Spaceborne radar sounders are an important class of remote sensing instruments which operate by recording backscattered electromagnetic waves in the vicinity of a solid planetary body. The incoming waves are generally transmitted by the radar itself (active sounding), although external signals of opportunity can also be used (passive sounding). There are currently two major planetary radar sounders under development, both headed to the Jovian icy moons (Europa, Ganymede and Callisto). Designing a radar sounder is a very challenging process involving careful leveraging of heritage and predictive tools, and in which backscattering simulators play a central role. This is especially true for coherent simulators, due to their higher accuracy and the possibility they offer to apply advanced processing techniques on the resulting simulated data, such as synthetic aperture radar focusing, or any other operation which requires field amplitude, phase and polarisation. For this reason, designing computationally-efficient coherent simulators is an important and active research area. The first contribution of this thesis is a novel multilayer coherent simulator based on the Stratton-Chu equation and the linear phase approximation, which can generate realistic simulated radar data on a wide range of surface and subsurface digital elevation models (DEM), using only a fraction of the computational resources that a finite-difference time-domain method would need. Thorough validation was conducted against both theoretical formulations and real data, which confirmed the accuracy of the method. The method was then generalised to noisy active and passive sounding, which is an important capability in the context of the proposed use of passive sounding on the Jovian icy moons. Provided that representative information about the surface and this external field exists, the simulator could compare the relative scientific value of active and passive sounding of a given target under given conditions. However, quality DEMs of the Jovian icy moons are scarce. For this reason we also present a comparative study of the fractal roughness of Europa and Mars (a much better studied body), where we derive fractal analogue maps of twelve types of Europan terrains on Mars. These maps could be used to guide the choice of Martian DEMs on which to perform representative backscattering simulations for future radar missions on Europa. Finally, we explore the possibility of entirely new radar architectures with the novel concept of the distributed radar. In a distributed sounder, very large across-track antennas can be synthesised from smallsats flying on selected orbits, providing a way to obtain a highly-directive antenna without the need to deploy large and complex structures in space. We develop an analytical formulation to treat the problem of beamforming with an array affected by perturbations on the positions of its array elements, and propose a set of Keplerian parameters that enable the concept.

Radar sounders (RS) are low frequency remote sensing instruments that profile the shallow subsurface of planetary bodies providing valuable scientific information. The prediction of the RS performance and the interpretation of the target properties from the RS data are challenging due to the complex electromagnetic interaction between many acquisition variables. RS simulations address this issue by forward modeling this complex interaction and simulating the radar response. However, existing simulators require detailed and subjective modeling of the target in order to produce realistic radargrams. For less-explored planetary bodies, such information is difficult to obtain with high accuracy. Moreover, the high computational requirements of conventional electromagnetic simulators prohibit the simulation of a large number of radargrams. Thus, it is not possible to generate and analyze a database of simulated radargrams representative of the acquisition scenario that would be very useful for both the RS design and the data analysis phase. To overcome these difficulties and to produce realistic simulated radargrams, this thesis proposes two novel approaches to the simulation and analysis of the radar response. The first contribution is a simulation approach that leverages the data available over geological analogs of the investigated target and reprocesses them to obtain the simulated radargrams. The second contribution is a systematic approach to the generation and analysis of a database of simulated radargrams representing the possible scenarios during the RS acquisition. The database is analyzed to predict the RS performance, to design the instrument parameters, and to support the development of automatic target detection algorithms. To demonstrate the proposed techniques the thesis addresses their use in two future RS instruments, which are at different phases of development: (1) the Radar for Icy Moons Exploration (RIME) and (2) a RS for Earth observation of the polar ice caps. The first contribution focuses on the analysis of the detectability of complex tectonic targets on the icy moons of Jupiter by RIME by simulating the radar response of 3D target models. The second contribution presents a feasibility study for an Earth orbiting RS based on the proposed simulation approaches.

This Ph.D. thesis presents advancements in the analysis of radar sounder data. Radar sounders (RSs) are remote sensors that transmit an electromagnetic (EM) wave at the nadir direction that penetrates the subsurface. The backscattered echoes captured by the RS antenna are coherently summed to generate an image of the subsurface profile known as a radargram. The first focus of this work is to automate the segmentation of radargrams using deep learning methodologies while minimizing the need for labeled training data. The surge in radar sounding data volume necessitates efficient automated methods. However, the amount of training labeled data in this field is strongly limited. This first work introduces a transfer learning framework based on deep learning tailored for radar sounder data that minimizes the training data requirements. This method automatically identifies and segments geological units within radargrams acquired in the cryosphere. With the cryosphere being a critical indicator of climate change, understanding its dynamics is paramount. Geological details within radargrams, such as the basal interface or the inland and floating ice, are key to this understanding. Our work shifts the focus to uncharted territory: the coastal areas of Antarctica. Novel targets such as floating ice and crevasses add complexity to the data, but the transfer learning framework minimizes the need for extensive labeled training data. The results, based on data from Antarctica, confirm the effectiveness of the approach, promising adaptability to other targets and radar data from existing and future planetary missions like RIME and SRS. The second focus of this thesis explores the generation of novel and improved geological data products by harnessing the unique characteristics of radar sounder data, including subsurface information and so-called “unwanted” clutter. The thesis introduces two methods that use RS data to generate geological products. The first contribution proposes a global high-frequency radar image of Mars. This product delivers a novel, comprehensive global radar image of Mars, capturing both surface and shallow subsurface structures. The method unlocks the potential to explore concealed Martian geology and further understand Martian geological features like dust, revealing possible candidate large dust deposits that were unknown until now. Furthermore, this method can potentially offer insights into celestial bodies beyond Mars, such as the detection of new lunar facets and Venusian geological formations. The third contribution aims to generate Digital Elevation Models (DEM) from single swath radargrams. The activity addresses the challenge of precise bed DEM estimations in Antarctica. Bed topography is critical in ice modeling and mass balance calculations, yet existing methods face limitations. To overcome these, we employ a generative adversarial network (GAN) approach that utilizes clutter information from single radargrams. This innovative technique promises to refine bed DEMs and enhance our understanding of glacier erosion and ice dynamics. The proposed methodologies were validated with data acquired on both Earth and Mars, showing promising results and confirming their effectiveness.

Radar Sounders (RSs) are active sensors that transmit in the nadir electromagnetic (EM) waves with a low frequency in the range of High-Frequency and Very-High-Frequency and relatively wide bandwidth. Such a signal penetrates the surface and propagates in the subsurface, interacting with dielectric interfaces. This interaction yields to backscattered echoes detectable by the antenna that are coherently summed and stored in radargrams. RSs are used for planetary exploration and Earth observation for their value in investigating subsurface geological structures and processes, which reveal the past geomorphological history and possible future evolution. RS instruments have several parameter configurations that have to be designed to achieve the mission science goals. On Mars, radargram visual analyses revealed the icy layered deposits and liquid water evidence in the poles. On the Earth, RSs showed relevant structures and processes in the cryosphere and the arid areas that help to monitor the subsurface geological evolution, which is critical for climate change. Despite the valuable results, visual analysis is subjective and not feasible for processing a large amount of data. Therefore, a need emerges for automatic methods extracting fast and reliable information from radargrams. The thesis addresses two main open issues of the radar-sounding literature: i) assessing target detectability in simulated orbiting radargrams to guide the design of RS instruments, and ii) designing automatic methods for information extraction from RS data. The RS design is based on assessing the performance of a given instrument parameter configuration in achieving the mission science goals and detecting critical targets. The assessment guides the parameter selection by determining the appropriate trade-off between the achievable performance and technical limitations. We propose assessing the detectability of subsurface targets (e.g., englacial layering and basal interface) from satellite radar sounders with novel performance metrics. This performance assessment strategy can be applied to guide the design of the SNR budget at the surface, which can further support the selection of the main EORS instrument parameters. The second contribution is designing automatic methods for analyzing radargrams based on fuzzy logic and deep learning. The first method aims at identifying buried cavities, such as lava tubes, exploiting their geometric and EM models. A fuzzy system is built on the model that detects candidate reflections from the surface and the lava tube boundary. The second and third proposed methods are based on deep learning, as they showed groundbreaking results in several applications. We contributed with an automatic technique for analyzing radargram acquired in icy areas to investigate the basal layer. To this end, radargrams are segmented with a deep learning network into literature classes, including englacial layers, bedrock, and echo-free zone (EFZ) and thermal noise, as well as new classes of basal ice and signal perturbation. The third method proposes an unsupervised segmentation of radargrams with deep learning for detecting subsurface features. Qualitative and quantitative experimental results obtained on planetary and terrestrial radargrams confirm the effectiveness of the proposed methods, which investigate new subsurface targets and allow an improvement in terms of accuracy when compared to other state-of-the-art methods.