Altimeter System Research Articles

Leaf area index retrieval with ICESat-2 photon counting LiDAR

This is the first study to inverse leaf area index (LAI) with photon counting LiDAR (i.e., Ice, Cloud, and Land Elevation Satellite-2/Advanced Topographic Laser Altimeter System, ICESat-2/ATLAS). A new framework composed of three major steps was applied: noise removal algorithm, photon classification algorithm, and the LAI estimation model. To evaluate the feasibility and accuracy of ICESat-2 derived LAI, the MODIS (Moderate Resolution Imaging Spectroradiometer) LAI product at the spatial resolution of 500-m over two sites (Amazon rainforest and Daxing’an mountains forests) were selected for comparison purpose. The results show satisfactory agreements (Amazon: R=0.693, RMSE=2.545; Daxing’an: R=0.626, RMSE=1.893; Two sites together: R=0.667, RMSE=2.433) between MODIS LAI and ICESat-2 derived LAI. Moreover, we also selected the LAI derived from Sentinel-2 to further validate the ICESat-2 derived LAI, and got better results than MODIS LAI (Amazon: R=0.752, RMSE=2.329; Daxing’an: R=0.704, RMSE=1.724). Our study found that: (1) The new applied framework can effectively inverse LAI with ICESat-2. (2) The ICESat-2 derived LAI are reliable. (3) ICESat-2 exists many advantages over MODIS in terms of LAI estimation, for example, it not only can alleviate the issue of saturation of MODIS when LAI is high, but also can mitigate the problem of poor inversion effect of MODIS where the vegetation types are diversity.

ICESat‐2 Atmospheric Channel Description, Data Processing and First Results

AbstractThe Advanced Topographic Laser Altimeter System (ATLAS) was launched aboard the Ice Cloud and land‐Elevation Satellite‐2 (ICESat‐2) satellite in September 2018. ATLAS is a single wavelength (532 nm) lidar system designed to acquire high resolution measurements of the earth's surface while also obtaining atmospheric backscatter from molecules, clouds, and aerosols. Because ATLAS is optimized for altimetry, the atmospheric data acquired is unique in many respects and requires non‐standard analysis techniques. For example, the high repetition rate laser limits the vertical extent of the profiles to just 14 km and causes atmospheric scattering from above 15 km to be added to the scattering in the lower 0–14 km profile. In addition, the limited vertical range of the acquired profiles renders it difficult to compute the magnitude of the solar background and hinders the application of standard calibration techniques. Despite these limitations, methods have been developed to successfully produce data products that have value to the atmospheric community for cloud and aerosol research and are currently available at the National Snow and Ice Data Center (NSIDC). In this paper we describe the ICESat‐2 atmospheric channel and the methods used to process the ATLAS raw photon count data to obtain calibrated backscatter and higher level products such as layer heights and type, blowing snow, column optical depth and apparent surface reflectance.

Tree height estimations for different forest canopies in natural secondary forests based on ULS, TLS and ultrasonic altimeter systems

Tree height estimations for different forest canopies in natural secondary forests based on ULS, TLS and ultrasonic altimeter systems

Calibration of the laser pointing bias of the GaoFen-7 satellite based on simulation waveform matching.

The two-beam GaoFen-7 (GF-7) laser altimeter is China's first formal spaceborne laser altimeter system for Earth observations. In this article, a calibration method based on simulation waveform matching is proposed to correct the laser pointing error now that the satellite is in orbit. In the method, the optimal position of the laser footprint is searched using simulated and actual waveforms. Then, the laser pointing is calibrated based on the laser footprint optimal position. In this paper, after calibration of the GF-7 laser pointing, infrared detectors are used to capture laser footprints for accuracy verification. The results show that the GF-7 laser pointing accuracy is greatly improved by the method; the laser pointing accuracy of beam 1 is approximately 5.4 arcsec, and that of beam 2 is approximately 5.7 arcsec. Subsequently, two laser footprints are selected for GF-7 laser calibration in the Helan Mountains, China, and AW3D30 digital surface model (DSM) and GPS/RTK data are used to verify the laser elevation measurement accuracy (EMA). The results show that the EMA of the GF-7 laser is significantly improved after calibration. Over flat terrain, the EMA of the GF-7 laser is improved by 10 times, from 3.74 ± 0.55 m to 0.35 ± 0.50 m, verifying the effectiveness of the proposed method.

Ocean Currents Reconstruction from a Combination of Altimeter and Ocean Colour Data: A Feasibility Study

Measuring the ocean surface currents at high spatio-temporal resolutions is crucial for scientific and socio-economic applications. Since the early 1990s, the synoptic and global-scale monitoring of the ocean surface currents has been provided by constellations of radar altimeters. By construction, altimeter constellations provide only the geostrophic component of the marine surface currents. In addition, given the effective spatial-temporal resolution of the altimeter-derived products (O (100 km) and O (10 days), respectively), only the largest ocean mesoscale features can be resolved. In order to enhance the altimeter system capabilities, we propose a synergistic use of high resolution sea surface Chlorophyll observations (Chl) and altimeter-derived currents’ estimates. The study is focused on the Mediterranean Sea, where the most energetic signals are found at spatio-temporal scales up to 10 km and a few days. The proposed method allows for inferring the marine surface currents from the evolution of the Chl field, relying on altimeter-derived currents as a first-guess estimate. The feasibility of this approach is tested through an Observing System Simulation Experiment, starting from biogeochemical model outputs distributed by the European Copernicus Marine Service. Statistical analyses based on the 2017 daily data showed that our approach can improve the altimeter-derived currents accuracy up to 50%, also enhancing their effective spatial resolution up to 30 km. Moreover, the retrieved currents exhibit larger temporal variability than the altimeter estimates over annual to weekly timescales. Our method is mainly limited to areas/time periods where/when Chl gradients are larger and are modulated by the marine currents’ advection. Its application is thus more efficient when the surface Chl evolution is not dominated by the biological activity, mostly occurring in the mid-February to mid-March time window in the Mediterranean Sea. Preliminary tests on the method applicability to satellite-derived data are also presented and discussed.

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

To derive shallow water bathymetry for coastal areas, a common approach is to deploy a scanning airborne bathymetric light detection and ranging (LiDAR) system or a shipborne echosounder for ground surveys. However, recent advancements in satellite remote sensing, including the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) offer new tools for generating satellite derived bathymetry (SDB). The key payload onboard ICESat-2 is the Advanced Topographic Laser Altimeter System (ATLAS), a micro-pulse, photon-counting LiDAR system, simultaneously emitting six separate 532 nm beams at 10 kHz pulse rate. However, despite its high resolution, the major limitation for bathymetry is that ICESat-2 only provides along-track height profiles, leaving observation gaps between the parallel ground tracks. Merging ICESat-2 observations with optical multispectral imagery, as demonstrated herein, provides an effective solution for deriving a full scene of water depth in light of the spectral attenuation behavior.This study aims to combine ICESat-2 and Sentinel-2 optical data to derive shallow water bathymetry (depth <20 m) at six islands and reefs in the South China Sea. ICESat-2 ATL03 point clouds of georeferenced photons are first filtered to determine the seafloor elevation along the ground track. Results indicate a root-mean-square error (RMSE) of 0.26–0.61 m as compared with independent observations from an airborne LiDAR campaign. Next, three semi-empirical functions, namely the Modified Linear/Polynomial/Exponential Ratio Models with its kernel formed by the log ratio between Sentinel-2′s green and blue bands, are used to fit the spectral data with ICESat-2 height profiles. After water depth mapping using the trained model, independent ICESat-2 point clouds are used to validate the Sentinel-2 derived bathymetry. The RMSE values of the three models using the weighted average of multiple images for these six islands are within 0.50–0.90 m in 0–15 m deep. We also demonstrate that a synthesis of satellite laser altimetry and optical remote sensing can produce SDB results that potentially meet the requirement of category C in Zones of Confidence (ZOC) of the Electronic Navigational Chart (ENC) in 0–8 m deep. It is foreseen that ICESat-2 will be a helpful tool for mapping coastal and shallow waters around the world especially where bathymetric data are unavailable.

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

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

Comparing airborne and spaceborne photon-counting LiDAR canopy structural estimates across different boreal forest types

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.

Quality assessment of open sourced digital elevation models in southeast coast of India

Digital elevation model (DEM) provides elevation values of the terrain. At present several open source DEMs are available and its vertical accuracy varies from place to place. The present study assessed the vertical accuracy of 1 arc-sec data of Advanced Land Observing Satellite digital surface model (AW3D30) Version-3, Shuttle Radar Topographic Mission (SRTM) DEM (V3), Carto DEM (V3R1) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM (V2) using Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System (ICESat/GLAS) and Real Time Kinematic Global Positioning System (RTK GPS) data in Southeast coast of India. Root mean square error (RMSE) was calculated for DEMs, showed the elevation accuracy as 2.48 m (AW3D30), 3.02 m (SRTM DEM), 3.88 m (Carto DEM) and 8.02 m (ASTER GDEM) in the Tamil Nadu state while it was 2.29 m (AW3D30), 3.10 m (SRTM DEM), 3.93 m (Carto DEM) and 8.98 m (ASTER GDEM) in the 10 km buffer zone of coastal region and the results are satisfactory than the official product’s specifications. Landform analysis was carried out at selected RTK GPS surveyed sites where the elevation of different coastal landforms is validated with DEMs and it was found the AW3D30 showed least RMSE than others. This paper suggests that AW3D30 is best for representing terrain elevation values followed by SRTM and Carto DEMs which are apt choices for application studies.

Regionalization of an Existing Global Forest Canopy Height Model for Forests of the Southern United States

A large-scale global canopy height map (GCHM) is essential for global forest aboveground biomass estimation. Four GCHMs have recently been built using data from the Geoscience Laser Altimeter System (GLAS) sensor aboard the Ice, Cloud, and land Elevation Satellite (ICESat), along with auxiliary spatial and climate information. The main objectives of this research were to find out how well a selected GCHM agrees with airborne lidar data from locations in the southern United States and to recalibrate that GCHM to more closely match the forest canopy heights found in the region. The airborne lidar resource was built from data collected between 2010 and 2012, available from in-house and publicly available sources, for sites that included a variety of vegetation types across the southern United States. EPA ecoregions were used to provide ecosystem information for the southern United States. The airborne lidar data were pre-processed to provide lidar-derived metrics, and assigned to four height categories—namely, returns from above 0 m, 1 m, 3 m, and 5 m. The assessment phase results indicated that the 90th and 95th percentiles of the airborne lidar height values were well-suited for use in the recalibration phase of the study. Simple linear regression was used to generate a new, recalibrated GCHM. It was concluded that the characterization of the agreement of a selected GCHM with local data, followed by the recalibration of the existing GCHM to the local region, is both viable and essential for future GCHMs in studies conducted at large scales.

Estimating global aerodynamic parameters in 1982–2017 using remote-sensing data and a turbulent transfer model

The bulk surface properties, including canopy height (h), zero-plane displacement height (d), roughness length (z0), and aerodynamic resistance (rb and rd), are crucial biophysical parameters that influence momentum, energy and mass exchanges at the land-atmosphere interface. The variabilities of these parameters are important for understanding possible impacts of the ecosystem on climate change, yet they have not been systematically evaluated due to the lack of large-scale, long-term observations. Here we provide global estimates of these bulk aerodynamic parameters, including d, z0, rb, and rd, for the period 1982–2017 based on remote-sensed leaf area index (LAI), h, and plant functional type dependent canopy morphological characteristics. The global h estimate is acquired from LAI using a semi-empirical relation in which the coefficients have been optimized based on the canopy height product from the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat). Two LAI products, Global Land Surface Satellite (GLASS) and Global Inventory Modeling and Mapping Studies (GIMMS), are used to calculate canopy height and parameters separately. The products derived from the above two LAI datasets agree very well (spatial correlation coefficient, SCC = 0.99, relative root-mean-square-error, rRMSE = 8.28% for h, SCC = 0.99, rRMSE = 12.15% for d, and SCC = 0.98, rRMSE = 13.78% for z0). Verification of the products against in-situ canopy height records from the FLUXNET and eddy-covariance (EC)-based d and z0 estimates shows that the estimates can reproduce the annual means and seasonal variations of the bulk surface properties. We found significant positive trends in global h (0.04–0.05% year−1), d (0.07% year−1) and z0 (0.07% year−1) associated with the Earth greening, but they are different from the trend in LAI (0.14–0.25% year−1). The differences between the trends in LAI and the trends in derived parameters are also found in different latitudes. For instance, in the Northern Hemisphere Polar region, the GLASS LAI increases by 0.48% year−1, leading to positive trends of 0.03% year−1 in h, 0.18% in d, and 0.20% year−1 in z0. The above results highlight the importance of comprehensively considering changes in LAI, canopy height, and aerodynamic parameters when evaluating the effects of ecosystem change on climate. This long-term dataset can be used in climate models to assess the response of bulk aerodynamic parameters to climate changes and the impact of vegetation dynamics on regional and global climate.

Inland water level measurement from spaceborne laser altimetry: Validation and comparison of three missions over the Great Lakes and lower Mississippi River

Satellite laser altimetry offers an opportunity to accurately measure inland water levels over the Earth’s surface. This paper described the validation and comparison of three spaceborne missions for inland water level retrievals, including the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud and Land Elevation Satellite-1 (ICESat-1), as well as the newly launched Advanced Topographic Laser Altimeter System (ATLAS) onboard ICESat-2 and the Global Ecosystem Dynamics Investigation (GEDI) lidar on the International Space Station. This study was conducted over the Great Lakes and lower Mississippi River using in-situ data from 22 gauging stations, where the potential for monitoring inland water dynamics was explored. The results showed that ICESat-2 provided lake water level retrievals with an unprecedented accuracy (RMSE = 0.06 m, biases =−0.01 ± 0.05 m), followed by ICESat-1 (RMSE = 0.10 m, biases = −0.04 ± 0.10 m), and then by GEDI (RMSE = 0.28 m, biases = −0.10 ± 0.23 m). ICESat-2 also offered more accurate river water level measurements (RMSE = 0.12 m, biases = −0.08 ± 0.07 m) than those of ICESat-1 (RMSE = 0.25 m, biases = −0.18 ± 0.16 m) and GEDI (RMSE = 0.40 m, biases = −0.24 ± 0.24 m). The analysis suggested that for all the altimeters, the strong beam achieved higher water level accuracies than those of the weak, and the nighttime acquisitions were slightly more accurate than those of the daytime. However, our result indicated no considerable differences between measurement accuracy and river width. Furthermore, the analysis demonstrated their great potential in monitoring inland water dynamics, especially with the combination of ICESat-2 and GEDI platforms. This study provided a comprehensive understanding of measurement accuracies of three recent missions and recommended that end users should employ the strong beam data acquired during the nighttime for accurate water level retrievals.

A Digital Terrain Modeling Method in Urban Areas by the ICESat-2 (Generating precise terrain surface profiles from photon-counting technology)

The ICE, Cloud and land Elevation Satellite-2 (ICES at-2) can provide new measurements of the Earth's elevations through photon-counting technology. Most research has focused on extracting the ground and the canopy photons in vegetated areas. Yet the extraction of the ground photons from urban areas, where the vegetation is mixed with artificial constructions, has not been fully investigated. This article proposes a new method to estimate the ground surface elevations in urban areas. The ICES at-2 signal photons were detected by the improved Density-Based Spatial Clustering of Applications with Noise algorithm and the Advanced Topographic Laser Altimeter System algorithm. The Advanced Land Observing Satellite-1 PALSAR –derived digital surface model has been utilized to separate the terrain surface from the ICES at-2 data. A set of ground-truth data was used to evaluate the accuracy of these two methods, and the achieved accuracy was up to 2.7 cm, which makes our method effective and accurate in determining the ground elevation in urban scenes.

A Self-Adaptive Denoising Algorithm Based on Genetic Algorithm for Photon-Counting Lidar Data

The ice, cloud, and land elevation satellite-2 (ICESat-2) is equipped with a photon-counting laser altimeter system and demonstrates outstanding ability to measure elevations in the ever-changing earth. However, the ICESat-2 data contain several noise photons affected by solar returns, and there are no reference data of signal or noise photons for evaluating the performance of denoising algorithms. In this letter, we propose a self-adaptive denoising algorithm based on a genetic algorithm (SADA-GA) for the ICESat-2 data, which uses the real-coded genetic algorithm to adaptively search for the global optimal denoising parameters in different data sets. The SADA-GA addresses the limitation of the selection method of the two parameters K and T in the localized statistics-based algorithm that normally cannot be applied to different data sets. To evaluate the algorithm performance, we created an ICESat-2 data set named WHU-PCL and compared the SADA-GA with two classic methods. The qualitative and quantitative analyses showed that our method can extract signal photons more efficiently from different ICESat-2 data sets and achieve the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> value of 0.99 in nighttime data. In addition, we analyzed the factors that affect the SADA-GA performance and found that the signal-to-noise ratio (SNR) is the most important parameter.

Retrieving Forest Canopy Elements Clumping Index Using ICESat GLAS Lidar Data

Clumping index (CI) is a canopy structural variable important for modeling the terrestrial biosphere, but its retrieval from remote sensing data remains one of the least reliable. The majority of regional or global CI products available so far were generated from multiangle optical reflectance data. However, these reflectance-based estimates have well-known limitations, such as the mere use of a linear relationship between the normalized difference hotspot and darkspot (NDHD) and CI, uncertainties in bidirectional reflectance distribution function (BRDF) models used to calculate the NDHD, and coarse spatial resolutions (e.g., hundreds of meters to several kilometers). To remedy these limitations and develop alternative methods for large-scale CI mapping, here we explored the use of spaceborne lidar—the Geoscience Laser Altimeter System (GLAS)—and proposed a semi-physical algorithm to estimate CI at the footprint level. Our algorithm was formulated to leverage the full vertical canopy profile information of the GLAS full-waveform data; it converted raw waveforms to forest canopy gap distributions and gap fractions of random canopies, which was used to estimate CI based on the radiative transfer theory and a revised Beer–Lambert model. We tested our algorithm over two areas in China—the Saihanba National Forest Park and Heilongjiang Province—and assessed its relative accuracies against field-measured CI and MODIS CI products. We found that reliable estimation of CI was possible only for GLAS waveforms with high signal-to-noise ratios (e.g., &gt;65) and at gentle slopes (e.g., &lt;12°). Our GLAS-based CI estimates for high-quality waveforms compared well to field-based CI (i.e., R2 = 0.72, RMSE = 0.07, and bias = 0.02), but they showed less correlation to MODIS CI (e.g., R2 = 0.26, RMSE = 0.12, and bias = 0.04). The difference highlights the impact of the scale effect in conducting comparisons of products with huge differences resolution. Overall, our analyses represent the first attempt to use spaceborne lidar to retrieve high-resolution forest CI and our algorithm holds promise for mapping CI globally.

Mapping Woody Volume of Mediterranean Forests by Using SAR and Machine Learning: A Case Study in Central Italy

In this paper, multi-frequency synthetic aperture radar (SAR) data at L- and C-bands (ALOS PALSAR and Envisat/ASAR) were used to estimate forest biomass in Tuscany, in Central Italy. The ground measurements of woody volume (WV, in m3/ha), which can be considered as a proxy of forest biomass, were retrieved from the Italian National Forest Inventory (NFI). After a preliminary investigation to assess the sensitivity of backscatter at C- and L-bands to forest biomass, an approach based on an artificial neural network (ANN) was implemented. The ANN was trained using the backscattering coefficient at L-band (ALOS PALSAR, HH and HV polarization) and C-band (Envisat ASAR in HH polarization) as inputs. Spatially distributed WV values for the entire test area were derived by the integration (fusion) of a canopy height map derived from the Ice, Cloud, and Land Elevation Geoscience Laser Altimeter System (ICESat GLAS) and the NFI data, in order to build a significant ground truth dataset for the training stage. The analysis of the backscattering sensitivity to WV showed a moderate correlation at L-band and was almost negligible at C-band. Despite this, the ANN algorithm was able to exploit the synergy of SAR frequencies and polarizations, estimating WV with average Pearson’s correlation coefficient (R) = 0.96 and root mean square error (RMSE) ≃ 39 m3/ha when applied to the test dataset and average R = 0.86 and RMSE ≃ 75 m3/ha when validated on the direct measurements from the NFI. Considering the heterogeneity of the scenario (Mediterranean mixed forests in hilly landscape) and the small amount of available ground measurements with respect to the spatial variability of different plots, the obtained results can be considered satisfactory. Moreover, the successful use of WV from global maps for implementing the algorithm suggests the possibility to apply the algorithm to wider areas or even to global scales.

A photon-counting LiDAR bathymetric method based on adaptive variable ellipse filtering

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with the advanced topographic laser altimeter system (ATLAS), is a new measurement strategy that includes a photon-counting technique. Employing such an advanced system facilitates highly accurate measurement of changes in the land surface of the Earth, as well as water depths, thereby providing a novel opportunity for nearshore bathymetry. The main problem in photon-counting bathymetry is the precise identification and separation of photons with different density distributions on the water surface and water-column. Further, automatic detection of the signal photon of the water surface and bottom from the noise photon presents a problem. To overcome such problems, we propose a novel, validated, adaptive variable ellipse filtering bathymetric method (AVEBM). This method can precisely identify and separate the photons in the above-water, water surface, and water-column regions. Filter parameters can be determined automatically by the density distribution of photons in different water environments and water depths, and the signal photons of the water surface and bottom can be detected precisely to conduct bathymetry. To ensure and improve the accuracy and reliability of the AVEBM, datasets of different photon densities were selected and employed in conducting experiments. Further, we used in situ data and remote sensing images to validate and estimate the detection accuracy of the signal photon and bathymetric accuracy. The experimental results indicated that bathymetric accuracy based on the AVEBM method reached 0.48 m of root mean square error (RMSE) when we employed three ATL03 datasets in combination for Qilianyu Islands. Moreover, the bathymetric accuracy reached 0.79 m of RMSE when comparing with the SDB result in Shanhu Island. Results showed that the proposed AVEBM method facilitated highly accurate signal photon detection and bathymetry.

Inversion of Terrain Slope and Roughness with Satellite Laser Altimeter Full-Waveform Data Assisted by Shuttle Radar Topographic Mission

Slope and roughness are basic geophysical properties of terrain surface, and also sources of error in satellite laser altimetry systems. The full-waveform satellite laser altimeter records the complete echo waveform backscattered from the target surface worldwide, so it may be used for both range measurement and inversion analysis of geometric parameters of the target surface. This paper proposes a new method for inversion of slope and roughness of the bare or near-bare terrain within laser footprint using full-waveform satellite laser altimeter data, Shuttle Radar Topographic Mission (SRTM) and topographic prior knowledge. To solve the non-uniqueness of the solution to the inversion problem, this paper used the SRTM and airborne Light Detection and Ranging (LiDAR) data in North Rhine-Westphalia, Germany, to establish a priori hypothesis about real information of topographic parameters. Then, under the constraints of prior hypothesis, the theoretical formulas and rules for slope and roughness inversion using the pulse-width broadening knowledge of satellite laser altimeter echo full-waveform were developed. Finally, based on the full-waveform data from the Geoscience Laser Altimeter System (GLAS) that was borne on ICE, Cloud, and Land Elevation Satellite (ICESat) and SRTM in the West Valley City, Utah and Jackson City, Wyoming, United States of America, the inversion was carried out. The experiment compares the results of proposed method with those of existing ones and evaluates the inversion results using high precision terrain slope and roughness information, which indicates that our proposed method is superior to the state-of-the-art methods, and the inversion accuracy for slope is 0.667° (Mean Absolute Error, MAE) and 1.054° (Root Mean Square Error, RMSE), the inversion accuracy for roughness is 0.171 m (MAE) and 0.250 m (RMSE).

An active-passive fusion strategy and accuracy evaluation for shallow water bathymetry based on ICESat-2 ATLAS laser point cloud and satellite remote sensing imagery

ABSTRACT The Advanced Topographic Laser Altimeter System (ATLAS) on ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) uses a 532 nm band photon-counting LiDAR (Light Detection and Ranging), which has certain penetrability to water bodies, and the measured data show that the bathymetric ability reaches nearly one Secchi depth. ATLAS has a limited number of beams and a fixed ground track, and only collects section elevation along the track direction. The fusion of active laser point cloud and passive optical remote sensing satellite image can fill the gap of shallow water depth data in a large range. This paper takes ATLAS as the research object, with the aim of exploring the effective algorithm flow of spaceborne active laser and passive optical fusion processing, and systematically evaluating the bathymetric accuracy of the fusion algorithm. An adaptive Gauss filtering technology based on the density of point cloud was firstly improved to achieve accurate denoising under the condition of uneven surface/underwater density. Subsequently, the depth of underwater points was calculated automatically through steps of water surface modelling, refraction correction, etc. Finally, the control points and check points were randomly extracted to solve the parameters of multispectral inversion model and verify the internal accuracy of the model. In this paper, the accuracy of ATLAS bathymetry was verified by Airborne LiDAR Bathymetry (ALB) data in Oahu Island, Hawaii, and the results indicate that the vertical root mean square error (RMSE) ranges from 0.56 m to 1.11 m. In Yongle islands and Qilianyu area of the South China Sea, WorldView-2 (WV2) 4 bands multispectral images and ATLAS data were used to carry out the active-passive fusion bathymetry, and the ALB and sonar data were used to evaluate the accuracy. Experimental results show that the internal compliance accuracy of the fusion model is better than 1.25 m (RMSE), and the real bathymetry accuracy is better than 1.42 m (RMSE). The above results reveal the great potential of active-passive fusion bathymetry based on ICESat-2 and other high-resolution remote sensing satellites, which can provide strong technical support for filling the blank of shallow water depth information.

Satellite Remote Sensing Image Stereoscopic Positioning Accuracy Promotion Based on Joint Block Adjustment With ICESat-2 Laser Altimetry Data

The next generation Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2)/Advanced Topographic Laser Altimeter System (ATLAS) was launched in 2018 to provide large amounts of spaceborne laser altimetry data. The joint block adjustment with laser altimetry data and satellite remote sensing images can promote limited stereoscopic positioning accuracy without ground control points (GCPs). However, there are two problems in the joint block adjustment, that is, the reliable ATLAS laser altimetry points (LAPs) selection and the discrepancies of image-object points. To solve the above problems, a surface point cloud extraction algorithm based on wavelet reconstruction was proposed. Then, the LAPs selection constraint model was established, so the terrain influence was resolved. Finally, a block joint adjustment method has been proposed with remote sensing images and the reliable LAPs considering error of their plane coordinates, which could achieve the accurate corresponding image points of the reliable LAPs, remarkably decrease the discrepancies of image-object points. Fourteen pairs of ATLAS data, seven ZY3-02 stereo images and eleven Mapping Satellite-1 stereo images in Zhengzhou, China were collected to validate the method performance. Experiment results have shown that the consistency of the image-object points and the positioning accuracy of stereoscopic images without GCPs have been improved. Compared with the free net block adjustment, the height, east and north positioning accuracy of ZY3-02 and Mapping Satellite-1 stereo images have increased by 61%, 56%, 60% and 56%, 38%, 37%, respectively.