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Synthetic aperture radar tomography (TomoSAR) at lower frequencies allows the reconstruction of the 3-D radar reflectivity of volume scatterers allowing access to their physical 3-D structure by means of multiangular SAR acquisitions. The performance of the reconstruction critically depends on the number and (spatial) distribution of the tomographic acquisitions (tracks). This dependence is addressed in this letter with respect to forest applications (volume scatters) at L-band. The letter discusses the optimum definition of tomographic configurations based on the peak sidelobe level (PSL) of the point spread function (PSF). For demonstration, a tomographic data set consisting of 15 acquisitions acquired by the DLR’s F-SAR system at L-band over the Traunstein test site in Germany is used, complemented by airborne LiDAR measurements. Three different reconstruction algorithms (Fourier beamforming, Capon beamforming, and compressive sensing) are implemented and compared to each other for scenarios with a reduced number of acquisitions. Although the limitation of the specific forest, the results show the potential of using the PSL of the PSF to define tomographic configurations optimized for forest structure applications. [ABSTRACT FROM AUTHOR]

Forest structure is a key parameter for forest applications, but it is difficult to be estimated at the required spatial and temporal scales. In this context, synthetic aperture radar Tomography (TomoSAR) that allows, at lower frequencies, the 3-D imaging of natural volume scatterers with high spatial and temporal resolution may be a game changer. The aim of this article is to evaluate three TomoSAR algorithms, Fourier beamforming (FB), Capon beamforming (CB), and compressive sensing (CS) with respect to their performance in the reconstruction of the 3-D forest reflectivity. The implications of volumetric forest scattering, as well as the temporal decorrelation of scatterers, are analyzed. The algorithms are compared on a set of simulated scenarios and then evaluated on an experimental L-band data set composed by four acquisition dates, each one consisting of five tomographic tracks. The data were acquired in 2014, within a time span of two months, over the Traunstein forest (Germany) using the F-SAR system. Additionally, discrete airborne Lidar has been used for a qualitative evaluation. The results indicate that the CS reconstruction is, for many practical cases, superior when compared to FB or CB reconstructions as they achieve higher vertical resolution, especially in cases with a lower number of acquisitions and complex forest scenarios. By combining acquisitions performed at different days, the effect of temporal decorrelation on each algorithm for two different tomographic implementations (repeat-pass vs. single-pass) has been assessed. The results indicate that simultaneously acquired image pairs allow a better reconstruction of the 3-D forest reflectivity. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) tomography (TomoSAR) allows the reconstruction of the vertical distribution of the power backscattered by natural volumes by combining multiple SAR images acquired with slightly different incidence angles. Being a “radar” quantity, the profile depends on the radar frequency, polarization, and acquisition geometry, the 3-D distribution of the scattering elements and their dielectric properties. The characterization of each one of these factors is crucial to enable the extraction of physical 3-D structure attributes from TomoSAR profiles. The objective of this paper is to investigate how the vertical distribution of the backscattered power at L-band is affected by seasonal- and weather-induced changes. Radiometric (e.g., ground and volume powers) and geometric (e.g., center of mass of the volume-only profiles and phase centers of the volume scattering layers) parameters have been estimated under different weather and season conditions and compared. Then, TomoSAR data sets affected by dielectric nonstationarity (i.e., variability) have been considered in order to assess the invariance degree of each radiometric and geometric parameters. This analysis has been carried out by processing four L-band airborne TomoSAR data sets acquired before and after a rainfall and in spring and autumn over the Traunstein forest (south of Germany). [ABSTRACT FROM AUTHOR]

The aim of this paper is to compare L- and P-band vertical backscattering profiles estimated by means of synthetic aperture radar (SAR) tomography and full light detection and ranging (lidar) waveforms in terms of their ability to distinguish different tropical forest structure types. The comparison relies on the unique DLR F-SAR and NASA Land, Vegetation and Ice Sensor (LVIS) lidar datasets acquired in 2016 in the frame of the AfriSAR campaign. In particular, F-SAR and LVIS data over three different test sites complemented by plot field measurements are used. First, the SAR and lidar three-dimensional (3-D) datasets are compared and discussed on a qualitative basis. The ability to penetrate into and through the canopy down to the ground is assessed at L- and P-band in terms of both the ground-to-volume power ratio and the performance to estimate the location of the underlying ground. The effect of polarimetry on the visibility of the ground is discussed as well. Finally, the 3-D measurements for each configuration are compared with respect to their ability to derive physical forest structure descriptors. For this, vertical structure indices derived from the volume-only 3-D radar reflectivity at L- and P-band and from the LVIS profiles are compared against each other as well as against plot-derived indices. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) remote sensing configurations are able to provide continuous measurements on global scales sensitive to the vertical structure of forests with a high spatial and temporal resolution. Furthermore, the development of tomographic SAR techniques allows the reconstruction of the three-dimensional (3-D) radar reflectivity opening the door for 3-D forest monitoring. However, the link between 3-D radar reflectivity and 3-D forest structure is not yet established. In this sense, this paper introduced a framework that allows a qualitative and quantitative interpretation of physical forest structure from tomographic SAR data at L-band. For this, forest structure is parameterized into a set of a horizontal and a vertical structure index. From inventory data, both indices can be derived from the spatial distribution and the dimensions of the trees. Similarly, two structure indices are derived from the 3-D spatial distribution of the local maxima of the reconstructed 3-D radar reflectivity profiles at L-band. The proposed methodology is tested by means of experimental tomographic L-band data acquired over the temperate forest site of Traunstein in Germany. The obtained horizontal and vertical structure indices are validated against the corresponding estimates obtained from inventory measurements and against the same indices derived from the vertical profiles of airborne Lidar data. The high correlation between the forest structure indices obtained from these three different data sources (expressed by correlation coefficients between 0.75 and 0.87) indicates the potential of the proposed framework. [ABSTRACT FROM AUTHOR]

Understanding the impact of soil and plant parameter changes in agriculture on Synthetic Aperture Radar (SAR) measurements is of great interest when it comes to monitor the temporal evolution of agricultural crops by means of SAR. In this regard, specific transitions between phenological stages in corn, barley, and wheat have been identified associated to certain dielectric and geometric changes, based on a time series of fully polarimetric multibaseline SAR data and in situ measurements. The data have been acquired in the frame of DLR's CROPEX campaign on six dates between May and July in 2014. The experiments reported in this paper address the sensitivity of X-, C-, and L-band to phenological transitions exploiting the availability of multiple baselines on each acquisition date. The application of tomographic techniques enables the estimation of the three-dimensional (3-D) backscatter distribution and the separation of ground and volume scattering components. Tomographic parameters have been derived at different frequencies, namely the center of mass of the profiles of the total and of the volume-only 3-D backscatter, and the ground and volume powers. Their sensitivity and ability to detect changes occurring on the ground and in the vegetation volume have been evaluated focusing on the added value provided by the 3-D resolution at the different frequencies and polarizations available. [ABSTRACT FROM AUTHOR]

The aim of this paper is to interpret and characterize the changes of the 3-D polarimetric scattering signatures of agricultural crops at C-band and to relate them to temporal changes of the soil and plant parameters. For this, a time series of multibaseline (MB) synthetic aperture radar (SAR) data acquired at C-band by the airborne F-SAR system of the German Aerospace Center over the Wallerfing test site in Germany was analyzed. The availability of MB SAR data enables the resolution of scattering contributions in height by means of SAR tomography. The tomographic profiles at different polarizations were analyzed regarding temporal changes for different crop types. First, it was investigated if the center of mass (CoM) of the vertical reflectivity profiles as a single parameter enables the tracking of changes in soil and vegetation. The results show that the vertical reflectivity profiles and their CoM do not allow resolving the ambiguity if a change originates from soil or vegetation dynamics as expected. Thus, the scattering contributions from ground and volume were separated in height, using a filtering approach, and used for the estimation of the ground and volume scattering powers by means of covariance matching. Comparing the outputs with coincident ground measurements showed that dielectric as well as geometric changes in the vegetation are traceable by the separated ground and volume powers. Finally, the estimated powers were analyzed with respect to orientation effects, i.e., to polarimetric anisotropic behavior. They were found to be not significant for the crops under study at C-band. [ABSTRACT FROM AUTHOR]

A two-layer model composed by ground and volume contributions has been proven suitable to describe the 3-D backscattering signatures of forest scenarios in a number of experiments. Under this hypothesis, the purpose of this letter is to investigate how synthetic aperture radar tomography (TomoSAR) can be used to estimate ground and volume polarimetric covariances and with which performance. An algorithm which is able to overcome the intrinsic ambiguity in the estimation problem is proposed, and it is shown to be a reliable alternative to the poorly performing full-rank Capon beamformer for estimating the ground polarimetric covariances. This performance improvement can be achieved, for instance, if an a priori knowledge of the ground topography (or an accurate estimate of it) is available. This analysis is carried out by processing an L-band TomoSAR stack acquired by the DLR’s E-SAR sensor over the temperate forest site of Traunstein. [ABSTRACT FROM PUBLISHER]

In forest and agricultural scattering scenarios, the backscattered synthetic aperture radar (SAR) signature consists, depending on the frequency, of the superposition of ground and volume scattering contributions. Using multibaseline SAR data, SAR tomography techniques allow resolving contributions occurring at different heights. Two algorithms for the separation of ground and volume scattering are compared with respect to their ability to provide a coherent volume component that can be further used for parameter inversion, both of them requiring only the a priori known ground topography. Once the volume-only coherences are available, the total ground and volume scattering powers are estimated by means of a least squares fitting. The objective of this letter is to quantitatively evaluate the performance of this estimation by means of a Monte Carlo analysis with simulated data focusing on the impact of vertical resolution, errors in the knowledge of the ground topography and phase calibration residuals. [ABSTRACT FROM PUBLISHER]

Forest height is an important parameter for the allometric estimation of above-ground forest biomass (AGB). However, variable forest stand densities limit the performance of the allometric estimation of AGB from height measurements alone. Recently, the use of vertical forest structure information as an indicator for the variation of stand density has been proposed and used to improve the allometric estimation of AGB from height measurements. In this letter, the use of vertical radar reflectivity profiles at L-band obtained from SAR tomography, as a proxy for vertical forest structure for the allometric estimation of AGB, is investigated. L-band reflectivity profiles, which are reconstructed from data at different polarizations (HH and HV) and acquired under “moist” and “dry” weather conditions, are investigated. The proposed allometric AGB estimator increases the correlation factor from 0.60 to 0.81 and reduces the root-mean-square error from 50.25 to 36.30 Mg/ha when compared with the AGB estimation from forest height alone. The effect of polarization and weather conditions on the AGB estimation performance is discussed. [ABSTRACT FROM PUBLISHER]

In this correspondence we derive explicit expressions for the hybrid Cramér-Rao lower bound (HCRB) on the estimation accuracy of signal direction of arrival (DOA) from data collected by randomly perturbed arrays. The presence of a wavefront spatial decorrelation, which is modeled as a multiplicative correlated noise, has also been taken into account in the data model, since it is typical in those applications involving extended sources. In particular, we consider perturbations in sensor positions. Existing approaches to DOA HCRB calculation do not consider the presence of multiplicative noise and are referred to the assumption of small perturbations only, still not being worked out explicitly. Here, we assume that the impinging wavefronts are coming from broadside or more generally from a narrow DOA sector, allowing the explicit derivation of the HCRB for any variance of the sensor positioning errors in the line-of-sight direction. This scenario corresponds to the typical operative condition of remote sensing systems such as synthetic aperture radar (SAR) multibaseline interferometers, for which a few HCRB sample curves are reported. [ABSTRACT FROM AUTHOR]