Spaceborne Laser Altimeter Research Articles

Positioning Improvement for Spaceborne Laser Footprint Based on Precisely Terrain Data

Abstract. Spaceborne laser altimetry represents a novel active remote sensing technology applicable to earth observation, which together with imaging spectroscopy and synthetic aperture radar as a core technology for data acquisition in the earth observation systems. However, the accuracy of horizontal positioning for laser footprints from spaceborne laser altimeters declines due to various factors such as the changes in the orbital environment and the deterioration of performance. Moreover, the limited frequency of in-orbit calibration of the spaceborne laser altimeters and the non-disclosure of calibration parameters mean that users are heavily reliant on positioning accuracy of the altimetry data provided. To address this issue, a new algorithm is proposed in this study for enhancing the accuracy of horizontal positioning for laser footprints in the absence of satellite altimeter pointing and ranging parameters. In this algorithm, high-resolution DSM is taken as the reference terrain data to take advantage of the higher precision in elevation over horizontal positioning of the laser footprints. By adjusting the horizontal position of the laser footprint within a small area, the algorithm achieves the optimal alignment of laser elevation data with the reference terrain. Then, the resulting shift in the horizontal position of the laser footprints is referenced to correct their horizontal positioning during that period. Based on the high-accuracy DSM data collected from the Xinjiang autonomous region in China and the data collected by the GF-7 satellite, simulation experiments are performed in this study to analyze and validate the proposed algorithm. According to the experimental results, the horizontal accuracy of the laser footprints improves significantly from 12.56 m to 3.11 m after optimization by the proposed method. With the elimination of 9.45 m horizontal error, accuracy is improved by 75.23%. This method is demonstrated as effective in further optimizing the horizontal position of laser altimetry data products in the absence of altimeter parameters and original data, which promotes the application of spaceborne laser data.

A novel spaceborne photon-counting laser altimeter denoising method based on parameter-adaptive density clustering

ABSTRACT To tackle the challenge of denoising spaceborne photon-counting laser altimeter point clouds with uneven noise density, this study proposes a denoising method based on adaptive parameter density clustering, which utilizes numerical simulations to achieve self-adaptation of key parameters (neighborhood radius Eps and minimum number of points MinPts ). First, taking the directional adaptive ellipse DBSCAN (DAE-DBSCAN) as an example, photons with different background photon count rates ( bckgrd _ rate ) are used to traverse Eps and MinPts to calculate their optimal values ( Eps and MinPts with the highest denoising accuracy). Then, a mathematical prediction model of bckgrd _ rate , Eps and MinPts was established. The actual background photon count rates were introduced into the key parameter prediction model to obtain the optimal Eps and MinPts . Finally, a denoising experiment was conducted using the simulated photons and the ATLAS data. The results show that the proposed method had higher accuracy than the constant parameter denoising method, with an F >0.95. Even for photons of complex mountainous terrain with a high background photon count rate, the denoising accuracy was still higher than 0.9. The proposed method improves the denoising accuracy of photons with different noise densities by adapting density clustering parameters.

Fine Calibration Method for Laser Altimeter Pointing and Ranging Based on Dense Control Points

Satellite laser altimetry technology, a novel space remote sensing technique, actively acquires high-precision elevation information about the Earth’s surface. However, the accuracy of laser altimetry can be compromised by alterations in the satellite-ground environment, thermal dynamics, and cosmic radiation. These factors may induce subtle variations in the installation and internal structure of the spaceborne laser altimeter on the satellite platform, diminishing measurement precision. In-orbit calibration is thus essential to enhancing the precision of laser altimetry. Through collaborative calculations between satellite and ground stations, we can derive correction parameters for laser pointing and ranging, substantially improving the accuracy of satellite laser altimetry. This paper introduces a sophisticated calibration method for laser altimeter pointing and ranging that utilizes dense control points. The approach interpolates discrete ground control point data into continuous simulated terrain using empirical Bayesian kriging, subsequently categorizing the data for either pointing or ranging calibration according to their respective functions. Following this, a series of calibration experiments are conducted, prioritizing “pointing” followed by “ranging” and continuing until the variation in the ranging calibration results falls below a predefined threshold. We employed experimental data from ground control points (GCPs) in Xinjiang and Inner Mongolia, China, to calibrate the GaoFen-7 (GF-7) satellite Beam 2 laser altimeter as per the outlined method. The calibration outcomes were then benchmarked against those gleaned from infrared laser detector calibration, revealing disparities of 1.12 s in the pointing angle and 2 cm in the ranging correction value. Post validation with ground control points, the measurement accuracy was refined to 0.15 m. The experiments confirm that the proposed calibration method offers accuracy comparable to that of infrared laser detector calibration and can facilitate the updating of 1:10,000 topographic maps utilizing stereo optical imagery. Furthermore, this method is more cost-effective and demands fewer personnel for ground control point collection, enhancing resource efficiency compared to traditional infrared laser detector calibration. The proposed approach surpasses terrain-matching limitations when calibrating laser ranging parameters and presents a viable solution for achieving frequent and high-precision in-orbit calibration of laser altimetry satellites.

Modeling and Correcting Building Boundary in ICESat-2 Spaceborne Laser Altimeter Data Considering the Extended Laser Spot Effect

Modeling and Correcting Building Boundary in ICESat-2 Spaceborne Laser Altimeter Data Considering the Extended Laser Spot Effect

A Pre-Pruning Quadtree Isolation Method with Changing Threshold for ICESat-2 Bathymetric Photon Extraction

The new generation of spaceborne laser altimeter, the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), which can simultaneously generate laser reflections on the water surface and underwater, is a potential data source for exploring water depth in nearshore environments. To achieve this scientific goal, accurate bathymetric photon extraction is needed. This study proposed a pre-pruning quadtree isolation (PQI) method with changing threshold. Firstly, the pre-pruning step is introduced. Photons are transformed into different levels in the isolated quadtree structure according to spatial density. Then, the frequency histogram of photon elevation and isolated level (IL) is generated, the IL thresholds in different depth ranges are calculated by the Otsu method, and the bathymetric photons are extracted. The results in the Culebra archive show that this method achieved a 92.71% F1 score. Noise rate and water depth are the main factors affecting the extraction of sounding photons. When the photon density gradually increases from 2–4 pts/m to 6–8 pts/m, the F1 score of PQI decreases by no more than two percent. In different depth ranges, the extraction results of PQI are also better than those of comparison methods. Therefore, PQI can provide reliable theoretical support for nearshore areas lacking water depth data.

Geometric positioning integrating optical satellite stereo imagery and a global database of ICESat-2 laser control points: A framework and key technologies

ABSTRACT Block Adjustment (BA) is one of the essential techniques for producing high-precision geospatial 3D data products with optical stereo satellite imagery. For block adjustment with few ground-control points or without ground control, the vertical error of the model is the decisive factor that constrains the accuracy of 3D data products. The elevation data obtained by spaceborne laser altimeter have the advantages of short update periods, high positioning precision, and low acquisition cost, providing sufficient data support for improving the elevation accuracy of stereo models through the combined BA. This paper proposes a geometric positioning model based on the integration of Optical Satellite Stereo Imagery (OSSI) and spaceborne laser altimeter data. Firstly, we elaborate the principle and necessity of this work through a literature review of existing methods. Then, the framework of our geo-positioning models. Secondly, four key technologies of the proposed model are expounded in order, including the acquisition and management of global Laser Control Points, the association of LCPs and OSSI, the block adjustment model combining LCPs with OSSI, and the accuracy estimation and quality control of the combined BA. Next, the combined BA experiment using Ziyuan-3 (ZY-3) OSSI and ICESat-2 laser data was carried out at the testing site in Shandong Province, China. Experimental results prove that our method can automatically select LCPs with high accuracy. The elevation deviation of the combined BA eventually achieved the Mean Error (ME) of 0.06 m and the Root Mean Square Error (RMSE) of 1.18 m, much lower than the ME of 13.20 m and the RMSE of 3.88 m before the block adjustment. A further research direction will be how to perform more adequate accuracy analysis and quality control using massive laser points as checkpoints.

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.

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.

A Compensation Method of Saturated Waveform for Space-Borne Laser Altimeter

Due to the difference in surface reflectivity, the laser measurement waveform data recorded in full waveform have a saturation phenomenon. When the signal is saturated, the echo waveform produces peak clipping and pulse spreading, which seriously restrict the accuracy of laser measurement results and the usability of data. Therefore, we conducted a ranging investigation on the “peak clipping” phenomenon of the saturated waveform and found a nonlinear time delay in the range, which is between the two extreme cases of saturated “dead time” and Gaussian fitting peak time as pulse signal reception time. Subsequently, based on the consistent relationship between the geometric characteristics of the high- and low-gain channels of the space-borne laser altimeter, we constructed a laser waveform saturation compensation model, namely, the laser pulse flight time delay compensation and the laser waveform peak intensity compensation, and carried out the data saturation compensation and validation with the dual-channel measurement data from the GaoFen-7 (GF-7) satellite. The experimental results showed that the saturation compensation model (SCM) proposed in this paper could restore the features of the saturated waveform signal and effectively improve the accuracy of the laser ranging. The accuracy of the laser waveform fitting result after saturation compensation improved from 0.7 ns (0.11 m) to 0.14 ns (0.02 m), which greatly improved the usability of the saturated laser measurement waveform data.

A new terrain matching method for estimating laser pointing and ranging systematic biases for spaceborne photon-counting laser altimeters

Precisely estimating on-orbit laser ranging and pointing systematic bias plays a significant role in obtaining high accuracy data product for spaceborne laser altimeters. Current terrain matching methods were mainly used to estimate the systematic bias for analog (or full waveform) laser altimeters or to evaluate the geolocation accuracy for both analog and photon-counting laser altimeters. The new generation photon-counting laser altimeter provides unprecedented high-density laser points on the ground and can obtain the along-track surface profiles even when observing terrains with complex topography. In this study, we propose a terrain matching algorithm to iteratively estimate the laser pointing and laser ranging systematic biases for spaceborne photon-counting laser altimeters. With the help of the ground truth topography, the systematic biases are obtained by matching the observed ranging profiles from a spaceborne photon-counting laser altimeter with the expected ranging profiles calculated from a reasonable model. Meanwhile, the theoretical model of the estimation precision is derived and denoted as confidence intervals. The data products and system parameters of the only current spaceborne photon-counting laser altimeter, i.e. Ice, Cloud, and Land Elevation Satellite-2 Advanced Topographic Laser Altimeter System (ICESat-2 ATLAS), are used to test the performance of the proposed method in Lianyungang City, China with local ground truth Digital Elevation Model (DEM). This method is proved that it can be further used to validate the geolocation accuracy, and the results show that the mean horizontal accuracy of ICESat-2 is approximately 1.7 m in the study site. It indicates that ICESat-2 has very good geolocation accuracy, even though the study site is a mountainous region with complex topography. In addition, the ranging model difference between analog and photon-counting laser altimeters is derived and quantitatively analyzed in this study. The proposed method can be not only used in the validation of geolocation accuracy but also as a potential calibration method for spaceborne photon-counting laser altimeters.

Geopositioning Improvement of ZY-3 Satellite Imagery Integrating GF-7 Laser Altimetry Data

Improving the accuracy of block adjustment with few or no ground control points (GCPs) is one of the core issues for satellite imagery high-precision mapping. The GaoFen-7 (GF-7) satellite laser altimeter system is the first Chinese formal spaceborne laser altimeter system, which is equipped with laser footprint cameras for the first time. Given the very high height accuracy and high planimetric accuracy of the instrument, this letter proposed an adjustment method of ZiYuan-3 (ZY-3) satellite imagery by integrating GF-7 laser altimetry data. The laser control points (LCPs) were automatically extracted according to the registration of the footprint images and stereo images. Furthermore, the combined adjustment was performed with the LCPs as the vertical control and horizontal constraint. The performance of the proposed method was evaluated by using 417 stereo scenes of ZY-3 images and 954 LCPs, covering an area of 158 000 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in Shandong, China. The results demonstrated that, only by using all LCPs as the vertical control, the root mean square error (RMSE) of elevation can be significantly improved from the original 8.82 to 1.40 m. Moreover, the planimetric RMSE decreased from the original 15.31 to 3.58 m with residual randomness when 45 LCPs were used as the horizontal constraint. Our method provides an alternative solution for satellite imagery geopositioning improvement without GCPs.

Classification of Laser Footprint Based on Random Forest in Mountainous Area Using GLAS Full-Waveform Features

Full-waveform spaceborne laser altimeter can provide more characteristic parameters of the laser footprint and rich vertical structure information on the target surface. This technology has the potential for land-cover classification, especially in hard-to-reach mountain areas. Classifying the land types based on the returned waveform can provide a convenient way for the online classification needs and assess the quality of footprint used as the ground control point in photogrammetry. This article presents a random forest (RF) classification using geoscience laser altimeter system waveform, in the west-central Yunnan Province, China. First, an improved threshold wavelet is performed to denoise the waveform, and then Gaussian decomposition is used to extract the typical characteristic features of footprint. Second, an RF algorithm is implemented to clarify the footprints into five types: flat, building, terrace, forest, and mountain. Finally, quantitative analysis is conducted with producer's accuracy (PA), user's accuracy (UA), overall accuracy (OA), precision, recall rate, F1-score, and kappa coefficient to compare the performance of RF with other classifiers, including linear support vector machine (linear-SVM), radial basis function SVM (RBF-SVM), logistic regression (LR), K-nearest neighbor (KNN), and naive Bayes (NB). The results show that all the six methods can accurately classify the flat land with 100.00% PA and UA. The RF also has the best performances in other four land types, with PA and UA of 98.14% and 100.00%, 97.24% and 95.49%, 98.64% and 96.03%, and 94.64% and 100.00%, respectively. The OA, precision, recall, F1-score, and kappa coefficient for the RF are 97.95%, 97.73%, 98.30%, 97.99%, and 0.9737, respectively; while 83.45%, 82.55%, 82.98%, 81.16%, and 0.7765 for NB, which has the worst performance. LR performs better than RBF-SVM, linear-SVM, and KNN. It also observes worse classification accuracy for all methods when the waveforms are more complex.

A Planimetric Location Method for Laser Footprints of the Chinese Gaofen-7 Satellite Using Laser Spot Center Detection and Image Matching to Stereo Image Product

Satellite stereo mapping, together with laser altimetry, can be used to obtain three-dimensional geospatial information. Spaceborne laser altimeter can provide high-accuracy elevation information; however, due to the lack of detailed intensity information, its planimetric accuracy is usually worse than the ranging accuracy. The Chinese Gaofen-7 (GF-7) satellite, which was designed for civilian mapping application, was launched on November 3, 2019. The GF-7 satellite’s main payloads are a laser altimeter system (with footprint camera) and a dual-linear charge-coupled device (CCD) mapping camera. According to the pixel coordinate of the laser footprint in the stereo image, the laser altimeter together with the footprint camera can provide planimetric geodetic coordinates for the control points of a higher accuracy than the other traditional satellite laser altimeters, and represents a new technology for satellite mapping. In this article, a laser footprint planimetric location method for the GF-7 satellite is proposed. The method is designed based on the main payload characteristics of GF-7 and the working modes of the laser altimeter by the combined use of subpixel phase correlation image matching and four types of laser spot center detection methods. The planimetric positioning accuracies of the laser spots in urban, suburban, farmland, forest, mountainous, and ice sheet areas were also analyzed. The experimental results show that the accuracy of planimetric location relative to stereo image for the laser footprint is 0.3–1.0 m (except for ice sheets ~12 m) when the footprint camera works under the synchronous mode, and 0.2–0.4 m when the footprint camera works under asynchronous mode (AM).

Performance evaluation of GEDI and ICESat-2 laser altimeter data for terrain and canopy height retrievals

With the advent of the next generation of space-based laser altimeters, ICESat-2 and GEDI, we are entering an exciting era of active remote sensing of forests that offers unprecedented opportunities for the observation of forest structure. Consistent comparisons of the accuracy of terrain and canopy height retrievals for these two missions are essential for continued improvement and further application. Because the time interval between the spaceborne products and validation data may introduce additional errors, we validate the newly released GEDI L2A product (version 2) and the ICESat-2 ATL08 product (version 4) using high-resolution, locally calibrated airborne lidar products acquired in the same year (2019) as the reference datasets. In addition, our study area contains 40 sites located in the U.S. mainland, Alaska, and Hawaii that encompass a variety of eco-climatic zones and vegetation cover types; thus, it avoids the uncertainties associated with small sample sizes and restricted spatial coverage. The results show that ICESat-2 and GEDI yield reasonable estimates of terrain height, with root mean squared errors (RMSEs) of 2.24 and 4.03 m for mid and low latitudes, respectively, and 0.98 m for high latitudes (ICESat-2 only). ICESat-2 outperforms GEDI across the board for terrain height retrieval, although they both have better accuracy than existing SRTM and GMTED DEM products. Analyses of the error factors suggest that steep slopes (>30°) present the greatest challenge for both GEDI and ICESat-2; in addition, tall (>20 m) and dense canopies (>90%) forest ecosystems also reduce the accuracy of the terrain height estimates. When ICESat-2 and GEDI data are used for canopy height retrieval, the use of only strong/power beam data acquired at night is recommended, as the overall RMSEs decrease from 7.21 and 5.02 m to 3.93 and 3.56 m, respectively, compared to using all data regardless of daytime and beam strength. GEDI outperforms ICESat-2 across the board for canopy height retrieval, as ICESat-2 has a larger potential bias for almost all forest types and cover conditions. ICESat-2 tends to overestimate the canopy height of dwarf shrublands and underestimate the canopy height of forest, and there is a gradual downward shift in the distribution of residuals with increasing canopy height. Overall, ICESat-2 with photon counting technology and GEDI with full waveform technology each represent the state of the art in spaceborne laser altimeters for terrain and canopy height retrieval. Combined, these two missions can take advantage of the unique strengths of each instrument.

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.

Multiscale Fusion Signal Extraction for Spaceborne Photon-Counting Laser Altimeter in Complex and Low Signal-to-Noise Ratio Scenarios

Extracting signal photons from noisy raw data is one of the critical processes for the new generation of spaceborne photon-counting laser altimeter. Affected by vast noise photon-counting events, the extraction of weak signal events still faces challenges in complex scenarios with low signal-to-noise ratio (SNR). Aiming to improve the extraction ability of signal photon events in these scenarios, a multiscale fusion signal extraction method was proposed, characterized by combining global spatial correlation constraint with optimized local spatial correlation constraint. The local constraint is implemented based on a density-based spatial clustering of applications with noise (DBSCAN) clustering method with adaptive parameter estimation, which is used to extract possible signal photons. A subsequent global constraint based on the spatial correlation of the terrain profiles is designed to remove the pseudo-signal photons clustered in the local constraints’ step. The global constraint is implemented based on a cost function, which is used to quantify different candidate paths. Our method was verified based on the actual Ice, Cloud, and land Elevation satellite-(ICESat2) data containing vegetation, mountains, and residential areas. The experimental results show that compared with the ICESat-2 extraction method, our method can significantly improve the precision and recall rate of signal photon events from the low SNR photon-counting data.

Surface heights and crevasse morphologies of surging and fast-moving glaciers from ICESat-2 laser altimeter data - Application of the density-dimension algorithm (DDA-ice) and evaluation using airborne altimeter and Planet SkySat data

NASA’s Ice, Cloud and land Elevation Satellite ICESat-2, launched September 15, 2018, carries the first space-borne multi-beam micro-pulse photon-counting laser altimeter system, the Advanced Topographic Laser Altimeter System (ATLAS). Observations from ATLAS are acquired in three pairs of weak and strong beams with 0.7 ​m nominal along-track spacing (under clear-sky conditions). The recording of the observations as a photon point cloud, which includes signal and background/noise events, requires a dedicated algorithm for identification of signal photons and determination of surface heights. The objectives of this paper are to demonstrate that measurements from ICESat-2 allow determination of heights over heavily crevassed ice surfaces and yield elevation profiles that present morphological characteristics that are typical of fast-moving and accelerating glaciers. Surface-height determination from the photon point cloud is facilitated by the density-dimension algorithm for ice surfaces, the DDA-ice. The DDA-ice returns surface heights at the 0.7 ​m sensor resolution for strong and weak beams, it utilizes a radial basis function for data aggregation and automatically adapts to changing environmental conditions and background characteristics, including time of day and apparent surface reflectance. In contrast, the official Land-Ice Along-Track Height Product, ATL06, provides surface heights at 40 ​m resolution with 20 ​m postings. The DDA-ice signal classification consistently identifies photons from complex reflectors in both the strong and weak ATLAS beams and hence constitutes a significant advance over the signal classification on the ATL03 Global Geolocated Photons Product. Results are evaluated using (1) airborne laser altimeter data collected during our ICESat-2 validation campaign over Negribreen, Svalbard, during surge, and (2) high-resolution (0.72 ​m or 0.86 ​m) satellite image data from Planet SkySat acquired over Ilulissat Ice Stream (Jakobshavn Isbræ), Greenland. Using DDA-ice analysis, ICESat-2 data allow discrimination of ice-surface types from surging glaciers (Negribreen) and continuously fast-moving and accelerating glaciers (Jakobshavn Isbræ) based on morphological characteristics.

Design and Data Processing of China's First Spaceborne Laser Altimeter System for Earth Observation: GaoFen-7

The GaoFen-7 (GF-7) satellite, which was launched on November 3, 2019, is China's first civilian submeter stereo mapping satellite. The satellite is equipped with the first laser altimeter officially in China for earth observation. Except for the laser altimeter, the GF-7 spaceborne laser altimeter system also includes two laser footprint cameras and a laser optical axis surveillance camera. The laser altimeter system is designed and used to assist improving the elevation accuracy without ground control points of the two line-array stereo mapping cameras. This article details the design of the GF-7 spaceborne laser altimeter system, its ranging performance in the laboratory, and its data processing method. The type of data products is also released. These data will play a vital role in the application of geography, glaciology, forestry, and other industries.

Mixed Centroid Method for Spaceborne Laser Altimeter Calibration

The on-orbit calibration of spaceborne laser altimeters is based on traditional geometric calibration in which the laser pointing angle error is the primary evaluation index. This study proposes a mixed centroid method for calculating the laser footprint center by using the geometric center of each energy level eliminating the abnormal energy level points, inverting the laser energy level distribution, and correcting the true footprint. Then, the mixed centroid method is compared with the traditional geometric center method and the centroid method. Simulation comparisons demonstrate that the proposed method offers high precision and low calibration cost, thereby improving the calibration accuracy of the target. The proposed method provides a reference algorithm for the ground field calibration of the next generation of high-resolution mapping satellites in China.

Iterative Pointing Angle Calibration Method for the Spaceborne Photon-Counting Laser Altimeter Based on Small-Range Terrain Matching

The satellite, Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) has been equipped with a new type of spaceborne laser altimeter, which has the benefits of having small footprints and a high repetition rate, and it can produce dense footprints on the ground. Focusing on the pointing angle calibration of this new spaceborne laser altimeter, this paper proposes a fast pointing angle calibration method using only a small range of terrain surveyed by airborne lidar. Based on the matching criterion of least elevation difference, an iterative pointing angle calibration method was proposed. In the experiment, the simulated photon-counting laser altimeter data and the Ice, Cloud and Land Elevation Satellite-2 data were used to verify the algorithm. The results show that when 1 km and 2.5 km lengths of track were used, the pointing angle error after calibration could be reduced to about 0.3 arc-seconds and less than 0.1 arc-seconds, respectively. Meanwhile, compared with the traditional pyramid search method, the proposed iterative pointing angle calibration method does not require well-designed parameters, which are important in the pyramid search method to balance calculation time and calibration result, and the iterative pointing angle calibration method could significantly reduce the calibration time to only about one-fifth of that of the pyramid search method.