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The estimation of the worldwide glacier mass balance between 2019 and 2023 was accomplished through the utilization of ICESat-2 ATL06 data by employing a quadratic surface model fitting approach. Glaciers have a mass change of −331.68 ± 59.07 Gt/yr during this four-year span, which can be equivalated to a sea level rise of 0.916 ± 0.163 mm/yr. Accelerated but contrasting patterns of glacier mass change have been observed, with an accelerated mass loss found in regions such as Svalbard, Russian Arctic, the High Mountain Asia, and the southern Andes. In contrast, Alaska exhibited a decelerated mass loss, and some Antarctic glaciers experienced a slight mass gain. In the maritime regions, land-terminating glaciers have experienced more extensive mass loss except Svalbard and the Russian Arctic. The analysis of seasonal glacier changes indicated that the majority of regions demonstrated their lowest glacier mass in the summer of 2022, and lost approximately 50% mass during 2022–2023. These results provide valuable reference data for the assessment of glacier mass balance using ICESat-2.

The launch of Sentinel-1 satellite introduces a novel approach for synthetic aperture radar (SAR) interferometry (InSAR). However, its capabilities for topographic mapping are reportedly limited. It is very challenging to create high-quality digital elevation models (DEMs) from InSAR data in mountain area. The main goal of the study was to generate a new high-quality DEM by combining Sentinel-1A and Sentinel-1B data with a short temporal baseline and multiple perpendicular baselines from 43 to 143 m. Five DEMs were produced from five interferometric pairs. The five DEMs were fused to generate a more reliable fused DEM. The performance of the new DEMs was evaluated against ICESat-2/ATL06 product and global DEM products (NASADEM, advanced spaceborne thermal emission and reflection radiometer (ASTER) global digital elevation model (GDEM), and advanced land observing satellite ALOS World 3D-30 m). The results showed that all Sentinel-1 DEMs performed better than ASTER GDEM, four of DEMs had higher accuracies than NASADEM. The fused DEM with the vertical error of 9.08 m for steep terrain (slope>20°) revealed higher accuracy than three global DEMs. The accuracy of DEM was related to terrain slope and land cover type. The accuracies of DEMs decreased as slope increased. The DEMs in glacier area revealed higher errors than those in bare rock. Besides, there was no clear relationship between the perpendicular baseline and vertical accuracy of DEM. The interferometric pair with the shortest baseline (43 m) produced the worst quality DEM, while the interferometric pair with slightly shorter baseline (69 m) produced the highest quality DEM. The study revealed the outstanding accuracy of the new DEM product, which is very valuable data for local glacier research.

ABSTRACTSpaceborne photon-counting light detection and ranging (LiDAR) have been extensively applied in shallow-water bathymetry. The density of underwater topographic photons (UTP) varies and is discontinuous due to sunlight noise, beam intensity, and seabed reflectivity, which differ from the land photon distribution due to the attenuation of water. Therefore, a general method for extracting and grading UTP is still lacking. We propose an active contour method combined with a variable convolution kernel method to calculate the photon range by considering the energy contributions of adjacent photons. Adaptive parameters under different observation conditions were determined to obtain the optimal convolution kernel using a kernel ridge regression model. This implies that the number of photons contained in the buffer zone was largest after the extracted UTP was fitted to a curve. Quantitative and qualitative verifications proved that the method performed well under different conditions. The photons obtained by the energy functional and the curve obtained by the fitting method were then used to grade the photons. Finally, an online developed UTP dataset and extraction framework were proposed to provide an applicable method for current and subsequent spaceborne photon-counting LiDAR.

The dynamics of glaciers serve as one of the most important indicators of climate change. Whilst current research has primarily concentrated on long-term interannual glacier mass balance and its response to climate change, glaciers may respond more rapidly to climate change, highlighting the urgent need for intra-annual mass balance estimations. Investigating seasonal or short-term variations in glacier mass balance not only enhances our understanding of the interactions between glaciers and the climate system but also provides crucial data for water resource management and ecological protection. The ICESat-2 and NASADEM datasets were used to estimate the inter- and intra-annual glacier mass balance changes in the mid-latitude Eurasia from 2019 to 2022. Additionally, the response of glacier mass balance to regional air temperature and precipitation values was analysed using ERA5-Land data and multiple regression analysis, respectively. From 2019 to 2022, glacier mass loss in mid-latitude Eurasia reached −45.02 ± 34.21 Gt per year, contributing to a global sea-level rise of 0.12 ± 0.09 mm per year. The glacier melt rate in the study area from 2019 to 2022 was 2.33 times higher than that from 2000 to 2019. With the exception of the Western Kunlun region, which experienced a weak accumulation rate of 0.04 ± 0.35 m w.e. per year, all other areas experienced ablation states. Seasonal mass balance responds differently to temperature and precipitation variations across seasons: higher temperatures in different seasons lead to more negative mass balances, while increased winter and spring precipitation can slow down glacier melt. Air temperature dominates the glacier mass balance changes in the study area. The intense heat in 2022 raised average glacier temperatures by 1.04 °C compared to 2019–2021, resulting in a more negative mass balance and an increased ice loss of −0.34 ± 1.01 m w.e. per year (−35.07 ± 103.22 Gt per year). This analysis indicates that glacier mass balance is highly sensitive to climate change, even on a seasonal scale. Moreover, the high precision and spatiotemporal resolution ICESat-2 data can facilitate the investigation of large-scale glacier mass balance on short time scales.

Abstract The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has great potential for development due to its advantages of the use of multiple beams, low energy consumption, high repetition frequency, and high measurement sensitivity. However, the weak photon signal emitted by the photon counting lidar is susceptible to the background noise caused by the sun and the atmosphere, which can seriously affect the processing and application of laser data. This paper proposes an improved DBSCAN clustering algorithm for denoising single photon laser point clouds in mountainous areas. Firstly, a grouping method based on elevation and distance statistics is proposed to reduce the influence of terrain undulations on denoising accuracy. Finally, an automatic radius search method is put forward to determine clustering radius of each group, automatically find the optimal radius, and improve the existing DBSCAN clustering method. The method proposed in this paper is compared with the classical DBSCAN algorithm. The results show that the proposed algorithm significantly improves denoising accuracy in mountainous areas and effectively filters out most background noise. Graphical Abstract

Abstract Peatlands in the Canadian boreal forest are being negatively impacted by anthropogenic climate change, the effects of which are expected to worsen. Peatland types and sub‐classes vary in their ecohydrological characteristics and are expected to have different responses to climate change. Large‐scale modelling frameworks such as the Canadian Model for Peatlands, the Canadian Fire Behaviour Prediction System and the Canadian Land Data Assimilation System require peatland maps including information on sub‐types and vegetation as critical inputs. Additionally, peatland class and vegetation height are critical variables for wildlife habitat management and are related to the carbon cycle and wildfire fuel loading. This research aimed to create a map of peatland sub‐classes (bog, poor fen, rich fen permafrost peat complex) for the Canadian boreal forest and create an inventory of peatland vegetation height characteristics using ICESat‐2. A three‐stage hierarchical classification framework was developed to map peatland sub‐classes within the Canadian boreal forest circa 2020. Training and validation data consisted of peatland locations derived from various sources (field data, aerial photo interpretation, measurements documented in literature). A combination of multispectral data, L‐band SAR backscatter and C‐Band interferometric SAR coherence, forest structure and ancillary variables was used as model predictors. Ancillary data were used to mask agricultural areas and urban regions and account for regions that may exhibit permafrost. In the first stage of the classification, wetlands, uplands and water were classified with 86.5% accuracy. In the second stage, within the wetland areas only, peatland and mineral wetlands were differentiated with 93.3% accuracy. In the third stage, constrained to only the peatland areas, bogs, rich fens, poor fens and permafrost peat complexes were classified with 71.5% accuracy. Then, ICESat‐2 ATL08 spaceborne lidar data were used to describe regional variations in peatland vegetation height characteristics and regional and class‐wise variations based on a boreal forest wide sample. This research introduced a comprehensive large‐scale peatland sub‐class mapping framework for the Canadian boreal forest, presenting the first moderate resolution map of its kind.

Space-based laser altimetry has revolutionized our capacity to characterize terrestrial ecosystems through the direct observation of vegetation structure and the terrain beneath it. Data from NASA's ICESat-2 mission provide the first comprehensive look at canopy structure for boreal forests from space-based lidar. The objective of this research was to create ICESat-2 aboveground biomass density (AGBD) models for the global entirety of boreal forests at a 30 m spatial resolution and apply those models to ICESat-2 data from the 2019–2021 period. Although limited in dense canopy, ICESat-2 is the only space-based laser altimeter capable of mapping vegetation in northern latitudes. Along each ICESat-2 orbit track, ground and vegetation height is captured with additional modeling required to characterize biomass. By implementing a similar methodology of estimating AGBD as GEDI, ICESat-2 AGBD estimates can complement GEDI's estimates for a full global accounting of aboveground carbon. Using a suite of field measurements with contemporaneous airborne lidar data over boreal forests, ICESat-2 photons were simulated over many field sites and the impact of two methods of computing relative height (RH) metrics on AGBD at a 30 m along-track spatial resolution were tested; with and without ground photons. AGBD models were developed specifically for ICESat-2 segments having land cover as either Evergreen Needleleaf or Deciduous Broadleaf Trees, whereas a generalized boreal-wide AGBD model was developed for ICESat-2 segments whose land cover was neither. Applying our AGBD models to a set of over 19 million ICESat-2 observations yielded a 30 m along-track AGBD product for the pan-boreal. The ability demonstrated herein to calculate ICESat-2 biomass estimates at a 30 m spatial resolution provides the scientific underpinning for a full, spatially explicit, global accounting of aboveground biomass.

ABSTRACTOpen source Global Digital Elevation Models (GDEMs) serve as an important base for studies in geosciences. However, these models contain vertical errors due to various reasons. In this study, data from two Satellite LiDAR altimetry systems, GEDI and ICESat-2, were used to improve the vertical accuracy of GDEMs. Three different machine learning methods, namely an Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), and a Convolutional Neural Network (CNN), were employed to improve existing DEM data with satellite LiDAR data. The methodology was tested in five areas with varying characteristics. Ground control data were selected from high accuracy DEMs generated from Airborne LiDAR and GNSS data. The use of ANN method improved the vertical accuracy of SRTM data from 6.45 to 3.72 m in Test area-4. Similarly, the CNN method demonstrated an improvement in the vertical accuracy of bare ground SRTM data increasing from 3.4 to 0.6 m in Test area-4. In Test area-5, the ANN method improved the vertical accuracy of SRTM data with slopes between 30 and 60%, increasing from 3.8 to 0.5 m. Notably, the results underscore the successful improvement of GDEMs across all test areas.

To tackle the challenge of denoising spaceborne photon-counting laser altimeter point clouds with uneven noise density, this study proposes a denoising method based on adaptive parameter density clustering, which utilizes numerical simulations to achieve self-adaptation of key parameters (neighborhood radius [Formula: see text] and minimum number of points [Formula: see text]). First, taking the directional adaptive ellipse DBSCAN (DAE-DBSCAN) as an example, photons with different background photon count rates ([Formula: see text]) are used to traverse [Formula: see text] and [Formula: see text] to calculate their optimal values ([Formula: see text] and [Formula: see text] with the highest denoising accuracy). Then, a mathematical prediction model of [Formula: see text], [Formula: see text] and [Formula: see text] was established. The actual background photon count rates were introduced into the key parameter prediction model to obtain the optimal [Formula: see text] and [Formula: see text]. Finally, a denoising experiment was conducted using the simulated photons and the ATLAS data. The results show that the proposed method had higher accuracy than the constant parameter denoising method, with an [Formula: see text] >0.95. Even for photons of complex mountainous terrain with a high background photon count rate, the denoising accuracy was still higher than 0.9. The proposed method improves the denoising accuracy of photons with different noise densities by adapting density clustering parameters.

The ICESat-2 satellite equipped with a new photon-counting laser altimeter has received much attention as a source of accurate elevation observations. However, in this research field, there is a lack of an open-source high-accuracy elevation control point dataset with the specific quality requirements at a global scale. To this end, using ICESat-2 altimeter data as the main data source, we constructed and organized a dataset as a useful supplement for this research field. The dataset was generated by a methodology based on detection environment evaluation, photon spatial analysis, and the redundant observation statistics. The dataset includes more than 600 million elevation control points and covers the global land areas, except for Greenland and Antarctica. The dataset has been validated by multiple digital elevation models (DEMs) from around the world (sourced from airborne LiDAR data). The results show that the dataset has high-accuracy elevation control points. The overall root-mean-square error (RMSE) of the original elevations of ICESat-2 is about 1.384–4.820 m, but the overall RMSE of the elevation control points in the new dataset is about 0.279–0.642 m. Moreover, the results obtained in this study show that the dataset is suitable for application within high vegetation cover areas.

The accuracy of digital elevation models (DEMs) is crucial for practical applications in complex terrain. In this study, various correction models were employed to evaluate and correct the 90 m SRTM3 DEM and TanDEM-X DEM data in the Qinghai-Tibet Plateau region. A simple and universal three-dimensional data co-registration method was utilized for horizontal alignment. The study demonstrated a significant reduction in horizontal discrepancies between datasets after data co-registration. By comparing the performance of five different correction methods, it was found that the RF model exhibited excellent accuracy and robustness, making it particularly suitable for applications that require high precision. The XGB and BPNN models offer a good balance between accuracy and time efficiency, making them suitable for research scenarios that are less sensitive to precision requirements. In contrast, the MLR and IDW methods performed poorly and are not recommended for high-precision applications. Furthermore, the corrected DEMs showed improved resistance to the influence of high slopes, enhancing their applicability in complex terrain areas. This study provides an important reference for the correction of high-quality DEMs in the Qinghai-Tibet Plateau region and offers valuable insights for DEM research in similar areas.

Our understanding of the impact of climate change on water availability and natural hazards in high-mountain regions is limited due to the spatial and temporal scarcity of ground observations of precipitation and snow. Freely available, satellite-based information about the snowpack is currently mainly limited to indirect measurements of snow-covered area or very coarse-scale snow water equivalent (SWE), but only for flat areas in lowlands without vegetation cover. Novel space-based laser altimeters, such as ICESat-2, have the potential to provide high-resolution snow depth data in worldwide mountain regions where no ground observations exist. However, these space-based laser altimeters come with spatial gaps between ground tracks, obtained without repetition at a give location. To overcome these drawbacks, here, we present a combined probabilistic data assimilation and deep learning approach to reconstruct spatio-temporal SWE from observations of snow depth along ground tracks, imitating ICESat-2 tracks in view of a potential future global application.Our approach is based on assimilating SWE and snow cover information in a degree-day model with an iterative ensemble smoother (IES) which allows temporally reconstructing SWE along hypothetical ground tracks separated by 3 km. As input, the degree-day model uses daily precipitation and downscaled air temperature from the ERA5 reanalysis. A feedforward neural network (FNN) is then used for spatial propagation of the daily mean and standard deviation of the updated SWE ensemble members obtained from the IES. The combined IES-FNN approach provides uncertainty-aware spatio-temporally continuous estimates of SWE.We tested our approach in the alpine Dischma valley (Switzerland) using high-resolution snow depth maps obtained from photogrammetric techniques mounted on airplanes and unmanned aerial system observations. Our results show that the IES-FNN model provides reliable estimates at a resolution of approximately 100 m. Even assimilating only one SWE observation during the year (combined with satellite-based melt-out date estimates) produces satisfying results when evaluating the IES-FNN SWE reconstructions on independent dates and smaller (

Supervised classification methods can distinguish between noise and signal in ice, cloud, and land elevation satellite-2 (ICESat-2) data across various feature perspectives and autonomously optimize parameters. Nevertheless, model generalization remains a significant limitation for practical applications. This study focuses on developing a universal denoising model for ICESat-2 using machine learning algorithms and analyzing its spatial transferability under various forest and terrain conditions. A photon-denoising feature parameter system is developed based on the analysis of the three-dimensional distribution of photons in forested regions. This system reduces the parameters dependent on absolute physical quantities and increases those that are less influenced by terrain and forest features to enhance the model’s transferability. Subsequently, automated machine learning algorithms (AutoML) are used for model selection and parameter optimization across six non-parametric regression models. We evaluate the accuracies of the local, global, and transfer models in estimating canopy height across four representative forested areas in China. Results show that the algorithm can effectively distinguish between signal and noise photons. The estimated canopy heights from signal photons are highly consistent with heights obtained using airborne laser scanning (ALS), exhibiting a Pearson correlation coefficient (r) of 0.89, root mean square errors (RMSE) of 3.75 m, relative root mean square error (rRMSE) of 0.27, relative bias (rBias) of −0.11 and mean Bias of −1.45 m. Notably, the accuracy of canopy height estimation by the global model has increased by an average of 21 % compared to ICESat-2 land-vegetation along-track products (ATL08). Furthermore, the model exhibits significant spatial transfer capabilities, with the accuracies of the transfer model exceeding those of ATL08 by margins ranging from 4 % to 41 %. This study marks a significant advancement in photon-denoising methodologies, providing a robust and transferable solution for large-scale environmental data analysis.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission, spanning the past five years, has collected extensive three-dimensional Earth observation data, facilitating the understanding of environmental changes on a global scale. Its key product, Land and Vegetation Height (ATL08), offers global land and vegetation height data for carbon budget and cycle modeling. Consistent measurement accuracy of ATL08 is crucial for reliable time series analysis. However, fluctuations in the temporal accuracy of ATL08 data have been ignored in previous studies, leading to unknown uncertainties in existing time-series analyses. To bridge the knowledge gap, this study analyzes 59 months of ATL08 version 006 data in Finland to assess terrain and surface height accuracy, with a focus on temporal fluctuations across six major land cover types. A random forest (RF) model is employed to quantify the relative importance of error factors affecting height accuracy. Moreover, the study assesses accuracy at two official spatial resolutions, i.e., 100 m × 11 m and 20 m × 11 m, to evaluate the capability of ATL08 for the high-resolution height retrieval. For the terrain, the 100 m segment shows a bias of 0.04 m, a mean absolute error (MAE) of 0.44 m, and a root mean square error (RMSE) of 0.66 m, while the 20 m segment exhibits a bias of 0.10 m, a MAE of 0.35 m, and an RMSE of 0.49 m. For the surface height, the 100 m segment shows a bias of −0.59 m, a MAE of 3.06 m, an RMSE of 4.52 m, a bias% of −3.45 %, a MAE% of 21.26 %, and an RMSE% of 31.40 %. The 20 m segment exhibits a bias of −0.72 m, a MAE of 3.51 m, an RMSE of 5.23 m, a bias% of −5.81 %, a MAE% of 28.52 %, and an RMSE% of 42.47 %. The results indicate that improving segment resolution enhances terrain accuracy but reduces surface height accuracy. According to the error factor analysis, surface coverage and beam type are crucial for terrain retrieval accuracy, with their effects varying over time. Seasonal changes, particularly the presence of snow, affect terrain retrieval accuracy, with the lowest accuracy observed around March each year. This study confirms the critical impact of surface height on its retrieval accuracy and suggests avoiding the use of ATL08 for retrieving low target surface heights, especially in steep terrains. Nevertheless, the analysis affirms the applicability of ATL08 for canopy height estimation in boreal forests, primarily composed of coniferous species, highlighting its potential for extensive spatial and temporal research. This contributes to bridging the gaps between accurate estimates and large area coverage in global carbon budget and cycle studies. Additionally, the findings reveal that similar issues may exist in other satellite laser altimetry missions, emphasizing the important impacts of temporal fluctuations in surface and terrain accuracy when utilizing satellite laser altimetry datasets.

Abstract Since the 2018 launch of NASA's ICESat‐2 satellite, numerous studies have documented the bathymetric measurement capabilities of the space‐based laser altimeter. However, a commonly identified limitation of ICESat‐2 bathymetric point clouds is that they lack accompanying spectral reflectance attributes, or even intensity values, which have been found useful for benthic habitat mapping with airborne bathymetric lidar. We present a novel method for extracting bathymetry from ICESat‐2 data and automatically adding spectral reflectance values from Sentinel‐2 imagery to each detected bathymetric point. This method, which leverages the cloud computing systems Google Earth Engine and NASA's SlideRule Earth, is ideally suited for “big data” projects with ICESat‐2 data products. To demonstrate the scalability of our workflow, we collected 3,500 ICESat‐2 segments containing approximately 1.4 million spectrally‐attributed bathymetric points. We then used this data set to facilitate training of a deep recurrent neural network for classifying benthic habitats at the ICESat‐2 photon level. We trained two identical models, one with and one without the spectral attributes, to investigate the benefits of fusing ICESat‐2 photons with Sentinel‐2. The results show an improvement in model performance of 18 percentage points, based on F1 score. The procedures and source code are publicly available and will enhance the value of the new ICESat‐2 bathymetry data product, ATL24, which is scheduled for release in Fall 2024. These procedures may also be applicable to data from NASA's upcoming CASALS mission.

Accurate bathymetric data is crucial for various aspects such as marine resource exploitation and marine ecological conservation. Currently, satellite-derived bathymetry (SDB) based on empirical and physical models has been widely utilized in constructing underwater terrain in shallow seas. However, the application of such SDB models is limited in remote island reef areas lacking in-situ measurement data. To overcome this issue, the manuscript proposes an unconstrained SDB optimization method without in-situ measurement data, utilizing satellite multispectral imagery (Geoeye-1) and spaceborne LiDAR data (ICESat-2). By classifying the seafloor substrate in coral reef areas into sandy and coral, based on the depth invariant index (DII), we employ an adaptive logarithmic ratio model for unconstrained SDB. The ICESat-2 LiDAR data are then used to correct the SDB results, achieving bathymetry optimization in the coral reef area of the Xisha Islands. Additionally, the proposed method is applied to Yuanzhi Island of the Xisha Islands, and the accuracy of the bathymetric results is evaluated against ALB (Airborne LiDAR Bathymetry) data. The findings demonstrate that compared to conventional methods, our method can improve the accuracy of SDB results with good adaptability. In the Yuanzhi Island area, the proposed method yields SDB results with an R2 of 0.93, an MAE (Mean Absolute Error) of 0.94, and an RMSE (Root Mean Square Error) of 1.12 m, compared to ALB data. The average error is less than 10 % of the maximum depth, essentially meeting the requirements of the International Hydrographic Organization (IHO) standards for depth measurement error when depth is

Shallow-water bathymetric maps provide vital geographic information for various coastal and marine applications such as environmental management, engineering construction, oil and gas resource exploration, and ocean fisheries. Recently, satellite-derived bathymetry (SDB) has emerged as an alternative approach to shallow-water bathymetry, particularly in hard-to-reach areas. In this research, an innovative approach to bathymetry was introduced. This method provides a reliable approach for generating high-accuracy and high-reliability shallow water bathymetry results. By using Sentinel-2 time series imagery combined with ICESat-2 data, four bathymetry results at different time points are produced based on four traditional bathymetry methods. For the results at each location, a statistical method is applied to evaluate the bathymetry results, remove erroneous data, and generate high-confidence bathymetry results. The validation results indicated that the accuracy of the proposed bathymetric method achieved an R² range of 0.96 to 0.99 and an RMSE between 0.42 and 1.18 meters. When contrasted with traditional methods that utilize a single temporal image, a notable enhancement in bathymetric accuracy was observed.

Shallow water environmental information is crucial for the study of marine ecosystems and human activities. There have been numerous satellite remote sensing studies focused on this area. However, accurate information acquisition from remote sensing data remains difficult in this region due to the complexity of the environment and the coupling between benthic reflectance and water column scattering. In this study, we developed a method to retrieve the diffuse attenuation coefficient (Kd), seafloor classification, and bathymetric maps by combining satellite laser altimetry and optical remote sensing imagery in shallow water areas. Firstly, the relationships between remote sensing reflectance (Rrs), water depth, and Kd were established based on radiative transfer theory. This method allows for the retrieval of Kd in shallow water regions, overcoming the limitations present in previous studies. Secondly, we eliminated the water column attenuation and obtained the bottom reflectance index (BRI). The BRI allowed us to determine the bottom reflectance and classify the seafloor using the Gaussian mixture model clustering method. This approach can effectively reduce the error in bathymetric inversion caused by variations in bottom reflectance. Finally, we developed a neural network model for bathymetric inversion. The model inputs consist of Rrs data and spectral shape data containing physical constraint information, aiming to achieve a robust estimation performance. We conducted the study in two experimental areas (the Bimini Islands and the Yongle Atoll) and compared the results with validation data to evaluate the algorithm performance. The results indicated an agreement between the estimated Kd and the validation data (inferred Kd490 values of 0.062 m−1 and 0.058 m−1, compared to a validation data range of 0.055–0.087 m−1 and 0.059–0.070 m−1, respectively). In addition, the seafloor classification accuracy was 86.74 % for the Yongle Atoll area. Finally, the neural network model accurately predicted the bathymetry in the two regions. The accuracy of the bathymetric maps improved significantly with seafloor classification, as indicated by reductions in root mean square error (RMSE) of 0.12 m and 0.15 m, and in mean absolute percentage error (MAPE) by 2.24 % and 5.87 %, respectively. Overall, the proposed method can be used to effectively decouple benthic and water column signals and accurately obtain Kd, bottom reflectance, and bathymetric information for shallow water environments, providing unprecedented information for assessing and monitoring ecosystems and facilitating further research.

Equipped with the Advanced Topographic Laser Altimeter System (ATLAS) of 532 nm, the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) is enabled to penetrate the water surface to retrieve water depth information at certain depths. However, signal photon extraction is significantly impacted by noise and varying terrain conditions, particularly in deep water regions with weaker signal photons. To address these challenges, this study proposed a novel bathymetric signal extraction method for ICESat-2/ATLAS data based on adaptive rotating ellipse and B-spline curve iterative filtering. First, raw photons are segregated into water surface and bottom photons using the specular return removal algorithm and combined with the RANdom SAmple Consensus (RANSAC) algorithm to derive water surface elevation. Second, our method employs the modified Ordering Points to Identify the Clustering Structure (OPTICS) with adaptive variable ellipse and B-spline curve iterative filtering to detect bottom signal photons. Following refraction and tidal corrections, water depth is computed. Finally, the bathymetric accuracy of the proposed algorithm is evaluated using manually labeled photons and airborne bathymetric LiDAR data. The experimental results indicate the proposed algorithm’s superior F_score value, which increased by about 6.6 % and 4.1 % compared with the high-confidence photons and Adaptive Variable Ellipse Filtering Bathymetric Method (AVEBM). Moreover, the Mean Absolute Deviation (MAE) of bathymetric accuracy is 0.47 m, the Root Mean Square Error (RMSE) is 0.55 m, and Coefficient of Determination (R2) of bathymetric accuracy is 0.93. The proposed method effectively extracts signal photons and provides accurate water depth for nearshore bathymetry estimation.

“Ice, Cloud, and Land Elevation Satellite-2” (ICESat-2) produces photon-point clouds that can be used to obtain nearshore bathymetric data through density-based filtering methods. However, most traditional methods simplified the variable spatial density distribution of a photon to a linear relationship with water depth, causing a limited extraction effect. To address this limitation, we propose a two-stage filtering method that considers spatial relationships. Stage one constructs the adaptive photon density threshold by mapping a nonlinear relationship between the water depth and photon density to obtain initial signal photons. Stage two adopts a seed-point expanding method to fill gaps in initial signal photons to obtain continuous signal photons that more fully reflect seabed topography. The proposed method is applied to ICESat-2 data from Oahu Island and compared with three other density-based filtering methods: AVEBM (Adaptive Variable Ellipse filtering Bathymetric Method), Bimodal Gaussian fitting, and Quadtree Isolation. Our method (F-measure, F = 0.803) outperforms other methods (F = 0.745, 0.598, and 0.454, respectively). The accuracy of bathymetric data gained from seabed photons filtered using our method can achieve 0.615 m (Mean Absolute Error) and 0.716 m (Root Mean Squared Error). We demonstrate the effectiveness of incorporating photon spatial relationships to enhance the filtering of seabed signal photons.

Quantifying forest carbon storage to better manage climate change and its effects requires accurate estimation of forest structural parameters such as canopy height. Variables from remote sensing data and machine learning models are tools that are being increasingly used for this purpose. This study modeled the canopy height of forest–savanna mosaics in the Sudano–Guinean zone of Togo. Relative heights were extracted from GEDI and ICESat-2 products, which were combined with optical, radar, and topographic variables for canopy height modeling. We tested four methods: Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN). The RF algorithm obtained the best predictions using 98% relative height (RH98). The best-performing result was obtained from variables extracted from GEDI data (r = 0.84; RMSE = 4.15 m; MAE = 2.36 m) and compared to ICESat-2 (r = 0.65; RMSE = 5.10 m; MAE = 3.80 m). Models that were developed during this study can be applied over large areas in forest–savanna mosaics, enhancing forest dynamics monitoring in line with REDD+ objectives. This study provides valuable insights for future spaceborne LiDAR and other remote sensing data applications in similar complex ecosystems and offers local decision-makers a robust tool for forest management.

Shallow water bathymetry is essential for maritime navigation, environmental monitoring, and coastal management. While traditional methods such as sonar and airborne LiDAR provide high accuracy, their high cost and time-consuming nature limit their application in remote and sensitive areas. Satellite remote sensing offers a cost-effective and rapid alternative for large-scale bathymetric inversion, but it still relies on significant in situ data to establish a mapping relationship between spectral data and water depth. The ICESat-2 satellite, with its photon-counting LiDAR, presents a promising solution for acquiring bathymetric data in shallow coastal regions. This study proposes a rapid bathymetric inversion method based on ICESat-2 and Sentinel-2 data, integrating spectral information, the Forel-Ule Index (FUI) for water color, and spatial location data (normalized X and Y coordinates and polar coordinates). An automated script for extracting bathymetric photons in shallow water regions is provided, aiming to facilitate the use of ICESat-2 data by researchers. Multiple machine learning models were applied to invert bathymetry in the Dongsha Islands, and their performance was compared. The results show that the XG-CID and RF-CID models achieved the highest inversion accuracies, 93% and 94%, respectively, with the XG-CID model performing best in the range from −10 m to 0 m and the RF-CID model excelling in the range from −15 m to −10 m.

Accurate estimation of building height is crucial for urban aesthetics and urban planning as it enables an accurate calculation of the shadow period, the effective management of urban energy consumption, and thorough investigation of regional climatic patterns and human-environment interactions. Although three-dimensional (3D) cadastral data, ground measurements (total station, Global Positioning System (GPS), ground laser scanning) and air-based (such as Unmanned Aerial Vehicle—UAV) measurement methods are used to determine building heights, more comprehensive and advanced techniques need to be used in large-scale studies, such as in cities or countries. Although satellite-based altimetry data, such as Ice, Cloud and land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI), provide important information on building heights due to their high vertical accuracy, it is often difficult to distinguish between building photons and other objects. To overcome this challenge, a self-adaptive method with minimal data is proposed. Using building photons from ICESat-2 and GEDI data and building footprints from the New York City (NYC) and Los Angeles (LA) open data platform, the heights of 50,654 buildings in NYC and 84,045 buildings in LA were estimated. As a result of the study, root mean square error (RMSE) 8.28 m and mean absolute error (MAE) 6.24 m were obtained for NYC. In addition, 46% of the buildings had an RMSE of less than 5 m and 7% less than 1 m. In LA data, the RMSE and MAE were 6.42 m and 4.66 m, respectively. It was less than 5 m in 67% of the buildings and less than 1 m in 7%. However, ICESat-2 data had a better RMSE than GEDI data. Nevertheless, combining the two data provided the advantage of detecting more building heights. This study highlights the importance of using minimum data for determining urban-scale building heights. Moreover, continuous monitoring of urban alterations using satellite altimetry data would provide more effective energy consumption assessment and management.

Abstract The ICESat‐2 (Ice, Cloud and Land Elevation Satellite‐2) photon‐counting laser altimeter technology required the design and development of very sophisticated onboard algorithms to collect, store and downlink the observations. These algorithms utilize both software and hardware solutions for meeting data volume requirements and optimizing the science achievable via ICESat‐2 measurements. Careful planning and dedicated development were accomplished during the pre‐launch phase of the mission in preparation for the 2018 launch. Once on‐orbit all of the systems and subsystems were evaluated for performance, including the receiver algorithms, to ensure compliance with mission standards and satisfy the mission science objectives. As the mission has progressed and the instrument performance and data volumes were better understood, there have been several opportunities to enhance ICESat‐2's contributions to Earth observation science initiated by NASA and the ICESat‐2 science community. We highlight multiple updates to the flight receiver algorithms, the onboard software for signal processing, that have extended ICESat‐2's data capabilities and allowed for advanced science applications beyond the original mission objectives.

Although the photon point cloud data acquired from ICESat-2/ATLAS can be efficiently employed in urban building height extraction, its universal applicability in undulating terrain scenarios is constrained, and there are noticeable issues of false positives and false negatives. This research establishes a terrain-adaptive methodological framework based on ICESat-2/ATLAS photon point cloud to extract high-precision, high-density building height data across varied urban topographical conditions. First, a terrain-adaptive elevation buffer is utilized to coarse denoise the photon point cloud, involving the removal of the majority of noise photons in the scene, thereby enhancing the efficiency of subsequent algorithms. Second, urban signal photons are extracted from the remaining original photons using the Adaptive Method Based on Single-Photon Spatial Distribution (SPSD-AM). This approach demonstrates high universality across various urban scenes, while simultaneously ensuring a stable accuracy of urban signal photon extraction. Subsequently, ground photons are extracted from the urban signal photons and fit the ground curve based on the Adaptive Method Based on Spatial Differences of Urban Signal Photons (USPSD-AM), which addresses the challenge of the potential mixing of ground and building photons in complex terrain scenarios. A precise ground curve is then employed to extract building photons from urban signal photons. In order to mitigate issues such as false positives and negatives, post-processing steps, including completion and denoising of building photons, are implemented. Finally, the acquired building photons and ground curve are adopted to extract accurate building height parameters. The precision verification results show that the extracted building heights are considerably consistent with the reference building heights. The mean RMSE and MAE are 0.273 m and 0.202 m for flat terrains and 1.168 m and 0.759 m for undulating terrains, respectively. The proposed method demonstrates superior applicability across diverse urban scenarios, providing a robust theoretical foundation for large-scale urban building height retrieval efforts.

Accurate land-sea classification of ATL03 datasets is the prerequisite for signal photons identification and higher-level data production. Current photon classification methods are mainly based on physical models and machine learning methods. Physical models necessitate a priori knowledge of the background noise rate, while machine learning methods require extensive manual intervention, which can be time-consuming. This work introduced a fast feature detection model (FFD model) for land-sea classification that eliminates the need for prior knowledge and is not constrained by time. The land-sea boundary usually experiences significant photon height changes and reflective intensity differences on both sides. The FFD model was developed to efficiently detect the inflection point of photon reflective intensity for preliminary land-sea classification. Minor classification errors were carefully corrected based on topographic geometric distribution in further refinement. The FFD model was applied to three typical land-sea boundary types to evaluate its classification accuracy. Validation of classification results with consistent Sentinel-2 images and manually tagging photons indicated that the FFD method exhibited substantial robustness to various land-sea boundary landforms. The FFD model can process nearly 105 raw photon classifications per second, with an average overall accuracy of 99.42 % for preliminary classification results. Coastal slope and surface reflection in land-sea boundary may have impact on classification accuracy. These effects were carefully eliminated in the refinement process, resulting in an average overall accuracy improvement to 99.86 %. Consequently, the FFD model represented a robust and efficient solution for automatic land-sea classification in coastal shallow water, coral reefs, and estuary areas.

Abstract The Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2) mission has collected surface elevation measurements for over 5 years. ICESat‐2 carries an instrument that emits laser light at 532 nm, and ice and snow absorb weakly at this wavelength. Previous modeling studies found that melting snow could induce significant bias to altimetry signals, but there is no formal assessment on ICESat‐2 acquisitions during the melting season. We performed two case studies over the Greenland Ice Sheet to quantify bias in ICESat‐2 signals over snow: one to validate Airborne Topographic Mapper (ATM) data against Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS‐NG) grain sizes, and a second to estimate ICESat‐2 bias relative to ATM. We used snow optical grain sizes derived from ATM and AVIRIS‐NG to attribute altimetry bias to snowpack properties. For the first case study, the mean and standard deviation of optical grain sizes were 340 ± 65 µm (AVIRIS‐NG) and 670 ± 420 µm (ATM). A mean altimetry bias of 4.81 ± 1.76 cm was found for ATM, with larger biases linked to increases in grain size. In the second case study, we found a mean grain size of 910 ± 381 µm and biases of 6.42 ± 1.77 cm (ICESat‐2) and 9.82 ± 0.97 cm (ATM). The grain sizes and densities needed to recreate biases with a model are uncommon in nature, so we propose that additional surface attributes must be considered to characterize ICESat‐2 bias over snow. The altimetry biases are within the accuracy requirements of the ICESat‐2 mission, but we cannot rule out more significant errors over coarse‐grained snow.

The traditional approach to water level monitoring, using sensor devices to automatically measure levels in tanks or reservoirs, is costly and requires regular maintenance. This makes satellite altimetry crucial for reducing monitoring expenses and enhancing the observation of inland water bodies. This study employs Sentinel-3 and ICESat-2 sensors to examine six major reservoirs in the Krishna River basin in India, each intersected by at least two altimetry tracks. A two-way retrieval method for Sentinel analysis, including individual wet tropospheric correction and back-substitution, determines water levels. A novel hydro-flatness test removes outliers, increasing efficiency and saving time, though it is limited when fewer than ten high-distortion footprints are present. The 2018-2022 time series was effectively built for all reservoirs after assessing sensor performance against Central Water Commission data. The study successfully generates water level time series for the reservoirs, with correlation coefficients above 0.96 and Mean Absolute Percentage Errors below 1%. The Root Mean Square Error (RMSE) remains under 0.45m for all reservoirs except Ujjani, monitored by Sentinel-3A, which has an RMSE of 0.53 and a correlation of 0.96. These results can be applied to flood forecasting, reservoir operations, bathymetry retrieval, river modeling, and long-term water level-discharge curves.

The ice, cloud, and land elevation satellite-2 (ICESat-2) is the only satellite that produces photon-counting light detection and ranging data, and is equipped with the advanced topographic laser altimeter system. ICESat-2 provides sea surface height product; however, its approach of the product is unsuitable for areas with sub-surface signals. Conventional denoising methods applied to sea surface photon data of variable density involve the use of different empirical parameters. Considering the distribution of sea surface signal photons, we propose a general open-source method using a dual-signal unmixing parameter (DSUMP), which incorporates the Gaussian distribution of dual-signal peaks to determine the sea surface range. This method facilitates the direct extraction of sea surface photons under various observation conditions—day or night, strong or weak beams, and including or excluding seabed photons—without requiring any variable parameters. The elevation error by DSUMP within 0.1m accounts for more than 97%. The mean absolute error is within 0.01 m compared to sea surface photons obtained via manual extraction. Different model parameters show stable denoising accuracy, only affects operating efficiency. The proposed method introduces a novel denoising technique for extracting sea surface elevation from ICESat-2 altimetry data, and its applicability can be extended to various point cloud data with similar distributions.

Satellite technology is an efficient tool, which can provide valuable observations for coastal areas from space. Compared with conventional bathymetric surveying approaches, remote sensing-based shallow water bathymetry retrieval methods have been widely used in recent years. Various empirical models have been proposed for deriving bathymetry of coastal shallow water, and prior topographic information is required to construct models. Traditional studies tend to select a cloud-free remote sensing image to map the coastal shallow water topography. As a result, in addition to the selection of empirical models, the high-quality remote sensing image and accurate prior topographic data are also of importance. This study aims to propose a method for mapping coastal shallow water bathymetry from multitemporal remote sensing imagery. Here, Sentinel-2 imagery time series are composited to produce a clear image, which can effectively avoid the contamination of clouds, water turbidity and other noises. ICESat-2 lidar altimeter data that contain accurate underwater elevations are used to provide topographic information. Moreover, Sentinel-2-based multispectral information and ICESat-2-based topographic information are combined for the coastal bathymetry retrieval by five empirical models (i.e., linear band model, ratio band model, support vector machine, neural network, and random forest). This proposed method is tested in Dongsha Atoll in South China Sea, and achieve a good performance [training: root mean square error (RMSE): 0.97 m ± 0.76 m, mean absolute percentage error (MAPE): 4.07% ± 0.046%, R-square (R2): 0.90 ± 0.14; validation: RMSE: 1.22 m ± 0.43 m, MAPE: 5.43% ± 0.035%, R2: 0.86 ± 0.089]. The comparison confirms that machine learning methods perform better than traditional methods, and the deep learning techniques can be further introduced in estimating shallow water bathymetry in the future, which is expected to achieve an excellent accuracy in bathymetry inversion.

Satellite altimetry has revolutionized river monitoring, particularly for hydrologists working on river flow monitoring in sparsely or ungauged areas. Despite this, there is a lack of a comprehensive evaluation of radar and LiDAR altimeters with varying sensor specifications for river water level retrieval, seasonal change characterization, and water surface slope (WSS) using gauged long-term water level and global navigation satellite system data. This study addresses this gap by combined evaluation of radar (ENVISAT to Sentinel-3) and LiDAR (ICESat-1, ICESat-2) altimeters along the Ganga River, from Prayagraj to Varanasi. We found that all the radar altimetry missions showed better accuracy for water level retrievals (R2 > = 0.8; RMSE 0.11 to 1.16 m) and water level change quantification (RMSE 0.59 m). However, Sentinel-3 with synthetic aperture radar (SAR) acquisition mode outperformed (RMSE 0.11 to 0.14 m) all the radar missions having low resolution mode. Despite LiDAR missions' high vertical accuracy, they show relatively lower accuracy in water level time series generation due to nonrepeating characteristics. In contrary, ICESat-2 demonstrates potential in capturing spatial and seasonal variability of WSS, enhancing the accuracy of surface water and ocean topography (SWOT) discharge products when combined with SWOT River database. This study provides a comprehensive baseline for end-users interested in utilizing radar and LiDAR missions for various hydrological applications, including river discharge estimation. Moreover, the studied river reach shares the SWOT calibration orbit, allowing the utilization of generated satellite and in-situ databases for the effective evaluation of SWOT measurements.

The coral reef geomorphic zone classification (CRGZC) map can provide a wealth of information for coastal management and protection. Remote sensing plays an important role in CRGZC by virtue of its speed, wide range, and low cost. Although many excellent results have been achieved in this field, there are still some shortcomings. With the development of machine learning, such methods are gradually introduced to CRGZC, yet the research and application of deep learning methods are still relatively few. In this article, based on ICESat-2 data and multispectral images, a deep learning model coupled with convolutional neural network (CNN) and random forest (RF) was proposed for coral reef geomorphic zone classification (CR_CRGZC). First, the priori bathymetry points were extracted from ICESat-2. Then, a near-shore bathymetry map was generated using a log-ratio model. Finally, topographic data and multispectral images were combined to achieve CRGZC through CR_CRGZC. The northeastern part of Coffin Island (CI) and the southern part of Punta Vaquero (PV) in Puerto Rico Island were selected as study areas. By comparing the classification results with those of CNN, RF, and maximum likelihood classification, CR_CRGZC outperformed the other classification methods. By quantitative analysis, the OA and Kappa coefficients of CR_CRGZC were 91.91% and 0.9013 in the CI region; and 89.91% and 0.8735 in the PV region, respectively. Under the same environmental requirements, this approach can map high-precision submeter CRGZC maps, providing a database for dynamic coral reef habitat mapping, which contributes to marine coastal ecosystem protection and coastal underwater topography monitoring.

To address the challenge of retrieving subcanopy topography using single-baseline single-polarization TanDEM-X interferometric synthetic aperture radar (InSAR) data, we propose a novel InSAR processing framework. Our methodology begins by employing the SINC model to estimate the penetration depth (PD). Subsequently, we establish a linear relationship between PD and phase center height (PCH) to generate a wall-to-wall PCH product. To achieve this, spaceborne light detection and ranging (LiDAR) data are employed to capture the elevation bias between actual ground elevation and InSAR-derived elevation. Finally, the subcanopy topography is derived by subtracting the PCH from the conventional InSAR-based digital elevation model (DEM). Moreover, this approach enables the simultaneous estimation of forest height from single-baseline TanDEM-X data by combining the estimated PD and PCH components. The approach has been validated against airborne LiDAR scanning (ALS) data over four diverse sites encompassing different forest types, terrain conditions, and climates. The derived subcanopy topography in the boreal and hemiboreal forest sites (Krycklan and Remningstorp) demonstrated notable improvement in accuracy. Additionally, the winter acquisitions outperformed the summer ones in terms of inversion accuracy. The achieved root-mean-square errors (RMSEs) for the winter scenarios were 2.45 m and 3.83 m, respectively, representing a 50% improvement over the InSAR-based DEMs. The forest heights are also close to the ALS measurements, with RMSEs of 2.70 m and 3.33 m, respectively. For the Yanguas site in Spain, characterized by rugged terrain, subcanopy topography in forest areas was estimated with an accuracy of 4.27 m, a 35% improvement over the original DEM. For the denser tropical forest site, only an average elevation bias could be corrected.

The ICESat-2 photon-counting lidar can simultaneously observe high-resolution radiometric and geometric characteristics of ocean waves. In this study, combining the wave radiometric and geometric characteristics, ICESat-2 can provide robust and accurate wind speeds over oceans that cannot be achieved by using a single characteristic. Specifically, with an along-track segment of 700 m, the sea surface photons and surface profile are detected and generated from ICESat-2 ATL03 geolocated photons by which the received energy and wave height are obtained. The unknown atmospheric transmittance and swell wave height have significant impacts on deriving the water surface reflectance (radiometric characteristic) and the wind-driven water height (geometric characteristic), respectively. We use all available ICESat-2 data in 2018 and 2019 to determine the unknown parameters and further separately estimate wind speeds. The radiation-based method performs better at low wind speeds, while the geometry-based method performs better at high wind speeds, and we combine each specific advantage to obtain more reliable wind speeds. In the Pacific Ocean, Atlantic Ocean, and Indian Ocean, thousands of ICESat-2 ground tracks from 2020 are used to estimate wind speeds, and the performance is validated against a trustworthy data source of ERA5 with a root-mean-square error (RMSE) of ∼0.3 m for wave heights and an RMSE of ∼1.5 m/s for wind speeds. ICESat-2 has shown great ability to provide independent wind speeds over oceans with high accuracy and resolution, which may fill the gap of wind speeds on a small scale as the large-scale results are well observed by spaceborne microwave devices.

Many current satellite-derived bathymetry methods usually rely on in-situ water depth, which limits their applications. In this study, an ICESat-2 assisted dual-band model (IDBM) is proposed, which can be used to derive bathymetry independent of in-situ water depth. First, the Sentinel-2 blue and green band diffuse attenuation coefficient is derived using ICESat-2 lidar photon data from adjacent deep water according to the Jerlov ${{K}_d}$ spectral curves and the sensor spectral response function. Then, these two coefficients are applied to the IDBM model to derive the water depth. Datasets for four study areas are used to validate the feasibility of the proposed IDBM model, in addition, the original QAA (quasi-analytical algorithm) assisted dual-band model (QDBM) is also used to derive bathymetry for comparison purposes. Experiment results show that the proposed IDBM model is effective and can achieve higher or similar accuracy compared to the original QDBM model. The average mean absolute error (MAE) and root mean square error (RMSE) of the IDBM model reach 1.23 and 1.67 m, while those of the QDBM model are 1.44 and 1.91 m, respectively. The performance of the IDBM model has improved to some extent. Especially for the SI case, the MAE and RMSE of the QDBM model are 1.46 and 2.00 m, while those of the IDBM model are 0.99 and 1.55 m, which are reduced by 0.47 and 0.45 m. In conclusion, the proposed IDBM model is feasible and effective to derive bathymetry without in-situ water depth.

The horizontal positioning error in spaceborne photon point clouds seriously constrains their elevation accuracy. To improve data quality for enhanced performance in scientific applications, this study proposes a photon correction method based on terrain feature identification, specifically for the photon-counting spaceborne lidar. Unlike the conventional terrain matching method, this approach accurately determines the horizontal positions of photons within a small-range area by establishing a matching relationship between the laser elevation turning points and the surface boundary lines. The feasibility of this method was verified using the satellite laser altimetry simulation platform, and the horizontal correction accuracy can reach within 0.6 m. Subsequently, the experiments were conducted to verify the geometric positioning accuracy of ICESat-2 across different areas, leveraging high-precision digital surface models. The results indicate that the average horizontal accuracy of ICESat-2 was 3.81 m, and the elevation accuracy was better than 0.5 m.

Synthesis of multi-satellite altimetry facilitates the acquisition of long-term changes in lake level but may induce biases due to inconsistent data sources, and thus remains largely unexplored. This study investigates the integrated application of Ice, Cloud, and land Elevation Satellite (ICESat), ICESat-2, and CryoSat-2 missions to provide consecutive lake level series in 2003–2020. The sample comprises 48 lakes in the world with gauge-based or Hydroweb water level data. The CryoSat-2 data are first adjusted to the ICESat-2 height reference based on the mean values of their monthly water level difference during their overlapping period (2018–2020). Then, the corrected CryoSat-2 data are used to link the ICESat and ICESat-2 data. Results show that in the sample lakes, the deviations between CryoSat-2 and ICESat-2 data vary and range from −0.78 m to 0.13 m. The data quality of the synthesized time series is evaluated by comparing against the validation data, with an average R2 of 0.84. This study shows that CryoSat-2 has the potential of filling the gap between ICESat and ICESat-2. The three altimeters can be expected to integrate effectively for monitoring lake water level changes in the past two decades.

Spaceborne photon-counting LiDAR is significantly affected by noise, and existing denoising algorithms cannot be universally adapted to different surface types and topographies under all observation conditions. Accordingly, a new denoising method is presented to extract signal photons adaptively. The method includes two steps. First, the local neighborhood radius is calculated according to photons' density, then the first-step denoising process is completed via photons’ curvature feature based on KNN search and covariance matrix. Second, the local photon filtering direction and threshold are obtained based on the first-step denoising results by RANSAC and elevation frequency histogram, and the local dense noise photons that the first-step cannot be identified are further eliminated. The following results are drawn: (1) experimental results on MATLAS with different topographies indicate that the average accuracy of second-step denoising exceeds 0.94, and the accuracy is effectively improves with the number of denoising times; (2) experiments on ICESat-2 under different observation conditions demonstrate that the algorithm can accurately identify signal photons in different surface types and topographies. Overall, the proposed algorithm has good adaptability and robustness for adaptive denoising of large-scale photons, and the denoising results can provide more reasonable and reliable data for sustainable urban development.

The ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) can collect earth surface elevation data with high precision on a global scale. However, the collected photon data contains a large amount of background noise due to the influence of sunlight, cloud reflection, and other factors. For photon data of different scenes, how to effectively denoise and achieve accurate classification of photon point clouds is crucial for subsequent applications. This study proposes a random forest based method for denoising and classifying ICESat-2 photon data in urban areas by fusing spectral features from Sentinel-2 images and spatial distribution features from photon data. The experimental results show that the method can effectively identify various types of photons. Compared with the reference data, the overall accuracy of photon denoising and classification is 95.97% on average, and the average kappa coefficient is 94.18%. Further analysis demonstrates that the addition of sentinel-2 spectral information can effectively improve the classification accuracy of photon point clouds in urban areas, and the photon classification method of combining photon lidar data and optical images can be a promising solution to improve classification accuracy.

ABSTRACTIce, Cloud, and land Elevation Satellite-2 (ICESat-2) provides effective photon-counting light detection and ranging (LiDAR) data for estimating forest height across extensive geographical areas. Although prior studies have illustrated canopy conditions during leaf-on and leaf-off phases may influence ICESat-2 derived forest heights, a comprehensive understanding of this effect remains incomplete. This study seeks to comprehensively assess how varying canopy conditions (leaf-on/leaf-off) affect ICESat-2 forest height retrieval and modelling. First, the accuracies of ICESat-2 terrain and canopy heights under leaf-on and leaf-off conditions were validated. Second, random forest algorithm was utilized to model forest height by integrating ICESat-2, Sentinel-2, and other ancillary datasets. Finally, we evaluated the influence of leaf-on and leaf-off conditions on forest height retrieval and modelling. Results reveal higher consistency between ICESat-2 and airborne LiDAR-derived terrain heights compared to the agreement between two canopy height datasets. Accuracies of ICESat-2 terrain and canopy heights are higher under leaf-off conditions in contrast to leaf-on conditions. Notably, the accuracies of ICESat-2 terrain and canopy heights under various conditions are closely linked to canopy cover. Furthermore, the accuracy of forest height modelling can be enhanced by combining ICESat-2 data collected during both leaf-on and leaf-off seasons with further eliminating low-quality samples.

Mapping forest canopy height is critical for climate modeling and forest management, and tropical forests present unique challenges for remote sensing due to their dense vegetation and complex structure. The advent of ICESat-2 and GEDI, two advanced lidar datasets, offers new opportunities for improving canopy height estimation. In this study, we used footprint-level canopy height products from ICESat-2 and GEDI, combined with features extracted from Landsat-8, PALSAR-2, and FABDEM products. The AutoGluon stacking ensemble learning algorithm was employed to construct inversion models, generating 30 m resolution continuous canopy height maps for the tropical forests of Puerto Rico. Accuracy validation was performed using the high-resolution G-LiHT airborne lidar products. Results show that tropical forest canopy height inversion remains challenging, with all models yielding relative root mean square errors (rRMSE) exceeding 0.30. The stacking ensemble model outperformed all base learners, and the GEDI-based map had slightly higher accuracy than the ICESat-2-based map, with RMSE values of 4.81 and 4.99 m, respectively. Both models showed systematic biases, but the GEDI-based model exhibited less underestimation for taller canopies, making it more suitable for biomass estimation. The proposed approach can be applied to other forest ecosystems, enabling fine-resolution canopy height mapping and enhancing forest conservation efforts.

The sea ice extent in the Weddell Sea exhibited a positive trend from the start of satellite observations in 1978 until 2016 but has shown a decreasing trend since then. This study analyzes seasonal and interannual variations in sea ice thickness using ICESat-2 laser altimetry data over the Weddell Sea from 2019 to 2022. Sea ice thickness was calculated from ICESat-2’s ATL10 freeboard product using the Improved Buoyancy Equation. Seasonal variability in ice thickness, characterized by an increase from February to September, is more pronounced in the eastern Weddell sector, while interannual variability is more evident in the western Weddell sector. The results were compared with field data obtained between 2019 and 2022, showing a general agreement in ice thickness distributions around predominantly level ice. A decreasing trend in sea ice thickness was observed when compared to measurements from 2003 to 2017. Notably, the spring of 2021 and summer of 2022 saw significant decreases in Sea Ice Extent (SIE). Although the overall mean sea ice thickness remained unchanged, the northwestern Weddell region experienced a noticeable decrease in ice thickness.

The ICESat-2 and SWOT satellite earth observation missions have provided highly accurate water surface slope (WSS) observations in global rivers for the first time. While water surface slope is expected to remain constant in time for approximately uniform flow conditions, we observe time varying water surface slope in many river reaches around the globe in the ICESat-2 record. Here, we investigate the causes of time variability of WSSs using simplified river hydraulic models based on the theory of steady, gradually varied flow. We identify bed slope or cross section shape changes, river confluences, flood waves, and backwater effects from lakes, reservoirs, or the ocean as the main non-uniform hydraulic situations in natural rivers that cause time changes of WSSs. We illustrate these phenomena at selected river sites around the world, using ICESat-2 data and river discharge estimates. The analysis shows that WSS observations from space can provide new insights into river hydraulics and can enable the estimation of river discharge from combined observations of water surface elevation and WSSs at sites with complex hydraulic characteristics.

Satellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, such reference data are too sparse to train the biomass retrieval model and approaches for calibrating that are independent from training data are sought. In this study, we compare the performance of one such calibration approach with the traditional regression modelling using reference measurements. The performance was evaluated at four sites representative of the major forest biomes in Europe focusing on growing stock volume (GSV) prediction from time series of C-band Sentinel-1 and Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS-2 PALSAR-2) backscatter measurements. The retrieval model was based on a Water Cloud Model (WCM) and integrated two forest structural functions. The WCM trained with plot inventory GSV values or calibrated with the aid of auxiliary data products correctly reproduced the trend between SAR backscatter and GSV measurements across all sites. The WCM-predicted backscatter was within the range of measurements for a given GSV level with average model residuals being smaller than the range of the observations. The accuracy of the GSV estimated with the calibrated WCM was close to the accuracy obtained with the trained WCM. The difference in terms of root mean square error (RMSE) was less than 5% units. This study demonstrates that it is possible to predict biomass without providing reference measurements for model training provided that the modelling scheme is physically based and the calibration is well set and understood.

Most work to date on satellite-derived bathymetry (SDB) depth change estimates water depth at individual times t1 and t2 using two separate models and then differences the model estimates. An alternative approach is explored in this study: a multi-temporal Sentinel-2 image is created by “stacking” the bands of the times t1 and t2 images, geographically coincident reference data for times t1 and t2 allow for “true” depth change to be calculated for the pixels of the multi-temporal image, and this information is used to fit a single model that estimates depth change directly rather than indirectly as in the model-differencing approach. The multi-temporal image approach reduced the depth change RMSE by about 30%. The machine learning modelling method (categorical boosting) outperformed linear regression. Overfitting of models was limited even for the CatBoost models having the maximum number of variables examined. The visible Sentinel-2 spectral bands contributed most to the model predictions. Though the multi-temporal stacked image approach produced clearly superior depth change estimates compared to the conventional approach, it is limited only to those areas for which geographically coincident multi-temporal reference/“true” depth data exist.

The leaf area index (LAI) is a critical variable for forest ecosystem processes. Passive optical and active LiDAR remote sensing have been used to retrieve LAI. LiDAR data have good penetration to provide vertical structure distribution and deliver the ability to estimate forest LAI, such as the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). Segment size and beam type are important for ICESat-2 LAI estimation, as they affect the amount of signal photons returned. However, the current ICESat-2 LAI estimation only covered a limited number of sites, and the performance of LAI estimation with different segment sizes has not been clearly compared. Moreover, ICESat-2 LAIs derived from strong and weak beams lack a comparative analysis. This study derived and evaluated LAI from ICESat-2 data over the National Ecological Observatory Network (NEON) sites in North America. The LAI estimated from ICESat-2 for different segment sizes (20, 100, and 200 m) and beam types (strong beam and weak beam) were compared with those from the airborne laser scanning (ALS) and the Copernicus Global Land Service (CGLS). The results show that the LAI derived from strong beams performs better than that of weak beams because more photon signals are received. The LAI estimated from the strong beam at the 200 m segment size shows the highest consistency with those from the ALS data (R = 0.67). Weak beams also present the potential to estimate LAI and have moderate agreement with ALS (R = 0.52). The ICESat-2 LAI shows moderate consistency with ALS for most forest types, except for the evergreen forest. The ICESat-2 LAI shows satisfactory agreement with the CGLS 300 m LAI product (R = 0.67, RMSE = 1.94) and presents a higher upper boundary. Overall, the ICESat-2 can characterize canopy structural parameters and provides the ability to estimate LAI, which may promote the LAI product generated from the photon-counting LiDAR.

Abstract In the summer of 2020, ESA changed the orbit of CryoSat‐2 to align periodically with NASA's ICESat‐2 mission, a campaign known as CRYO2ICE, which allows for near‐coincident CryoSat‐2 and ICESat‐2 observations in space and time over the Arctic until summer 2022, where the CRYO2ICE Antarctic campaign was initiated. This study investigates the Arctic CRYO2ICE radar and laser freeboards acquired by CryoSat‐2 and ICESat‐2, respectively, during the winter seasons of 2020–2022, and derives snow depths from their differences along the orbits. Along‐track snow depth observations can provide high‐resolution snow depth distributions which are vital for air‐ice‐ocean heat and momentum transfer, understanding light transmission, and snow‐ice‐interactions. Generally, ICESat‐2 is backscattered at a surface above the elevation of the CryoSat‐2 signal. CRYO2ICE snow depths are thinner than the daily model‐ or passive‐microwave‐based snow depth composites used for comparison, with differences being most pronounced in the Atlantic and Pacific Arctic. Satellite‐derived and model‐based snow estimates show similar seasonal accumulation over first‐year ice, but CRYO2ICE has limited seasonal accumulation over multi‐year ice which is linked to a slow increase in ICESat‐2, and to some extent CryoSat‐2, freeboards. We present a first estimation of spaceborne along‐track snow depth estimates with average uncertainty of 10–11 ± 2–3 cm for 7‐km segments, with random and systematic contributions of 7 and 4 cm. These observations show the potential for along‐track dual‐frequency observations of snow depth from the future Copernicus mission CRISTAL; but they also highlight uncertainties in radar penetration and the correlation length scales of snow topography that still require further research.

Abstract This study focuses on utilizing the increasing availability of satellite trajectory data from global navigation satellite system‐enabled low‐Earth orbiting satellites and their precision orbit determination (POD) solutions to expand and refine thermospheric model validation capabilities. The research introduces an updated interface for the GEODYN‐II POD software, leveraging high‐precision space geodetic POD to investigate satellite drag and assess density models. This work presents a case study to examine five models (NRLMSIS2.0, DTM2020, JB2008, TIEGCM, and CTIPe) using precise science orbit (PSO) solutions of the Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2). The PSO is used as tracking measurements to construct orbit fits, enabling an evaluation according to each model's ability to redetermine the orbit. Relative in‐track deviations, quantified by in‐track residuals and root‐mean‐square errors (RMSe), are treated as proxies for model densities that differ from an unknown true density. The study investigates assumptions related to the treatment of the drag coefficient and leverages them to eliminate bias and effectively scale model density. Assessment results and interpretations are dictated by the timescale at which the scaling occurs. DTM2020 requires the least scaling (∼−7%) to achieve orbit fits closely matching the PSO within an in‐track RMSe of 7 m when scaled over 2 weeks and 2 m when scaled daily. The remaining models require substantial scaling of the mean density offset (∼30 − 75%) to construct orbit fits that meet the aforementioned RMSe criteria. All models exhibit slight over or under‐sensitivity to geomagnetic activity according to trends in their 24‐hr scaling factors.

Abstract A major advance in global bathymetric observation occurred in 2018 with the launch of NASA’s ICESat‐2 satellite, carrying a green‐wavelength, photon‐counting lidar, the Advanced Topographic Laser Altimeter System (ATLAS). Although bathymetric measurement was not initially a design goal for the mission, pre‐ and post‐launch studies revealed ATLAS’s notable bathymetric mapping capability. ICESat‐2 bathymetry has been used to support a wide range of coastal and nearshore science objectives. However, analysis of ICESat‐2 bathymetry in numerous locations around the world revealed instances of missing or clipped bathymetry in areas where bathymetric measurement should be feasible. These missing data were due to the ATLAS receiver algorithms not being optimized for bathymetry capture. To address this, two updates have been made to ICESat‐2’s receiver algorithm parameters with the goal of increasing the area for which ICESat‐2 can provide bathymetry. This paper details the parameter changes and presents the results of a two‐phased study designed to investigate ICESat‐2’s bathymetry enhancements at both local and global scales. The results of both phases confirm that the new parameters achieved the intended goal of increasing the amount of bathymetry provided by ICESat‐2. The site‐specific phase demonstrates the ability to fill critical bathymetric data gaps in open ocean and coastal settings. The global analysis shows that the area of potential bathymetry approximately doubled, with 6.1 million km2 of new area in which bathymetric measurements may be feasible. These enhancements are anticipated to facilitate a range of science objectives and close the gap between ICESat‐2 bathymetry and offshore sonar data.

Abstract Boreal forest heights are associated with global carbon stocks and energy budgets. The launch of the Advanced Topographic Laser Altimeter System (ATLAS) onboard the NASA's Ice, Cloud and Land Elevation Satellite (ICESat‐2) enables canopy vertical structure measurement at a global scale. However, with a photon‐counting laser system, ICESat‐2 contains high uncertainties in the estimated canopy heights, requiring appropriate quality control before being applied to canopy height modelling. We adopted a multivariate quality control approach (i.e. the Cook's distance) to improve the quality of ICESat‐2 samples. The controlled ICESat‐2 data were then input as the response variable for predicting boreal forest heights based on spatially continuous satellite data and machine learning (ML) regression models. The examined ML regressors include artificial neural networks (ANN), gradient boosting machine (GBM), random forest (RF) and support vector regression (SVR). The proposed quality control effectively removes low‐quality ICESat‐2 samples and enhances the correlations between ICESat‐2 and airborne laser scanning (ALS) observations. Moreover, the controlled ICESat‐2 samples help achieve a trade‐off between sample quality and quantity for all ML regressors, generating close canopy heights to ALS‐derived counterparts. Overall, RF and GBM make better canopy height predictions than ANN and SVR. Of all explanatory variables, the normalized difference vegetation index calculated based on the first red edge band of Sentinel‐2 (NDVIredEdge1) is considered the most important. The proposed quality control on ICESat‐2 sample selection and canopy height model (CHM) development workflow will greatly benefit forest structure investigations in the Arctic community.