Imaging estuarine surface salinity using Sentinel-2: a case study in the Pearl River Estuary

Abstract Optical remote sensing for estuarine surface salinity relies on correlation with water color parameters, facing limitations in spectra, resolution, and hydrodynamics, particularly in multi-outlet estuaries. In this study, Sentinel-2 images with 10m resolution were selected, and geographic coordinates, imaging date and band combinations were incorporated as inputs for a neural network model to capture spatiotemporal salinity distribution. By repeatedly constructing the model with varied inputs to mitigate the impact of input sample variations and ensure robustness, the final model, N2L-3, achieved high accuracy (R2 = 0.916 and overall RMSE below 1.5 PSU) and was chosen to estimate the estuarine surface salinity in the Pearl River Estuary. Our results revealed high-salinity offshore water intruding the eastern estuary channel during flood tides, gradually merging upstream with freshwater. Low-salinity water reached the Pearl River Front Channel and East River North Branch. During ebb tides, due to estuarine outflows, low-salinity water spread through river mouths and shifted eastward near Humen.