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.

Land surface reflectance (LSR) is well known as an essential variable to understand land surface properties. The Geostationary Ocean Color Imager (GOCI) be able to observe not only the ocean but also the land with the high temporal and spatial resolution thanks to its channel specification. In this study, we describe the land atmospheric correction algorithm and present the quality of results through comparison with Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ data for GOCI-II. The GOCI LSR shows similar spatial distribution and quantity with MODIS LSR for both healthy and unhealthy vegetation cover. Our results agreed well with in-situ-based reference LSR with a high correlation coefficient (>0.9) and low root mean square error (<0.02) in all 8 GOCI channels. In addition, seasonal variation according to the solar zenith angle and phenological dynamics in time-series was well presented in both reference and GOCI LSR. As the results of uncertainty analysis, the estimated uncertainty in GOCI LSR shows a reasonable range (<0.04) even under a high solar zenith angle over 70°. The proposed method in this study can be applied to GOCI-II and can provide continuous satellite-based LSR products having a high temporal and spatial resolution for analyzing land surface properties.

Because ice surface temperature (IST) controls snow melt, sea ice growth and air–ocean heat exchange, it is a key parameter in analyses of the Arctic climate system. However, optical satellite-based IST data often have missing values due to the satellite orbit, clouds and polar night. In this study, we estimated IST using a deep neural network (DNN) algorithm based on meteorological, sea ice and geometric variables that are strongly correlated with IST. Moderate-Resolution Imaging Spectroradiometer (MODIS)/Terra IST data were used, and the input data were 2 m air temperature (Ta), 30-year averaged Ta (Ta climatology), total column water vapour (TCWV), solar zenith angle (SZA), local solar noon angle (LSN) and latitude. The data were classified into six cases according to sea ice age (SIA) and Ta to create an efficient DNN model that supplemented the IST data missing from the existing dataset. Model validation based on the MODIS IST data revealed a correlation coefficient of 0.94, root mean square error of 3.54 K and relative root mean square error of 1.35%, showing high accuracy.

Eunkyung Lee, Jae-Hyun Ahn, Kyeong-Sang Lee. Korean J. Remote Sens. 2025;41:975-83. https://doi.org/10.7780/kjrs.2025.41.6.4