Sun-Hwa Kim

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.

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.

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

Jinyeong Kim, Sun-Hwa Kim, Jeong Eun, Sung-Joo Yoon, Han-Sol Ryu, Tae-Ho Kim. Korean J. Remote Sens. 2025;41:1077-90. https://doi.org/10.7780/kjrs.2025.41.6.11

Various fusion methods of optical satellite images have been proposed for monitoring heterogeneous farmlands requiring high spatial and temporal resolution. In this study, a three-meter normalized difference vegetation index (NDVI) was generated by applying the spatiotemporal fusion (STF) method to simultaneously generate a full-length normalized difference vegetation index time series (SSFIT) and enhanced spatial and temporal adaptive reflectance fusion method (ESTARFM) to the NDVI of Sentinel-2 (S2) and PlanetScope (PS), using images from 2019 to 2021 of rice paddy and heterogeneous cabbage fields in Korea. Before fusion, S2 was processed with the maximum NDVI composite (MNC) and the spatiotemporal gap-filling technique to minimize cloud effects. The fused NDVI image had a spatial resolution similar to PS, enabling more accurate monitoring of small and heterogeneous fields. In particular, the SSFIT technique showed higher accuracy than ESTARFM, with a root mean square error of less than 0.16 and correlation of more than 0.8 compared to the PS NDVI. Additionally, SSFIT takes four seconds to process data in the field area, while ESTARFM requires a relatively long processing time of five minutes. In some images where ESTARFM was applied, outliers originating from S2 were still present, and heterogeneous NDVI distributions were also observed. This spatiotemporal fusion (STF) technique can be used to produce high-resolution NDVI images for any date during the rainy season required for time-series analysis.

Various fusion methods of optical satellite images were proposed for monitoring heterogeneous farmlands requiring high spatial and temporal resolution. In this study, 3m normalized difference vegetation index (NDVI) was generated by applying spatiotemporal fusion method to simultaneously generate full-length normalized difference vegetation index time series (SSFIT) and enhanced spatial and temporal adaptive reflectance fusion method (ESTARFM), to NDVI of Sentinel-2 (S2) and PlanetScope (PS) from 2019 to 2021 for rice paddy and heterogeneous cabbage fields. Before fusion, S2 was applied with the maximum NDVI composite (MNC) and the spatio-temporal gap-filling technique to minimize the cloud effects. The fused NDVI image had a spatial resolution similar to PS, enabling more accurate monitoring of small and heterogeneous fields. In particular, the SSFIT technique showed higher accuracy than ESTARFM with a root mean square error of less than 0.16 and correlation of more than 0.8 compared to PS NDVI, and was processed faster than the ESTARFM code. In some images where ESTARFM was applied, outliers originating from the S2 were still present and heterogeneous NDVI distributions were also shown. This spatio-temporal fusion (STF) technique can be used to produce high-resolution NDVI images for any date during the rainy season required for time-series analysis.

Sun-Hwa Kim, Jeong Eun, Tae-Ho Kim. Korean J. Remote Sens. 2024;40:957-63. https://doi.org/10.7780/kjrs.2024.40.6.1.7