A spatiotemporally weighted intelligent method for exploring fine-scale distributions of surface dissolved silicate in coastal seas

Abstract

The transfer of dissolved silicate (DSi) from land to coastal environments is a crucial part of global biogeochemical cycling. However, the retrieval of coastal DSi distribution is challenging due to the spatiotemporal non-stationarity and nonlinearity of modeling processes and the low resolution of in situ sampling. To explore the coastal DSi changes in a higher spatiotemporal resolution, this study developed a spatiotemporally weighted intelligent method based on a geographically and temporally neural network weighted regression (GTNNWR) model, a Data-Interpolating Empirical Orthogonal Functions (DINEOF) model, and satellite observations. For the first time, the complete surface DSi concentrations of 2182 days at the 500-meter and 1-day resolution in the coastal sea of Zhejiang Province, China, were obtained (Testing R² = 78.5 %) by using 2901 in situ records with concurrent remote sensing reflectance. The long-term and large-scale distributions of DSi reflected the changes in coastal DSi under the influences of rivers, ocean currents, and biological effects across multiple spatiotemporal scales. Benefiting from the high-resolution modeling, this study found that the surface DSi concentration had at least 2 declines during a diatom bloom process, which can provide crucial signals for the timely monitoring and early warning of diatom blooms and guide the management of eutrophication. It was also indicated that the correlation coefficient between the monthly DSi concentration and the Yangtze River Diluted Water velocities reached -0.462**, quantitatively revealing the significant influence of the terrestrial input. In addition, the daily-scale DSi fluctuations resulting from typhoon transits were finely characterized, which greatly reduces the monitoring cost compared with the field sampling. Therefore, this study developed an effective data-driven-based method to help explore the fine-scale dynamic changes of surface DSi in coastal seas.

Keywords: Coastal waters; Diatom bloom; Dissolved silicate; Geographically and temporally neural network weighted regression; High spatiotemporal resolution.