Model-Based Decomposition of Dual-Pol SAR Data: Application to Sentinel-1

In this study, we advance a new family of model-based decompositions adapted for dual-pol synthetic aperture radar data. These are formulated using the Stokes vector formalism, coupled to mappings from full quad-pol decomposition theory. A generalized model-based decomposition is developed, which allows separation of an arbitrary Stokes vector into partially polarized and polarized wave components. We employ the widely used random dipole cloud as a volume model but, in general, non-dipole options can be used. The cross-polarized phase δ, and the α angle, which is a function of the ratio between wave components, measure the transformation of polarization state on reflection. We apply the decomposition to dual-pol data provided by Sentinel-1 covering different scenarios, such as agricultural, forest, urban and glacial land-ice. We show that the polarized term of received polarization state is not usually the same as the transmitted one, and can therefore be used for key applications, e.g., classification and geo-physical parameter estimation. We show that, for vegetated terrain, depolarization is not the only influencing factor to Sentinel-1 backscattered intensities and, in the case of vertical crops (e.g., rice), this allows the crop orientation effects to be decoupled from volume scattering in the canopy. We demonstrate that coherent dual-pol systems show strong phase signatures over glaciers, where the polarized contribution significantly affects the backscattered state, resulting in elliptical polarization on receive. This is a key result for Sentinel-1, for which dual-pol phase analysis coupled to dense time series offer great opportunities for land-ice monitoring.This work was funded by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Projects TEC2017-85244-C2-1-P and PID2020-117303GB-C22, and by the University of Alicante under grant VIGROB-114

Rice Phenology Monitoring by means of SAR Polarimetry at X-Band

The feasibility of retrieving the phenological stage of rice fields at a particular date by employing coherent copolar dual-pol X-band radar images acquired by the TerraSAR-X sensor has been investigated in this paper. A set of polarimetric observables that can be derived from this data type has been studied by using a time series of images gathered during the whole cultivation period of rice. Among the analyzed parameters, besides backscattering coefficients and ratios, we have observed clear signatures in the correlation (in magnitude and phase) between channels in both the linear and Pauli bases, as well as in parameters provided by target decomposition techniques, like entropy and alpha from the eigenvector decomposition. A new model-based decomposition providing estimates of a random volume component plus a polarized contribution has been proposed and employed in interpreting the radar response of rice. By exploiting the signatures of these observables in terms of the phenology of rice, a simple approach to estimate the phenological stage from a single pass has been devised. This approach has been tested with the available data acquired over a site in Spain, where rice is cultivated, ensuring ground is flooded for the whole cultivation cycle, and sowing is carried out by randomly spreading the seeds on the flooded ground. Results are in good agreement with the available ground measurements despite some limitations that exist due to the reduced swath coverage of the dual-pol HHVV mode and the high noise floor of the TerraSAR-X system.This work was supported by the Spanish Ministry of Science and Innovation (MICINN) and EU FEDER under Projects TEC2008-06764-C02-02 and TEC2011-28201-C02-02, by the National Program of Human Resources Movability under Grant PR2009-0364, by the University of Alicante under Project GRE08J01, and by Generalitat Valenciana under Projects GV/2009/079 and ACOMP/2010/082

Influence of Incidence Angle on the Coherent Copolar Polarimetric Response of Rice at X-Band

The coherent nature of the acquisition by TerraSAR-X of both copolar channels (HH and VV) enables the generation of many different polarimetric observables with physical interpretation, as have recently been used for monitoring rice fields. In this letter, the influence of incidence angle upon these polarimetric observables is analyzed by comparing three stacks of images that were acquired simultaneously at different incidence angles (22°, 30°, and 40°) during a whole cultivation campaign. We show that the response of observables related to dominance (entropy, ratios of components) and type of scattering mechanisms (alpha angles) is not greatly influenced by incidence angle at some stages: early and advanced vegetative phases, and maturation. Moreover, the acquisition geometry drives the sensitivity to the presence of the initial stems and tillers, being detected earlier at shallower angles. This analysis is a necessary step before studying potential methodologies for combining different orbits and beams for reducing the time between acquisitions for monitoring purposes.This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under Project TEC2011-28201-C02-02, and by the Generalitat Valenciana under Project ACOMP/2014/136

Polarimetric Response of Rice Fields at C-Band: Analysis and Phenology Retrieval

A set of ten RADARSAT-2 images acquired in fully polarimetric mode over a test site with rice fields in Seville, Spain, has been analyzed to extract the main features of the C-band radar backscatter as a function of rice phenology. After observing the evolutions versus phenology of different polarimetric observables and explaining their behavior in terms of scattering mechanisms present in the scene, a simple retrieval approach has been proposed. This algorithm is based on three polarimetric observables and provides estimates from a set of four relevant intervals of phenological stages. The validation against ground data, carried out at parcel level for a set of six stands and up to nine dates per stand, provides a 96% rate of coincidence. Moreover, an equivalent compact-pol retrieval algorithm has been also proposed and validated, providing the same performance at parcel level. In all cases, the inversion is carried out by exploiting a single satellite acquisition, without any other auxiliary information.This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and European Union FEDER under Project TEC2011-28201-C02-02

Thermal Noise Removal From Polarimetric Sentinel-1 Data

This study proposes, for the first time, an approach to remove thermal noise from the wave coherency matrix, C₂, estimated from single-look complex dual-polarization Interferometric Wide Swath mode Sentinel-1 synthetic aperture radar data. The approach is straightforward; it exploits the ThermalNoiseRemoval module, provided by the European Space Agency (ESA) in its Sentinel Application Platform (SNAP) software, to remove thermal noise from the channel intensities. Then, noise correction on the complex data is applied, in order to estimate the noise-free C₂ matrix. As a further novelty, the proposed approach can be implemented in SNAP, through the use of a processing graph that is here provided. The method is applied on a dense time series of Sentinel-1 data, collected on an agricultural area located near Seville, Spain. The impact of thermal noise on the estimation of the eigendecomposition parameters of C₂, i.e., entropy (H₂), average alpha angle (α₂), and anisotropy (A₂), is assessed for different land-cover types, namely river, rice, forest, and urban areas. Monte Carlo simulations are implemented to assess the performance of the proposed approach in estimating H₂, α₂, and A₂. Results show that the proposed noise removal method improves the estimation of these parameters for the considered land-cover classes

Using GEDI Waveforms for Improved TanDEM-X Forest Height Mapping: A Combined SINC + Legendre Approach

In this paper, we consider a new method for forest canopy height estimation using TanDEM-X single-pass radar interferometry. We exploit available information from sample-based, space-borne LiDAR systems, such as the Global Ecosystem Dynamics Investigation (GEDI) sensor, which offers high-resolution vertical profiling of forest canopies. To respond to this, we have developed a new extended Fourier-Legendre series approach for fusing high-resolution (but sparsely spatially sampled) GEDI LiDAR waveforms with TanDEM-X radar interferometric data to improve wide-area and wall-to-wall estimation of forest canopy height. Our key methodological development is a fusion of the standard uniform assumption for the vertical structure function (the SINC function) with LiDAR vertical profiles using a Fourier-Legendre approach, which produces a convergent series of approximations of the LiDAR profiles matched to the interferometric baseline. Our results showed that in our test site, the Petawawa Research Forest, the SINC function is more accurate in areas with shorter canopy heights (<~27 m). In taller forests, the SINC approach underestimates forest canopy height, whereas the Legendre approach avails upon simulated GEDI forest structural vertical profiles to overcome SINC underestimation issues. Overall, the SINC + Legendre approach improved canopy height estimates (RMSE = 1.29 m) compared to the SINC approach (RMSE = 4.1 m)

Principal Component Analysis (PCA) in Radar Polarimetry

Statistical and computational techniques for revealing the internal structure that underlies the set of random correlated data exists in a great variety at present, and target decomposition theorems, either in the coherent or incoherent formulation, are well established. In spite of this fact a rather innovative and new concept is presented in this contribution which was proposed for radar polarimetry by late Dr. Ernst Lüneburg. In turn the Principal Component Analysis is considered to possibly add value to existing approaches, and it allows for an interpretation of polarimetric synthetic aperture radar measurements using variables obtained via linear transformation. Starting with the Sinclair backscatter matrix which will be further transformed into the so called target feature vector by stacking column elements of S and generating the covariance matrices averaged over a certain pixel array we show, how the Sinclair backscatter matrix is decomposed into the sum of a maximum of four 2 x 2 elementary point scatter matrices which are weighted by the principal components, whereas the variances of this components agree with the eigenvalues of the covariance matrix. In conclusion this mathematical development defines a decomposition which expresses scattering mechanism from distributed targets in terms of scattering matrices via an incoherent step

A New Polarimetric Change Detector in Radar Imagery

In modern society, the anthropogenic influences on ecosystems are central points to understand the evolution of our planet. A polarimetric synthetic aperture radar may have a significant contribution in tackling problems concerning land use change, since such data are available with any-weather conditions. Additionally, the discrimination capability can be enhanced by the polarimetric analysis. Recently, an algorithm able to identify targets scattering an electromagnetic wave with any degree of polarization has been developed, which makes use of a vector rearrangement of the elements of the coherency matrix. In the present work, this target detector is modified to perform change detection between two polarimetric acquisitions, for land use monitoring purposes. Regarding the selection of the detector parameters, a physical rationale is followed, developing a new parameterization of the algebraic space where the detector is defined. As it will be illustrated in the following, this space is 6-D complex with restrictions due to the physical feasibility of the vectors. Specifically, a link between the detector parameters and the angle differences of the eigenvector model is obtained. Moreover, a dual polarimetric version of the change detector is developed, in case quad-polarimetric data are not available. With the purpose of testing the methodology, a variety of data sets were exploited: quad-polarimetric airborne data at L-band (E-SAR), quad-polarimetric satellite data at C-band (Radarsat-2), and dual-polarimetric satellite data at X-band (TerraSAR-X). The algorithm results show agreement with the available information about land changes. Moreover, a comparison with a known change detector based on the maximum likelihood ratio is presented, providing improvements in some conditions. The two methodologies differ in the analysis of the total amplitude of the backscattering, where the proposed algorithm does not take this into consideration.This work was supported in part by the Spanish Ministry of Science and Innovation (MICINN) and EU FEDER, under Projects TEC2008-06764-C02-02 and TEC2011-28201-C02-02