-sky” surface broadband albedo and directional reflectance. This algorithm was implemented operationally by the National Oceanic and Atmospheric Administration (NOAA) to generate the GOES-R land surface albedo product suite with a daily updated clear-sky satellite observation database. The “operational” land surface albedo estimation from ABI and AHI data was validated against ground measurements at the SURFRAD sites and OzFlux sites and compared with the existing satellite products, including MODIS, Visible infrared Imaging Radiometer (VIIRS), and Global Land Surface Satellites (GLASS) albedo products, where good agreement was found with bias values of −0.001 (ABI) and 0.020 (AHI) and root-mean-square-errors (RMSEs) less than 0.065 for the hourly albedo estimation. Directional surface reflectance estimation, evaluated at more than 74 sites from the Aerosol Robotic Network (AERONET), was proven to be reliable as well, with an overall bias very close to zero and RMSEs within 0.042 (ABI) and 0.039 (AHI). Results show that the albedo and reflectance estimation can satisfy the NOAA accuracy requirements for operational climate and meteorological applications.

Reviews and syntheses: Ongoing and emerging opportunities to improve environmental science using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites

“…Current efforts to estimate surface reflectance from ABI, Advanced Himawari Imager (AHI), and the Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorological Satellite (COMS) include generating lookup tables from the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model (He et al, 2019;Tian et al, 2010;Vermote et al, 1997;Yeom et al, 2018Yeom et al, , 2020. Optimal estimation methods that estimate surface BRF from SEVIRI have been extended to estimate surface broadband albedo and surface reflectance from ABI and the AHI on Himawari-8 (Govaerts et al, 2010;He et al, 2019He et al, , 2012Wagner et al, 2010). These algorithms estimate surface reflectance and broadband surface albedo by minimizing the difference between TOA BRF estimated through radiative transfer modeling and measured by the satellite (He et al, 2019(He et al, , 2012.…”

“…Optimal estimation methods that estimate surface BRF from SEVIRI have been extended to estimate surface broadband albedo and surface reflectance from ABI and the AHI on Himawari-8 (Govaerts et al, 2010;He et al, 2019He et al, , 2012Wagner et al, 2010). These algorithms estimate surface reflectance and broadband surface albedo by minimizing the difference between TOA BRF estimated through radiative transfer modeling and measured by the satellite (He et al, 2019(He et al, , 2012. Unlike the surface reflectance algorithm currently used for SEVIRI, the algorithm for ABI and AHI takes the diurnal variation of aerosol optical depth into account (He et al, 2019).…”

Reply on RC2

“…These algorithms estimate surface reflectance and broadband surface albedo by minimizing the difference between TOA BRF estimated through radiative transfer modeling and measured by the satellite (He et al, 2019(He et al, , 2012. Unlike the surface reflectance algorithm currently used for SEVIRI, the algorithm for ABI and AHI takes the diurnal variation of aerosol optical depth into account (He et al, 2019). Originally developed for atmospheric correction of MODIS imagery, the Multi-Angle Implementation of Atmospheric Correction (MAIAC) has also been adapted to provide provisional daytime surface reflectance every 10 minutes for bands 1 -6 of AHI with plans to extend the algorithm to ABI (Li et al, 2019b).…”

Estimation of Land Surface Incident and Net Shortwave Radiation from Visible Infrared Imaging Radiometer Suite (VIIRS) Using an Optimization Method

“…Figure 2 illustrates the framework of the algorithm. An optimization method has been used [52,54] to estimate surface reflectance and broadband albedo. In our previous research, we developed a similar approach for incident shortwave radiation estimation from MODIS data by revising the cost function considering both satellite observations and optional constraints, including aerosol optical depth (AOD), cloud optical depth (COD), surface reflectance products, and albedo climatology [51].…”