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Photosynthetically available radiation (PAR) has a key role in generating primary production at the ocean surface. Various satellite sensors were used to estimate PAR at the ocean, but GOCI, the world’s first geostationary ocean color sensor, didn’t service PAR as an official product yet. In this study, PAR for the ocean around Korean Peninsula was estimated by using GOCI and validation was carried out. GOCI daily PAR was estimated from plane-parallel theory based model and corrected with in-situ measurements PAR data which had been collected at two ocean research stations. Corrected GOCI daily PAR has a high R2 value (0.99) with in-situ measurements. GOCI daily PAR also shows a high accuracy in terms of root-mean-square error (RMSE) and mean bias error (MBE); 4.98 % and -0.52 %, respectively. As a result of the comparison with other sensors derived PAR data, GOCI daily PAR shows the highest performance at the ocean around Korean Peninsula. MODIS and AHI derived daily PAR have lower accuracy than GOCI. MODIS daily PAR has RMSE of 10.40 % and -4.15 % with in-situ measurements, and AHI daily PAR has 6.51 % and -4.65 %, respectively. GOCI daily PAR help to understand the marine environment around the Korean Peninsula. For the further study, PAR from GOCI-II will be discussed.

Photosynthetically available radiation (PAR) reaching the ocean surface controls phytoplankton growth, primary productivity, and evolution within marine ecosystems. Therefore, accurate daily PAR estimates are important for a broad range of marine biology and biogeochemistry applications. In this study, hourly data from Geostationary Ocean Color Imager (GOCI), the world’s first geostationary ocean color sensor, was employed to estimate daily mean PAR at the ocean surface around the Korean Peninsula using a budget model based on plane-parallel theory. In situ PAR data collected from two ocean research stations (Socheong-cho and Ieodo) were used to evaluate the accuracy of the GOCI PAR estimates. First, the instantaneous in situ measurements were checked for calibration and exposure errors against Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer calculations under the clearest sky conditions and adjusted to eliminate biases. After adjustment, the root-means-square error (RMSE) between 6S and in situ PAR data was reduced from 6.08 (4.81%) and 3.82 (3.93%) mol/m2/day to 2.85 (2.26%) and 1.74 (1.21%) mol/m2/day at the Socheong-cho and Ieodo stations, respectively, and the coefficient of determination R2 was 0.99. Then, the GOCI daily mean PAR estimated by the initial algorithm were corrected using the 2015 adjusted in situ daily PAR measurements collected under clear-sky conditions. The daily mean PAR values derived from GOCI data in all conditions were improved after the correction, with RMSE reduced from 4.58 (8.30%) to 2.57 (4.65%) mol/m2/day and R2 = 0.97. The comparison statistics were similar for 2015 and 2016 combined, with RMSE of 2.52 (4.38%) and mean bias error (MBE) of –0.40 (–0.70%), indicating that the correction was also effective in cloudy conditions. On the other hand, daily PAR estimates from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Himawari Imager (AHI) yielded larger RMSE of 6.24 (10.40%) mol/m2/day and MBE of –2.49 (–4.15%) mol/m2/day (MODIS) and RMSE of 3.71 (6.51%) mol/m2/day and MBE of –2.65 (–4.65%) mol/m2/day (AHI) against in situ measurements. The GOCI-based daily PAR model developed in this study is reliable and suitable for investigating the marine environment around the Korean Peninsula.

In coastal region, estuaries have dynamic conditions such as river discharge, tidal current, wave and sand ridge. These conditions generate suspended sediments processes in the estuary. Suspended sediment process can be used to explain reason for geological changes in the coastal area. To understand these processes, we use Geostationary Ocean Color Imager to monitor estuarine suspended sediments in this study. GOCI has high time resolution (1hour) and can provide daily SSC images. We compared daily GOCI SSC images with in situ SSC and conducted time-series analysis using monthly averaged GOCI SSC images. In dry season, net sediment fluxes were calculated. We can understand morphological changes in the estuary using these net sediment fluxes data. Using monthly averaged SSC images, the area influenced by river discharge in wet season can be explain. In the future, we will research about limitation of river discharge in wet season using optical satellite images.