ICESat-2 Bathymetric Signal Reconstruction Method Based on a Deep Learning Model with Active–Passive Data Fusion

Abstract

When carrying out SDB (satellite-derived bathymetry) in island area based on ICESat-2 (Ice, Cloud, and land Elevation Satellite 2) data, it is often found that the ICESat-2 bathymetric signals are partially missing due to the influence of thick aerosols such as clouds and fog. This not only hinders the accurate extraction of the along-track underwater topography, but also restricts the active–passive fusion bathymetry based on ICESat-2 data and multi/hyperspectral remote sensing images. In this paper, aiming at the partially missing ICESat-2 bathymetric signals, combined with passive optical remote sensing images, and based on an LSTM (long short-term memory) deep recurrent neural network model, an ICESat-2 bathymetric signal reconstruction method based on active–passive data fusion is proposed. It is found that this method can effectively reconstruct the local missing bathymetric signals. When the reconstructed ICESat-2 bathymetric data are applied to carry out active–passive fusion and bathymetric inversion, the accuracy indices are better than those of the inversion results of the data with partial missing signals, and the performance is comparable to that of the original data without missing data, which is of great value for the bathymetric application of ICESat-2 data in island and reef areas.