The accurate assessment of large-scale and complex coastal waters is a grand challenge due to the spatial nonstationarity and complex nonlinearity involved in integrating remote sensing and in situ data. We developed a water quality assessment method based on a newly proposed geographically neural network weighted regression (GNNWR) model to address that challenge and obtained a highly accurate and realistic water quality distribution on the basis of the comprehensive index of Chinese Water Quality Classification Standards. Using geostationary ocean color imager (GOCI) data and observations from 1240 water quality sampling sites, we conducted experiments for a typical large-scale coastal area of the Zhejiang Coastal Sea (ZCS), People's Republic of China. The GNNWR model achieved higher prediction performance (average R2 = 84%) in comparison to the widely used models, and the obtained water quality classification (WQC) maps in May of 2015-2017 and August 2015 can depict intuitively reasonable spatiotemporal patterns of water quality in the ZCS. Furthermore, an analysis of WQC maps successfully illustrated how terrestrial discharges, anthropogenic activities, and seasonal changes influenced the coastal environment in the ZCS. Finally, we identified essential regions and provided targeted regulatory interventions for them to facilitate the management and restoration of large-scale and complex coastal environments.
Keywords: Coastal water quality; Geographically neural network weighted regression; Large-scale areas; Remote sensing; Spatial nonstationarity; Water quality assessment.