In this paper we present a first demonstration of spaceborne L-band SAR Tomography over a forested scenario. To this aim, we process a SAOCOM stack of nine acquisitions with across-track baselines theoretically achieving a vertical resolution around 22 m. To obtain a correct TomoSAR imaging, we phase calibrate the stack by properly accounting for forest volumetric structure, which drives the choice of calibrating with smaller baselines. Finally, we ground steer calibrated TomoSAR to achieve a consistent reconstruction of forest structure above ground and derive a forest height map. Comparison with reference canopy height gives a very good overall agreement, proving the effectiveness of our approach and making the experiment an interesting test-bed in view of future long-wavelength SAR missions.

In this work we present a first spaceborne L-band forest SAR Tomography (TomoSAR). We process a stack of 9 real SAOCOM acquisitions over Amazonas, a scenario mostly consisting of tropical rainforest with average tree height of about 25 m to 35 m. We phase calibrate the stack with Phase Linking algorithm, in order to obtain a consistent reconstruction of forest over terrain and compare TomoSAR results with different spectral estimators. We achieve good results with MUSIC spectral estimator, coping with spatial and temporal baselines and compare the upper TomoSAR envelope with LiDAR forest height. The overall agreement is quite good, confirming the effectiveness of our approach.

This paper studies the potential of X-band space borne SAR tomography for characterizing the structure of snow packs. The proposed solution is based on a constellation of 4 or 5 small satellites operated in a specific MIMO-FDM configuration, whose geometrical and spectral features are optimized so as to reach vertical resolution and ambiguity figures equivalent to 25 monostatic sensors. Estimation performance bounds, computed for specific scenarios, show that this concept is able to accurately capture the internal structure of shallow or deep snow-packs from a single measurement and to unambiguously estimate Snow Water Equivalent using two observations, with a very high accuracy and without assumptions on the structure or thickness of the measured cover.

Bistatic Correlation Tomography is an innovative radar technique capable of retrieving the vertical structure of the target. By combining pairs of simultaneous SAR images collected in multiple bistatic passes, the distribution in elevation of the backscattered power can be reconstructed. This processing technique is particularly interesting for the observation of natural targets because it is not sensitive to temporal decorrelation. For this reason, we consider its application to forestry. The goal of this work is twofold: In the first place, an analytical model for assessing the performances of Correlation Tomography is derived and validated through a numerical simulation. Then, a procedure for recovering tomographic products in the presence of very irregular baselines is proposed. This procedure is then tested using real data from the TomoSense campaign.

This work investigates a new concept to finely resolve the vertical structure of natural media, like snow, ice, vegetation, by using a formation of spaceborne Synthetic Aperture Radars (SAR) mounted onboard different satellites. The formation is assumed to operate in Multiple Input Multiple Output (MIMO) mode by implementing a Frequency Division Multiplexing (FDM) access scheme, where all satellites transmit simultaneously on different frequency bands and receive the echoes scattered by the Earth’s surface in all transmitted bands. In so-doing, a formation onNsatellites is used to produceN2SAR images. By the principle of Diffraction Tomography, each of these images represents a distinct set of wavenumbers, i.e. a distinct region of the spatial spectrum of the observed scene. The vertical separation between any two sets of wavenumbers defines the interferometric differential wavenumber, which determines the sensitivity of that particular pair to the vertial structure of the observed scene. Fine vertical resolution is achieved by developing a novel approach to set the satellite positions in such a way that the resulting interferometric differential wavenumbers form an almost uniformly-spaced array of maximum length under the constraint of a given height of ambiguity and interferometric coherence magnitude. As a result, we show two examples where formations of 4 or 5 satellites are deployed to provide the equivalent of 17 and 26 monostatic acquisitions, respectively. Such figures are comparable to the best airborne and ground-based systems available as of today, and indicate the concrete possibility to image the vertical structure of natural targets from space at fine resolution. The concept here developed to deploy the formation is referred to as Minimum Redundancy Wavenumber Illumination (MRWI), as it is shown to be a generalization to distributed targets of the principle of Minimum Redundancy Virtual Array (MRVA) used in array theory. The analysis is supported by results from synthetic data generated by numerical simulations.

BIOMASS is new ESA Earth Explorer, scheduled for launch in 2024. It will be the first P-band spaceborne Synthetic Aperture Radar (SAR), featuring full polarimetry, repeat pass interferometry and tomography. Its main objective is global routine monitoring of forested areas, its secondary objectives include exploration of glacier dynamics, subsurface geology and sub-canopy terrain topography. In order to correctly retrieve information, interferometric calibration has to address main disturbances affecting repeat pass P-band SAR: ionosphere, baseline errors and troposphere. In this paper we present the scientific design of BIOMASS interferometric calibration processor, detailing the state-of-the-art correction steps implemented. Some preliminary results obtained by processing simulated acquisitions are illustrated to motivate the proposed techniques.

ESA BIOMASS will be the first spaceborne P-band SAR, and is designed to produce annual, near-global maps of forest biomass. Biomass-independent scene properties can be mitigated by interferometric pre-processing of the acquisitions, after which backscatter is inverted by a model to estimate biomass, relying on some external reference biomass. The model is simple enough to be invertible, while still representing the spatiotemporal variability of parameters in a global scenario. However, it requires a reliable, global reference biomass dataset, with enough coverage to reflect the variability of the chosen model. The global algorithm is also computationally efficient, so can be deployed in the ground segment. In this paper we briefly discuss the design of the global estimation algorithm for BIOMASS and propose a reference biomass dataset for the algorithm. Then, we discuss the robustness of the proposed algorithm using some reference biomass data for three different parameter variability setups.

The BIOMASS DEM Product Prototype Processor (BIO-DEMPP) is being developed in the frame of ESA's Earth Explorer BIOMASS mission. The prototype includes a complete interferometric SAR chain, from the stack co-registration until the mosaicking of the derived height products (Digital Elevation and Digital Terrain Models). This paper presents an overview of the BIODEMPP architectural design and its validation strategy, as well as first results obtained with simulated BIOMASS-like data.

Scheduled for launch in 2023, ESA's seventh Earth Explorer Mission, BIOMASS, will carry the first P-band synthetic aperture radar (SAR) to be flown in space, to gather fully polarimetric acquisitions over forested areas worldwide in interferometric and tomographic modes. This paper presents the algorithms developed to estimate biophysical parameters from BIOMASS measurements and their implementation in the BIOMASS level 2 (L2) prototype processor. The L2 processor will generate global maps of forest Above Ground Biomass (AGB), Forest Height (FH), Forest disturbance (FD). Accurate generation of these products requires the L2 processor to be closely inter-linked with the BIOMASS interferometric processor, in order to produce phase-calibrated interferometric stacks, retrieve sub-canopy terrain topography, and generate a 3D representation of forest structure by use of SAR tomography. AGB estimation results are here shown using BIOMASS-like acquisitions derived from campaign data acquired over six tropical forests in South America and Equatorial Africa.

This paper describes the rationale and development of the estimation techniques for the level-2 data products of the European Space Agency's 7th Earth Explorer BIOMASS mission. BIOMASS is planned for launch in 2023 and will carry the first-ever P-band synthetic aperture radar (SAR) onboard a satellite. It has been designed to produce consistent global maps of the Earth's forests during a nominal five-year lifetime. Fully polarimetric SAR data will be collected and the satellite orbit will be selected for repeat-pass interferometry and tomography in separated mission phases. Mission requirements call for three level-2 data products: above-ground biomass, forest height and forest disturbance. The paper also discusses the expected limitations of the estimation techniques and remaining problems to be addressed.

The ESA BIOMASS mission will be the 7th Earth Explorer measuring the above-ground biomass (AGB) in the world's forests. The current ESA Level-2 (L2) implementation study focuses on defining and implementing the main algorithms for forest parameter retrieval from BIOMASS data. After the first year of L2 study innovative results were achieved: the development of ground cancellation, in particular, has proved to be huge value, since it removes from the data the effects of environmental variability and contributions unrelated to the forest carried in the ground scattering. In this paper the current processor implementation and validation activities of the L2 team will be described.

In this work we investigate the role of volume scattering obtained from tomography and ground/volume decomposition in retrieving AGB (Above Ground Biomass). Results here presented originate from the BIOMASS L2 study, aimed at defining and implementing the tomographic and interferometric processors of the BIOMASS mission. In particular we aim at discussing whether, and to what extent, ground/volume decomposition can provide a valid alternative to tomography. To do this, both are tested based on the P-Band data collected at the forest site of Paracou, French Guiana, during the TropiSAR campaign, and validated against in-situ AGB measurements in terms of correlation and sensitivity of the retrievals. Quite surprisingly, results indicate that volumebackscattered power as obtained by ground/volume decomposition is almost unsensitive to AGB, notwithstanding different solutions for volume scattering are tested, and lead to conclusion that forest structure actually plays a non-negligible role in AGB retrieval in tropical areas.

The ESA BIOMASS mission will be the first spaceborne mission specifically dedicated to the study of forests. In this framework, ESA is running the “Level-2 implementation study”, focused on defining and implementing the main algorithms for forest parameters retrieval from BIOMASS data. During the first year, the science group involved in the study has carried out R&D activities in order to identify the most viable solutions. In this paper, an overview of the concepts and techniques behind the development of the BIOMASS L2-prototype processor are described, with a focus on each of the main mission products: biomass, forest height and forest disturbance map.

Every day, ships of different type, size and origin cross the world seas. Not only for commerce and transport, but also for illegal activities. In addition to conventional positioning and tracking systems, detection with Earth observation satellites is an effective means to monitor human movements across the sea. The European Copernicus Programme operates towards this goal, through the definition of border and maritime surveillance as one of its main tasks. This paper describes an Object Based Image Analysis (OBIA) workflow developed for ship detection, monitoring and tracking with high-resolution satellite images. Here, it has been used to simulated medium-resolution multispectral (MS) and Synthetic Aperture Radar (SAR) images representative of the Sentinel components of Copernicus. First results confirm that the method proposed can be efficiently used by European agencies for monitoring the explosive growth of illegal flows in the Mediterranean Sea.

In this letter, we propose a new methodology to estimate the absolute digital elevation model (DEM) of an area by radargammetric-like processing of interferometric multibaseline synthetic aperture radar (SAR) data from two opposite-side surveys. Two DEMs of the imaged area obtained from two opposite-side tomographic SAR views are coregistered, correcting residual baseline errors. This methodology combines the great accuracy of multibaseline interferometric processing with precise stereo plotting typical of opposite-side radargrammetry, requires no texture matching and no control points, and is applicable also in the case of few a priori information about the site topography.

In this paper, first results concerning the characterization of the subsurface of ice sheets and glaciers through single and multipolarization synthetic aperture radar (SAR) tomography (TomoSAR) are illustrated. To this aim, the processing of data acquired in the framework of the European Space Agency IceSAR 2012 campaign is discussed. IceSAR 2012 was conceived so as to support the secondary objectives of the future Earth Explorer mission BIOMASS, which will be a SAR instrument with media penetration capabilities due to the use of the P-band frequency. In this regard, a tomographic study of ice was motivated by the fact that cryospheric remote sensing is of fundamental importance in order to understand more in depth the morphology and the dynamic processes regulating ice sheets. The main objective of the tomographic experiment of the campaign herein discussed was indeed to assess the capability of P-band SAR to retrieve any information about ice subsurface structure. Imaging has been achieved through TomoSAR techniques, applied to airborne multibaseline data acquired in the southwest of Greenland. Different imaging approaches are compared, and the main results achieved are presented: It is found that scattering in the upper layers of glacial subsurface can be achieved up to an extent of about 20-60 m, conditional on the different types of glaciological zone observed. Moreover, clear morphological structures have been found beneath the ice surface at one of the investigated sites.

In this paper we propose a new methodology to estimate the absolute Digital Elevation Model (DEM) of an area by radargammetric-like processing of interferometric multi-baseline SAR data from two opposite-side surveys. First, two separate DEMs of the imaged area are obtained from two opposite-side tomographic SAR views. Both the DEMs thus obtained are affected by residual baseline errors. Coregistration of the DEMs is thus performed, so as to correct these errors. This methodology combines the great accuracy of multi-baseline interferometric processing with precise stereo plotting typical of opposite-side radargrammetry, requires no texture matching and no control points and can be applied also in the case of few a-priori information about the site topography.

This paper presents the preliminary results obtained within a research project aimed to assess the feasibility of a system to monitor the immigration flows in the Southern Mediterranean Sea by solely relying on images coming from scientific and commercial satellites, which already operates on a regular basis. “Space Shepherd”, a project funded by Politecnico di Milano, Italy, has the ultimate goal of integrating information coming from a number of satellites to 1) monitor remotely the Southern Mediterranean Sea, 2) detect the presence of possible vessels, 3) identify the migrant vessels and keep the authorities informed, 4) track the vessels and issue warnings in case of danger, 5) support the search and rescue operations. The methodology for scheduling image acquisitions is presented, as well as the algorithms for automatic detection of vessels in both optical and SAR images. The performances of the system are discussed, and its feasibility is assessed.

The following paper concerns preliminary results from a phenomenological analysis of vessel scattering carried out on Synthetic Aperture Radar Spotlight data. Time-frequency analysis tools have been applied to real data acquired from the German satellite Terrasar X, in order to detect and characterize the different scattering mechanisms contributing to the received signal. The impact and correction of data perturbations due to the motion of the ship are also discussed.

Due to the increased melting of ice sheets over the last decades, monitoring of ice dynamics and structure with remote sensing instruments is of extreme importance to achieve a deeper insight on related environmental issues. The study presented in this paper documents an attempt of mapping ice structure with P-band SAR tomography. First results from ESA IceSAR 2012 campaign carried out in south-west Greenland are presented. It is found that significant penetration in the upper layers of glacial subsurface can be achieved up to an extent of about 20-60 m, conditional on the different type of glaciological zone observed.

In this paper we propose a strategy for phase calibrating repeat-pass Synthetic Aperture Radar (SAR) data for tomographic applications. The problem is tackled by observing that multi-baseline data provide equations enough to jointly estimate aircraft and target positions, up to a rototranslation of the coordinate system used for representing the imaged scene. Such a rototranslation is shown to correspond to the forward operator null space, which can be accounted for in order to project the results in the desired coordinate frame. Volumetric scatterers are treated by exploiting the Phase Linking algorithm, which allows to represent them as equivalent phase centers. Results are shown from synthetic data and real SAR data acquired over snow and ice.