We developed land surface temperature (LST) retrieval algorithms based on the time of day and water vapor content using the Himawari-8/AHI (Advanced Himawari Imager) data, which is the Japanese next generation geostationary satellite. To develop the LST retrieval algorithms, we simulated the spectral radiance using the radiative transfer model (MODTRAN4) by applying the atmospheric profiles (SeeBor), diurnal variation of LST and air temperature, spectral emissivity of land surface, satellite viewing angle, and spectral response function of Himawari-8/AHI. To retrieve the LST from Himawari-8 data, a linear type of split-window method was used in this study. The Himawari-8 LST algorithms showed a high correlation coefficient (0.996), and a small bias (0.002 K) and root mean square error (RMSE) (1.083 K) between prescribed LSTs and estimated LSTs. However, the accuracy of LST algorithms showed a slightly large RMSE when the lapse rate was larger than 10 K, and the brightness temperature difference was greater than 6 K. The cross-validation of Himawari-8/AHI LST using the MODIS (Terra and Aqua Moderate Resolution Imaging Spectroradiometer) LST showed that annual mean correlation coefficient, bias, and RMSE were 0.94, +0.45 K, and 1.93 K, respectively. The performances of LST algorithms were slightly dependent on the season and time of day, generally better during the night (warm season) than during the day (cold season).

Land surface temperature (LST) is an important geophysical element for understanding Earth systems and land–atmosphere interactions. In this study, we developed a nonlinear split-window LST retrieval algorithm for the observation area of GEO-KOMPSAT-2A (GK2A), the next-generation geostationary satellite in Korea. To develop the GK2A LST retrieval algorithm, radiative transfer model simulation data, considering various impacting factors, were constructed. The LST retrieval algorithm was developed with a total of six equations as per day/night and atmospheric conditions (dry/normal/wet), considering the effects of diurnal variation of LST and atmospheric conditions on LST retrieval. The emissivity of each channel required for LST retrieval was calculated in real-time with the vegetation cover method using the consecutive 8-day cycle vegetation index provided by GK2A. The indirect validation of the results of GK2A LST with Moderate Resolution Imaging Spectroradiometer (MODIS) LST Collection 6 showed a high correlation coefficient (0.969), slightly warm bias (+1.227 K), and root mean square error (RMSE) (2.281 K). Compared to the MODIS LST, the GK2A LST showed a warm bias greater than +1.8 K during the day, but a relatively small bias (<+0.7 K) at night. Based on the results of the validation with in situ measurements from the Tateno station of the Baseline Surface Radiation Network, the correlation coefficient was 0.95, bias was +0.523 K, and RMSE was 2.021 K.

Jihyun Choi, Youn-Young Choi, Jae-Heon Jeong, Inkwon Baek, Sun-Hwa Kim. Korean J. Remote Sens. 2026;42:181-96. https://doi.org/10.7780/kjrs.2026.42.2.2

Maintaining radiometric consistency throughout the mission lifetime is essential for the quantitative application of high-resolution optical satellite imagery, particularly for sensors without onboard absolute radiometric calibration systems. This study presents an operational absolute radiometric calibration framework for the Compact Advanced Satellite 500-1 (CAS500-1) based on repeated field campaigns conducted during 2023–2024. Reflectance-based vicarious calibration was performed independently for the summer (TDI 1) and winter (TDI 2) operating modes using ground-measured surface reflectance, quality-controlled atmospheric observations, sensor viewing geometry, and MODTRAN6 radiative transfer simulations. Annual gain and offset coefficients were derived through band-wise linear regression between simulated top-of-atmosphere (TOA) radiance and CAS500-1 digital numbers (DNs). The reliability of the calibration was evaluated through campaign-specific uncertainty assessment, independent cross-calibration with Sentinel-2A/B over Libya-4 and RadCalNet sites, and operational image-based validation. The 2024 calibration showed strong linear relationships between simulated TOA radiance and CAS500-1 DN, with campaign-specific combined uncertainties ranging from 4.59% to 7.77%. Sentinel-2 cross-calibration provided independent support for the field-derived coefficients in the visible bands, although the agreement varied with spectral band, site, and operating mode. Image-based validation showed that negative TOA radiance occurred at low proportions for most images and was primarily concentrated over low-radiance surfaces when elevated values occurred. Repeated observations over a stable asphalt reference site showed generally consistent temporal behavior in the visible bands, whereas the NIR band exhibited greater absolute variability that was also present in the original DN measurements. The results demonstrate that operational radiometric calibration should be managed through an integrated framework combining field calibration, uncertainty quantification, independent cross-calibration, and product-level validation. Continued PICS and reference-site monitoring is required to further distinguish long-term sensor-response changes from calibration-related variability.