Cost-efficient bathymetric mapping method based on massive active–passive remote sensing data

Abstract

Accurate bathymetric mapping is critical for island reef research, coastal ecosystem monitoring, and nearshore engineering. Increasing amounts of remote sensing data have promoted the development of optical bathymetric remote sensing. However, large-scale and efficient bathymetric mapping of shallow oceanic islands is difficult due to the limited availability of high-quality optical remote sensing images. In this study, we propose a cost-efficient method for bathymetric mapping based on a quadratic polynomial ratio model (QPRM) of massive active–passive remote sensing data. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data represent active data and a priori data, and Sentinel-2 data from the Google Earth Engine represent passive data. The key step is to build a QPRM based on the median value of the blue-green band logarithmic ratio from massive Sentinel-2 data. The method is applied to six typical areas located in the Pacific Ocean, Indian Ocean, and South China Sea, and the results showed that the root mean square error (RMSE) and mean absolute error (MAE) of bathymetric mapping in the water depth range of 0–20 m were 0.49–0.71 m and 0.29–0.49 m, respectively. In addition, the RMSE decreased with an increase in the number of Sentinel-2 images, and the results were relatively stable when the number reached approximately 150. The QPRM results were also compared with that of the classical linear ratio model (CLRM) and multiple fitting methods to calculate the median water depth, and the findings showed that the QPRM method has a slight advantage in terms of accuracy and efficiency. The proposed method, which does not depend on in situ data, effectively calculates precise water depths and removes the effects of complex underwater optical paths and surface instabilities, such as waves, boats, and clouds. Therefore, it has great potential for bathymetric mapping of oceanic islands and reefs worldwide.