Abstract
The Korea Meteorological Administration successfully launched Korea’s next-generation meteorological satellite, Geo-KOMPSAT-2A (GK-2A), on 5 December 2018. It belongs to the new generation of GEO (Geostationary Elevation Orbit) satellite which offers capabilities to disseminate high spatial- (0.5–2 km) and high temporal-resolution (10 min) observations over a broad area, herein a geographic disk encompassing the Asia–Oceania region. The targeted objective is to enhance our understanding of climate change, owing to a bulk of coherent observations. For such, we developed an algorithm to map the land surface albedo (LSA), which is a major Essential Climate Variable (ECV). The retrieval algorithm devoted to GK-2A/Advanced Meteorological Imager (AMI) data considered Japan’s Himawari-8/Advanced Himawari Imager (AHI) data for prototyping, as this latter owns similar specifications to AMI. Our proposed algorithm is decomposed in three major steps: atmospheric correction, bidirectional reflectance distribution function (BRDF) modeling and angular integration, and narrow-to-broadband conversion. To perform BRDF modeling, the optimization method using normalized reflectance was applied, which improved the quality of BRDF modeling results, particularly when the number of observations was less than 15. A quality assessment was performed to compare our results to those of Moderate Resolution Imaging Spectroradiometer (MODIS) LSA products and ground measurement from Aerosol Robotic Network (AERONET) sites, Australian and New Zealand flux tower network (OzFlux) site and the Korea Flux Network (KoFlux) site from throughout 2017. Our results show dependable spatial and temporal consistency with MODIS broadband LSA data, and rapid changes in LSA due to snowfall and snow melting were well expressed in the temporal profile of our results. Our outcomes also show good agreement with the ground measurements from AERONET, OzFlux and KoFlux ground-based network with root mean square errors (RMSE) of 0.0223 and 0.0306, respectively, which is close to the accuracy of MODIS broadband LSA. Moreover, our results reveal still more reliable LSA products even when clouds are frequently present, such as during the summer monsoon season. It shows that our results are useful for continuous LSA monitoring.
Highlights
land surface albedo (LSA) represents the ratio of solar radiance reflected from land surface across the entire spectrum of solar radiation
We propose a LSA-retrieval algorithm for GK-2A/Advanced Meteorological Imager (AMI) aimed at achieving sustained and consistent LSA monitoring in the Asia–Oceania region
Because AMI data are available from July 2019 following in-orbit testing, we developed the LSA algorithm using Himawari-8/Advanced Himawari Imager (AHI), which has very similar channel specification and observation cycle to GK-2A/AMI [40], with the benefit to analyze seasonal variation in LSA
Summary
LSA (abbreviations not defined in the text and can be found in Table 1) represents the ratio of solar radiance reflected from land surface across the entire spectrum of solar radiation. One is a traditional algorithm that is composed of the most intuitive methods (atmospheric correction, BRDF modeling, and N2B conversion) for estimating LSA from satellite measurement It is used in operating system for LSA retrieval from MODIS [17] and PROBA-V [18]. In light of these advantages, the SCOPE-CM, which is a network of agencies and operators of environmental satellite systems and interfaces, makes an effort to estimate global LSA products as ECVs based on a constellation of GEO satellites through one of the pilot projects [22] In this respect, GEO satellites are the best tool with which to produce continuous, long-term, and high-quality LSA over a large region for the purpose of studying and monitoring the Earth’s surface.
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