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Sea ice thickness is an essential variable to understand and forecast the dynamic ice cover and can be estimated by satellite altimetry. Nevertheless, it is affected by uncertainties especially from snow depth, a key parameter to derive it from ice freeboard. We developed a snow depth product based on the differences between CryoSat-2 SAR Ku and IceSat-2 laser altimeters which have different snow penetration capabilities. We compared this product to other existing snow depth products. This comparison indicates correlations all above 0.37 and reaching 0.91 relative to airborne snow radar. We estimate the snow depth uncertainties of our product to be 0.06 m on average. Combining two frequencies to simultaneously measure ice and total freeboard reduces the uncertainties in ice thickness by around a factor of two compared with a single-frequency solution combined with a snow depth climatology. The lack of in situ measurements is a limiting factor to assess the advantages of coincident CryoSat-2 and ICESat-2 acquisitions. Validation data are of major importance for upcoming missions. [ABSTRACT FROM AUTHOR]

In the active–passive fusion-based bathymetry inversion method using single-temporal images, image data often suffer from errors due to inadequate atmospheric correction and interference from neighboring land and water pixels. This results in the generation of noise, making high-quality data difficult to obtain. To address this problem, this paper introduces a multi-temporal image fusion method. First, a median filter is applied to separate land and water pixels, eliminating the influence of adjacent land and water pixels. Next, multiple images captured at different times are fused to remove noise caused by water surface fluctuations and surface vessels. Finally, ICESat-2 laser altimeter data are fused with multi-temporal Sentinel-2 satellite data to construct a machine learning framework for coastal bathymetry. The bathymetric control points are extracted from ICESat-2 ATL03 products rather than from field measurements. A backpropagation (BP) neural network model is then used to incorporate the initial multispectral information of Sentinel-2 data at each bathymetric point and its surrounding area during the training process. Bathymetric maps of the study areas are generated based on the trained model. In the three study areas selected in the South China Sea (SCS), the validation is performed by comparing with the measurement data obtained using shipborne single-beam or multi-beam and airborne laser bathymetry systems. The root mean square errors (RMSEs) of the model using the band information after image fusion and median filter processing are better than 1.82 m, and the mean absolute errors (MAEs) are better than 1.63 m. The results show that the proposed method achieves good performance and can be applied for shallow-water terrain inversion. [ABSTRACT FROM AUTHOR]

Advancements in scientific inquiry and practical applications have created a higher demand for the accuracy of global digital elevation models (GDEMs), especially for GDEMs whose main data source is optical imagery. To address this challenge, integrating GDEM and satellite laser altimeter data (global coverage and high-accuracy ranging) is an important research direction, in addition to the technological enhancement of the main data source. In this paper, we describe the datasets and algorithms used to generate a GDEM product (IC2-GDEM) by correcting ASTER GDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model) elevation data with ICESat-2 altimeter data. The algorithm scheme presents the details of the strategies used for the various challenges, such as the processing of DEM boundaries, the fusion of the different data, and the geographical layout of the satellite laser altimeter data. We used a high-accuracy global elevation control point dataset and multiple high-accuracy local DEMs as the validation data for a comprehensive assessment at the global scale. The results from the validation comparison show that the elevation accuracy of IC2-GDEM is evidently superior to that of the ASTER GDEM product: (1) the RMSE reduction ratio of the corrected GDEM elevation is between 16 % and 82 %, and the average reduction ratio is about 47 %; and (2) from the analysis of the different topographies and land covers, this error reduction is effective even in areas with high topographic relief (>15°) and high vegetation cover (>60%). ASTER GDEM has been in use for more than a decade, and many historical datasets and models are based on its elevation data. IC2-GDEM facilitates seamless integration with these historical datasets, which is essential for longitudinal studies examining long-term environmental change, land use dynamics, and climate impacts. Meanwhile, IC2-GDEM can serve as a new complementary data source for existing DEMs (such as Copernicus DEM) mainly sourced from synthetic aperture radar (SAR) observation. By cross-validating qualities, filling data gaps, and conducting multi-scale analyses, it can lead to more reliable and comprehensive scientific discoveries, thereby improving the overall quality and reliability of Earth science research. The IC2-GDEM product is openly available at 10.11888/RemoteSen.tpdc.301229 (Xie et al., 2024). [ABSTRACT FROM AUTHOR]

The intertidal zone, as a dynamic ecosystem at the interface of land and sea, plays a critical role in environmental protection and disaster mitigation. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is equipped with the Advanced Topographic Laser Altimeter System (ATLAS) with the ability to penetrate the water bodies, enabling its use for bathymetric measurements. However, the complex land cover types and frequent environmental changes in intertidal zones pose significant challenges for precise measurement and dynamic monitoring. In an effort to address the denoising challenges of ICESat-2 photon point cloud data in such complex environments, this study proposes an adaptive photon denoising method that is capable of dynamically adjusting the denoising strategy for different types of photon data. ATL03 data from four typical intertidal zones were selected for denoising experiments. The results indicated that the proposed adaptive denoising method achieved average recall, precision, and F-score values of 0.9885, 0.9927, and 0.9906, respectively, demonstrating excellent denoising performance and stability. This method provides an effective data processing approach for high-precision monitoring of intertidal zone topography. [ABSTRACT FROM AUTHOR]

This study evaluates global radar‐derived digital elevation models (DEMs), namely the Shuttle Radar Topography Mission (SRTM), NASADEM and GLO‐30 DEMs. We evaluate their accuracy over bare‐earth terrain and characterize elevation biases induced by forests using global Lidar measurements from the Ice, Cloud, and Land Elevation Satellite (ICESat)'s Geoscience Laser Altimeter System (GLAS), the Global Ecosystem Dynamics Investigation (GEDI) and the ICESat‐2 Advanced Topographic Laser Altimeter System (ATLAS) instruments collected on locally flat terrain. Our analysis is based on error statistics calculated for each 1°×1° $1{}^{\circ}\times 1{}^{\circ}$ DEM tile, which are then summarized as global error percentiles, providing a regional characterization of DEM quality. We find NASADEM to be a significant improvement upon the SRTM V3. Over bare ground areas, the mean elevation bias and root mean square error (RMSE) improved from 0.68 to 2.50 m respectively to 0.00 and 1.5 m as compared to ICESat/GLAS. GLO‐30 is more accurate with bare ground elevation bias and RMSE were below 0.05 and 0.55 m. Similar improvements were observed when compared to GEDI and ICESat‐2 measurements. The DEM biases associated with the presence of vegetation vary linearly with canopy height, and more closely follow the 50th $5{0}^{th}$ percentile of Lidar Relative Height (RH50). Other factors such as canopy density, radar frequency and Lidar technology also contribute to observed elevation biases. This global analysis highlights the potential of various technologies for mapping of Earth's topography, and the need for more advanced remote sensing observations that can resolve vegetation structure and sub‐canopy ground elevation. Plain Language Summary: We compare the accuracy of three global maps of Earth's surface topography that were derived from spaceborne radar missions, namely, SRTM, NASADEM, and GLO30. The accuracy is evaluated using spaceborne Lidar data sets from three missions: ICESAT, ICESAT‐2, and GEDI. Lidars are typically more accurate in estimating elevation of the ground, but their measurements are like sparse point measurements. We evaluated the radar‐derived DEMs separately over bare ground regions and forests. We found that NASADEM is a significant improvement over the SRTM DEM with an error of 1.5 m in bare ground areas. But the GLO30 DEM is more accurate with error of only 0.55 m. The presence of forests causes radar‐derived elevation to be higher than the ground, somewhere mid‐canopy. Key Points: NASA's Digital Elevation Model is better than the Shuttle Radar Topography Mission Digital Elevation Model, but GLO‐30 is the most accurateAll radar‐derived elevations exhibit a strong correlation with forest canopy height with bias similar to the Lidar's relative height 50The Ice, Cloud and land Elevation Satellite‐2 Lidar measurements provide a better Lidar alternative for detecting the ground below forest [ABSTRACT FROM AUTHOR]

Forests are an important component of the Earth's ecosystems. Forest canopy height is an important fundamental indicator for quantifying forest ecosystems. The current spaceborne photon-counting Light Detection and Ranging (LiDAR) technique has photon cloud characteristic parameters to estimate forest canopy height, and factors such as the sampling window size have not been quantitatively studied. To better understand the precision for estimating canopy height using spaceborne photon-counting LiDAR ICESat-2/ATLAS (Ice, Cloud, and Land Elevation Satellite-2/Advanced Topographic Laser Altimeter System), this study quantified the impact of photon-counting characteristic parameters, sampling window size, and forest cover. Estimation accuracy was evaluated across nine study areas in North America. The findings revealed that when the photon-counting characteristic parameter was set to H70 (70% of canopy height) and the sampling window length was 20 m, the estimation results aligned more closely with the airborne validation data, yielding superior accuracy evaluation indicators with a root mean square error (RMSE) of 4.13 m. Under forest cover of 81%–100%, our algorithms exhibited high estimation accuracy. These study results offer novel perspectives for the application of spaceborne photon-counting LiDAR ICESat-2/ATLAS in forestry. [ABSTRACT FROM AUTHOR]

Satellite altimetry data, with its increasing density and quality, has become the primary source for marine deflection of the vertical (DOV) and gravity anomaly modelling. Limited by orbital inclinations, the precision of the meridian component of the gridded deflection of the vertical (GDOV) calculated by traditional altimetry satellites is significantly better than that of the prime vertical component, and the excessive precision difference between these two components restricts the inversion precision of marine gravity anomaly model. The study of cross-track deflection of the vertical (CTDOV) is enabled by the multibeam synchronous observation mode of the new laser altimetry satellite, Ice, Cloud and Land Elevation Satellite-2 (ICESat-2). Based on the remove-restore method, residual geoid gradients are first calculated in this paper using three approaches: along-track (A-T), cross-track (C-T) and an integration of along-track and cross-track. Vertical deflections are then computed on a 1′ × 1′ grid using the least squares collocation (LSC) method, and the precision is verified against the SIO V32.1_DOV model. An optimized combination is proposed to address the issue of precision differences between the meridian and prime vertical components, and to enhance the precision of DOV inversion. A new DOV combination is formed by combining the meridian component from along-track deflection of the vertical (ATDOV) with the prime vertical component from cross-track deflection of the vertical (CTDOV) based on the remove-restore method. The Philippine Sea (0°–35°N, 120°–150°E) is selected as the test area to verify the feasibility of the optimized combination. The results indicate that the optimized combination of the meridian and prime vertical components achieved test precision of 2.63 and 3.33 μrad, respectively, when compared against the SIO V32.1_DOV model. The precision gap between the components is effectively narrowed by this approach, which maintains the precision of the meridian component and enhances that of the prime vertical component, thereby achieving optimal inversion precision for gravity anomalies. [ABSTRACT FROM AUTHOR]

Anomalies displaying significant deviations between terrain elevation products acquired from spaceborne full-waveform LiDAR and reference elevations are frequently observed in assessment studies. While the predominant focus is on "normal" data, recognizing anomalies within datasets obtained from the Geoscience Laser Altimeter System (GLAS) and the Global Ecosystem Dynamics Investigation (GEDI) is essential for a comprehensive understanding of widely used spaceborne full-waveform data, which not only facilitates optimal data utilization but also enhances the exploration of potential applications. Nevertheless, our comprehension of anomalies remains limited as they have received scant specific attention. Diverging from prevalent practices of directly eliminating outliers, we conducted a targeted exploration of anomalies in forested areas using both transmitted and return waveforms from the GLAS and the GEDI in conjunction with airborne LiDAR point cloud data. We unveiled that elevation anomalies stem not from the transmitted pulses or product algorithms, but rather from scattering sources. We further observed similarities between the GLAS and the GEDI despite their considerable disparities in sensor parameters, with the waveforms characterized by a low signal-to-noise ratio and a near exponential decay in return energy; specifically, return signals of anomalies originated from clouds rather than the land surface. This discovery underscores the potential of deriving cloud-top height from spaceborne full-waveform LiDAR missions, particularly the GEDI, suggesting promising prospects for applying GEDI data in atmospheric science—an area that has received scant attention thus far. To mitigate the impact of abnormal return waveforms on diverse land surface studies, we strongly recommend incorporating spaceborne LiDAR-offered terrain elevation in data filtering by establishing an elevation-difference threshold against a reference elevation. This is especially vital for studies concerning forest parameters due to potential cloud interference, yet a consensus has not been reached within the community. [ABSTRACT FROM AUTHOR]

The global marine gravity anomaly model is predominantly recovered from along-track radar altimeter data. Despite significant advances in gravity anomaly recovery, the improvement of the gravity anomaly model remains constrained by the absence of cross-track geoid gradients and the reduction in radar altimeter data, especially in coastal and high-latitude regions. ICESat-2 laser altimetry, with a three-pair laser beam configuration, a small footprint, and a near-polar orbit, facilitates the determination of cross-track geoid gradients and provides valid observations in certain regions. We present an ICESat-2 altimeter data processing strategy that includes the determination of cross-track geoid gradients and the combination of along-track and cross-track geoid gradients. Utilizing these methods, we developed a new global marine gravity model, SDUST2022GRA, from radar and laser altimeter data. Different weight determination methods were applied to each type of altimeter datum. The precision and spatial resolution of SDUST2022GRA were assessed against published altimeter-derived global gravity anomaly models (DTU17, V32.1, NSOAS22) and shipborne gravity measurements. SDUST2022GRA achieved a global precision of 4.43 mGal , representing an improvement of approximately 0.22 mGal over existing altimeter-derived models. In local coastal and high-latitude regions, SDUST2022GRA showed an enhancement of 0.16–0.24 mGal compared to the other models. The spatial resolution of SDUST2022GRA is approximately 20 km in certain regions, which is slightly superior to the other models. The percentage contribution of ICESat-2 to the improvement of the gravity anomaly model is 4.3 % in low- to mid-latitude regions by comparing SDUST2022GRA with ICESat-2 to SDUST2021GRA without ICESat-2, and this is increasing in coastal regions. These assessments suggest that SDUST2022GRA is a reliable global marine gravity anomaly model. The SDUST2022GRA data are freely available at https://doi.org/10.5281/zenodo.8337387 (Li et al., 2023). [ABSTRACT FROM AUTHOR]

In the ecological context of global climate change, ensuring the stable carbon sequestration capacity of forest ecosystems, which is among the most important components of terrestrial ecosystems, is crucial. Forest fires are disasters that often burn vegetation and damage forest ecosystems. Accurate recognition of firegrounds is essential to analyze global carbon emissions and carbon flux, as well as to discover the contribution of climate change to the succession of forest ecosystems. The common recognition of firegrounds relies on remote sensing data, such as optical data, which have difficulty describing the characteristics of vertical structural damage to post-fire vegetation, whereas airborne LiDAR is incapable of large-scale observations and has high costs. The new generation of satellite-based photon counting radar ICESat-2/ATLAS (Advanced Topographic Laser Altimeter System, ATLAS) data has the advantages of large-scale observations and low cost. The ATLAS data were used in this study to extract three significant parameters, namely general, canopy, and topographical parameters, to construct a recognition index system for firegrounds based on vertical structure parameters, such as the essential canopy, based on machine learning of the random forest (RF) and extreme gradient boosting (XGBoost) classifiers. Furthermore, the spatio-temporal parameters are more accurate, and widespread use scalability was explored. The results show that the canopy type contributed 79% and 69% of the RF and XGBoost classifiers, respectively, which indicates the feasibility of using ICESat-2/ATLAS vertical structure parameters to identify firegrounds. The overall accuracy of the XGBoost classifier was slightly greater than that of the RF classifier according to 10-fold cross-validation, and all the evaluation metrics were greater than 0.8 after the independent sample test under different spatial and temporal conditions, implying the potential of ICESat-2/ATLAS for accurate fireground recognition. This study demonstrates the feasibility of ATLAS vertical structure parameters in identifying firegrounds and provides a novel and effective way to recognize firegrounds based on different spatial–temporal vertical structure information. This research reveals the feasibility of accurately identifying fireground based on parameters of ATLAS vertical structure by systematic analysis and comparison. It is also of practical significance for economical and effective precise recognition of large-scale firegrounds and contributes guidance for forest ecological restoration. [ABSTRACT FROM AUTHOR]

This contribution presents an assessment of the horizontal and vertical accuracy of altimetry observations collected by NASA's Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission. We selected the terrain-matching method to determine the position of laser altimeter profiles within a precisely known surface, represented by a digital elevation model (DEM). We took this classical approach a step further, approximated the DEM by planar surface patches, and calculated the optimal position of the laser profile by minimizing the square sum of the elevation differences between reference DEMs and ICESat-2 profiles. We found the highly accurate DEMs of the McMurdo Dry Valleys (DV), Antarctica, ideal for this research because of their stable landscape and rugged topography. Here, we summarize recent improvements of this approach to estimate the horizontal and vertical accuracy of ICESat-2 observations and assess the quality of new ICESat-2 data releases. [ABSTRACT FROM AUTHOR]

Advancements in scientific inquiry and practical applications have put forward a higher demand for the accuracy of global digital elevation models (GDEMs), especially for GDEMs whose main data source is optical imagery. To address this challenge, integrating GDEM and satellite laser altimeter data (global coverage and high-accuracy ranging) is one of the important research directions, in addition to the technological enhancement of the main data source. In this paper, we describe the datasets and algorithms used to generate a GDEM product (IC2-GDEM) by correcting ASTER GDEM elevation data with ICESat-2 altimeter data. The algorithm scheme presents the details of the strategies used for the various challenges, such as the processing of DEM boundaries, the fusion of the different data, the geographical layout of the satellite laser altimeter data, etc. We used a high-accuracy global elevation control point dataset and multiple high-accuracy local DEMs as the validation data for a comprehensive assessment at a global scale. The results from the validation comparison present that the elevation accuracy of IC2-GDEM is evidently superior to that of the ASTER GDEM product. The root-mean-square error (RMSE) reduction ratio of the corrected GDEM elevation is between 16 % and 82 %, and the average reduction ratio is about 47 %. From the analysis of the different topographies and land covers, it was also found that this error reduction is effective even in areas with high topographic relief (>15°) and high vegetation cover (>60 %). ASTER GDEM has been in use for more than a decade, and many historical datasets and models are based on its elevation data. IC2-GDEM facilitates seamless integration with these historical datasets, which is essential for longitudinal studies examining long-term environmental change, land use dynamics, and climate impacts. Meanwhile, IC2-GDEM can serve as a new complementary data source to existing DEMs (Copernicus DEM, etc.) mainly sourced from synthetic aperture radar (SAR) observation. By cross-validating qualities, filling data gaps, conducting multi-scale analyses,etc., it can lead to more reliable and comprehensive scientific discoveries, thereby improving the overall quality and reliability of earth science research. IC2-GDEM product is openly released via https://doi.org/10.11888/RemoteSen.tpdc.301229 (Xie et al., 2024). [ABSTRACT FROM AUTHOR]

Space-based atmospheric backscatter lidars provide critical information about the vertical distribution of clouds and aerosols, thereby improving our understanding of the climate system. They are additionally useful for detecting hazards to aviation and human health, such as volcanic plumes and man-made pollution events. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP, 2006–2023), Cloud-Aerosol Transport System (CATS, 2015–2017), and Advanced Topographic Laser Altimeter System (ATLAS 2018–present) are three such lidars that operated within the past 20 years. The signal-to-noise ratio (SNR) for these lidars is significantly lower in daytime data compared with nighttime data due to the solar background signal increasing the detector response noise. Averaging horizontally across profiles has been the standard way to increase SNR, but this comes at the expense of resolution. Modern, deep learning-based denoising algorithms can be applied to improve the SNR without coarsening resolution. This paper describes how one such model architecture, Dense Dense U-Net (DDUNet), was trained to denoise CATS 1064 nm raw signal data (photon counts) using artificially noised nighttime data. Simulated CATS daytime 1064 nm data were then created to assess the model's performance. The denoised simulated data increased the daytime SNR by a factor of 2.5 (on average) and decreased minimum detectable backscatter (MDB) to ~ 7.3 × 10 − 4 km−1sr−1, which is lower than the CALIOP 1064 nm night MDB value of 8.6 × 10 − 4 km−1sr−1. Layer detection was performed on simulated 2 km horizontal resolution denoised and 60 km averaged data. Despite the finer resolution input, the denoised layers had more true positives, fewer false positives, and an overall Jaccard Index of 0.54 versus 0.44 when compared to the layers detected on averaged data. Layer detection was also performed on a full month of denoised daytime CATS data (Aug. 2015) to detect layers for comparison with CATS standard Level 2 (L2) product layers. The detection on the denoised data yielded 2.33 times more, higher-quality bins within detected layers at 2.7–33 times finer resolution than the CATS L2 products. [ABSTRACT FROM AUTHOR]

Because of the high cost of manual surveys, the analysis of spatial change of forest structure at the regional scale faces a difficult challenge. Spaceborne LiDAR can provide global scale sampling and observation. Taking this opportunity, dense natural forest canopy cover (NFCC) observations obtained by combining spaceborne LiDAR data, plot survey, and machine learning algorithm were used as spatial attributes to analyze the spatial effects of NFCC. Specifically, based on ATL08 (Land and Vegetation Height) product generated from Ice, Cloud and land Elevation Satellite-2/Advanced Topographic Laser Altimeter System (ICESat-2/ATLAS) data and 80 measured plots, the NFCC values located at the LiDAR's footprint locations were predicted by the ML model. Based on the predicted NFCC, the spatial effects of NFCC were analyzed by Moran's I and semi-variogram. The results showed that (1) the Random Forest (RF) model had the strongest predicted performance among the built ML models (R2=0.75, RMSE=0.09); (2) the NFCC had a positive spatial correlation (Moran's I = 0.36), that is, the CC of adjacent natural forest footprints had similar trends or values, belonged to the spatial agglomeration distribution; the spatial variation was described by the exponential model (C0 = 0.12×10-2, C= 0.77×10-2, A0 = 10200 m); (3) topographic factors had significant effects on NFCC, among which elevation was the largest, slope was the second, and aspect was the least; (4) the NFCC spatial distribution obtained by SGCS was in great agreement with the footprint NFCC (R2 = 0.59). The predictions generated from the RF model constructed using ATL08 data offer a dependable data source for the spatial effects analysis. [ABSTRACT FROM AUTHOR]

The ICESat‐2 (Ice, Cloud and Land Elevation Satellite‐2) photon‐counting laser altimeter technology required the design and development of very sophisticated onboard algorithms to collect, store and downlink the observations. These algorithms utilize both software and hardware solutions for meeting data volume requirements and optimizing the science achievable via ICESat‐2 measurements. Careful planning and dedicated development were accomplished during the pre‐launch phase of the mission in preparation for the 2018 launch. Once on‐orbit all of the systems and subsystems were evaluated for performance, including the receiver algorithms, to ensure compliance with mission standards and satisfy the mission science objectives. As the mission has progressed and the instrument performance and data volumes were better understood, there have been several opportunities to enhance ICESat‐2's contributions to Earth observation science initiated by NASA and the ICESat‐2 science community. We highlight multiple updates to the flight receiver algorithms, the onboard software for signal processing, that have extended ICESat‐2's data capabilities and allowed for advanced science applications beyond the original mission objectives. Plain Language Summary: NASA launched its second Earth observing laser altimeter in 2018 with mission objectives of collecting observations in support of Earth science as a window into climate change impacts on our planet. Pre‐launch studies focused on specific instrument settings and on‐board data processing to support the mission objectives without violating data volume constraints. Once the instrument was on‐orbit and operational, evaluation of the algorithms for success in signal detection, signal finding, and signal telemetry was undertaken. In response to the evaluation, updates have been made to optimize the data provided by the mission. Key Points: Since the 2018 ICESat‐2 (Ice, Cloud and Land Elevation Satellite‐2) launch multiple updates have been made to the onboard flight receiver algorithm parameters to improve access and utility of the data for a multi‐disciplinary science communityThe adjustments have been primarily in the in the vertical telemetry band settings and have facilitated enhanced observations of blowing snow and increased detection opportunities of bathymetry in nearshore environmentsThe parameter changes have been made to mitigate data losses in certain situations and to advance science applications outside of the primary science objectives of the ICESat‐2 mission [ABSTRACT FROM AUTHOR]

The wave-affected marginal ice zone (MIZ) is an essential part of the sea ice cover and crucial to the atmosphere–ice–ocean interaction in the polar region. While we primarily rely on in situ campaigns for studying MIZs, significant challenges exist for the remote sensing of MIZs by satellites. This study develops a novel retrieval algorithm for wave-affected MIZs based on the delay-Doppler radar altimeter on board CryoSat-2 (CS2). CS2 waveform power and waveform stack statistics are used to determine the part of the sea ice cover affected by waves. Based on the CS2 data since 2010, we generate a climate record of wave-affected MIZs in the Atlantic Arctic, spanning 12 winters between 2010 and 2022 (10.5281/zenodo.8176585,). The MIZ record indicates no significant change in the mean MIZ width or the extreme width, although large temporal and spatial variability is present. In particular, extremely wide MIZ events (over 300 km) are observed in the Barents Sea, whereas in other parts of the Atlantic Arctic, MIZ events are typically narrower. We also compare the CS2-based retrieval with the retrievals based on the laser altimeter of ICESat2 and the synthetic aperture radar images from Sentinel-1. Under spatial and temporal collocation, we attain good agreement among the MIZ retrievals based on the three different types of satellite payloads. Moreover, the traditional sea-ice-concentration-based definition of MIZ yields systematically narrower MIZs than CS2, and no statistically significant correlation exists between the two. Beyond its application to CS2, the proposed retrieval algorithm can be adapted to historical and future radar altimetry campaigns. The synergy of multiple satellites can improve the spatial and temporal representation of the altimeters' observation of the MIZs. [ABSTRACT FROM AUTHOR]

Icebergs are big chunks of ice floating on the ocean surface, and melting of icebergs contributes for the major part of freshwater flux into ocean. Dynamic monitoring of the icebergs in Antarctica and accurate estimation of their volume are important for predicting the trend of freshwater budget of the Southern Ocean. The iceberg freeboard is a key parameter for measuring the thickness and volume of an iceberg and is defined as the difference between the elevation of iceberg surface and sea level. So far, freeboards of icebergs have been successfully extracted using InSAR DEM, and the laser and radar altimeter. However, uncertainties exist in these results mainly caused by missed detection of small icebergs due to the spatially sparse and temporally incomplete data coverage. In addition to the above techniques, optical images can also be used to extract the iceberg freeboard based on its geometric relationship with shadow length, which can effectively compensate for the above shortcomings. Although the feasibility has been preliminarily presented, the precision and extensive application of shadow-height method deserves further research, such as estimating the basal melting of icebergs. In this work, we tested an optical image-based freeboard extraction method over icebergs in Prydz Bay, Antarctica. A normalized shadow pixel index (NSPI) is designed to identify iceberg shadows with different shapes in HY-1C/D CZI and Sentinel-2 MSI optical images. The iceberg freeboard can be determined with an acceptable precision (2 m) in optical images compared with laser altimeter (i.e. ICESat-2) measurements. Moreover, basal melting of icebergs has been assessed according to the variation of freeboard using repeated optical observations. The results indicate that icebergs in the study area were with a mean freeboard of about 56 m in early December 2022, and experienced a decrease in freeboard of 1.9 m within two months, in correspondence with the Antarctic seasonal trend. The methodological framework, therefore, turns out to be a reliable complementary approach to studying the iceberg freeboard in polar regions. [ABSTRACT FROM AUTHOR]

To address the matching challenge between the High Resolution Imaging Science Experiment (HiRISE) Digital Elevation Model (DEM) and the Mars Orbiter Laser Altimeter (MOLA) DEM, we propose a terrain matching framework based on the combination of point cloud coarse alignment and fine alignment methods. Firstly, we achieved global coarse localization of the HiRISE DEM through nearest neighbor matching of key Intrinsic Shape Signatures (ISS) points in the Fast Point Feature Histograms (FPFH) feature space. We introduced a graph matching strategy to mitigate gross errors in feature matching, employing a numerical method of non-cooperative game theory to solve the extremal optimization problem under Karush–Kuhn–Tucker (KKT) conditions. Secondly, to handle the substantial resolution disparities between the MOLA DEM and HiRISE DEM, we devised a smoothing weighting method tailored to enhance the Voxelized Generalized Iterative Closest Point (VGICP) approach for fine terrain registration. This involves leveraging the Euclidean distance between distributions to effectively weight loss and covariance, thereby reducing the results' sensitivity to voxel radius selection. Our experiments show that the proposed algorithm improves the accuracy of terrain registration on the proposed Curiosity landing area's, Mawrth Vallis, data by nearly 20%, with faster convergence and better algorithm robustness. [ABSTRACT FROM AUTHOR]

The empirical approach of satellite-derived bathymetry provides a straightforward, easily-implemented, and very effective way of estimating shallow-water depths from high-spatial-resolution satellite images. However, this approach has a great challenge that it requires high-quality in-situ depth data, which often do not exist or are not available due to financial and/or technical reasons, as the depth calibration data. To address this problem, this article assesses the feasibility of replacing high-quality in-situ depth data with four kinds of possible sourced depth data in satellite-derived empirical bathymetry. These four kinds of depth data are satellite altimeter depth data from laser altimeter systems on some earth observing satellites; satellite photogrammetric depth data, which are derived from satellite stereo-images with the photogrammetric method; nautical chart depth data, which come from existing electronic or paper charts; and radiative transfer model-based depth data, which are simulated in accordance with a bio-optical radiative transfer model (RTM). Correspondingly, four kinds of empirical bathymetry's model calibration data are referred to as altimeter data, photogrammetric data, chart data, and RTM data, respectively. The objective of this article is to assess the similarities, differences, limitations, and applicability of these four kinds of possible model calibration data, in order to know which of them is most suitable for a certain empirical bathymetry task. WorldView-2 and Sentinel-2A multi-spectral images covering four typical shallow water areas in Hawaii and Xisha Islands and Stumpf's logarithmic band ratio model were used to compare and contrast the bathymetric performance of these four kinds of possible model calibration data. The result showed that all four kinds of possible model calibration data can be used for satellite-derived empirical bathymetry when the high-quality in-situ depth data are not provided, but they will provide different bathymetric accuracy values. According to five evaluation indexes (i.e., bathymetric performance, data availability, data quality, complexity of data generation, and data adaptability), the altimeter data perform best, the chart data perform second best, the RTM data follow with the third performance, and the photogrammetric data perform markedly worse than other three kinds of model calibration data. Such factors as the in-situ property, spatial coverage, co-registration and independence of model calibration data must be taken into consideration as well during a model calibration data selection. [ABSTRACT FROM AUTHOR]

The Nectarian-aged Crisium basin is characterised by prolonged lunar volcanism ranging from the Imbrian age to the Eratosthenian period. In this study, two shallow lunar domes designated Cleomedes 1 and 2 are described and identified using terrestrial telescopic imagery, Lunar Reconnaissance Orbiter (LRO) Wide-Angle Camera (WAC) images, the Laser Altimeter Digital Elevation Model (LOLA DEM), and the LRO WAC-based GLD100 Digital Terrain Model, along with data from the Chandroyaan-1 Moon Mineralogy Mapper and Kaguya Multiband Imager. The dome Cleomedes 1 is located at 21.8°N, 54.8°E, with a diameter of 4.0 km and height of 40 + 5 m, yielding an average flank slope of 1.14 ± 0.1°. The dome Cleomedes 2 is located at 21.9°N, 55.2°E, with a base diameter of 3.5 km and height of 35 + 5 m, yielding an average flank slope of 1.1 ± 0.1°. These domes are not reported in the USGS/-707 map by Casella & Binder. Based on the morphometric properties, the physical conditions under which the domes were formed (lava viscosity, effusion rate, magma rise speed, and the geometries of the feeder dikes) are inferred. [ABSTRACT FROM AUTHOR]

In the summer of 2020, ESA changed the orbit of CryoSat‐2 to align periodically with NASA's ICESat‐2 mission, a campaign known as CRYO2ICE, which allows for near‐coincident CryoSat‐2 and ICESat‐2 observations in space and time over the Arctic until summer 2022, where the CRYO2ICE Antarctic campaign was initiated. This study investigates the Arctic CRYO2ICE radar and laser freeboards acquired by CryoSat‐2 and ICESat‐2, respectively, during the winter seasons of 2020–2022, and derives snow depths from their differences along the orbits. Along‐track snow depth observations can provide high‐resolution snow depth distributions which are vital for air‐ice‐ocean heat and momentum transfer, understanding light transmission, and snow‐ice‐interactions. Generally, ICESat‐2 is backscattered at a surface above the elevation of the CryoSat‐2 signal. CRYO2ICE snow depths are thinner than the daily model‐ or passive‐microwave‐based snow depth composites used for comparison, with differences being most pronounced in the Atlantic and Pacific Arctic. Satellite‐derived and model‐based snow estimates show similar seasonal accumulation over first‐year ice, but CRYO2ICE has limited seasonal accumulation over multi‐year ice which is linked to a slow increase in ICESat‐2, and to some extent CryoSat‐2, freeboards. We present a first estimation of spaceborne along‐track snow depth estimates with average uncertainty of 10–11 ± 2–3 cm for 7‐km segments, with random and systematic contributions of 7 and 4 cm. These observations show the potential for along‐track dual‐frequency observations of snow depth from the future Copernicus mission CRISTAL; but they also highlight uncertainties in radar penetration and the correlation length scales of snow topography that still require further research. Plain Language Summary: Estimates of snow depth on sea ice are currently either outdated or limited in resolution, thus a need to derive high‐resolution snow depth on sea ice is crucial. Former studies have computed snow depth on sea ice using the difference in penetration between radar (Ku‐band) and laser (or Ka‐band radar) altimeters from different satellite missions, assuming the Ku‐band radar and laser/Ka‐band are reflected at the bottom and top of the snow pack, respectively. Those studies have resulted in monthly composites of snow depth due to a limited overlap of individual tracks between the satellite missions. Since the CRYO2ICE (CryoSat‐2/ICESat‐2 Resonance) campaign was initiated in July 2020, we have for the first time the possibility of investigating near‐coincident observations of spaceborne radar and laser altimeters. These results are important to initiate relevant discussions on snow depth retrieval in preparation for the future dual‐frequency altimeter mission, CRISTAL (Copernicus Polar Ice and Snow Topography Altimeter), expected to launch in 2028. Key Points: CRYO2ICE (C2I) orbits are investigated over sea ice for the first time and snow depth is computed along the orbitsC2I snow depth has an uncertainty of 10–11 cm along 7 km segments with 5–95% confidence interval of 7–16 cm for both winter seasonsAlong‐track snow depth is compared with modeled and passive‐microwave estimates and C2I is thinner for the Pacific and Atlantic Arctic [ABSTRACT FROM AUTHOR]

The lunar south pole is a region of focused scientific and exploration interest, with several crewed and robotic missions to this region planned within the next decade. Understanding the mineralogy of the region is essential to inform landing site characterization and selection and provides the key context for interpreting samples and in situ observations. At high latitudes, extreme illumination conditions (high phase angles) can negatively impact the data quality of orbital instruments. This is especially true for passive near‐infrared spectrometers such as the Moon Mineralogy Mapper (M3) and the Kaguya Spectral Profiler, which measure the spectral properties of the surface using reflected sunlight. Using Moon Mineralogy Mapper data, we observed that the south polar region is associated with a detectable mafic signature consistent with the presence of pyroxenes. The strongest mafic signatures are associated with the South Pole—Aitken Basin, suggesting that impact melt and basin ejecta from the lower crust and upper mantle are present within this region. This observation is validated in several ways: (a) comparisons between M3 data acquired during different mission phases, (b) comparisons between multiple spectral parameters sensitive to the presence of mafic minerals, (c) comparisons between the north and south lunar polar regions, and (d) comparisons with publicly available Kaguya polar mineralogy maps and Lunar Prospector elemental abundances. We also investigate the nature of an anomalous high‐albedo region within 2–3° of the south pole observed in Lunar Orbiter Laser Altimeter reflectance data exhibiting a spatially conflicting apparent FeO abundance pattern between several data sets. Plain Language Summary: The lunar south pole region is a region of focused scientific and exploration interest, with several crewed and robotic missions to this region currently planned within the next decade and beyond. The rocks and minerals at the lunar south pole are very different from those encountered during the Apollo missions and will provide insight into a wide range of important science questions. It is important to understand the abundance and distribution of these different materials to effectively choose landing sites and plan surface science experiments and sampling targets. We combine several data sets from orbital satellites to determine the types and distribution of rocks across the south polar region. We see evidence for the presence of lower crust and upper mantle materials excavated by a nearby giant impact basin. Key Points: Moon Mineralogy Mapper data shows that the south polar region exhibits a mafic signature consistent with the presence of pyroxenesMafic signatures are spatially correlated with ejecta from the South Pole—Aitken Basin, which may be sampled by Artemis astronautsAn anomalous high‐albedo region within ∼3° of the south pole exhibits spatially conflicting apparent FeO abundance patterns between several data products [ABSTRACT FROM AUTHOR]

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• Photometric correction of IIRS images obtained from the Chandrayaan-2 mission. • IIRS data were found to be comparatively darker than Chandrayaan-1 M3. • Derived IIRS reflectances show minimal variation compared to M3 values. • Derived phase function fits for 950, 1500, and 1700 nm channels have high R2. • Absence of major artifacts (including cross-tracks) on IIRS data. India's second mission to the Moon, Chandrayaan-2, carried one of the most advanced imaging spectrometers ever sent to image a planetary surface – the IIRS (Imaging Infra-Red Spectrometer) – a 250 band spectrometer developed at the Space Applications Centre (SAC), ISRO, India. In this study, IIRS data between 800 and 2000 nm have been used to derive photometrically corrected reflectance from calibrated radiance data supplied by ISRO's online archive (Pradan) of Chandrayaan-2 data. Additionally, the photometric properties of three IIRS bands (950, 1500, and 1700 nm) have been studied in detail. Since the images obtained by IIRS are at different viewing geometry orientations, the purpose of performing a photometric correction on IIRS images is to re-project each pixel to a standard viewing geometry of incidence angle, i = 30°, emission angle, e = 0° and, phase angle, α = 30°. In order to correct the lunar limb darkening effect, a combination of the Lommel-Seeliger function and McEwen's model was used. In addition, topographic data from the merged Digital Elevation Model of Lunar Reconnaissance Orbiter – Lunar Orbiter Laser Altimeter (LRO-LOLA) and SELENE Terrain Camera (TC) were utilized to remove effects from local topography. A photometric function was then applied on four images captured by IIRS which were included in the first public release of data in August 2021. Radiance and reflectance (obtained after implementing photometric correction on IIRS images) values were compared with L1 and L2 M3 (Moon Mineralogy Mapper) data from Chandrayaan-1. It was found that although IIRS images are much darker than M3, corrected IIRS reflectance images had a much smaller variation in terms of reflectance values with M3. Moreover, the associated spectral reddening and reflectance values are within range when compared with data from other lunar missions. In addition, the reflectance spectra derived from the corrected IIRS images have been plotted for each image. However, the spectra exhibit significantly deeper absorptions than their corresponding M3 data for the same pixel. Phase functions derived from three bands have a good correlation with the reflectance values and phase angles. Consequently, this function can be applied to all IIRS images as and when more data are released to the public. [ABSTRACT FROM AUTHOR]

Spaceborne laser altimetry represents a novel active remote sensing technology applicable to earth observation, which together with imaging spectroscopy and synthetic aperture radar as a core technology for data acquisition in the earth observation systems. However, the accuracy of horizontal positioning for laser footprints from spaceborne laser altimeters declines due to various factors such as the changes in the orbital environment and the deterioration of performance. Moreover, the limited frequency of in-orbit calibration of the spaceborne laser altimeters and the non-disclosure of calibration parameters mean that users are heavily reliant on positioning accuracy of the altimetry data provided. To address this issue, a new algorithm is proposed in this study for enhancing the accuracy of horizontal positioning for laser footprints in the absence of satellite altimeter pointing and ranging parameters. In this algorithm, high-resolution DSM is taken as the reference terrain data to take advantage of the higher precision in elevation over horizontal positioning of the laser footprints. By adjusting the horizontal position of the laser footprint within a small area, the algorithm achieves the optimal alignment of laser elevation data with the reference terrain. Then, the resulting shift in the horizontal position of the laser footprints is referenced to correct their horizontal positioning during that period. Based on the high-accuracy DSM data collected from the Xinjiang autonomous region in China and the data collected by the GF-7 satellite, simulation experiments are performed in this study to analyze and validate the proposed algorithm. According to the experimental results, the horizontal accuracy of the laser footprints improves significantly from 12.56 m to 3.11 m after optimization by the proposed method. With the elimination of 9.45 m horizontal error, accuracy is improved by 75.23%. This method is demonstrated as effective in further optimizing the horizontal position of laser altimetry data products in the absence of altimeter parameters and original data, which promotes the application of spaceborne laser data. [ABSTRACT FROM AUTHOR]

The ICESat‐2 and GEDI missions were launched in 2018, becoming the new generation of space‐borne laser altimeters. These missions provide unprecedented global geodetic elevations, opening great opportunities for water level monitoring. The potential of these altimeters has been demonstrated in open‐water environments such as lakes, rivers, and reservoirs. However, detailed evaluations in vegetated environments, such as wetlands, floodplains, and other areas not constrained by water canal networks, are essential for continued improvement and further hydrological application. We developed a systematic accuracy assessment of ICESat‐2 ATL08, and GEDI L2A products to monitor spatial‐temporal water level and depth dynamics over the South Florida Everglades wetlands. The evaluation was performed on data acquired between 2020 and 2021, using gauge‐based water level and depth estimates as references. The results showed an RMSE of 0.17 m (water level) and 0.15 m (water depth) for ICESat‐2 and 0.75 m (water level) and 0.37 m (water depth) for GEDI. The analysis suggested that nighttime acquisitions were more accurate for both missions than daytime ones. The low‐power beams achieved slightly higher accuracies than those of the high‐power beams over the evaluated wetlands. Water level retrieval was more problematic in densely vegetated areas; however, we derived a correction model based on the leaf area index that improved the accuracy by up to 75% for water depth retrievals from GEDI. Furthermore, the analysis provides new insights to understand the potential of the altimeters in monitoring the spatial‐temporal dynamics of water levels in the evaluated wetlands. Plain Language Summary: Monitoring water dynamics in wetlands is essential for understanding the ecosystem's functioning. Satellite‐based measurement of wetland water levels is an effective way of studying wetlands hydrology. In recent decades, available data from new satellites has increased, representing an opportunity to advance wetland monitoring. This study evaluated the latest generation space technology—satellite laser altimeter—which uses laser pulses to measure the elevation of land, ice, water, and vegetation on the Earth's surface. We used data from the ICESat‐2 and GEDI laser altimeters to measure water levels over the South Florida Everglades, a wide‐wetland area monitored by a dense ground‐based monitoring network. We evaluated the altimeters' accuracy by comparing them with the ground‐based measurements. Our findings demonstrated a strong agreement between the space‐ and ground‐based water levels, suggesting that altimeters can be used to monitor water levels in various wetlands. However, the altimeter characteristics and wetland vegetation influenced the observation's accuracy. The errors for ICESa‐2 were 0.17 m, and for GEDI were 0.75 m. We proposed a correction that reduced errors by up to 75% for GEDI. In summary, the new laser altimeters provide valuable measurements that can improve our understanding of water dynamics in wetlands worldwide, especially in data‐scarce regions. Key Points: We present a systematic accuracy assessment of the ICESat‐2 and GEDI missions for water level and depth retrieval in the Everglades wetlandsThe study proposes a correction model for the observations of the altimeters based on the leaf area indexThe results demonstrate the potential of the missions for monitoring the spatial‐temporal dynamics of water level and depth in wetlands [ABSTRACT FROM AUTHOR]

• Multi-wavelength signatures of volatiles have been detected in lunar south pole. • Low axial tilt allows permanently shaded cold traps like Shackleton Crater. • Shackleton Crater exhibits above average albedo vis-a-vis lunar south pole. • Certain regions in the lunar south pole exhibit albedo even higher than Shackleton. Shackleton Crater on the Moon is named in honor of a pivotal figure of the Heroic Age of Antarctic Exploration Sir Ernest Henry Shackleton. The Crater was formed about 3.6 billion years ago at the Lunar South Pole. Due to the low (1.5°) tilt of the lunar axis of rotation, the interiors of the crater receive almost no direct sunlight and observations by passive hyperspectral sensors are only possible due to reflected light. Active sensors are however able to illuminate the interior surface of the crater and make observations. Here we present the results of study of the crater using data from multiple sensors onboard two missions of Indian Space Research Organization (ISRO) and National Aeronautics and Space Administration (NASA). Hyperspectral observations from Moon Mineralogy Mapper (M3) on-board Chandrayaan-1, temperature from Diviner Lunar Radiometer Experiment (DLRE), albedo from Lunar Orbiter Laser Altimeter (LOLA) and Off/On band ratios from Lyman-Alpha Mapping Project (LAMP) on board Lunar Reconnaissance Orbiter (LRO) have been used. Permanently Shaded Regions (PSRs) are identified and Shackleton has been found, as reported, to be a PSR. Emphasis has been laid on LOLA albedo analysis of lunar south pole (80°-90° South). It is observed that the Shackleton Crater exhibits significantly higher albedo compared to its surrounding regions. Some regions exhibit albedo even higher than the Shackleton Crater in the lunar south pole region. The findings of high albedo, coupled with high Off/On band ratio of LAMP, extremely low DLRE temperatures in the permanently shaded regions and detection of hydration features in the M3 hyperspectral data suggests higher probability of hydration in the Shackleton Crater. [ABSTRACT FROM AUTHOR]

Crustal thickness (Moho surface relief) is closely related to geotectonic, which is the predominant research topic in lunar geophysics. For lunar crustal depth inversion, to address the problem of downward continuation in traditional spectral domain methodologies, we adopt a solution from gravity disturbance (geophysical gravity anomaly). It is available to investigate crustal thickness at arbitrary surface outside the geoid. Using gravity data from the Gravity Recovery and Interior Laboratory (GRAIL) mission and terrain information recovered from the Lunar Orbiter Laser Altimeter (LOLA) mission, global crustal depth of the Moon is recovered by the extended Vening Meinesz-Moritz method. The first application result of this technique on the Moon is validated by four types of released models in the form of spherical harmonic expansion with 310°. Moreover, from the statistics, our lunar Moho solution demonstrates that the average crustal thickness is approximately 41.5 km. The maximum value of 83.1 km and the minimum value of 0.8 km are located in the area of high mountains and Moscoviense mascon, respectively. [ABSTRACT FROM AUTHOR]

Satellite laser altimetry technology, a novel space remote sensing technique, actively acquires high-precision elevation information about the Earth's surface. However, the accuracy of laser altimetry can be compromised by alterations in the satellite-ground environment, thermal dynamics, and cosmic radiation. These factors may induce subtle variations in the installation and internal structure of the spaceborne laser altimeter on the satellite platform, diminishing measurement precision. In-orbit calibration is thus essential to enhancing the precision of laser altimetry. Through collaborative calculations between satellite and ground stations, we can derive correction parameters for laser pointing and ranging, substantially improving the accuracy of satellite laser altimetry. This paper introduces a sophisticated calibration method for laser altimeter pointing and ranging that utilizes dense control points. The approach interpolates discrete ground control point data into continuous simulated terrain using empirical Bayesian kriging, subsequently categorizing the data for either pointing or ranging calibration according to their respective functions. Following this, a series of calibration experiments are conducted, prioritizing "pointing" followed by "ranging" and continuing until the variation in the ranging calibration results falls below a predefined threshold. We employed experimental data from ground control points (GCPs) in Xinjiang and Inner Mongolia, China, to calibrate the GaoFen-7 (GF-7) satellite Beam 2 laser altimeter as per the outlined method. The calibration outcomes were then benchmarked against those gleaned from infrared laser detector calibration, revealing disparities of 1.12 s in the pointing angle and 2 cm in the ranging correction value. Post validation with ground control points, the measurement accuracy was refined to 0.15 m. The experiments confirm that the proposed calibration method offers accuracy comparable to that of infrared laser detector calibration and can facilitate the updating of 1:10,000 topographic maps utilizing stereo optical imagery. Furthermore, this method is more cost-effective and demands fewer personnel for ground control point collection, enhancing resource efficiency compared to traditional infrared laser detector calibration. The proposed approach surpasses terrain-matching limitations when calibrating laser ranging parameters and presents a viable solution for achieving frequent and high-precision in-orbit calibration of laser altimetry satellites. [ABSTRACT FROM AUTHOR]

Due to the presence of sea ice, determining the sea surface height in the Arctic Ocean remains a significant challenge. State-of-the-art Arctic Mean Sea Surface (MSS) products are primarily derived from radar altimetry missions like CryoSat-2. However, the ICESat-2 laser altimeter can offer valuable sea surface observations up to 88° N latitude, extending the observational reach. This paper analyses the performance of combined CryoSat-2 and ICESat-2 data in determining the Arctic sea surface. Comparisons of overlapping observations from both missions reveal excellent consistency, with an inter-mission bias of less than 1 cm in the Arctic. Different geophysical corrections are considered, and the results suggest that only the ocean tide correction needs to be unified, while other corrections show minimal discrepancies. The MSS derived from combined data boasts both superior spatial coverage and precision compared to individual missions. The impact of summer melt pond is also discussed. The data from June, July and August are seriously contaminated, but only have limited effect on the mean sea surface calculation. Overall, the combined use of CryoSat-2 and ICESat-2 data offers a promising approach to accurately determining the Arctic sea surface, paving the way for improved understanding of sea level change and its implications in this critical region. [ABSTRACT FROM AUTHOR]

A major advance in global bathymetric observation occurred in 2018 with the launch of NASA's ICESat‐2 satellite, carrying a green‐wavelength, photon‐counting lidar, the Advanced Topographic Laser Altimeter System (ATLAS). Although bathymetric measurement was not initially a design goal for the mission, pre‐ and post‐launch studies revealed ATLAS's notable bathymetric mapping capability. ICESat‐2 bathymetry has been used to support a wide range of coastal and nearshore science objectives. However, analysis of ICESat‐2 bathymetry in numerous locations around the world revealed instances of missing or clipped bathymetry in areas where bathymetric measurement should be feasible. These missing data were due to the ATLAS receiver algorithms not being optimized for bathymetry capture. To address this, two updates have been made to ICESat‐2's receiver algorithm parameters with the goal of increasing the area for which ICESat‐2 can provide bathymetry. This paper details the parameter changes and presents the results of a two‐phased study designed to investigate ICESat‐2's bathymetry enhancements at both local and global scales. The results of both phases confirm that the new parameters achieved the intended goal of increasing the amount of bathymetry provided by ICESat‐2. The site‐specific phase demonstrates the ability to fill critical bathymetric data gaps in open ocean and coastal settings. The global analysis shows that the area of potential bathymetry approximately doubled, with 6.1 million km2 of new area in which bathymetric measurements may be feasible. These enhancements are anticipated to facilitate a range of science objectives and close the gap between ICESat‐2 bathymetry and offshore sonar data. Key Points: Recent updates to ICESat‐2's receiver algorithm parameters have boosted the bathymetric measurement capabilitiesThe new updates allow ICESat‐2 to consistently measure bathymetry up to 41 m deepThe updates allow ICESat‐2 to potentially measure over 6.1 million km2 of new bathymetry in both coastal and ocean settings [ABSTRACT FROM AUTHOR]

The GaoFen7 (GF7) optical satellite is the first Chinese civilian sub-meter high-resolution stereo mapping satellite and is equipped with a double linear array camera and laser altimeter to achieve large-scale topographic mapping. To improve the accuracy of attitude determination, an attitude determination system comprised of four star sensors is loaded. According to the measurement accuracy and steady performance, the star sensors 1a and 1b is usually used together for satellite attitude calculation, which is called the conventional mode of attitude determination. Then, the combination of star sensors 2a and 2b is called the unconventional mode of attitude determination. Affected by variations in the incident angle of sunlight and solar radiation, thermal deformation occurs in the body and installation structure of the star sensor, which causes Attitude Low-Frequency Error (ALFE) and seriously influences the consistency of attitude determination results of different combination modes for multiple star sensors system. This study proposes an ALFE analysis and calibration approach for the multiple star sensors system of GF7 satellite to ensure the consistency of attitude determination results of different combination modes. Based on the statistical characteristics of the angles of the three axes, the installation parameters of the four star sensors are first calibrated. After analyzing the characteristics of the optical axis angles within 1420 orbit periods over 135 days, the segmented ALFE compensation model between the unconventional and conventional modes is proposed based on the Fourier series model and input parameter of latitude. Based on the on-orbit installation parameters and the ALFE model, the precise attitude determination results of the unconventional mode are calculated. Experimental results show that the attitude determination consistency after compensation is better than 2". Moreover, the reliable application time range of the compensation model is 30 days to satisfy the requirements for high-precision attitude determination of GF7 satellite. [ABSTRACT FROM AUTHOR]

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

The present‐day distribution of mare basalts on the Moon is an important constraint on the timing, duration, and flux of volcanism on the Moon. In this work, we find the global distribution of visible mares and cryptomares using the effective density (ρeff) of the Moon, which is sensitive to the vertical density distribution of the crust. We compute ρeff using the Gravity Recovery and Interior Laboratory (GRAIL) data and the Lunar Orbiter Laser Altimeter (LOLA) topography data. We apply this ρeff approach to the search of cryptomare for the first time, and we use a higher resolution grid and larger search area to constrain visible mare thicknesses compared to previous work. We use a Bayesian approach to find the distribution of mare thicknesses and density gradients of the underlying crust that is consistent with the localized ρeff. Assuming a bulk density of the mare basalts of 3,460 kg/m3, we find a mean visible mare thickness of 0.7−0.1+0.2 ${0.7}_{-0.1}^{+0.2}$ km which is consistent with previous work. We find a candidate cryptomare volume of 3.6−1.1+1.7×106 ${3.6}_{-1.1}^{+1.7}\times {10}^{6}$ km3 representing more than half of the total mare volume on Moon. This volume is consistent with the higher end of the previously proposed values. Finally, we search for dark halo craters (DHC) globally to obtain geologic evidence of cryptomares and find 258 craters. Our results are consistent with extensive ancient volcanism, which explains the previously identified discrepancy between the early high volcanic flux predicted by thermal models of the Moon and the dearth of ancient volcanic deposits currently exposed. Plain Language Summary: Mare basalts are the most abundant type of lava that erupted on the Moon. It is important to constrain the distribution of basalts to understand the timing, duration, and flux of volcanism on the Moon. We can observe that 16% of the surface is covered by mare basalts but additional mare deposits exist, covered by a thin layer of lighter‐looking material. These hidden mare deposits are called cryptomaria and they are likely older than the exposed mare. We use gravity and topography data to map the global distribution of visible mares and cryptomares. These data are sensitive to the density signature of mare basalts. We obtain a higher resolution map of mare thicknesses than provided before, over a larger area, and uncertainties on these values. Additionally, we map dark halo craters (DHCs), which are considered geologic evidence of cryptomares because they expose underlying cryptomares as part of their ejecta material. Combining our gravitationally inferred cryptomare distribution and the distribution of DHCs, we find a total volume of mare of 6.8 × 106 km3 with 50% of this volume being cryptomare. Our results are consistent with a more extensive ancient volcanism than previously thought. Key Points: We constrain the global distribution of visible and hidden mare basalts on the Moon using gravity and topography dataAssuming a bulk density of 3,460 kg/m3, we find a total mare volume of 7.0 × 106 km3, with over 50% of cryptomareWe find new cryptomare regions, and our results are consistent with a higher volume of ancient mares than previously thought [ABSTRACT FROM AUTHOR]

Sea ice is important for Earth's energy budget as it influences surface albedo and air–sea fluxes in polar regions. On its margins, waves heavily impact sea ice. Routine and repeat observations of waves in sea ice are currently lacking, and therefore a comprehensive understanding of how waves interact with sea ice and are attenuated by it is elusive. In this paper, we develop methods to separate the two-dimensional (2D) surface wave spectra from sea-ice height observations made by the ICESat-2 (IS2) laser altimeter, a polar-orbiting satellite. A combination of a linear inverse method, called generalized Fourier transform (GFT), to estimate the wave spectra along each beam and a Metropolis–Hastings (MH) algorithm to estimate the dominant wave's incident angle was developed. It allows us to estimate the 2D wave signal and its uncertainty from the high-density, unstructured ATL03 ICESat-2 photon retrievals. The GFT is applied to re-binned photon retrievals on 25 km segments for all six beams and outperforms a discrete Fourier transform (DFT) in accuracy while having fewer constraints on the data structure. The MH algorithm infers wave direction from beam pairs every 25 km using coherent crests of the most energetic waves. Assuming a dominant incident angle, both methods together allow a decomposition into 2D surface wave spectra with the advantage that the residual surface heights can potentially be attributed to other sea-ice properties. The combined GFT–MH method shows promise in routinely isolating waves propagating through sea ice in ICESat-2 data. We demonstrate its ability on a set of example ICESat-2 tracks, suggesting a detailed comparison against in situ data is necessary to understand the quality of retrieved spectra. [ABSTRACT FROM AUTHOR]

The Artemis exploration zone is a topographically complex impact-cratered terrain. Steep undulating slopes pose a challenge for walking extravehicular activities (EVAs) anticipated for the Artemis III and subsequent missions. Using 5 m/pixel Lunar Orbiter Laser Altimeter (LOLA) measurements of the surface, an automated Python pipeline was developed to calculate traverse paths that minimize metabolic workload. The tool combines a Monte Carlo method with a minimum-cost path algorithm that assesses cumulative slope over distances between a lander and stations, as well as between stations. To illustrate the functionality of the tool, optimized paths to permanently shadowed regions (PSRs) are calculated around potential landing sites 001, nearby location 001(6), and 004, all within the Artemis III 'Connecting Ridge' candidate landing region. We identified 521 PSRs and computed (1) traverse paths to accessible PSRs within 2 km of the landing sites, and (2) optimized descents from host crater rims into each PSR. Slopes are limited to 15° and previously identified boulders are avoided. Surface temperature, astronaut body illumination, regolith bearing capacity, and astronaut-to-lander direct view are simultaneously evaluated. Travel times are estimated using Apollo 12 and 14 walking EVA data. A total of 20 and 19 PSRs are accessible from sites 001 and 001(6), respectively, four of which maintain slopes <10°. Site 004 provides access to 11 PSRs, albeit with higher EVA workloads. From the crater rims, 94 % of PSRs can be accessed. All round-trip traverses from potential landing sites can be performed in under 2 h with a constant walk. Traverses and descents to PSRs are compiled in an atlas to support Artemis mission planning. • Development of an automated pipeline to calculate optimized traverse paths for EVAs. • Mapping of 521 PSRs using LOLA data within the Artemis III 'Connecting Ridge' region. • We evaluate paths to PSRs within 2 km of potential landing sites 001, 001(6), and 004. • 94 % of these PSRs can be accessed from the host crater rim on slopes below 15°. • An atlas compiles all traverses and descents to PSRs to support mission planning. [ABSTRACT FROM AUTHOR]

Many forest cover maps have been generated by using optical and/or microwave images, but these forest cover maps have large area and spatial discrepancies. To date, few studies have assessed forest cover maps in terms of two biophysical parameters used in forest definition: canopy height and canopy coverage. We generated annual forest cover maps from 2007 to 2010 and evergreen forest cover maps from 2000 to 2021 in the Brazilian Amazon using the images from the Phased Array type L-band Synthetic Aperture Radar and the time series images from the Moderate Resolution Imaging Spectroradiometer, using the forest definition of the Food and Agriculture Organization of the United Nations (>5 m tree height and >10 % canopy coverage) as the reference. We used the canopy height and canopy coverage datasets from the Geoscience Laser Altimeter System during 2003–2007 to assess annual forest cover maps from 2007 to 2010 and annual evergreen forest cover maps from 2003 to 2007, and the results show high accuracy of these forest cover and evergreen forest cover maps. These annual forest cover maps and annual evergreen forest cover maps provide data support for the analyses of the causes, processes, and consequences of forest cover changes in the Brazilian Amazon (10.6084/m9.figshare.21445626; Qin and Xiao, 2022a; 10.6084/m9.figshare.21445590; Qin and Xiao, 2022b). [ABSTRACT FROM AUTHOR]

Reliable tree canopy cover (TCC) products are vital for national forest inventory, land process modeling and forest dynamic monitoring. The new generation of space-based laser altimeter, GEDI, offers a three-dimensional (3D) insight on the forest structure, shaping the paradigm of structural variable estimation. However, the generality of newly released GEDI level-2 TCC product version 2 was less investigated across various forest types. Additionally, satellite-derived product validation usually suffers from the geolocation mismatch between satellite and reference data. In this study, we comprehensively validated the GEDI TCC product across seven forest types using the reference TCC derived from several public and private aerial LiDAR datasets after geographical registration, and crossly compared with a commonly-used passive satellite product (i.e., GFCC TCC). As the reference aerial TCC maps were derived using various aerial LiDAR instruments, we investigated the consistency of TCC estimation among them using simulation datasets and found that the distributions of TCC relative bias (biasR, %) were almost identical and the differences of relative RMSE (rRMSE, %) was less than 0.2%. Through the registration process, we found that the geolocation offsets of GEDI footprints tended to be independent of azimuth directions and their average was about 10 m, verifying the necessity of registration during the validation process. Importantly, the post-registration validation of GEDI TCC showed an average RMSE of 0.10 and an average R2 of 0.85 for all forest types, resulting in a decrease of RMSE of up to 0.15 and an increase of R2 of up to 0.33 compared to the pre-registration validation. The inter-comparison also exhibited improved consistency between GEDI and GFCC TCC products after registration. Further, we found a non-negligible dependence of GEDI TCC on the slope factor but almost independence on forest type, encouraging the spread of GEDI TCC product. [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]

Sea ice coverage is a key indicator of changes in the global climate. Estimates of sea ice area and extent are primarily derived from satellite measurements of surface microwave emissions, from which local sea ice concentration (SIC) is derived. Passive microwave (PM) satellite sensors remain the sole global product for understanding SIC variability. Using a dataset of more than 27,000 high-resolution airborne optical images, we first examine biases in commonly-used products that emerge from challenges in sampling thin sea ice fractures and melt ponds on the sea ice surface. We show that the ICESat-2 (IS2) laser altimeter effectively samples these surface features and we develop a new, independent SIC product, which we term the Linear Ice Fraction (LIF). On monthly timescales, we show using an emulator that the LIF product offers an independent estimate of sea ice concentration over 60 % of the Arctic sea ice cover with similar-or-better error qualities compared to PM data. IS2 and its high-precision measurements of the sea ice surface should be considered for augmenting PM-SIC measurements in the future. [ABSTRACT FROM AUTHOR]

Sea ice monitoring by polar orbiting satellites has been developed over more than four decades and is today one of the most well-established applications of space observations. This article gives an overview of data product development from the first sensors to the state-of-the-art regarding retrieval methods, new products and operational data sets serving climate monitoring as well as daily operational services including ice charting and forecasting. Passive microwave data has the longest history and represents the backbone of global ice monitoring with already more than four decades of consistent observations of ice concentration and extent. Time series of passive microwave data is the primary climate data set to document the sea ice decline in the Arctic. Scatterometer data is a valuable supplement to the passive microwave data, in particular to retrieve ice displacement and distinguish between firstyear and multiyear ice. Radar and laser altimeter data has become the main method to estimate sea ice thickness and thereby fill a gap in the observation of sea ice as an essential climate variable. Data on ice thickness allows estimation of ice volume and masses as well as improvement of the ice forecasts. The use of different altimetric frequencies also makes it possible to measure the depth of the snow covering the ice. Synthetic Aperture Radar (SAR) has become the work horse in operational ice observation on regional scale because high-resolution radar images are delivered year-round in nearly all regions where national ice services produce ice charts. Synthetic Aperture Radar data are also important for sea ice research because the data can be used to observe a number of sea ice processes and phenomena, like ice type development and sea ice dynamics, and thereby contribute to new knowledge about sea ice. The use of sea ice data products in modelling and forecasting services as well as in ice navigation is discussed. Finally, the article describes future plans for new satellites and sensors to be used in sea ice observation. [ABSTRACT FROM AUTHOR]

Wave-affected marginal ice zone (MIZ) is an integral part of the sea ice cover and key to the atmosphere-ice-ocean interaction in the polar region. While we mainly rely on in-situ campaigns for studying MIZs, great challenges still exist for the remote sensing of the MIZs by satellites. In this study we develop a novel retrieval algorithm for wave-affected MIZs based on the delay-Doppler radar altimeter onboard CryoSat-2 (CS2). CS2 waveform power and the waveform stack statistics are used to determine the part of the ice cover affected by waves. Based on the CS2 data since 2010, we generate a climate record of wave-affected MIZs in the Atlantic Arctic, spanning 12 winters between 2010 and 2022. As indicated by the MIZ record, no significant change of either the mean MIZ width or the extreme width is detected, although large temporal and spatial variability is present. In particular, extremely wide MIZs (over 300 km) are observed in the Barents Sea, while in other part of the Atlantic Arctic, MIZs are generally narrower. We also compare the CS2-based retrieval with those based on the laser altimeter of ICESat2 and the synthetic aperture radar images from Sentinel-1. Under spatial and temporal collocation, we attain good agreement among the MIZ retrievals based on the three different types of satellite payloads. Moreover, the traditional sea ice concentration based definition MIZ yields systematically narrower MIZs than CS2, and there is no statistically significant correlation between the two. Besides CS2, the proposed retrieval algorithm can be adapted for various historical and future radar altimetry campaigns. The synergy of multiple satellites can further improve the spatial and temporal representation of the altimeters' observation of the MIZs.c [ABSTRACT FROM AUTHOR]

The CryoSat-2 radar altimeter and ICESat-2 laser altimeter can provide complementary measurements of the freeboard and thickness of Arctic sea ice. However, both sensors face significant challenges for accurately measuring the ice freeboard when the sea ice is melting in summer months. Here, we used crossover points between CryoSat-2 and ICESat-2 to compare elevation retrievals over summer sea ice between 2018–2021. We focused on the electromagnetic (EM) bias documented in CryoSat-2 measurements, associated with surface melt ponds over summer sea ice which cause the radar altimeter to underestimate elevation. The laser altimeter of ICESat-2 is not susceptible to this bias but has other biases associated with melt ponds. So, we compared the elevation difference and reflectance statistics between the two satellites. We found that CryoSat-2 underestimated elevation compared to ICESat-2 by a median difference of 2.4 cm and by a median absolute deviation of 5.3 cm, while the differences between individual ICESat-2 beams and CryoSat-2 ranged between 1–3.5 cm. Spatial and temporal patterns of the bias were compared to surface roughness information derived from the ICESat-2 elevation data, the ICESat-2 photon rate (surface reflectivity), the CryoSat-2 backscatter, and the melt pond fraction derived from Sentinel-3 Ocean and Land Color Instrument (OLCI) data. We found good agreement between theoretical predictions of the CryoSat-2 EM melt pond bias and our new observations; however, at typical roughness <0.1 m the experimentally measured bias was larger (5–10 cm) compared to biases resulting from the theoretical simulations (0–5 cm). This intercomparison will be valuable for interpreting and improving the summer sea ice freeboard retrievals from both altimeters. [ABSTRACT FROM AUTHOR]

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), which can achieve high-precision ground detection. However, due to its low pulse energy and high sensitivity, it is also affected by noise when obtaining data. Therefore, data denoising is critical to the subsequent processing and application. In this study, a multilevel filtering algorithm is proposed to denoise the daytime photon cloud data with high background noise. Firstly, the Random Sample Consensus (RANSAC) algorithm is used to roughly denoise the daytime photon cloud data with high background noise, and a signal point cloud buffer is established to remove most of the noise points. Subsequently, the horizontal continuity parameter is calculated based on the photon cloud data following the rough denoising process. Based on this parameter, the shape and size of the search domain of the results of the subsequent fine denoising algorithm are adaptively improved. Finally, three filtering directions (a horizontal direction, an intra-group unified direction, and an adaptive direction for each photon) are proposed, and a hybrid algorithm combining the Ordering Points to Identify the Clustering Structure (OPTICS) density clustering algorithm and the Relative Neighboring Relationship K-nearest neighbors-based noise removal (RNR−KNNB) algorithm is employed to accurately denoise the photon cloud data in the three filtering directions. Furthermore, the RANSAC algorithm based on a sliding overlap window is used to remove outliers for the weak beam fine denoising photon cloud data. The results indicate that, for the strong beams, the denoising accuracy of the multilevel filtering algorithm in the three filtering directions is comparable (Rs ≥ 0.96, F ≥ 0.67), and they are all better than that of the ATL08 algorithm (Rs/Rn/p/F = 0.85/0.67/0.52/0.65). For weak beams, the denoising accuracy of the multilevel filtering algorithm in the horizontal direction and the intra-group unified direction is similar (Rs = 0.92, F = 0.69), and it is superior to the denoising results in the adaptive direction of each photon and the ATL08 algorithm (Rs/Rn/p/F = 0.94/0.84/0.51/0.65, 0.88/0.87/0.55/0.67, respectively). For strong and weak beams, the p-value and F-value of the denoising results of multilevel filtering algorithms in three different filtering directions increase with the increase of SNR value. It is demonstrated that SNR is an important factor affecting the denoising results of algorithms. The multilevel filtering algorithm proposed in the study can effectively achieve precise denoising of daytime photon cloud data with high background noise, and the three different filtering directions have different effects on the denoising results of strong and weak beam photon cloud data. This can provide technical and methodological guidance for subsequent photon cloud data filtering processing. [ABSTRACT FROM AUTHOR]

Bathymetric data, comprising elevations of submerged surfaces (e.g., seafloor or lake bed), constitute a critical need for a wide range of science and application focus areas, such as safety of marine navigation, benthic habitat mapping, flood inundation modeling, and coastal engineering. Over the past decade, the availability of nearshore bathymetric data has increased dramatically due to advances in satellite-derived bathymetry (SDB). One notable advance occurred with the 2018 launch of NASA's Ice, Cloud, and land Elevation Satellite 2 (ICESat-2), carrying the Advanced Topographic Laser Altimeter System (ATLAS). However, much like other Earth observing satellites, ATLAS is often hampered by obstructions, such as clouds, which block the sensor's view of the Earth's surface. In this study, we introduce the Deep Occlusion Recovery of Satellite Lidar From ICESat-2 Network (DORSL-FIN) to recover partially occluded bathymetric profiles. We show that DORSL-FIN is able to accurately recover occluded bathymetry and outperforms other methods of interpolation. [ABSTRACT FROM AUTHOR]

Radar satellite altimeters enable the determination of the mean sea surface to centimeter accuracy, which can be degraded in coastal areas because of the lack of valid altimetry observations due to land contamination and the altimeter footprint size. In 2018, the National Aeronautics and Space Administration launched ICESat-2, a laser altimetry mission equipped with the Advanced Topographic Laser Altimeter System, providing measurements every 0.7 m in the along-track direction. Taking into account the complexity of the Norwegian coastline, this study aims to evaluate coastal observations from ICESat-2 in order to use it to update the existing mean sea surface for Norway, NMBU18. We, therefore, determined the mean sea surface using only ICESat-2 observations and compared it with mean sea level observations from 23 permanent tide gauges along the entire coast and 21 temporary tide gauges in Norway's largest and deepest fjord, Sognefjorden. We also included two global mean sea surface models and NMBU18 for comparison. The results have shown that ICESat-2 is indeed able to provide more valid observations in the coastal zone, which can be used to improve the mean sea surface model, especially along the coast. [ABSTRACT FROM AUTHOR]

Dams and reservoirs are human-made infrastructures that have attracted increasing attention because of their societal and environmental significance. Towards better management and conservation of reservoirs, a dataset of reservoir-catchment characteristics is needed, considering that the amount of water and material flowing into and out of reservoirs depends on their locations on the river network and the properties of the upstream catchment. To date, no dataset exists for reservoir-catchment characteristics. The aim of this study is to develop the first database featuring reservoir-catchment characteristics for 3254 reservoirs with storage capacity totaling 682 595 km3 (73.2 % of reservoir water storage capacity in China) to support the management and conservation of reservoirs in the context of catchment level. To ensure a more representative and accurate mapping of local variables of large reservoirs, reservoir catchments are delineated into full catchments (their full upstream contributing areas) and intermediate catchments (subtracting the area contributed by upstream reservoirs from the full upstream part of the current reservoir). Using both full catchments and intermediate catchments, characteristics of reservoir catchments were extracted, with a total of 512 attributes in six categories (i.e., reservoir and catchment body characteristics, topography, climate, soil and geology, land cover and use, and anthropogenic activity characteristics). Besides these static attributes, time series of 15 meteorological variables of catchments were extracted to support hydrological simulations for a better understanding of drivers of reservoir environment change. Moreover, we provide a comprehensive and extensive reservoir dataset on water level (data available for 20 % of 3254 reservoirs), water surface area (99 %), storage anomaly (92 %), and evaporation (98 %) from multisource satellites such as radar and laser altimeters and images from Landsat and Sentinel satellites. These products significantly enhance spatial and temporal coverage in comparison to existing similar products (e.g., 67 % increase in spatial resolution of water level and 225 % increase in storage anomaly) and contribute to our understanding of reservoir properties and functions within the Earth system by incorporated national or global hydrological modeling. In situ data of 138 reservoirs are employed in this study as a valuable reference for evaluation, thus enhancing our confidence in the data quality and enhancing our understanding of the accuracy of current satellite datasets. Along with its extensive attributes, the Reservoir dataset in China (Res-CN) can support a broad range of applications such as water resources, hydrologic/hydrodynamic modeling, and energy planning. Res-CN is on Zenodo through 10.5281/zenodo.7664489 (Shen et al., 2022c). [ABSTRACT FROM AUTHOR]

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

The second-generation spaceborne LiDAR-Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) carries the Advanced Topographic Laser Altimeter System (ATLAS), which can penetrate a certain depth of water, and is one of the important means to obtain the water depth information of nearshore water. However, due to the influence of the atmospheric environment, water quality and color, the system itself and other factors, the photon point cloud introduces survey noise, which restricts the survey accuracy and reliability of nearshore water depth. Therefore, in this study, we presented a photon denoising algorithm for layered processing of submarine surface. Firstly, rough denoising of the original photon data was completed by smoothing filtering. Then, elevation histogram statistics were carried out on the photon data, two peaks of the histogram were fitted by a double Gaussian function, and the intersection of two curves was then taken to separate the water surface and underwater photons. The surface photons were denoised by the DBSCAN clustering algorithm. Then according to the distribution characteristics of underwater signal photons, a single-photon point cloud filtering bathymetric method was proposed based on improved local distance statistics (LDSBM), which was used for fine denoising of underwater point cloud data. Finally, the Gaussian function was used to fit the frequency histogram, and the signal photons were screened to extract the water depth information. In this study, 13 groups of the ATL03 dataset from the Xisha Islands, the St. Thomas and the Acklins Island were used for denoising. The denoising results were compared with the signal photons manually marked and the signal photons extracted by the official built-in method (OM). The experimental results showed that, compared with the official method results of ATL03, the LDSBM had a higher F value (comprehensive evaluation index), with an average of more than 96.70%. In conclusion, the proposed underwater single-photon point cloud filtering bathymetric method was superior to the traditional algorithm and could recover terrain information accurately. [ABSTRACT FROM AUTHOR]

The geometrical measurement precision of laser spots is affected by the deviation between the parameters of the laser altimeter and the laboratory measurement results, and the inversion accuracy of surface object height is also limited. The measurement parameters and the load state can be obtained by calibration of the laser altimeter system. Usually, ground detectors are deployed to calibrate the measurement parameters of the laser altimeter, including the divergence angle and the energy distribution of the laser beam. A calibration method for a laser footprint spot without a calibration field was proposed in this paper, focused on the airborne large-footprint laser altimeter system. The geometric parameters of the laser spot were calibrated through the laser echo waveforms of a specific terrain. The experimental results show that geometric calibration of the large-footprint laser altimeter can be achieved in the area of the step surface. The divergence angle of the laser beams obtained from the six experimental areas is 4.604 ± 0.359 mRad, and the consistency of the energy distribution from each laser spot reaches 92.67%. A new method of on-orbit calibration and verification is provided for the satellite laser altimeter system. [ABSTRACT FROM AUTHOR]