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The penetration of microwave signals into snow and ice, especially in dry conditions, introduces a bias in digital elevation models generated by means of synthetic aperture radar (SAR) interferometry. This bias depends directly on the vertical backscattering distribution in the subsurface. At the same time, the sensitivity of interferometric SAR measurements on the vertical backscattering distribution provides the potential to derive information about the subsurface of glaciers and ice sheets from SAR data, which could support the assessment of their dynamics. The aim of this article is to improve the interferometric modeling of the vertical backscattering distribution in order to support subsurface structure retrieval and penetration bias estimation. Vertical backscattering distributions are investigated at different frequencies and polarizations on two test sites in the percolation zone of Greenland using fully polarimetric X-, C-, L-, and P-band SAR data. The vertical backscattering distributions were reconstructed by means of SAR tomography and compared to different vertical structure models. The tomographic assessment indicated that the subsurface in the upper percolation zone is dominated by scattering layers at specific depths, whereas a more homogeneous scattering structure appears in the lower percolation zone. The performance of the evaluated structure models, namely an exponential function with a vertical shift, a Gaussian function, and a Weibull function, was evaluated. The proposed models improve the representation of the data compared with existing models while the complexity is still low to enable potential model inversion approaches. The tomographic analysis and the model assessment is therefore a step forward toward subsurface structure information and penetration bias estimation from SAR data. [ABSTRACT FROM AUTHOR]

Compact polarimetry is a hybrid dual-polarization imaging mode, which is used to enable wide swath coverages and provide more polarimetric information compared with the conventional dual-polarimetric imaging modes (HH/HV and VH/VV). In applications of compact polarimetric synthetic aperture radar (Pol-SAR), pseudo quad-polarimetric (quad-pol) image reconstruction is an important technique. In this study, we propose a least-squares (LS) based method to estimate the quad-pol covariance elements for the linear π/4 compact polarimetric mode. Different from existing quad-pol reconstruction approaches, which use an iterative approach to refine the model solution based on multi-look data, the LS method uses a set of data points to best fit the reconstruction model and is applicable to both multi-look and single-look complex data. In this study, a decomposition-based three-component reconstruction model is exploited to construct the system of non-linear equations. Then, the minimization problem is addressed in a local window for the cross-polarized term, which is optimized with bound constraints. Furthermore, the $m - {\alpha _s}$ decomposition for the linear compact mode is developed, which is used to approximate the reconstruction model parameter for the LS model function. Experiments are performed on C-band RADARSAT-2 data collected over agriculture fields, an urban area, and an area with complex terrain types. In comparison with the iterative-based methods, the LS-based reconstruction method shows its superiority in estimating both the cross-polarized term and the co-polarized phase difference. [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]

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]

An iterative, generalized hybrid polarimetric decomposition, combining model-based and eigen-based techniques together with a generalized vegetation model, is developed for soil moisture retrieval under agricultural vegetation cover. The algorithm is physically based without the need of empirical calibration or fitting with auxiliary data and runs in two iterations. The algorithm is applied on L-band fully polarimetric data sets acquired by DLR’s E-SAR sensor. The flights were conducted within the AgriSAR, OPAQUE, and SARTEO campaigns carried out between 2006 and 2008 on three different test sites. The algorithm achieves inversion rates always higher than 95% for a variety of crop types at different phenological stages. The validation is performed against in situ time-domain reflectometry (TDR), frequency-domain reflectometry (FDR), and gravimetric measurements. The moisture levels range from 5 vol.% to 40 vol.%. The achieved root-mean-square error (RMSE) levels stay between 4.0 vol.% and 4.4 vol.% for all three sites across different vegetation and soil types, comprising the entire phenological cycle (e.g., April–July 2006). [ABSTRACT FROM PUBLISHER]

Temporal decorrelation is the most critical issue for the successful inversion of polarimetric SAR interferometry (Pol-InSAR) data acquired in an interferometric repeat-pass mode, typical for satellite or lower frequency airborne SAR systems. This paper provides a quantitative estimation of temporal decorrelation effects at L-band for a wide range of temporal baselines based on a unique set of multibaseline Pol-InSAR data. A new methodology that allows to quantify individual temporal decorrelation components has been developed and applied. Temporal decorrelation coefficients are estimated for temporal baselines ranging from 10 min to 54 days and converted to height inversion errors caused by them. The temporal decorrelations of \gammaTV (volume temporal decorrelation) and \gammaTG (ground temporal decorrelation) depend not only on the wind-induced movement but also strongly on the rain-induced dielectric changes in volume and on the ground at temporal baseline on the order of day or longer. At temporal baselines on the order of minutes, the wind speed is a critical parameter and the speed of 2 m/s already hampers the application of Pol-InSAR forest parameter inversion. The approach is supported and validated by using L-band E-SAR repeat-pass data acquired in the frame of three dedicated campaigns, BioSAR 2007, TempoSAR 2008, and TempoSAR 2009. [ABSTRACT FROM PUBLISHER]