Land Elevation Satellite Research Articles

Mapping the Forest Height by Fusion of ICESat-2 and Multi-Source Remote Sensing Imagery and Topographic Information: A Case Study in Jiangxi Province, China

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

Detection and Attribution of Alpine Inland Lake Changes by Using Random Forest Algorithm

The alpine inland lake dynamics have been good indicators of changes in terrestrial hydrological cycles under global climate change. However, the relationship between alpine inland lake and climatic factors remained largely uncertain. This study examines the spatial-temporal change of the fluctuation of the lake by using dense time series Landsat TM/ETM/OLI images to delineate water boundary information based on the Random Forest algorithm and using ICESat (Ice, Cloud and land Elevation Satellite) dataset to monitor changes in variations of water level. Variations of Qinghai Lake (QHL) were analyzed from 1987 to 2020 and the mechanism of these changes was discussed with meteorological data. The results indicated that the QHL fluctuated strongly showing a pattern of shrinkage–expansion over the last three decades. The lake storage significantly decreased by −2.58 × 108 m3·yr−1 (R2 = 0.86, p < 0.01) from 1989 to 2004 and sharply increased (6.92 × 108 m3·yr−1, R2 = 0.92, p < 0.01) after 2004. The relationship between the lake and climate over the last 30 years implies that the decreasing evaporation and increasing precipitation were the major factors affecting the fluctuation of lake storage. Meanwhile, the temporal heterogeneity of the driving mechanism of climate change led to the phased characteristics of lake storage change. In detail, obvious warming led to the shrinkage of the QHL before 2004 through increasing evaporation, while humidifying and accelerating wind stilling dominated the expansion of the QHL after 2004 by increasing precipitation and decreasing evaporation. This paper indicated that the frameworks of multi-source remote sensing and accurate detection of water bodies were required to protect the high-altitude lakes from further climate changes based on the findings of this paper of the QHL recently. The framework presented herein can provide accurate detection and monitoring of water bodies in different locations in the Qinghai-Tibet Plateau, and provide a necessary basis for future political activities and decisions in terms of sustainable water resource management.

Evaluating Greenland surface-mass-balance and firn-densification data using ICESat-2 altimetry

Abstract. Surface-mass-balance (SMB) and firn-densification (FD) models are widely used in altimetry studies as a tool to separate atmospheric-driven from ice-dynamics-driven ice-sheet mass changes and to partition observed volume changes into ice-mass changes and firn-air-content changes. Until now, SMB models have been principally validated based on comparison with ice core and weather station data or comparison with widely separated flight radar-survey flight lines. Firn-densification models have been primarily validated based on their ability to match net densification over decades, as recorded in firn cores, and the short-term time-dependent component of densification has rarely been evaluated at all. The advent of systematic ice-sheet-wide repeated ice-surface-height measurements from ICESat-2 (the Ice Cloud, and land Elevation Satellite, 2) allows us to measure the net surface-height change of the Greenland ice sheet at quarterly resolution and compare the measured surface-height differences directly with those predicted by three FD–SMB models: MARv3.5.11 (Modèle Atmosphérique Régional version 3.5.11) and GSFCv1.1 and GSFCv1.2 (the Goddard Space Flight Center FD–SMB models version 1.1 and 1.2). By segregating the data by season and elevation, and based on the timing and magnitude of modelled processes in areas where we expect minimal ice-dynamics-driven height changes, we investigate the models' accuracy in predicting atmospherically driven height changes. We find that while all three models do well in predicting the large seasonal changes in the low-elevation parts of the ice sheet where melt rates are highest, two of the models (MARv3.5.11 and GSFCv1.1) systematically overpredict, by around a factor of 2, the magnitude of height changes in the high-elevation parts of the ice sheet, particularly those associated with melt events. This overprediction seems to be associated with the melt sensitivity of the models in the high-elevation part of the ice sheet. The third model, GSFCv1.2, which has an updated high-elevation melt parameterization, avoids this overprediction.

Satellite-Derived Bathymetry with Sediment Classification Using ICESat-2 and Multispectral Imagery: Case Studies in the South China Sea and Australia

Achieving coastal and shallow-water bathymetry is essential for understanding the marine environment and for coastal management. Bathymetric data in shallow sea areas can currently be obtained using SDB (satellite-derived bathymetry) with multispectral satellites based on depth inversion models. In situ bathymetric data are crucial for validating empirical models but are currently limited in remote and unapproachable areas. In this paper, instead of using the measured water depth data, ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) ATL03 bathymetric points at different acquisition dates and multispectral imagery from Sentinel-2/GeoEye-1 were used to train and evaluate water depth inversion empirical models in two study regions: Shanhu Island in the South China Sea, and Heron Island in the Great Barrier Reef (GBR) in Australia. However, different sediment types also influenced the SDB results. Therefore, three types of sediments (sand, reef, and coral/algae) were analyzed for Heron Island, and four types of sediments (sand, reef, rubble and coral/algae) were analyzed for Shanhu Island. The results show that accuracy generally improved when sediment classification information was considered in both study areas. For Heron Island, the sand sediments showed the best performance in both models compared to the other sediments, with mean R2 and RMSE values of 0.90 and 1.52 m, respectively, representing a 5.6% improvement of the latter metric. For Shanhu Island, the rubble sediments showed the best accuracy in both models, and the average R2 and RMSE values were 0.97 and 0.65 m, respectively, indicating an RMSE improvement of 15.5%. Finally, bathymetric maps were generated in two regions based on the sediment classification results.

Combining ICESat-2 photons and Google Earth Satellite images for building height extraction

Building heights are one of the crucial data for comprehending the functions of urban systems. Employing optical remote sensing imagery, the shadow-based method is one of the most promising methods which have been proposed for estimating building height. However, the existing shadow-based studies for building height estimation are restricted to a small area due to the lack of building height annotations and ignorance of building azimuth variations. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) allows large-scale building height retrieval in the along-track direction and thus can be taken as ground truth building height annotations to support the shadow-based algorithms for large-scale building height extraction. Here, we proposed an approach for extracting building height by combining Google Earth Satellite (GES) images and ICESat-2 photons. Building and shadow instances were first extracted using a U-Net deep learning framework. Based on the building height annotations retrieved from ICESat-2 photons, an improved shadow-based building height estimation model by minimizing the global error across all sample buildings was developed. A typical urban area located in the city center of Shanghai, China with an area of around 90 km2 was selected to validate the proposed method. In total 15,966 buildings were successfully extracted and the results indicated that the estimated building heights have high accuracy with an absolute mean error of 4.08 m. Moreover, the proposed method shows a better performance compared to the existing shadow-based method and existing building height datasets. The method holds great potential for large-scale building-level height retrieval which contributes to further studies of urban morphologies.

A noise removal algorithm based on adaptive elevation difference thresholding for ICESat-2 photon-counting data

Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has shown great potential for near-shore bathymetry. Unavoidably, the photon data acquired has a lot of noise. In order to remove noise more accurately, a photon denoising algorithm based on adaptive elevation difference thresholding (AEDTA) is proposed under the assumption that the terrain is continuously changing. The algorithm uses a Gaussian function obtained by fitting an elevation histogram to ICESat-2 data for the extraction of underwater photons. Repeatedly count the elevation difference histogram of underwater photons and adaptively determine the threshold. And the final denoised underwater signal photons are obtained. Compared with the conventional clustering method of ordering points to identify the clustering structure (OPTICS), our algorithm achieves better photon detection results for underwater topography for different densities of ICESat-2 data and has the advantage of parameter adaption without human intervention. ICESat-2 data from Ganquan Island, Dongdao Island and Oahu Island were selected for testing, and the ICESat-2 bathymetry results calculated by the algorithm were compared with in situ bathymetry data to assess the reliability and accuracy of the proposed denoising algorithm. The results show that the MAE of the bathymetry results calculated using the ICESat-2 signal photon data extracted by the method ranged from 0.54 to 1.57 m and the RMSE ranged from 0.64 to 1.74 m in different seas. The algorithm can provide high quality denoised data for bathymetric mapping around coastal areas and islands. Meanwhile, the AEDTA algorithm also has a good denoising effect on terrestrial photons, and the effect of cloud shading on the Gaussian fit analysis of ICESat-2 data cannot be ignored.

Densifying and Optimizing the Water Level Series for Large Lakes from Multi-Orbit ICESat-2 Observations

Satellite laser altimetry has been widely used for monitoring surface height changes in inland waters. However, constructing time series of water levels is partially limited in temporal resolution only based on the individual orbit of altimeter observations. To densify and optimize the time series of altimetry-based water levels is crucial to the scientific understanding of lake hydrologic dynamics. This paper focuses on synthesizing the multi-orbit on-lake observations from the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) to densify and refine the water level time series for large lakes. The approach of synthesizing water level time series has been validated through experiments applied to 18 large lakes worldwide, resulting in an average R of 0.93, RMSE of 0.14 m, MAE of 0.12 m, NSE of 0.67, and CV of 2.86, according to the hydrologic gauge stations. The evaluation results demonstrate that our approach can provide an effective solution for densifying the water level series of large lakes covered by multi-orbit ICESat-2 observations. Further, the approach can be extended to monitor the high-frequency variation of other lakes covered by the multiple ICESat-2 orbits. This approach provides the potential of generating higher-frequency estimates of water levels based on satellite altimetry, which could not only help to reveal the characteristics of the seasonal dynamics of lakes but also be used to investigate the abrupt water level changes due to hydrological extreme events (e.g., floods, droughts, etc.).

Synthesis of the ICESat/ICESat-2 and CryoSat-2 observations to reconstruct time series of lake level

ABSTRACT Synthesis of multi-satellite altimetry facilitates the acquisition of long-term changes in lake level but may induce biases due to inconsistent data sources, and thus remains largely unexplored. This study investigates the integrated application of Ice, Cloud, and land Elevation Satellite (ICESat), ICESat-2, and CryoSat-2 missions to provide consecutive lake level series in 2003–2020. The sample comprises 48 lakes in the world with gauge-based or Hydroweb water level data. The CryoSat-2 data are first adjusted to the ICESat-2 height reference based on the mean values of their monthly water level difference during their overlapping period (2018–2020). Then, the corrected CryoSat-2 data are used to link the ICESat and ICESat-2 data. Results show that in the sample lakes, the deviations between CryoSat-2 and ICESat-2 data vary and range from −0.78 m to 0.13 m. The data quality of the synthesized time series is evaluated by comparing against the validation data, with an average R 2 of 0.84. This study shows that CryoSat-2 has the potential of filling the gap between ICESat and ICESat-2. The three altimeters can be expected to integrate effectively for monitoring lake water level changes in the past two decades.

ICESat-2 Bathymetric Signal Reconstruction Method Based on a Deep Learning Model with Active–Passive Data Fusion

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.

Automatic land-sea classification in a nearshore environment using satellite-based photon-counting LiDAR data.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) can measure the global surface with unprecedented resolution. Accurate classification of land and sea data is the prerequisite for generating high-quality data products. Current land-sea classification methods rely on assisted data or manual participation, and the automation degree cannot meet the needs of massive data processing. Therefore, using the land-sea difference of photon-counting LiDAR data, an index called normalized photon rate-elevation ratio (NPRER) is designed. Inspired by this, an automatic land-sea classification method is proposed, and the results are obtained through preliminary classification, reclassification, and post-processing enhancement. The results in Cook Inlet, Alaska, show that NPRER can measure the probability of sea appearance in the nearshore environment. At the same time, the automatic classification method can achieve an overall accuracy of 97.98%. The changes in the coastal type, data collection time, and classification feature sets have little influence on this method. Therefore, the method provides a reliable technical scheme for improving the automation of land-sea classification of satellite-based photon-counting LiDAR data.

An Automatic Algorithm to Extract Nearshore Bathymetric Photons Using Pre-Pruning Quadtree Isolation for ICESat-2 Data

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) equips with a novel photon-counting LiDAR system, which can generate underwater reflections in nearshore environments. However, due to the water reflection, scattering, and absorption, the distribution of bathymetric photons in the nearshore data varies with depth. The existing bathymetric photon extraction algorithms need more adaptability to seafloor topography. The changing density of bathymetric photons and the fluctuation of underwater topography make the noise removal of nearshore data full of challenges. This study proposed a bathymetric photon extraction algorithm using pre-pruning quadtree isolation (PQI). Firstly, the pre-pruning step judges whether to stop the growth of quadtree in advance during quadtree isolation (QI) to avoid excessive division of noise photons. Secondly, the maximum inter-class variance algorithm (also called the Otsu method) obtains the best threshold of isolation depth and extracts bathymetric photons. The algorithm was tested on the Florida coast. The results show that the PQI algorithm can wholly and accurately extract bathymetric photons with different acquisition times from the data. The F1-score of the extracted results is 93.96%. This study provides an intelligent solution to processing bathymetric data in nearshore environments worldwide.

Exploring Photon-Counting Laser Altimeter ICESat-2 in Retrieving LAI and Correcting Clumping Effect

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) employs a unique multibeam photon counting approach to acquire a near-continuously sampled profile and provides more precise technology for mapping the leaf area index (LAI) at the global scale. The inversion accuracy of LAI is affected by the clumping effect, which has been an open question for spaceborne laser scanning (SLS). Here, we present a segmented method based on the path length distribution model to calculate the clumping-corrected LAI independently using ICESat-2 data. The results showed that the LAI derived by the proposed method with a 200 m segment was consistent with the airborne laser scanning (ALS)-derived LAI, with a root mean squared error (RMSE) of 0.37. A satisfactory agreement (RMSE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=1.03$ </tex-math></inline-formula> ) was also shown between moderate resolution imaging spectroradiometer (MODIS) LAI and ICESat-2 LAI. Moreover, the LAI derived by the proposed method was on average 31.72% higher than the LAIe derived by Beer’s law, which indicated that the proposed method achieved the purpose of correcting the clumping effect. The gap probability was calculated by the 200 m moving window and the path length distribution was obtained by the 1 m moving window as the model input had the highest accuracy. In addition, the limitation of the point cloud data and the time lag of ICESat-2 acquisitions and ALS observations may affect the inversion accuracy of LAI. This study proposed a feasible way to correct the clumping effect and invert LAI independently using ICESat-2 data, which has the potential to characterize vegetation structure precisely at regional and global scales.

Adaptive Clustering-Based Method for ICESat-2 Sea Ice Retrieval

The great potential of NASA’s Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) to retrieve sea ice heights has been demonstrated. However, the presence of a significant number of noise photons in the ICESat-2 data makes accurate monitoring of sea ice changes challenging. This paper proposes an adaptive clustering and kernel density estimation-based (AC-KDE) method for estimating sea ice heights in ICESat-2 photon clouds. First, the adaptive clustering method effectively detects sea ice signal photons. The method’s input parameters are determined based on the ATLAS parameters and the LiDAR transmission equation. Then, the adaptive-count signal photon aggregates are used to estimate sea ice heights, and a variable along-track resolution is obtained using the kernel density estimation method. The AC-KDE method is applied to the MABEL and ICESat-2 data, and we compare it with other denoising algorithms, including the HBM, DBSCAN, OPTICS, UMD_RDA, DDM, and ILSM algorithms. The results indicate that the proposed method outperforms these algorithms in extracting signal photons with higher accuracy scores and F-scores, which are 0.97 & 0.97, 0.92 & 0.90, and 0.89 & 0.72 under high-medium-low signal-to-noise ratio conditions, respectively. Additionally, the retrieved sea ice heights are compared with the ATL07 heights. The AC-KDE heights show a significant correlation with coincident ATM heights, and have a lower RMSE value (0.066 m) compared to ATL07 heights (0.104 m). The AC-KDE method also demonstrates a vertical height precision of 0.01 m over flat leads. The proposed method can effectively extract signal photons and accurately estimate sea ice heights in polar regions.

A Density and Distance-Based Method for ICESat-2 Photon-Counting Data Denoising

The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) dynamically monitors water depth in shallow waters around islands and reefs. Noise removal is a prerequisite for accurate reconstruction of seafloor topography based on ICESat-2 data products. To this end, we propose a Density and Distance-Based Method (DDBM) to extract seafloor signal photons from ICESat-2 data. DDBM first separates the photons into three parts: above-water, water surface and water-column. The water-column photons consist of seafloor signal photons and noise photons. DDBM adopts a two-step denoising strategy to remove noise in water-column photons to obtain pure and complete signal photons. In the first step, an orientation-variable adaptive ellipse filter is developed, which can adaptively adjust the parameters according to the water depth to remove low-density noise photons. In the second step, a novel Distance-Based Filter (DBF) is designed for stubborn high-density noise clusters. These noise clusters are far from the signal photons, and the DBF removes them by a distance threshold derived from the Otsu threshold method. We select three high-density ICESat-2 datasets to validate DDBM. Compared with the reference data, the comprehensive evaluation indexes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> of DDBM in all datasets are above 0.99 and the highest is 0.998, showing superior performance.

Machine-Learning-Method-Based Inversion of Shallow Bathymetric Maps Using ICESat-2 ATL03 Data

The application of empirical methods for satellite-derived bathymetry is limited by the lack of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> bathymetric data in remote, inaccessible areas. This challenge has been addressed with the launch of ICESat-2 (Ice, Cloud, and land Elevation Satellite-2). This study provides an accurate bathymetric photon extraction process for ICESat-2 ATL03 data, and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value of the bathymetric photons obtained using this process and airborne bathymetric LiDAR data is up to 99%. Next, based on two types of remote sensing data, ICESat-2 and Sentinel-2, machine learning (ML) models, including linear regression (LR), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), were trained to obtain bathymetric maps. The experimental results show that the mean RMSE (root mean square error), mean MAE (mean absolute error), and mean MRE (mean relative error) values of the LR models are less than 3.02 m, 2.38 m, and 86.03%, respectively. The mean RMSE, MAE, and MRE values of the LightGBM and CatBoost models are less than 0.91 m, 0.66 m, and 23.17%, respectively. It is concluded that the proposed denoising process for ICESat-2 ATL03 data is effective, and the results of the bathymetric maps obtained using these data are satisfactory. Thus, the proposed approach is effective, and this strategy can be used to replace conventional bathymetric inversion methods to obtain high-accuracy bathymetric maps.

Detection of Signal and Ground Photons From ICESat-2 ATL03 Data

The Advanced Topographic Laser Altimeter System (ATLAS) laser altimeter aboard the Ice, Cloud, and Land Elevation Satellite (ICESat-2) can measure the elevation of the Earth’s surface with unprecedented spatial detail. However, the quality of the derived signal and ground photons depends on the signal-to-noise ratio and canopy coverage. Current algorithms underperform for data collected during daytime over mountain areas with dense canopy. We demonstrate a novel procedure for signal photon detection and subsequent ground photon detection from ICESat-2 ATL03 data. We first introduce a gravity-based density model to characterize the anisotropic properties of photon distribution. Through jointly using the photon densities from the weak–strong beam pair, we are able to find key photons that have high probability being signals. A directional regional growing approach then takes these key photons as seeds to label all remaining signal photons. Finally, we introduce a weighted iterative median filter (WIMF) algorithm to identify ground photons whose height is closest to the estimated ground surface. A total of 36 ATL03 beams of two entire counties in USA are used for test and evaluation. Compared to the ATL03 and ATL08 algorithms, our signal photon finding method is more robust to the variation of topography, canopy coverage, and data collection time. Remarkably, the mislabeling caused by the after-pulsing effect does not present in our detected signal photons. Comparing current ATL03 and ATL08 products, the detected ground photons from our method are more consistent with reference to the 3DEP DEM, especially for strong beam data collected during daytime in dense canopy, high relief areas.

Automated Detection and Depth Determination of Melt Ponds on Sea Ice in ICESat-2 ATLAS Data—The Density-Dimension Algorithm for Bifurcating Sea-Ice Reflectors (DDA-Bifurcate-Seaice)

As climate warms and the transition from a perennial to a seasonal Arctic sea-ice cover is imminent, understanding melt ponding is central to understanding changes in the new Arctic. NASA’s Ice, Cloud and land Elevation Satellite (ICESat-2) has the capacity to provide measurements and monitoring of the onset of melt in the Arctic and on melt progression. Yet ponds are currently not identified on the ICESat-2 standard sea-ice products, in which only a single surface is determined. The objective of this paper is to introduce a mathematical algorithm that facilitates automated detection of melt ponds in ICESat-2 ATLAS data, retrieval of two surface heights, pond surface and bottom, and measurements of depth and width of melt ponds. With the Advanced Topographic Laser Altimeter System (ATLAS), ICESat-2 carries the first space-borne multi-beam micro-pulse photon-counting laser altimeter system, operating at 532 nm frequency. ATLAS data are recorded as clouds of discrete photon points. The Density-Dimension Algorithm for bifurcating sea-ice reflectors (DDA-bifurcate-seaice) is an auto-adaptive algorithm that solves the problem of pond detection near the 0.7 m nominal along-track spacing of ATLAS data, utilizing the radial basis function for calculation of a density field and a threshold function that automatically adapts to changes in background, apparent surface reflectance and some instrument effects. The DDA-bifurcate-seaice is applied to large ICESat-2 data sets from the 2019 and 2020 melt seasons in the multi-year Arctic sea-ice region. Results are evaluated by comparison to those from a manually forced algorithm.

Forest Canopy Height Extraction Method Based on ICESat-2/ATLAS Data

Ice, cloud, and land elevation satellite (ICESat-2)/Advanced Topographic Laser Altimeter System (ATLAS) multibeam micropulse photoncounting light detection and ranging (LiDAR) can be effectively applied to extract forest canopy height. However, the ICESat-2/ATLAS photon point cloud interfered with the signal-to-noise ratio (SNR), fraction vegetation coverage (FVC), and terrain slope. The main challenge of this research is to extract high-precision canopy heights. Therefore, this article improves the canopy height extraction method based on the ICESat-2/ATL08 theoretical algorithm. First, an adaptive filter, Threshold Segmentation based on Spatial Clustering and Bimodal Reconstruction (TS-SCABR), is proposed, which can adapt to different SNR scenarios. Then, combined with the gradient method, the discontinuous data are detrended in sections to eliminate the edge mutation problem of the detrended data. Based on the detrended data, the iterative filtering algorithm of the local terrain is employed to fit the ground curve, and the mutation detection and empirical mode decomposition (EMD)-digital smoothing polynomial (DISPO) filtering remove the pseudoground photons to identify the data of ground and nonground photons accurately. Finally, the percentile statistics method is utilized to extract the canopy-top photons from the nonground photons according to their elevation difference. The results indicate that, under different natural conditions, the improved algorithm has better adaptability than the previous algorithm. Compared with the original ATL08 ATBD algorithm, the canopy height accuracy is significantly improved, especially in low FVC and high slope scenarios. When the FVC is lower than 25%, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{2}$ </tex-math></inline-formula> increases by 50.3%, and the root mean square error (RMSE) is reduced by 2.175 m, and when the slope is higher than 45°, it increases by 41.7%, and the RMSE is reduced by 2.159 m. Therefore, the algorithm has apparent advantages in inverting the canopy height in a mountainous environment with lush forests.

Improved terrain estimation from spaceborne lidar in tropical peatlands using spatial filtering

Tropical peatlands are estimated to hold carbon stocks of 70 Pg C or more as partly decomposed organic matter, or peat. Peat may accumulate over thousands of years into gently mounded deposits called peat domes with a relief of several meters over distances of kilometers. The mounded shapes of tropical peat domes account for much of the carbon storage in these landscapes, but their subtle topographic relief is difficult to measure. As many of the world's tropical peatlands are remote and inaccessible, spaceborne laser altimetry data from missions such as NASA's Global Ecosystem Dynamics Investigation (GEDI) on the International Space Station (ISS) and the Advanced Topographic Laser Altimeter System (ATLAS) instrument on the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory could help to describe these deposits. We evaluate retrieval of ground elevations derived from GEDI waveform data, as well as single-photon data from ATLAS, with reference to an airborne lidar dataset covering an area of over 300 km2 in the Belait District of Brunei Darussalam on the island of Borneo. Spatial filtering of GEDI L2A version 2, algorithm 1 quality data reduced mean absolute deviations from airborne-lidar-derived ground elevations from 8.35 m to 1.83 m, root-mean-squared error from 15.98 m to 1.97 m, and unbiased root-mean-squared error from 13.62 m to 0.72 m. Similarly, spatial filtering of ATLAS ATL08 version 3 ground photons from strong beams at night reduced mean absolute deviations from 1.51 m to 0.64 m, root-mean-squared error from 3.85 m to 0.77 m, and unbiased root-mean-squared error from 3.54 m to 0.44 m. We conclude that despite sparse ground retrievals, these spaceborne platforms can provide useful data for tropical peatland surface altimetry if postprocessed with a spatial filter.

Inversion of Deflection of the Vertical in the South China Sea Using ICESat-2 Sea Surface Height Data

The traditional altimetry satellites based on pulse-limited radar altimeter only calculate along-track deflection of the vertical (DOV), which results in poorer precision of the prime vertical component than that of the meridian component and limits the precision of the marine gravity field inversion. We expect an improvement in the higher precision prime vertical component using the Ice, Cloud and land Elevation Satellite 2 (ICESat-2) sea surface height (SSH) data. In this paper, the 2′ × 2′ gridded DOVs derived from along-beam DOVs, cross-beam DOVs, and joint along-cross beam DOVs in the South China Sea (SCS; 0°–23°N, 103°–120°E) are calculated with the weighted least squares method, respectively. The inverse Vening–Meinesz (IVM) formula is applied to derive 2′ × 2′ gravity anomalies over the SCS from ICESat-2-derived gridded DOVs. In addition, the XGM2019e_2159-DOV and SIO V31.1-DOV models are used to assess the precision of the gridded DOVs. The XGM2019e_2159-GRA, SIO V31.1-GRA models, and ship-borne gravity anomalies are also adopted to evaluate the quality of gravity anomalies. The results show that the gridded DOVs calculated by the joint along-cross beam DOVs have the highest precision among the three gridded DOVs determined by ICESat-2. The precision of difference between gravity anomalies derived from the joint along-cross beam DOV and the above verification data are higher than those derived from the along-beam and cross-beam DOVs. We conclude that the joint along-cross beam DOV can effectively improve the precision of the gridded DOV, which is conducive to the inversion of a high-precision marine gravity field.