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.

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.

Snow cover plays an important role in climate and hydrology, at both global and regional scales. Most previous studies have used static threshold techniques to detect snow cover, which can lead to errors such as misclassification of snow and clouds, because the reflectance of snow cover exhibits variability and is affected by several factors. Therefore, we present a simple new algorithm for mapping snow cover from Moderate Resolution Imaging Spectroradiometer (MODIS) data using dynamic wavelength warping (DWW), which is based on dynamic time warping (DTW). DTW is a pattern recognition technique that is widely used in various fields such as human action recognition, anomaly detection, and clustering. Before performing DWW, we constructed 49 snow reflectance spectral libraries as reference data for various solar zenith angle and digital elevation model conditions using approximately 1.6 million sampled data. To verify the algorithm, we compared our results with the MODIS swath snow cover product (MOD10 L2). Producer's accuracy, user's accuracy, and overall accuracy values were 92.92%, 78.41%, and 92.24%, respectively, indicating good overall classification accuracy. The proposed algorithm is more useful for discriminating between snow cover and clouds than threshold techniques in some areas, such as those with a high viewing zenith angle.

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.

Air temperature (Ta), defined as the temperature 2 m above the land’s surface, is one of the most important factors for environment and climate studies. Ta can be measured by obtaining the land surface temperature (LST) which can be retrieved with the 11- and 12-µm bands from satellite imagery over a large area, and LST is highly correlated with Ta. To measure the Ta in a broad area, we studied a Ta retrieval method through Deep Neural Network (DNN) using in-situ data and satellite data of South Korea from 2014 to 2017. To retrieve accurate Ta, we selected proper input variables and conditions of a DNN model. As a result, Normalized Difference Vegetation Index, Normalized Difference Water Index, and 11- and 12-µm band data were applied to the DNN model as input variables. And we also selected proper condition of the DNN model with test various conditions of the model. In validation result in the DNN model, the best accuracy of the retrieved Ta showed an correlation coefficient value of 0.98 and a root mean square error (RMSE) of 2.19 K. And then we additional 3 analysis to validate accuracy which are spatial representativeness, seasonal analysis and time series analysis. We tested the spatial representativeness of the retrieved Ta. Results for window sizes less than 132 × 132 showed high accuracy, with a correlation coefficient of over 0.97 and a RMSE of 1.96 K and a bias of −0.00856 K. And in seasonal analysis, the spring season showed the lowest accuracy, 2.82 K RMSE value, other seasons showed high accuracy under 2K RMSE value. We also analyzed a time series of six the Automated Synoptic Observing System (ASOS) points (i.e., locations) using data obtained from 2018 to 2019; all of the individual correlation coefficient values were over 0.97 and the RMSE values were under 2.41 K. With these analysis, we confirm accuracy of the DNN model was higher than previous studies. And we thought the retrieved Ta can be used in other studies or climate model to conduct urban problems like urban heat islands and to analyze effects of arctic oscillation.