Subsurface Mapping of Deserts and Polar Regions Using Radar Data on Earth and Mars

There are abundant resources buried underground that are difficult to be investigated remotely. This thesis is concerned with the development and utility of various novel processing methods for different radar instruments in the field of subsurface mapping on Earth and Mars. Firstly, advanced Synthetic Aperture Radar (SAR) imaging and Interferometric SAR (InSAR) techniques are applied to assess their potential for revealing subsurface features in the eastern Sahara Desert. The radar penetration depth at L-band (1.25 GHz) is estimated to be 1-2 m over paleochannels in the Sahara Desert, given an initial assumption that radar penetration occurs in the sand accumulation areas. The L-band frequency of previous and existing spaceborne SAR mission is shown to limit the penetration depth to a few metres below the surface. However, over the terrestrial ice-sheets, a radar instrument, the Multi-Coherent Radar Depth Sounder (MCoRDS) from the NASA Operation Ice Bridge (OIB) mission, can penetrate the ice sheet down to 3 km, revealing extensive englacial layers. An automated layer tracing method based on the Continuous Wavelet Transform (CWT) and Hough Transform (HT) is proposed to detect and digitise these englacial layers in Greenland. The results show that this proposed method can restore at least 72% of the isochrones when compared with previous results. Given the research interests of the department and inspired by the similarity of the layering phenomenon between the Earth and Martian polar regions, the layer tracing method is adjusted and applied to SHAllow RADar (SHARAD) radargrams from the Mars Reconnaissance Orbiter. This method is demonstrated on the SHARAD data in Promethei Lingula as this 6 is the only region with coherent subsurface echo returns near the south pole, resulting in the extraction of six distinct subsurface interfaces, which record past depositional and erosional history and may be associated with past climate change on Mars

Combination of MRO SHARAD and deep-learning-based DTM to search for subsurface features in Oxia Planum, Mars

Context. Oxia Planum is a mid-latitude region on Mars that attracts a great amount of interest worldwide. An orbiting radar provides an effective way to probe the Martian subsurface and detect buried layers or geomorphological features. The Shallow radar orbital radar system on board the NASA Mars reconnaissance orbiter transmits pulsed signals towards the nadir and receives returned echoes from dielectric boundaries. However, radar clutter can be induced by a higher topography of the off-nadir region than that at the nadir, which is then manifested as subsurface reflectors in the radar image. Aims. This study combines radar observations, terrain models, and surface images to investigate the subsurface features of the ExoMars landing site in Oxia Planum. Methods. Possible subsurface features are observed in radargrams. Radar clutter is simulated using the terrain models, and these are then compared to radar observations to exclude clutter and identify possible subsurface return echoes. Finally, the dielectric constant is estimated with measurements in both radargrams and surface imagery. Results. The resolution and quality of the terrain models greatly influence the clutter simulations. Higher resolution can produce finer cluttergrams, which assists in identifying possible subsurface features. One possible subsurface layering sequence is identified in one radargram. Conclusions. A combination of radar observations, terrain models, and surface images reveals the dielectric constant of the surface deposit in Oxia Planum to be 4.9–8.8, indicating that the surface-covering material is made up of clay-bearing units in this region

A High-Resolution Digital Terrain Model Mosaic of the Mars 2020 Perseverance Rover Landing Site at Jezero Crater

We demonstrate the capabilities of a published MADNet monocular height estimation network in producing a refined digital terrain model (DTM) mosaic at 50 cm/pixel resolution for the Mars 2020 Perseverance rover landing site in Jezero crater on Mars. Our approach utilizes the publicly available Mars 2020 Terrain Relative Navigation (TRN) High-Resolution Imaging Science Experiment (HiRISE) Digital Terrain Model (DTM) mosaic, which was originally created by the United States Geological Survey (USGS) Astrogeology Science Centre. Our resultant HiRISE MADNet DTM mosaic is strictly matched with the original HiRISE TRN DTM and orthoimage mosaics. These mosaics are themselves co-aligned with the USGS TRN Context Camera (CTX) based DTM and orthoimage mosaics, as well as the ESA/DLR/FUB (European Space Agency/German Aerospace Center/Free University Berlin) High Resolution Stereo Camera (HRSC) level 5 DTM and orthoimage mosaics. In this paper, we provide a brief description of the technical details, and present both visual and quantitative assessments of the refined MADNet HiRISE Jezero DTM mosaic product. This DTM product is now publicly available at http://dx.doi.org/10.17169/refubium-38359

Large Area High-Resolution 3D Mapping of the Von Kármán Crater: Landing Site for the Chang’E-4 Lander and Yutu-2 Rover

We demonstrate the creation of a large area of high-resolution (260 × 209 km2 at 1 m/pixel) DTM mosaic from the Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images over the Chang’E-4 landing site at Von Kármán crater using an in-house deep learning-based 3D modelling system developed at University College London, called MADNet, trained with lunar orthorectified images and digital terrain models (DTMs). The resultant 1 m DTM mosaic is co-aligned with the Chang’E-2 (CE-2) and the Lunar Orbiter Laser Altimeter (LOLA)—SELenological and Engineering Explorer (SELENE) blended DTM product (SLDEM), providing high spatial and vertical congruence. In this paper, technical details are briefly discussed, along with visual and quantitative assessments of the resultant DTM mosaic product. The LROC NAC MADNet DTM mosaic was compared with three independent DTM datasets, and the mean differences and standard deviations are as follows: PDS photogrammetric DTM at 5 m grid-spacing had a mean difference of −0.019 ± 1.09 m, CE-2 DTM at 20 m had a mean difference of −0.048 ± 1.791 m, and SLDEM at 69 m had a mean difference of 0.577 ± 94.940 m. The resultant LROC NAC MADNet DTM mosaic, alongside a blended LROC NAC and CE-2 MADNet DTM mosaic and a separate LROC NAC, orthorectified image mosaic, are made publicly available via the ESA planetary science archive’s guest storage facility

Subpixel-Scale Topography Retrieval of Mars Using Single-Image DTM Estimation and Super-Resolution Restoration

We propose using coupled deep learning based super-resolution restoration (SRR) and single-image digital terrain model (DTM) estimation (SDE) methods to produce subpixel-scale topography from single-view ESA Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) and NASA Mars Reconnaissance Orbiter High Resolution Imaging Science Experiment (HiRISE) images. We present qualitative and quantitative assessments of the resultant 2 m/pixel CaSSIS SRR DTM mosaic over the ESA and Roscosmos Rosalind Franklin ExoMars rover’s (RFEXM22) planned landing site at Oxia Planum. Quantitative evaluation shows SRR improves the effective resolution of the resultant CaSSIS DTM by a factor of 4 or more, while achieving a fairly good height accuracy measured by root mean squared error (1.876 m) and structural similarity (0.607), compared to the ultra-high-resolution HiRISE SRR DTMs at 12.5 cm/pixel. We make available, along with this paper, the resultant CaSSIS SRR image and SRR DTM mosaics, as well as HiRISE full-strip SRR images and SRR DTMs, to support landing site characterisation and future rover engineering for the RFEXM22

Rapid Single Image-Based DTM Estimation from ExoMars TGO CaSSIS Images Using Generative Adversarial U-Nets

The lack of adequate stereo coverage and where available, lengthy processing time, various artefacts, and unsatisfactory quality and complexity of automating the selection of the best set of processing parameters, have long been big barriers for large-area planetary 3D mapping. In this paper, we propose a deep learning-based solution, called MADNet (Multi-scale generative Adversarial u-net with Dense convolutional and up-projection blocks), that avoids or resolves all of the above issues. We demonstrate the wide applicability of this technique with the ExoMars Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) 4.6 m/pixel images on Mars. Only a single input image and a coarse global 3D reference are required, without knowing any camera models or imaging parameters, to produce high-quality and high-resolution full-strip Digital Terrain Models (DTMs) in a few seconds. In this paper, we discuss technical details of the MADNet system and provide detailed comparisons and assessments of the results. The resultant MADNet 8 m/pixel CaSSIS DTMs are qualitatively very similar to the 1 m/pixel HiRISE DTMs. The resultant MADNet CaSSIS DTMs display excellent agreement with nested Mars Reconnaissance Orbiter Context Camera (CTX), Mars Express’s High-Resolution Stereo Camera (HRSC), and Mars Orbiter Laser Altimeter (MOLA) DTMs at large-scale, and meanwhile, show fairly good correlation with the High-Resolution Imaging Science Experiment (HiRISE) DTMs for fine-scale details. In addition, we show how MADNet outperforms traditional photogrammetric methods, both on speed and quality, for other datasets like HRSC, CTX, and HiRISE, without any parameter tuning or re-training of the model. We demonstrate the results for Oxia Planum (the landing site of the European Space Agency’s Rosalind Franklin ExoMars rover 2023) and a couple of sites of high scientific interest

Fusion of multi-frequency interferometric results by using Kalman filter to generate high quality DEM

Using conventional two-pass interferometry to generate DEM with high frequency SAR data may be confronted with the problem of poor coherence in areas covered by dense vegetation, while DEM generated from low frequency SAR data interferometry is less sensitive to the topographic relief Therefore, this article aims at generating high quality DEM by fusing multi-frequency interferometric results obtained from two-pass interferometry by using Kalman filter. Meanwhile, a process of coregistration of multi-frequency SAR data which are of different pixel sizes and imaging geometries is proposed. And data from Envisat ASAR and TerraSAR-X are used for case study and the accuracy of fused DEM are verified by ASTER GDEM.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000349688103058&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Engineering, Electrical & ElectronicGeosciences, MultidisciplinaryRemote SensingEICPCI-S(ISTP)

A New Method for Automatically Tracing Englacial Layers from MCoRDS Data in NW Greenland

Englacial layering reflects ice dynamics within the ice bodies, which improves understanding of ice flow variation, past accumulation rates and vertical flows transferring between the surface and the underlying bedrock. The internal layers can be observed by using Radar Echo Sounding (RES), such as the Multi-channel Coherent Radar Depth Sounder (MCoRDS) used in NASA’s Operation IceBridge (OIB) mission. Since the 1960s, the accumulation of the RES data has prompted the development of automated methods to extract the englacial layers. In this study, we propose a new automated method that combines peak detection methods, namely the CWT-based peak detection or the Automatic Phase Picker (APP), with a Hough Transform (HT) to trace boundaries of englacial layers. For CWT-based peak detection, we test it using two different wavelets. The proposed method is tested with twelve MCoRDS radio echograms, which are acquired south of the Northern Greenland Eemian (NEEM) ice drilling site, where the folding of ice layers was observed. The method is evaluated in comparison to the isochrones that were extracted in an independent study. In comparison, the proposed new automated method can restore more than 70% of the englacial layers. This new automated layer-tracing method is publicly available on github

Large Area High-Resolution 3D Mapping of Oxia Planum: The Landing Site for the ExoMars Rosalind Franklin Rover

International audienceWe demonstrate an end-to-end application of the in-house deep learning-based surface modelling system, called MADNet, to produce three large area 3D mapping products from single images taken from the ESA Mars Express’s High Resolution Stereo Camera (HRSC), the NASA Mars Reconnaissance Orbiter’s Context Camera (CTX), and the High Resolution Imaging Science Experiment (HiRISE) imaging data over the ExoMars 2022 Rosalind Franklin rover’s landing site at Oxia Planum on Mars. MADNet takes a single orbital optical image as input, provides pixelwise height predictions, and uses a separate coarse Digital Terrain Model (DTM) as reference, to produce a DTM product from the given input image. Initially, we demonstrate the resultant 25 m/pixel HRSC DTM mosaic covering an area of 197 km × 182 km, providing fine-scale details to the 50 m/pixel HRSC MC-11 level-5 DTM mosaic. Secondly, we demonstrate the resultant 12 m/pixel CTX MADNet DTM mosaic covering a 114 km × 117 km area, showing much more detail in comparison to photogrammetric DTMs produced using the open source in-house developed CASP-GO system. Finally, we demonstrate the resultant 50 cm/pixel HiRISE MADNet DTM mosaic, produced for the first time, covering a 74.3 km × 86.3 km area of the 3-sigma landing ellipse and partially the ExoMars team’s geological characterisation area. The resultant MADNet HiRISE DTM mosaic shows fine-scale details superior to existing Planetary Data System (PDS) HiRISE DTMs and covers a larger area that is considered difficult for existing photogrammetry and photoclinometry pipelines to achieve, especially given the current limitations of stereo HiRISE coverage. All of the resultant DTM mosaics are co-aligned with each other, and ultimately with the Mars Global Surveyor’s Mars Orbiter Laser Altimeter (MOLA) DTM, providing high spatial and vertical congruence. In this paper, technical details are presented, issues that arose are discussed, along with a visual evaluation and quantitative assessments of the resultant DTM mosaic products

Time-Series Analysis on Persistent Scatter-Interferometric Synthetic Aperture Radar (PS-InSAR) Derived Displacements of the Hong Kong–Zhuhai–Macao Bridge (HZMB) from Sentinel-1A Observations

The synthetic aperture radar interferometry (InSAR) technique has been applied in monitoring the deformation of infrastructures, such as bridges, highways, railways and subways. Persistent scatterer (PS)-InSAR is one of the InSAR techniques, which utilises persistent scatterers to derive long-term displacements. This study applied time-series methods to post-process the PS-InSAR-derived time-series displacements with the use of 86 Sentinel-1A acquisitions spanning from 6 January 2018 to 27 November 2020. Empirical mode decomposition (EMD) and seasonal and trend decomposition using loess (STL) were combined to estimate the seasonal component of the total time-series displacements. Then, a temperature correlation map was generated by correlating the seasonal component with the temperature variation. Results show that the thermal expansion phenomenon is pronounced on the buildings of the Zhuhai–Macao Passenger Terminal as well as the bridge and road connecting to the Hong Kong International Airport (HKIA), while it is less obviously observed at the main Hong Kong-Zhuhai-Macao Bridge (HZMB). In addition, sudden changes between subsidence and uplift can be detected through the p-values derived by applying the augmented Dickey-Fuller (ADF) test to the residual signals after removing the linear and seasonal components from the original ones