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Since turbid waters make it extremely difficult for light to penetrate the water column due to attenuation, performing satellite‐derived bathymetry using spectral images and/or photon‐counting lidars is a very challenging problem in turbid waters. The photon‐counting lidar borne on Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2) can obtain high‐resolution and accurate height estimations of sea surfaces, that do not depend on the water clarity. Using the ocean surface waves calculated from ICESat‐2 and classical wave theory that models the relationship between the water depth and wavelength, we propose a method to estimate the depths in opaque waters. The performance is validated in different water qualities with a mean absolute percent error of 9%∼22%. The basic theory of estimating water depths in this study is entirely different from that of conventional laser bathymetry, and this method has the potential to fill the gap of space‐based bathymetric data sets in turbid waters. Plain Language Summary: In clear waters, optical instruments such as lidar and spectral cameras are the most used satellite‐based sensors to obtain water depths. These instruments can measure water depths by receiving the reflected light signal from the seafloor. However, as turbid waters make it extremely difficult for light to penetrate the water column, the seafloor is almost undetectable in turbid waters. Alternatively, the lidar on the Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2) satellite provides unprecedented dense two‐dimensional geolocated points on the water surface, from which ocean wave characteristics can be precisely calculated. When ocean waves approach coastal areas, their wavelengths decrease as water depths decrease. The phenomenon of wavelength changes can be used to derive water depths in coastal areas, even operating in opaque waters. In this study, we investigate the technical process of how ICESat‐2 obtains high‐resolution ocean waves that propagate and evolve from the oceans to the coasts and the basic theory of estimating water depths by observing wavelength changes. With global‐scale ICESat‐2 data, this new method can be used to obtain the near‐shore water depth regardless of the water quality and can also be expanded to understand the wave characteristics in many specific coastal areas or even the globe. Key Points: A method to obtain near‐shore water depths even in opaque waters is proposed using Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2) lidar dataICESat‐2 can observe high‐resolution ocean waves that propagate and evolve from the oceans to the coastsThe linear wave theory is modeled to estimate water depths by observing wavelength changes from ICESat‐2 [ABSTRACT FROM AUTHOR]

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]

This contribution identifies the uncertainty sources influencing the component uncertainties for an airborne LiDAR bathymetry (ALB) measurement and presents the models for various component uncertainties (arising from the device, environment, and target for ALBs). Since various instrumental and environmental factors introduce vertical and horizontal uncertainties in ALB data, these uncertainties should be first analyzed and then precisely modeled to ensure the accuracy of the measurements. For this purpose, ten different effects that influence the accuracy of ALB data are systematically analyzed and modeled for four aspects in this article: the device aspect (laser pointing deflection, trajectory uncertainty, and boresight/lever arm offset), environmental aspect (atmospheric limitation, refraction on the sea surface, refraction in water, scattering in water, and water level fluctuation), target aspect (irregular bottom), and other aspect (accuracy of coordinate transformation model). In addition, the effect of the laser spot size is also discussed. To verify the presented uncertainty models, an ALB survey was operated around Yuanzhi Island in the South China Sea. For a water depth of 10 m, the theoretical overall root-sum-squares (RSS) for ten different effects of an ALB system (approximately 34 cm calculated using the total vertical uncertainty (TVU) models) is generally in accordance with the actual performance of the ALB data (approximately 39 cm) performance. The difference is mainly attributed to the limited accuracy of the ground truth data, and the difference between the water depth and laser ranging is reasonable. In this process, the topography data in the same region captured by a shipborne multibeam echo sounder (MBES) were used as the ground truth. The results indicated that for the typical ALB system, the laser pointing uncertainty and refraction uncertainty on the sea surface are primary uncertainty sources and should be corrected in a higher priority to meet the seafloor topographic accuracy demand of the International Hydrographic Organization (IHO) Standards for Hydrographic Surveys (S-44). The proposed uncertainty models can be used not only to guide the actual measurement of an ALB system but also to provide the uncertainty correction reference for ALB data postprocessing. [ABSTRACT FROM AUTHOR]

With new Ice, Cloud, and land Elevation Satellite (ICESat)-2 lidar (Light detection and ranging) datasets and classical Landsat imagery, a method was proposed to monitor annual changes of lake water levels and volumes for 35 years dated back to 1980s. Based on the proposed method, the annual water levels and volumes of Lake Mead in the USA over 1984–2018 were obtained using only two-year measurements of the ICESat-2 altimetry datasets and all available Landsat observations from 1984 to 2018. During the study period, the estimated annual water levels of Lake Mead agreed well with the in situ measurements, i.e., the R2 and RMSE (Root-mean-square error) were 1.00 and 1.06 m, respectively, and the change rates of lake water levels calculated by our method and the in situ data were −1.36 km3/year and −1.29 km3/year, respectively. The annual water volumes of Lake Mead also agreed well with in situ measurements, i.e., the R2 and RMSE were 1.00 and 0.36 km3, respectively, and the change rates of lake water volumes calculated by our method and in situ data were −0.57 km3/year and −0.58 km3/year, respectively. We found that the ICESat-2 exhibits a great potential to accurately characterize the Earth's surface topography and can capture signal photons reflected from underwater bottoms up to approximately 10 m in Lake Mead. Using the ICESat-2 datasets with a global coverage and our method, accurately monitoring changes of annual water levels/volumes of lakes—which have good water qualities and experienced significant water level changes—is no longer limited by the time span of the available satellite altimetry datasets, and is potentially achievable over a long-term period. [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]

Currently, in-situ data and the time duration of altimeter data are limitations in calculating the water level and water volume of lakes and reservoirs from remotely sensed data. A novel method is proposed to estimate the temporal change in water levels and water volumes for lakes with only remotely sensed data. First, the surface profiles, including the ground and the underwater bottom, were extracted from the MABEL (Multiple Altimeter Beam Experimental Lidar) photon-counting lidar raw data via a new surface detecting algorithm. Second, the lake boundaries between land and water in different years were identified using a thresholding method based on the annual median Landsat composite. Third, water levels were calculated by matching the lidar surface profiles with the lake boundaries based on the nearby georeferenced coordinates. Finally, the water volumes in different years were estimated via the contours (i.e., lake boundaries) with different elevations. Lake Mead was selected as the study area, which is the largest reservoir in the United States in terms of water capacity. With only one day measuring lidar points in February 2012 and over 20 years of Landsat images (from 1987 to 2007), the water levels and water volumes in different years were estimated and compared with the in-situ data. Our results performed well in accordance with the in-situ measurements; the R-square of the water levels and water volumes were both over 0.99; the RMSE of the interannual variations of water levels and water volumes were 0.96 m and 0.31 km3, respectively. The MABEL was used as a technology demonstrator for the satellite photon-counting laser altimeter and had similar data to the ICESat-2 dataset. Future ICESat-2 datasets will broaden this method to estimate water volumes for remote lakes from the 1980s, where no in-situ data are available (such as the Tibetan Plateau and polar regions with thousands of remote and wild lakes), which could not be achieved in previous studies. • A new method to estimate water volumes for lakes with only remotely sensed data • Mapped water extents and lake boundaries using annual Landsat composite • Extracted profile of over and underwater surface from photon-counting lidar data • Obtained water levels by matching lidar profiles with the lake boundaries • Estimated water volumes of Lake Mead over 1987–2007 and verified by in-situ data [ABSTRACT FROM AUTHOR]

Airborne or space-borne photon-counting lidar can provide successive photon clouds of the Earth's surface. The distribution and density of signal photons are very different because different land cover types have different surface profiles and reflectance, especially in coastal areas where the land cover types are various and complex. A new adaptive signal photon detection method is proposed to extract the signal photons for different land cover types from the raw photons captured by the MABEL (Multiple Altimeter Beam Experimental Lidar) photon-counting lidar in coastal areas. First, the surface types with 30 m resolution are obtained via matching the geographic coordinates of the MABEL trajectory with the NLCD (National Land Cover Database) datasets. Second, in each along-track segment with a specific land cover type, an improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm with adaptive thresholds and a JONSWAP (Joint North Sea Wave Project) wave algorithm is proposed and integrated to detect signal photons on different surface types. The result in Pamlico Sound indicates that this new method can effectively detect signal photons and successfully eliminate noise photons below the water level, whereas the MABEL result failed to extract the signal photons in vegetation segments and failed to discard the after-pulsing noise photons. In the Atlantic Ocean and Pamlico Sound, the errors of the RMS (Root Mean Square) wave height between our result and in-situ result are −0.06 m and 0.00 m, respectively. However, between the MABEL and in-situ result, the errors are −0.44 m and −0.37 m, respectively. The mean vegetation height between the East Lake and Pamlico Sound was also calculated as 15.17 m using the detecting signal photons from our method, which agrees well with the results (15.56 m) from the GFCH (Global Forest Canopy Height) dataset. Overall, for different land cover types in coastal areas, our study indicates that the proposed method can significantly improve the performance of the signal photon detection for photon-counting lidar data, and the detected signal photons can further obtain the water levels and vegetation heights. The proposed approach can also be extended for ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) datasets in the future. [ABSTRACT FROM AUTHOR]

Airborne light detection and ranging (LiDAR) full waveforms and multibeam echo sounding (MBES) backscatter data contain rich information about seafloor features and are important data sources representing seafloor topography and geomorphology. Currently, to classify seafloor types using MBES, curve features are extracted from backscatter angle responses or grayscale, and texture features are extracted from backscatter images based on gray level co-occurrence matrix (GLCM). To classify seafloor types using LiDAR, waveform features are extracted from bottom returns. This paper comprehensively considers the features of both LiDAR waveforms and MBES backscatter images that include the eight feature factors of the LiDAR full waveforms (amplitude, peak location, full width half maximum (FWHM), skewness, kurtosis, area, distance, and cross-section) and the eight feature factors of MBES backscatter images (mean, standard deviation (STD), entropy, homogeneity, contrast, angular second moment (ASM), correlation, and dissimilarity). Based on a support vector machine (SVM) algorithm with different kernel functions and penalty factors, a new seafloor classification method that merges multiple features is proposed for a beneficial exploration of acousto-optic fusion. The experimental results of the seafloor classification around Yuanzhi Island in the South China Sea indicate that, when LiDAR waveform features are merged (using an Optech Aquarius system) with MBES backscatter image features (using a Sonic 2024) to classify three types of sands, reefs, and rocks, the overall accuracy is improved to 96.71%, and the kappa reaches 0.94. After merging multiple features, the classification accuracies of the SVM, genetic algorithm SVM (GA-SVM) and particle swarm optimization SVM (PSO-SVM) increase by an average of 9.06%, 3.60%, and 2.75%, respectively. [ABSTRACT FROM AUTHOR]