Abstract Accurate bathymetric data is essential for marine, coastal ecosystems, and related studies. In the past decades, a lot of studies were investigated to obtain bathymetric data in shallow waters using satellite remotely sensed data. Satellite multispectral imagery has been widely used to estimate shallow water depths based on empirical models and physics-based models. However, the in-situ water depth information is essential (as the priori) to use the empirical model in a specific area, which limits its application, especially for remote reefs. In this study, the bathymetric maps in shallow waters were produced based on empirical models with only satellite remotely sensed data (i.e., the new ICESat-2 bathymetric points and Sentinel-2 multispectral imagery). The bathymetric points from the spaceborne ICESat-2 lidar were used in place of the in-situ auxiliary bathymetric points to train the classical empirical models (i.e., the linear model and the band ratio model). The bathymetric points were firstly extracted from noisy ICESat-2 raw data photons by an improved point cloud processing algorithm, and then were corrected for bathymetric errors (which were caused by the refraction effect in the water column, the refraction effect on the water surface, and the fluctuation effect on the water surface). With the trained empirical models and Sentinel-2 multispectral images, the bathymetric maps were produced for Yongle Atoll, in the South China Sea and the lagoon near Acklins Island and Long Cay, to the southeast of Bahama with four-date Sentinel-2 images. The bathymetry performance (including the accuracy and consistency of multi-date data) was evaluated and compared with the in-situ measurements. The results indicate that the bathymetric accuracy is well, and the RMSE is lower or close to 10% of the maximum depth for the two models with four-date images in two study areas. The consistency of multi-date data is well with the mean R2 of 0.97. The main novelties of this study are that the accuracy bathymetric points can be obtained from the ICESat-2 raw data using the proposed signal processing and error correction method, and using the ICESat-2 bathymetric points, the satellite multispectral imagery based on empirical models is no longer limited by local priori measurements, which were essential in previous studies. Hence, In the future, with the help of free and open-access satellite data (i.e., ICESat-2 data and Sentinel-2 imagery), this approach can be extended to a larger scale to obtain bathymetric maps in the shallow water of coastal areas, surroundings of islands and reefs, and inland waters.

中文翻译：

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使用 ICESat-2 激光雷达和 Sentinel-2 影像数据集的卫星测深

摘要 准确的测深数据对于海洋、沿海生态系统和相关研究至关重要。在过去的几十年里，大量的研究被调查以利用卫星遥感数据获得浅水区的测深数据。卫星多光谱图像已被广泛用于基于经验模型和基于物理模型的浅水深度估计。然而，原位水深信息对于在特定区域使用经验模型是必不可少的（作为先验），这限制了它的应用，特别是对于偏远的珊瑚礁。在这项研究中，浅水区的测深图是基于仅使用卫星遥感数据（即新的 ICESat-2 测深点和 Sentinel-2 多光谱图像）的经验模型制作的。来自星载ICESat-2激光雷达的测深点被用来代替原位辅助测深点来训练经典的经验模型（即线性模型和带比模型）。首先通过改进的点云处理算法从嘈杂的 ICESat-2 原始数据光子中提取测深点，然后对测深误差（由水柱中的折射效应、水面的折射效应、以及对水面的波动影响）。使用经过训练的经验模型和 Sentinel-2 多光谱图像，为南海的永乐环礁和巴哈马东南部的阿克林斯岛和长礁附近的泻湖制作了四期 Sentinel-2 图像的测深图。对测深性能（包括多日期数据的准确性和一致性）进行了评估，并与原位测量进行了比较。结果表明，在两个研究区，四个日期图像的两个模型的测深精度良好，RMSE低于或接近最大深度的10%。多日期数据的一致性很好，均值 R2 为 0.97。本研究的主要创新点是可以使用所提出的信号处理和纠错方法从ICESat-2原始数据中获得准确的测深点，并且使用ICESat-2测深点，基于经验模型的卫星多光谱图像为不再受先前研究中必不可少的局部先验测量的限制。因此，在未来，借助免费和开放访问的卫星数据（即，