Satellite-derived sediment distribution mapping using ICESat-2 and SuperDove

Nearshore sediment distribution is a significant component of fundamental geographic data, but the inherent optical properties of the water column make the large-scale observation of sediment particularly difficult. The development of satellite-based imaging sensors has enabled earth observation to capture more types and finer granularities of ground information, making it possible to obtain the target sediment distribution on a large scale. We use the proposed wavelet clustering algorithm to separate noise points and signal photons from ICESat-2 and then combine multispectral images and a quasi-analytical algorithm to calculate substrate reflectance to reduce the influence of the inherent properties of the water body. Furthermore, a new deep learning model, TSDANet, is being built to combine substrate reflectance and multispectral image data from SuperDove for nearshore sediment identification. We use field data to validate the method through a large number of experiments. The experiments demonstrate that the wavelet clustering algorithm has a coefficient of determination (R2) of 0.90 and 0.95 for signal point extraction over a large scale of high and low photon densities compared with a digital elevation model, respectively. Compared to single-source data, the TSDANet sediment classification kappa improves by 6.44% on average and 8.28% at its maximum. The results indicate that the identification and classification of sediment using ICESat-2 and SuperDove data is a new and highly feasible method.

Automated summary

This study develops a method based on satellite remote sensing and deep learning to identify nearshore sediments using multispectral images and a quasi-analytical algorithm. The experiments demonstrate that the proposed method achieves high accuracy in signal point extraction and sediment classification.