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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]

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]

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]

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]

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]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has excellent potential for obtaining water depth information around islands and reefs. Combining the density-based spatial clustering of applications with noise algorithm (DBSCAN) and multiscale quadtree analysis, we propose a new photon-counting lidar denoising method to discard the large amount of noise in ICESat-2 data. First, the kernel density estimation (KDE) is used to preprocess the point cloud data, and a threshold is set to remove the noise photons on the sea surface. Next, the DBSCAN algorithm is used to preliminarily remove underwater noise photons. Then, the quadtree segmentation and Otsu algorithm are used for fine denoising to extract accurate bottom signal photons. Based on ICESat-2 pho-ton-counting data from six typical islands and reefs worldwide, the proposed method outperforms other algorithms in terms of denoising effect. Compared to in situ data, the determination coefficient (R2) reaches 94.59%, and the root mean square error (RMSE) is 1.01 m. The proposed method can extract accurate underwater terrain information, laying a foundation for offshore bathymetry. [ABSTRACT FROM AUTHOR]

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

Accurately estimating building heights is crucial for various applications, including urban planning, climate studies, population estimation, and environmental assessment. However, this remains a challenging task, particularly for large areas. Satellite-based Light Detection and Ranging (LiDAR) has shown promise, but it often faces difficulties in distinguishing building photons from other ground objects. To address this challenge, we propose a novel method that incorporates building footprints, relative positions of building and ground photons, and a self-adaptive buffer for building photon selection. We employ the Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2) photon-counting LiDAR, specifically the ICESat-2/ATL03 data, along with building footprints obtained from the New York City (NYC) Open Data platform. The proposed approach was applied to estimate the heights of 17,399 buildings in NYC, and the results showed strong consistency with the reference building heights. The root mean square error (RMSE) was 8.1 m, and for 71% of the buildings, the mean absolute error (MAE) was less than 3 m. Furthermore, we conducted an extensive evaluation of the proposed approach and thoroughly investigated the influence of terrain, region, building height, building density, and parameter selection. We also verified the effectiveness of our approach in an experimental area in Beijing and compared it with other existing methods. By leveraging ICESat-2 LiDAR data, building footprints, and advanced selection techniques, the proposed approach demonstrates the potential to accurately estimate building heights over broad areas. [ABSTRACT FROM AUTHOR]

This report presents an analysis of the potential of using the information product ATL08 derived from the ATLAS/ICESat-2 (Advanced Topographic Laser Altimeter System/Ice, Cloud, and land Elevation Satellite) satellite lidar to determine the height of protective forest stands. Height measurements, corresponding to vegetation based on lidar data from 2019 to 2022, were compared with the results of aerial photography processing conducted in Volgograd oblast in 2022. A significant strong correlation was found between the mean and maximum canopy heights determined from aerial survey and laser scanning data for 20 × 14-m segments with a woody and shrub vegetation cover exceeding 50%. For the mean canopy height, the root mean square error (RMSE) is ±0.7 m and coefficient of determination R2 = 0.85; for the maximum canopy height, RMSE = 2.2 m and R2 = 0.83. Comparison of projective cover using lidar data, calculated as the ratio of the number of photons above a certain threshold height to the total number of photons in the segment, with aerial photography data showed insufficient accuracy of this approach. The results suggest that ATL08 lidar data holds promise for evaluating the height of protective forest stands, although it may not be suitable for determining projective cover. [ABSTRACT FROM AUTHOR]

The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is extensively utilized in various fields, such as forestry, oceanography, geology, and hydrology. However, due to limitations in radar side-view imaging, the SRTM DEM still contains gaps and anomalies, particularly in areas with an intricate topography, like forests. To enhance the accuracy of the SRTM DEM in forested regions, commonly employed approaches include regression analysis and artificial neural networks (ANN). Nevertheless, existing regression methods struggle to accurately capture the intricate nonlinear relationship between the error and influencing factors. Additionally, traditional ANN models are susceptible to overfitting, resulting in subpar accuracy. Deep Belief Network (DBN) is a highly precise algorithm in deep learning. However, the intricate combination of hyperparameters often leads to limited generalization ability and model robustness when correcting DEM. The present study proposes an error prediction model based on the DBN optimized by Particle Swarm Optimization (PSO) for SRTM DEM correction. By utilizing the PSO algorithm, we aim to identify the optimal combination of hyperparameters of DBN, including the number of neurons in the hidden layer and the learning rates. The experiment focuses on two regions in Hunan Province, China, characterized by abundant vegetation cover. The reference data utilized for comparison is ICESat/GLAS data. The experimental results demonstrate that the mean error (ME) and root mean square error (RMSE) of the SRTM DEM corrected by the proposed algorithm in these two regions are significantly reduced by 93.5%–96.0% and 21.5%–23.5%, respectively. Moreover, there is an improvement of over 26.1% in accuracy within complex terrain areas. Specifically, in broadleaf forest, the PSO-DBN method exhibits a remarkable accuracy improvement of 26.2%, while the DBN-corrected SRTM DEM shows an improvement of 15.3%. In coniferous forest, the PSO-DBN method achieves an accuracy improvement of 14.8%, whereas the DBN-corrected SRTM DEM demonstrates a gain of 5.8%. The approach provides a more effective and robust tool for correcting SRTM DEM or other similar DEMs over vegetated mountain areas. [ABSTRACT FROM AUTHOR]

Satellite-derived bathymetry enables the non-contact derivation of large-scale shallow water depths. Hyperspectral satellite images provide more information than multispectral satellite images, making them theoretically more effective and accurate for bathymetry inversion. This paper focuses on the use of hyperspectral satellite images (PRISMA) for bathymetry inversion and compares the retrieval capabilities of multispectral satellite images (Sentinel-2 and Landsat 9) in the southeastern waters of Molokai Island in the Hawaiian Archipelago and Yinyu Island in the Paracel Archipelago. This paper proposes an attention-based band optimization one-dimensional convolutional neural network model (ABO-CNN) to better utilize the increased spectral information from multispectral and hyperspectral images for bathymetry inversion, and this model is compared with a traditional empirical model (Stumpf model) and two deep learning models (feedforward neural network and one-dimensional convolutional neural network). The results indicate that the ABO-CNN model outperforms the above three models, and the root mean square errors of retrieved bathymetry using the PRISMA images are 1.43 m and 0.73 m in the above two study areas, respectively. In summary, this paper demonstrates that PRISMA hyperspectral imagery has superior bathymetry inversion capabilities compared to multispectral images (Sentinel-2 and Landsat 9), and the proposed deep learning model ABO-CNN is a promising candidate model for satellite-derived bathymetry using hyperspectral imagery. With the increasing availability of ICESat-2 bathymetric data, the use of a combination of the proposed ABO-CNN model and the ICEsat-2 data as the training data provides a practical approach for bathymetric retrieval applications. [ABSTRACT FROM AUTHOR]

Digital elevation models (DEMs) are the fundamental datasets for coastal ecosystem monitoring, and several global open‐accessed DEMs have recently been reported. In coastal regions, a comprehensive vertical accuracy assessment of these DEMs has not yet been carried out. In this study, eight open‐access DEM datasets, including SRTM‐3, SRTM‐1, TanDEM‐X, ASTER GDEM v3, MERIT DEM, AW3D30, NASADEM, and CoastalDEM, were investigated across the coastal region of the Chinese mainland using high accuracy ICESat‐2 data as a reference. Statistical tools including mean absolute error (MAE) and root mean square error (RMSE) were selected to describe the data error/uncertainty and spatial distribution. Moreover, the effects of elevation ranging, slope degree, geomorphogenesis and landuse on vertical accuracy were further analyzed to assess their applicability. The assessment results revealed that the CoastalDEM and NASADEM datasets had the highest accuracy, with MAE values of 1.68 and 1.88 m and RMSE values of 2.55 and 2.61 m, for 3‐arc second and 1‐arc second resolution DEMs, respectively. Other DEMs with close accuracies include AW3D30, SRTM‐1, MERIT, and SRTM‐3 DEM. The results proved that the CoastalDEM outperformed other datasets, indicating its applicability in coastal regions. [ABSTRACT FROM AUTHOR]

[Display omitted] • An overlay method of water depth index from massive Sentinel-2 data is proposed. • Active-passive fusion bathymetry using ICESat-2 and Sentinel-2 data. • The effects of complex optical paths and surface instabilities were eliminated. • This method exhibits high accuracy and efficiency in water depth inversion. • The impact of Sentinel-2 numbers on the bathymetry accuracy is quantitated. Accurate bathymetric mapping is critical for island reef research, coastal ecosystem monitoring, and nearshore engineering. Increasing amounts of remote sensing data have promoted the development of optical bathymetric remote sensing. However, large-scale and efficient bathymetric mapping of shallow oceanic islands is difficult due to the limited availability of high-quality optical remote sensing images. In this study, we propose a cost-efficient method for bathymetric mapping based on a quadratic polynomial ratio model (QPRM) of massive active–passive remote sensing data. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data represent active data and a priori data, and Sentinel-2 data from the Google Earth Engine represent passive data. The key step is to build a QPRM based on the median value of the blue-green band logarithmic ratio from massive Sentinel-2 data. The method is applied to six typical areas located in the Pacific Ocean, Indian Ocean, and South China Sea, and the results showed that the root mean square error (RMSE) and mean absolute error (MAE) of bathymetric mapping in the water depth range of 0–20 m were 0.49–0.71 m and 0.29–0.49 m, respectively. In addition, the RMSE decreased with an increase in the number of Sentinel-2 images, and the results were relatively stable when the number reached approximately 150. The QPRM results were also compared with that of the classical linear ratio model (CLRM) and multiple fitting methods to calculate the median water depth, and the findings showed that the QPRM method has a slight advantage in terms of accuracy and efficiency. The proposed method, which does not depend on in situ data, effectively calculates precise water depths and removes the effects of complex underwater optical paths and surface instabilities, such as waves, boats, and clouds. Therefore, it has great potential for bathymetric mapping of oceanic islands and reefs worldwide. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The TanDEM-X Digital Elevation Model (DEM) is limited by the radar side-view imaging mode, which still has gaps and anomalies that directly affect the application potential of the data. Many methods have been used to improve the accuracy of TanDEM-X DEM, but these algorithms primarily focus on eliminating systematic errors trending over a large area in the DEM, rather than random errors. Therefore, this paper presents the least-squares collocation-based error correction algorithm (LSC-TXC) for TanDEM-X DEM, which effectively eliminates both systematic and random errors, to enhance the accuracy of TanDEM-X DEM. The experimental results demonstrate that TanDEM-X DEM corrected by the LSC-TXC algorithm reduces the root mean square error (RMSE) from 6.141 m to 3.851 m, resulting in a significant improvement in accuracy (by 37.3%). Compared to three conventional algorithms, namely Random Forest, Height Difference Fitting Neural Network and Back Propagation in Neural Network, the presented algorithm demonstrates a reduction in the RMSEs of the corrected TanDEM-X DEMs by 6.5%, 7.6%, and 18.1%, respectively. This algorithm provides an efficient tool for correcting DEMs such as TanDEM-X for a wide range of areas. [ABSTRACT FROM AUTHOR]

Block Adjustment (BA) is one of the essential techniques for producing high-precision geospatial 3D data products with optical stereo satellite imagery. For block adjustment with few ground-control points or without ground control, the vertical error of the model is the decisive factor that constrains the accuracy of 3D data products. The elevation data obtained by spaceborne laser altimeter have the advantages of short update periods, high positioning precision, and low acquisition cost, providing sufficient data support for improving the elevation accuracy of stereo models through the combined BA. This paper proposes a geometric positioning model based on the integration of Optical Satellite Stereo Imagery (OSSI) and spaceborne laser altimeter data. Firstly, we elaborate the principle and necessity of this work through a literature review of existing methods. Then, the framework of our geo-positioning models. Secondly, four key technologies of the proposed model are expounded in order, including the acquisition and management of global Laser Control Points, the association of LCPs and OSSI, the block adjustment model combining LCPs with OSSI, and the accuracy estimation and quality control of the combined BA. Next, the combined BA experiment using Ziyuan-3 (ZY-3) OSSI and ICESat-2 laser data was carried out at the testing site in Shandong Province, China. Experimental results prove that our method can automatically select LCPs with high accuracy. The elevation deviation of the combined BA eventually achieved the Mean Error (ME) of 0.06 m and the Root Mean Square Error (RMSE) of 1.18 m, much lower than the ME of 13.20 m and the RMSE of 3.88 m before the block adjustment. A further research direction will be how to perform more adequate accuracy analysis and quality control using massive laser points as checkpoints. [ABSTRACT FROM AUTHOR]

Forest canopy height is defined as the distance between the highest point of the tree canopy and the ground, which is considered to be a key factor in calculating above-ground biomass, leaf area index, and carbon stock. Large-scale forest canopy height monitoring can provide scientific information on deforestation and forest degradation to policymakers. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in 2018, with the Advanced Topographic Laser Altimeter System (ATLAS) instrument taking on the task of mapping and transmitting data as a photon-counting LiDAR, which offers an opportunity to obtain global forest canopy height. To generate a high-resolution forest canopy height map of Jiangxi Province, we integrated ICESat-2 and multi-source remote sensing imagery, including Sentinel-1, Sentinel-2, the Shuttle Radar Topography Mission, and forest age data of Jiangxi Province. Meanwhile, we develop four canopy height extrapolation models by random forest (RF), Support Vector Machine (SVM), K-nearest neighbor (KNN), Gradient Boosting Decision Tree (GBDT) to link canopy height in ICESat-2, and spatial feature information in multi-source remote sensing imagery. The results show that: (1) Forest canopy height is moderately correlated with forest age, making it a potential predictor for forest canopy height mapping. (2) Compared with GBDT, SVM, and KNN, RF showed the best predictive performance with a coefficient of determination (R2) of 0.61 and a root mean square error (RMSE) of 5.29 m. (3) Elevation, slope, and the red-edge band (band 5) derived from Sentinel-2 were significantly dependent variables in the canopy height extrapolation model. Apart from that, Forest age was one of the variables that the RF moderately relied on. In contrast, backscatter coefficients and texture features derived from Sentinel-1 were not sensitive to canopy height. (4) There is a significant correlation between forest canopy height predicted by RF and forest canopy height measured by field measurements (R2 = 0.69, RMSE = 4.02 m). In a nutshell, the results indicate that the method utilized in this work can reliably map the spatial distribution of forest canopy height at high resolution. [ABSTRACT FROM AUTHOR]

Accurate and frequent monitoring of inland water body levels is essential for water sources and their ecological protection and management. Water bodies such as natural lakes and manmade dams provide an ecological environment for endangered animals and endemic plants and serve as irrigation and water source for human activities. However, in situ measurements or fixed observation stations are not always available for small to large water bodies. Here, we investigate water level changes at a regional level where endangered endemic species live in the Ramsar site, a nationally protected park, and inland waters using the National Aeronautics and Space Administration's Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) satellite launched in 2018. Most of the inland water bodies show high R2 values ranging from 0.28 to 0.99 and the root mean square error values are widely distributed lower than 1 m. This study shows the potential and limitations of the new altimetry data source ICESat-2 ATL13 product for monitoring the long-term behavior of inland water reservoirs for sustainable monitoring. [ABSTRACT FROM AUTHOR]

The number of in-situ stations measuring river discharge, one of the Essential Climate Variables (ECV), is declining steadily, and numerous basins have never been gauged. With the aim of improving data availability worldwide, we propose an easily applicable and transferable approach to estimate reach-scale discharge solely using remote sensing data that is suitable for filling gaps in the in-situ network. We combine 20 years of satellite altimetry observations with high-resolution satellite imagery via a hypsometric function to observe large portions of the reach-scale bathymetry. The high-resolution satellite images, which are classified using deep learning image segmentation, allow for detecting small rivers (narrower than 100 m) and can capture small width variations. The unobserved part of the bathymetry is estimated using an empirical width-to-depth function. Combined with precise satellite-derived slope measurements, river discharge is calculated at multiple consecutive cross-sections within the reach. The unknown roughness coefficient is optimized by minimizing the discharge differences between the cross-sections. The approach requires minimal input and approximate boundary conditions based on expert knowledge but is not dependent on calibration. We provide realistic uncertainties, which are crucial for data assimilation, by accounting for errors and uncertainties in the different input quantities. The approach is applied globally to 27 river sections with a median normalized root mean square error of 12% and a Nash–Sutcliffe model efficiency of 0.560. On average, the 90% uncertainty range includes 91% of the in-situ measurements. • Discharge from satellite altimetry and imagery without in-situ calibration. • Median NRMSE of 12% in 27 river reaches and easily transferable to other locations. • Discharge uncertainties with a median confidence coverage probability of 98% • Deep-learning classification of high-res images captures small river width variations. • River roughness optimized based on mass conservation over multiple cross-sections. [ABSTRACT FROM AUTHOR]

The water surface slope (WSS) of rivers is essential for estimating flow velocity and discharge. It is also helpful as a correction applied to range measurements of satellite altimetry missions to derive water level time series at a virtual station. Using radar altimetry, WSS can only be roughly estimated and is limited to wide rivers because of its coarse spatiotemporal resolution. In contrast, the lidar sensor onboard Ice, Cloud, and Land Elevation Satellite 2 (ICESat‐2) can also observe small rivers. Using ICESat‐2's unique measurement geometry with six parallel laser beams, we derive instantaneous WSS along and across the satellite's ground track, time‐variable WSS (with an average of 5 days of records in the studied epoch between October 2018 and October 2021), and average WSS on reach‐scale. Although the method can be applied globally, this study is limited to 815 reaches in Europe and North America where sufficient validation data is available. We compare the ICESat‐2 WSS with time‐variable WSS derived from multiple gauges and constant data from the "SWOT River Database." For 89% of the studied reaches, ICESat‐2 can be used to estimate the average WSS with a median absolute error of 23 mm/km. We also show the possible performance gain at multiple virtual stations (VS) in the "Database for Hydrological Time Series of Inland Waters" (https://dahiti.dgfi.tum.de), applying the WSS as a correction for altimetry satellites' ground track variability. We correct 137 VS for the derived ICESat‐2 WSS and yield improvements in the root mean square error by up to 30 cm or 66%. Key Points: Simultaneous obs. from Ice, Cloud, and Land Elevation Satellite 2's (ICESat‐2's) unique multibeam lidar altimeter are used to estimate instantaneous reach‐scale water surface slope (WSS)For 89% of 815 studied reaches, we find ICESat‐2 can be used to estimate WSS with a median absolute error of 23 mm/km relative to gauge dataWe correct water level time series from radar satellite altimetry for the derived WSS and obtain improvements of up to 30 cm (66%) root mean square error [ABSTRACT FROM AUTHOR]

The new-generation photon-counting laser altimeter aboard the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has acquired unprecedented high-density laser data on the global surface. The continuous analysis and calibration of potential systematic biases in laser data are important for generating highly accurate data products. Current studies mainly calibrate the absolute systematic bias of laser altimeters based on external reference data. There are few studies that focus on the analysis and calibration of relative systematic biases in long-term laser data. This paper explores a method for systematic biases analysis and calibration of ICESat-2 laser data based on track crossovers for the first time. In the experiment, the simulated data and ICESat-2 data were used to verify the algorithm. The results show that, during the three-year period in orbit, the standard deviation (STD) and bias of the crossover differences of the ICESat-2 terrain data were 0.82 m and −0.03 m, respectively. The simulation validation well demonstrate that the crossover adjustment can calibrate the relative bias between different beams. For ICESat-2 data, the STD of the estimated systematic bias after crossover adjustment was 0.09 m, and the mean absolute error (MAE) was 0.07 m. Compared with airborne lidar data, the bias and root mean square error (RMSE) of the ICESat-2 data remained basically unchanged after adjustment, i.e., −0.04 m and 0.38 m, respectively. This shows that the current ICESat-2 data products possess excellent internal and external accuracy. This study shows the potential of crossover for evaluating and calibrating the accuracy of spaceborne photon-counting laser altimeter data products, in terms of providing a technical approach to generate global/regional high-accuracy point cloud data with consistent accuracy. [ABSTRACT FROM AUTHOR]

The bottom depth of coastal benthic habitats plays a vital role in the coastal ecological environment and navigation. In optically shallow waters (OSWs), seafloor reflectance has an impact on the remotely sensed data, and thus, water depth can be retrieved from the remote sensing reflectance ( R r s λ ) values provided by satellite imagery. Empirical methods for depth estimation are mainly limited by field measurements coverage. In addition, owing to the diverse range of water bio-optical properties in coastal regions, the high-precision models that could be applied to all OSWs are insufficient. In this study, we developed a novel bottom-depth retrieval method based on Hydrolight simulated datasets, in which R r s λ were generated from radiative transfer theory instead of actual satellite data. Additionally, this method takes into consideration the variable conditions of water depth, chlorophyll concentrations, and bottom reflectance. The bottom depth can be derived from R r s λ using a data-driven machine learning method based on the random forest (RF) model. The determination coefficient (R2) was greater than 0.98, and the root mean squared error ( R M S E ) was less than 0.4 m for the training and validation datasets. This model shows promise for use in different coastal regions while also broadening the applications that utilize satellite data. Specifically, we derived the bottom depth in three areas in the South China Sea, i.e., the coastal regions of Wenchang city, Xincun Bay, and Huaguang Reef, based on Sentinel-2 imagery. The derived depths were validated by the bathymetric data acquired by spaceborne photon-counting lidar ICESat-2, which was able to penetrate clean shallow waters for sufficient bottom detection. The predicted bottom depth showed good agreement with the true depth, and large-scale mapping compensated for the limitations resulting from along-track ICESat-2 data. Under a variety of circumstances, this general-purpose depth retrieval model can be effectively applied to high spatial resolution imagery (such as that from Sentinel-2) for bottom depth mapping in optically shallow waters. [ABSTRACT FROM AUTHOR]

Background: Fast and accurate forest aboveground biomass (AGB) estimation and mapping is the basic work of forest management and ecosystem dynamic investigation, which is of great significance to evaluate forest quality, resource assessment, and carbon cycle and management. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), as one of the latest launched spaceborne light detection and ranging (LiDAR) sensors, can penetrate the forest canopy and has the potential to obtain accurate forest vertical structure parameters on a large scale. However, the along-track segments of canopy height provided by ICESat-2 cannot be used to obtain comprehensive AGB spatial distribution. To make up for the deficiency of spaceborne LiDAR, the Sentinel-2 images provided by google earth engine (GEE) were used as the medium to integrate with ICESat-2 for continuous AGB mapping in our study. Ensemble learning can summarize the advantages of estimation models and achieve better estimation results. A stacking algorithm consisting of four non-parametric base models which are the backpropagation (BP) neural network, k-nearest neighbor (kNN), support vector machine (SVM), and random forest (RF) was proposed for AGB modeling and estimating in Saihanba forest farm, northern China. Results: The results show that stacking achieved the best AGB estimation accuracy among the models, with an R2 of 0.71 and a root mean square error (RMSE) of 45.67 Mg/ha. The stacking resulted in the lowest estimation error with the decreases of RMSE by 22.6%, 27.7%, 23.4%, and 19.0% compared with those from the BP, kNN, SVM, and RF, respectively. Conclusion: Compared with using Sentinel-2 alone, the estimation errors of all models have been significantly reduced after adding the LiDAR variables of ICESat-2 in AGB estimation. The research demonstrated that ICESat-2 has the potential to improve the accuracy of AGB estimation and provides a reference for dynamic forest resources management and monitoring. [ABSTRACT FROM AUTHOR]

Inland lakes or reservoirs are the main surface water resources, related to people's survival and social development. On-site water level data are critical for water resource management, while satellite laser and remote sensing data are a significant tool for long-term change monitoring. In this study, we took the Miyun Reservoir, the most important source of drinking water in Beijing, as the research area. We used Landsat images and the Ice, Cloud, and Land Elevation Satellite (ICESat-2) for small-scale water level change monitoring and inversion across unrecorded periods. First, we proposed a new two-step denoising method based on local statistics to denoise the ATL03 single photon data from 2018 to 2020, and extracted the effective elevation information of Miyun Reservoir. Second, based on Landsat images from 1984 to 2020, the MNDWI index was used to extract the water area and generated a percentage map of water occurrence. Finally, the water and land boundary elevation were selected from the effective photon elevation information according to the boundary position under each percentage of the water generation percentage graph, the area, and the water level elevation model (A–E model) was established, and the water level values of unrecorded periods were inverted. The denoising results showed that the denoising accuracy of all data is higher than 95%. The area of Miyun Reservoir has changed greatly in the past 37 years, especially in the northeast corner and northern region. The inverted water level elevation had a strong correlation with the measured value, the correlation coefficient reached 0.97, the root mean square error (RMSE) was 0.553 m, and the estimated water level value was highly consistent with the recorded value and fluctuated dramatically from 1984 to 2020. This method complements the traditional water level mapping method, provides a new idea for the monitoring of water level changes in lakes or reservoirs without field records, and also contributes to the timely and effective monitoring and management of global lake and reservoir water level and time changes. [ABSTRACT FROM AUTHOR]

• Combining ICESat-2 and GEE can monitor water volume change in large areas precisely. • Precision of lake volume change is mainly affected by water level measurement. • ICESat-2 laser altimetry data has high accuracy in estimating water level change. • Seasonal variations in water level of natural lake and reservoir vary significantly. • Reservoirs generally show larger variations than lakes in water volume change. Monitoring the water level and volume changes of lakes and reservoirs is essential for deepening our understanding of the temporal and spatial dynamics of water resources in the Yellow River Basin, with a view to better utilizing and managing water resources. In recent years, there have been many studies on monitoring water level and volume changes in inland waters, but they were mainly focused on radar altimetry and the full waveform LiDAR ICESat, which was retired in 2010. Few studies based on the latest photon-counting LiDAR ICESat-2 have been reported. Compared with previous sensors, ICESat-2 has great advantages in footprint size, transmitting frequency, pulse number, etc, but its performance in monitoring water level and volume changes in inland waters has not been fully explored. Here we investigated the spatial distribution of water level and volume changes of 11 lakes and 8 reservoirs in the Yellow River Basin based on ICESat-2 and Google Earth Engine, and analyzed the factors affecting the measurement uncertainties. In-situ validation of lake level in Lake Qinghai indicates that the Root Mean Square Error (RMSE) of our result is only 7 cm after the reference coordinate system conversion. We found that the water level trend of the natural lake shows significant seasonal variations, while the water level trend of the reservoir shows a sharp rise and fall. In addition, precipitation plays a decisive role in the changes in natural lake levels and indirectly affects the artificial control of reservoirs' water discharges. The uncertainty of water volume change monitoring is mainly affected by water level measurement uncertainty for lakes, while for reservoirs, that is affected by the combination of water level and area measurement uncertainties. The stability of lake level measurement increases with the increase in photon counts. The introduction of ICESat-2 ATL13 Significant Wave Height might lead larger standard deviation in water level measurement. According to the law of propagation of uncertainty, the uncertainty of the water volume change estimation by the combination of ICESat-2 and GEE is less than 9 %. [ABSTRACT FROM AUTHOR]

Forest inventories require up-to-date data on dominant tree height and stand top height from forest sample plots. These data are used to characterize the vertical structure of forests, providing a baseline for volume and yield tables as well as many other biomass studies. Obtaining height information through ground measurement is laborious, costly, and time-consuming. The aim of this study is to estimate stand top heights of the Artvin-Hatila Valley's forests using freely available laser scanning (LiDAR) data from the ICESat-2 satellite for the first time in Turkey. For this purpose, the dominant tree heights, traditionally measured by digital hypsometer in 52 sample plots, were evaluated by stand types and compared with the ICESat-2 canopy data. Then, two data sets were modeled using the Convolutional Neural Network (CNN) and simple regression methods. The model accuracies were evaluated with correlation (Pearson's R), coefficient of determination (R2), and root mean squared error (RMSE) using ground-based data. The results showed that the CNN-based model performed better than the linear regression model in height estimation. Its R, R2, and RMSE values were .82, .68, and 4.2 m, respectively. As for stand types, broadleaves-dominated, mature, and fully covered stands seem more appropriate for top height modeling with spaceborne LiDAR data. Degraded, coniferous, and young stands, as well as non-forest areas, barely allow accurate top height estimations due to their complex canopy surfaces and small openings among trees. Given the promising results, we conclude that satellite-based LiDAR systems provide opportunities to forest professionals as a free auxiliary data source for operational forest management in Turkey. [ABSTRACT FROM AUTHOR]

Accurate bathymetric and topographical information is crucial for coastal and marine applications. In the past decades, owing to its low cost and high efficiency, satellite-derived bathymetry has been widely used to estimate the depth of shallow water in coastal areas. However, insufficient spectral bands and availability of in situ water depths limit the application of satellite-derived bathymetry. Currently, the investigation about the bathymetric potential of hyperspectral imaging is relatively insufficient based on datasets of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). In this study, Zhuhai-1 hyperspectral images and ICESat-2 datasets were utilized to perform nearshore bathymetry and explore the bathymetric capability by selecting different bands based on classical empirical models (the band ratio model and the linear band model). Furthermore, experimental results achieved at the South China Sea indicate that the combination of blue (2 and 3 band) and green (9 band) bands and the combination of red (10 and 12 band) and near-infrared (29 band) bands are most suitable to achieve nearshore bathymetry. Correspondingly, the highest accuracy of bathymetry reached root mean square error values of 0.98 m and 1.19 m for different band combinations evaluated through bathymetric results of reference water depth. The bathymetric accuracy of Zhuhai-1 image is similar with that of Sentinel-2 when employing the blue and green bands. The combination of red and near-infrared bands has a higher bathymetric accuracy for Zhuhai-1 image than that for Sentinel-2 image. [ABSTRACT FROM AUTHOR]

• Terrain profiles surveyed by the ICESat-2 represented properly the topography of the reservoir. • The combination of altimetric data and optical images generated contours with acceptable accuracy. • The elevation-area-volume curves had satisfactory values of correlation and RMSE. The semiarid region of Brazil is highly dependent on reservoirs, and precise bathymetric data are essential for understanding the dynamics and storage variability of these water bodies. This study aimed to develop the application of bathymetric calculation using the observed water level and ICESat-2 satellite terrain profile data of large and medium-sized reservoirs. The combination of satellite images with altimetric data allows for the extraction of water contours, whose points contain elevation information. A digital terrain model (DTM) was generated, and an elevation-area-volume (EAV) curve was calculated for two reservoirs in Northeast Brazil, Sobradinho and Poço da Cruz, which have capacities of 34.116 and 0.4837 km3, respectively. The water level from ICESat-2 was compared to that of the observed data from the Sobradinho, and DTM and EAV curves were analyzed for the two reservoirs. Using the water level from Sobradinho reservoir stations, this method covered 99% of the useful volume. The ICESat-2 data covered 50% of the storage capacity in the Sobradinho and 89% in Poço da Cruz. The in-situ and ICESat-2 data-based EAV curves showed volumes with Pearson's correlations equal to 0.99 for both reservoirs, and normalized root mean square errors of 2.62% and 4.13% for Sobradinho and Poço da Cruz, respectively. The results revealed that this strategy can be economic and viable, as it uses available free data with the potential to generate high-resolution bathymetry in reservoirs with different geometric characteristics. [ABSTRACT FROM AUTHOR]

In order to evaluate the feasibility of the remote sensing estimation using spaceborne Lidar ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) data for the forest Aboveground Biomass (AGB) in the mountains region, the Random Forest Regression (RFR) model was conducted by combining the Advanced Terrain Laser Altimeter System (ATLAS) photon point cloud data and 54 sample plots in Shangri-La, a typical mountain area in northwest Yunnan, Southwest China. On the basis of the data denoising and classification, the 50 canopy parameters and 3 topographic factors of 74 873 footprints were extracted. A biomass model was established with 53 parameters as the independent variables after the hyper-parametric optimizing RF, and the biomass data was collected from 54 remote sensing plots to serve as dependent variables. The Root Mean Square Error (RMSE), coefficient of determination (R2) and overall estimation accuracy (P1) were used to evaluate the model accuracy. The results showed that: 1) There was the highest significant correlation of the number canopy photons parameters with the forests aboveground biomass (P<0.01) by a correlation analyzing between the 53 footprint parameters and footprint biomass. The other parameters, landsat percentage canopy, canopy photon rate, slope, number of photons and apparent surface reflectance were significantly correlated with biomass (0.01

The monitoring and analysis of the water level in the Mekong River is of major importance in water resources security, management, and geostrategic cooperation. This study proposed a new approach for monitoring and analysis of changes in water level of the Mekong River by using ICESat-2 spaceborne laser altimetry. River water levels were extracted from ICESat-2 inland water level data (ATL13). Then, the remote-sensed water level was quantitatively compared with the in situ water level measured by hydrological stations. Finally, the responses of water level to natural and anthropogenic factors were explored. The results showed that the ATL13 products provided river water level data with a high precision (root mean square error (RMSE) = 0.24 m, bias = −0.11 m) and a low measurement uncertainty (median of standard deviations (MSD) = 0.04). The analysis indicated that ATL13 products under different beam intensities and acquisition times can be applied to derive river water level. However, the use of nighttime measurements achieved slightly higher accuracies. The seasonal characteristics of river water level (flooding season from May to October and dry season from November to April of the next year) were because of natural factors, such as upstream flow and rainfall. A comparison of water level changes among different periods showed that hydropower development generally decreased the range of water level in the flooding season and increased water level in the dry season, thereby mitigating the current uneven spatial and temporal distribution characteristics of water resources in Mekong basin. [ABSTRACT FROM AUTHOR]

Using the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) global high-resolution elevation measurements, it is possible to distinguish individual surface ocean waves. With the vast majority of ocean surveying missions using radar satellites, ICESat-2 observations are an important addition to ocean surveys. ICESat-2 can also provide additional observations not possible with radar. In this paper, we consolidate the ICESat-2 ocean observations by comparing the significant wave height (SWH) with coincident CryoSat-2 radar observations during the CRYO2ICE campaign from August 2020 to August 2021. We use 136 orbit segments, constrained to the Pacific and Atlantic oceans as well as the Bering Sea, to compare observations to show the level of agreement between these systems. Three models based on ICESat-2 are used in the comparison: the standard ocean data output (ATL12), a method of modeling the individual surface waves using the geolocated photons and, functioning as a baseline, an approach using the standard deviation of the ocean surface. We find the following correlations between the SWHs from the models and the SWHs from CryoSat-2: 0.97 for ATL12, 0.95 for the observed waves model, and 0.97 for the standard deviation model. In the same comparison, we find mean differences relative to the observed SWHs for each model, as well as errors, which increase as the SWH increases. The SWH observed from ICESat-2 is found to agree with observations from CryoSat-2, with limitations due to changes in the sea state between the satellite observations. Observing the individual surface waves from ICESat-2 can therefore provide additional observed properties of the sea state that can be used alongside other global observations. [ABSTRACT FROM AUTHOR]

Acquiring accurate and timely datasets on tidal flat topography is fundamental for studies, management, and policy‐making in coastal zones. In this study, we report a fusion method of freely accessed and global‐scale Sentinel‐2 (imagery) and Ice,Cloud, and Land Elevation Satellite‐2 (ICESat‐2) (laser altimetry) datasets for deriving tidal flat topography. The basic idea is to construct a model on characterizing the specific relationship between the elevations of the tidal flat (from ICESat‐2 data) and corresponding inundation frequencies (from Sentinel‐2 time‐series). The proposed method was validated for the largest tidal flat on the Chinese continental shelf with a root mean square error of 0.23 m. We highlight that this method solely relies on satellite data and does not require any in‐situ data because ICESat‐2 provides dense and accurate along‐track points as valuable prior elevation information, which enhance the ability to obtain tidal flat topography in many coastal regions even in the absence of local in‐situ data. Plain Language Summary: Global tidal flat topography is still unavailable at present, although it is the foundation of many global‐scale coastal‐related issues, such as coastal vulnerability estimation to sea level rise and flooding, coastal wetland monitoring, habitat conservation, site selection of offshore wind farms and ports, and land use planning. To generate tidal flat topography, accurate vertical and horizontal information is simultaneously acquired. The Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2) satellite provides dense three‐dimensional points with high vertical accuracy, which can replace field surveying (e.g., by handheld/airborne/shipborne devices) for obtaining these three‐dimensional points. The time‐series imagery of the Sentinel‐2 satellite can map accurate inundation frequencies of tidal flats (i.e., the frequency of a location heaves out or is covered by water due to ocean tides) with high horizontal resolution. Integrating the advantages of ICESat‐2 and Sentinel‐2 makes it possible to obtain global topography for tidal flats with only satellite remotely sensed data. With freely accessed and global‐scale ICESat‐2 and Sentinel‐2 data, it can be expanded to obtain tidal flat topography in many specific coastal regions or even globally. Key Points: A method to generate tidal flat topography in coastal zones was proposed using satellite remotely sensed dataIce,Cloud, and Land Elevation Satellite‐2 provides accurate along‐track profiles as prior elevation information; therefore, in‐situ data are no longer neededGoogle Earth Engine was used to speed up implementation and can expand this method for large scale applications [ABSTRACT FROM AUTHOR]

The shore zone is the most active zone in the atmosphere, hydrosphere, biosphere and lithosphere of nature, and has the environmental characteristics of both ocean and land. The ICESat-2 satellite provides height measurements of shore zone using a photon-counting LiDAR. The purpose of this study is to explore the application potential of ICESat-2 satellite data in shore zone classification. Saint Lawrence Island, Alaska, was chosen as the study area. Firstly, in this study, the upper and lower boundaries of the shore zone of the study area were extracted based on Google Earth images. The slope and width between the two boundaries were then calculated according to the formula. Secondly, six statistical indicators (standard deviation, relative standard deviation, average absolute deviation, relative average deviation, absolute median error and quartile deviation) related to the substrate and sediment classification that could reflect the characteristics of the shore zone profile were extracted, and the statistical indicators were used as input parameters of the softmax regression model for classification. Finally, the accuracy of the shore zone classification was validated using the ShoreZone classification system. The results show that, among the 246 shore zone sections in the study area, 86% (212) has been correctly classified. The results therefore indicate that ICESat-2 data can be used to support the characterization of shore zone morphology. [ABSTRACT FROM AUTHOR]

NASA's Ice, Cloud, and land Elevation Satellite‐2 (ICESat‐2) laser altimeter launched in Fall 2018, providing an invaluable addition to the polar altimetry record generated by ESA's CryoSat‐2 radar altimeter. The simultaneous operation of these two satellite altimeters enables unique comparison studies of sea ice altimetry, utilizing the different frequencies and profiling strategies of the two instruments. Here, we use freeboard data from ICESat‐2 to assess Antarctic snow freeboard retrievals from CryoSat‐2. We first discuss updates made to a previously published CryoSat‐2 retrieval process and show how this Version 2 algorithm improves upon the original method by comparing the new retrievals to ICESat‐2 in specific along‐track profiles as well as on the basin‐scale. In two near‐coincident along‐track profiles, we find mean snow freeboard differences (standard deviations of differences) of 0.3 (9.3) and 7.6 cm (9.6 cm) with 25 km binned correlation coefficients of 0.77 and 0.89. Monthly mean freeboard differences range between −2.9 (10.8) and 6.6 cm (16.8 cm) basin wide, with the largest differences typically occurring in Austral fall months that is hypothesized to be related to new ice growth and the use of static snow backscatter coefficients in the retrieval. Monthly mean correlation coefficients range between 0.57 and 0.80. While coincident data show good agreement between the two sensors, they highlight issues related to geometric and frequency sampling differences that can impact the freeboard distributions. Plain Language Summary: Measuring sea ice freeboard from space is an important first step in estimating its thickness. A previous study had developed a new method of measuring freeboard over Antarctic sea ice using ESA's CryoSat‐2 altimeter, however, few validation data existed at the time to determine how well it performed. In this paper, we improve the CryoSat‐2 processing and make use of data from NASA's ICESat‐2 altimeter for comparisons with the CryoSat‐2 data. While agreement is strong overall, there are still differences between the measurements that we hypothesize come from the different footprint sizes and wavelengths of the two instruments. Key Points: We present an updated CryoSat‐2 Antarctic snow freeboard retrieval methodThese improved CryoSat‐2 snow freeboard retrievals show strong agreement with ICESat‐2 data both along‐track and basin‐wideThis assessment highlights difficulties in laser‐radar comparisons brought on by frequency and geometric sampling discrepancies [ABSTRACT FROM AUTHOR]

With the development of photon-counting sensors, spaceborne photon-counting lidars have shown many advantages in mapping underwater topography. Although a space based lidar is normally a profiling system, the depth penetration and the vertical accuracy achieved with a bathymetric lidar is superior to imagery (that only provides relative depths). Therefore, many satellite derived bathymetry products use both active/passive spaceborne data to achieve spatial coverage as well as absolute depth measurements. Compared with shipborne and airborne bathymetric systems, satellite derived bathymetry data can have a global coverage, especially covering some remote areas where airborne/shipborne systems are hard or very expensive to reach. In this paper, a theoretical model is proposed to quantitatively analyze the maximum bathymetric depth of a satellite photon-counting lidar, which considers the system parameters, environmental effects, and the constraint of the SNR (signal to noise ratio). System parameters of ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) as well as the MODIS (Moderate-resolution Imaging Spectroradiometer) and NCEP (National Centers for Environmental Prediction) datasets are used to provide systematic and environmental inputs to the model, and the ICESat-2 actual bathymetric data are used to verify the maximum depth estimated by the proposed model. In six different sites with different water qualities, solar angles and wind speeds, the theoretical maximum bathymetric depths agree well with the actually achieved ICESat-2 lidar depths, in terms of MAE (mean absolute error) of 0.50 m and the RMSE (root mean square error) of 0.60 m. The errors in all study areas are within 14% of the corresponding maximum depths. Additionally, the non-linear response of a photon-counting lidar, independent parameter impacts on the theoretical model, and the errors in the experimental process were quantitatively analyzed. In the future, this theoretical model can be used to evaluate the maximum bathymetric depth for a bathymetry investigation when applying system parameters and environmental parameters. In addition, it can be used to optimize the hardware parameters of a photon-counting lidar in its early design process. [ABSTRACT FROM AUTHOR]

ICESat ‐2’s laser data has the highest elevation accuracy up to date, and its observation range covers the global land, which can be used as basic data for the high ‐ precision global ground elevation reference. Based on ICESat‐2 /ATLAS global laser data product ATL08, this paper obtained ICESat‐2 la‐ ser points on the global land, and studied the method of extracting global elevation control points based on elevation reference and attribute parameters, and used reference elevation data to verify their accuracy. The obtained laser points were verified by airborne laser data in Shandong experimental field and Henan experi‐ mental field in China. And the root mean square error (RMSE) were 1.11 m and 1.39 m respectively before filtering. After filtering with elevation reference and slope constraints, the mean RMSE were 0.69 m and 0.57 m respectively, and the corresponding data retention rates were 61.38% and 60.00%, which proved that the proposed method in this paper could effectively improve the elevation accuracy while ensuring the data retention rate. The airborne laser data from the western, central and eastern United States experimen‐ tal fields were used to verify the elevation control points. The RMSE of each experimental field was less than 0.9 m, which proved that the extraction method proposed in this paper could be used to extract eleva‐ tion control points worldwide. This method can automatically extract global elevation control points with high density and high precision, providing support for the stereo mapping of domestic high‐resolution satel‐ lites without or with few ground control points, and assistance in evaluating the quality of DEM/DSM products. [ABSTRACT FROM AUTHOR]

The ICESat-2 data products work well in extracting terrain elevations and canopy heights in boreal forests. However, identifying ground photons in mountainous dense forests remains a challenge due to high canopy covers, complex terrains, and limited penetrability, particularly in tropical areas. Accurate identification of ground photons is crucial for improving the performance of ICESat-2 data. This study proposes an algorithm called OPIC to optimize ground photons provided by ICESat-2 data products. The OPIC algorithm involves removing outliers of ground photons, reclassifying photons misclassified as canopies or noises, and extracting optimized ground elevations. The OPIC algorithm is evaluated in four distinct forests located in subtropical southern China, tropical Indonesia, temperate Estonia and tropical America. The results show significant improvements in the number of valid segments and the accuracy of canopy height estimations. The ICESat-2 product of Land and Vegetation Height (ATL08) provides canopy heights by segments with an along-track distance resolution of 20 meters. In forests with canopy covers ≥85% on terrains with slopes ≥15°, the ATL08 is only able to provide canopy height estimations for 10 out of 368 segments (i.e., 2.7%) at the Chinese test site, but the OPIC algorithm improves this to 157 segments (i.e., 42.7%). At the Indonesian test site, the OPIC algorithm improves the number of valid segments from 11 to 369 out of 453 segments (i.e., from 2.4% to 81.5%). Similarly, the OPIC algorithm improves the number of valid segments from 353 to 1148 out of 2776 segments (i.e., from 12.7% to 41.4%) at the American test site. The airborne lidar data is utilized as reference data to evaluate the performance of OPIC. For valid segments provided in ATL08, the OPIC can reduce the relative root mean square error (%RMSE) of canopy height estimations from 31.64% m to 24.97% at the Chinese site, from 25.74% to 18.84% at the Indonesian site, from 7.27% to 2.47% at the Estonian site, and from 27.97% to 23.87% at the American site. For all segments provided by ATL08 in each test site disregarding terrains and canopy covers, the OPIC algorithm is able to increase the number of valid segments from 383 to 1244 out of 2252 (i.e., from 17.0% to 55.2%) at the Chinese site, from 173 to 1276 out of 1482 (i.e., from 11.7% to 86.1%) at the Indonesian site, from 8638 to 10,149 out of 10,344 (i.e., from 83.5% to 98.1%) at the Estonian site, and from 6145 to 9383 out of 16,150 (i.e., from 38.0% to 58.1%) at the American site, while maintaining approximately the same estimation accuracy of canopy heights as the valid segments of ICESat-2 data products. These results demonstrate the potential of the OPIC algorithm to provide more valid segments and more accurate estimations of canopy heights in mountainous dense forests. • ICESat-2 faces challenges in identifying ground photons in mountainous dense forest. • An algorithm (OPIC) is proposed to optimize ground photons of ICESat-2 data product. • Reclassifying ground photons misclassified as canopies and noises, and outliers are removed. • OPIC can improve ratio of valid segments from 11.7% to 86.1% in tropical forests. • Estimation errors of canopy heights can be decreased by OPIC from 25.74% to 18.84%. [ABSTRACT FROM AUTHOR]

The upcoming Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission will offer prospects for mapping and monitoring biomass and carbon of terrestrial ecosystems over large areas using photon counting LiDAR data. In this paper, we aim to develop a methodology to derive terrain elevation and vegetation canopy height from test-bed sensor data and further pre-validate the capacity of the mission to meet its science objectives for the ecosystem community. We investigated a novel methodological framework with two essential steps for characterizing terrain and canopy height using Multiple Altimeter Beam Experimental LiDAR (MABEL) data and simulated ICESat-2 data with various vegetation conditions. Our algorithm first implements a multi-level noise filtering approach to minimize noise photons and subsequently classifies the remaining photons into ground and top of canopy using an overlapping moving window method and cubic spline interpolation. Results of noise filtering show that the design of the multi-level filtering process is effective to identify background noise and preserve signal photons in the raw data. Moreover, calibration results using MABEL and simulated ICESat-2 data share similar trends with the retrieved terrain being more accurate than the retrieved canopy height, and the nighttime results being better than corresponding daytime results. Compared to the results of simulated ICESat-2 data, MABEL data achieve lower accuracy for ground and canopy heights in terms of root mean square error (RMSE), which may partly result from the inconsistency between MABEL and reference data. Specifically, simulated ICESat-2 data using 115 various nighttime and daytime scenarios, yield average RMSE values of 1.83 m and 2.80 m for estimated ground elevation, and 2.70 m and 3.59 m for estimated canopy height. Additionally, the accuracy assessment of percentile heights of simulated ICESat-2 data further substantiates the robustness of the methodology from different perspectives. The methodology developed in this study illustrates plausible ways of processing the data that are structurally similar to expected ICESat-2 data and holds the potential to be a benchmark for further method adjustment once genuine ICESat-2 are available. [ABSTRACT FROM AUTHOR]

Lakes comprise the largest element of terrestrial surface liquid freshwater bodies, playing an indispensable part in the Earth's water cycle and alleviating floods and droughts. The seasonal variation of lake water level (LWLsv) and storage (LWSsv) reflects the periodic hydrologic fluctuations and related driving forces of the water balances at the basin scale. China, a vastly diverse country that descends from the "Roof of the World" to monsoonal coast zones, hosts a wide distribution of lakes with diverse hydro-climatological and topographic features. Most previous studies focused on monitoring long-term changes of lakes in China, while the seasonality (mostly regarding the lake area) of lake hydrologic dynamics was only investigated for typical lakes or local basins/zones due to the limited spatial coverage and temporal resolution of various observation data. Benefiting from the finer footprints and increased beams of ICESat-2 laser altimeter, we examined the seasonal variations of LWLsv of Chinese lakes (3473 lakes historically larger than 1 km2 during the 1980s–2010s) on a national scale from 2019 to 2021. Then, the machine learning algorithm termed the extreme gradient boosting tree was employed to model the LWLsv of the lakes that were not monitored by ICESat-2. We further quantized the national-scale LWSsv by combining the LWLsv estimates and lake area data. Results show that the mean LWLsv of the 1255 lakes observed by ICESat-2 during 2019–2021 is 0.44 ± 0.07 m. Among them, 1167 lakes have the LWLsv <1 m, and 12 lakes have the LWLsv exceeding 3 m. The accuracy evaluation indicates that the XGBoost model performs well in predicting LWLsv results for unobserved lakes, with the coefficient of determination of 0.76, the mean absolute error of 0.14 m, and the root mean square error of 0.03 m. Overall, the predicted LWLsv of unobserved lakes has a similar spatial pattern to that of the observed lakes. The LWSsv is estimated at 77.29 ± 6.87 Gt in total, but exhibits obvious spatial heterogeneity in China. The Middle and Lower Reaches of Yangtze River Basin and the endorheic Qiangtang Plateau Basin rank the first two contributors of the net LWSsv, which were respectively attributed to the most significant LWLsv changes and the largest lake group area. This national-scale quantification of the LWLsv and LWSsv helps advance our scientific understanding of the seasonal hydrologic behaviors for Chinese lakes in regulating the water cycle and providing a valuable reference for regional water resource management. • Seasonal variations of LWLsv for Chinese lakes were assessed using ICESat-2. • The LWLsv of unobserved lakes was reconstructed by machine learning models. • The LWLsv and LWSsv of Chinese lakes was estimated at national scale. • The LWLsv and LWSsv of lakes showed substantial spatial heterogeneity in China. [ABSTRACT FROM AUTHOR]

NASA's Ice, Cloud and land Elevation Satellite-2 (ICESat-2) mission was launched in September 2018. The sole instrument onboard ICESat-2 is ATLAS, a highly precise laser that now provides routine, very-high-resolution, surface height measurements across the globe, including over the Arctic. To further improve the detection accuracy of the sea ice concentration (SIC), we demonstrate a new processing chain that can be used to convert the along-track sea ice freeboard products (ATL10) obtained by ICESat-2 into the SIC, with our initial efforts being focused on the Arctic. For this conversion, we primarily make use of the classification results from the type (sea ice or lead) and segment length data gathered from ATL10. The along-track SIC is the ratio of the area that is covered by sea ice segments to the area of all of the along-track segments. We generated a monthly gridded SIC product with a 25 km resolution and compared this to the NSIDC Climate Data Record (CDR) sea ice concentration. The highest correlation was determined to be 0.7690 in September at high latitudes and the lowest correlation was found to be 0.8595 in June at mid-latitudes. The regions with large standard deviations in summer and autumn are mainly distributed in the thin-ice areas at mid-latitudes. In the Laptev Sea and Kara Sea of east Siberia, the differences in the standard deviation were large; the maximum bias was −0.1566, in November, and the minimum bias was −0.0216, in June. ICESat-2 shows great potential for the accurate estimation of the SIC. [ABSTRACT FROM AUTHOR]

ICESat-2 altimeter data could be used to estimate sea ice freeboard and thickness values with a higher measuring accuracy than that achievable with data provided by previous altimeter satellites. This study developed an improved algorithm considering variable lead proportions based on the lowest point method to derive the sea surface height for the retrieval of Antarctic sea ice freeboard and thickness values from ICESat-2 ATL-07 data. We first collocated ICESat-2 tracks to corresponding Sentinel-1 SAR images and calculated lead (seawater) proportions along each track to estimate the sea surface height in the Antarctic Ocean. Then, the Antarctic sea ice freeboard and thickness were estimated based on a local sea surface height reference and a static equilibrium equation. Finally, we assessed the accuracy of our improved algorithm and ICESat-2 data product in the retrieval of the Antarctic sea ice thickness by comparing the calculated values to ship-based observational sea ice thickness values acquired during the 35th Chinese Antarctic Research Expedition (CHINARE-35). The results indicate that the Antarctic sea ice freeboard estimated with the improved lowest point method was slightly larger than that estimated with the ICESat-2 data product algorithm. The root mean squared error (RMSE) of the improved lowest point method was 35 cm with the CHINARE-35 measured sea ice thickness, which was smaller than that determined with the ICESat-2 data product algorithm (65 cm). Our improved algorithm could provide more accurate data on the Antarctic sea ice freeboard and thickness, thus supporting Antarctic sea ice monitoring and the evaluation of its change under global effects. [ABSTRACT FROM AUTHOR]

Lakes play a key role in the global water cycle, providing essential water resources and ecosystem services for humans and wildlife. Quantifying long-term changes in lake volume at a global scale is therefore important to the sustainability of humanity and natural ecosystems. Yet, such an estimate is still unavailable because, unlike lake area, lake volume is three-dimensional, challenging to be estimated consistently across space and time. Here, taking advantage of recent advances in remote sensing technology, especially NASA's ICESat-2 satellite laser altimeter launched in 2018, we generated monthly volume series from 2003 to 2020 for 9065 lakes worldwide with an area ≥ 10 km2. We found that the total volume of the 9065 lakes increased by 597 km3 (90% confidence interval 239–2618 km3). Validation against in situ measurements showed a correlation coefficient of 0.98, an RMSE (i.e., root mean square error) of 0.57 km3 and a normalized RMSE of 2.6%. In addition, 6753 (74.5%) of the lakes showed an increasing trend in lake volume and were spatially clustered into nine hot spots, most of which are located in sparsely populated high latitudes and the Tibetan Plateau; 2323 (25.5%) of the lakes showed a decreasing trend in lake volume and were clustered into six hot spots—most located in the world's arid/semi-arid regions where lakes are scarce, but population density is high. Our results uncovered, from a three-dimensional volumetric perspective, spatially uneven lake changes that aggravate the conflict between human demands and lake resources. The situation is likely to intensify given projected higher temperatures in glacier-covered regions and drier climates in arid/semi-arid areas. The 15 hot spots could serve as a blueprint for prioritizing future lake research and conservation efforts. [ABSTRACT FROM AUTHOR]

The lack of ground control points (GCPs) affects the elevation accuracy of digital surface models (DSMs) generated by optical satellite stereo images and limits the application of high-resolution DSMs. It is a feasible idea to use ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) laser altimetry data to improve the elevation accuracy of optical stereo images, but it is necessary to accurately match the two types of data. This paper proposes a DSM registration strategy based on terrain similarity (BOTS), which integrates ICESat-2 laser altimetry data without GCPs and improves the DSM elevation accuracy generation from optical satellite stereo pairs. Under different terrain conditions, Worldview-2, SV-1, GF-7, and ZY-3 stereo pairs were used to verify the effectiveness of this method. The experimental results show that the BOTS method proposed in this paper is more robust when there are a large number of abnormal points in the ICESat-2 data or there is a large elevation gap between DSMs. After fusion of ICESat-2 data, the DSM elevation accuracy extracted from the satellite stereo pair is improved by 73~92%, and the root mean square error (RMSE) of Worldview-2 DSM reaches 0.71 m. [ABSTRACT FROM AUTHOR]

Shallow-water depth information is essential for ship navigation and fishery farming. However, the accurate acquisition of shallow-water depth has been a challenge for marine mapping. Combining Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) bathymetry data with multispectral data, satellite-derived bathymetry is a promising solution through which to obtain bathymetric information quickly and accurately. This study proposes a photon refraction correction method considering sea-surface undulations to address errors in the underwater photons obtained by the ICESat-2. First, the instantaneous sea surface and beam emission angle are integrated to determine the sea-surface incidence angle. Next, the distance of photon propagation in water is determined using sea-surface undulation and Snell's law. Finally, position correction is performed through geometric relationships. The corrected photons were combined with the multispectral data for bathymetric inversion, and a bathymetric map of the Yongle Atoll area was obtained. A bathymetric chart was created using the corrected photons and the multispectral data in the Yongle Atoll. Comparing the results of different refraction correction methods with the data measured shows that the refraction correction method proposed in this paper can effectively correct bathymetry errors: the root mean square error is 1.48 m and the R2 is 0.86. [ABSTRACT FROM AUTHOR]

With the advent of the next generation of space-based laser altimeters, ICESat-2 and GEDI, we are entering an exciting era of active remote sensing of forests that offers unprecedented opportunities for the observation of forest structure. Consistent comparisons of the accuracy of terrain and canopy height retrievals for these two missions are essential for continued improvement and further application. Because the time interval between the spaceborne products and validation data may introduce additional errors, we validate the newly released GEDI L2A product (version 2) and the ICESat-2 ATL08 product (version 4) using high-resolution, locally calibrated airborne lidar products acquired in the same year (2019) as the reference datasets. In addition, our study area contains 40 sites located in the U.S. mainland, Alaska, and Hawaii that encompass a variety of eco-climatic zones and vegetation cover types; thus, it avoids the uncertainties associated with small sample sizes and restricted spatial coverage. The results show that ICESat-2 and GEDI yield reasonable estimates of terrain height, with root mean squared errors (RMSEs) of 2.24 and 4.03 m for mid and low latitudes, respectively, and 0.98 m for high latitudes (ICESat-2 only). ICESat-2 outperforms GEDI across the board for terrain height retrieval, although they both have better accuracy than existing SRTM and GMTED DEM products. Analyses of the error factors suggest that steep slopes (>30°) present the greatest challenge for both GEDI and ICESat-2; in addition, tall (>20 m) and dense canopies (>90%) forest ecosystems also reduce the accuracy of the terrain height estimates. When ICESat-2 and GEDI data are used for canopy height retrieval, the use of only strong/power beam data acquired at night is recommended, as the overall RMSEs decrease from 7.21 and 5.02 m to 3.93 and 3.56 m, respectively, compared to using all data regardless of daytime and beam strength. GEDI outperforms ICESat-2 across the board for canopy height retrieval, as ICESat-2 has a larger potential bias for almost all forest types and cover conditions. ICESat-2 tends to overestimate the canopy height of dwarf shrublands and underestimate the canopy height of forest, and there is a gradual downward shift in the distribution of residuals with increasing canopy height. Overall, ICESat-2 with photon counting technology and GEDI with full waveform technology each represent the state of the art in spaceborne laser altimeters for terrain and canopy height retrieval. Combined, these two missions can take advantage of the unique strengths of each instrument. • The latest GEDI and ICESat-2 terrain and canopy height products were evaluated. • Locally calibrated airborne lidar products allow consistent accuracy assessment. • ICESat-2 and GEDI yield reasonable terrain and canopy height estimates. • For canopy height retrieval, the use of strong-beam and night data is recommended. • Both ICESat-2 and GEDI represent the state of the art in spaceborne laser altimeters. [ABSTRACT FROM AUTHOR]