Longer wavelength synthetic aperture radars (SARs) are precious in the remote sensing of forested areas, being sensitive to contributions from the whole vegetation layer and from the ground below. The electromagnetic properties of such contributions are retrieved from multipolarimetric acquisitions, whereas their vertical structure is retrieved from multibaseline acquisitions through tomographic imaging. Combining baseline and polarization diversity provides most information, allowing the decomposition of the SAR signal into ground- and volume-only contributions. A formal treatment of this problem is provided with the algebraic synthesis technique, which extends the concepts of PolInSAR. The decomposition, however, is shown to be ambiguous in that different solutions are equally consistent with the data. The main goal of this paper is to discuss this topic in light of the experimental results from a tomographic and polarimetric analysis of the boreal forest within the Krycklan River catchment, Northern Sweden, investigated at P- and L-bands during the ESA campaign BioSAR 2008. Different solutions to the decomposition problem will be discussed by examining the corresponding vertical structures accessible through tomographic techniques. Elements are shown supporting the idea that ground-volume interactions play a nonnegligible role at P-band, and a solution is proposed to isolate contributions from direct volume backscattering. The retrieval of forest top height is discussed as well, leading to the conclusion that such parameter is robust against erroneous choices in the identification of volume-only contributions, thus corroborating the PolInSAR approach for the analysis of single-baseline data

This paper focuses on multiimage synthetic aperture radar interferometry (InSAR) in the presence of distributed scatterers, paying particular attention to the role of target decorrelation in the estimation process. This phenomenon is accounted for by splitting the analysis into two steps. In the first step, we estimate the interferometric phases from the data, whereas in the second step, we use these phases to retrieve the physical parameters of interest, such as line-of-sight (LOS) displacement and residual topography. In both steps, we make the hypothesis that target statistics are at least approximately known. This approach is suited both to derive the performances of InSAR with different decorrelation models and for providing an actual estimate of LOS motion and topography. Results achieved from Monte Carlo simulations and a set of repeated pass ENVISAT images are shown

The primary objective of the European Space Agency's 7 th Earth Explorer mission, BIOMASS, is to determine the worldwide distribution of forest above-ground biomass (AGB) in order to reduce the major uncertainties in calculations of carbon stocks and fluxes associated with the terrestrial biosphere, including carbon fluxes associated with Land Use Change, forest degradation and forest regrowth. To meet this objective it will carry, for the first time in space, a fully polarimetric P-band synthetic aperture radar (SAR). Three main products will be provided: global maps of both AGB and forest height, with a spatial resolution of 200 m, and maps of severe forest disturbance at 50 m resolution (where "global" is to be understood as subject to Space Object tracking radar restrictions). After launch in 2022, there will be a 3-month commissioning phase, followed by a 14-month phase during which there will be global coverage by SAR tomography. In the succeeding interferometric phase, global polarimetric interferometry Pol-InSAR coverage will be achieved every 7 months up to the end of the 5-year mission. Both Pol-InSAR and TomoSAR will be used to eliminate scattering from the ground (both direct and double bounce backscatter) in forests. In dense tropical forests AGB can then be estimated from the remaining volume scattering using non-linear inversion of a backscattering model. Airborne campaigns in the tropics also indicate that AGB is highly correlated with the backscatter from around 30 m above the ground, as measured by tomography. In contrast, double bounce scattering appears to carry important information about the AGB of boreal forests, so ground cancellation may not be appropriate and the best approach for such forests remains to be finalized. Several methods to exploit these new data in carbon cycle calculations have already been demonstrated. In addition, major mutual gains will be made by combining BIOMASS data with data from other missions that will measure forest biomass, structure, height and change, including the NASA Global Ecosystem Dynamics Investigation lidar deployed on the International Space Station after its launch in December 2018, and the NASA-ISRO NISAR Land S-band SAR, due for launch in 2022. More generally, space-based measurements of biomass are a core component of a carbon cycle observation and modelling strategy developed by the Group on Earth Observations. Secondary objectives of the mission include imaging of sub-surface geological structures in arid environments, generation of a true Digital Terrain Model without biases caused by forest cover, and measurement of glacier and icesheet velocities. In addition, the operations Here Eff denotes fossil fuel emissions; Elb is net land biospheric emissions, comprising both Land Use 94 Change and ecosystem dynamics, and including alterations to biomass stocks linked to process 95 responses to climate change, nitrogen deposition and rising atmospheric CO2; ΔCatmos is the change in 96 atmospheric CO2; and Uland and Uocean are net average uptake by t...

In this paper, a new methodology is proposed for the analysis of forested areas basing on multipolarimetric multibaseline synthetic aperture radar (SAR) surveys. Such a methodology is based on three hypotheses: 1) statistical uncorrelation of the different scattering mechanisms (SMs), such as ground, volume, and ground-trunk scattering; 2) independence of volumetric and temporal coherence losses of each SM on the choice of the polarimetric channel; and 3) invariance (up to a scale factor) of the average polarimetric signature of each SM with respect to the choice of the track. Under these hypotheses, the data covariance matrix can be expressed as a Sum of Kronecker Products, after which it follows that K SMs 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 model-free and hybrid approaches, while yielding the best Least Square solution given the hypothesis of K SMs. It will be shown that this approach to SM separation is consistent with the inversion procedures usually exploited in single-baseline polarimetric SAR interferometry. Experimental validation of this methodology is provided on the basis of the P-band data set relative to the forest site of Remningstorp, Sweden, acquired by German Aerospace Center's E-SAR airborne system in the framework of the European Space Agency campaign BioSAR.

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