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With the development of photon-counting sensors, spaceborne photon-counting lidars have shown many advantages in mapping underwater topography. Although a space based lidar is normally a profiling system, the depth penetration and the vertical accuracy achieved with a bathymetric lidar is superior to imagery (that only provides relative depths). Therefore, many satellite derived bathymetry products use both active/passive spaceborne data to achieve spatial coverage as well as absolute depth measurements. Compared with shipborne and airborne bathymetric systems, satellite derived bathymetry data can have a global coverage, especially covering some remote areas where airborne/shipborne systems are hard or very expensive to reach. In this paper, a theoretical model is proposed to quantitatively analyze the maximum bathymetric depth of a satellite photon-counting lidar, which considers the system parameters, environmental effects, and the constraint of the SNR (signal to noise ratio). System parameters of ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) as well as the MODIS (Moderate-resolution Imaging Spectroradiometer) and NCEP (National Centers for Environmental Prediction) datasets are used to provide systematic and environmental inputs to the model, and the ICESat-2 actual bathymetric data are used to verify the maximum depth estimated by the proposed model. In six different sites with different water qualities, solar angles and wind speeds, the theoretical maximum bathymetric depths agree well with the actually achieved ICESat-2 lidar depths, in terms of MAE (mean absolute error) of 0.50 m and the RMSE (root mean square error) of 0.60 m. The errors in all study areas are within 14% of the corresponding maximum depths. Additionally, the non-linear response of a photon-counting lidar, independent parameter impacts on the theoretical model, and the errors in the experimental process were quantitatively analyzed. In the future, this theoretical model can be used to evaluate the maximum bathymetric depth for a bathymetry investigation when applying system parameters and environmental parameters. In addition, it can be used to optimize the hardware parameters of a photon-counting lidar in its early design process. [ABSTRACT FROM AUTHOR]

Accurate bathymetric data is essential for marine, coastal ecosystems, and related studies. In the past decades, a lot of studies were investigated to obtain bathymetric data in shallow waters using satellite remotely sensed data. Satellite multispectral imagery has been widely used to estimate shallow water depths based on empirical models and physics-based models. However, the in-situ water depth information is essential (as the priori) to use the empirical model in a specific area, which limits its application, especially for remote reefs. In this study, the bathymetric maps in shallow waters were produced based on empirical models with only satellite remotely sensed data (i.e., the new ICESat-2 bathymetric points and Sentinel-2 multispectral imagery). The bathymetric points from the spaceborne ICESat-2 lidar were used in place of the in-situ auxiliary bathymetric points to train the classical empirical models (i.e., the linear model and the band ratio model). The bathymetric points were firstly extracted from noisy ICESat-2 raw data photons by an improved point cloud processing algorithm, and then were corrected for bathymetric errors (which were caused by the refraction effect in the water column, the refraction effect on the water surface, and the fluctuation effect on the water surface). With the trained empirical models and Sentinel-2 multispectral images, the bathymetric maps were produced for Yongle Atoll, in the South China Sea and the lagoon near Acklins Island and Long Cay, to the southeast of Bahama with four-date Sentinel-2 images. The bathymetry performance (including the accuracy and consistency of multi-date data) was evaluated and compared with the in-situ measurements. The results indicate that the bathymetric accuracy is well, and the RMSE is lower or close to 10% of the maximum depth for the two models with four-date images in two study areas. The consistency of multi-date data is well with the mean R2 of 0.97. The main novelties of this study are that the accuracy bathymetric points can be obtained from the ICESat-2 raw data using the proposed signal processing and error correction method, and using the ICESat-2 bathymetric points, the satellite multispectral imagery based on empirical models is no longer limited by local priori measurements, which were essential in previous studies. Hence, In the future, with the help of free and open-access satellite data (i.e., ICESat-2 data and Sentinel-2 imagery), this approach can be extended to a larger scale to obtain bathymetric maps in the shallow water of coastal areas, surroundings of islands and reefs, and inland waters. • Estimating bathymetric topography with only satellite remotely sensed data. • Using new ICESat-2 lidar points and Sentinel-2 multispectral imageries. • Proposing signal detection and bathymetric error correction method for ICESat-2. • Training empirical models by ICESat-2 bathymetric points to estimate water depths. • Drawing and validating bathymetry in two study areas with multi-date datasets. [ABSTRACT FROM AUTHOR]