| dc.description.abstract | Bathymetric map is crucial for various applications, such as ocean related research and navigation safety. However, retrieving accurate water depths is always a challenging task. Traditionally, the water depth is measured by shipborne sonar system, but shallow waters are difficult for ships to access and this method is also constrained by its limited swath. While advanced airborne LiDAR system can measure wider than sonar of shallow water, however, it takes huge costs and has to consider air traffic issues. Recently, a spaceborne LiDAR sensor onboard ICEsat-2 can also provide some water depth measurement, but the spatial resolution is relatively low. Therefore, using optical satellite imagery to derive water depth becomes a potential alternate way. Satellite image offers periodic and wide coverage with lower costs, which can overcome the limitations of the other methods.
Deriving water depth from satellite imagery is not a straightforward task due to the complicated nonlinear system between factors such as water depth, water quality, and seafloor type. Based on the complexity, machine learning (ML)-based models have demonstrated effective capabilities. In this study, three models, NN (Neural Network), APMLP (Adjacent-Pixel Multilayer Perceptron), and CNN (Convolutional Neural Network), were adopted to estimate water depth in the Dongsha Atoll. The 2 datasets are integrated from Sentinel-2 and PlanetScope satellite imageries with the ground truth obtained by LiDAR measurement. Then these 3 models were trained by these datasets separately and analyzed their results. Additionally, we investigated the impact of amount of training data and the number of hidden layers on model performances.
The experimental results showed that the NN had the largest errors among them, and APMLP outperformed the other models when it was configured with multiple hidden layers (MAE = 0.78 m; RMSE = 1.57 m). Furthermore, this study investigated the feature importance to assess the influence of each spectral bands on the trained models. Regardless of the satellite imagery used, the results indicated that all models identified the green band as the most crucial feature for depth retrieval. The behavior is consistent with the optical property of shallow sea water, Despite the blue light has the strongest penetrating ability, it is easily scattered by the atmosphere, therefore, the green band will be the easiest to penetrate to the bottom of water. This emphasize the reliability and accuracy of the models′ estimations. | en_US |