Substantial potential of ICESat-2 photon-counting laser altimetry for reconstructing the lake water depth on the Tibetan Plateau

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.

Abstract. The polar ice sheets serve as natural thermostats, regulating Earth’s temperatures. The Greenland Ice Sheet (GrIS), the second-largest ice sheet, is a critical indicator of climate change and global warming. Estimating the volume of supraglacial lakes on the GrIS, which is directly linked to the extent of melting in the Arctic ice sheet, requires information on both lake area and water depth. Conventional bathymetric methods (i.e., airborne bathymetric LiDAR, shipborne echo-sounder) are commonly used for accurate water depth measurement. However, polar supraglacial lakes face challenging conditions, leading to uncertainties in their spatial and temporal distribution. To overcome the limitations, this study combines Sentinel-2 and ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) to estimate bathymetry and detect changes in lake volume on the GrIS from 2019 to 2023. Firstly, Sentinel-2 images were pre-processed, and ICESat-2 single-photon LiDAR points were extracted using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method, followed by the bathymetric corrections as the training data. Subsequently, three bathymetry models (i.e., log-linear, log-ratio, and BP (Back Propagation) neural network) were constructed using Sentinel-2 images and ICESat-2 data. Lastly, the highresolution ArcticDEM (Arctic Digital Elevation Model) was used as the validation data to assess the satellite-derived bathymetry accuracy. In this study, the log-ratio model yielded the best results with the R2, RMSE, and MAE of 0.92, 0.79 m (lower than 10% of the maximum depth), and 0.62 m. The results demonstrate the feasibility of the integrated active and passive remote sensing approach for bathymetry in Arctic supraglacial lakes.

The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission provides effective data for measuring global ground elevation and forest height. Unlike the ICESat, ICESat-2 emits three pairs of beams. Each pair includes a strong beam and a weak beam with an energy ratio of 4:1. To evaluate the performance of ICESat-2's strong and weak beams in estimating ground elevation and forest height, the ICESat-2 data in four different scenarios were analyzed; 1) ICESat-2's strong beams in the daytime, 2) ICESat-2's weak beams in the daytime, 3) ICESat-2's strong beams in the nighttime, and 4) ICESat-2's weak beams in the nighttime. The results indicate that the weak beams perform as well as strong beams in estimating ground elevations due to high coefficient of determination (R2) values and low root-mean-squared error (RMSE) values regardless of the daytime and nighttime data. While for forest height estimation, ICESat-2's weak beams perform worse than strong beams and the daytime data perform worse than the nighttime data. These results suggest that all ICESat-2 data are suitable for ground elevation extraction, while the ICESat-2's weak beams in the daytime are not suited to estimate forest height.

When carrying out SDB (satellite-derived bathymetry) in island area based on ICESat-2 (Ice, Cloud, and land Elevation Satellite 2) data, it is often found that the ICESat-2 bathymetric signals are partially missing due to the influence of thick aerosols such as clouds and fog. This not only hinders the accurate extraction of the along-track underwater topography, but also restricts the active–passive fusion bathymetry based on ICESat-2 data and multi/hyperspectral remote sensing images. In this paper, aiming at the partially missing ICESat-2 bathymetric signals, combined with passive optical remote sensing images, and based on an LSTM (long short-term memory) deep recurrent neural network model, an ICESat-2 bathymetric signal reconstruction method based on active–passive data fusion is proposed. It is found that this method can effectively reconstruct the local missing bathymetric signals. When the reconstructed ICESat-2 bathymetric data are applied to carry out active–passive fusion and bathymetric inversion, the accuracy indices are better than those of the inversion results of the data with partial missing signals, and the performance is comparable to that of the original data without missing data, which is of great value for the bathymetric application of ICESat-2 data in island and reef areas.

Estimating aboveground biomass and forest canopy cover with simulated ICESat-2 data

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.

Canopy spatial structure plays an essential role in ecosystem function and the carbon cycle. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provided continuous three-dimensional sampling observation that can be used to derive canopy structure parameters. Although ICESat-2 data is delivering global estimates of forest structure, analysis of the performance of ICESat-2 data across a range of forest conditions remains limited. Therefore, the overall goal of this study was to evaluate the structural estimates of plant area index (PAI) from ICESat-2 data over temperate deciduous forest structural types. The PAI was derived using the geolocated photon data (ATL03) and the segment-based path length distribution method based on 100-m ICESat-2 vegetation product data (ATL08) segments. The ground-measured data used to evaluate the accuracy of PAI inversion at 100-m ATL08 segments was collected in the Saihanba forest reservation, northern China, which was covered by temperate deciduous needle-leaved forest. The results showed that the ICESat-2 PAI was in good agreement with ground-measured data, which indicated that the method had a better performance in retrieving PAI with ICESat-2 data. Moreover, we compared the effects of the characteristic of signal photons in the segments on the accuracy of PAI inversion and found that the accuracy of PAI inversion was limited by the quality of signal photons. Findings from this study highlight the method for estimating PAI with ICESat-2 data that may be suitable for a range of cover types.

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

Rapid and accurate ecological surveys of mangroves are of great significance for coastal protection and global carbon balance assessments. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2)/Advanced Topographic Laser Altimeter System (ATLAS) data provide an opportunity to conduct large-scale surveys of mangroves. The purpose of this study was to assess the expressiveness of ICESat-2 data for ground and canopy height retrievals in mangroves. First, the ICESat-2 data were processed to obtain the ground and canopy heights of mangrove areas. Second, the accuracies of the ground and canopy heights retrieved from the ICESat-2 data were verified by airborne light detection and ranging (LiDAR) data. Finally, we analyzed the influence of various factors on the ground and canopy height estimation accuracies. The results showed that the average errors of ICESat-2 for the ground and canopy heights were 0.28 and −0.21 m and that the root mean squared errors (RMSEs) were 0.96 and 2.50 m. The accuracies of the ICESat-2 ground and canopy height estimates differed significantly when day/night and strong/weak beams were used. The strong beams at night provided the most accurate estimations of canopy height (RMSE% = 24.4%) and are thus the most suitable choice for studying mangrove areas. In addition, the results indicated that slope is the variable that has the greatest influence on the accuracy of the ground elevation estimates of the four factors above, while the accuracy of canopy height estimates is significantly affected by the canopy height itself. Overall, our study found that ICESat-2 data are suitable for ecological investigations of mangroves.

Accurate estimation of ground elevation on a large scale is essential and worthwhile in topography, geomorphology, and ecology. The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission, launched in September 2018, offers an opportunity to obtain global elevation data over the earth's surface. This paper aimed to evaluate the performance of ICESat-2 data for ground elevation retrieval. To fulfill this objective, our study first tested the availability of existing noise removal and ground photon identification algorithms on ICESat-2 data. Second, the accuracy of ground elevation data retrieved from ICESat-2 data was validated using airborne LiDAR data. Finally, we explored the influence of various factors (e.g., the signal-to-noise ratio (SNR), slope, vegetation height and vegetation cover) on the estimation accuracy of ground elevation over forest, tundra and bare land areas in interior Alaska. The results indicate that the existing noise removal and ground photon identification algorithms for simulated ICESat-2 data also work well for ICESat-2 data. The overall mean difference and RMSE values between the ground elevations retrieved from the ICESat-2 data and the airborne LiDAR-derived ground elevations are -0.61 m and 1.96 m, respectively. In forest, tundra and bare land scenarios, the mean differences are -0.64 m, -0.61 m and -0.59 m, with RMSE values of 1.89 m, 2.05 m, and 1.76 m, respectively. By analyzing the influence of four error factors on the elevation accuracy, we found that the slope is the most important factor affecting the accuracy of ICESat-2 elevation data. The elevation errors increase rapidly with increasing slope, especially when the slope is greater than 20°. The elevation errors decrease with increasing SNR, but this decrease varies little once the SNR is greater than 10. In forest and tundra areas, the errors in the ground elevation also increase with increasing vegetation height and the amount of vegetation cover.

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) satellite uses a synchronized multi-beam photon-counting method to collect data from three pairs of synchronous ground tracks. The sampling rate along the ground tracks is designed to be ∼0.7 m, much smaller than that used in conventional radar altimeters. Hence, it is reasonable to expect an improvement in marine gravity recovery over coastal zones using ICESat-2 data. ICESat-2 provides valid sea surface height (SSH) measurements and a standard data product (ATL12) over ocean areas. This led us to consider the possibility of investigating its ability to calculate the deflection of vertical (DOV) and marine gravity anomalies. We processed ATL12 data about 22 months over the South China Sea (0°–23°N, 103°–120°E) and verified the ability of ICESat-2 SSH measurements to be used in calculating directional components of DOV. The results show that the ICESat-2 SSH data have a similar centimeter-magnitude accuracy level as data from the Jason-2 satellite. Furthermore, the accuracy of cross-track deflection of vertical (CTDOV) calculations between non-identical side beams is lower. For along-track points, the difference in accuracy between the solution of the prime component and the meridional component is significantly reduced, the prime component accuracy is significantly better than the directional components of the gridded deflection of vertical (GDOV), although the enhancement is weak for the meridional component. We also implemented the inversion of the ICESat-2 single mission based on the inverse Vening Meinesz formula, and verified the capability of ICESat-2 gravity field detection using shipborne gravity measurements and XGM2019 gravity field model, and found that the accuracy is 1.35 mGal and 2.47 mGal, respectively. ICESat-2 deserves the attention of the altimetry community, and its advantages are expected to make it an alternative data source for multi-mission fusion inversion of the ocean gravity field in the future.

The leaf area index (LAI) is a critical variable for forest ecosystem processes. Passive optical and active LiDAR remote sensing have been used to retrieve LAI. LiDAR data have good penetration to provide vertical structure distribution and deliver the ability to estimate forest LAI, such as the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). Segment size and beam type are important for ICESat-2 LAI estimation, as they affect the amount of signal photons returned. However, the current ICESat-2 LAI estimation only covered a limited number of sites, and the performance of LAI estimation with different segment sizes has not been clearly compared. Moreover, ICESat-2 LAIs derived from strong and weak beams lack a comparative analysis. This study derived and evaluated LAI from ICESat-2 data over the National Ecological Observatory Network (NEON) sites in North America. The LAI estimated from ICESat-2 for different segment sizes (20, 100, and 200 m) and beam types (strong beam and weak beam) were compared with those from the airborne laser scanning (ALS) and the Copernicus Global Land Service (CGLS). The results show that the LAI derived from strong beams performs better than that of weak beams because more photon signals are received. The LAI estimated from the strong beam at the 200 m segment size shows the highest consistency with those from the ALS data (R = 0.67). Weak beams also present the potential to estimate LAI and have moderate agreement with ALS (R = 0.52). The ICESat-2 LAI shows moderate consistency with ALS for most forest types, except for the evergreen forest. The ICESat-2 LAI shows satisfactory agreement with the CGLS 300 m LAI product (R = 0.67, RMSE = 1.94) and presents a higher upper boundary. Overall, the ICESat-2 can characterize canopy structural parameters and provides the ability to estimate LAI, which may promote the LAI product generated from the photon-counting LiDAR.

The monitoring of forested ecosystems relies on an accurate description of forest structure. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2), launched in September 2018, carries the Advanced Topographic Laser Altimeter System (ATLAS), a Light Detection and Ranging (LiDAR) instrument capable of detecting individual photons reflected back from vegetation canopy. ICESat-2 data is delivering global estimates of forest structure; however, analysis of the performance of ICESat-2 on-orbit data across a range of forest conditions remains limited. This study derives structural estimates of (i) canopy height, (ii) canopy cover and (iii) canopy height variability from ICESat-2 data acquired in snow-free and low atmospheric scattering conditions over different boreal forest structural types in Ontario, Canada. ICESat-2 structural estimates were derived from the Global Geolocated Photon Data (ATL03) and Land and Vegetation Height (ATL08) data products and compared against single-photon detection airborne LiDAR (Leica SPL100). An extensive network of ground plots were used to stratify the study area into three distinct forest structural groups, each resulting from different stand development stages. ICESat-2 and SPL100 estimates of canopy height were compared at the ATL03 photon level, whereas estimates of height variability and canopy cover were compared for spatial analysis units (AU; mean size = 1287 m2). ICESat-2 photons returned from the top of the canopy underestimated canopy height relative to SPL100 by an average of 2.3 m overall and corresponded most strongly to the 90th percentile (P90) of coincident airborne SPL100 returns (root mean square difference (RMSD) = 2.9 m and correlation coefficient (r) = 0.84). The lowest average underestimation of SPL P90 was observed in homogeneous stands that were relatively simple, and single-layered with a single dominant species (RMSD = 2.5 m, r = 0.84). We observed the least agreement of ICESat-2 and SPL forest structural metrics in over-mature stands with complex structure and greater variability in canopy heights (RMSD = 3.5 m, r = 0.64). For the AUs, the strength of the relationship between SPL100 and ICESat-2 canopy height percentiles increased with increasing height percentiles (e.g. P25 RMSD% = 77.8%; P95 RMSD% = 23.7%). ICESat-2 generally underestimated canopy height variability relative to the SPL100 data, with both data having similar absolute variability (standard deviation of canopy heights RMSD% = 26.8%, r = 0.75), but lower agreement in relative variability (coefficient of variation of canopy heights RMSD% = 33.9%, r = 0.45). Herein we propose the use of the vegetation fill index as a method to estimate canopy cover with ICESat-2. Comparison of SPL100 and ICESat-2 vegetation fill indices at the AU level resulted in strong agreement overall (RMSD% = 19.7%; r = 0.57). These observations and results contribute to the overall objective of building a comprehensive understanding of the performance of ICESat-2 for characterizing vegetation structure in boreal forest environments.

A semi-empirical scheme for bathymetric mapping in shallow water by ICESat-2 and Sentinel-2: A case study in the South China Sea

Enhancing Large-Area DEM modeling of GF-7 stereo imagery: Integrating ICESat-2 data with Multi-characteristic constraint filtering and terrain matching correction