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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 34153

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Special Issue Editors

Dr. Stefano Tebaldini

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Guest Editor

Politecnico di Milano, Department of Information, Electronics, and Bioengineering, 20133 Milan, Italy
Interests: radar remote sensing; diffraction tomography; inverse problems; EM imaging; multi-channel SAR processing; signal and image processing

Prof. Dr. Andreas Reigber

E-Mail Website
Co-Guest Editor

Microwaves and Radar Institute, SAR Technology Department, German Aerospace Center (DLR), 82234 Wessling, Germany
Interests: airborne SAR sensors; radar remote sensing; diffraction tomography; inverse problems; EM imaging; multi-channel SAR processing; signal and image processing
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Prof. Laurent Ferro-Famil

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Co-Guest Editor

Institute of Electronics and Telecommunications of Rennes, University of Rennes 1, 263, Av. Leclerc Campus Beaulieu, Bât 11D, 35042 Rennes, FranceCESBIO, 18 Av Belin, BPI 2801, 31401 Toulouse, France
Interests: EM and radar imaging; SAR tomography; signal processing; snow remote sensing; inverse problems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of synthetic aperture radar (SAR) data is currently a standard for many scientific, commercial, and civilian applications. SAR systems provide a unique combination of features relevant to remote sensing, such as large spatial coverage, fine resolution, and all-weather operation capabilities. Moreover, the SAR signal can penetrate for meters, or even tens of meters, into natural media that are nontransparent at optical frequencies, providing access to information which is hidden to optical and hyper-spectral sensors.

In this context, the introduction of SAR tomography has opened up the way to a completely new approach to look at SAR data. Tomographic SAR surveys require illumination under different view angles to form a data-stack containing multiple SAR images of the same area. The data-stack is then focused via digital signal processing to produce a collection of voxels that represent the backscattered energy in three dimensions, thus allowing direct imaging of the interior of the illuminated media.

The inclusion of the third dimension marked a milestone in the development of SAR applications and technologies. Space agencies, in the first place, have increasingly been investing in SAR tomography in the last few years, funding both airborne and ground-based campaigns aimed at evaluating the potentials of SAR tomography and its feasibility in the context of spaceborne remote sensing. As a token of its potential, SAR tomography has been assigned a dedicated 14-month acquisition phase in the context of the forthcoming ESA Earth Explorer mission BIOMASS, to be launched in 2022, and it has increasingly been considered as a selling point for future bistatic missions operating at L-Band and higher frequencies.

The use of SAR tomography has been demonstrated by different research teams in different application contexts. Results include, in the first place, a characterization of the interior structure of natural media, such as forested areas, snow, ice sheets and glaciers, well beyond the capabilities of conventional SAR imagery and SAR interferometry (InSAR). Tomographic methods have also been demonstrated for tracking temporal variations of the interior of natural media, as well as to provide enhanced sensitivity to bio- and geophysical parameters.

These outstanding scientific achievements have been flanked by the introduction of new data processing methods. As made clear since early works, tomographic imaging poses strict requirements in terms of signal processing. This spurred the development of enhanced algorithms for phase calibration, focusing from incomplete or irregular data, space/time multidimensional analysis, and the introduction of three-dimensional time domain focusing approaches.

Following this brief introduction to the wonders of tomographic SAR imaging, we would like to invite you to participate in a Special Issue of Remote Sensing focusing on SAR tomography of natural media. The Special Issue is intended to bring to the reader’s attention any aspect relevant to tomographic imaging of distributed media. Hence, we encourage both experimental contributions, presenting case studies and scientific findings, and theoretical contributions relevant to any aspect of tomographic processing. The received manuscripts will be peer-reviewed by experts to select outstanding papers for final inclusion in the Special Issue. The submission deadline is 30 June 2020.

Dr. Stefano Tebaldini
Prof. Dr. Andreas Reigber
Dr. Laurent Ferro-Famil
Guest Editors

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Keywords

SAR
SAR Tomography
forestry
snow
ice
glacier
phase calibration
three-dimensional SAR focusing
differential SAR tomography
BIOMASS
Tandem-L

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Published Papers (9 papers)

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22 pages, 6031 KiB

Open AccessArticle

Evaluation of P-Band SAR Tomography for Mapping Tropical Forest Vertical Backscatter and Tree Height

by Naveen Ramachandran, Sassan Saatchi, Stefano Tebaldini, Mauro Mariotti d’Alessandro and Onkar Dikshit

Remote Sens. 2021, 13(8), 1485; https://doi.org/10.3390/rs13081485 - 13 Apr 2021

Cited by 9 | Viewed by 3193

Abstract

Low-frequency tomographic synthetic aperture radar (TomoSAR) techniques provide an opportunity for quantifying the dynamics of dense tropical forest vertical structures. Here, we compare the performance of different TomoSAR processing, Back-projection (BP), Capon beamforming (CB), and MUltiple SIgnal Classification (MUSIC), and compensation techniques for estimating forest height (FH) and forest vertical profile from the backscattered echoes. The study also examines how polarimetric measurements in linear, compact, hybrid, and dual circular modes influence parameter estimation. The tomographic analysis was carried out using P-band data acquired over the Paracou study site in French Guiana, and the quantitative evaluation was performed using LiDAR-based canopy height measurements taken during the 2009 TropiSAR campaign. Our results show that the relative root mean squared error (RMSE) of height was less than 10%, with negligible systematic errors across the range, with Capon and MUSIC performing better for height estimates. Radiometric compensation, such as slope correction, does not improve tree height estimation. Further, we compare and analyze the impact of the compensation approach on forest vertical profiles and tomographic metrics and the integrated backscattered power. It is observed that radiometric compensation increases the backscatter values of the vertical profile with a slight shift in local maxima of the canopy layer for both the Capon and the MUSIC estimators. Our results suggest that applying the proper processing and compensation techniques on P-band TomoSAR observations from space will allow the monitoring of forest vertical structure and biomass dynamics. Full article

(This article belongs to the Special Issue SAR Tomography of Natural Media)

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39 pages, 18797 KiB

Open AccessArticle

Tomographic Performance of Multi-Static Radar Formations: Theory and Simulations

by Ilgin Seker and Marco Lavalle

Remote Sens. 2021, 13(4), 737; https://doi.org/10.3390/rs13040737 - 17 Feb 2021

Cited by 12 | Viewed by 3463

Abstract

3D imaging of Earth’s surface layers (such as canopy, sub-surface, or ice) requires not just the penetration of radar signal into the medium, but also the ability to discriminate multiple scatterers within a slant-range and azimuth resolution cell. The latter requires having multiple radar channels distributed in across-track direction. Here, we describe the theory of multi-static radar tomography with emphasis on resolution, SNR, sidelobes, and nearest ambiguity location vs. platform distribution, observation geometry, and different multi-static modes. Signal-based 1D and 2D simulations are developed and results for various observation geometries, target distributions, acquisition modes, and radar parameters are shown and compared with the theory. Pros and cons of multi-static modes are compared and discussed. Results for various platform formations are shown, revealing that unequal spacing is useful to suppress ambiguities at the cost of increased multiplicative noise. In particular, we demonstrate that the multiple-input multiple-output (MIMO) mode, in combination with nonlinear spacing, outperforms the other modes in terms of ambiguity, sidelobe levels, and noise suppression. These findings are key to guiding the design of tomographic SAR formations for accurate surface topography and vegetation mapping. Full article

(This article belongs to the Special Issue SAR Tomography of Natural Media)

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16 pages, 6591 KiB

Open AccessArticle

Potential of P-Band SAR Tomography in Forest Type Classification

by Dinh Ho Tong Minh, Yen-Nhi Ngo and Thu Trang Lê

Remote Sens. 2021, 13(4), 696; https://doi.org/10.3390/rs13040696 - 14 Feb 2021

Cited by 9 | Viewed by 3684

Abstract

Forest type classification using spaceborne remote sensing is a challenge. Low-frequency Synthetic Aperture Radar (SAR) signals (i.e., P-band, ∼0.69 m wavelength) are needed to penetrate a thick vegetation layer. However, this measurement alone does not guarantee a good performance in forest classification tasks. SAR tomography, a technique employing multiple acquisitions over the same areas to form a three-dimensional image, has been demonstrated to improve SAR’s capability in many applications. Our study shows the potential value of SAR tomography acquisitions to improve forest classification. By using P-band tomographic SAR data from the German Aerospace Center F-SAR sensor during the AfriSAR campaign in February 2016, the vertical profiles of five different forest types at a tropical forest site in Mondah, Gabon (South Africa) were analyzed and exploited for the classification task. We demonstrated that the high sensitivity of SAR tomography to forest vertical structure enables the improvement of classification performance by up to 33%. Interestingly, by using the standard Random Forest technique, we found that the ground (i.e., at 5–10 m) and volume layers (i.e., 20–40 m) play an important role in identifying the forest type. Together, these results suggested the promise of the TomoSAR technique for mapping forest types with high accuracy in tropical areas and could provide strong support for the next Earth Explorer BIOMASS spaceborne mission which will collect P-band tomographic SAR data. Full article

(This article belongs to the Special Issue SAR Tomography of Natural Media)

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20 pages, 10596 KiB

Open AccessArticle

Analysis of the Double-Bounce Interaction between a Random Volume and an Underlying Ground, Using a Controlled High-Resolution PolTomoSAR Experiment

by Ray Abdo, Laurent Ferro-Famil, Frederic Boutet and Sophie Allain-Bailhache

Remote Sens. 2021, 13(4), 636; https://doi.org/10.3390/rs13040636 - 10 Feb 2021

Cited by 7 | Viewed by 2437

Abstract

The radar response of vegetated environments, and forested areas in particular, are usually modeled using a very simple structure made of a random volume, representing a cloud of vegetation particles, lying over a semi-infinite medium with a rough interface, associated with the underlying ground. This Random Volume over Ground model can efficiently handle double-bounce scattering mechanisms, or arbitrary volume reflectivity profiles. This paper proposes to analyze a specific component of the Random Volume over Ground simplified scattering model, which concerns the double-bounce interaction between the ground and the volume. This specific contribution is not considered by classical characterization techniques and is studied in this work using a controlled experiment involving a Synthetic Aperture Radar operated in a Polarimetric and Tomographic configuration in order to image in 3D a controlled miniaturized scene composed of volume lying over a ground. It is shown that ground/volume double-bounce scattering, which remains focused at the ground level even in 3D imaging mode, and has polarimetric patterns that differ largely from those usually expected from double-bounce reflections, with volume-like features, such as a strong cross-polarized reflectivity or decorrelation between co-polarized channels. Moreover, it is shown that the full rank polarimetric patterns of the ground-volume mechanism are tightly linked to the reflectivity of the volume and may mask the ground response. As a consequence, isolating the ground response using 3D imaging does not permit to avoid a generally very strong distortion of the soil response by the double-bounce reflection, and the estimation of different geophysical parameters of the ground, such as its humidity or roughness are significantly altered. Full article

(This article belongs to the Special Issue SAR Tomography of Natural Media)

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20 pages, 20702 KiB

Open AccessArticle

Forest Height Estimation Using a Single-Pass Airborne L-Band Polarimetric and Interferometric SAR System and Tomographic Techniques

by Yue Huang, Qiaoping Zhang and Laurent Ferro-Famil

Remote Sens. 2021, 13(3), 487; https://doi.org/10.3390/rs13030487 - 30 Jan 2021

Cited by 19 | Viewed by 3427

Abstract

This paper addresses forest height estimation for boreal forests at the test site of Edson in Alberta, Canada, using dual-baseline PolInSAR dataset measured by Intermap’s single-pass system. This particular dataset is acquired by using both ping-pong and non-ping-pong modes, which permit forming a dual-baseline TomoSAR configuration, i.e., an extreme configuration for tomographic processing. A tomographic approach, based on polarimetric Capon and MUSIC estimators, is proposed to estimate the elevation of tree top and of underlying ground, and hence forest height is estimated. The resulting forest DTM and DSM over the test site are validated against LiDAR-derived estimates, demonstrating the undeniable capability of the single-pass L-band PolInSAR system for forest monitoring. Full article

(This article belongs to the Special Issue SAR Tomography of Natural Media)

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28 pages, 46991 KiB

Open AccessArticle

Single-Look SAR Tomography of Urban Areas

by Gustavo Daniel Martín-del-Campo-Becerra, Andreas Reigber, Matteo Nannini and Scott Hensley

Remote Sens. 2020, 12(16), 2555; https://doi.org/10.3390/rs12162555 - 8 Aug 2020

Cited by 10 | Viewed by 3635

Abstract

Synthetic aperture radar (SAR) tomography (TomoSAR) is a multibaseline interferometric technique that estimates the power spectrum pattern (PSP) along the perpendicular to the line-of-sight (PLOS) direction. TomoSAR achieves the separation of individual scatterers in layover areas, allowing for the 3D representation of urban zones. These scenes are typically characterized by buildings of different heights, with layover between the facades of the higher structures, the rooftop of the smaller edifices and the ground surface. Multilooking, as required by most spectral estimation techniques, reduces the azimuth-range spatial resolution, since it is accomplished through the averaging of adjacent values, e.g., via Boxcar filtering. Consequently, with the aim of avoiding the spatial mixture of sources due to multilooking, this article proposes a novel methodology to perform single-look TomoSAR over urban areas. First, a robust version of Capon is applied to focus the TomoSAR data, being robust against the rank-deficiencies of the data covariance matrices. Afterward, the recovered PSP is refined using statistical regularization, attaining resolution enhancement, suppression of artifacts and reduction of the ambiguity levels. The capabilities of the proposed methodology are demonstrated by means of strip-map airborne data of the Jet Propulsion Laboratory (JPL) and the National Aeronautics and Space Administration (NASA), acquired by the uninhabited aerial vehicle SAR (UAVSAR) system over the urban area of Munich, Germany in 2015. Making use of multipolarization data [horizontal/horizontal (HH), horizontal/vertical (HV) and vertical/vertical (VV)], a comparative analysis against popular focusing techniques for urban monitoring (i.e., matched filtering, Capon and compressive sensing (CS)) is addressed. Full article

(This article belongs to the Special Issue SAR Tomography of Natural Media)

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23 pages, 22969 KiB

Open AccessArticle

Signal Processing Options for High Resolution SAR Tomography of Natural Scenarios

by Yanghai Yu, Mauro Mariotti d’Alessandro, Stefano Tebaldini and Mingsheng Liao

Remote Sens. 2020, 12(10), 1638; https://doi.org/10.3390/rs12101638 - 20 May 2020

Cited by 26 | Viewed by 5749

Abstract

Synthetic Aperture Radar (SAR) Tomography is a technique to provide direct three-dimensional (3D) imaging of the illuminated targets by processing SAR data acquired from different trajectories. In a large part of the literature, 3D imaging is achieved by assuming mono-dimensional (1D) approaches derived from SAR Interferometry, where a vector of pixels from multiple SAR images is transformed into a new vector of pixels representing the vertical profile of scene reflectivity at a given range, azimuth location. However, mono-dimensional approaches are only suited for data acquired from very closely-spaced trajectories, resulting in coarse vertical resolution. In the case of continuous media, such as forests, snow, ice sheets and glaciers, achieving fine vertical resolution is only possible in the presence of largely-spaced trajectories, which involves significant complications concerning the formation of 3D images. The situation gets even more complicated in the presence of irregular trajectories with variable headings, for which the one theoretically exact approach consists of going back to raw SAR data to resolve the targets by 3D back-projection, resulting in a computational burden beyond the capabilities of standard computers. The first aim of this paper is to provide an exhaustive discussion of the conditions under which high-quality tomographic processing can be carried out by assuming a 1D, 2D, or 3D approach to image formation. The case of 3D processing is then further analyzed, and a new processing method is proposed to produce high-quality imaging while largely reducing the computational burden, and without having to process the original raw data. Furthermore, the new method is shown to be easily parallelized and implemented using GPU processing. The analysis is supported by results from numerical simulations as well as from real airborne data from the ESA campaign AlpTomoSAR. Full article

(This article belongs to the Special Issue SAR Tomography of Natural Media)

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22 pages, 6192 KiB

Open AccessArticle

Calibration of a Ground-Based Array Radar for Tomographic Imaging of Natural Media

by Albert R. Monteith, Lars M. H. Ulander and Stefano Tebaldini

Remote Sens. 2019, 11(24), 2924; https://doi.org/10.3390/rs11242924 - 6 Dec 2019

Cited by 5 | Viewed by 3065

Abstract

Ground-based tomographic radar measurements provide valuable knowledge about the electromagnetic scattering mechanisms and temporal variations of an observed scene and are essential in preparation for space-borne tomographic synthetic aperture radar (SAR) missions. Due to the short range between the radar antennas and a scene being observed, the tomographic radar observations are affected by several systematic errors. This article deals with the modelling and calibration of three systematic errors: mutual antenna coupling, magnitude and phase errors and the pixel-variant impulse response of the tomographic image. These errors must be compensated for so that the tomographic images represent an undistorted rendering of the scene reflectivity. New calibration methods were described, modelled and validated using experimental data. The proposed methods will be useful for future ground-based tomographic radar experiments in preparation for space-borne SAR missions. Full article

(This article belongs to the Special Issue SAR Tomography of Natural Media)

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29 pages, 11160 KiB

Open AccessFeature PaperReview

TomoSAR Mapping of 3D Forest Structure: Contributions of L-Band Configurations

by Matteo Pardini, Victor Cazcarra-Bes and Konstantinos P. Papathanassiou

Remote Sens. 2021, 13(12), 2255; https://doi.org/10.3390/rs13122255 - 9 Jun 2021

Cited by 16 | Viewed by 2962

Abstract

Synthetic Aperture Radar (SAR) measurements are unique for mapping forest 3D structure and its changes in time. Tomographic SAR (TomoSAR) configurations exploit this potential by reconstructing the 3D radar reflectivity. The frequency of the SAR measurements is one of the main parameters determining the information content of the reconstructed reflectivity in terms of penetration and sensitivity to the individual vegetation elements. This paper attempts to review and characterize the structural information content of L-band TomoSAR reflectivity reconstructions, and their potential to forest structure mapping. First, the challenges in the accurate TomoSAR reflectivity reconstruction of volume scatterers (which are expected to dominate at L-band) and to extract physical structure information from the reconstructed reflectivity is addressed. Then, the L-band penetration capability is directly evaluated by means of the estimation performance of the sub-canopy ground topography. The information content of the reconstructed reflectivity is then evaluated in terms of complementary structure indices. Finally, the dependency of the TomoSAR reconstruction and of its structural information to both the TomoSAR acquisition geometry and the temporal change of the reflectivity that may occur in the time between the TomoSAR measurements in repeat-pass or bistatic configurations is evaluated. The analysis is supported by experimental results obtained by processing airborne acquisitions performed over temperate forest sites close to the city of Traunstein in the south of Germany. Full article

(This article belongs to the Special Issue SAR Tomography of Natural Media)

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