Ice, Cloud, And Land Elevation Satellite-2 Data Research Articles

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.

Landsat-Scale Regional Forest Canopy Height Mapping Using ICESat-2 Along-Track Heights: Case Study of Eastern Texas

Spaceborne profiling lidar missions such as the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) are collecting direct elevation measurements, supporting the retrieval of vegetation attributes such as canopy height that are crucial in forest carbon and ecological studies. However, such profiling lidar systems collect observations along predefined ground tracks which limit the spatially complete mapping of forest canopy height. We demonstrate that the fusion of ICESat-2 along-track canopy height estimates and ancillary Landsat and LANDFIRE (Landscape Fire and Resource Management Planning Tools Project) data can enable the generation of spatially complete canopy height data at a regional level in the United States. We developed gradient-boosted regression models relating canopy heights with ancillary data values and used them to predict canopy height in unobserved locations at a 30 m spatial resolution. Model performance varied (R2 = 0.44 − 0.50, MAE = 2.61–2.80 m) when individual (per month) Landsat data and LANDFIRE data were used. Improved performance was observed when combined Landsat and LANDFIRE data were used (R2 = 0.69, MAE = 2.09 m). We produced a gridded canopy height product over our study area in eastern Texas, which agreed moderately (R2 = 0.46, MAE = 4.38 m) with independent airborne lidar-derived canopy heights. Further, we conducted a comparative assessment with the Global Forest Canopy Height product, an existing 30 m spatial resolution canopy height product generated using GEDI (Global Ecosystem Dynamics Investigation) canopy height and multitemporal Landsat data. In general, our product showed better agreement with airborne lidar heights than the global dataset (R2 = 0.19 MAE = 5.83 m). Major differences in canopy height values between the two products are attributed to land cover changes, height metrics used (98th in this study vs 95th percentile), and the inherent differences in lidar sampling and their geolocation uncertainties between ICESat-2 and GEDI. On the whole, our integration of ICESat-2 data with ancillary datasets was effective for spatially complete canopy height mapping. For better modeling performance, we recommend the careful selection of ICESat-2 datasets to remove erroneous data and applying a series of Landsat data to account for phenological changes. The canopy height product provides a valuable spatially detailed and synoptic view of canopy heights over the study area, which would support various forestry and ecological assessments at an enhanced 30 Landsat spatial resolution.

A novel bathymetry signal photon extraction algorithm for photon-counting LiDAR based on adaptive elliptical neighborhood

Ice Cloud and Land Elevation Satellite-2 (ICESat-2) uses a 532-nm laser with a strong seawater penetration ability, and can provide reliable water depth information for sea areas that in situ bathymetric data are lacking. However, because of the complexity of underwater terrain, background noise, optical signal attenuation in water, and other factors, there are still some obstacles to accurately extract bathymetry photons using ICESat-2 data This paper proposes an novel bathymetry signal extraction algorithm based on an adaptive elliptical neighborhood window. The direction-adaptive elliptical neighborhood strategy ensures the directionality and continuity of signal extraction. The size-adaptive neighborhood strategy satisfies the demand for extraction of both shallow, dense signals and deep, sparse signals. The kernel density estimation (KDE) based strategy for sea surface elevation extraction and above-water signal separation makes it possible to determine the sea surface elevation accurately while correctly separating the above-water photons. Four study areas with different environmental conditions in the Xisha Islands, the Bahamas, and the Gulf of Aden were selected, and the key results are as follows. (1) For a water-land interface area with dense noise, the proposed algorithm can effectively reduce the impact of land and accurately eliminate noise photons in shallow water. (2) For an extremely sparse signal in a deep-water area, which is difficult to process using classical algorithms, the proposed algorithm can accurately extract signal photons in water approximately 50 m deep while effectively extracting the dense signal photons in shallow water. (3) In the extraction results obtained using this algorithm, the signal strip has better continuity and smoothness than the classical algorithm and therefore can portray the seafloor terrain more completely. As the in situ water depth confirms, the overall mean absolute error (MAE) and mean relative error (MRE) of the water depth information extracted by the proposed algorithm are 0.79 m and 7.54%, respectively, which indicate high reliability.

Extraction of high-accuracy control points using ICESat-2 ATL03 in urban areas

• A method of extracting high-accuracy control points from ATL03 in urban areas is proposed. • Extract and classify building and ground photons in different urban areas. • The elevation and plane accuracy of control points are evaluated. • ICESat-2 ATL03 photons in urban areas can act as control points. • The land cover in urban areas has significant impact on the accuracy of control points. The ice, cloud, and land elevation satellite-2 (ICESat-2) can effectively measure global surface elevations and be used as a new data source for global elevation control. ICESat-2 has provided surface-specific data products on land/vegetation (ATL08), ice sheet (ATL06), inland water (ATL13), etc., but not on urban areas. The current research using ICESat-2 data to extract control points mostly based on products already provided, focusing on land, forest and other areas. Few studies have analyzed the feasibility of extracting control points in urban areas, as well as the position and elevation accuracy of control points. In this paper, we proposed a method for extracting high-accuracy control points from ATL03 in urban areas and evaluated the elevation and plane accuracy. First, building and ground photons were extracted and classified according to the photon shape features and neighborhood relationships, thereby addressing the insufficiency of ICESat-2 of not classifying photons in urban areas. Then, high-accuracy photons were selected from the ground photons as the laser control points considering the influence of land cover. Finally, the solid and undulating city outline was used for shape matching with DSM, thereby addressing the problem of plane accuracy evaluation in flat areas. We verified our method in two areas (Nanjing and Auckland) and showed that the method can adequately classify buildings and ground photons in different scenarios. The plane accuracy was better than 3.8 m, which lies well within ICESat-2′s mission requirement, with an elevation accuracy of better than 0.5 m, which meets the accuracy requirements of large-scale mapping, suggesting that photons in urban areas can also provide high-accuracy control data for stereo mapping.

Shallow Water Bathymetry Mapping from ICESat-2 and Sentinel-2 Based on BP Neural Network Model

Accurate shallow water bathymetry data are essential for coastal construction and management, marine traffic, and shipping. With the development of remote sensing satellites and sensors, the satellite-derived bathymetry (SDB) method has been widely used for bathymetry in shallow water areas. However, traditional satellite bathymetry requires in-situ bathymetric data. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with the advanced high-resolution topographic laser altimeter system (ATLAS) provides a new technical tool and makes up for the shortcomings of traditional bathymetric methods in shallow waters. In this study, a new method is proposed to automatically detect photons reflected from the shallow seafloor with ICESat-2 altimetry data. Two satellite bathymetry models were trained, to obtain shallow water depth from Sentinel-2 satellite images. First, sea surface and seafloor signal photons from ICESat-2 were detected in the Oahu (in the U.S. Hawaiian Islands) and St. Thomas (in the U.S. Virgin Islands) sampling areas, to obtain water depths along the surface track. The results show that the RMSE is between 0.35 and 0.71 m and the R2 is greater than 0.92, when compared to the airborne LiDAR bathymetry (ALB) data in the field. Second, the ICESat-2 bathymetric points from Oahu Island are used to train the Back Propagation (BP) neural network model and obtain the SDB. The RMSE is between 0.97 and 1.43 m and the R2 is between 0.90 and 0.96, which are better than the multi-band ratio model with RMSE of 1.03–1.57 m and R2 of 0.89–0.95. The results show that the BP neural network model can effectively improve bathymetric accuracy, when compared to the traditional multi-band ratio model. This approach can obtain shallow water bathymetry more easily, without the in-situ bathymetric data. Therefore, it extends to a greater extent with the free ICESat-2 and Sentinel-2 satellite data for bathymetry in shallow water areas, such as coastal, island and inland water bodies.

Biases Analysis and Calibration of ICESat-2/ATLAS Data Based on Crossover Adjustment Method

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.

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.

Estimating aboveground carbon stocks of urban trees by synergizing ICESat-2 LiDAR with GF-2 data

Accurately mapping carbon stocks of urban trees is necessary for urban managers to design strategies to mitigate climate change. However, the aboveground carbon stocks of urban trees are usually underestimated by passive remote sensing data because of the signal saturation problem. The research is the first attempt to develop a framework to map aboveground carbon density of trees in urban areas by synergizing Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) LiDAR data with Gaofen-2 (GF-2) imagery. The framework consists of three key steps. First, we used a support vector machine classifier to classify GF-2 images and extracted urban tree regions. Second, we estimated the tree carbon density of ICESat-2 strips by developing a ICESat-2 photon feature-based aboveground carbon density estimation model. Third, we mapped the carbon density of urban trees by developing a synergistic model between ICESat-2 and GF-2 data based on an object-oriented method. We tested the approach for the areas within the fifth ring road of Beijing, China. The results showed that the 50th percentile height (PH50) of nighttime photons was a good predictor for estimating carbon density of urban trees, with a R2 of 0.69 and a Root Mean Square Error (RMSE) of 2.81 kg C m−2. Using the spectral features generated by GF-2 imagery, we could further extrapolate the carbon density estimated by ICESat-2 strip data to a full coverage of accurate mapping carbon density by urban trees, resulting in a R2 of 0.64 and a RMSE of 2.32 kg C m−2. The carbon stocks within the fifth ring road of Beijing were 8.28 × 108 kg in total, with the mean carbon density of 3.52 kg C m−2. Such estimations were larger than that of previous study using passive remote sensing data only, suggesting the integration of spaceborne LiDAR and spectral data could greatly reduce the underestimation of carbon stocks of urban trees. Our approach can more accurately estimate carbon stocks of urban trees and has the potential to be applicable in other cities.

Deriving vertical profiles of chlorophyll-a concentration in the upper layer of seawaters using ICESat-2 photon-counting lidar.

Chlorophyll-a concentration (chl-a) is a great indicator for estimating phytoplankton biomass and productivity levels and is also particularly useful for monitoring the water quality, biodiversity and species distribution, and harmful algal blooms. A great deal of studies investigated to estimate chl-a concentrations using ocean color remotely sensed data. With the development of photon-counting sensors, spaceborne photon-counting lidar can compensate for the shortcomings of passive optical remote sensing by enabling ocean vertical profiling in low-light conditions (e.g., at night). Using geolocated photons captured by the first spaceborne photon-counting lidar borne on ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2), this research reported methods for deriving vertical profiles of chl-a concentration in the upper layer of ocean waters. This study first calculates the average numbers of backscattered subaqueous photons of ICESat-2 at different water depths, and then estimates the optical parameters in water column based on a discrete theoretical model of the expected number of received signal photons. With the estimated optical parameters, vertical profiles of chl-a concentration are calculated by two different empirical algorithms. In two study areas (mostly with Type I open ocean waters and small part of Type II coastal ocean waters), the derived chl-a concentrations are generally consistent when validated by BGC-Argo (Biogeochemical Argo) data in the vertical direction (MAPEs<15%) and compared with MODIS (Moderate Resolution Imaging Spectroradiometer) data in the along-track direction (average R2>0.86). Using globally covered ICESat-2 data, this approach can be used to obtain vertical profiles of chl-a concentration and optical parameters at a larger scale, which will be helpful to analyze impact factors of climate change and human activities on subsurface phytoplankton species and their growth state.

Estimation of biomass burning emissions by integrating ICESat-2, Landsat 8, and Sentinel-1 data

Anthropogenic carbon emissions directly contribute to global warming, which has induced severe environmental concerns like extreme droughts and devastating fires. To evaluate the effects of fires on carbon cycling and climate change, it is crucial to accurately estimate the amount of carbon released during fires. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission offers spaceborne light detection and ranging (LiDAR) measurements of global forests, providing excellent opportunities for monitoring forest dynamics and carbon emissions. The ICESat-2 Land and vegetation height product (ATL08), Landsat 8 data, and Sentinel-1 data were integrated to estimate biomass burning emissions with a three-phase hierarchy model. In Phase 1, the least absolute shrinkage and selection operator (LASSO) regression was used to establish the relationship between ATL08 LiDAR metrics and reference aboveground biomass (AGB), R2 0.77 and RMSE 56.67 Mg ha−1. In Phase 2, the LASSO regression predicted AGB for all ATL08 segments. A Random Forest Regression model was trained with Landsat 8 reflectance data and derived vegetation indices, Sentinel-1 data, and terrain variables as predictors and with the LASSO predicted AGB as the response variable, R2 0.71 and RMSE 45.91 Mg ha−1. Wall-to-wall maps of pre-fire and post-fire AGB were produced with the Random Forest model. In Phase 3, the difference between the pre-fire and the post-fire AGB was converted to carbon emissions. The estimated biomass burning emissions of the 2018 Carr fire group, the 2018 Camp fire, and the 2019 Walker fire in California are 3.54 (± 0.067) Tg C, 1.66 (± 0.041) Tg C, and 0.45 (± 0.022) Tg C, respectively. Forests have the highest biomass burning emissions with an average emission of 30.09 Mg ha−1, approximately twice and four times the average emissions from shrubs and grasses. This study highlights the merit of integrating spaceborne remote sensing data, including LiDAR data, optical data, and radar data, for estimating biomass burning emissions, opening an avenue for accurate forest biomass and carbon dynamics monitoring, as well as climate change mitigation.

Assessment of the potential use of ICESat-2 data for bathymetric mapping of small lakes of Kazakhstan

Lake bathymetry is of great importance for water resources management and hydrological modeling. Bathymetric mapping of lakes was predominantly conducted with the use of highly-priced methods such as airborne lidars, active imaging sonars, multibeam echosounders. With the advancements in GIS and emergence of remotely sensed data new approaches for bathymetry extraction were developed. However, despite a high motivation to obtain bathymetric information for small lakes from remotely sensed data, there is a lack of reliable methods that can be implemented under various climate conditions and on a wide scale. In this paper several remote-sensing-based methods for bathymetry mapping of small lakes are discussed. The new Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data was used to extract bathymetric information on three small lakes of Kazakhstan. The assessment of ICESat-2 for lake bathymetry extraction was conducted using field measurements as the validation data.

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.

ICESAT-2 Lidar retrievals for a smoke and clouds case: estimates in the south america region

The present study aims to estimate the height of smoke aerosols and clouds from the Advanced Topographic Laser Altimeter System (ATLAS) instrument sensor onboard the Ice, Cloud and land Elevation Satellite-2 (ICESat-2). The study was conducted during the dry season on September 14th of 2020, when there was a large number spot of biomass burning fires and smoke aerosol layers over the Amazon and the South American region. Most often, the smoke plumes move with southward winds, along the east of the Andes. During this period occasionally polar cold fronts move from the south, meeting the smoke aerosol layers. Initially, daily images from the polar orbit satellites such as Terra, Aqua, NOAA and Suomi were used to identify the spatial location of smoke plumes and clouds over the region. Then, ICESat-2 tracks were analyzed to evaluate their presence in the study area. The results show that ICESat-2 data retrieval is able to estimate the height of the aerosol layer and the top of the cloud. Some tracks show clearly higher clouds, with tops reaching about 12-13 km, when there is the presence of a cold front or deep convective cumulonimbus. Over clear sky regions where smoke is observed, estimated heights from the lidar are on the order of 3-4 km. Local profiles of temperature obtained from radiosondes show inversion layers at similar heights as detected by the lidar sensor. These inversion layers seem to be associated with the warming effect of the absorbing biomass burning aerosols.

Altimetric observation of wave attenuation through the Antarctic marginal ice zone using ICESat-2

Abstract. The Antarctic marginal ice zone (MIZ) is a highly dynamic region where sea ice interacts with ocean surface waves generated in ice-free areas of the Southern Ocean. Improved large-scale (satellite-based) estimates of MIZ extent and variability are crucial for understanding atmosphere–ice–ocean interactions and biological processes and detection of change therein. Legacy methods for defining the MIZ are typically based on sea ice concentration thresholds and do not directly relate to the fundamental physical processes driving MIZ variability. To address this, new techniques have been developed to measure the spatial extent of significant wave height attenuation in sea ice from variations in Ice, Cloud and land Elevation Satellite-2 (ICESat-2) surface heights. The poleward wave penetration limit (boundary) is defined as the location where significant wave height attenuation equals the estimated error in significant wave height. Extensive automated and manual acceptance/rejection criteria are employed to ensure confidence in along-track wave penetration width estimates due to significant cloud contamination of ICESat-2 data or where wave attenuation is not observed. Analysis of 304 ICESat-2 tracks retrieved from four months of 2019 (February, May, September and December) reveals that sea-ice-concentration-derived MIZ width estimates are far narrower (by a factor of ∼ 7 on average) than those from the new technique presented here. These results suggest that indirect methods of MIZ estimation based on sea ice concentration are insufficient for representing physical processes that define the MIZ. Improved large-scale measurements of wave attenuation in the MIZ will play an important role in increasing our understanding of this complex sea ice zone.

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.

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.

Accuracy Assessment of ICESat-2 Ground Elevation and Canopy Height Estimates in Mangroves

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.

Retrieving Forest Canopy Height From ICESat-2 Data by an Improved DRAGANN Filtering Method and Canopy Top Photons Classification

Lots of noise photons limit the application of Ice, Cloud and land Elevation Satellite-2 (ICESat-2) in forest canopy height retrieval. Abundant noise photons lead to unstable filtering results, thus affecting the canopy top photons classification. Therefore, this study proposes a method that takes account of the background noise level in the photon cloud. First, we propose an improved Differential, Regressive, and Gaussian Adaptive Nearest Neighbor (DRAGANN) filtering approach based on the DRAGANN filtering method. To obtain a more stable filtering result, large-scale and small-scale search radiuses are combined to improve the DRAGANN filtering performance. Second, we retrieve sub-canopy terrain topography by the multi-scale window detection method from the filtered photon cloud. Finally, a robust photon acquisition criterion based on the elevation difference of the filtered photons and the uneven density of signal photons is proposed to extract the canopy-top surface, which aims to mitigate the influence of inconsistencies of residual noise photons along the track. In addition, considering the fluctuation of the canopy surface, we use a non-spline interpolation method to obtain a continuous canopy surface, which avoids the Runge phenomenon caused by the spline interpolation method. The ICESat-2 data acquired in the Harvard Forest Region (HARV) is selected to assess the proposed method. The filtering performance of the improved DRAGANN approach shows stable than that of the DRAGANN approach. The proposed method&#x2019;s root means square error (RMSE) and coefficient of determination (R2) reach 3.85 m and 0.55, respectively. The results indicate that the proposed method can retrieve reliable forest canopy height from the ICESat-2 data and performs significantly better than the ATL08 canopy height product in the test site.