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AbstractICESat-2 has made a significant contribution to polar research, although its application in oceanography is relatively limited. The precision and resolution of its sea surface height (SSH) products are crucial for ensuring their effectiveness in oceanographic applications. Therefore, this study presents a comprehensive assessment of the precision and resolution of ICESat-2 ATL12 SSH product, utilizing Cryosat-2 and Jason-3 altimeter data and tide gauge measurements as references. First, ICESat-2 exhibits significant data gaps over open oceans, with sampling distances exceeding 5 km. However, the mean sampling distance in coastal regions is only 1.2 km. Second, the sea surface height obtained by ICESat-2 has good precision, with a standard deviation of approximately 8 cm for crossover differences with Cryosat-2. Third, there is a significant negative bias compared to Cryosat-2 and Jason-3, which can be classified into systematic and time-varying biases. The systematic bias is primarily attributable to discrepancies in geophysical corrections, whereas the time-varying bias is predominantly caused by scattering. In addition, the time-varying characteristic of the bias exert a significant influence on sea surface height, with a STD of 8–9 cm. This indicates that the precision of ATL12 SSHs is inconsistence, which should be given attention when it is used. [ABSTRACT FROM AUTHOR]

"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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study evaluated six open‐access digital elevation models (DEMs) for the Del Azul Creek Basin in the Argentine Chaco‐Pampean Plain: Shuttle Radar Topography Mission, Advanced Land Observing Satellite Phased Array L‐Band Synthetic Aperture Radar, TerraSAR‐X Add‐On for Digital Elevation Measurements (TanDEM‐X), NASADEM Global DEM, Forest and Building height biases were removed from Copernicus GLO 30 DEM V1‐0 (FABDEM), and TanDEM‐X 30 m Edited DEM (EDEM). Statistical metrics were calculated for (i) residuals between DEMs and the Ice, Cloud and Land Elevation Satellite‐2 (ICESat‐2); (ii) the minimum distance between DEM‐derived drainage networks and those from the Buenos Aires Provincial Water Authority; and (iii) DEM‐derived slopes in shallow water bodies compared with the Joint Research Centre's Global Surface Water Mapping product. Analyses were performed for four elevation and seven slope bands. TanDEM‐X had the smallest errors compared to ICESat‐2 (median 0.19 m, NMAD 0.38 m), followed by FABDEM (median 0.31 m, NMAD 0.23 m). EDEM performed best in drainage networks (median 99.45 m, NMAD 117.16 m), followed by FABDEM. In general, the vertical error increased with elevation and the accuracy of the drainage network estimates improved. The vertical accuracy decreased with steeper slopes, with FABDEM performing the best across all slope ranges. FABDEM exhibited the best performance in determining seasonally dispersed shallow water bodies, demonstrating its overall usefulness for hydrological applications in large‐scale plains characterized by aeolian geoforms of lowland accumulation and erosion. Assessing freely available products provides valuable resources for researchers and professionals and can guide decision making for managing hydrological resources, including flood risk and infrastructure development. [ABSTRACT FROM AUTHOR]

The ICESat-2 Land and Vegetation Height (ATL08) product is a new control point dataset for large-scale topographic mapping and geodetic surveying. However, its elevation accuracy is typically affected by multiple factors. The study aims to propose a new approach to classify ATL08 terrain estimates into different accuracy levels and extract reliable ground control points (GCPs) from ICESat-2 ATL08. Specifically, the methodology is divided into three stages. First, the ATL08 terrain estimates are matched with the raw ATL03 photon cloud data, and the ATL08 terrain estimates are used to fit a continuous terrain curve. Then, using the fitted continuous terrain curve and raw ATL03 photon cloud data, a pseudo-waveform is generated for grading the ATL08 terrain estimates. Finally, all the ATL08 terrain estimates are graded based on the peak characteristics of the generated pseudo-waveform. To validate the feasibility of the proposed method, four study areas from the National Ecological Observatory Network (NEON), characterized by various terrain features and forest types were selected. High-accuracy airborne lidar data were used to evaluate the accuracy of graded ICESat-2 terrain estimates. The results demonstrate that the method effectively classified all ATL08 terrain estimates into different accuracy levels and successfully extracted high-accuracy GCPs. The root mean square errors (RMSEs) of the first accuracy level in the four selected study areas were 0.99 m, 0.51 m, 1.88 m, and 0.65 m, representing accuracy improvement of 51.7%, 58.2%, 83.1%, and 68.8%, respectively, compared to the original ATL08 terrain estimates before classifying. Additionally, a comparison with the conventional threshold-based GCP extraction method demonstrated the superior performance of our proposed approach. This study introduces a new approach to extract high-quality elevation control points from ICESat-2 ATL08 data, particularly in forested areas. [ABSTRACT FROM AUTHOR]

Beginning in March 2022, the Antarctic Peninsula's Hektoria Glacier experienced an unprecedented retreat of ∼23 km over 1.5 years, one of the fastest observed glacier retreats on record. Improving constraints on the drivers of such extreme events is key to understanding glacier change around the continent and future sea‐level rise. We use satellite remote sensing and reanalysis data to characterize changes in Hektoria, a former Larsen B Ice Shelf tributary, over the last ∼20 years and document a period of retreat from 2002 to 2011, and readvancement from 2011 to 2022. We find that the long‐term ice front and velocity response (2002–2022) correlated more strongly with changes in modeled ocean temperatures compared to surface air temperatures. However, the acute loss of buttressing support following fast ice collapse paired with a near‐contemporaneous extreme atmospheric river in the region likely catalyzed the unprecedented 2022–2023 retreat. Plain Language Summary: The Antarctic Ice Sheet is one of the largest sources for future sea level rise, yet how much and how fast ice is lost to the ocean here remains relatively unknown. Ice shelves can buttress glaciers from flowing quickly into the ocean, stabilizing their movement and limiting mass discharge. As ice shelves retreat or break up, glaciers accelerate, adding mass to the ocean. In this study, we use imagery and elevation data collected from airborne studies and satellites to characterize how Hektoria Glacier—a marine‐terminating glacier located on the Eastern Antarctic Peninsula that was previously a Larsen B Ice Shelf tributary—has changed over the past 20 years. We compare these changes with available ocean and air temperatures in the region to determine how they influenced the observed fluctuations over time. We find that Hektoria retreated from 2002 to 2011 and readvanced from 2011 to 2022, followed by an unprecedented retreat of ∼23 km between March 2022 and August 2023. We find that abrupt changes in stress following buttressing loss drives glacier change, while modeled ocean temperatures wield influence on Hektoria's long‐term fluctuations and atmospheric temperatures drive shorter term changes in glacier response. Key Points: Hektoria Glacier retreated ∼23 km between March 2022 and August 2023—one of the fastest observed marine‐terminating glacier retreatsChanges in buttressing support and mid‐depth ocean temperatures served as primary drivers for change at Hektoria between 2002 and 2022Understanding long‐ and short‐term glacier response to ocean and atmospheric variability is key to improved sea level rise predictions [ABSTRACT FROM AUTHOR]

Studying glacier changes is essential for understanding global glacier mass balance, patterns in climate change, and the potential consequences they may have. The glaciers of Geladandong Mountain serve as a crucial water source for the Yangtze River and its downstream regions. Any changes in the glacier at the river source will directly impact the ecological environment and water recycling process downstream. This study presents an approach for monitoring Geladandong Mountain glacier elevation changes using ICESat-2 data combined with a Binned Multilevel Regression Fitting Method (BMRFM). We analysed multi-year ICESat-2 data, considering various topographic features, to understand glaciers' annual and seasonal elevation changes. Our research reveals significant spatial heterogeneity in glacier elevation changes, influenced by altitude, slope, and aspect factors. It demonstrates the effectiveness of ICESat-2 in glacier monitoring, offering insights into the impacts of climate change on glacier dynamics. Moreover, the results indicate a predominant trend of ongoing melting at lower altitudes, with a small amount of accretion at higher elevations. Over the last two decades, the overall elevation change rate is −0.23 ± 0.12 metres per year. Seasonal analysis shows substantial glacier thickening from April to August, primarily due to increased precipitation offsetting elevated temperatures. This study offers a robust method for monitoring glaciers using satellite data, thereby advancing the precision and reliability of glacier research. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: NASA's Ice, Cloud, and land Elevation Satellite 2 (ICESat‐2) uses green laser beams to map the elevation of the Earth's surface. These beams can also penetrate water, allowing the satellite to map the seafloor elevation up to a certain depth. These measurements are important for research on many topics, including shallow marine habitat mapping. In many cases, ICESat‐2 seafloor depths are combined with color information from satellite imagery. However, it is difficult to combine large quantities of these two data types because they are distributed by different systems and come in different formats. In this paper, we present a workflow for ICESat‐2 seafloor mapping that uses free, cloud computing resources from NASA and Google. By leveraging these computing resources, we can combine large amounts of ICESat‐2 data with satellite imagery. These large data sets are highly sought after for projects in artificial intelligence. We demonstrate this by building our own artificial intelligence model to map shallow marine habitats from ICESat‐2 data. A statistical analysis shows that combining ICESat‐2 with satellite imagery increases accuracy by 18 percentage points. Future NASA satellite missions will use similar sensors to ICESat‐2 and this work may be useful for future research with those satellites. Key Points: We present a cloud computing workflow for ICESat‐2 bathymetry extraction and data fusion that facilitates deep learningWe introduce a novel approach for extracting bathymetric photons from ICESat‐2 using techniques from graph theory and network analysisWe demonstrate the efficacy of our techniques by training a recurrent neural network to classify benthic habitats at the photon scale [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The empirical formula (EF) method, which do not rely on topographic data, stands as the prevailing technique for estimating lake water storage variation (LWSV). However, for smaller lakes, the sporadic monitoring frequency of satellite altimetry fails to adequately support this method, presenting a challenge in accurately gauging LWSV. Using Lake Chahannur, a lake in China with an area smaller than 50 km2, as a case study, seven schemes based on the EF method and the Area-Volume-Height (A-V-H) curve method were designed to estimate the LWSV of this undersized lake. The efficacy and precision of each scheme were evaluated against field-measured elevations. Findings reveal that due to the limited satellite altimetry monitoring, both the EF method and the H-driven A-V-H curve schemes struggle to provide consistent and comprehensive estimations. In the A-driven A-V-H curve schemes, terrain data from SRTM DEM suffers from mask processing and substantial errors, with the former posing challenges for shrinking lakes and the latter significantly compromising estimation accuracy. While field-measured elevations boast high precision, the interpolation process leads to terrain maps lacking in detail, with site density becoming a crucial factor influencing the accuracy of LWSV estimation. The combination of terrain reconstruction and A-driven pattern emerges as the most promising, boasting high accuracy, rich detail, and significantly reduced reliance on satellite altimetry monitoring, making it particularly suitable for small lakes. Chahannur's bottom elevation ranges between 1271.71 and 1273.44 m, and the lake shows a downward trend in water volume from 1991 to 2020, with fluctuations totaling approximately 35 million m3. This study serves as a vital addition to the field of LWSV estimation, potentially broadening the scope of estimation from large-scale lakes to a wider array of global surface water bodies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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In September 2018, NASA launched the ICESat-2 satellite into a 500 km high Earth orbit. It carried a truly unique lidar system, i.e., the Advanced Topographic Laser Altimeter System or ATLAS. The ATLAS lidar is capable of detecting single photons reflected from a wide variety of terrain (land, ice, tree leaves, and underlying terrain) and even performing bathymetric measurements due to its green wavelength. The system uses a single 5-watt, Q-switched laser producing a 10 kHz train of sub-nanosecond pulses, each containing 500 microjoules of energy. The beam is then split into three "strong" and three "weak" beamlets, with the "strong" beamlets containing four times the power of the "weak" beamlets in order to satisfy a wide range of Earth science goals. Thus, ATLAS is capable of making up to 60,000 surface measurements per second compared to the 40 measurements per second made by its predecessor multiphoton instrument, the Geoscience Laser Altimeter System (GLAS) on ICESat-1, which was terminated after several years of operation in 2009. Low deadtime timing electronics are combined with highly effective noise filtering algorithms to extract the spatially correlated surface photons from the solar and/or electronic background noise. The present paper describes how the ATLAS system evolved from a series of unique and seemingly unconnected personal experiences of the author in the fields of satellite laser ranging, optical antennas and space communications, Q-switched laser theory, and airborne single photon lidars. [ABSTRACT FROM AUTHOR]

The intense changes in glaciers in the southeastern Tibetan Plateau (SETP) have essential impacts on regional water resource management. In order to study the seasonal fluctuations of glaciers in this region and their relationship with climate change, we focus on the Yigong Zangbo River Basin in the SETP, extract the annual and seasonal variations of glaciers in the basin during 2018–2023, and analyze their spatio-temporal characteristics through the seasonal-trend decomposition using the LOESS (STL) method. Finally, combining the Extreme Gradient Boosting (XGBoost) model and the Shapley additive explanations (SHAP) model, we assess the comprehensive impact of meteorological factors such as temperature and snowfall on glacier changes. The results indicate that glaciers in the Yigong Zangbo River Basin experienced remarkable mass loss during 2018–2023, with an average annual melting rate of −0.83 ± 0.12 m w.e.∙yr−1. The glacier mass exhibits marked seasonal fluctuations, with increases in January–March (JFM) and April–June (AMJ) and noticeable melting in July–September (JAS) and October–December (OND). The changes over these four periods are 2.12 ± 0.04 m w.e., 0.93 ± 0.15 m w.e., −1.58 ± 0.19 m w.e., and −1.32 ± 0.17 m w.e., respectively. Temperature has been identified as the primary meteorological driver of glacier changes in the study area, surpassing the impact of snowfall. This study uses advanced altimetry data and meteorological data to monitor and analyze glacier changes, which provides valuable data for cryosphere research and also validates a set of replicable research methods, which provides support for future research in related fields. [ABSTRACT FROM AUTHOR]

The photon-counting laser altimeter on the Ice, Cloud and Land Elevation 2 (ICESat-2) satellite offers a solution for tracking the dynamic water level variations in medium and large inland lakes. We utilize the monthly ATL13 global inland water data of ICESat-2 satellite from 2018 to 2020 to estimate and analyze the water level change in Poyang Lake. The measured data from Hukou, Xingzi and Kangshan hydrological stations are used for verification and error correction, and the water level and rainfall data of each station are combined to analyze the dynamic variation of Poyang Lake water level and reveal the underlying drivers. The results show that, the annual water level of Poyang Lake varied sharply with obvious seasonal variations and an overall upward trend;the high water level period was from June to October, which peaked from July to September. The linear correlation coefficient of water levels between ICESat-2 and measured data is above 0. 846, rising to 0. 974 after error correction. The Root Mean Square Error (RMSE) is 1. 660 m, 1. 073 m, and 0. 836 m for Hukou, Xingzi, and Kangshan stations, respectively;error correction and recalculation can decrease the RMSE to 0. 663 m, 0. 659 m, and 0. 440 m for Hukou, Xingzi, and Kangshan stations, respectively, enhancing the measurement accuracy by nearly one meter. The variation of water level in Poyang Lake is highly correlated with the change of rainfall, the reduced precipitation during periods from January to February and October to December corresponds to the declining water level in dry season, while the increased rainfall from March to October corresponds to the water level rise in wet season, and the precipitation concentration period from July to September aligns with the peak of water level in Poyang Lake. [ABSTRACT FROM AUTHOR]

Objectives: To achieve the accurate monitoring of sea ice conditions in the key area of Arctic passage, high resolution sea ice thickness is essential. However, sea ice thickness with kilometer-scale resolution cannot meet the requirement at present. Methods: Considering the relationship between sea ice thickness and image albedo, this paper tried to establish a regression model between the ICESat-2 ATL10 sea ice freeboard product and the high resolution Sentinel-2 optical image albedo by taking advantage of the intensive altimetry data of ICESat-2 along the orbit to obtain dense sea ice freeboard. Based on high resolution sea ice thickness calculated from dense freeboard with the hydrostatic equilibrium model and combined with high resolution sea ice concentration derived from Sentinel-2 image, we classified ice areas into different navigational categories according to the ice-break capability of ship and compared optimal route design at multiple spatial scales. Results: Regression models established from the ATL10 sea ice freeboard and the Sentinel-2 image albedo have good fitting accuracy. The R² of the regression model is higher than 0.5, the average deviation is less than 0.05 m, and the root mean square error of the accuracy validation is less than 0.2 m. High resolution sea ice parameters derived by the proposed method can describe the distribution of fine leads between floating ice which are difficult to be captured by low resolution sea ice parameters. Conclusions: High resolution sea ice parameters can describe the details of sea ice conditions more accurately. Therefore, the proposed high resolution inversion method of sea ice parameters can improve the capability of navigation planning for ships in Arctic passage, further improving the navigation safety. [ABSTRACT FROM AUTHOR]

The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) is an active spaceborne remote sensing system that utilizes photon-counting LiDAR to capture highly detailed information about under-vegetation terrain and forest structure over vast spatial regions. It facilitates the accurate retrieval of terrain elevation and canopy height information, critical for assessing the global carbon budget and understanding the role of forests in climate change mitigation. However, challenges arise from the characteristics of the ICESat-2 photon-counting LiDAR data, such as their linear distribution, extensive spatial coverage, and substantial residual noise. These challenges hinder the performances of the state-of-the-art methods when applied on ICESat-2 data for extracting ground or top of canopy, while they perform well on airborne LiDAR that is featured with planar distribution, small coverage, and high signal-to-noise ratio. Consequently, this study proposes a novel algorithm termed Adaptive Linear Cloth Simulation Filtering (ALCSF), for the automated extraction of ground and top-of-canopy photons from ICESat-2 signal photons. The ALCSF algorithm innovatively introduces a cloth strip model as a reference to accommodate the distribution characteristics of ICESat-2 photons. Additionally, it employs a terrain-adaptive strategy to adjust the rigidity of the cloth strip by utilizing terrain slope information, thus making ALCSF applicable to large-scale areas with significant topographical changes. Furthermore, the proposed ALCSF addresses noise interference by simultaneously considering the movability of particles of the cloth strip model and the photon distribution during iterative adjustments of the cloth strip. The performance of the ALCSF is evaluated by comparing it with the ICESat-2 Land–Vegetation Along-Track Products (ATL08) across twelve datasets that encompass various times of day and scenes. In the results, the ALCSF exhibits notable improvements over ATL08 products, effectively reducing the root mean square error (RMSE) of ground elevation by 21.8% and canopy height by 25.8%, with superior performance in preserving terrain details. This highlights the significance of ALCSF as a valuable tool for enhancing the accuracy of ICESat-2 land and vegetation products, ultimately contributing to the estimation of the global carbon budget in future studies. [ABSTRACT FROM AUTHOR]

Antarctica has an active subglacial hydrological system, with interconnected subglacial lakes fed by subglacial meltwater. Subglacial hydrology can influence basal sliding, inject freshwater into the sub‐ice‐shelf cavity, and impact sediment transport and deposition which can affect the stability of grounding lines (GLs). We used satellite altimetry data from the ICESat, ICESat‐2, and CryoSat‐2 missions to document the second recorded drainage of Engelhardt Subglacial Lake (SLE), which began in July 2021 and discharged more than 2.3 km3 of subglacial water into the Ross Ice Shelf cavity. We used differential synthetic aperture radar interferometry from RADARSAT‐2 and TerraSAR‐X alongside ICESat‐2 repeat‐track laser altimetry (RTLA) and REMA digital elevation model strips to detect 2–13 km of GL retreat since the previous drainage event in 2003–06. Combining these satellite observations, we evaluated the mechanism triggering SLE drainage, the cause of the observed GL retreat, and the interplay between subglacial hydrology and GL dynamics. We find that: (a) SLE drainage was initiated by influx from a newly identified upstream lake; (b) the observed GL retreat is mainly driven by the continued retreat of Engelhardt Ice Ridge and long‐term dynamic thinning that caused a grounded ice plain to reach flotation; and (c) SLE drainage and GL retreat were largely independent. We also discuss the possible origins and influence of a 27 km grounded promontory found to protrude seaward from the GL. Our observations demonstrate the importance of high‐resolution satellite data for improving the process‐based understanding of dynamic and complex regions around the Antarctic Ice Sheet margins. Plain Language Summary: Large volumes of water flow beneath the Antarctic Ice Sheet through an interconnected network of rivers and lakes. This water system impacts slipperiness at the base of the ice, affecting how fast it moves. It also delivers freshwater into the ocean, directly contributing to sea‐level rise and increasing melt beneath the floating ice shelves. In this study, we use satellite data to track the 2021–24 drainage of Engelhardt Subglacial Lake in West Antarctica. This lake is located close to the Ross Ice Shelf grounding line, the point at the edge of the ice sheet where the ice first lifts off the bedrock and starts to float on the ocean. This region of the grounding line has retreated by up to 13 km since the last lake drainage in 2003–06. Here, we investigate what caused the drainage, the reasons for the grounding line retreat, and whether the two processes are connected. We also report the growth of a grounded promontory extending 27 km out to sea, which may be evidence of a former ice stream moraine or melt channel. These findings help to improve our limited understanding of the relationship between subglacial hydrology and grounding line dynamics in Antarctica. Key Points: Satellite altimetry detects second recorded drainage of Engelhardt Subglacial Lake in July 2021, triggered by an influx from an upstream lakeThe grounding line retreated by 2–13 km since the last drainage in 2003, linked to the retreat of Engelhardt Ice Ridge and ice plain ungroundingSatellite observations suggest that the 2021–24 lake drainage and grounding line retreat were largely independent processes [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

There now exists two decades of basin-wide coverage of Arctic sea ice from three dedicated polar-orbiting altimetry missions (ICESat, CryoSat-2, and ICESat-2) launched by NASA and ESA. Here, we review our retrieval approaches and discuss the composite record of Arctic ice thickness (2003–2023) after appending two more years (2022–2023) to our earlier records. The present availability of five years of snow depth estimates—from differencing lidar (ICESat-2) and radar (CryoSat-2) freeboards—have benefited from the concurrent operation of two altimetry missions. Broadly, the dramatic volume loss (5500 km3) and Arctic-wide thinning (0.6 m) captured by ICESat (2003–2009), primarily due to the decline in old ice coverage between 2003 and 2007, has slowed. In the central Arctic, away from the coasts, the CryoSat-2 and shorter ICESat-2 records show near-negligible thickness trends since 2007, where the winter and fall ice thicknesses now hover around 2 m and 1.3 m, from a peak of 3.6 m and 2.7 m in 1980. Ice volume production has doubled between the fall and winter with the faster-growing seasonal ice cover occupying more than half of the Arctic Ocean at the end of summer. Seasonal ice behavior dominates the Arctic Sea ice's interannual thickness and volume signatures. [ABSTRACT FROM AUTHOR]

Forest Canopy Height (FCH) is a crucial parameter that offers valuable insights into forest structure. Spaceborne LiDAR missions provide accurate FCH measurements, but a significant challenge is their point-based measurements lacking spatial continuity. This study integrated ICESat-2's ATL08-derived FCH values with multi-temporal and multi-source remote sensing (RS) datasets to generate continuous FCH maps for northern forests in Iran. Sentinel-1/2, ALOS-2 PALSAR-2, and FABDEM datasets were prepared in Google Earth Engine (GEE) for FCH mapping, each possessing unique spatial and geometrical characteristics that differ from those of the ATL08 product. Given the importance of accurately representing the geometrical characteristics of the ATL08 segments in modeling FCH, a novel Weighted Kernel (WK) approach was proposed in this paper. The WK approach could better represent the RS datasets within the ATL08 ground segments compared to other commonly used resampling approaches. The correlation between all RS data features improved by approximately 6% compared to previously employed approaches, indicating that the RS data features derived after convolving the WK approach are more predictive of FCH values. Furthermore, the WK approach demonstrated superior performance among machine learning models, with random forests outperforming other models, achieving a coefficient of determination (R2) of 0.71, root mean square error (RMSE) of 4.92 m, and mean absolute percentage error (MAPE) of 29.95%. Furthermore, in contrast to previous studies using only summer datasets, this study included spring and autumn data from Sentinel-1/2, resulting in a 6% increase in R2 and a 0.5-m decrease in RMSE. The proposed methodology filled the research gaps and improved the accuracy of FCH estimations. [ABSTRACT FROM AUTHOR]

The huge volume of data from the Ice, Cloud and land Elevation Satellite-2 (ICESat-2), designed for mapping polar ice, sea ice, and continental vegetation, requires a highly automated data analysis and reliable terrain classification. In particular, we have developed a method to identify 4 distinct terrain categories in observed terrain, namely ocean, land, sea ice, and ice sheets. This study performed the following efforts: first, the spatial distribution characteristics for each of the 4 categories within individual ICESat-2 "major frames" along the orbit were extracted; second, these features were fed into Classification and Regression Tree (CART) and Random Forest (RF) for training; and lastly, post-processing enhancement was used to improve the classification results. Based on the 76,891 major frame samples (10,764,740 m along track) acquired via various ICESat-2 datasets, the accuracy of the two model were calculated using ten-fold cross-validation. The results indicate that the RF algorithm obtained higher classification accuracy (average accuracy [AA] = 0.9353, overall accuracy [OA] = 0.9342, and Cohen's Kappa coefficient [kappa] = 0.9122) when compared with the CART algorithm (AA = 0.9066, OA = 0.9057, and kappa = 0.8743). Overall, our approach can effectively reduce the workload of human field investigation or visual inspection of altimetry data, improve the accuracy for Earth surface classification, and add to the variety of ways to obtain global surface information from ICESat-2 data. [ABSTRACT FROM AUTHOR]

Building height is a crucial indicator when studying urban environments and human activities, necessitating accurate, large-scale, and fine-resolution calculations. However, mainstream machine learning-based methods for inferring building heights face numerous challenges, including limited sample data and slow update frequencies. Alternatively, satellite laser altimetry technology offers a reliable means of calculating building heights with high precision. Here, we initially calculated building heights along satellite orbits based on building-rooftop contour vector datasets and ICESat-2 ATL03 photon data from 2019 to 2022. By integrating multi-source passive remote sensing observation data, we used the inferred building height results as reference data to train a random forest model, regressing building heights at a 10 m scale. Compared with ground-measured heights, building height samples constructed from ICESat-2 photon data outperformed methods that indirectly infer building heights using total building floor number. Moreover, the simulated building heights strongly correlated with actual observations at a single-city scale. Finally, using several years of inferred results, we analyzed building height changes in Tianjin from 2019 to 2022. Combined with the random forest model, the proposed model enables large-scale, high-precision inference of building heights with frequent updates, which has significant implications for global dynamic observation of urban three-dimensional features. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.