A Semi-Empirical Chlorophyll-a Retrieval Algorithm Considering the Effects of Sun Glint, Bottom Reflectance, and Non-Algal Particles in the Optically Shallow Water Zones of Sanya Bay Using SPOT6 Data.

Chlorophyll-a (Chl-a) concentration retrieval is essential for water quality monitoring, aquaculture, and guiding coastline infrastructure construction. Compared with common ocean color satellites, land observation satellites have the advantage of a higher resolution and more data sources for retrieving the concentration of Chl-a from optically shallow waters. However, the sun glint (R-sg), bottom reflectance (R-b), and non-algal particle (NAP) derived from terrigenous matter affect the accuracy of Chl-a concentration retrieval using land observation satellite image data. In this paper, we propose a semi-empirical algorithm based on the remote sensing reflectance (R-rs) of SPOT6 to retrieve the Chl-a concentration in Sanya Bay (SYB), considering the effect ofR(sg),R-b, and NAP. In this semi-empirical algorithm, the Cox-Munk anisotropic model and radiative transfer model (RTM) were used to reduce the effects ofR(sg)andR(b)onR(rs), and the Chl-a concentration was retrieved by the Chl-a absorption coefficient at 490 nm (a(phy)(490)) to remove the effect of NAP. The semi-empirical algorithm was in the form of Chl-a = 43.3[a(phy)(490)](1.454), wherea(phy)(490) was calculated by the total absorption coefficient and the absorption coefficients of each component by empirical algorithms. The results of the Chl-a concentration retrieval show the following: (1) SPOT6 data are available for Chl-a retrieval using this semi-empirical algorithm in oligotrophic or mesotrophic coastal waters, and the accuracy of the algorithm can be improved by removing the effects ofR(sg),R-b, and NAP (R(2)from 0.71 to 0.93 and root mean square error (RMSE) from 0.23 to 0.11 ug/L); (2) empirical algorithms based on the blue-green band are suitable for oligotrophic or mesotrophic coastal waters, and the algorithm based on the blue-green band difference Chl-a index (DCI) has stronger anti-interference in terms of the effects of sun glint and bottom reflectance than the algorithm based on the blue-green ratio (BGr); (3) in the case of ignoringR(sg)unrelated to inherent optical properties (IOPs), NAP is the biggest interference factor when >9.5 mg/L and the effect of bottom reflectance should be considered when the water depth (H) <5 m in SYB; and (4) the inherent optical properties of the waters in SYB are dominated by NAP (Chl-a = 0.2-2.6 ug/L and NAP = 2.2-30.1 mg/L), and the nutrients are concentrated by enclosed terrain and southeast current. This semi-empirical algorithm for Chl-a concentration retrieval has the potential to monitor Chl-a in oligotrophic and mesotrophic coastal waters using other land observation satellites (e.g., Landsat8 OLI, ASTER, and GaoFen2).