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 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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.94) by comparing with in-situ airborne lidar data around Ganquan Island, which is better than the result (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 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.

Highlights

  • I N MARINE and coastal zones on the Earth’s surface, shallow water serves as crucial habitats for ecosystems to maintain the sustainability and biodiversity [1]

  • The basic step of our method is to derive bathymetry using the linear regression model based on a single Sentinel-2 image and ICESat-2 bathymetric points

  • A multitemporal stacking method was applied to derive more accurate shallow water bathymetry based on multitemporal bathymetry maps

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Summary

Introduction

I N MARINE and coastal zones on the Earth’s surface, shallow water serves as crucial habitats for ecosystems to maintain the sustainability and biodiversity [1]. The underwater bathymetry is a fundamental parameter to reflect the property of shallow water [2]. Shallow water bathymetric data were normally collected by ship-based single-beam/multibeam echo sounders and airborne bathymetric lidars [4], [5]. These bathymetric survey approaches face some challenges in specific applications. Ship-based or airborne bathymetry is challenging at large scales due to the high costs

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