Comparisons of algorithms to estimate water turbidity in the coastal areas of China

Abstract

ABSTRACTTurbidity is an important indicator of water environments and water-quality conditions. Ocean colour remote sensing has proved to be an efficient way of monitoring water turbidity because of its wide synoptic coverage and repeated regular sampling. However, operational tasks are still challenging in high-turbidity waters, especially in estuaries and the coastal regions of China. In these areas, the existing algorithms derived from remote-sensing reflectance (Rrs) are usually invalid because it is difficult to correctly estimate the reflectance Rrs from satellite data such as Moderate Resolution Imaging Spectroradiometer (MODIS) data. A new algorithm that uses Rayleigh-corrected reflectance (Rrc) instead of Rrs has been recently introduced and was used to estimate water turbidity in Zhejiang (ZJ) coastal areas from Geostationary Ocean Color Imager (GOCI) data. The Rrc algorithm has previously shown a capability to estimate water turbidity. However, its performance still requires careful evaluation. In this article, we compared the new Rrc algorithm with two other existing algorithms. Differences among the three algorithms were assessed by comparing the results from using Rrc data and Rrs reflectance data derived from both GOCI and MODIS imagery data. The capability of the new Rrc algorithm to estimate water turbidity in larger areas and extended seasons in the coastal seas of China was also estimated. The results showed that the new Rrc algorithm is suitable for the coastal waters of China, especially for highly turbid waters.