Estimation of sea surface salinity (SSS) using the Geostationary Ocean Color Imager-II (GOCI-II) measurements in the East China Sea (ECS) was conducted from July to September of 2021, when the first-year observations are available after GOCI-II launch in 2020. The SSS in the ECS is mainly affected by the Changjiang River plume, which varies from under 20–35 psu, and the discharged freshwater disperses from the river mouth towards Jeju Island, Korea. For the SSS estimation, a multi-layer perceptron neural network (MPNN) was employed to train the nonlinear processes of GOCI-II spectral measurements as inputs and the SSS of Soil Moisture Active Passive (SMAP) as the target. Because GOCI-II has four additional spectral bands (380, 510, 620 and 709 nm) compared to the bands in the first generation of GOCI (413, 443, 490, 555, 660 and 680 nm), we developed a new MPNN algorithm and analysed (1) how much these new spectral measurements increase SSS accuracy, and (2) how the enhanced spatial and temporal resolution of GOCI-II make SSS features different from GOCI. The first results showed that the root mean square error (RMSE) and coefficient of determination (R²) were 0.68 psu and 0.92, respectively. Furthermore, R², when compared with in-situ measurements at the Ieodo Ocean Research Station (I-ORS), increased as much as 0.23 (0.20–0.43) for the 10-band model, which performed much better than previous 6-band model. The second result suggests how the improved spatial features at more frequent SSS measurements can be utilized to avoid low salinity intrusion into the aquafarming sites off the coast of Korea. More importantly, we elucidated why the MPNN algorithm performs better than conventional SSS estimation methods by comparing various optical properties with SSS variation; thus, the newly developed model can provide SSS not only in ECS but also low salinity water near southwest Korean coasts at hourly spatial resolution of 250 m.

The Ieodo plume is a distinctive suspended sediment plume near the Ieodo Ocean Research Station (I-ORS), located in the middle of the northern East China Sea. Because the Ieodo plume exhibits multiple different spatial scales, this study conducted an integrated remote sensing observation using satellites and unmanned aerial vehicles (UAVs) to observe its development and dispersion. Sentinel-2 and Geostationary Ocean Color Imager-II (GOCI-II) data were used to determine the plume’s spatial characteristics, broad-scale behavior, hourly variability, and turbidity characteristics. Also, TPXO model outputs were employed to evaluate the relationship between plume occurrence and tides, together with satellite imagery. Plume was repeatedly observed near the top of the Ieodo Seamount, with an affected extent of 11.4 ± 3.2 km in the east–west direction and 14.3 ± 4.1 km in the north–south direction. Moreover, hourly variations observed using GOCI-II showed that the Ieodo plume rotated clockwise with shifting tidal currents, forming a counterclockwise curved band or a ring-shaped structure. Total suspended solids (TSSs) in the plume reached their maximum when the southward component of the TPXO tidal current was dominant. Based on UAV optical surveys at the I-ORS, fine-scale morphology at the early stage of plume development was revealed, and it was confirmed that the Ieodo plume can occur even when it is not detected by satellite imagery. Furthermore, the u- and v-velocity vectors of the propagating Ieodo plume were derived by applying large-scale particle image velocimetry (LSPIV) to geometrically corrected sequential UAV imagery obtained in I-ORS. Plume speed was greatest near the source during the initial stage (0.81 ± 0.30 m s⁻¹) and gradually decreased to 0.34 ± 0.29 m s⁻¹ over distance. Based on the results above, we propose that the Ieodo plume is primarily generated by a pressure reduction associated with tidally accelerated currents over topography, driven by the Bernoulli effect. This study shows that an integrated satellite and UAV observation framework can effectively monitor rapidly evolving suspended sediment plumes. It can further help improve our understanding of dynamically driven submesoscale marine events.