High-Precision Digital Surface Model Extraction from Satellite Stereo Images Fused with ICESat-2 Data

The lack of ground control points (GCPs) affects the elevation accuracy of digital surface models (DSMs) generated by optical satellite stereo images and limits the application of high-resolution DSMs. It is a feasible idea to use ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) laser altimetry data to improve the elevation accuracy of optical stereo images, but it is necessary to accurately match the two types of data. This paper proposes a DSM registration strategy based on terrain similarity (BOTS), which integrates ICESat-2 laser altimetry data without GCPs and improves the DSM elevation accuracy generation from optical satellite stereo pairs. Under different terrain conditions, Worldview-2, SV-1, GF-7, and ZY-3 stereo pairs were used to verify the effectiveness of this method. The experimental results show that the BOTS method proposed in this paper is more robust when there are a large number of abnormal points in the ICESat-2 data or there is a large elevation gap between DSMs. After fusion of ICESat-2 data, the DSM elevation accuracy extracted from the satellite stereo pair is improved by 73~92%, and the root mean square error (RMSE) of Worldview-2 DSM reaches 0.71 m.

Influence of Atmospheric Scattering on the Accuracy of Laser Altimetry of the GF-7 Satellite and Corrections

Satellite laser altimetry can obtain sub-meter or even centimeter-scale surface elevation data over large areas, but it is inevitably affected by scattering caused by clouds, aerosols, and other atmospheric particles. This laser ranging error caused by scattering cannot be ignored. In this study, we systematically combined existing atmospheric scattering identification technology used in satellite laser altimetry and observed that the traditional algorithm cannot effectively estimate the laser multiple scattering of the GaoFen-7 (GF-7) satellite. To solve this problem, we used data from the GF-7 satellite to analyze the importance of atmospheric scattering and propose an identification scheme for atmospheric scattering data over land and water areas. We also used a look-up table and a multi-layer perceptron (MLP) model to identify and correct atmospheric scattering, for which the availability of land and water data reached 16.67% and 26.09%, respectively. After correction using the MLP model, the availability of land and water data increased to 21% and 30%, respectively. These corrections mitigated the low identification accuracy due to atmospheric scattering, which is significant for facilitating satellite laser altimetry data processing.

First results of undersea muography with the Tokyo-Bay Seafloor Hyper-Kilometric Submarine Deep Detector

Tidal measurements are of great significance since they may provide us with essential data to apply towards protection of coastal communities and sea traffic. Currently, tide gauge stations and laser altimetry are commonly used for these measurements. On the other hand, muography sensors can be located underneath the seafloor inside an undersea tunnel where electric and telecommunication infrastructures are more readily available. In this work, the world’s first under-seafloor particle detector array called the Tokyo-bay Seafloor Hyper-Kilometric Submarine Deep Detector (TS-HKMSDD) was deployed underneath the Tokyo-Bay seafloor for conducting submarine muography. The resultant 80-day consecutive time-sequential muographic data were converted to the tidal levels based on the parameters determined from the first-day astronomical tide height (ATH) data. The standard deviation between ATH and muographic results for the rest of a 79-day measurement period was 12.85 cm. We anticipate that if the length of the TS-HKMSDD is extended from 100 m to a full-scale as large as 9.6 km to provide continuous tidal information along the tunnel, this muography application will become an established standard, demonstrating its effectiveness as practical tide monitor for this heavy traffic waterway in Tokyo and in other important sea traffic areas worldwide.

A High-Resolution Antarctic Grounding Zone Product from ICESat-2 Laser Altimetry

The Antarctic grounding zone, which is the transition between the fully grounded ice sheet to freely floating ice shelf, plays a critical role in ice sheet instability, mass budget calculations and ice sheet model projections. It is therefore important to continuously monitor its location and migration over time. Here we present the first ICESat-2-derived high-resolution grounding zone product of the Antarctica Ice Sheet, including three important boundaries: the inland limit of tidal flexure (Point F), inshore limit of hydrostatic equilibrium (Point H) and the break-in-slope (Point Ib). This dataset was derived from automated techniques developed in this study, using ICESat-2 laser altimetry repeat tracks between 30 March 2019 and 30 September 2020. The new grounding zone product has a near complete coverage of the Antarctica Ice Sheet with a total of 21346 Point F, 18149 Point H and 36765 Point Ib identified, including the difficult to survey grounding zones, such as the fast-flowing glaciers draining into the Amundsen Sea Embayment. The locations of newly derived ICESat-2 landward limit of tidal flexure agree well with the most recent differential synthetic aperture radar interferometry (DInSAR) observations in 2018, with the mean absolute separation and standard deviation of 0.02 and 0.02 km, respectively. By comparing the ICESat-2-derived grounding zone with the previous grounding zone products, we find up-to 15 km grounding line retreat on the Crary Ice Rise of Ross Ice Shelf and the pervasive landward grounding line migration along the Amundsen Sea Embayment during the past two decades. We also identify the presence of ice plain on the Filchner-Ronne Ice Shelf and the influence of oscillating ocean tides on the grounding zone migration. The product derived from this study is available at https://doi.org/10.5523/bris.bnqqyngt89eo26qk8keckglww (Li et al., 2021) and is archived and maintained at the National Snow and Ice Data Center.

Combined Block Adjustment for Optical Satellite Stereo Imagery Assisted by Spaceborne SAR and Laser Altimetry Data

To ensure the accuracy of large-scale optical stereo image bundle block adjustment, it is necessary to provide well-distributed ground control points (GCPs) with high accuracy. However, it is difficult to acquire control points through field measurements outside the country. Considering the high planimetric accuracy of spaceborne synthetic aperture radar (SAR) images and the high elevation accuracy of satellite-based laser altimetry data, this paper proposes an adjustment method that combines both as control sources, which can be independent from GCPs. Firstly, the SAR digital orthophoto map (DOM)-based planar control points (PCPs) acquisition is realized by multimodal matching, then the laser altimetry data are filtered to obtain laser altimetry points (LAPs), and finally the optical stereo images’ combined adjustment is conducted. The experimental results of Ziyuan-3 (ZY-3) images prove that this method can achieve an accuracy of 7 m in plane and 3 m in elevation after adjustment without relying on GCPs, which lays the technical foundation for a global-scale satellite image process.

Towards machine learning assisted error identification in orbital laser altimetry for tides derivation

<p>Laser altimetry experiments on the NASA MESSENGER mission [1], and on the currently on cruise ESA/JAXA BepiColombo Mission [2,3] did and are going to yield, respectively, a plethora of range measurements of the surface of Mercury. Orbital laser altimetry can be used to derive tidal parameters, which can in turn be used to infer properties of a body’s interior [4,5]. The derivation of tidal parameters requires large datasets of precise and accurate measurements. Errors as well as outliers can degrade the quality of the computed tidal parameters. While many outliers can be filtered though conventional automated processes, other errors could only be identified by human supervision. In the face of the amount of data involved, systematic user interaction at the error identification step becomes unpractical. A neural network trained with user expertise could help spotting outliers and errors and would improve the derived parameters in accuracy and precession. We started developing a neural network based on the pytorch framework[6] and compared the performance with a small training dataset form the MESSENGER Laser Altimeter (MLA) for a linear and a convolutional network. The results were much in favour of the linear network [7]. In this presentation we explore the reasons behind bad convolutional network performance with extended training and test datasets. We are going to show our results for the filtered datasets and the impact this has on the derived tidal parameters. The filtering with an artificial neural network might be useful for other applications, as well.</p> <p>1. Cavanaugh, J. F. et al. The Mercury Laser Altimeter Instrument for the MESSENGER Mission. Space Sci Rev 131, 451–479 (2007).</p> <p>2. Benkhoff, J. et al. BepiColombo—Comprehensive exploration of Mercury: Mission overview and science goals. Planetary and Space Science 58, 2–20 (2010).</p> <p>3. Thomas, N. et al. The BepiColombo Laser Altimeter. Space Sci Rev 217, 25 (2021).</p> <p>4. Thor, R. N. et al. Determination of the lunar body tide from global laser altimetry data. J Geod 95, 4 (2021).</p> <p>5. Thor, R. N. et al. Prospects for measuring Mercury’s tidal Love number h2 with the BepiColombo Laser Altimeter. A&A 633, A85 (2020).</p> <p>6. Paszke, A., et al., PyTorch: An Imperative Style, High-Performance Deep Learning Library, In: Advances in Neural Information Processing Systems 32, pp 8024–8035, 2019.</p> <p>7. Stenzel, O., Thor, R., and Hilchenbach, M.: Error identification in orbital laser altimeter data by machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14749, https://doi.org/10.5194/egusphere-egu21-14749, 2021.</p>