Sea surface salinity (SSS) is crucial for estimating sea surface density (SSD) and understanding the sea surface property in the ocean. In the East China Sea (ECS), SSS change during the summer season was mainly influenced by the Changjiang River discharge. The Changjiang diluted water (CDW) affected biogeochemical environments and ecosystems in the ECS. In this dissertation, SSS estimation models were developed based on various ocean color satellite sensors and machine learning methods. In Chapter Ⅰ, the importance of SSS and density fronts to chlorophyll-a concentration (Chl) was determined by using long-term (25 years) SSS measurements. In Chapters Ⅱ and Ⅲ, the development of SSS estimation algorithms for Geostationary Ocean Color Imager (GOCI)-Ⅰ and GOCI-Ⅱ to produce high-resolution SSS was described, respectively.Firstly, the long-term sea surface salinity (SSS) in the East China Sea (ECS) was estimated based on Ocean Colour Climate Change Initiative (OC-CCI) data using machine learning during the summer season (June to September) from 1997 to 2021. The Changjiang Diluted Water (CDW) in the ECS propagates northeastward and longitudinally formed ocean fronts. To determine the CDW distribution, three fronts were investigated: (1) CDW fronts based on chlorophyll-a concentration (Chl), SSS, and sea surface temperature (SST); (2) the CDW front based on sea surface density (SSD); and (3) the CDW front for nutrients distribution. The Chl fronts were well matched with SSS fronts, suggesting that Chl variation in the ECS is highly correlated with the CDW. Furthermore, the SSD fronts spatially matched well with nitrogen concentration. Sea level anomaly (SLA) variation with SSD also detected, indicating that CDW had sufficiently large effects on SLA so that they may be detectable by altimeter measurements. This result suggests the influences of steric height changes and inflow from river are significant in the ECS. In addition, continuous long-term SSD developed in this study enable researchers to detect the CDW front and its influences on the ECS marine environment.Secondly, the SSS detection algorithm was developed based on the ocean color measurements by GOCI in high spatial and temporal resolution using a multi-layer perceptron neural network (MPNN). Among the various combinations of input parameters, combinations with three to six bands of GOCI remote sensing reflectance (Rrs), SST, longitude, and latitude were most appropriate for estimating the SSS. According to model validations with the Soil Moisture Active Passive (SMAP) and Ieodo Ocean Research Station (I-ORS) SSS measurements, the coefficient of determination (R2) were 0.81 and 0.92 and the root means square errors (RMSEs) were 1.30 psu and 0.30 psu, respectively. In addition, a sensitivity analysis revealed the importance of SST and the red-wavelength spectral signal for estimating the SSS. Finally, hourly estimated SSS images were used to illustrate the hourly CDW distribution. With the model developed in this study, the near real-time SSS distribution in the ECS can be monitored using GOCI and SST data.Lastly, estimation of SSS using the GOCI-II measurements in the ECS was conducted from July to September of 2021, when the first-year observations are available after the GOCI-II launched in 2020. The SSS in the ECS is mainly affected by the Changjiang River plume, which varies from under 20 to 35 psu, and the discharged freshwater disperses from the river mouth toward Jeju Island, Korea. For the SSS estimation, the MPNN was employed to train the nonlinear processes of GOCI-II spectral measurements as inputs and the SSS of 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), I developed a new MPNN algorithm and analyzed (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 RMSE and R2 were 0.68 psu and 0.92, respectively. Furthermore, R2, when compared with the in-situ measurement at the I-ORS, increased as much as 0.23 (0.20 to 0.43) for the 10-band model, which performed much better than the previous 6-bands 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, I 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 in low salinity water near southwest Korean coasts in hourly and spatial resolution of 250 m.This dissertation analyzed the development of the summertime SSS estimation models depends on various ocean color measurements. The models were acceptable to produce SSS distribution in the ECS despite the uncertainties of SSS estimation models being relatively high near the Changjiang River mouth. This dissertation will contribute to monitoring of CDW extension in near-real time and understanding the behavior of biogeochemical properties depending on CDW.