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.

The Geostationary Ocean Color Imager-II (GOCI-II) provides high-frequency multispectral observations over East Asia, offering considerable potential for terrestrial monitoring. Surface reflectance (SR) is a fundamental land product that represents surface spectral properties after accounting for atmospheric effects and serves as a primary input to downstream products such as surface albedo and vegetation indices. Consequently, uncertainties in SR retrieval can propagate through the processing chain and affect the accuracy and consistency of these derived land products. However, the original GOCI-II land SR algorithm was developed before launch using GOCI proxy data and employed a temporal minimum-reflectance selection scheme, limiting its ability to fully account for the actual spectral, geometric, and atmospheric characteristics of GOCI-II observations. In this study, we developed and evaluated an improved GOCI-II-specific atmospheric correction framework based on a 6SV lookup table (LUT) constructed for all 12 reflective bands. The revised retrieval incorporates pixel-level observation geometry, aerosol optical depth, water vapor, ozone, and terrain elevation and produces observation-specific clear-sky SR without temporal minimum compositing. The original and improved land products were evaluated using all 12 Local Area slots throughout 2023. SR was validated against MODIS MCD19A1, broadband albedo against MODIS MCD43A3, and NDVI and EVI against MODIS MOD13Q1 and GK2A/AMI products. For SR, the largest improvements occurred at shorter wavelengths: RMSE decreased from 0.271 to 0.042 at 412 nm, from 0.196 to 0.032 at 443 nm, and from 0.134 to 0.036 at 490 nm. Improvements were also observed at longer wavelengths, and a common-pixel analysis confirmed that the reductions in bias and RMSE persisted when the original and improved products were evaluated using an identical set of valid pixels. The revised processing also improved the downstream land products. The RMSE of broadband black-sky albedo decreased from 0.037 to 0.025, while that of white-sky albedo decreased from 0.039 to 0.035. For vegetation indices, NDVI RMSE decreased from 0.403 to 0.142 against MODIS MOD13Q1 and from 0.342 to 0.100 against GK2A/AMI, while EVI RMSE decreased from 0.274 to 0.111 and from 0.268 to 0.071, respectively. These results demonstrate that sensor-specific post-launch atmospheric correction substantially improves GOCI-II SR and that enhanced accuracy of this fundamental upstream product contributes to greater consistency and accuracy throughout the downstream GOCI-II land-product chain.