U-Net Super-Resolution Model of GOCI to GOCI-II Image Conversion

Abstract

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 18 February 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 model to enhance the GOCI remote-sensing reflectance (R 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 R 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 R 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 R rs not only simulated spatial patterns, similar to GOCI-II CHL map, but also corrected the overestimated GOCI CHL map. The U-Net super-resolution model may help to produce more reliable and fine-scale R 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.