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.

中文翻译：

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一种高效的索引，可直接从 sentinel-2 图像中对滩涂进行稳健映射


作为潮间带的重要组成部分，滩涂 （TF） 是资源丰富的区域，物质和能量交换最为密集。然而，由于人类活动和极端气候条件的双重威胁，TF 正在大规模消失。尽管 TF 很重要，但由于其复杂性和动态性，准确映射 TF 已被证明具有挑战性。然而，潮汐影响显着增强了 TF 的多样性和可变性，而悬浮颗粒物引入了浑浊度，挑战了用于区分水和陆地的传统指标。本研究重点研究了位于江苏中部沿海的世界上最大的潮间带沉积系统，该地区具有复杂的沉积特征和动态的 TF 条件。通过对不同年份、季节和潮汐阶段 TFs 的光谱特性进行定量分析，本研究确定了 TFs 的两个独特光谱特征：均匀的低反射率值和梯形光谱形状。利用低反射率、梯形光谱形状中段的平坦性以及临界带的初始增加和下降趋势，开发了一种新的潮汐平坦指数 （TFI）。实验结果表明，TFI 适用于跨年份、季节和潮汐阶段的稳健和直接的 TF 映射，在 12 种不同情况下实现超过 0.95 的 F1 分数。与其他指数和基于规则的方法相比，TFI 提供了更高的准确性、阈值稳定性、背景和云抑制。 该研究还扩展到其他全球丰富的 TFs 地区，以证明拟议指数在各种环境中的普遍性和适用性，包括其在划定年度 TFs 范围方面的有效性。本研究为基于单个 Sentinel-2 多光谱图像的 TFs 自动制图提供了技术支持。