Deriving Highly Accurate Shallow Water Bathymetry From Sentinel-2 and ICESat-2 Datasets by a Multitemporal Stacking Method

Abstract

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 multi-date 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 study, using only satellite data, including multi-temporal Sentinel-2 images and ICESat-2 data, a multi-temporal 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 multi-temporal Sentinel-2 images can achieve a mean 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.

A Cross-Sensor-Based Approach to Estimate Depth Values in Nearshore Coastal Waters, Case Study: Nayband Bay, Persian Gulf

“…Moreover, it can be concluded that integration of harmonised L8/OLI and S2A/MSI imagers may minimize the errors which are usually come from atmospheric interferences, and consequently may increase the final estimated depth values. However, many studies showed that, in comparison with single image, the use of multitemporal Sentinel-2 images may improve the estimated bathymetric data (Evagorou et al, 2019;Xu et al, 2021;Lumban-Gaol et al, 2022). It should be noted that Landsat-9 satellite has launched on September 2021with the same OLI imager and an 8-day time interval with Landsa-8 satellite.…”

Satellite-Derived Bathymetry of an Inland Reservoir in India

“…Most of the above mentioned models for SDB are supervised learning based models, where the models are trained to estimate depth of water columns from satellite data/images using certain ground truth data. The works use depth information collected from acoustic systems [11]- [13], [15], [16], [20]- [23], [31]- [35], airborne LiDAR systems [10], [11], [14], [17], [19], [26], [28], [36]- [38] or LiDAR data obtained from Ice, Cloud, and Elevation Satellite-2 (ICESat-2) satellites [18], [29], [30], as ground truth data. The study areas of interest in most of the above works belong to coastal regions [10]- [12], [14], [15], [17]- [19], [21], [28], [32], [34], [37], [39] and only a small number of works study SDB for inland water bodies [13], [16], [38].…”

“…The works use depth information collected from acoustic systems [11]- [13], [15], [16], [20]- [23], [31]- [35], airborne LiDAR systems [10], [11], [14], [17], [19], [26], [28], [36]- [38] or LiDAR data obtained from Ice, Cloud, and Elevation Satellite-2 (ICESat-2) satellites [18], [29], [30], as ground truth data. The study areas of interest in most of the above works belong to coastal regions [10]- [12], [14], [15], [17]- [19], [21], [28], [32], [34], [37], [39] and only a small number of works study SDB for inland water bodies [13], [16], [38]. As mentioned, most of the above works consider the reflectance values collected by satellites as input for estimating depth of water columns.…”

Assessing the impact of cirrus cloud thickness on ICESat-2's maximum bathymetric capabilities in coastal waters

“…1 It provides the foundation for analyzing shoreline changes and deepens our understanding of the impact of climate change phenomena such as global warming, sea-level rise, ocean acidification, and pollution on nearshore environments. 2,3 High-precision bathymetric data offer invaluable information for coastal geomorphological analysis, navigational safety, sediment studies, fisheries, mineral exploration, coastal engineering, and resource conservation. [4][5][6] While traditional shipborne multibeam echo sounders perform well in deeper waters, they pose higher risks in shallow waters with corals and rocks.…”