ICESat Research Articles

Deriving high-accuracy lake water-level changes from multi-source satellite datasets

ABSTRACT Satellite data have exhibited a great potential to track lake water-level changes. By integrating multi-source satellite datasets (i.e., Ice, Cloud and land Elevation Satellite-2 (ICESat-2) lidar datasets, Landsat imagery, and Global Surface Water Dataset (GSWD)), we proposed a method to track annual water-level changes of Lake Mead, USA over 1984–2018. Specifically, we first extracted ICESat-2 profiles as elevation information. Second, we used Landsat imagery to extract annual lake boundaries and calculate lake areas. Third, we constructed the relationship between elevations and inundation frequencies provided by the GSWD for ICESat-2 points, and applied it to extract annual water levels by using inundation frequency of Landsat derived lake boundaries. Finally, we created the relationship between lake water level and lake areas via a cubic polynomial model to refine the annual lake water levels. Our water level results agreed well with the in-situ data (R 2 = 0.99, p < 0.01, RMSE = 0.64 m). Our results showed that the ICESat-2 data performed well in providing accurate elevation information around the shore of lake. Using satellite datasets, our proposed method has the potential to derive water levels for water bodies worldwide in the future.

Extracting shallow water depth from the fusion of multi-temporal ICESat-2 data and multi-spectral imageries

With the development of satellite remote sensing and laser altimetry data, the fusion of laser altimetry data and multi-spectral images for shallow water depth inversion has become an economically convenient way. However, there are few methods that take into account the temporal dimension of data to integrate the results of water depth inversion in multiple temporal phases. In response to this issue, this article proposed a shallow water depth inversion method that integrates multi-source and multi-temporal remote sensing data. This method utilized the random forest (RF) algorithm to estimate the water depth values at different time, taking into account the contextual information of Sentinel-2 imagery and using the overall least squares as the theoretical model to fuse the multi-temporal water depth inversion results. This article took the Yongle Islands in the South China Sea as the research area and conducted the shallow water inversion experiments using ICESat-2 (Ice, Cloud and Land Elevation Satellite 2) data from 5 temporal phases and one Sentinel-2 imagery. The results show that the mean value of RMSE (root mean square error) and R2 (determination coefficient) of the proposed method using single temporal imagery was 1.53m and 0.7610, which outperformed the inversion accuracy of traditional methods that ignore image context information. The RMSE and R2 of the multi-temporal fusion model was 1.15m and 0.8622, which was 0.08m and 0.0199m higher than existing median filtering fusion method.

Arctic Sea Ice Topography Information From RADARSAT Constellation Mission (RCM) Synthetic Aperture Radar (SAR) Backscatter

AbstractSea ice topography information can be obtained from altimetry but these data are spatially and temporally limited compared to recent synthetic aperture radar (SAR) missions such as the RADARSAT Constellation Mission (RCM). We analyze the relationship between sea ice roughness and height obtained from three Ice, Cloud, and Land Elevation Satellite (ICESat)‐2 tracks on two dates in March 2022, with RCM backscatter from 17 images in the McClintock Channel, Canadian Arctic. We analyze how this relationship varies with ice type, polarization, and incidence angle. We find particularly notable relationships between sea ice roughness and horizontal‐transmit/vertical‐receive backscatter for first‐year ice, and sea ice height and backscatter for multi‐year ice. We develop a preliminary model for winter sea ice roughness retrieval using RCM. In comparison with independent ICESat‐2 data in our study region, we find the model performs effectively at estimating a roughness distribution and key roughness statistics, and characterizes spatial variations in roughness at a sub‐kilometer scale.

Assessing Thermospheric Neutral Density Models Using GEODYN's Precision Orbit Determination

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

Intercomparisons and Evaluations of Satellite-Derived Arctic Sea Ice Thickness Products

Currently, Arctic sea ice thickness (SIT) data with extensive spatiotemporal coverage primarily comes from satellite observations, including CryoSat-2, Soil Moisture and Ocean Salinity (SMOS), and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). The studies of the intercomparison and evaluation of multi-source satellite products in recent years are limited. In this study, three latest version products of ICESat-2, CryoSat-2, and CS2SMOS (a merged product of CryoSat-2 and SMOS) were examined from October to April, between 2018 and 2022. Three types of observation including airborne data from the Operation IceBridge (OIB) and IceBird, and in situ data from Beaufort Gyre Exploration Project (BGEP) are selected as the reference in the evaluation. The intercomparison results show that the mean SIT is generally largest in ICESat-2, second largest in CryoSat-2, and smallest in CS2SMOS. The SIT in CryoSat-2 is closer to the SIT in ICESat-2. The thickness displayed by the three satellite products starts to increase at different freezing months, varying between October and November. The three satellite products demonstrated the strongest agreements in SIT in the Beaufort Sea and Central Arctic regions, and exhibited the most distinct differences in the Barents Sea. In the evaluation with OIB data, three satellite-derived SIT were generally underestimated and CS2SMOS demonstrates the closest match. The evaluation using IceBird data indicates an underestimation for all satellites, with CryoSat-2 showing the best agreement. In the assessment with BGEP data, ICESat-2 displayed a more pronounced degree of overestimation or underestimation compared to the other two satellites, and CS2SMOS exhibited the optimal agreement. Based on the comprehensive consideration, CS2SMOS demonstrated the best performance with the airborne and in situ observational data, followed by CryoSat-2 and ICESat-2. The intercomparison and evaluation results of satellite products can contribute to a further understanding of the accuracies and uncertainties of the latest version SIT retrieval and the appropriate selection and utilization of satellite products.

Nearshore Bathymetry from ICESat-2 LiDAR and Sentinel-2 Imagery Datasets Using Physics-Informed CNN

The recently developed Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2), furnished with the Advanced Terrain Laser Altimeter System (ATLAS), delivers considerable benefits in providing accurate bathymetric data across extensive geographical regions. By integrating active lidar-derived reference seawater depth data with passive optical remote sensing imagery, efficient bathymetry mapping is facilitated. In recent times, machine learning models are frequently used to define the nonlinear connection between remote sensing spectral data and water depths, which consequently results in the creation of bathymetric maps. A salient model among these is the convolutional neural network (CNN), which effectively integrates contextual information concerning bathymetric points. However, current CNN models and other machine learning approaches mainly concentrate on recognizing mathematical relationships within the data to determine a water depth function and remote sensing spectral data, while oftentimes disregarding the physical light propagation process in seawater before reaching the seafloor. This study presents a physics-informed CNN (PI-CNN) model which incorporates radiative transfer-based data into the CNN structure. By including the shallow water double-band radiative transfer physical term (swdrtt), this model enhances seawater spectral features and also considers the context surroundings of bathymetric pixels. The effectiveness and reliability of our proposed PI-CNN model are verified using in situ data from St. Croix and St. Thomas, validating its correctness in generating bathymetric maps with a broad experimental R2 accuracy exceeding 95% and remaining errors below 1.6 m. Preliminary results suggest that our PI-CNN model surpasses conventional methodologies.

A method for constructing directional surface wave spectra from ICESat-2 altimetry

Abstract. 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.

Annual maps of forest cover in the Brazilian Amazon from analyses of PALSAR and MODIS images

Abstract. 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 (&gt;5 m tree height and &gt;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 (https://doi.org/10.6084/m9.figshare.21445626; Qin and Xiao, 2022a; https://doi.org/10.6084/m9.figshare.21445590; Qin and Xiao, 2022b).

Automated Building Height Estimation Using Ice, Cloud, and Land Elevation Satellite 2 Light Detection and Ranging Data and Building Footprints

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

A Novel ICESat-2 Signal Photon Extraction Method Based on Convolutional Neural Network

When it comes to the application of the photon data gathered by the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), accurately removing noise is crucial. In particular, conventional denoising algorithms based on local density are susceptible to missing some signal photons when there is uneven signal density distribution, as well as being susceptible to misclassifying noise photons near the signal photons; the application of deep learning remains untapped in this domain as well. To solve these problems, a method for extracting signal photons based on a GoogLeNet model fused with a Convolutional Block Attention Module (CBAM) is proposed. The network model can make good use of the distribution information of each photon’s neighborhood, and simultaneously extract signal photons with different photon densities to avoid misclassification of noise photons. The CBAM enhances the network to focus more on learning the crucial features and improves its discriminative ability. In the experiments, simulation photon data in different signal-to-noise ratios (SNR) levels are utilized to demonstrate the superiority and accuracy of the proposed method. The results from signal extraction using the proposed method in four experimental areas outperform the conventional methods, with overall accuracy exceeding 98%. In the real validation experiments, reference data from four experimental areas are collected, and the elevation of signal photons extracted by the proposed method is proven to be consistent with the reference elevation, with R2 exceeding 0.87. Both simulation and real validation experiments demonstrate that the proposed method is effective and accurate for extracting signal photons.

Vertical accuracy of open-source remote sensing data (AW3D30, TanDEM-X, ATLAS) for understory terrain estimation

Understory terrain is an important fundamental indicator for monitoring changes in forest ecosystems. However, in-depth analysis of the accuracy of existing international open-source DEM products is still lacking for understory environments. The DEM data produced by Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) carries an advanced topographic LiDAR altimeter system (ATLAS) that contains elevation information of the ground surface as well as features such as the vegetation above. It can reflect the elevation of the ground surface, including the part obscured by vegetation or canopy, and can be better used to monitor changes in ground surface elevation. However, few existing studies have been analyzed the accuracy of open-source DEM products (include ATLAS, AW3D30 DEM, and TanDEM-X data) for estimating the ground in understory environments. Therefore, this study selected Aiken County, Cook Inlet, and Yucatan as study areas to evaluate ICESat-2/ATLAS data, AW3D30 DEM and TanDEM-X data using the airborne G-LiHT system’s post-processing DTM. The study also quantitatively evaluated the influence of slope, aspect, and land vegetation type on elevation accuracies of different open-source DEM products. The experimental results showed that the ATLAS data at the footprint scale produced more accurate evaluation indicators than the AW3D30 DEM and TANDEM-X data products. The average evaluation indicator R 2 was 0.98, RMSE was 1.44 m, and STD was 1.25 m. The results showed that ATLAS can be used as validation data to evaluate the accuracy of open-source DEM in estimating the vertical direction. At the regional scale, AW3D30 was the most robust and had the most stable performance in most of the tests (R 2 is 0.95, RMSE is 6.37 m and STD is 5.02 m) and is therefore the best choice for research on understory terrain. Slope had a greater impact on the accuracy of data estimation compared to aspect and surface vegetation type. The difference in RMSE for estimating forest understory terrain was 3.11 m for ATLAS products, 29.37 m for AW3D30 DEM data and 54.26 m for TanDEM-X data over the different slopes.

Genetic Programming Guided Mapping of Forest Canopy Height by Combining LiDAR Satellites with Sentinel-1/2, Terrain, and Climate Data

Accurately mapping the forest canopy height is vital for conserving forest ecosystems. Employing the forest height measured by satellite light detection and ranging (LiDAR) systems as ground samples to establish forest canopy height extrapolation (FCHE) models presents promising opportunities for mapping large-scale wall-to-wall forest canopy height. However, despite the potential to provide more samples and alleviate the stripe effect by synergistically using the data from two existing LiDAR datasets, Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), the fundamental differences in their operating principles create measurement biases, and thus, there are few studies combining them for research. Furthermore, previous studies have typically employed existing regression algorithms as FCHE models to predict forest canopy height, without customizing a model that achieves optimal performance based on the current samples. These shortcomings constrain the accuracy of predicting forest canopy height using satellite LiDAR data. To surmount these difficulties, we proposed a genetic programming (GP) guided method for mapping forest canopy height by combining the GEDI and ICESat-2 LiDAR data with Sentinel-1/2, terrain, and climate data. In this method, GP autonomously constructs the fusion model of the GEDI and ICESat-2 datasets (hereafter GIF model) and the optimal FCHE model based on the explanatory variables for the specific study area. The outcomes demonstrate that the fusion of the GEDI and ICESat-2 data shows high consistency (R2 = 0.85, RMSE = 2.2m, pRMSE = 11.24%). The synergistic use of the GEDI and ICESat-2 data, coupled with the optimization of the FCHE model, substantially improves the precision of forest canopy height predictions, and finally achieves R2, RMSE, and pRMSE of 0.64, 3.38m, and 16.08%, respectively. In summary, our research presents a reliable approach to accurately estimate forest canopy height using remote sensing data by addressing measurement biases between the GEDI and ICESat-2 data and overcoming the limitations of traditional FCHE models.

A Density-Based Multilevel Terrain-Adaptive Noise Removal Method for ICESat-2 Photon-Counting Data.

The photon point clouds collected by the high-sensitivity single-photon detector on the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) are utilized in various applications. However, the discretely distributed noise among the signal photons greatly increases the difficulty of signal extraction, especially the edge noise adjacent to signals. To detect signal photons from vegetation coverage areas at different slopes, this paper proposes a density-based multilevel terrain-adaptive noise removal method (MTANR) that identifies noise in a coarse-to-fine strategy based on the distribution of noise photons and is evaluated with high-precision airborne LiDAR data. First, the histogram-based successive denoising method was used as a coarse denoising process to remove distant noise and part of the sparse noise, thereby increasing the fault tolerance of the subsequent steps. Second, a rotatable ellipse that adaptively corrects the direction and shape based on the slope was utilized to search for the optimal filtering direction (OFD). Based on the direction, sparse noise removal was accomplished robustly using the Otsu's method in conjunction with the ordering points to identify the clustering structure (OPTICS) and provide a nearly noise-free environment for edge searching. Finally, the edge noise was removed by near-ground edge searching, and the signal photons were better preserved by the surface lines. The proposed MTANR was validated in four typical experimental areas: two in Baishan, China, and two in Taranaki, New Zealand. A comparison was made with three other representative methods, namely differential, regressive, and Gaussian adaptive nearest neighbor (DRAGANN), used in ATL08 products, local distance statistics (LDS), and horizontal ellipse-based OPTICS. The results demonstrated that the values of the F1 score for the signal photon identification achieved by the proposed MTANR were 0.9762, 0.9857, 0.9839, and 0.9534, respectively, which were higher than those of the other methods mentioned above. In addition, the qualitative and quantitative results demonstrated that MTANR outperformed in scenes with steep slopes, abrupt terrain changes, and uneven vegetation coverage.

ICESat-2 ATL08 Verileri Kullanılarak Veri Toplama Zamanının Orman Kanopi Örtüsü Tahmini Üzerindeki Etkisinin Değerlendirilmesi

Aim of study: This study investigates the estimation success of using day and night segments in producing Forest Canopy Cover (FCC) maps with the Canopy Cover Estimation Model (CCEM) for the years 2020 and 2022.&#x0D; Area of study: The study area covers 17 interconnected counties situated in the southeastern part of Texas state, adjacent to the state of Louisiana, and near the southern coastlines, known for their extensive forested areas.&#x0D; Material and methods: This study incorporated both day and night acquisition segments from Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data for a comprehensive comparison of their effectiveness in mapping the forest canopy cover using the CCEM. &#x0D; Main results: The study’s findings reveal that night segment-derived FCC maps outperform those derived from day segments, showing higher kappa coefficients of 0.77 and 0.83 for the years 2020 and 2022, respectively. In addition, notable differences were observed among classes of FCC estimations successes for day and night segment-derived maps.&#x0D; Research highlights: This study introduces a significant finding that the FCC maps derived from night segments yield more accurate results than those derived from day segments. The study further discovers a notable difference in the forest canopy cover classification success, particularly with a lower accuracy observed in the Moderate Forest Canopy Cover (MFCC) category.

Effect of leaf-on and leaf-off canopy conditions on forest height retrieval and modelling with ICESat-2 data

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

An optimized denoising method for ICESat-2 photon-counting data considering heterogeneous density and weak connectivity

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) can obtain underwater elevation due to its strong penetration ability. However, the photons recorded by ICESat-2 include a large amount of noise that needs to be removed. Although density-based clustering methods can finish signal photon extraction, heterogeneous density and weak connectivity in photon data distribution impede their denoising performance, especially for sparse signals in deep water and drastic topographic change areas. In this paper, a novel fused denoising method based on the local outlier factor and inverse distance metric is proposed to overcome the above problems. The local outlier factor and inverse distance metric are calculated based on K-nearest neighbors (KNNs), taking into account not only the difference in density but also the directional uniformity of the data distribution. Using six trajectories under various seabed topographies, the proposed method is compared with state-of-the-art ICESat-2 photon denoising algorithms and official ATL03 results. The results indicate that the overall accuracy of the proposed method can surpass 96%, and the proposed method maintains higher recall but also has a lower false positive rate. Compared with the results of other methods, the proposed method can better adopt areas with abrupt topographic changes and deep water. The extracted signal strips are more unbroken and continuous. This study can contribute to pioneering a new perspective for ICESat-2 photon-counting data denoising research that is limited to using only density-based algorithms.

Methods to Improve the Accuracy and Robustness of Satellite-Derived Bathymetry through Processing of Optically Deep Waters

Selecting a representative optical deep-water area is crucial for accurate satellite-derived bathymetry (SDB) based on semi-theoretical and semi-empirical models. This study proposed a deep-water area selection method where potential areas were identified by integrating remote sensing imagery with existing global bathymetric data. Specifically, the effects of sun glint correction for deep-water areas on SDB estimation were investigated. The results indicated that the computed SDB had significant instabilities when different optical deep-water areas without sun glint correction were used for model training. In comparison, when sun glint correction was applied, the SDB results from different deep-water areas had greater consistency. We generated bathymetric maps for the Langhua Reef in the South China Sea and Buck Island near the U.S. Virgin Islands using Sentinel-2 multispectral images and 70% of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) bathymetry data. Additionally, 30% of the ICESat-2 bathymetry data and NOAA NGS Topo-bathy Lidar data served as the validation data to evaluate the qualities of the computed SDB, respectively. The results showed that the average quality of the SDB significantly improved with sun glint correction application by a magnitude of 0.60 m in terms of the root mean square error (RMSE) for two study areas. Moreover, an evaluation of the SDB data computed from different deep-water areas showed more consistent results, with RMSEs of approximately 0.4 and 1.4 m over the Langhua Reef and Buck Island, respectively. These values were consistently below 9% of the maximum depth. In addition, the effects of the optical image selection on SDB inversion were investigated, and the SDB calculated from the images over different time periods demonstrated similar results after applying sun glint correction. The results showed that this approach for optical deep-water area selection and correction could be used for improving the SDB, particularly in challenging scenarios, thereby enhancing the accuracy and robustness of SDB.

Reconstructing subglacial lake activity with an altimetry-based inverse method

Abstract Subglacial lake water-volume changes produce ice-elevation anomalies that provide clues about water flow beneath glaciers and ice sheets. Significant challenges remain in the quantitative interpretation of these elevation-change anomalies because the surface expression of subglacial lake activity depends on basal conditions, rate of water-volume change, and ice rheology. To address these challenges, we introduce an inverse method that reconstructs subglacial lake activity from altimetry data while accounting for the effects of viscous ice flow. We use a linearized approximation of a Stokes ice-flow model under the assumption that subglacial lake activity only induces small perturbations relative to a reference ice-flow state. We validate this assumption by accurately reconstructing lake activity from synthetic data that are produced with a fully nonlinear model. We then apply the method to estimate the water-volume changes of several active subglacial lakes in Antarctica by inverting data from NASA's Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) laser altimetry mission. The results show that there can be substantial discrepancies (20% or more) between the inversion and traditional estimation methods due to the effects of viscous ice flow. The inverse method will help refine estimates of subglacial water transport and further constrain the role of subglacial hydrology in ice-sheet evolution.

Evaluation of Slope Correction Methods to Improve Surface Elevation Change Estimation over Antarctic Ice Sheet using SARAL/AltiKa

Antarctic Ice Sheet (AIS) surface elevation change plays a crucial role in understanding the ice sheet mass balance. The present study focuses on improving AIS surface elevation estimations by incorporating slope correction methods called Direct Method (DM) using SARAL/AltiKa 40 Hz geophysical data record for 2013 (Exact Repeat Mission) and 2020 (Drifting Phase) with terrain slope ranges from 0° to 0.85°. The NASA's Ice, Cloud, and land Elevation Satellite (ICESat) Digital Elevation Model (DEM) has been utilized as a priori topography model to retrieve slope of the AIS terrain. While comparing the two direct methods (DM1 &amp; DM2) based slope corrected elevations with Operation Ice Bridge (OIB) elevation data of November 2013, the RMSE resulted in 0.35 and 0.37 m and biases of the order of 0.26 m and 0.28 m for DM1 and DM2 respectively. Moreover, comparison with ICESat DEM showed the RMSE of the order of 1.81 and 2.09 m, and biases of the order of 0.95 and 0.99 m for DM1 and DM2, respectively. It has been observed that DM1 is the most suitable method for correcting terrain slope with the lowest RMSE and bias. Moreover, the slope induced error correction methods show utmost importance in estimating accurate elevation, especially over undulating terrain of AIS.

Monitoring Coastal Waves with ICESat-2

The coastal zone faces an ever-growing risk associated with climate-driven change, including sea level rise and increased frequency of extreme natural hazards. Often the location and dynamism of coastal regions makes them a formidable environment to adequately study with in-situ methods. In this study we use Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) to make measurements of basic wave parameters and wave directionality in the coastal zones of the Hawaiian Islands and North Carolina, USA. Our goal was to leverage as much of the full resolution data available in the ATL03 data product to generate wave metrics out from shore up to ~25 km. Using a combination of statistical and signal processing methods, including cross-correlation and wavelets, we can use ICESat-2 to generate basic wave metrics, including significant wave heights with an accuracy of ±0.5 m. In some profiles we can identify wave shoaling, which could be useful to infer bathymetry and coastal dynamics. In areas with complex wave dynamics, the nature of how ICESat-2 measures elevations (parallel laser altimetry beams) can make extracting some wave parameters, especially wavelength and directionality, more challenging. These wave metrics can provide important data in support of validating wave and tidal models and may also prove useful in extended ICESat-2 applications like bathymetric corrections and satellite-derived bathymetry.