Signal Photon Extraction Method for Weak Beam Data of ICESat-2 Using Information Provided by Strong Beam Data in Mountainous Areas

Abstract

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) can measure the elevations of the Earth’s surface using a sampling strategy with unprecedented spatial detail. In the daytime of mountainous areas where the signal–noise ratio (SNR) of weak beam data is very low, current algorithms do not always perform well on extracting signal photons from weak beam data (i.e., many signal photons were missed). This paper proposes an effective algorithm to extract signal photons from the weak beam data of ICESat-2 in mountainous areas. First, a theoretical equation of SNR for ICESat-2 measured photons in mountainous areas was derived to prove that the available information provided by strong beam data can be used to assist the signal extraction of weak beam data (that may have very low SNR in mountainous areas). Then, the relationship between the along-track slope and the noise level was used as the bridge to connect the strong and weak beam data. To be specific, the along-track slope of the weak beam was inversed by the slope–noise relationship obtained from strong beam data, and then was used to rotate the direction of the searching neighborhood in the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. With the help of this process, the number of signal photons included in the searching neighborhood will significantly increase in mountainous areas and will be easily detected from the measured noisy photons. The proposed algorithm was tested in the Tibetan Plateau, the Altun Mountains, and the Tianshan Mountains in different seasons, and the extraction results were compared with the results from the ATL03 datasets, the ATL08 datasets, and the classical DBSCAN algorithm. Based on the ground-truth signal photons obtained by visual inspection, the parameters of the classification precision, recall, and F-score of our algorithm and three other algorithms were calculated. The modified DBSCAN could achieve a good balance between the classification precision (93.49% averaged) and recall (89.34% averaged), and its F-score (more than 0.91) was higher than that of the other three methods, which successfully obtained a continuous surface profile from weak beam data with very low SNRs. In the future, the detected signal photons from weak beam data are promising to assess the elevation accuracy achieved by ICESat-2, estimate the along-track and cross-track slope, and further obtain the ground control points (GCPs) for stereo-mapping satellites in mountainous areas.

Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm

“…The ICESat-2 carries an advanced terrain laser altimeter system (ATLAS), which emits three pairs of beams. The distance between each pair of beams is about 3.3 km, and the distance between two trajectories in the beam is 90 m. Three pairs of laser beams represent six ground trajectories, and the corresponding data numbers from left to right are, respectively, Ground Track 1 Left (GT1L) to Ground Track 3 Right (GT3R) [14,19,47,48].…”

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

“…In these studies, the volume and direction of the ellipsoid searching area were determined by a principal component analysis (PCA) method. However, the study areas of all above research were relatively flat with an elevation change less than 20 m. Zhang et al [18] demonstrated the relation between the signal-to-noise ratio (SNR) and several influential factors, including beam type, data collection time, and topography. As the slope increases, the SNR decreases sharply, and the same pattern applies for the sun presence and the SNR as well [18].…”

“…However, the study areas of all above research were relatively flat with an elevation change less than 20 m. Zhang et al [18] demonstrated the relation between the signal-to-noise ratio (SNR) and several influential factors, including beam type, data collection time, and topography. As the slope increases, the SNR decreases sharply, and the same pattern applies for the sun presence and the SNR as well [18]. Such situations become worse for the weak beam due to a further decrease on the SNR caused by a lower transmit energy [18].…”

“…Such situations become worse for the weak beam due to a further decrease on the SNR caused by a lower transmit energy [18]. Therefore, Zhang et al [18] took the advantage of the relationship between SNR and slope obtained from the strong beam to estimate the along-track slope of the corresponding weak beam and succeeded in identifying more reliable signal photons for the weak beam in mountain areas. However, their research area did not contain much forest coverage.…”

Very High-Resolution Satellite-Derived Bathymetry and Habitat Mapping Using Pleiades-1 and ICESat-2

“…The noise arises from several sources, including the laser pulse being scattered by the atmosphere, the solar background noise effects, and the detector dark noise. In our study, the main noise source is associated with photons that are scattered by particles in the water column [22]. Based on this first clustering, photons from the seabed were identified and corrected for the refraction effect occurring at the air-water interface.…”