Accurate terrain slope and aspect estimation is essential for landscape geomorphology. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its high-density sampling in space, centimeter-level accuracy in elevation, and repeated reference ground track (RGT) mechanism, enables high-precision terrain parameter calculation. In this study, we propose a method for estimating terrain slope and aspect by integrating multi-input information of the along-track, cross-track, and pulse width information. A spatial proximity-based weighting algorithm is employed to optimize the integration. Validations with local digital elevation models (DEMs) are conducted in four mountainous regions across North America. Experimental results indicate that the proposed method achieved the best RMSEs in slope (3.8°) and aspect (10.9°) estimations compared with current approaches. Overall, our method confirms the effectiveness and robustness of integrating multi-input information, providing a reliable methodological basis for high-precision terrain parameter retrieval in future applications of multi-beam photon-counting lidars.

The diffuse attenuation coefficient$K_{d}$continues to play a crucial role in oceanographic research works. Recently, Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has shown its great ability to estimate$K_{d}$using the water column decay profiles. However, the weak water column backscattered signals are vulnerable to afterpulses and solar background noise, making this way perform not well in the daytime and in nearshore areas. In this study, a method to estimate$K_{d}$is proposed which innovatively uses ICESat-2 bathymetric signal intensities. The main principle is to calculate the attenuation in water column transmission by bathymetric lidar equations. Since the seafloor signal level is much stronger than that of the water column, a significant advantage is the greater noise immunity, i.e., the ability to operate under strong background noise and afterpulses interference. The performance is validated against the moderate-resolution imaging spectroradiometer (MODIS) ocean color measurements with mean relative differences (MRDs) of <32% using both daytime and nighttime ICESat-2 data in six sea and large lake nearshore areas. Based on the new generation of spaceborne lidar data, this study explores a new path to monitor water qualities in nearshore areas. This method is applicable where seafloor photons exist in both daytime and nighttime.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) carries the new generation spaceborne photon-counting lidar, Advanced Topographic Laser Altimeter System (ATLAS). ICESat-2/ATLAS has an excellent performance for obtaining precise geometric surface profiles of land and oceans, by which the surface parameters such as the significant wave height (SWH) over oceans can be further obtained. As the strong beams have better data quality, they are currently used to obtain the sea surface parameters. The weak beams could double the spatial coverage area if they can also be successfully used. However, this potential is constrained by the lower signal-to-noise ratio (SNR) of weak beams. To exploit the performance of weak beams, this study proposes a method to extract sea surface signal photons, which are further accumulated to calculate the SWHs. This study explores the effect of the data processing window length on the result of the denoising algorithm and how many sea surface signal photons should be accumulated to estimate the reliable SWHs with ICESat-2 weak beams. The calculated SWH shows good agreement with ECMWF reanalysis 5 (ERA5) data, with the root mean square error (RMSE) under 0.3 m. The method proposed in this study enables the acquisition of SWH values in certain regions where no ATL12 official data are available.

The recent capabilities of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) photon counting lidar in monitoring water levels have been demonstrated through its precise elevation measurement and small footprint. The accuracy in water level measurements is, however, significantly impacted by the first photon bias, especially when photon-counting detectors are fully saturated due to particular reflections from calm water surfaces. In this study, we propose an analytical model to correct the first photon bias in scenarios where the ICESat-2/Advanced Topographic Altimeter System (ATLAS) is fully saturated. Notably, the model innovatively recovers and estimates the required signal level using after-pulses, which are typically considered as noise. These after-pulses can be used to effectively estimate the signal level when the detector is fully saturated. The experiment analysis, conducted on eight ICESat-2 ground tracks over calm water surfaces near the Great Lakes and the Tibetan Plateau, indicates that the actual received signal photons can surpass 200 and in some cases, reach up to 600 counts for strong beams, introducing a first photon bias exceeding 15 cm. The findings prove that 1) after-pulses can be used to retrieve water surface elevation and reflectance when the primary surface return is distorted by detector saturation and 2) calm waters reflect 5–40 times more than ice and snow surfaces, where first photon bias is a predominant error in water level measurements. The method holds great significance for the accurate monitoring of water levels in small inland water bodies using ICESat-2 and may also inform the design of lidar systems for inland water observations.

Photon-counting laser altimeter is an advanced remote sensing observation equipment, which provides detailed surface profile information, exemplified by the advanced topographic laser altimeter system (ATLAS) on Ice, Cloud, and land Elevation Satellite-2 (ICESat-2). However, the high sensitivity of a photon-counting laser altimeter introduces noisy geolocated photons, posing a tremendous challenge in signal extraction from noise photons with low signal-to-noise ratios (SNRs). An efficient signal extraction algorithm is critical for further applications of ICESat-2 data, and the spatial density-based algorithms perform well and have been verified in various scenarios, e.g., canopy and ground detection, sea-ice freeboard detection, and bathymetry. Currently, the geometric parameters in density-based algorithms are usually empirically determined, and the main challenge is to adaptively set the optimal geometric parameters in variable scenarios. In this study, a theoretical mapping model that correlates the performance metrics (e.g., number of true positive, false positive, and false negative photons) with the algorithm parameters and the lidar system parameters is derived. The performance of this model is verified using Monte Carlo simulated data of bare lands and vegetated areas with$R^{2}$exceeding 0.99, and also verified using ICESat-2 data over land, ocean, vegetation, and ice areas with$R^{2}$exceeding 0.94. Based on the model, the signal extraction capacity in different SNRs, channel numbers, and signal durations are discussed, offering a theoretical foundation for determining the optimal parameters to extract ICESat-2 signal photons and also for better designing hardware parameters of photon-counting laser altimeters.

Accurate beach topography mapping is crucial for understanding coastal dynamics and mitigating climate change impacts. However, traditional methods such as airborne LiDAR have limitations, leading to substantial gaps in national-scale elevation data. This study presents an innovative framework to reconstruct missing elevation data along New Zealand’s coastline by integrating airborne LiDAR, Sentinel-2 optical imagery, and geometric features (distance) using machine learning methods. Our results show that Artificial Neural Network (ANN) emerged as the best model (test set: R²=0.79, RMSE=0.91 m; validation set: 0.79, RMSE=0.93 m), outperforming other models in accuracy. The produced 10-m DEM for national-scale sandy beaches expands area coverage by 286.6% (114.15 km²), filling gaps in 1249 beaches, including remote areas such as Stewart Island. This novel framework offers a scalable solution for improving the comprehensiveness and accuracy of beach topography. It provides essential support for inundation prediction, habitat management, and the development of climate adaptation strategies, thereby facilitating more informed decision-making in coastal zone management and climate change mitigation efforts.

Atmospheric turbulence is a significant factor that affects the radiometry of spaceborne laser pulses. Depending on the meteorological data from the National Centers for Environmental Prediction (NCEP) dataset and phase screens simulated from fractal interpolation method, the optical fields of a transmitted laser pulse propagating through the turbulence are modeled in this study. By calculating the ratio of the received energy with and without turbulence, an energy index is introduced to quantitatively evaluate the turbulence impact. Taking the ICESat-2 Lidar as an example, the distributions of the energy index at three simulated areas with weak, moderate, and strong turbulence are investigated. The results indicate that the means of energy index reaches 0.90 for moderate and weak turbulences, which corresponds to 10% laser energy loss, but would decrease to 0.70 for strong turbulence corresponding to 30% laser energy loss. It implies that the strong turbulence impact should be compensated for the radiometric correction of laser pulses. In addition, the proposed method is validated by comparing the energy index and the atmospheric transmittance derived from the ATL09 data over three areas with different surface types. The mean absolute percentage errors (MAPEs) are below 5% and the root mean square errors (RMSEs) are less than 0.05, which proves that our proposed method is effective for simulating the influence of atmospheric turbulence on the radiometry of spaceborne laser pulses.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) that carries the Advanced Topographic Laser Altimeter System (ATLAS) photon-counting LiDAR (PCL) provides an astonishing opportunity for Earth observations. The precise identification of PCL signal photons is the prerequisite for ICESat-2 data applications in Earth observations. However, due to the influence of variable topography and vegetation structures, denoising PCL photons remains a significant challenge, particularly in mountainous forest areas. The overall goal of this study is to develop an adaptive photon denoising method tailored for mountainous forest areas. Focusing on study areas with Tahoe National Forest and two simulated datasets, the specific goals are to: 1) setting the neighborhood size based on elevation frequency histogram (EFH) and wavelet transform criterion, which considers the effects of noise and vegetation structure on signal photons; 2) setting the neighborhood direction using detrend operation, which could fit the terrain curve more accurately; and 3) setting the discrimination threshold according to the constraints of the signal and noise rates, which could identify noise photons with complex vegetation structures. The results from ICESat-2 data demonstrate that the terrain elevation and relative height of 95% (RH95) derived using the proposed method exhibit superior performance, with the coefficient of determinations (CODs) of 1.00 and 0.62, respectively.

The spaceborne laser altimeter achieves a vertical accuracy of a few tens of centimeters in land elevation measurements. Adjusting the satellite stereo images with the constraint of laser altimetry data is an effective method in improving the accuracy of photogrammetric measurements. This study presents a new approach to adjust the stereo imagery using the ICESat-2 derived laser control points (LCPs) and achieve a better photogrammetric measurement in horizon. The building boundaries which have both distinct geometric features in ICESat-2 point data and texture features in stereo images are used. Comparing with the adjustment method that only considers the vertical constraints from laser altimetry data, the proposed method considers the effective horizontal and vertical constraints. Using WorldView-3 imagery in San Diego, USA and GaoFen-7 imagery in St. George, USA, the photogrammetric adjustment indicates ~47%/~37% accuracy improvements in horizon and ~27%/~38% accuracy improvements in elevation after adjustment, respectively.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is equipped with a new type of photon-counting laser altimetry system, demonstrating significant potential for global mapping. However, the ICESat-2 data contain a large amount of noise photons influenced by solar background, making data processing challenging. In this letter, we propose an end-to-end deep neural network called VOJA-Net, which utilizes semantic information from multilevel decoders to learn multilevel feature representations, thus enhancing denoising performance. First, we design a module called ICESat spatial transformer (IS Transformer), specifically for extracting spatial features from ICESat-2 data to deepen the network’s understanding of the spatial distribution differences between valid photons and noise. Second, we construct a joint attention fusion module named joint attention fusion (JA Fusion), which employs a multibranch attention mechanism to avoid the network from overlearning features of dense parts of the ICESat-2 point cloud, thereby enhancing the network’s ability to recognize sparse valid photons. Finally, we design a multiscale denoising loss (MSDLoss) function to guide network model training, promoting the network to achieve optimal denoising effects. On our carefully annotated ICESat-2 point cloud dataset, the final model achieved$F1$score and mIoU of 93.34% and 73.40%, respectively, demonstrating the competitiveness of the proposed approach.

Recently, an increasing number of studies have progressively explored the radiometric properties of ICE, Cloud, and land Elevation Satellite-2 (ICESat-2), enabling this satellite and its payload to provide support for not only geometric but also radiometric applications. However, due to potential changes in the optical throughput and electronics of the instrument during flight, the essential radiometric corrections are needed for quantitative radiometric applications. In this study, using ICESat-2 signal and noise data on icesheet and desert surfaces, which have a relatively stable reflectance and clear sky, a radiometric correction method is proposed to describe the changes in instrument parameters, i.e., to estimate how much the signal and noise should be exactly scaled. With instrument and environmental parameters provided by the ICESat-2 ATL04/ATL09 product, signal and noise models are used to calculate the theoretical signals and noise levels. The theoretical predictions are then compared with the actual measured signals and noise results by ICESat-2 to obtain the scale factors (or radiometric correction factors) for signal and noise, respectively. The results indicate that all three photon counting electronics (PCEs) (corresponding to three laser pairs) exhibit very close scale factor values (i.e., ~1.9), i.e., ICESat-2 receives both signal and noise nearly double the expected values. The noise scale factors$F_{\text {noise}}$over icesheet are expected to yield the most accurate scale factors because$F_{\text {noise}}$will change very little with and without layers. We also analyze the annual average radiometric drift of ICESat-2, which indicates the decreases of ~2.5% ($F_{\text {signal}}$) and ~2.2% ($F_{\text {noise}}$) from 2019 to 2022. This study demonstrates the consistency of signal and noise in radiation and also helps to understand the radiation closure and data fusion between the passive background noise and active laser signal for a spaceborne lidar.

The extraction of canopy heights from spaceborne lidar received waveforms over mountainous areas is a challenging task, as the waveform signal broadening and overlapping effects make the vegetation and ground returns difficult to identify. This study aims to obtain the deconvoluted waveform from the received waveform using a Richardson–Lucy deconvolution algorithm for better separation of the vegetation and ground returns from overlapped waveform components. Specifically, the canopy top elevation is extracted from the start bin of the deconvoluted waveform and the ground elevation is calculated as the elevation centroid of the ground return, which is determined by shuttle radar topography mission (SRTM) data and deconvoluted waveform. The canopy height is the difference between the canopy top and the ground elevations. Eight track data of the Global Ecosystem Dynamics Investigation (GEDI) lidar over the rugged Larkspur Mountain of Colorado are employed to validate the proposed method. The results demonstrate that the mean error (ME), mean absolute error (MAE), and root-mean-square (rms) error of derived canopy heights over rugged terrain with surface slopes more than 20° are greatly reduced by 86.7%, 23.2%, and 19.5% relative to GEDI canopy height products, respectively. The results prove that the proposed method is more applicable to derive canopy heights over rugged mountainous areas.

Recently, spaceborne active lidars relying on backscattered laser signal can observe thin clouds, but the laser beam cannot penetrate clouds with large optical thickness. The new generation photon-counting lidar on Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), whose noise can be treated as observations of a green band camera, provides an excellent opportunity to fuse active and passive information to retrieve cloud optical thickness (COT). Clouds significantly increase the background noise and sharply attenuate the signal returning from oceans, which makes it feasible to observe thin and thick clouds by combining active and passive information of ICESat-2. In this study, we first derive the passive background noise and active signal models over oceans for spaceborne lidars, which considers medium contributions from clouds, aerosols, air molecules, ocean surface, and subsurface. ICESat-2 measured surface signals and noise rates in open oceans of Western Pacific are used to verify the models with auxiliary environment datasets. The results indicate that the theoretical predictions have the mean absolute error (MAE) of less than 0.31 counts for signal and the mean absolute percentage error (MAPE) of less than 35% for noise. Then, based on these theoretical models, we propose a COT estimation method combining ICESat-2 active signal and passive noise data without extra auxiliary datasets, and the MAEs between ICESat-2 retrieved COTs and Himawari-8 (H8) cloud products are less than 1.2 (COTs range from 0 to exceeding 20) over open oceans. In general, the proposed method not only expands the observation range of retrieved COTs compared to methods solely relying on signal or noise data but also has great significance for assessing the availability of lidar surface signal, i.e., producing cloud mask.

The canopy cover is a fundamental parameter in forest inventory. The launch of Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) that carries the Advanced Topographic Laser Altimeter System (ATLAS) photon-counting lidar provides an astonishing opportunity to assess canopy covers at a large scale. Currently, the canopy covers were calculated as the proportion of vegetation photons to total signal photons using ICESat-2/ATLAS data without considering the radiometric distortion caused by photon-counting detectors and the surface reflectance of vegetation and ground. The overall goal of this study is to investigate a method to derive more accurate canopy covers considering the radiometric correction and surface reflectance correction with ICESat-2 photon data. With focusing on two study areas, Slaughter (SLAU) and Lenoir Landing (LENO) in USA, the specific purposes are to: 1) propose a radiometric correction model based on the lidar equation and response mechanism of photon-counting detectors to recover accurate vegetation and ground photons; 2) estimate the reflectance ratio between vegetation and ground (RVG) according to the vegetation radiative transfer model and the density of spatial cluster method; 3) derive original and compensated canopy covers with ICESat-2 classified photons; 4) evaluate the accuracy of derived canopy covers relative to local airborne reference canopy covers; and 5) explore the effects of undergrowth vegetation and land cover types on the canopy covers. The coefficients of correlation (${R}$) and root-mean-square errors (RMSEs) of the compensated canopy covers are 0.86 and 0.15 at SLAU and 0.59 and 0.16 at LENO, compared with those for original canopy covers with 0.71 and 0.18, and 0.45 and 0.21, respectively. As the undergrowth vegetation and diverse land cover types have an impact on the retrieval accuracy of canopy covers, we can employ the photons of different species to obtain their specific reflectance ratios to achieve a higher precision.

ICESat-2/ATLAS can obtain nearly continuous profiles of ground targets. At present, fusing with other satellite-based data sources such as images is an important usage for ICESat-2 data. Some scenarios such as urban areas and applications such as stereo photogrammetry require registration features with a high geolocation accuracy when fusing. The building boundaries would be very appropriate features, as they not only have obvious geometric features in ICESat-2 data, but also has textural features which are distinct in images. However, in ICESat-2 geolocated photons, the building boundary normally expands than its actual boundary. This non-negligible blurring phenomenon of geometric boundaries is caused by the extended laser spot of ICESat-2. This study theoretically and practically proposes a solution for these blurred building boundaries in ICESat-2 data. We first derive a theoretical model to describe the spatial convolution of laser spots (or called the extended laser spot effect) on building boundaries and then propose a method to estimate the exact building boundary points from for ICESat-2 data. The signal model and boundary point correcting method are validated using typical 23 targets in four tracks of ICESat-2 in Hutt City, New Zealand, where local airborne lidar points with high density and accuracy are available. After correcting the horizontal offset of ICESat-2, the horizontal accuracy of the determined building boundary locations has an RMSE of ~1 m, which is much better than that directly obtained by the signal photons from the ATL03 and ATL08 product (with RMSEs of ~6m). The results indicate this study can provide significant feature points for accurate registration and fusion between ICESat-2 data and other data sources in urban areas.

The new generation photon-counting lidar on Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) can obtain the subsurface optical properties of sea waters. Recent studies highlight the applications of deriving the lidar attenuation coefficient$K_{\mathrm {lidar}}$and then substituting$K_{\mathrm {lidar}}$into the bio-optical model to obtain more information of sea waters. As commonly used bio-optical models are built for the diffuse attenuation coefficient$K_{d}$that are traditionally derived by passive ocean color sensors, whether$K_{\mathrm {lidar}}$derived from ICESat-2 can be directly used as$K_{d}$is a fundamental question. Given that$K_{d}$is an apparent optical property (AOP) in the water column rather than an inherent optical property (IOP),$K_{d}$is closely related to the zenith angle of the incident light. The zenith angle of the incident light of the sunlight is normally tens of degrees for ocean color sensors, while the maximum laser off-nadir angle is ~1.5° for the ICESat-2 lidar. To demonstrate this issue, we select hundreds of ground tracks of ICESat-2 in both open ocean and coastal sea waters and compare the derived$K_{\mathrm {lidar}}$with their corresponding Moderate Resolution Imaging Spectroradiometer (MODIS)-derived$K_{d}$. The results indicate that the corrected results of$1.2\times K_{\mathrm {lidar}}$, instead of the direct results of$K_{\mathrm {lidar}}$, are more consistent with MODIS$K_{d}$. This study is of great significance to the better fusion of active lidar data and passive optical data in ocean observations.

The passive multispectral imaging and active bathymetric lidar make a great achievement for bathymetry in optically shallow waters. However, due to the attenuation of the water column precludes deep penetration of the light, accurately obtaining the underwater topography in turbid waters through remote sensing techniques, both passive and active, is still a challenging task. Airborne lidars can obtain water surface topography with high accuracy and resolution, which can further be used to derive the water depth based on wave theory. In this study, an ‘indirect’ method to determine water depth is proposed using airborne lidar measured water surface points. As the wavelength and wave direction can be accurately tracked from the water surface topography, a 20m×20m underwater topography near Ganquan Island, South China Sea, is generated with an RMSE of 0.91 m and a MAPE of 7.1%. The basic theory of deriving water depths is totally different from airborne lidar bathymetry, i.e., this method is independent of water clarity and can be used in turbid waters or even works with near infrared airborne lidar that can only obtain water surface points.

Over the past few decades, spaceborne passive ocean color sensors that measure the solar radiance have provided an unprecedented source of scientific knowledge on marine biology. Recently, spaceborne active lidars that measure the backscattered laser signal from the ocean subsurface provide new insights in deriving vertical profiles of ocean subsurface and obtaining shallow water bathymetry. The solar radiation is the signal source of passive ocean color sensors but acts as the primary noise source of satellite-based lidars, which may limit the extraction and application of the weak subaqueous signal in the daytime. Based on the perspective of the reciprocity, the background noise of the ICESat-2 spaceborne photon-counting lidar in six channels (or pixels) has potential to be regarded as the signal of an “ocean color camera” with a very narrow band. In this study, the remote sensing reflectance, that is the fundamental data of ocean color sensors, is theoretically linked to and accurately transferred from ICESat-2 noise data with an average Mean Absolute Percentage Error (MAPE) of less than 20% compared to thein-situmeasurements. With this method, not only the remote sensing reflectanceRrscan be retrieved from ICESat-2 under strong background noise, which enhances the capability of ICESat-2 to monitor the diurnal variation, but also numerous quantitative applications by passive remote sensing sensors may be achievable by the noise data of spaceborne photon-counting lidars in the future. In addition, a spaceborne lidar can synchronously detect active laser signal and passive solar radiation, which may bring new insights in the data fusion and verification of active and passive techniques.

The signal strength is a fundamental parameter in radiometric applications for satellite lidars. Different from full-waveform lidars, photon-counting lidars cannot record the returned signal strength but only respond to the presence of the photon event and may miss some returned photons due to the dead time effect, i.e., introduce radiometric distortion. Based on the lidar equation and the response mechanism of photon-counting detectors, we propose a radiometric correction model to remove the impact of the nonlinear response and dead time of detectors for the photon-counting lidar borne on ICESat-2. The returned signal photon number is corrected by the proposed model with respect to the photon event number per shot (PNPS) and surface slope derived from ATL03/ATL08 products. Then, the optical throughput calibration factor of ICESat-2 is obtained from ATL06 products over high Antarctic plateau where has given reflectance and clear atmosphere, which is generally equal to 0.52. The atmospheric attenuation induced by the molecular, cloud, and aerosol is calculated from ATL09 products. In addition, the corrected radiometric parameters including the calculated surface reflectance and apparent surface reflectance (ASR) are applied to classify land cover types along laser tracks over snow-covered terrains. The results indicate that the signal strength and calibration constant are reliable after corrections, but the atmospheric attenuation is sometimes inaccurate, which further influences the derived surface reflectance. In classifications, the overall accuracy and Kappa coefficient based on the corrected ASR can achieve the best classification results with 88.80% and 0.69. The proposed radiometric correction model is very essential to radiometric applications for photon-counting lidars such as ICESat-2, especially for data captured on ice and bare land with relatively high reflectance.

Airborne LiDAR bathymetry (ALB) is a useful technology for seamless topobathymetric mapping, offering high acquisition rate and point density. However, in very shallow waters (90%). In the simulated dataset, the root mean square error of the laser travel time between the estimated and truth values is 0.22 ns (corresponding to 2.5-cm slant range). The results indicate that the proposed method provides a new solution for filling the bathymetric gap in very shallow water, which is very essential for topobathymetry mapping.

Detailed information on lake bathymetry is essential for both hydrology-related studies and water resource management. Conventionally, lake bathymetry was mapped using high-cost approaches (e.g., ship/boat-based multibeam echosounders or airborne bathymetric lidars). With only satellite remotely sensed data sets, a method for deriving high-resolution bathymetry for dynamic areas was proposed by combining the new Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) lidar data and the Landsat-based Global Surface Water Data Set (GSWD). First, ICESat-2 can provide accurate along-track topographic points after the point cloud processing and bathymetric error correction, and the GSWD can supply water occurrence information within the lake dynamic area between 1984 and 2018. Second, using the derived relationship between the elevation and water occurrence, the bathymetric map of Lake Mead, USA, was produced with the dynamic area exceeding 235 km2, elevation ranging nearly 37 m, and a resolution of 30 m. The local reference data (i.e., the airborne topographic lidar data and ship/boat-based bathymetric data) in six areas around Lake Mead were used for the validations. In general, the produced lake bathymetry achieved an accuracy of approximately 2 m in elevation with$R^{2}$of 0.97. The proposed method is promising to obtain global bathymetry for inland water bodies (e.g., the lake and reservoir) and coastal areas (e.g., the tidal zone) where water level fluctuations are strong and the water clarity is sufficient.

Full-waveform satellite LiDAR can be used to retrieve the terrestrial surface information by decomposing its received waveforms. However, it is challenging to accurately extract the parameters of each component from a non-Gaussian overlapped waveform, which happens in steep mountain or urban areas. Therefore, a synthetic algorithm with the boosted Richardson–Lucy (RL) deconvolution, layered extraction, and gradient descent is proposed to implement the skew-normal decomposition for the received waveforms. To validate the performance of the proposed algorithm, we developed waveform decomposition experiments for three types of data, including known-parameter waveforms, simulated waveforms, and Global Ecosystem Dynamics Investigation (GEDI) satellite LiDAR waveforms. Meanwhile, we figured out the evaluation metrics involving correlation coefficients (CCs); root mean square errors (RMSEs); extracted parameter errors; and successful, missing, and unwanted rates for the decomposed waveforms. Through comparing the decomposed results of the proposed algorithm and the classical direct Gaussian decomposition (DGD) algorithms, we discovered that 1) the average CC has a growth of 4% and the average RMSE has a reduction of 60%; 2) the average errors of extracted amplitude, peak position, and pulsewidth have mitigated with 3.9%, 2.2%, and 5.1%, respectively; and 3) the successful detection rate increases by 40% and the unwanted and the missing rate decrease by 5% and 35% for the 2000 groups of known-parameter waveforms. In addition, the average CCs have slight growth of 3% and 1.2%, and the average RMSEs have significant reductions of 43% and 49% for the simulated and GEDI LiDAR waveforms, respectively. This research provides a preferable waveform decomposing approach conductive to characterizing the terrestrial information from the overlapping skew-normal full waveforms.

Surface-water-level changes reflect Earth's water resource variations (e.g., trends and fluctuations) and they are helpful to understand potential drivers (e.g., climate change and human activities). Currently, Australia is facing serious water crisis owing to rainfall shortage and climate change, and national-scale data set on surface-water-level changes is required for supporting sustainable water resource management. Here, we used all Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM)+/Operational Land Imager (OLI) data available on Google Earth Engine to obtain annual surface water during 2003-2019 and produced 1506 boundaries of water bodies with areas greater than 1 km2across Australia. The produced surface water map in Australia is more accurate than the existing global lake databases (e.g., the Global Lakes and Wetlands Database), which can be downloaded for free. Then, 52 water bodies (lakes and reservoirs) with areas larger than 1 km2and available Ice, Cloud, and land Elevation Satellite (ICESat/ICESat-2) data for more than 5 years were combined to estimate trends in surface water levels in Australia. Across Australia, from 2003 to 2019, the area-weighted mean of water level change rates is -0.046 m/year with 17 lakes (32.7%) with increasing water levels and 35 lakes (67.3%) decreasing with water levels. In detail, the largest lakes (>100 km2) dominate the total change trend and most of the largest lakes underwent decreasing trend (-0.046 m/year), whereas the mean water levels of small lakes (2) increased in the past 17 years. In situ water levels of three typical lakes/reservoirs were used to validate our estimation results, which exhibited a very good agreement (R2= 0.98).

As an essential indicator of climatic and environmental change, water levels of lakes are sensitive to various natural factors and anthropogenic activities from local to global scales. Understanding the global lake water level change can help to uncover the earth's water resource change and its response to the potential drivers. To date, most studies on lake water level changes were performed at local and regional scales. However, comprehensive information on the magnitude of lake water level changes at a global scale remains poorly understood. Here, with the help of the lake polygons from the global lakes and wetlands database (GLWD) at a global scale, we analyzed all available Ice, Cloud, and Land Elevation Satellite (ICESat) data to estimate the change in water levels of 14 981 lakes with areas greater than 0.1 km2 and reservoirs with storage capacities greater than 0.5 km3 around the world. We found global lakes exhibiting a significant spatial heterogeneity with 58.68% increase and 41.32% decrease in water levels during 2003 and 2009. The average change rate of global lake levels is 0.013 m/yr. We discovered an obvious and broad lake level increase in North America and Siberia region, Tibetan Plateau, and the Amazon basin. Our results provide detailed satellite-based evidence of the global increase in lake levels. In the future, more works should be conducted to estimate the lake level change at a longer temporal scale using multi-source satellite data and investigate the potential drivers under the climate warming and growing human footprint.

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.

Geographic information describing coral reefs plays an important role in constructing electronic chart systems and protecting the ecological environment of the ocean. To derive geographic information of coral reefs more effectively, this paper proposes a methodology to detect coral reefs by combining airborne LiDAR bathymetry (ALB) bottom waveform and bathymetric feature data. A feature vector was established by deriving bottom waveform variables (the peak amplitude, pulsewidth, area, skewness, kurtosis, and backscatter cross section) and bathymetric variables (the depth standard deviation, slope, bathymetric position index, Gaussian curvature, mean curvature, and roughness). Using a support vector machine classifier, coral reefs were detected by distinguishing two classes (coral reefs and others) on the seafloor. To evaluate the classification performance of coral reefs, the developed method was applied to Yuanzhi Island, South China Sea surveys, and verified by field data (aerial digital camera images and underwater video images). The results showed that the classification overall accuracy of coral reefs can be greatly improved from 80.59%/90.31% when ALB bottom waveform or bathymetric variables features were used separately to 93.57% when using a combination of ALB bottom waveform and bathymetric features. In addition, the kappa coefficient can also be greatly improved from approximately 0.61/0.80 to 0.87. And the new proposed method performs better compared to the current classification method using ALB data to detect coral reefs with an overall accuracy of 90.92% and Kappa of 0.81. This highlights the potential of ALB data, combining waveform data and bathymetric data, for precisely detecting coral reefs in shallow water areas.

Water depth can be measured using airborne LiDAR bathymetry (ALB). However, when the green laser beam passes through the air-water interface, the sea surface slope greatly affects the laser propagation path, significantly influencing the accuracy of the measured seafloor topography. To reduce its influence, a refraction correction method at the air-water interface based on the sea surface profile and ray tracing is proposed. First, the 3-D sea surface profile is fit based on the least-squares criteria and the wave spectrum, using the laser point data reflected by the sea surface. Then, on the basis of the sea surface slope, the geolocation biases of the laser points are corrected by tracing every laser transmission path at the air-water interface. The developed method is used to correct the ALB data collected in the South China Sea, and verified by the topography data captured by a ship-borne multibeam echo sounder. Before the refraction correction, the mean absolute error (MAE) is 14.2 cm, and the root-mean-square error (RMSE) is 17.5 cm. After the refraction correction, the MAE and RMSE decrease to 7.2 and 8.3 cm, respectively. The developed method can effectively improve the bathymetric accuracy of the ALB data.