A highly efficient index for robust mapping of tidal flats from sentinel-2 images directly

Abstract

As an essential component of the intertidal zone, tidal flats (TFs) are areas rich in resources where with the most intense material and energy exchanges. However, due to the dual threats of human activities and extreme climate conditions, TFs are disappearing on a large scale. Despite their importance, accurately mapping TFs has proved challenging due to their complex and dynamic nature. Nevertheless, Tidal influences significantly enhance the diversity and variability of TFs, and suspended particulates introduce turbidity that challenges conventional indices used for distinguishing between water and land. This study focuses on the world’s largest intertidal sedimentary system located along the central coast of Jiangsu, an area characterized by complex sedimentary features and dynamic TF conditions. Through quantitative analysis of the spectral characteristics of TFs at different years, seasons, and tidal stages, this study identifies two unique spectral features of TFs: uniformly low reflectance values and a trapezoidal spectral shape. Leveraging the low reflectance, the flatness of the middle segment in the trapezoidal spectral shape, and the initial increase followed by a decreasing trend across critical bands, a novel Tidal Flat Index (TFI) has been developed. Experimental results indicate that TFI is suitable for robust and direct TF mapping across years, seasons, and tidal stages, achieving F1 scores exceeding 0.95 in 12 different scenarios. Compared to other indices and rule-based methods, TFI offers greater accuracy, threshold stability, background and cloud suppression. The study also extends to other globally rich TFs regions to demonstrate the universality and applicability of the proposed index in various environments, including its effectiveness in delineating annual TFs extents. This study offers technical support for the automatic mapping of TFs based on single Sentinel-2 multispectral images.