A universal method to recognize global big rivers estuarine turbidity maximum from remote sensing

Abstract

The study of estuarine turbidity maximum (ETM) has a long history. However, the algorithms and criteria for ETM identification vary significantly across estuaries and hydrological regimes. Moreover, almost all of these methods depend on derived water parameters, such as suspended sediment concentration and turbidity, which inevitably result in inherent errors in the ETM results. To overcome these disadvantages and develop a standard ETM recognition method that has good applicability in most estuaries, this study analyzed the spectral characteristics of 23 big river estuaries worldwide using Landsat and Sentinel sensor images. Based on the difference in band reflectance between the ETM and normal water bodies, we first proposed a universal method, defined as the product of the ratio of blue, green and red bands to their average value over the entire estuary, namely, Red Green Blue Turbidity (RGBT). Combined with the corresponding remote sensing images, the ETM distributions in the 23 estuaries were extracted and analyzed. It was found that the ETM recognition results for the Pearl River Estuary on different dates (2004, 2015) were consistent with those of previous studies. The validation accuracies (Q) reached 0.8335 and 0.8800, respectively, illustrating the effectiveness of the RGBT method in the Pearl River Estuary. For the other 22 estuaries, the RGBT-based ETM recognition results were evaluated using the corresponding visual interpretation. Comparisons and details of the ETM boundaries indicate that the method works well for all types of estuaries. It also included accurately identifying slightly turbid plumes from maritime wind turbines and bridge piers. The validation accuracy exceeded 0.9 (0.9025–0.9733) in seven estuaries, and surpassed 0.7898 in the remaining 15 estuaries. The RGBT method generally achieved higher accuracy for estuaries in Asia and Europe, followed by estuaries in America and Oceania, with a relatively lower accuracy for estuaries in Africa. But the variation in the accuracy in different regions was small. The average validation accuracy of all estuaries and different seasons was as high as 0.9027. This demonstrates that the unified method with same criterion can directly and effectively recognize ETM distributions from multi-source remote sensing data in different estuaries worldwide.