Machine Learning Based Estimation of Coastal Bathymetry From ICESat-2 and Sentinel-2 Data

Satellite technology is an efficient tool, which can provide valuable observations for coastal areas from space. Compared with conventional bathymetric surveying approaches, remote sensing-based shallow water bathymetry retrieval methods have been widely used in recent years. Various empirical models have been proposed for deriving bathymetry of coastal shallow water, and prior topographic information is required to construct models. Traditional studies tend to select a cloud-free remote sensing image to map the coastal shallow water topography. As a result, in addition to the selection of empirical models, the high-quality remote sensing image and accurate prior topographic data are also of importance. This study aims to propose a method for mapping coastal shallow water bathymetry from multitemporal remote sensing imagery. Here, Sentinel-2 imagery time series are composited to produce a clear image, which can effectively avoid the contamination of clouds, water turbidity and other noises. ICESat-2 lidar altimeter data that contain accurate underwater elevations are used to provide topographic information. Moreover, Sentinel-2-based multispectral information and ICESat-2-based topographic information are combined for the coastal bathymetry retrieval by five empirical models (i.e., linear band model, ratio band model, support vector machine, neural network, and random forest). This proposed method is tested in Dongsha Atoll in South China Sea, and achieve a good performance [training: root mean square error (RMSE): 0.97 m +/- 0.76 m, mean absolute percentage error (MAPE): 4.07% +/- 0.046%, R-square (R-2): 0.90 +/- 0.14; validation: RMSE: 1.22 m +/- 0.43 m, MAPE: 5.43% +/- 0.035%, R-2: 0.86 +/- 0.089]. The comparison confirms that machine learning methods perform better than traditional methods, and the deep learning techniques can be further introduced in estimating shallow water bathymetry in the future, which is expected to achieve an excellent accuracy in bathymetry inversion.