Satellite-based vegetation indices are an essential element in understanding the Earth's surface. In this study, we estimated the normalized difference vegetation index (NDVI) using Himawari-8/Advanced Himawari Imager (AHI) data and analyzed the sensitivity of products to atmospheric and surface correction. We used the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer model for atmospheric correction, and kernel-based semi-empirical bidirectional reflectance distribution function (BRDF) model to remove surface anisotropic effects. From this, top-of-atmosphere, top-of-canopy, and normalized NDVIs were produced. A sensitivity analysis showed that the normalized NDVI had the lowest number of missing values compared with the others and almost no low peaks during the study period. These results were validated by Terra and Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) and Project for On-Board Autonomy/Vegetation (PROBA) NDVI product, showing the root mean square error (RMSE) and bias of 0.09 and + 0.04 (MODIS) and 0.09 and − 0.04 (PROBA), respectively. These results also satisfied the FP7 Geoland2/BioPar project-defined user requirements (threshold: 0.15; target: 0.10).

In this study, we compared four net radiation products: the fifth generation of European Centre for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate (ERA5), National Centers for Environmental Prediction (NCEP), Clouds and the Earth’s Radiant Energy System Energy Balanced and Filled (EBAF), and Global Energy and Water Exchanges (GEWEX), based on ground observation data and intercomparison data. ERA5 showed the highest accuracy, followed by EBAF, GEWEX, and NCEP. When analyzing the validation grid, ERA5 showed the most similar data distribution to ground observation data. Different characteristics were observed between the reanalysis data and satellite data. In the case of satellite-based data, the net radiation value tended to increase at high latitudes. Compared with the reanalysis data, Greenland and the central Arctic appeared to be overestimated. All data were highly correlated, with a difference of 6–21 W/m2 among the products examined in this study. Error was attributed mainly to difficulties in predicting long-term climate change and having to combine net radiation data from several sources. This study highlights criteria that may be helpful in selecting data for future climate research models of this region.

We propose a methodology employing an interpolation technique on the Second Simulation of a Satellite Signal (6S) look-up table (LUT) to improve surface reflectance retrieval using Himawari-8/Advanced Himawari Imager (AHI). A minimum curvature surface (MCS) technique was used to refine the 6S LUT, and the solar zenith angle (SZA) and viewing zenith angle (VZA) increments were narrowed by 0.5°. The interpolation processing time was relatively short, about 3172 s per channel, and the interpolated xa and xb were well represented by the changes in SZA and VZA. An evaluation of the interpolated xa and xb for six cases revealed a relative mean absolute error of less than 5% for all channels and cases; however, a slight difference was evident for higher values of SZA and VZA. To evaluate the surface reflectance, we compared the surface reflectance derived using 6S LUT with that calculated using 6S only. Application of the interpolated 6S LUT showed a lower relative root mean square error (RRMSE) of 0.65% to 9.29% for all channels, than before interpolation. The improvement in surface reflectance measurements increased with the SZA. For a SZA above 75°, the RRMSE improved significantly for all channels (by 11.33-45.1%). In addition, when the MCS method was applied, the surface reflectance measurements improved without spatial discontinuity and showed good agreement with 6S results in a linear profile analyses. Thus, the method proposed can improve LUT based surface reflectance measurements in less time and increase the availability of surface reflectance data based on geostationary satellites.

Rapid warming of the Arctic has resulted in widespread sea ice loss. Sea ice radiative forcing (SIRF) is the instantaneous perturbation of Earth’s radiation at the top of the atmosphere (TOA) caused by sea ice. Previous studies focused only on the role of albedo on SIRF. Skin temperature is also closely related to sea ice changes and is one of the main factors in Arctic amplification. In this study, we estimated SIRF considering both surface albedo and skin temperature using radiative kernels. The annual average net-SIRF, which consists of the sum of albedo-SIRF and temperature-SIRF, was calculated as −54.57 ± 3.84 W/m2 for the period 1982–2015. In the net-SIRF calculation, albedo-SIRF and temperature-SIRF made similar contributions. However, the albedo-SIRF changed over the study period by 0.12 ± 0.07 W/m2 per year, while the temperature-SIRF changed by 0.22 ± 0.07 W/m2 per year. The SIRFs for each factor had different patterns depending on the season and region. In summer, rapid changes in the albedo-SIRF occurred in the Kara and Barents regions. In winter, only a temperature-SIRF was observed, and there was little difference between regions compared to the variations in albedo-SIRF. Based on the results of the study, it was concluded that the overall temperature-SIRF is changing more rapidly than the albedo-SIRF. This study indicates that skin temperatures may have a greater impact on the Arctic than albedo in terms of sea ice surface changes.

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.