Empirical models have been widely used to retrieve shallow water bathymetry from multispectral/hyperspectral satellite imagery. In traditional studies on deriving the topography and monitoring its temporal changes, a single date satellite image without clouds corresponded to a bathymetric map and multidate images corresponded to multiple bathymetric maps. The satellite image noise caused by various environmental conditions and satellite sensors can inevitably introduce errors or gaps in deriving bathymetric maps. Also, empirical models are limited in some remote areas due to the lack of prior bathymetric points. In this article, using only satellite data, including multitemporal Sentinel-2 images and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data, a multitemporal stacking method was developed to derive highly accurate and cloud free shallow water bathymetry with accuracy of approximately 1 m and the depth range exceeding 22 m. The proposed method was tested and validated by an airborne bathymetric lidar. To be specific, our method using multitemporal Sentinel-2 images can achieve a mean root mean square error (RMSE) of 1.08 m ( R ² = 0.94) by comparing with in-situ airborne lidar data around Ganquan Island, which is better than the result ( R ² = 0.92, RMSE = 1.46 m) derived from single date image based methods.Also, the gaps in a bathymetric map due to clouds or other noise can be avoidable benefitting from the stacking of multiple date satellite images. In the future, this satellite data driven method can be further extended to the globe to produce highly accurate and cloud free bathymetry around clear shallow water benefited from prior ICESat-2 bathymetric data.

中文翻译：

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通过多时相叠加方法从 Sentinel-2 和 ICESat-2 数据集推导出高精度的浅水深度测量

经验模型已被广泛用于从多光谱/高光谱卫星图像中检索浅水测深。在传统的地形推导和监测其时间变化的研究中，没有云的单日期卫星图像对应于测深图，多日期图像对应于多个测深图。由各种环境条件和卫星传感器引起的卫星图像噪声不可避免地会在推导测深图时引入错误或差距。此外，由于缺乏经验模型，在一些偏远地区受到限制。先前的测深点。在本文中，仅使用卫星数据，包括多时相 Sentinel-2 图像和冰、云和陆地高程卫星 2 (ICESat-2) 数据，开发了一种多时相叠加方法，以推导出高精度且无云的浅水测深精度约为 1 m，深度范围超过 22 m。所提出的方法通过机载测深激光雷达进行了测试和验证。具体来说，我们使用多时相 Sentinel-2 图像的方法可以实现 1.08 m ( R ² = 0.94) 通过与甘泉岛周围的原位机载激光雷达数据，优于结果（ R ² = 0.92, RMSE = 1.46 m) 源自基于单一日期图像的方法。此外，可以从多个日期卫星图像的叠加中受益，避免由于云或其他噪声导致的测深图中的差距。未来，这种卫星数据驱动的方法可以进一步扩展到全球，以利用先前的 ICESat-2 测深数据在清澈的浅水周围产生高精度和无云的测深。