P band synthetic aperture radar (SAR) tomography represents a powerful tool for characterizing the 3-D structure of tropical forests from their electromagnetic response. Current techniques separate the responses of the forest canopy and the underlying ground using SAR tomography, polarimetric diversity, and complex processing techniques. This letter shows that similar performance and better stability may be achieved using single-polarization data and parametric tomographic focusing, performed using a low-dimensional model. The vertical density of reflectivity of a tropical forest is modeled, at P band, using a Dirac function for the ground and a narrow peak for the volume. The performance of the proposed method is evaluated using P band tomographic data acquired during the TropiSAR campaign, and the results show that it can accurately and reliably estimate key structural parameters of the observed topical forest.

Exploring vertical forest structures worldwide via remote sensing faces challenges. Recent technologies like waveform light detection and ranging (LiDAR) from NASA’s global ecosystem dynamics investigation (GEDI) and SAR tomography (TomoSAR) from future European Space Agency (ESA) BIOMASS offer promising solutions. This article assesses the performance of spaceborne GEDI and TomoSAR airborne data from an ESA’s TomoSense campaign to highlight the important role of GEDI measurements in BIOMASS algorithm training and establishing precise site-specific processing parameters. Our study in Germany’s Eifel National Park delves into the precision of GEDI and P-band TomoSAR in measuring surface [digital terrain model (DTM)] and vegetation [canopy height model (CHM)] heights. Results demonstrate that GEDI and P-band TomoSAR offer high-resolution and precise surface and vegetation heights and vertical profile measurements. While GEDI relative height (RH) at 98% (RH98) was previously recommended for tropical forests, our findings advocate for RH85 as the optimal metric for temperate forests. The research supports improving the accuracy of both DTM and CHM utilizing GEDI beams with full-power lasers coupled with high sensitivity and signal-to-noise ratio (SNR). Ground elevation measurements are more accurate than canopy height estimates for temperate forests, with DTM RMSE about 2 m and CHM RMSE about 3 m for GEDI and TomoSAR measurements. By analyzing the vertical structure of monthly GEDI data, we note a 1-m shift in the volume peak between GEDI’s leaf-on and leaf-off periods. At the same time, TomoSAR consistently exhibits a lower volume peak by about 2 m compared to GEDI during leaf-on seasons. In conclusion, our research underscores the complementary roles of TomoSAR and GEDI in accurately mapping diverse forest types, thereby bolstering the effectiveness of the BIOMASS mission.

Synthetic Aperture Radar (SAR) tomography has been successfully applied to the characterization of forest using 3D imaging. Nevertheless, the information content extracted by this technique is limited by the resolution in range and elevation. Also, it is demonstrated that a small number of parameters allows the reconstruction of tomograms that are close to the measured ones. In this paper, forest tomograms are reconstructed by an inversion method using forest scattering models with few parameters. Key forest parameters, such as ground position zg, forest height hvand the ratio between ground intensity and volume are determined using mono-polarized P-band data. These results are compared for different types of forest scattering models with verification data from the TropiSAR and TomoSense campaigns.

Our study highlights the important role of GEDI measurements in BIOMASS algorithm training and the establishment of precise site-specific processing parameters. Combining GEDI measurements at sparse coordinates and SAR tomography (TomoSAR) estimates enables the creation of detailed canopy height maps (CHM). While relative height (RH) at 98% (RH98) was previously recommended for tropical forests, our findings advocate for RH85 as the optimal metric for temperate forests. Emphasis is placed on selecting shots with over 90% sensitivity for ground return detection and GEDI beams equipped with full-power lasers. Additionally, we show the GEDI profile data’s unique capacity to investigate annual changes, revealing significant volume contributions during leaf-on periods and increased ground importance during leaf-off seasons.

Quantification of forest’s vertical structure in the tropics using remote sensing is a challenge. NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne LiDAR data, whereas the ESA’s next Earth Explorer BIOMASS mission will acquire multiple acquisitions over the same areas to form three-dimensional images through SAR tomography (TomoSAR) technique. We show that GEDI and P-band TomoSAR can directly measure vegetation heights and vertical profiles with high resolution and precision. The GEDI vegetation height error is 5 m at the tropical sites, similar to the expected performance of the future spaceborne BIOMASS mission. These results suggest GEDI measurements, i.e., RH98 from full power shots with sensitivity greater than 98%, will provide a good reference of forest structure to calibrate the BIOMASS mission algorithms.

Synthetic aperture radar tomographic imaging reconstructs the three-dimensional reflectivity of a scene from a set of coherent acquisitions performed in an interferometric configuration. In forest areas, a high number of elements backscatter the radar signal within each resolution cell. To reconstruct the vertical reflectivity profile, state-of-the-art techniques perform a regularized inversion implemented in the form of iterative minimization algorithms. We show that light-weight neural networks can be trained to perform this inversion with a single feed-forward pass, leading to fast reconstructions that could better scale to the amount of data provided by the future BIOMASS mission. We train our encoder-decoder network using simulated data and validate our technique on real L-band and P-band data.

Measuring the vertical structure of tropical forests using remote sensing technology is challenging. To overcome this, active sensors, such as P-band Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR), are used to penetrate thick vegetation layers. NASA’s Global Ecosystem Dynamics Investigation (GEDI) uses spaceborne LiDAR data. In contrast, the European Space Agency’s (ESA) BIOMASS mission uses multiple acquisitions of SAR data to create 3D images through a technique called SAR tomography (TomoSAR). The paper discusses the forest’s vertical structure, such as volume peak (or volume scattering center), penetration, and reflectivity, using GEDI and airborne P-band TomoSAR by analyzing measurements at tropical forest sites in South America and Africa. It was found that the location of the volume peak in TomoSAR is consistently lower than in GEDI, with a range of 2-4 m depending on the polarization and the height of the forest layers. Compared to GEDI, TomoSAR data has a better ground reflection for vegetation taller than 25 m. GEDI and TomoSAR data can accurately capture vertical information in the canopy levels (between 10-40 m), displaying a strong correlation in the volume layers. The highest correlation occurs around 30 m above ground level, aligning with previous research in developing algorithms for the BIOMASS mission in aboveground biomass retrieval. Together, TomoSAR and GEDI are robust and comparable in studying tropical forests and support the BIOMASS mission for global biomass mapping.

SAR tomography represents a unique way to characterize forested areas from their 3-D density of reflectivity. As shown by numerous studies, coherent 2-D SAR images with intermediate horizontal resolution may be processed through spectral analysis techniques in order to focus 3-D cubes of reflectivity, and to provide an electromagnetic description of forets which is generally sufficient to estimates its main features, such as underlying ground topography, tree height and above-ground biomass. Nevertheless, a refined analysis of the vertical structure of a forest is usually highly limited by the vertical resolution and by the presence of side-lobes and focusing artifacts whose separation from the actual response may be problematic. As shown in this paper, direct deconvolution of the vertical impulse response using high-resolution techniques is an underdetermined inverse problem with an infinite number of plausible solutions. The use of basis functions, such as those proposed by Aguilera et al. [1], as well as a set of signal properties likely to be well adapted to the reflectivity of forested areas, allows to drastically reduce the size of the solution domain. This paper iuses an efficient iterative technique to estimate the intrinsic vertical reflectivity profile of forest measured at L and P bands

3-D imaging using SAR tomography is a well-recognized technique for the characterization of forested areas. Studies revealed that the intensity of radar echoes originating from specific locations within the canopy of forest could be used to estimate its above ground biomass. Moreover, a recent work proposed an estimation technique using a pair of interferometric SAR images only. The images are combined in order to cancel contributions from the ground, and to roughly estimate the volume reflectivity. This paper proposes to study the influence of temporal decorrelation of this minimalist approach, which relies on the hypothesis of perfectly correlated signals. A model, based on second order statistics, is proposed and is used to predict the influence of temporal decorrelation of the relative error of the above ground biomass estimation over tropical forests measured at$\mathrm{P}$band.

Estimating tropical forests vertical structure using remote sensing is a challenge. Active sensors such as low-frequency Synthetic Aperture Radar (SAR) operating at P-band, with a wavelength of ~ 69 cm wavelength, and Light Detection and Ranging (LiDAR) are able to penetrate thick vegetation layers. While NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne liDAR data, the ESA’s next Earth Explorer BIOMASS mission will acquire multiple acquisitions over the same areas to form three-dimensional images through SAR tomography (TomoSAR) technique. Our study shows the potential value of GEDI and TomoSAR acquisitions in producing accurate estimates of forests vertical structure. By analyzing airborne P-band TomoSAR, airborne LiDAR, and spaceborne GEDI LiDAR at a tropical forest site in Paracou, French Guiana, South America, we show that both GEDI and P-band TomoSAR can directly measure surface, vegetation heights, and vertical profiles with high resolution and precision. Airborne TomoSAR is of higher quality than GEDI due to better penetration properties and precision. However, the GEDI vegetation height root-mean-square error is less than 5 m, for an average forest height value around 30 m at the Paracou site, which is similar to the expected performance of the future spaceborne BIOMASS mission. These results suggest GEDI measurements, i.e. shots with sensitivity greater than 98%, will provide a good reference of forest structure to calibrate the BIOMASS mission algorithms.

This paper addresses some advanced PolSAR approaches combined with time-frequency and tomographic techniques for characterization of urban areas. Three case studies are dedicated to demonstrate the effectiveness of these polarimetric approaches, using spaceborne SAR data such as Sentinel-1, RadarSAT2 and TerraSAR/Tandem-X. The results show advanced PolSAR approaches can provide a refined characterization of urban areas from space.

This aims to compare the performance of different observations modes of the future BIOMASS mission for the characterization of tropical forests. In particular, it provides indicators of variability for different typical descriptors of the SAR response of a forest, and computes estimates using real data acquired at P band in the frame of the TropiSAR campaign. Results show that the best performance, reached in the Polarimetric and Tomographic mode, degrades slightly in the single-polarization case and significantly when the vertical tomographic resolution is reduced. Nevertheless, it is shown that in this latter case, the use of priors estimated in the high-resolution tomographic phase and consisting on the estimate of the wave extinction, leads to a spectacular improvement of the performance. This results reveals important in the frame of the BIOMASS missions which plans to phases with different acquisition configurations.

This article addresses the 3-D reconstruction of urban areas using a minimal number of Synthetic Aperture Radar (SAR) acquisitions, that is, a set of three images, characterized by intermediate spatial resolution features. In such extreme conditions, conventional tomographic techniques reveal unadapted to refined 3-D imaging purposes, either due to the resulting intrinsic coarse vertical resolution, or to the low dimensionality of the data set, that prevents any separation of complex mixed scattering patterns. A new high-resolution (HR) tomographic estimator, based on a polarimetric signal subspace fitting criterion, is proposed to overcome these limitations, as this method adapts to the statistical behavior of the backscattered signals using robust metrics. The optimization of the corresponding focusing criterion is led through a new polarimetric alternating projection algorithm, characterized by a low computational cost, which may also be used to optimize the polarimetric the deterministic maximum likelihood criterion. The proposed polarimetric signal subspace fitting technique is shown to outperform the other studied HR techniques over both simulated signals and data acquired by the DLR’s ESAR sensor at L-band over Dresden city, Germany. Finally full-rank polarimetric tomographic estimators are proposed that generalize nonparametric polarimetric estimators, and permit to estimate second-order polarimetric representations in 3-D, instead of unitary rank target vector with their conventional versions. This approach makes it possible to characterize polarimetric scattering mechanisms in 3-D.

This paper addresses multiple scatterer detection over man-made areas using SAR tomography. Diverse high-resolution tomographic estimators are compared, showing that stochastic maximum likelihood and signal subspace fitting techniques are more efficient and accurate than deterministic maximum likelihood technique when estimating coherent scatterers. Based on these two techniques, detection schemes are proposed and their effectiveness for scatterer detection is demonstrated by using multi-baseline L-band SAR data over a test site containing man-made objects .

This paper presents a new approach for the study of ship discrimination in complex sea ice ocean environment using single polarimetric synthetic aperture radar data and sub-spectra strategy. A statistic descriptor related to the signal coherence in the Time-Frequency domain, is proposed to enhance the ship/background contrast and improve discrimination capabilities. Using RADARSAT-2 single polarization data over complex sea ice scenes in Arctic ocean, experimental results demonstrate the efficiency of this method in terms of ship location retrieval and response characterization.

Polarimetric synthetic aperture radar tomography (Pol-TomoSAR) allows to achieve a 3-D characterization over urban areas using multiple polarimetric acquisitions. However, using spaceborne datasets, such as TerraSAR-X, it is difficult to localize the distributed or uncorrelated scattering patterns along elevation due to the temporal decorrelation. In order to overcome this limitation, this paper proposes polarimetric correlation tomographic techniques based on Tandem-mode images. The key of this technique is to build a covariance matrix from the observed Tandem coherence pairs, and then apply conventional covariance-based tomographic techniques. This processing allows to extract both coherent and distributed scatterers. The resulting 3-D reconstruction is more refined and detailed, compared to the one derived from TerraSAR-X data. Seven TSX/TDX pairs in fully polarimetric mode over a small county in Yunnan province, China, are used to demonstrate the effectiveness of this technique for the characterization of urban environments.

This paper summarizes a set of studies led on the topic of polarimetric coherence optimization for the coherent processing of stacks of polarimetric SAR images. It is shown that coherence maximization may be understood differently depending on the application at hand. Extracting polarimetric coherent signals embedded in noise or in a severe background requires to use polarimetric diversity as a supplementary mean for discovering organized speckles patterns, whereas classical MB-PolinSAR coherence optimization gives more importance to the polarimetric scattering mechanisms that extremize coherence values. This paper reviews different techniques able to cope with an arbitrary number of images and that are characterized by their low degree of computational complexity, conferred by the favored use of analytical solutions. The usefulness of these techniques is demonstrated using various kinds of applications to real spaceborne and airborne data sets.

This paper proposes different processing techniques for the polarimetric 3-D imaging of forested areas using multi-baseline interferometric SAR data, acquired in tandem configuration from spaceborne SAR sensors. Tandem-like acquisition modes, based on the simultaneous measurement of interferometric pairs, represent a high-potential alternative for the tomographic imaging of scenes with rapidly decorrelating scattering features using a spaceborne SAR. The counterpart related to this independent interferometric sampling lies in the restricted amount of available information, whose processing requires specific techniques. These methods as well as their potential for boreal forest characterization are evaluated in the frame of the preparation of the SAOCOM CS mission using ESA's BIOSAR II campaigna data sets acquired at L band by the DLR ESAR sensor.

Despite its ability to characterize 3-D environments, synthetic aperture radar (SAR) tomographic imaging, when applied to the characterization of targets concealed beneath forest canopies, may appear as an ill-conditioned estimation problem, with a complex mixture of numerous scattering mechanisms measured from a few different positions. Among the set of tomographic estimators that may be used to characterize such complex scattering environments, nonparametric tomographic techniques are more robust to focus on artifacts but limited in resolution and, hence, may fail to discriminate objects, whereas parametric ones provide better vertical resolution but cannot adequately handle continuously distributed volumetric scattering densities, characteristic of forest canopies. This letter addresses a new wavelet-based sparse tomographic estimation method for the 3-D imaging and discrimination of underfoliage objects that overcomes these limitations. The effectiveness of this new approach is demonstrated using L-band airborne tomographic SAR data acquired by the German Aerospace Center over Dornstetten, Germany.

This paper presents new principles and techniques to perform High Resolution (HR) 3-D imaging of volumetric environments using Polarimetric SAR Tomography (POLTOMSAR) and Multi-Baseline Polarimetric and Interferometric SAR (MB-POL-inSAR) data. Unlike classical polarimetric spectral estimation approaches which consider polarization as way to improve the discrimination between vertically aligned scatterers, or to estimate unitary rank polarimetric scattering features [1] [2], this paper provides full rank techniques which permit to estimate 3-D coherency matrices that can be characterized using classical polarimetric processing algorithms. The algorithms investigated here, Beamformer, Capon and MUSIC, have a relatively low numerical complexity and varying levels of resolution. A novel approach is developed to estimate 3-D full rank polarimetric covariance matrices with HR spatial properties.

SAR imaging of concealed targets beneath the canopies has to face a complex mixture of diverse scattering mechanisms. To characterize this complex scattering environment, nonparametric tomographic estimators are more robust to focusing artefacts but limited in resolution. Parametric tomographic estimators provide better vertical resolution but fail to adequately characterize continuously distributed volumetric scatterers such as forest canopies. To overcome these limitations, this paper addresses a new wavelet-based sparse estimation method for 3D imaging and characterization for underfoliage objects. The effectiveness of this new approach is demonstrated by using L-band Multi-Baseline PolInSAR Data over Dornstetten, Germany.

The structure of forests is an important indicator of ecosystem dynamics and enables the modeling and monitoring of ecological change. Synthetic aperture radar tomography (TomoSAR) provides scene reflectivity estimation of vegetation along elevation coordinates. Due to the advantages of superresolution imaging and a small number of measurements, compressive sensing (CS) inversion techniques for SAR tomography were successfully developed and applied. This paper addresses the 3-D imaging of forested areas based on the framework of CS using fully polarimetric (FP) multibaseline SAR interferometric (MB-InSAR) tomography at P-band. A new CS-based FP MB-InSAR tomography method is proposed: a sum of Kronecker product (SKP) decomposition-based CS FP MB-InSAR tomography method (FP-SKPCS TomoSAR method). The method, based on an assumption that the reflectivity signal of a single scattering mechanism (SM) is more sparse than that of a composite of SMs, recovers the reflectivity profile of different SMs by using the CS technique. This method not only allows superresolution imaging with a low number of acquisitions but also can estimate the polarimetric SM of the vertical structure of forested areas. The effectiveness of these novel techniques for polarimetric SAR tomography is demonstrated using FP P-band airborne data sets acquired by the ONERA SETHI airborne system over a test site in Paracou, French Guiana, and the results of the vertical structure of forested areas derived by the method are verified by in situ test data.

The geomorphic features of alluvial fans in arid and semi-arid areas can contain vast amounts of information for the study of paleoclimatic and paleoenvironmental changes. Taking the Shule River Alluvial Fan (SRAF) as study area, the research of surface roughness estimation, one of the important geomorphic features of the arid and semi-arid alluvial fans, was carried out by using Radarsat-2 polarimetric synthetic aperture radar (SAR) data in this paper. A modified roughness inversion model was developed to solve the roughness overvalued problem when the conventional models are used in arid surface of alluvial fans directly. In this model, the dielectric constant of the gravels exposed in the surface, instead of the moisture, becomes a more important influencing factor on backscattering coefficients. After comparing the results retrieved from the conventional and modified roughness invention models, the correlation coefficient increases from 0.68 to 0.85, and the absolute difference between the inversion and field measured value reduces obviously. As a result, the proposed model improves the accuracy effectively and is suitable for the roughness parameter inversion in the arid surface of alluvial fans.

This paper addresses under-foliage target detection using diverse detection schemes. Compared with classical detection schemes as GLRT and SSF-based detection, isolated scatterer selection is potential to eliminate volume effects and detect under-foliage targets. The full-rank polarimetric spectral estimators are also applied for under-foliage detection. The effectiveness of diverse detection schemes are demonstrated by using L-band Multi-Baseline PolInSAR Data over Dornstetten, Germany.

This paper presents new principles and techniques to perform High Resolution (HR) 3-D imaging of volumetric environments using Polarimetric SAR Tomography (POLTOMSAR) and Multi-Baseline Polarimetric and Interferometric SAR (MB-POL-inSAR) data. Unlike classical polarimetric spectral estimation approaches which consider polarization as way to improve the discrimination between vertically aligned scatterers, this paper provides full rank techniques which permit to estimate 3-D coherency matrices that can be characterized using classical polarimetric processing algorithms.

This paper addresses the characterization of tropical forest structure by estimating their heights, underlying ground topography, vertical structure function and ground-to-volume ratio. The hybrid tomographic estimator can accurately estimate the tree top heights and the underlying topography. In order to separate the ground and volume contributions, model-based two-component fitting techniques are proposed to reconstruct the vertical structure function of forests. The proposed techniques are applied to P-Band Multi-baseline PolInSAR data acquired during the TropiSAR campaign over the test site of Paracou in French Guiana.

This paper addresses the imaging of objects located under a forest cover using polarimetric synthetic aperture radar tomography (POLTOMSAR) at L-band. High-resolution spectral estimators, able to accurately discriminate multiple scattering centers in the vertical direction, are used to separate the response of objects and vehicles embedded in a volumetric background. A new polarimetric spectral analysis technique is introduced and is shown to improve the estimation accuracy of the vertical position of both artificial scatterers and natural environments. This approach provides optimal polarimetric features that may be used to further characterize the objects under analysis. The effectiveness of this novel technique for POLTOMSAR is demonstrated using fully polarimetric L-band airborne data sets acquired by the German Aerospace Center (DLR)'s E-SAR system over the test site in Dornstetten, Germany.

This paper presents principles and techniques to perform tomographic imaging of volumetric natural environments using Multi-Baseline Polarimetric and Interferometric SAR (MB POL-inSAR) data. The objective of this work is to provide robust techniques to reconstruct the 3-D structure of media and to estimate some of their physical parameters. The pro posed approaches are based on results obtained in [1] on the robust estimation of MB-POL-inSAR quantities, on the POLINSAR model presented in [2] and on the tomographic techniques introduced in [3] and [4].

This paper addresses the characterization of tropical forests by estimating their heights and the underlying ground topography. A novel hybrid spectral approach is proposed and applied to P-Band Multi-baseline PolInSAR data acquired during the TropiSAR campaign over the test site of Paracou in French Guiana. Experimental results demonstrates that tropical forest heights and the underlying ground topography can be accurately estimated by this method, compared with those derived by single-baseline PolInSAR parameter retrieval techniques. The estimated quantities are validated against LiDAR measurements during TropiSAR campaign.

SAR tomography is the extension of conventional two dimensional SAR imaging principle to three dimensions. In order to improve the vertical resolution with respect to classical Fourier-based methods, high resolution approaches are used in this paper to perform SAR tomography. Both nonparametric spectral estimators, like beamforming and Capon and parametric ones, like MUSIC, maximum likelihood, are applied to real data sets and compared in terms of scatterer location accuracy and resolution. This paper addresses the discrimination of coherent scatterers presented in the natural environment and a joint approach of estimation and detection is proposed.

This paper presents principles and robust techniques to estimate physical parameters of natural environments using Multi-Baseline Polarimetric and Interferometric SAR (MB-POL-inSAR) data. The first part of this paper concerns the abstract topic of MB-POL-inSAR coherence optimization The second part is dedicated to the general estimation of the coherence line model parameters [1]. It is demonstrated that the line parameters can be estimated in an analytical and robust way by using the whole available POL-inSAR information.

Diverse spectral estimations methods, i.e. MUSIC, Maximum Likelihood (ML), Weighted Subspace fitting (WSF), are proposed and applied to the estimation of building height dense urban environments and to the retrieval of scatterers' physical properties. Compared to other estimators, the polarimetric WSF estimator is optimally model adaptive and results in reduced sidelobes induced by irregularly sampled baselines.

The advances in Polarimetric SAR Interferometry (PolInSAR) techniques provide a promising way to recover ground characteristics such as sub-canopy soil moisture and roughness using SAR data. Spectral analysis techniques have been applied to extract the vegetation and building parameters. Yamada et al proposed the ESPRIT algorithm to estimate vegetation height; Sauer et al apply the spectral analysis techniques to estimate building heights and extract physical properties from Multi-baseline (MB) PolinSAR data. In these applications, the parameters are mainly estimated from the phase information or phase center, but the validity of the sub-canopy soil backscattering or reflectivity estimation from polarimetric spectral analysis technique is not investigated. In this paper, the ground scattering center is first located by po-larimetric MUSIC algorithm and then the ground reflectivity is recovered using a polarimetric least-square method. The validity of the polarimetric spectral analysis technique for the sub-canopy ground reflectivity estimation is demonstrated using simulated and real SAR data.