A simple and effective algorithm to retrieve total suspended matter from GOCI data in Hangzhou Bay, China

Hangzhou Bay (HZB) is particularly known for its high concentrations and rapid variations of total suspended matter (TSM) due to river input and tidal induced resuspending. Extreme TSM concentrations (C_TSM) of HZB usually exceed the application ranges of many existing remote sensing algorithms. In this study, a simple and efficient algorithm suitable for high dynamic C_TSM based on Rayleigh-corrected reflectance (ρ_rc) data from the Geostationary Ocean Color Imager (GOCI) was established. The parameters of this algorithm were determined by assembling a satellite-ground synchronous dataset from 2013 to 2020 using 3 buoys, ensuring that it was capable of monitoring C_TSM varying across four orders of magnitudes. Performance comparisons between recalibrated prevalence C_TSM retrieval algorithms based on surface water-leaving reflectance and the ρ_rc-based method in this study were also carried out. Results show that, the proposed algorithm in this study performed best with R² and mean absolute percentage error (MAPE) values of 0.75 and 45.41 %, respectively. When the proposed algorithm was applied on the GOCI data of 2019, although monthly changes of C_TSM in the HZB were observed in a pattern similar to that of NOAA C_TSM products, with high values occurring in the winter and lower values in the summer, the accuracy of daily results showed a significant improvement with R² of 0.65 versus 0.11, and MAPE of 38.86 % versus 75.06 %. And results derived using GOCI in this study can observe C_TSM fluctuations on small scales due to sediment resuspension during different tidal periods. Additionally, transferability of the proposed algorithm was examined with Landsat-8 and GOCI-II data. Overall, the findings of this study provided a concise and practical C_TSM algorithm to estimate more valid C_TSM at highly dynamic turbid coastal area.