Non-cooperative bistatic SAR clock drift compensation for tomographic acquisitions

In the last years, an important amount of research has been headed towards the measurement of above-ground forest biomass with polarimetric Synthetic Aperture Radar (SAR) tomography techniques. This has motivated the proposal of future bistatic SAR missions, like the recent non-cooperative SAOCOM-CS and PARSIFAL from CONAE and ESA. It is well known that the quality of SAR tomography is directly related to the phase accuracy of the interferometer that, in the case of non-cooperative systems, can be particularly affected by the relative drift between onboard clocks. In this letter, we provide insight on the impact of the clock drift error on bistatic interferometry, as well as propose a correction algorithm to compensate its effect. The accuracy of the compensation is tested on simulated acquisitions over volumetric targets, estimating the final impact on tomographic profiles

Joint multi-baseline SAR interferometry

We propose a technique to provide interferometry by combining multiple images of the same area. This technique differs from the multi-baseline approach in literature as (a) it exploits all the images simultaneously, (b) it performs a spectral shift preprocessing to remove most of the decorrelation, and (c) it exploits distributed targets. The technique is mainly intended for DEM generation at centimetric accuracy, as well as for differential interferometry. The problem is framed in the contest of single-input multiple-output (SIMO) channel estimation via the cross-relations (CR) technique and the resulting algorithm provides significant improvements with respect to conventional approaches based either on independent analysis of single interferograms or multi-baselines phase analysis of single pixels of current literature, for those targets that are correlated in all the images, like for long-term coherent areas, or for acquisitions taken with a short revisit time (as those gathered with future satellite constellations)

Mapping above-ground biomass in tropical forests with ground-cancelled P-band SAR and limited reference data

This paper introduces the CASINO (CAnopy backscatter estimation, Subsampling, and Inhibited Nonlinear Optimisation) algorithm for above-ground biomass (AGB) estimation in tropical forests using P-band (435 MHz) synthetic aperture radar (SAR) data. The algorithm has been implemented in a prototype processor for European Space Agency's (ESA's) 7th Earth Explorer Mission BIOMASS, scheduled for launch in 2023. CASINO employs an interferometric ground cancellation technique to estimate canopy backscatter (CB) intensity. A power law model (PLM) is then used to model the dependence of CB on AGB for a large number of systematically distributed SAR data samples and a small number of calibration areas with a known AGB. The PLM parameters and AGB for the samples are estimated simultaneously within pre-defined intervals using nonlinear minimisation of a cost function. The performance of CASINO is assessed over six tropical forest sites on two continents: two in French Guiana, South America and four in Gabon, Africa, using SAR data acquired during airborne ESA campaigns and processed to simulate BIOMASS acquisitions. Multiple tests with only two randomly selected calibration areas with AGB > 100 t/ha are conducted to assess AGB estimation performance given limited reference data. At 2.25 ha scale and using a single flight heading, the root-mean-square difference (RMSD) is ≤ 27% for at least 50% of all tests in each test site and using as reference AGB maps derived from airborne laser scanning data. An improvement is observed when two flight headings are used in combination. The most consistent AGB estimation (lowest RMSD variation across different calibration sets) is observed for test sites with a large AGB interval and average AGB around 200–250 t/ha. The most challenging conditions are in areas with AGB < 200 t/ha and large topographic variations. A comparison with 142 1 ha plots distributed across all six test sites and with AGB estimated from in situ measurements gives an RMSD of 20% (66 t/ha)

The BIOMASS level 2 prototype processor : design and experimental results of above-ground biomass estimation

BIOMASS is ESA’s seventh Earth Explorer mission, scheduled for launch in 2022. The satellite will be the first P-band SAR sensor in space and will be operated in fully polarimetric interferometric and tomographic modes. The mission aim is to map forest above-ground biomass (AGB), forest height (FH) and severe forest disturbance (FD) globally with a particular focus on tropical forests. This paper presents the algorithms developed to estimate these biophysical parameters from the BIOMASS level 1 SAR measurements and their implementation in the BIOMASS level 2 prototype processor with a focus on the AGB product. The AGB product retrieval uses a physically-based inversion model, using ground-canceled level 1 data as input. The FH product retrieval applies a classical PolInSAR inversion, based on the Random Volume over Ground Model (RVOG). The FD product will provide an indication of where significant changes occurred within the forest, based on the statistical properties of SAR data. We test the AGB retrieval using modified airborne P-Band data from the AfriSAR and TropiSAR campaigns together with reference data from LiDAR-based AGB maps and plot-based ground measurements. For AGB estimation based on data from a single heading, comparison with reference data yields relative Root Mean Square Difference (RMSD) values mostly between 20% and 30%. Combining different headings in the estimation process significantly improves the AGB retrieval to slightly less than 20%. The experimental results indicate that the implemented retrieval scheme provides robust results that are within mission requirements

A Maximum Likelihood basedPhase Estimate for Multi-Channel SAR Interferometry

The paper introduces a novel technique that exploits either single or multiple baseline (or multiple frequency) SAR acquisitions to provide a quite accurate and local estimate of the interferometric phases, related both to the topography or to the targets motion (subsidences, etc.). The paper approaches an optimal method, in Maximum Likelihood sense, that applies to coherent, distributed targets. Promising results in topography reconstruction from ENVISAT dataset are reported

Polarimetric SAR tomographic of forest scenarios

In this paper a technique is described for the joint exploitation of several multi-polarimetric SAR images, to the aim of yielding a separate tomographic reconstruction of each of the different Scattering Mechanisms (SM) (such as ground and volume scattering ) that contribute to the received signal. Under large hypotheses it will be shown that the data covariance matrix can be expressed as a Sum of Kronecker Products (SKP), after which it follows that K scattering mechanisms are uniquely identified by K(K-1) real numbers. This result provides the basis to perform SM separation by employing not only model based approaches, generally retained in literature, but also purely algebraic approaches, while yielding the best Least Square solution given the hypothesis of K SMs. Experimental results will be shown basing on airborne data relative to the forest site of Remningstorp, Sweden, acquired by DLR’s ESAR in the framework of the ESA campaign BioSAR2007. Furthermore, it will be shown that the SKP structure also provides a straightforward tool for discussing the well posing of the model inversion problem

Honey, we shrunk the interferogram matrix!

reserved3F.Rocca; S. Tebaldini; A. RucciRocca, Fabio; Tebaldini, Stefano; Rucci, Alessi