The Ice, Cloud, and Land Elevation Satellite - 2 Mission: A Global Geolocated Photon Product Derived From the Advanced Topographic Laser Altimeter System

Mapping global mangrove canopy height by integrating Ice, Cloud, and Land Elevation Satellite-2 photon-counting LiDAR data with multi-source images

Large-scale and precise measurement of mangrove canopy height is crucial for understanding and evaluating wetland ecosystems' condition, health, and productivity. This study generates a global mangrove canopy height map with a 30 m resolution by integrating Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) photon-counting light detection and ranging (LiDAR) data with multi-source imagery. Initially, high-quality mangrove canopy height samples were extracted using meticulous processing and filtering of ICESat-2 data. Subsequently, mangrove canopy height models were established using the random forest (RF) algorithm, incorporating ICESat-2 canopy height samples, Sentinel-2 data, TanDEM-X DEM data and WorldClim data. Furthermore, a global 30 m mangrove canopy height map was generated utilizing the Google Earth Engine platform. Finally, the global map's accuracy was evaluated by comparing it with reference canopy heights derived from both space-borne and airborne LiDAR data. Results indicate that the global 30 m resolution mangrove height map was found to be consistent with canopy heights obtained from space-borne (r = 0.88, Bisa = -0.07 m, RMSE = 3.66 m, RMSE% = 29.86 %) and airborne LiDAR (r = 0.52, Bisa = -1.08 m, RMSE = 3.39 m, RMSE% = 39.05 %). Additionally, our findings reveal that mangroves worldwide exhibit an average height of 12.65 m, with the tallest mangrove reaching a height of 44.94 m. These results demonstrate the feasibility and effectiveness of using ICESat-2 data integrated with multi-source imagery to generate a global mangrove canopy height map. This dataset offers reliable information that can significantly support government and organizational efforts to protect and conserve mangrove ecosystems.

High spatial resolution elevation change dataset derived from ICESat-2 crossover points on the Tibetan Plateau

Understanding elevation changes on the Tibetan Plateau is crucial to comprehend the changes in topography, landscape, climate, environmental conditions, and water resources. However, some of the current products that track elevation changes only cover specific surface types or limited areas, and others have low spatial resolution. We propose an algorithm to extract ICESat-2 crossover points dataset for the Tibetan Plateau, and form a dataset. The crossover points dataset has a density of 2.015 groups/km², and each group of crossover points indicates the amount of change in elevation before and after a period of time over an area of approximately 17 meters in diameter. Comparing ICESat-2 crossover points data with existing studies on glaciers and lakes, we demonstrated the reliability of the derived elevation changes. The ICESat-2 crossover points provide a refined data source for understanding high-spatial-resolution elevation changes on the Tibetan Plateau. This dataset can provide validation data for various studies that require high-precision or high-resolution elevation change data on the Tibetan Plateau.

Study of 256 fiber array biaxial LiDAR optical assembly measurements

This paper presents a method for measuring the optical assembly results based on multi-beam biaxial LiDAR. This method analyzes the optical assembly parameters of a LiDAR system affecting the LiDAR operation, and an experimental measurement system is built using a collimator to simulate the infinity imaging field. An InGaAs infrared camera is used to take pictures of the laser spot from the LiDAR transmitter and receiver, and then fit the laser spot images with Gaussian equations to calculate the biaxial LiDAR optical assembly results. Finally, the possible effecting factors of LiDAR alignment results are analyzed. This method is experimentally proven to achieve the measurement of the optical assembly results of a large scale multi-beam LiDAR. The possibility of further optimizing the measurement method by shaping the transmit laser is also reported.

ICESat-2 river surface slope (IRIS): A global reach-scale water surface slope dataset

The global reach-scale "ICESat-2 River Surface Slope" (IRIS) dataset comprises average and extreme water surface slopes (WSS) derived from ICESat-2 observations between October 2018 and August 2022 as a supplement to 121,583 reaches from the "SWOT Mission River Database" (SWORD). To gain full advantage of ICESat-2's unique measurement geometry with six parallel lidar beams, the WSS is determined across pairs of beams or along individual beams, depending on the intersection angle of spacecraft orbit and river centerline. Combining both approaches maximizes spatial and temporal coverage. IRIS can be used to research river dynamics, estimate river discharge, and correct water level time series from satellite altimetry for shifting ground tracks. Additionally, by referencing SWORD as a common database, IRIS may be used in combination with observations from the recently launched SWOT mission.

Measuring glacier mass changes from space-a review

Glaciers distinct from the Greenland and Antarctic ice sheets are currently losing mass rapidly with direct and severe impacts on the habitability of some regions on Earth as glacier meltwater contributes to sea-level rise and alters regional water resources in arid regions. In this review, we present the different techniques developed during the last two decades to measure glacier mass change from space: digital elevation model (DEM) differencing from stereo-imagery and synthetic aperture radar interferometry, laser and radar altimetry and space gravimetry. We illustrate their respective strengths and weaknesses to survey the mass change of a large Arctic ice body, the Vatnajökull Ice Cap (Iceland) and for the steep glaciers of the Everest area (Himalaya). For entire regions, mass change estimates sometimes disagree when a similar technique is applied by different research groups. At global scale, these discrepancies result in mass change estimates varying by 20%-30%. Our review confirms the need for more thorough inter-comparison studies to understand the origin of these differences and to better constrain regional to global glacier mass changes and, ultimately, past and future glacier contribution to sea-level rise.

3D compressive imaging system with a single photon-counting detector

For photon-counting based compressive imaging systems, it is difficult to obtain 3D image with intensity and depth information precisely due to the dead time and shot noise effect of photon-counting detectors. In this study, we design and achieve a 3D compressive imaging system using a single photon-counting detector. To overcome the radiometric distortion arising from the dead time and shot noise, considering the response mechanism of photon-counting detectors, a Bayesian posterior model is derived and a Reversible jump Markov chain Monte Carlo (RJMCMC)-based method is proposed to iteratively obtain model parameters. Experimental and simulation results indicate that the 3D image of targets can be effectively and accurately reconstructed with a smaller number of repeated illuminations and no longer restricted by the photon flux conditions (i.e., breaking through the upper limit of the received signal level). The proposed Bayesian RJMCMC-based radiometric correction method is not only beneficial to single-photon 3D compressive imaging system, but also to any other photon-counting based systems, e.g., photon-counting lidars. In addition, limiting condition of recovering the actual photon number for photon-counting imaging or lidar systems is also quantitatively analyzed, which is of great significance to the system scheme design.

ICESat-2 laser data denoising algorithm based on a back propagation neural network

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) photon data is the emerging satellite-based LiDAR data, widely used in surveying and mapping due to its small photometric spot and high density. Since ICESat-2 data collect weak signals, it is difficult to denoise in shallow sea island areas, and the quality of the denoising method will directly affect the precision of bathymetry. This paper proposes a back propagation (BP) neural network-based denoising algorithm for the data characteristics of shallow island reef areas. First, a horizontal elliptical search area is constructed for the photons in the dataset. Suitable feature values are selected in the search area to train the BP neural network. Finally, data with a geographic location far apart, including daily and nightly data, are selected respectively for experiments to test the generality of the network. By comparing the results with the confidence labels provided in the official documents of the ATL03 dataset, the DBSCAN algorithm, and the manual visual interpretation, it is proved that the denoising algorithm proposed in this paper has a better processing effect in shallow island areas.

Nearshore bathymetry and seafloor property studies from Space lidars: CALIPSO and ICESat-2

In shallow nearshore waters, seafloor heights and properties can be accurately measured by the current generation of space-based elastic backscatter lidars: CALIOP, flying aboard the CALIPSO satellite and ATLAS aboard ICESat-2. CALIOP's 532 nm volume depolarization ratios, together with the ratios of the attenuated backscatter coefficients measured at 532 nm and 1064 nm, can efficiently distinguish optically shallow waters from nearby land surfaces and deep oceans. ATLAS's high vertical resolution photon measurements can accurately determine seafloor depths in shallow water bodies, characterize seafloor reflectance, and provide assessments of ocean biomass concentrations in the intervening water column. By adding bathymetry, seafloor optical properties (e.g., reflectance, depolarization ratio and attenuated backscatter), and nighttime observations, space lidar measurements obtained in nearshore waters can provide a wealth of unique information to complement existing satellite-based ocean color remote sensing capabilities. The results reported here demonstrate the feasibility of using satellite lidars for nearshore seafloor ecosystem analyses, which in turn provide critical insights for studies of coastal navigation and seabed topography changes due to disasters, as well as the temporal and spatial morphological evolution of coastal systems.

Measuring Vegetation Heights and Their Seasonal Changes in the Western Namibian Savanna Using Spaceborne Lidars

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.

Registration and Combined Adjustment for the Laser Altimetry Data and High-Resolution Optical Stereo Images of the GF-7 Satellite

The GF-7 satellite is China’s first civil sub-meter resolution stereo mapping satellite, aiming at 1:10,000-scale mapping. To achieve this goal, apart from the stereo optical cameras that reach sub-meter resolution, the GF-7 satellite is equipped with a laser altimetry system capable of obtaining three-dimensional laser points (LPs) with high elevation accuracy. However, the combination of laser altimetry data and optical stereo images has not been thoroughly studied. In this paper, we exploit the images recorded by the highly integrated laser footprint cameras and propose a hierarchical phase correlation method based on a geographic pyramid for the registration of laser altimetry data and high-resolution optical stereo images, which lays a solid foundation for the following combined adjustment. Experiments show that the proposed registration method can automatically locate the LPs on high-resolution stereo images and meet the requirements of bundle adjustment. A series of bundle adjustment experiments were carried out, showing that laser altimetry data can significantly enhance the vertical accuracy of optical image stereo mapping and that elevation accuracy can reach roughly 1.0 m (RSME) without ground control points. Therefore, this study could be a good guide for global high-precision DSM acquisition with the GF-7 satellite.

Inter- and Intra-Annual Glacier Elevation Change in High Mountain Asia Region Based on ICESat-1&2 Data Using Elevation-Aspect Bin Analysis Method

Glaciers are sensitive indicators of climate change and have a significant influence on regional water cycle, human survival and social development. Global warming has led to great changes in glaciers over the High Mountain Asia (HMA) region. Glacier elevation change is a measure of glacier mass balance driven by the processes of energy and mass exchange between the glacier surface and the atmosphere which are influenced by climatic factors and glacier surface properties. In this study, we estimated the inter-annual and intra-annual elevation changes of glaciers in the HMA region in 2003–2020 using Ice, Cloud and land Elevation Satellite (ICESat) data and Shuttle Radar Terrain Mission (SRTM) digital elevation model (DEM) data by developing an “elevation-aspect bin analysis method” that considered the difference of glacier elevation changes in different elevations and aspects of glacier topography. The results showed that: (1) The inter-annual change of glacier elevation in 2003–2020 had large spatial heterogeneity. Glacier elevation reduction mainly occurred in the marginal region of the HMA with the maximum decline in the Nyainqentanglha region, while glacier elevation showed increase in the West Kunlun of inner HMA regions in 2003–2020. The glacier elevation change rate showed an accelerating reduction trend in most of the HMA regions, except in the west HMA where the glacier elevation reduction rate showed slowdown tendency. Specifically, the glacier elevation change rate in the entire HMA was −0.21 ± 0.12 m/year in 2003–2008 and −0.26 ± 0.11 m/year in 2003–2020, respectively. (2) The intra-annual change of HMA glacier elevation in 2019 and 2020 showed obvious spatiotemporal heterogeneity, and the glacier thickening period was gradually delayed from the marginal area to the inner area of the HMA. The glaciers in the western marginal part of the HMA (the Tienshan Mountains, Pamir and Hindu Kush and Spiti Lahaul) and Karakoram thickened in winter or spring, the glaciers in the Nyainqentanglha Mountains exhibited spring accumulation. The glaciers in West Kunlun accumulated in two time periods, i.e., from March to June and from July to September. The glaciers in the Inner Tibetan Plateau and Bhutan and Nepal areas experienced spring or summer accumulation, especially in June or July. Moreover, we found that the inter-annual and intra-annual change of glacier elevation could be explained by the changes in temperature and precipitation. A similar analysis can be extended to mountain glaciers in other regions of the world, and glacier change trends could be further explored over a longer time span with the continuous operation of ICESat-2.

Assessing the Ability to Quantify Bathymetric Change over Time Using Solely Satellite-Based Measurements

Coastal regions are undergoing rapid change, due to natural and anthropogenic forcings. A current constraint in understanding and modeling these changes is the lack of multi-temporal bathymetric data, or recursive observations. Often, it is difficult to obtain the repeat observations needed to quantify bathymetric change over time or events. However, the recent availability of ICESat-2 bathymetric lidar creates the option to map coastal bathymetry from solely space-based measurements via satellite-derived bathymetry with multispectral imagery (IS-2/SDB). This compositional space-based bathymetric mapping technique can assess temporal change along the coasts without other remote sensing or in situ data. However, questions exist as to the accuracy of the technique relative to both quantitative uncertainties and the ability to resolve the spatial patterns of erosion and deposition in the nearshore environment, indicative of geomorphologic change. This paper addresses the concept using data from the Florida panhandle (Northern Gulf of Mexico) collected by Sentinel-2 and ICESat-2 at two epochs to assess the feasibility of using IS-2/SDB for bathymetric change detection at scientifically relevant scales, spatial resolutions and accuracies. The comparison of the satellite-only result is compared to airborne data collected at similar epochs to reveal both quantitatively and qualitatively the utility of this technique.

Airborne Validation of ICESat-2 ATLAS Data over Crevassed Surfaces and Other Complex Glacial Environments: Results from Experiments of Laser Altimeter and Kinematic GPS Data Collection from a Helicopter over a Surging Arctic Glacier (Negribreen, Svalbard)

The topic of this paper is the airborne evaluation of ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) measurement capabilities and surface-height-determination over crevassed glacial terrain, with a focus on the geodetical accuracy of geophysical data collected from a helicopter. To obtain surface heights over crevassed and otherwise complex ice surface, ICESat-2 data are analyzed using the density-dimension algorithm for ice surfaces (DDA-ice), which yields surface heights at the nominal 0.7 m along-track spacing of ATLAS data. As the result of an ongoing surge, Negribreen, Svalbard, provided an ideal situation for the validation objectives in 2018 and 2019, because many different crevasse types and morphologically complex ice surfaces existed in close proximity. Airborne geophysical data, including laser altimeter data (profilometer data at 905 nm frequency), differential Global Positioning System (GPS), Inertial Measurement Unit (IMU) data, on-board-time-lapse imagery and photographs, were collected during two campaigns in summers of 2018 and 2019. Airborne experiment setup, geodetical correction and data processing steps are described here. To date, there is relatively little knowledge of the geodetical accuracy that can be obtained from kinematic data collection from a helicopter. Our study finds that (1) Kinematic GPS data collection with correction in post-processing yields higher accuracies than Real-Time-Kinematic (RTK) data collection. (2) Processing of only the rover data using the Natural Resources Canada Spatial Reference System Precise Point Positioning (CSRS-PPP) software is sufficiently accurate for the sub-satellite validation purpose. (3) Distances between ICESat-2 ground tracks and airborne ground tracks were generally better than 25 m, while distance between predicted and actual ICESat-2 ground track was on the order of 9 m, which allows direct comparison of ice-surface heights and spatial statistical characteristics of crevasses from the satellite and airborne measurements. (4) The Lasertech Universal Laser System (ULS), operated at up to 300 m above ground level, yields full return frequency (400 Hz) and 0.06–0.08 m on-ice along-track spacing of height measurements. (5) Cross-over differences of airborne laser altimeter data are −0.172 ± 2.564 m along straight paths, which implies a precision of approximately 2.6 m for ICESat-2 validation experiments in crevassed terrain. (6) In summary, the comparatively light-weight experiment setup of a suite of small survey equipment mounted on a Eurocopter (Helicopter AS-350) and kinematic GPS data analyzed in post-processing using CSRS-PPP leads to high accuracy repeats of the ICESat-2 tracks. The technical results (1)–(6) indicate that direct comparison of ice-surface heights and crevasse depths from the ICESat-2 and airborne laser altimeter data is warranted. Numerical evaluation of height comparisons utilizes spatial surface roughness measures. The final result of the validation is that ICESat-2 ATLAS data, analyzed with the DDA-ice, facilitate surface-height determination over crevassed terrain, in good agreement with airborne data, including spatial characteristics, such as surface roughness, crevasse spacing and depth, which are key informants on the deformation and dynamics of a glacier during surge.

An Improved Algorithm for the Retrieval of the Antarctic Sea Ice Freeboard and Thickness from ICESat-2 Altimeter Data

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

Radiometric Assessment of ICESat-2 over Vegetated Surfaces

The ice, cloud, and land elevation satellite-2 (ICESat-2) is providing global elevation measurements to the science community. ICESat-2 measures the height of the Earth’s surface using a photon counting laser altimeter, ATLAS (advanced topographic laser altimetry system). As a photon counting system, the number of reflected photons per shot, or radiometry, is a function primarily of the transmitted laser energy, solar elevation, surface reflectance, and atmospheric scattering and attenuation. In this paper, we explore the relationship between detected scattering and attenuation in the atmosphere against the observed radiometry for three general forest types, as well as the radiometry as a function of day versus night. Through this analysis, we found that ATLAS strong beam radiometry exceeds the pre-launch design cases for boreal and tropical forests but underestimates the predicted radiometry over temperate forests by approximately half a photon. The weak beams, in contrast, exceed all pre-launch conditions by a factor of two to six over all forest types. We also observe that the signal radiometry from day acquisitions is lower than night acquisitions by 10% and 40% for the strong and weak beams, respectively. This research also found that the detection ratio between each beam-pair was lower than the predicted 4:1 values. This research also presents the concept of ICESat-2 radiometric profiles; these profiles provide a path for calculating vegetation structure. The results from this study are intended to be informative and perhaps serve as a benchmark for filtering or analysis of the ATL08 data products over vegetated surfaces.

Classifying the Nunivak Island Coastline Using the Random Forest Integration of the Sentinel-2 and ICESat-2 Data

Shore zone information is essential for coastal habitat assessment, environmental hazard monitoring, and resource conservation. However, traditional coastal zone classification mainly relies on in situ measurements and expert knowledge interpretation, which are costly and inefficient. This study classifies a shore zone area using satellite remote sensing data only and investigates the effect of the statistical indicators from Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) information with the Sentinel-2 data-derived spectral variables on the prediction results. Google Earth Engine was used to synthesize long time-series Sentinel-2 images, and different features were calculated for this synthetic image. Then, statistical indicators reflecting the characteristics of the shore zone profile were extracted from ICESat-2. Finally, a random forest algorithm was used to develop characteristics and shore zone classification. Comparing the results with the data measured shows that the proposed method can effectively classify the shore zone; it has an accuracy of 83.61% and a kappa coefficient of 0.81.

Forest Canopy Height Mapping by Synergizing ICESat-2, Sentinel-1, Sentinel-2 and Topographic Information Based on Machine Learning Methods

Spaceborne LiDAR has been widely used to obtain forest canopy heights over large areas, but it is still a challenge to obtain spatio-continuous forest canopy heights with this technology. In order to make up for this deficiency and take advantage of the complementary for multi-source remote sensing data in forest canopy height mapping, a new method to estimate forest canopy height was proposed by synergizing the spaceborne LiDAR (ICESat-2) data, Synthetic Aperture Radar (SAR) data, multi-spectral images, and topographic data considering forest types. In this study, National Geographical Condition Monitoring (NGCM) data was used to extract the distributions of coniferous forest (CF), broadleaf forest (BF), and mixed forest (MF) in Hua’ nan forest area in Heilongjiang Province, China. Accordingly, the forest canopy height estimation models for whole forest (all forests together without distinguishing types, WF), CF, BF, and MF were established, respectively, by Radom Forest (RF) and Gradient Boosting Decision Tree (GBDT). The accuracy for established models and the forest canopy height obtained based on estimation models were validated consequently. The results showed that the forest canopy height estimation models considering forest types had better performance than the model grouping all types of forest together. Compared with GBDT, RF with optimal variables had better performance in forest canopy height estimation with Pearson’s correlation coefficient (R) and the root-mean-squared error (RMSE) values for CF, BF, and MF of 0.72, 0.59, 0.62, and 3.15, 3.37, 3.26 m, respectively. It has been validated that a synergy of ICESat-2 with other remote sensing data can make a crucial contribution to spatio-continuous forest canopy height mapping, especially for areas covered by different types of forest.

Retrieval of DTM under Complex Forest Stand Based on Spaceborne LiDAR Fusion Photon Correction

The new generation of satellite-borne laser radar Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data has been successfully used for ground information acquisition. However, when dealing with complex terrain and dense vegetation cover, the accuracy of the extracted understory Digital Terrain Model (DTM) is limited. Therefore, this paper proposes a photon correction data processing method based on ICESat-2 to improve the DTM inversion accuracy in complex terrain and high forest coverage areas. The correction value is first extracted based on the ALOS PALSAR DEM reference data to correct the cross-track photon data of ICESat-2. The slope filter threshold is then selected from the reference data, and the extracted possible ground photons are slope filtered to obtain accurate ground photons. Finally, the impacts of cross-track photon and slope filtering on fine ground extraction from the ICESat-2 data are discussed. The results show that the proposed photon correction and slope filtering algorithms help to improve the extraction accuracy of forest DTM in complex terrain areas. Compared with the forest DTM extracted without the photon correction and slope filtering methods, the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) are reduced by 51.90~57.82% and 49.37~53.55%, respectively. To the best of our knowledge, this is the first study demonstrating that photon correction can improve the terrain inversion ability of ICESat-2, while providing a novel method for ground extraction based on ICESat-2 data. It provides a theoretical basis for the accurate inversion of canopy parameters for ICESat-2.

Very High-Resolution Satellite-Derived Bathymetry and Habitat Mapping Using Pleiades-1 and ICESat-2

Accurate and reliable bathymetric data are needed for a wide diversity of marine research and management applications. Satellite-derived bathymetry represents a time saving method to map large shallow waters of remote regions compared to the current costly in situ measurement techniques. This study aims to create very high-resolution (VHR) bathymetry and habitat mapping in Mayotte island waters (Indian Ocean) by fusing 0.5 m Pleiades-1 passive multispectral imagery and active ICESat-2 LiDAR bathymetry. ICESat-2 georeferenced photons were filtered to remove noise and corrected for water column refraction. The bathymetric point clouds were validated using the French naval hydrographic and oceanographic service Litto3D® dataset and then used to calibrate the multispectral image to produce a digital depth model (DDM). The latter enabled the creation of a digital albedo model used to classify benthic habitats. ICESat-2 provided bathymetry down to 15 m depth with a vertical accuracy of bathymetry estimates reaching 0.89 m. The benthic habitats map produced using the maximum likelihood supervised classification provided an overall accuracy of 96.62%. This study successfully produced a VHR DDM solely from satellite data. Digital models of higher accuracy were further discussed in the light of the recent and near-future launch of higher spectral and spatial resolution satellites.

Improved Filtering of ICESat-2 Lidar Data for Nearshore Bathymetry Estimation Using Sentinel-2 Imagery

The accurate estimation of nearshore bathymetry is necessary for multiple aspects of coastal research and practices. The traditional shipborne single-beam/multi-beam echo sounders and Airborne Lidar bathymetry (ALB) have a high cost, are inefficient, and have sparse coverage. The Satellite-derived bathymetry (SDB) method has been proven to be a promising tool in obtaining bathymetric data in shallow water. However, current empirical SDB methods for multispectral imagery data usually rely on in situ depths as control points, severely limiting their spatial application. This study proposed a satellite-derived bathymetry method without requiring a priori in situ data by merging active and passive remote sensing (SDB-AP). It realizes rapid bathymetric mapping with only satellite remotely sensed data, which greatly extends the spatial coverage and temporal scale. First, seafloor photons were detected from the ICESat-2 raw photons based on an improved adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which could calculate the optimal detection parameters for seafloor photons by adaptive iteration. Then, the bathymetry of the detected seafloor photons was corrected because of the refraction that occurs at the air–water interface. Afterward, the outlier photons were removed by an outlier-removal algorithm to improve the retrieval accuracy. Subsequently, the high spatial resolution (0.7 m) ICESat-2 derived bathymetry data were gridded to match the Sentinel-2 data with a lower spatial resolution (10 m). All of the ICESate-2 gridded data were randomly separated into two parts: 80% were employed to train the empirical bathymetric model, and the remaining 20% were used to quantify the inversion accuracy. Finally, after merging the ICESat-2 data and Sentinel-2 multispectral images, the bathymetric maps over St. Thomas of the United States Virgin Islands, Acklins Island in the Bahamas, and Huaguang Reef in the South China Sea were produced. The ICESat-2-derived results were compared against in situ data over the St. Thomas area. The results showed that the estimated bathymetry reached excellent inversion accuracy and the corresponding RMSE was 0.68 m. In addition, the RMSEs between the SDB-AP estimated depths and the ICESat-2 bathymetry results of St. Thomas, Acklins Island, and Huaguang Reef were 0.96 m, 0.91 m, and 0.94 m, respectively. Overall, the above results indicate that the SDB-AP method is effective and feasible for different shallow water regions. It has great potential for large-scale and long-term nearshore bathymetry in the future.

Theoretical model considering optimal ranging performance and energy efficiency for photon-counting lidars with multiple detectors

With an orbital speed of several kilometers per second, space-borne photon-counting lidars can only perform several to a few tens of measurements on the same target. The ranging uncertainty can exceed tens of centimeters with a pulse width of several nanoseconds and has a great impact on the total ranging error. When multiple photons are recorded (using multiple detectors) in a single laser shot, the ranging uncertainty can be effectively suppressed by the average method. Higher signal levels (receiving more photons) introduce a better ranging uncertainty, but a worse ranging bias is caused by the dead time effect of photon-counting detectors. In this study, a theoretical ranging performance model is proposed to address that question: What signal levels and how many detectors are the optimum selection to achieve a better ranging performance? A photon-counting lidar system with four photomultiplier tubes is used to verify the proposed ranging performance model. Experiments are conducted under nine sets of different signal levels, and the ranging performance of experiment results agree well with the theoretical predictions. The average residual error is 0.31 cm and all error ratios are less than 10%. The energy efficiency and ranging uncertainty are further quantitatively analyzed. When multiple detectors are employed, the total ranging error (i.e., the root sum square of the ranging bias and ranging uncertainty) has the minimum value. For a space-borne photon-counting lidar, the expected signal photon number in each detector is recommended as 0.5∼1 (wasting a small part of the received energy). With a 4×4 detector array and a received pulse width of 1.5 ns, the total ranging error can reach approximately 5 cm when the expected signal photon number is close to 10. This theoretical model is essential in estimating the ranging performance for a give photon-counting lidar and in optimizing the design of lidar system parameters.

Automated Water Level Monitoring at the Continental Scale from ICESat-2 Photons

Of the approximately 6700 lakes and reservoirs larger than 1 km2 in the Contiguous United States (CONUS), only ~430 (~6%) are actively gaged by the United States Geological Survey (USGS) or their partners and are available for download through the National Water Information System database. Remote sensing analysis provides a means to fill in these data gaps in order to glean a better understanding of the spatiotemporal water level changes across the CONUS. This study takes advantage of two-plus years of NASA’s ICESat-2 (IS-2) ATLAS photon data (ATL03 products) in order to derive water level changes for ~6200 overlapping lakes and reservoirs (>1 km2) in the CONUS. Interactive visualizations of large spatial datasets are becoming more commonplace as data volumes for new Earth observing sensors have markedly increased in recent years. We present such a visualization created from an automated cluster computing workflow that utilizes tens of billions of ATLAS photons which derives water level changes for all of the overlapping lakes and reservoirs in the CONUS. Furthermore, users of this interactive website can download segmented and clustered IS-2 ATL03 photons for each individual waterbody so that they may run their own analysis. We examine ~19,000 IS-2 derived water level changes that are spatially and temporally coincident with water level changes from USGS gages and find high agreement with our results as compared to the in situ gage data. The mean squared error (MSE) and the mean absolute error (MAE) between these two products are 1 cm and 6 cm, respectively.

Quantifying Surface-Height Change Over a Periglacial Environment With ICESat-2 Laser Altimetry

We use Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) laser altimetry crossovers and repeat tracks collected over the North Slope of Alaska to estimate ground surface-height change due to the seasonal freezing and thawing of the active layer. We compare these measurements to a time series of surface deformation from Sentinel-1 interferometric synthetic aperture radar (InSAR) and demonstrate agreement between these independent observations of surface deformation at broad spatial scales. We observe a relationship between ICESat-2-derived surface subsidence/uplift and changes in normalized accumulated degree days, which is consistent with the thermodynamically driven seasonal freezing and thawing of the active layer. Integrating ICESat-2 crossover estimates of surface-height change yields an annual time series of surface-height change that is sensitive to changes in snow cover during spring and thawing of the active layer throughout spring and summer. Furthermore, this time series exhibits temporal correlation with independent reanalysis datasets of temperature and snow cover, as well as an InSAR-derived time series. ICESat-2-derived surface-height change estimates can be significantly affected by short length-scale topographic gradients and changes in snow cover and snow depth. We discuss optimal strategies of post-processing ICESat-2 data for permafrost applications, as well as the future potential of joint ICESat-2 and InSAR investigations of permafrost surface-dynamics.

Rapid Formation of an Ice Doline on Amery Ice Shelf, East Antarctica

Surface meltwater accumulating on Antarctic ice shelves can drive fractures through to the ocean and potentially cause their collapse, leading to increased ice discharge from the continent. Implications of increasing surface melt for future ice shelf stability are inadequately understood. The southern Amery Ice Shelf has an extensive surface hydrological system, and we present data from satellite imagery and ICESat-2 showing a rapid surface disruption there in winter 2019, covering ∼60 km2. We interpret this as an ice-covered lake draining through the ice shelf, forming an ice doline with a central depression reaching 80 m depth amidst over 36 m uplift. Flexural rebound modeling suggests 0.75 km3 of water was lost. We observed transient refilling of the doline the following summer with rapid incision of a narrow meltwater channel (20 m wide and 6 m deep). This study demonstrates how high-resolution geodetic measurements can explore critical fine-scale ice shelf processes.

Global Estimation and Assessment of Monthly Lake/Reservoir Water Level Changes Using ICESat-2 ATL13 Products

Accurate and detailed information on lake/reservoir water levels and temporal changes around the globe is urgently required for water resource management and related studies. The traditional satellite radar altimeters normally monitor water level changes of large lakes and reservoirs (i.e., greater than 1 km2) around the world. Fortunately, the recent Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) makes it possible to monitor water level changes for some small lakes and reservoirs (i.e., less than 1 km2). ICESat-2 ATL13 products provide observations of inland water surface heights, which are suitable for water level estimation at a global scale. In this study, ICESat-2 ATL13 products were used to conduct a global estimation and assessment of lake/reservoir water level changes. We produced monthly water levels for 13,843 lakes and reservoirs with areas greater than 0.1 km2 and all-season ATL13 products across the globe, in which 2257 targets are smaller than 1 km2. In total, the average valid number of months covered by ICESat-2 is 5.41 months and only 204 of 13,843 lakes and reservoirs have water levels in all the months in 2019. In situ water level data from 21 gauge stations across the United States and 12 gauge stations across Australia were collected to assess the monthly lake/reservoir water levels, which exhibited a high accuracy (RMSE = 0.08 m, r = 0.999). According to comparisons between the monthly water levels and changes from ATL08 products in another study and ATL13 products in this study, we found that both products can accurately estimate the monthly water level of lakes and reservoirs, but water levels derived from ATL13 products exhibited a higher accuracy compared with water levels derived from ATL08 products (RMSE = 0.28 m, r = 0.999). In general, the ATL13 product is more convenient because the HydroLAKES mask of inland water bodies, the orthometric height (with respect to the EGM2008 geoid) of water surfaces, and several data quality parameters specific to water surfaces were involved in the ATL13 product.

Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests

Global Ecosystem Dynamics Investigation (GEDI) satellite mission is expanding the spatial bounds and temporal resolution of large-scale mapping applications. Integrating the recent GEDI data into Airborne Laser Scanning (ALS)-derived estimations represents a global opportunity to update and extend forest models based on area based approaches (ABA) considering temporal and spatial dynamics. This study evaluates the effect of combining ALS-based aboveground biomass (AGB) estimates with GEDI-derived models by using temporally coincident datasets. A gradient of forest ecosystems, distributed through 21,766 km2 in the province of Badajoz (Spain), with different species and structural complexity, was used to: (i) assess the accuracy of GEDI canopy height in five Mediterranean Ecosystems and (ii) develop GEDI-based AGB models when using ALS-derived AGB estimates at GEDI footprint level. In terms of Pearson’s correlation (r) and rRMSE, the agreement between ALS and GEDI statistics on canopy height was stronger in the denser and homogeneous coniferous forest of P. pinaster and P. pinea than in sparse Quercus-dominated forests. The GEDI-derived AGB models using relative height and vertical canopy metrics yielded a model efficiency (Mef) ranging from 0.31 to 0.46, with a RMSE ranging from 14.13 to 32.16 Mg/ha and rRMSE from 38.17 to 84.74%, at GEDI footprint level by forest type. The impact of forest structure confirmed previous studies achievements, since GEDI data showed higher uncertainty in highly multilayered forests. In general, GEDI-derived models (GEDI-like Level4A) underestimated AGB over lower and higher ALS-derived AGB intervals. The proposed models could also be used to monitor biomass stocks at large-scale by using GEDI footprint level in Mediterranean areas, especially in remote and hard-to-reach areas for forest inventory. The findings from this study serve to provide an initial evaluation of GEDI data for estimating AGB in Mediterranean forest.

Enabling Value Added Scientific Applications of ICESat-2 Data With Effective Removal of Afterpulses

The Advanced Topographic Laser Altimeter System (ATLAS) aboard the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) has been making very high resolution measurements of the Earth's surface elevation since October 2018. ATLAS uses photomultiplier tubes (PMTs) as detectors in photon counting mode, so that a single photon reflected back to the receiver triggers a detection within the ICESat-2 data acquisition system. However, one characteristic of ICESat-2 detected photons is the possible presence of afterpulses, defined as small amplitude pulses occurring after the primary signal pulse due to photon arrival. The disadvantage of these afterpulses is that they often confound the accurate measurements of low level signals following a large amplitude of signal and can degrade energy resolution and cause errors in pulse counting applications. This paper discusses and summarizes the after-pulsing effects exhibited by the ATLAS PMTs based on on-orbit measurements over different seasons and geographic regions. The potential impacts of these after-pulsing effects on altimetry and ocean subsurface retrievals are discussed.

ICESat-2 Early Mission Synopsis and Observatory Performance

The Advanced Topographic Laser Altimetry System (ATLAS) onboard the NASA Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) is the newest Earth observing satellite for global elevation studies. The primary objectives for ICESat-2 follow the objectives of its predecessor, ICESat and also focus on providing cryospheric measurements to determine ice sheet mass balance, and monitor both sea ice thickness and extent. However, the global observations support secondary science objectives as well such as biomass estimation, inland water elevation, sea state height and aerosol concentrations. Since launch of ICESat-2, ATLAS has collected more than a trillion measurements. This study provides a mission overview, a description of the operational components that enable the altimeter products for science, on-orbit observatory performance, and assessment of the spacecraft attitude control systems that enable repeat measurements to within 10 m and pointing control within ±45 m. These metrics should be considered for ground-based validation campaigns or science investigations.

Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm

The forest canopy height (FCH) plays a critical role in forest quality evaluation and resource management. The accurate and rapid estimation and mapping of the regional forest canopy height is crucial for understanding vegetation growth processes and the internal structure of the ecosystem. A stacking algorithm consisting of multiple linear regression (MLR), support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF) was used in this paper and demonstrated optimal performance in predicting the forest canopy height by synergizing Sentinel-2 images acquired from the cloud-based computation platform Google Earth Engine (GEE) with data from ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). This research was conducted to achieve continuous mapping of the canopy height of plantations in Saihanba Mechanical Forest Plantation, which is located in Chengde City, northern Hebei province, China. The results show that stacking achieved the best prediction accuracy for the forest canopy height, with an R2 of 0.77 and a root mean square error (RMSE) of 1.96 m. Compared with MLR, SVM, kNN, and RF, the RMSE obtained by stacking was reduced by 25.2%, 24.9%, 22.8%, and 18.7%, respectively. Since Sentinel-2 images and ICESat-2 data are publicly available, this opens the door for the accurate mapping of the continuous distribution of the forest canopy height globally in the future.

ICESat-2/ATLAS Onboard Flight Science Receiver Algorithms: Purpose, Process, and Performance

The Advanced Topographic Laser Altimetry System (ATLAS) is the sole instrument on the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2). Without some method of reducing the transmitted data, the volume of ATLAS telemetry would far exceed the normal X-band downlink capability or require many more ground station contacts. The ATLAS Onboard Flight Science Receiver Algorithms (hereinafter Receiver Algorithms or Algorithms) control the amount of science data that is telemetered from the instrument, limiting the data volume by distinguishing surface echoes from background noise, and allowing the instrument to telemeter data from only a small vertical region about the signal. This is accomplished through the transfer of the spacecraft's location and attitude to the instrument every second, use of an onboard Digital Elevation Model, implementation of signal processing techniques, and use of onboard relief and surface type reference maps. Extensive ground testing verified the performance of the Algorithms. On-orbit analysis shows that the Algorithms are working as expected from the ground testing; they are performing well and meeting the mission requirements.

ICESat-2 Elevation Retrievals in Support of Satellite-Derived Bathymetry for Global Science Applications

Bathymetry retrievals from 2D, multispectral imagery, referred to as Satellite-Derived Bathymetry (SDB), afford the potential to obtain global, nearshore bathymetric data in optically clear waters. However, accurate SDB depth retrievals are limited in the absence of "seed depths." The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) space-based altimeter has proven capable of accurate bathymetry, but methods of employing ICESat-2 bathymetry for SDB retrievals over broad spatial extents are immature. This research aims to establish and test a baseline methodology for generating bathymetric surface models using SDB with ICESat-2. The workflow is operationally efficient (17-37 min processing time) and capable of producing bathymetry of sufficient vertical accuracy for many coastal science applications, with RMSEs of 0.96 and 1.54 m when using Sentinel-2 and Landsat 8, respectively. The highest priorities for further automation have also been identified, supporting the long-range goal of global coral reef habitat change analysis using ICESat-2-aided SDB.

Human alteration of global surface water storage variability

Knowing the extent of human influence on the global hydrological cycle is essential for the sustainability of freshwater resources on Earth^1,2. However, a lack of water level observations for the world's ponds, lakes and reservoirs has limited the quantification of human-managed (reservoir) changes in surface water storage compared to its natural variability³. The global storage variability in surface water bodies and the extent to which it is altered by humans therefore remain unknown. Here we show that 57 per cent of the Earth's seasonal surface water storage variability occurs in human-managed reservoirs. Using measurements from NASA's ICESat-2 satellite laser altimeter, which was launched in late 2018, we assemble an extensive global water level dataset that quantifies water level variability for 227,386 water bodies from October 2018 to July 2020. We find that seasonal variability in human-managed reservoirs averages 0.86 metres, whereas natural water bodies vary by only 0.22 metres. Natural variability in surface water storage is greatest in tropical basins, whereas human-managed variability is greatest in the Middle East, southern Africa and the western USA. Strong regional patterns are also found, with human influence driving 67 per cent of surface water storage variability south of 45 degrees north and nearly 100 per cent in certain arid and semi-arid regions. As economic development, population growth and climate change continue to pressure global water resources⁴, our approach provides a useful baseline from which ICESat-2 and future satellite missions will be able to track human modifications to the global hydrologic cycle.

Signal Photon Extraction Method for Weak Beam Data of ICESat-2 Using Information Provided by Strong Beam Data in Mountainous Areas

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) can measure the elevations of the Earth’s surface using a sampling strategy with unprecedented spatial detail. In the daytime of mountainous areas where the signal–noise ratio (SNR) of weak beam data is very low, current algorithms do not always perform well on extracting signal photons from weak beam data (i.e., many signal photons were missed). This paper proposes an effective algorithm to extract signal photons from the weak beam data of ICESat-2 in mountainous areas. First, a theoretical equation of SNR for ICESat-2 measured photons in mountainous areas was derived to prove that the available information provided by strong beam data can be used to assist the signal extraction of weak beam data (that may have very low SNR in mountainous areas). Then, the relationship between the along-track slope and the noise level was used as the bridge to connect the strong and weak beam data. To be specific, the along-track slope of the weak beam was inversed by the slope–noise relationship obtained from strong beam data, and then was used to rotate the direction of the searching neighborhood in the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. With the help of this process, the number of signal photons included in the searching neighborhood will significantly increase in mountainous areas and will be easily detected from the measured noisy photons. The proposed algorithm was tested in the Tibetan Plateau, the Altun Mountains, and the Tianshan Mountains in different seasons, and the extraction results were compared with the results from the ATL03 datasets, the ATL08 datasets, and the classical DBSCAN algorithm. Based on the ground-truth signal photons obtained by visual inspection, the parameters of the classification precision, recall, and F-score of our algorithm and three other algorithms were calculated. The modified DBSCAN could achieve a good balance between the classification precision (93.49% averaged) and recall (89.34% averaged), and its F-score (more than 0.91) was higher than that of the other three methods, which successfully obtained a continuous surface profile from weak beam data with very low SNRs. In the future, the detected signal photons from weak beam data are promising to assess the elevation accuracy achieved by ICESat-2, estimate the along-track and cross-track slope, and further obtain the ground control points (GCPs) for stereo-mapping satellites in mountainous areas.

FOSTER-An R package for forest structure extrapolation

The uptake of technologies such as airborne laser scanning (ALS) and more recently digital aerial photogrammetry (DAP) enable the characterization of 3-dimensional (3D) forest structure. These forest structural attributes are widely applied in the development of modern enhanced forest inventories. As an alternative to extensive ALS or DAP based forest inventories, regional forest attribute maps can be built from relationships between ALS or DAP and wall-to-wall satellite data products. To date, a number of different approaches exist, with varying code implementations using different programming environments and tailored to specific needs. With the motivation for open, simple and modern software, we present FOSTER (Forest Structure Extrapolation in R), a versatile and computationally efficient framework for modeling and imputation of 3D forest attributes. FOSTER derives spectral trends in remote sensing time series, implements a structurally guided sampling approach to sample these often spatially auto correlated datasets, to then allow a modelling approach (currently k-NN imputation) to extrapolate these 3D forest structure measures. The k-NN imputation approach that FOSTER implements has a number of benefits over conventional regression based approaches including lower bias and reduced over fitting. This paper provides an overview of the general framework followed by a demonstration of the performance and outputs of FOSTER. Two ALS-derived variables, the 95^th percentile of first returns height (elev_p95) and canopy cover above mean height (cover), were imputed over a research forest in British Columbia, Canada with relative RMSE of 18.5% and 11.4% and relative bias of -0.6% and 1.4% respectively. The processing sequence developed within FOSTER represents an innovative and versatile framework that should be useful to researchers and managers alike looking to make forest management decisions over entire forest estates.

Early ICESat-2 on-orbit Geolocation Validation Using Ground-Based Corner Cube Retro-Reflectors

The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2), an Earth-observing laser altimetry mission, is currently providing global elevation measurements. Geolocation validation confirms the altimeter’s ability to accurately position the measurement on the surface of the Earth and provides insight into the fidelity of the geolocation determination process. Surfaces well characterized by independent methods are well suited to provide a measure of the ICESat-2 geolocation accuracy through statistical comparison. This study compares airborne lidar data with the ICESat-2 along-track geolocated photon data product to determine the horizontal geolocation accuracy by minimizing the vertical residuals between datasets. At the same location arrays of corner cube retro-reflectors (CCRs) provide unique signal signatures back to the satellite from their known positions to give a deterministic solution of the laser footprint diameter and the geolocation accuracy for those cases where two or more CCRs were illuminated within one ICESat-2 transect. This passive method for diameter recovery and geolocation accuracy assessment is implemented at two locations: White Sands Missile Range (WSMR) in New Mexico and along the 88°S latitude line in Antarctica. This early on-orbit study provides results as a proof of concept for this passive validation technique. For the cases studied the diameter value ranged from 10.6 to 12 m. The variability is attributed to the statistical nature of photon-counting lidar technology and potentially, variations in the atmospheric conditions that impact signal transmission. The geolocation accuracy results from the CCR technique and airborne lidar comparisons are within the mission requirement of 6.5 m.

Estimating Terrain Slope from ICESat-2 Data in Forest Environments

The global digital elevation measurement (DEM) products such as SRTM DEM and GDEM have been widely used for terrain slope retrieval in forests. However, the slope estimation accuracy is generally limited due to the DEMs’ low vertical accuracy over complex forest environments. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission shows excellent potential for slope estimation because of the high elevation accuracy and unique design of beam pairs. This study aimed to explore the possibility of ICESat-2 data for terrain slope retrieval in the United States forests. First, raw ICESat-2 data were processed to obtain accurate ground surfaces. Second, two different methods based on beam pairs were proposed to derive terrain slopes from the ground surfaces. Third, the estimated slopes were validated by airborne LiDAR-derived slopes and compared with SRTM-derived slopes and GDEM-derived slopes. Finally, we further explored the influence of surface topography and ground elevation error on slope estimation from ICESat-2 data. The results show that the ground surface can be accurately extracted from all scenarios of ICESat-2 data, even weak beams in the daytime, which provides the basis for terrain slope retrieval from ICESat-2 beam pairs. The estimated slope has a strong correlation with airborne LiDAR-derived slopes regardless of slope estimation methods, which demonstrates that the ICESat-2 data are appropriate for terrain slope estimation in complex forest environments. Compared with the method based on along- and across-track analysis (method 1), the method based on plane fitting of beam pairs (method 2) has a high estimation accuracy of terrain slopes, which indicates that method 2 is more suitable for slope estimation because it takes full advantage of more ground surface information. Additionally, the results also indicate that ICESat-2 performs much better than SRTM DEMs and GDEMs in estimating terrain slopes. Both ground elevation error and surface topography have a significant impact on terrain slope retrieval from ICESat-2 data, and ground surface extraction should be improved to ensure the accuracy of terrain slope retrieval over extremely complex environments. This study demonstrates for the first time that ICESat-2 has a strong capability in terrain slope retrieval. Additionally, this paper also provides effective solutions to accurately estimate terrain slopes from ICESat-2 data. The ICESat-2 slopes have many potential applications, including the generation of global slope products, the improvement of terrain slopes derived from the existing global DEM products, and the correction of vegetation biophysical parameters retrieved from space-borne LiDAR waveform data.

Orbiting and In-Situ Lidars for Earth and Planetary Applications

At NASA Goddard Space Flight Center, we have been developing spaceborne lidar instruments for space sciences. We have successfully flown several missions in the past based on mature diode pumped solid-state laser transmitters. In recent years, we have been developing advanced laser technologies for applications such as laser spectroscopy, laser communications, and interferometry. In this article, we will discuss recent experimental progress on these systems and instrument prototypes for ongoing development.

Understanding of Contemporary Regional Sea-Level Change and the Implications for the Future

Global sea level provides an important indicator of the state of the warming climate, but changes in regional sea level are most relevant for coastal communities around the world. With improvements to the sea-level observing system, the knowledge of regional sea-level change has advanced dramatically in recent years. Satellite measurements coupled with in situ observations have allowed for comprehensive study and improved understanding of the diverse set of drivers that lead to variations in sea level in space and time. Despite the advances, gaps in the understanding of contemporary sea-level change remain and inhibit the ability to predict how the relevant processes may lead to future change. These gaps arise in part due to the complexity of the linkages between the drivers of sea-level change. Here we review the individual processes which lead to sea-level change and then describe how they combine and vary regionally. The intent of the paper is to provide an overview of the current state of understanding of the processes that cause regional sea-level change and to identify and discuss limitations and uncertainty in our understanding of these processes. Areas where the lack of understanding or gaps in knowledge inhibit the ability to provide the needed information for comprehensive planning efforts are of particular focus. Finally, a goal of this paper is to highlight the role of the expanded sea-level observation network-particularly as related to satellite observations-in the improved scientific understanding of the contributors to regional sea-level change.

First Evidence of Peat Domes in the Congo Basin using LiDAR from a Fixed-Wing Drone

The world’s most extensive tropical peatlands occur in the Cuvette Centrale depression in the Congo Basin, which stores 30.6 petagrams of carbon (95% CI, 6.3–46.8). Improving our understanding of the genesis, development and functioning of these under-studied peatlands requires knowledge of their topography and, in particular, whether the peat surface is domed, as this implies a rain-fed system. Here we use a laser altimeter mounted on an unmanned airborne vehicle (UAV) to measure peat surface elevation along two transects at the edges of a peatland, in the northern Republic of Congo, to centimetre accuracy and compare the results with an analysis of nearby satellite LiDAR data (ICESat and ICESat-2). The LiDAR elevations on both transects show an upward slope from the peatland edge, suggesting a surface elevation peak of around 1.8 m over ~20 km. While modest, this domed shape is consistent with the peatland being rainfed. In-situ peat depth measurements and our LiDAR results indicate that this peatland likely formed at least 10,000 years BP in a large shallow basin ~40 km wide and ~3 m deep.

Using ICESat-2 to Estimate and Map Forest Aboveground Biomass: A First Example

National Aeronautics and Space Administration’s (NASA’s) Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides rich insights over the Earth’s surface through elevation data collected by its Advanced Topographic Laser Altimeter System (ATLAS) since its launch in September 2018. While this mission is primarily aimed at capturing ice measurements, ICESat-2 also provides data over vegetated areas, offering the capability to gain insights into ecosystem structure and the potential to contribute to the sustainable management of forests. This study involved an examination of the utility of ICESat-2 for estimating forest aboveground biomass (AGB). The objectives of this study were to: (1) investigate the use of canopy metrics for estimating AGB, using data extracted from an ICESat-2 transect over forests in south-east Texas; (2) compare the accuracy for estimating AGB using data from the strong beam and weak beam; and (3) upscale predicted AGB estimates using variables from Landsat multispectral imagery and land cover and canopy cover maps, to generate a 30 m spatial resolution AGB map. Methods previously developed with simulated ICESat-2 data over Sam Houston National Forest (SHNF) in southeast Texas were adapted using actual data from an adjacent ICESat-2 transect over similar vegetation conditions. Custom noise filtering and photon classification algorithms were applied to ICESat-2’s geolocated photon data (ATL03) for one beam pair, consisting of a strong and weak beam, and canopy height estimates were retrieved. Canopy height parameters were extracted from 100 m segments in the along-track direction for estimating AGB, using regression analysis. ICESat-2-derived AGB estimates were then extrapolated to develop a 30 m AGB map for the study area, using vegetation indices from Landsat 8 Operational Land Imager (OLI), National Land Cover Database (NLCD) landcover and canopy cover, with random forests (RF). The AGB estimation models used few canopy parameters and suggest the possibility for applying well-developed methods for modeling AGB with airborne light detection and ranging (lidar) data, using processed ICESat-2 data. The final regression model achieved a R2 and root mean square error (RMSE) value of 0.62 and 24.63 Mg/ha for estimating AGB and RF model evaluation with a separate test set yielded a R2 of 0.58 and RMSE of 23.89 Mg/ha. Findings provide an initial look at the ability of ICESat-2 to estimate AGB and serve as a basis for further upscaling efforts.

Pervasive ice sheet mass loss reflects competing ocean and atmosphere processes

Quantifying changes in Earth's ice sheets and identifying the climate drivers are central to improving sea level projections. We provide unified estimates of grounded and floating ice mass change from 2003 to 2019 using NASA's Ice, Cloud and land Elevation Satellite (ICESat) and ICESat-2 satellite laser altimetry. Our data reveal patterns likely linked to competing climate processes: Ice loss from coastal Greenland (increased surface melt), Antarctic ice shelves (increased ocean melting), and Greenland and Antarctic outlet glaciers (dynamic response to ocean melting) was partially compensated by mass gains over ice sheet interiors (increased snow accumulation). Losses outpaced gains, with grounded-ice loss from Greenland (200 billion tonnes per year) and Antarctica (118 billion tonnes per year) contributing 14 millimeters to sea level. Mass lost from West Antarctica's ice shelves accounted for more than 30% of that region's total.

Maker Buoy Variants for Water Level Monitoring and Tracking Drifting Objects in Remote Areas of Greenland

Meltwater runoff from the Greenland Ice Sheet changes water levels in glacial lakes and can lead to glacial lake outburst flooding (GLOF) events that threaten lives and property. Icebergs produced at Greenland's marine terminating glaciers drift into Baffin Bay and the North Atlantic, where they can threaten shipping and offshore installations. Thus, monitoring glacial lake water levels and the drift of icebergs can enhance safety and aid in the scientific studies of glacial hydrology and iceberg-ocean interactions. The Maker Buoy was originally designed as a low-cost and open source sensor to monitor surface ocean currents. The open source framework, low-cost components, rugged construction and affordable satellite data transmission capabilities make it easy to customize for environmental monitoring in remote areas and under harsh conditions. Here, we present two such Maker Buoy variants that were developed to monitor water level in an ice-infested glacial lake in southern Greenland and to track drifting icebergs and moorings in the Vaigat Strait (Northwest Greenland). We describe the construction of each design variant, methods to access data in the field without an internet connection, and deployments in Greenland in summer 2019. The successful deployments of each Maker Buoy variant suggest that they may also be useful in operational iceberg management strategies and in GLOF monitoring programs.

Canopy and Terrain Height Retrievals with ICESat-2: A First Look

NASA’s Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) launched in fall 2018 and has since collected continuous elevation data over the Earth’s surface. The primary scientific objective is to measure the cryosphere for studies related to land ice and sea ice characteristics. The vantage point from space, however, provides the opportunity to measure global surfaces including oceans, land, and vegetation. The ICESat-2 mission has dedicated products to the represented surface types, including an along-track elevation profile of terrain and canopy heights (ATL08). This study presents the first look at the ATL08 product and the quantitative assessment of the canopy and terrain height retrievals as compared to airborne lidar data. The study also provides qualitative examples of ICESat-2 observations from selected ecosystems to highlight the broad capability of the satellite for vegetation applications. Analysis of the mission’s preliminary ATL08 data product accuracy using an ICESat-2 transect over a vegetated region of Finland indicates a 5 m offset in geolocation knowledge (horizontal accuracy) well within the 6.5 m mission requirement. The vertical RMSE for the terrain and canopy height retrievals for one transect are 0.85 m and 3.2 m respectively.