Improved Filtering of ICESat-2 Lidar Data for Nearshore Bathymetry Estimation Using Sentinel-2 Imagery

Abstract

The accurate estimation of nearshore bathymetry is necessary for multiple aspects of coastal research and practices. The traditional shipborne single-beam/multi-beam echo sounders and Airborne Lidar bathymetry (ALB) have a high cost, are inefficient, and have sparse coverage. The Satellite-derived bathymetry (SDB) method has been proven to be a promising tool in obtaining bathymetric data in shallow water. However, current empirical SDB methods for multispectral imagery data usually rely on in situ depths as control points, severely limiting their spatial application. This study proposed a satellite-derived bathymetry method without requiring a priori in situ data by merging active and passive remote sensing (SDB-AP). It realizes rapid bathymetric mapping with only satellite remotely sensed data, which greatly extends the spatial coverage and temporal scale. First, seafloor photons were detected from the ICESat-2 raw photons based on an improved adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which could calculate the optimal detection parameters for seafloor photons by adaptive iteration. Then, the bathymetry of the detected seafloor photons was corrected because of the refraction that occurs at the air–water interface. Afterward, the outlier photons were removed by an outlier-removal algorithm to improve the retrieval accuracy. Subsequently, the high spatial resolution (0.7 m) ICESat-2 derived bathymetry data were gridded to match the Sentinel-2 data with a lower spatial resolution (10 m). All of the ICESate-2 gridded data were randomly separated into two parts: 80% were employed to train the empirical bathymetric model, and the remaining 20% were used to quantify the inversion accuracy. Finally, after merging the ICESat-2 data and Sentinel-2 multispectral images, the bathymetric maps over St. Thomas of the United States Virgin Islands, Acklins Island in the Bahamas, and Huaguang Reef in the South China Sea were produced. The ICESat-2-derived results were compared against in situ data over the St. Thomas area. The results showed that the estimated bathymetry reached excellent inversion accuracy and the corresponding RMSE was 0.68 m. In addition, the RMSEs between the SDB-AP estimated depths and the ICESat-2 bathymetry results of St. Thomas, Acklins Island, and Huaguang Reef were 0.96 m, 0.91 m, and 0.94 m, respectively. Overall, the above results indicate that the SDB-AP method is effective and feasible for different shallow water regions. It has great potential for large-scale and long-term nearshore bathymetry in the future.

Investigating the Shallow-Water Bathymetric Capability of Zhuhai-1 Spaceborne Hyperspectral Images Based on ICESat-2 Data and Empirical Approaches: A Case Study in the South China Sea

“…Moreover, the validation results of SDB models using blue and green bands of Zhuhai-1 (ZBR2, 9, ZLB3, 9, and ZLB3, 9, 12) image showed a similar accuracy to that of Sentinel-2 image (SBR2, 3, SLB2, 3, and SLB2, 3, 4), which further confirmed The most commonly used band-combination strategy for shallow-water bathymetry may be the employment of blue and green bands, of which principle and potential have been revealed in previous research [52]. The blue and green bands of Sentinel-2 image have been widely and successfully used for shallow-water bathymetry [53]. In this study, relative high correlations were observed for the blue and green bands between Zhuhai-1 and Sentinel-2 (R 2 > 0.7, Figure 13).…”

“…The most commonly used band-combination strategy for shallow-water bathymetry may be the employment of blue and green bands, of which principle and potential have been revealed in previous research [52]. The blue and green bands of Sentinel-2 image have been widely and successfully used for shallow-water bathymetry [53]. In this study, relative high correlations were observed for the blue and green bands between Zhuhai-1 and Sentinel-2 (R 2 > 0.7, Figure 13).…”

Satellite-derived bathymetry combined with Sentinel-2 and ICESat-2 datasets using machine learning

“…Despite the accuracy of existing DBSCAN methods, it is still very difficult to determine the values of key algorithm parameters due to the number of raw photons and the complexity of the underwater terrain. In our previous study, we proposed an adaptive ellipse DBSCAN (AE-DBSCAN) algorithm for ICESat-2 photon data processing (Xie et al, 2021). AE-DBSCAN can calculate the optimal detection parameters by adaptive iteration.…”

Use of ICEsat-2 and Sentinel-2 Open Data for the Derivation of Bathymetry in Shallow Waters: Case Studies in Sardinia and in the Venice Lagoon

“…Based on these assumptions and considering the increasing need for continuous and large-scale monitoring of environmental evolution along coastal belts, remote sensing (RS) offers a practical alternative for the extraction of depth information and the achievement of fast, repetitive, and low-cost bathymetry. In particular, the application of satellite-derived bathymetry (SDB) has achieved widespread adoption [10][11][12][13][14][15][16][17][18][19][20][21][22]. To this end, the availability of Copernicus (https://www.copernicus.eu/en, accessed on 30 May 2023) open data and their integration with other spatial data, which has opened up new horizons for downstream satellite applications [23], offers important benefits.…”

“…In particular, the SDB literature mentions two approaches: (i) the physically based one, which relies on the physics of the exponential attenuation of light with depth in the water column and its reflection from either the water column or the seabed [17,18]; (ii) the empirical approach, without considering spectral, radiometric, and environmental parameters. The latter derives seabed estimates based on the statistical similarity of spectral signatures defined within different classes by defining specific training vectors [19,20] and relies on bathymetric data measured in situ to calibrate satellite images and validate the outcomes; hence, the SDB depth accuracy is limited in the absence of in situ depths [21].…”