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The use of ocean color sensors presents limitations to monitoring coastal environmental changes and capturing fine spatial features below 1 km due to low spatial (0.5–1 km) and temporal (1 day) resolutions. The geostationary ocean color imager (GOCI)-II, launched on February 18, 2020, is a follow-up mission to GOCI, operated from June 27, 2010 to March 31, 2021. GOCI-II imagery, with a spatial resolution of 250 m, detects more detailed spatial structures of ocean dynamics compared to GOCI with a spatial resolution of 500 m. This study aims to develop a U-Net super-resolution (SR) model to enhance the GOCI remote-sensing reflectance ( $\text{R}_{\text {rs}}$ ) imagery to the same spatial resolution as GOCI-II. The U-Net model is trained with eight paired bands (412, 443, 490, 555, 660, 680, 745, and 865 nm) of GOCI and GOCI-II $\text{R}_{\text {rs}}$ around the waters of the Korean Peninsula. The consistency level between GOCI and GOCI-II images indicated GOCI sensor degradation, especially in the blue bands, during its last mission period from December 2020 to March 2021. Through quantitative and qualitative evaluations, we found that the U-Net $\text{R}_{\text {rs}}$ image had greater spectral information with higher consistency compared to the G1-bicubic image by bicubic interpolation of GOCI. In particular, the U-Net results improved the consistency in the blue bands (412, 443, and 490 nm). Qualitative evaluations also showed that U-Net corrected the blue band underestimation in degraded GOCI images. In addition, chlorophyll-a concentration (CHL) map from the U-Net $\text{R}_{\text {rs}}$ not only simulated spatial patterns, similar to GOCI-II CHL map, but also corrected the overestimated GOCI CHL map. The U-Net SR model may help to produce more reliable and fine-scale $\text{R}_{\text {rs}}$ products from GOCI similar to those of GOCI-II, and to enable long-term ocean color monitoring around the waters of the Korean Peninsula.