Estimating and validating instantaneous PAR from GOCI-I data

Geostationary-orbit sensors provide high-frequency observations, offering advantages over sun-synchronous sensors for studying short-term ocean dynamics, particularly in regions such as the East Sea/Japan Sea and Yellow Sea, where daily variability is significant. Primary production (PP), crucial for analyzing the carbon cycle, is typically estimated on a global and daily scale from space, but understanding daily changes in PP at a regional scale is also important. Estimating instantaneous PP (iPP) requires knowledge of instantaneous photosynthetically available radiation (iPAR), which is the planar solar flux reaching the surface at wavelengths from 400 to 700 nm, a fundamental controlling variable. Here, using a plane-parallel theory-based PAR model, iPAR from GOCI-I was estimated for the ocean around the Korean Peninsula and evaluated against in-situ iPAR measurements from ECO-PAR sensors deployed at two ocean research stations, Socheong-cho and Ieodo, from 2015 to 2020. Data from 2015 to 2017 were used for the training set, and data from 2018 to 2020 for validation. In-situ measurements from 2015 to 2017 were checked against expected values from radiative transfer simulations, and a 2nd order polynomial regression was applied to correct the measurements. The GOCI-I iPAR estimates showed good agreement with the corrected data, with an RMSE of 10.36% and an MBE of 1.55% from 2015 to 2017. Evaluation against in-situ data from 2018 to 2020 also showed similar RMSE (10.04%) and MBE (0.74%). The accuracy of GOCI-I iPAR was further compared with that of iPAR data from the advanced Himawari imager (AHI) and the moderate resolution imaging spectrometer (MODIS). iPAR values from AHI and MODIS exhibited higher RMSE and MBE than GOCI-I iPAR and lower R². These findings demonstrate that GOCI-I iPAR is a valuable dataset for assessing diurnal variability in oceanic PP around the Korean Peninsula, with implications for improved regional carbon cycle studies and ecosystem monitoring.