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.
Highlights
Land surface albedo is one of the most important variables in the study of the surface radiation budget, climate change, and the hydrologic cycle [1]
Compared to the number of studies focused on estimating surface albedo from polar orbiting satellites data e.g., [10,11,12,13,14,15], there have been fewer studies on geostationary albedo product development in the past decades e.g., [16,17,18], probably because of the reduced shortwave spectral bands of sensors such as the Geostationary Operational Environment Satellites (GOES), the Meteosat Visible and Infrared Imager (MVIRI), and the Fengyun-2 Visible and Infrared Spin Scan Radiometer (VISSR)
The objective of this paper is to introduce the theoretical basis of the Geostationary Operational Environment Satellite-R (GOES-R) surface albedo product suite algorithm as well as the operational implementation, and to evaluate the performance of the current algorithm with GOES-16 Advanced Baseline Imager (ABI) and Himawari-8 Advanced Himawari Imager (AHI) data
Summary
Land surface albedo is one of the most important variables in the study of the surface radiation budget, climate change, and the hydrologic cycle [1]. While it is possible to estimate surface reflectance from the near-infrared signal where the atmospheric impact (e.g., aerosol scattering) is low, the absence of the near-infrared band from these sensors makes it difficult to use the classic atmospheric correction method [19] To overcome this problem, Knapp et al [20] proposed a temporal composite method to deal with single band GOES data by selecting the darkest observation within the temporal window to minimize the impacts of cloud and aerosol over vegetated pixels. With the development of multi-spectral geostationary sensors, such as the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), this composite approach was extended by considering the diurnal variation in surface reflectance and applied along with SEVIRI to estimate aerosol properties [21] The studies of both Knapp et al [20] and Popp et al [21] rely on temporal information corresponding to about 10 days; the surface anisotropy changes with snow, rain, and vegetation growth. To react to rapid surface changes, Geiger et al [17] employed an empirical formulation of latitude to estimate the AOD and integrate the atmospherically corrected reflectance into the daily albedo with the SEVIRI data
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