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The Advanced Topographic Laser Altimetry System (ATLAS) onboard the NASA Ice, Cloud and land Elevation Satellite-2 (ICESat-2) is the newest and most recent Earth observing satellite for global elevation studies. The primary objectives for ICESat-2 follow that of its predecessor, ICESat, and focus on providing cryospheric measurements to determine ice sheet mass balance, and monitor both sea ice thickness and extent. However, the global observations support secondary science objectives such as biomass estimation, inland water elevation, sea state height and aerosol concentrations. In all, ATLAS measurements support 7 along-track geophysical products with multiple gridded products to provide regional and global change detection for seasonal and annual cycles. Since the launch of ICESat-2, the instrument has operated nominally and collected more than a trillion measurements. This paper provides an overview of the mission, a description of the operational components that support the altimeter products for science discovery and on-orbit observatory performance.

Satellite altimetry is advantageous for measuring water surface elevations (WSE) globally. However, biases of time series can be caused by uneven water surfaces, as nadir pointing measurements are often collected at different locations across a lake. This study demonstrates how two‐dimensional WSE difference maps derived from the SWOT mission can enhance ICESat‐2 WSE time series. First, the SWOT‐derived WSE difference maps showed strong agreement with in situ data over Lake Erie (R2 = 0.79). For Lake Powell, this method improves the R2 of the ICESat‐2 time series from 0.62 to 0.89. Furthermore, spatial variability in water surface, as estimated using SWOT data, accounts for 44% and 16% of the uncertainty in median WSE fluctuations in global lakes and reservoirs, respectively. By analyzing 81,133 lakes worldwide, this study identifies hotspots of bias and offers an advantage for integrating multi‐satellite altimetry data, facilitating more accurate and long‐term hydrological monitoring across diverse global landscapes.

The Surface Water and Ocean Topography (SWOT) mission, initially designed to observe oceans and inland waters, proves valuable for mapping intertidal flat topography. This study presents a fusion method that integrates freely available SWOT interferometric altimetry data with Sentinel‐2 imagery. The density‐based spatial clustering of applications with noise algorithm is employed to accurately identify observation data located in the intertidal zone. Intertidal topography with a 10 m resolution were generated from L2_HR_PIXC data collected during SWOT's science phase (July 2023–October 2024) in the intertidal region along the coast of Jiangsu Province, China. These Digital Elevation Models were compared with laser altimetry observations from the ICESat‐2. SWOT observations show high accuracy, with RMSEs of 0.24 m (multi‐cycle) and 0.41 m (single‐cycle), confirming their potential for intertidal monitoring. This study presents a data processing strategy that does not rely on ground‐based observations, demonstrating potential for application in a broader range of regions.

The Advanced Topographic Laser Altimeter System (ATLAS) aboard the Ice, Cloud, and land Elevation Satellite‐2 (ICESat‐2) has been making very high resolution measurements of the Earth’s surface elevation since October 2018. ATLAS uses photomultiplier tubes (PMTs) as detectors in photon counting mode, so that a single photon reflected back to the receiver triggers a detection within the ICESat‐2 data acquisition system. However, one characteristic of ICESat‐2 detected photons is the possible presence of afterpulses, defined as small amplitude pulses occurring after the primary signal pulse due to photon arrival. The disadvantage of these afterpulses is that they often confound the accurate measurements of low level signals following a large amplitude of signal and can degrade energy resolution and cause errors in pulse counting applications. This paper discusses and summarizes the after‐pulsing effects exhibited by the ATLAS PMTs based on on‐orbit measurements over different seasons and geographic regions. The potential impacts of these after‐pulsing effects on altimetry and ocean subsurface retrievals are discussed. Plain Language Summary After‐pulsing effects occurring in the ICESat‐2 Advanced Topographic Laser Altimeter System (ATLAS) are characterized from the on‐orbit measurements acquired over different surface types. Multiple echoes due to after‐pulsing effects in the ATLAS photomultiplier tubes (PMTs) are clearly seen below the Earth’s surface where the signal should be totally attenuated. The afterpulses captured from on‐orbit measurements are caused by three different reasons: (1) the effects of the dead‐time circuit (∼3 ns) due to PMT saturation; (2) the effects of optical reflections within the ATLAS receiver optical components; (3) PMT afterpulses. The echoes separated by ∼0.45 m are attributed to the effect of the dead‐time circuit (∼3 ns) due to PMT saturation. The echoes at ∼2.3 and ∼4.2 m below the primary surface returns are caused by the optical reflections within the ATLAS receiver optical components, while the echoes from ∼10 to ∼45 m away from the primary surface signal are due to the PMT afterpulses with a longer time delay. The ICESat‐2 ATLAS instrument response is derived from both a measurement of the transmitted laser pulse shape and measured photon events arising from land surfaces with different surface albedos. The ICESat‐2 on‐orbit measurements demonstrate that the ATLAS impulse response during different months and over different surface types is essentially identical. Key Points The effects of after‐pulsing by the Advanced Topographic Laser Altimeter System (ATLAS) aboard the ICESat‐2 satellite are described The transient response of the ATLAS receiver is characterized over different measurement regimes The potential impacts of these detector artifacts on ICESat‐2 science studies are discussed

Recent studies show that the Ice, Clouds, and Land Elevation Satellite‐2 (ICESat‐2) can achieve decimeter‐level accuracy over forested and mountainous sites in the western United States, as well as over the glaciers of Alaska. However, there has yet to be an assessment on ICESat‐2 snow depths over the boreal forests and tundra of Alaska, both of which are significant reservoirs of snow during the winter season. We present two case studies of retrieving snow depth using ICESat‐2 over Alaska. We focus on two field sites used by the NASA SnowEx 2022/2023 campaigns: Farmer's Loop/Creamer's Field near Fairbanks, AK (forest) and Upper Kuparuk/Toolik on the Arctic North Slope (tundra). When validated against airborne LiDAR flown by the University of Alaska, Fairbanks (UAF), we find median biases of −6.3 to +2.1 cm among three ICESat‐2 data products in the tundra region. Biases over the boreal forest are higher at 7.5–13 cm. Utilizing the open source tool SlideRule, we observe little change in results when filtering by the ICESat‐2 signal photon confidence scheme or by the vegetation filter. However, uncertainties in snow depth decrease with coarser Sliderule‐derived snow depths. The number of signal photons (i.e., signal strength) has an influence on retrievals, with a large number of photons per ICESat‐2 return providing more accurate snow depths. The initial results are promising, and we expect to expand this effort to other ICESat‐2 overpasses over the SnowEx field sites.

The Surface Water and Ocean Topography (SWOT) mission promises quasi‐global monitoring of glacial lakes, yet the elevation difference arising from its Ka‐band radar penetrating lake snow/ice cover remains unquantified. This poses a challenge to assessing their level changes. We present the first quantification of this difference by intercomparing SWOT and ICESat‐2 surface elevations over 260 glacial lakes (>1 km2) globally. Given ICESat‐2’s laser measures the snow/ice surface, we leverage near‐synchronous observations to treat the elevation difference as an empirical measure of the snow/ice penetration effect of SWOT. We find a globally consistent negative difference (SWOT minus ICESat‐2) of −0.25 m over the 1‐year period. This difference is dependent on season, amplifying by more than double from −0.15 m (warm) to −0.34 m (cold), with peak monthly differences nearly reaching −1 m. This study provides a framework for reconciling SWOT and ICESat‐2 observations, enabling long‐term, high‐resolution records of glacial lake variability.

Detecting submerged targets in shallow waters from satellite platforms remains a challenge, as the optical spectral information of targets is significantly distorted by the absorption and scattering effects of the water column. In this study, we propose a new framework as the bathymetry‐informed target extraction, which integrates the spaceborne lidar data and multispectral imagery. By using lidar assisted Satellite‐Derived Bathymetry model, we convert the complex multispectral information into relative depth data. Through this transformation, the challenging issue of distorted color domain image segmentation is converted into the task of depth anomaly detection. The method is validated on submerged artificial stone weirs and breakwaters in typical open ocean and coastal waters, which indicates significant improvements in target detection rate and reliability compared to direct color‐based methods. This approach promises large‐scale surveys of submerged targets in shallow waters, offering an alternative solution to on‐site surveys such as shipborne sonars.

Lakes provide important water resources and many essential ecosystem services. Some of Earth's largest lakes recently reached record‐low levels, suggesting increasing threats from climate change and anthropogenic activities. Yet, continuous monitoring of lake levels is challenging at a global scale due to the sparse in situ gauging network and the limited spatial or temporal coverage of satellite altimeters. A few pioneering studies used water areas and hypsometric curves to reconstruct water levels but suffered from large uncertainties due to the lack of high‐quality hypsometry data. Here, we propose a novel proxy‐based method to reconstruct multi‐decadal water levels from 1992 to 2018 for both large and small lakes using Landsat images and ICESat (2003–2009) and recently launched ICESat‐2 (2018+) laser altimeters. Using the new method, we evaluate reconstructed levels of 342 lakes worldwide, with sizes ranging from 1 to 81,844 km2. Reconstructed water levels have a median root‐mean‐square error (RMSE) of 0.66 m, equivalent to 57% of the standard deviation of monthly level variability. Compared with two recently reconstructed water level data sets, the proposed method reduces the median RMSE by 27%–32%. The improvement is attributable to the new method's robust construction of high‐quality hypsometry, with a median R2 value of 0.92. Most reconstructed water level time series have a bi‐monthly or higher frequency. Given that ICESat‐2 and Landsat can observe hundreds of thousands of water bodies, this method can be applied to conduct an improved global inventory of time‐varying lake levels and thus inform water resource management more broadly than existing methods. Key Points Landsat images and laser altimeters were leveraged to reconstruct multi‐decadal lake levels of both large and small lakes Reconstructed water levels were validated against observed levels on 342 global lakes with a median error of 0.66 m Most of the reconstructed lake level time series have a bi‐monthly or higher frequency

Forecasting flood and drought events requires accurate modeling tools. Hydraulic river models are based on estimates of riverbed geometry which are traditionally collected in situ. The novel Ice, Cloud and Land Elevation Satellite 2 [ICESat‐2] lidar altimetry mission with 6 simultaneous high‐resolution laser beams provides the opportunity to define river cross‐section geometries as well as observe water surface elevation [WSE] and water surface slope spatially resolved along the river chainage. This paper describes a method to utilize terrain altimetry and water surface slope estimates to define complete river geometries from ICESat‐2 data products, using the diffusive wave approximation to calculate depth in the submerged section not penetrated by the lidar. Exemplifying the method, cross‐sections are defined for a stretch of the Mekong River. Hydrodynamic model results of the stretch are compared with ICESat‐2 WSE estimates and in situ gauging station time series. Insights in river characteristics from satellite imagery and the ICESat‐2 slope estimates allow for fine‐tuning of the cross‐sections using spatially varying Manning numbers. The final model achieves a root mean square error against the ICESat‐2 WSE of 0.676 m and average Kling‐Gupta Efficiency against gauging station time series of 0.880. The method is limited by the diffusive wave approximation resulting in inaccurate cross‐section estimates in sections with supercritical flow or significant acceleration. Errors can be identified from ICESat‐2 WSE estimates and reduced with additional cross‐sections. Combined with hydrological models, the method will allow for cross‐section definition without in situ data. Plain Language Summary The depth and width of the river channel are important factors when seeking to predict floods. To predict water level in a river, computer models for flood forecasting must be informed with river channel geometry at cross‐sections along the river. Such cross‐sections are traditionally measured in the field which is expensive and time consuming. This paper describes a method to estimate river cross‐sectional shape from land and water height measurements from the satellite ICESat‐2. When the satellite path crosses the river, the precise laser instrument onboard outlines the surface to indicate the river channel shape, but it does not penetrate the water. We found that it was possible to estimate the shape of the submerged part of the cross‐section using the water surface slope obtained from the satellite's unique instrument. Evaluating the method on a stretch of the Mekong River, we were able to model water level along the river with accuracy sufficient for flood forecasting. In some sections, where the flow speed changes quickly, a hydraulic model with the defined cross‐sections does not reproduce observed water levels. Using satellite crossings from days with steadier flow reduces the error. The method allows for building hydraulic river models without cross‐section surveys. Key Points Land elevation and water surface slope estimates from the ICESat‐2 lidar altimetry mission are combined to define river cross‐sections RMSE of 0.676 m and KGE of 0.88 is achieved for a hydrodynamic model of a stretch of the Mekong River using the derived cross‐sections Sections with significant flow acceleration lead to inaccurate cross‐sections, but errors are reduced with additional ICESat‐2 crossings

Lake surface morphology, an essential yet underexplored feature of hydrological systems, remains poorly understood, including its effects on water volume estimation. This study investigates north‐south surface profiles of 147 lakes worldwide using ICESat‐2 altimetry data (2018–2024). A meticulous selection process, followed by DBSCAN clustering and moving window averaging, enabled the construction of detailed elevation profiles. Based on metrics of linearity, concavity, and convexity, we classified lake surface morphology into four types: linear, concave, convex, and others. Concave surfaces were the most common (67/147 lakes), while convex surfaces were the least common (11/147 lakes). The formation of these surface types is influenced by factors such as groundwater flow, salinity gradients, and lakebed variations. Surface elevation differences ranged from 0.016 to 2.565 m, averaging 0.221 m. To address the impacts of these variations, we calculated equivalent water surface height (heq) and demonstrated that concave lakes overestimate lake surface height by an average of 0.093 m. Although the volumetric overestimation may seem small (e.g., 0.78% for Lake Victoria), 41% of lakes showed surface elevation differences exceeding their mean monthly variation, posing a risk of trend misinterpretation (e.g., Lake Victoria: elevation difference 0.59 m, standard deviation 0.16 m, monthly variation 0.044 m). These results underscore the critical importance of incorporating lake surface morphology into hydrological assessments, offering a new perspective that could lead to more accurate and comprehensive water volume estimations and ultimately improve global water resource management. Key Points Global lake surfaces are classified into concave, convex, linear, and mixed types with ICESat‐2, refining surface variability studies Wind, groundwater, salinity, and lakebed topography are suggested as factors shaping lake surface morphology Uneven lake surfaces affect volume estimation, with errors quantified using equivalent surface height