Polarimetric Interferometric Synthetic Aperture Radar Research Articles

Adaptive model based on polarimetric decomposition using correlation coefficient in horizontal–vertical and circular basis

This paper presents two decomposition schemes for polarimetric synthetic aperture radar data. The proposed schemes intend to overcome the problem of scattering ambiguity and reduce the volume scattering power in oriented urban areas. The first proposed scheme uses an empirical volume model based on the correlation coefficients of the Pauli component in the horizontal–vertical basis, whereas the second one employs a volume model defined on correlation coefficients of the Pauli components expressed in the circular basis. The correlation coefficients are calculated from polarimetric interferometric synthetic aperture radar (PolInSAR) data. The characteristics adopted from these volume models are used to enhance the results of the decomposition schemes. The scattering powers estimated from the proposed methods give promising results compared to existing methods in the literature, particularly in urban areas since all the oriented built-up areas are well discriminated as double or odd bounce scattering. The methods are evaluated using the experimental airborne SAR sensor (E-SAR) PolInSAR L band data acquired on the Oberpfaffenhofen test site in Germany.

A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data

The random volume over ground (RVoG) model associates vegetation vertical structure parameters with multiple complex interferometric coherence observables. In this paper, on the basis of the RVoG model, a truncated singular value decomposition (TSVD)-based method is proposed for forest height inversion from single-baseline polarimetric interferometric synthetic aperture radar (PolInSAR) data. In addition, in order to improve the applicability of TSVD for this issue, a new truncation method is proposed for TSVD. Differing from the traditional three-stage method, the TSVD-based inversion method estimates the pure volume coherence directly from the complex interferometric coherence, and estimates the forest height from the estimated pure volume coherence with a least-squares method. As a result, the TSVD-based method can adjust the contributions of the polarizations in the estimation of the model parameters and avoid the null ground-to-volume ratio assumption. The simulated experiments undertaken in this study confirmed that the TSVD-based method performs better than the three-stage method in forest height inversion. The TSVD-based method was also applied to E-SAR P-band data acquired over the Krycklan Catchment, Sweden, which is covered with mixed pine forest. The results showed that the TSVD-based method improves the root-mean-square error by 48.6% when compared to the three-stage method, which further validates the performance of the TSVD-based method.

Retrieval of agricultural crop height from space: A comparison of SAR techniques

This paper deals with the retrieval of agricultural crop height from space by using multipolarization Synthetic Aperture Radar (SAR) images. Coherent and incoherent crop height estimation methods are discussed for the first time with a unique TanDEM-X dataset acquired over rice cultivation areas. Indeed, with its polarimetric and interferometric capabilities, the TanDEM-X mission enables the tracking of crop height through interferometric SAR (InSAR), polarimetric interferometric SAR (PolInSAR) and the inversion of radiative transfer-based backscattering model. The paper evaluates the three aforementioned techniques simultaneously with a data set acquired in September 2014 and 2015 over rice fields in Turkey during their reproductive stage. The assessment of the absolute height accuracy and the limitations of the approaches are provided. In-situ measurements conducted in the same cultivation periods are used for validation purposes. The PolInSAR and morphological backscattering model results showed better performance with low RMSEs (12 and 13 cm) compared to the differential InSAR result having RMSE of 18 cm. The spatial baseline, i.e. the distance between satellites, is a key parameter for coherent methods such as InSAR and PolInSAR. Its effect on the absolute height accuracy is discussed using TanDEM-X pairs separated by a baseline of 101.7m and 932m. Although the InSAR based approach is demonstrated to provide sufficient crop height accuracy, the availability of a precise vegetation-free digital elevation model and a structurally dense crop are basic requirements for achieving high accuracy. The PolInSAR approach provides reliable crop height estimation if the spatial baseline is large enough for the inversion. The impact of increasing spatial baseline on the absolute accuracy of the crop height estimation is evident for both methods. However, PolInSAR is more cost-efficient, e.g. there is no need for phase unwrapping and any external vegetation free surface elevation data. Instead, the usage of radiative transfer based backscattering models provides not only crop height but also other biophysical properties of the crops with consistent accuracy. The efficient retrieval of crop height with backscattering model is achieved by metamodelling, which makes the computational cost of backscattering inversion comparable to the ones of the coherent methods. However, effectiveness depends on not only the backscattering model, but also the integration of agronomic crop growth rules. Motivated by these results, a combination of backscattering and PolInSAR inversion models would provide a successful method of future precision farming studies.

Improved SNR Optimum Method in POLDINSAR Coherence Optimization

The traditional methods for coherence optimization in the framework of multibaseline (MB) polarimetric differential interferometric synthetic aperture radar (DInSAR) applications, such as Best, MB1 equal scattering mechanism (MB1-ESM), suboptimum scattering mechanism (SOM), and exhaustive search polarimetric optimization (ESPO), all have some disadvantages. The MB2-ESM method just can be preferred for its normal accuracy and fast computing time. Signal–noise ratio optimum (SNR-OPT) in the coarse grid followed by the conjugated gradient method (SNR-OPT-CG-CGM) can be selected because of its higher accuracy and acceptable cost time. SNR-OPT has higher computational efficiency compared with ESPO because it makes the 4-D coherence optimization problem transform into two independent 2-D optimization problems (“2 $+$ 2” optimization problem). However, SNR-OPT still costs much time. In this letter, we propose a new method which can further make this “2 $+$ 2” optimization problem transform into one 2-D and two independent 1-D optimization problems (“ $2+1+1$ ” optimization problem). Thus, the computational efficiency will be improved much more compared with SNR-OPT, and meanwhile, the accuracy just decreases a little. Seven full polarimetric RADARSAT-2 images are taken for experiment, and the results also show that the improved SNR-OPT-CG-CGM method is a better method considering the tradeoff between computation time and accuracy compared with other methods for DINSAR applications.

The Dual-Band PolInSAR Method for Forest Parametrization

There are basically two methods for retrieving the ground and the canopy height from interferometric synthetic aperture radar (InSAR) in forested areas. The first method is based on the difference between InSAR height estimations from dual-band (DB) systems, typically operating at X- and P-bands HH. The second method is based on the modeling of the polarimetric (Pol) InSAR response along the forest vertical structure, typically at L- or P-band. This paper proposes the combination of both methods, so that the ground and total tree height estimations are improved and become available alongside the interferometric forest height with the usage of polarimetric data and the RVoG model adoption in both bands. In the proposed method (DB-PolInSAR), first, the ground phase is retrieved from the RVoG inversion through a straightforward line fit of the P-Band polarimetric data in the complex plane. Fixing the ground height coming from the previous P-band inversion and applying the RVoG model to the X-band interferometric data, we estimate the total tree height. Repeat-pass dual-polarimetric (HH and HV) P-band data and single-pass three-baseline HH X-band data acquired with the airborne OrbiSAR sensor of Bradar over the Amazon region of Urucu are used to demonstrate the proposed method. Comparisons between the dual-band PolInSAR and the dual-band single-polarization cases are performed for five different P-band single-baseline configurations. Better ground estimation over range is obtained with the proposed method. Furthermore, the three-antenna single-pass X-band data enabled a robust RVoG total tree height inversion.

The Impact of Forest Density on Forest Height Inversion Modeling from Polarimetric InSAR Data

Forest height is of great significance in analyzing the carbon cycle on a global or a local scale and in reconstructing the accurate forest underlying terrain. Major algorithms for estimating forest height, such as the three-stage inversion process, are depending on the random-volume-over-ground (RVoG) model. However, the RVoG model is characterized by a lot of parameters, which influence its applicability in forest height retrieval. Forest density, as an important biophysical parameter, is one of those main influencing factors. However, its influence to the RVoG model has been ignored in relating researches. For this paper, we study the applicability of the RVoG model in forest height retrieval with different forest densities, using the simulated and real Polarimetric Interferometric SAR data. P-band ESAR datasets of the European Space Agency (ESA) BioSAR 2008 campaign were selected for experiments. The test site was located in Krycklan River catchment in Northern Sweden. The experimental results show that the forest density clearly affects the inversion accuracy of forest height and ground phase. For the four selected forest stands, with the density increasing from 633 to 1827 stems/Ha, the RMSEs of inversion decrease from 4.6 m to 3.1 m. The RVoG model is not quite applicable for forest height retrieval especially in sparsely vegetated areas. We conclude that the forest stand density is positively related to the estimation accuracy of the ground phase, but negatively correlates to the ground-to-volume scattering ratio.

Forest Height Estimation by Means of Pol-InSAR Data Inversion: The Role of the Vertical Wavenumber

This paper examines the multifaceted effect of the effective spatial baseline, as expressed through the vertical (interferometric) wavenumber, on the inversion of forest height from polarimetric interferometric synthetic aperture radar (Pol-InSAR) data. First, the role of the vertical wavenumber in relating forest height to the interferometric (volume) coherence is introduced. Through the review of the forest height inversion from Pol-InSAR data, the effect of the vertical wavenumber on the inversion performance is evaluated. The selection of optimum with respect to forest height inversion performance, vertical wavenumbers is discussed. The impact of the acquisition geometry and terrain slopes on the vertical wavenumber and their consideration in the inversion methodology is addressed. The individual effects discussed are demonstrated by means of airborne repeat pass Pol-InSAR acquisitions in L- and P-band acquired over different forest conditions, including a boreal, a temperate, and a tropical forest test site. The achieved forest height inversion performance is validated against reference height data derived from airborne LIDAR acquisitions.

Extraction of Structural and Dynamic Properties of Forests From Polarimetric-Interferometric SAR Data Affected by Temporal Decorrelation

This paper addresses the important yet unresolved problem of estimating forest properties from polarimetric-interferometric radar images affected by temporal decorrelation. We approach the problem by formulating a physical model of the polarimetric-interferometric coherence that incorporates both volumetric and temporal decorrelation effects. The model is termed random-motion-over-ground (RMoG) model, as it combines the random-volume-over-ground (RVoG) model with a Gaussian-statistic motion model of the canopy elements. Key features of the RMoG model are: 1) temporal decorrelation depends on the vertical structure of forests; 2) volumetric and temporal coherences are not separable as simple multiplicative factors; and 3) temporal decorrelation is complex-valued and changes with wave polarization. This third feature is particularly important as it allows compensating for unknown levels of temporal decorrelation using multiple polarimetric channels. To estimate model parameters such as tree height and canopy motion, we propose an algorithm that minimizes the least square distance between model predictions and complex coherence observations. The algorithm was applied to L-band NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar data acquired over the Harvard Forest (Massachussetts, USA). We found that the RMS difference at stand level between estimated RMoG-model tree height and NASA's lidar Laser Vegetation and Ice Sensor tree height was within 12% of the lidar-derived height, which improved significantly the RMS difference of 37% obtained using the RVoG model and ignoring temporal decorrelation. This result contributes to our ability of estimating forest biomass using in-orbit and forthcoming polarimetric-interferometric radar missions.

Soil Salinity Characterization Using Polarimetric InSAR Coherence: Case Studies in Tunisia and Morocco

The phenomenon of soil salinization in semi-arid regions is getting amplified and accentuated by both anthropogenic practices and climate change. Land salinization mapping and monitoring using conventional strategies are insufficient and difficult. Our work aims to study the potential of synthetic aperture radar (SAR) for mapping and monitoring of the spatio-temporal dynamics of soil salinity using interferometry. Our contribution in this paper consists of a statistical relationship that we establish between field salinity measurement and InSAR coherence based on an empirical analysis. For experimental validation, two sites were selected: 1) the region of Mahdia (central Tunisia) and 2) the plain of Tadla (central Morocco). Both sites underwent three ground campaigns simultaneously with three Radarsat-2 SAR image acquisitions. The results show that it is possible to estimate the temporal change in soil electrical conductivity (EC) from SAR images through the InSAR technique. It has been shown that the radar signal is more sensitive to soil salinity in HH polarization using a small incidence angle. However, for the HV polarization, a large angle of incidence is more suitable. This is, under considering the minimal influence of roughness and moisture surfaces, for a given InSAR coherence.

Polarization Impact in TanDEM-X Data Over Vertical-Oriented Vegetation: The Paddy-Rice Case Study

It has been recently shown that the TanDEM-X mission is capable of tracking the plant growth of rice paddies. The precision of the elevation measure depends on the physical interaction between the synthetic aperture radar (SAR) signal and the canopy. In this letter, this interaction is studied by considering the signal polarization. In particular, the vertical and horizontal wave polarizations are compared, and their performance in the temporal mapping of the crop height is analyzed. The temporal elevation difference analysis shows a monotonically increasing trend within the reproductive stage of the canopy, with maximum height discrepancies between polarizations of about 9 cm. From an operational point of view of InSAR-based vegetation height measurements, this letter demonstrates that the oriented structure of the canopy shall be considered not only in polarimetric InSAR studies but also in the interpretation of bistatic spaceborne interferometric elevation models.

Extracting DEM from airborne X-band data based on PolInSAR

Abstract. Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) is a new trend of SAR remote sensing technology which combined polarized multichannel information and Interferometric information. It is of great significance for extracting DEM in some regions with low precision of DEM such as vegetation coverage area and building concentrated area. In this paper we describe our experiments with high-resolution X-band full Polarimetric SAR data acquired by a dual-baseline interferometric airborne SAR system over an area of Danling in southern China. Pauli algorithm is used to generate the double polarimetric interferometry data, Singular Value Decomposition (SVD), Numerical Radius (NR) and Phase diversity (PD) methods are used to generate the full polarimetric interferometry data. Then we can make use of the polarimetric interferometric information to extract DEM with processing of pre filtering , image registration, image resampling, coherence optimization, multilook processing, flat-earth removal, interferogram filtering, phase unwrapping, parameter calibration, height derivation and geo-coding. The processing system named SARPlore has been exploited based on VC++ led by Chinese Academy of Surveying and Mapping. Finally compared optimization results with the single polarimetric interferometry, it has been observed that optimization ways can reduce the interferometric noise and the phase unwrapping residuals, and improve the precision of DEM. The result of full polarimetric interferometry is better than double polarimetric interferometry. Meanwhile, in different terrain, the result of full polarimetric interferometry will have a different degree of increase.

Woodland Extraction from High-Resolution CASMSAR Data Based on Dempster-Shafer Evidence Theory Fusion

Mapping and monitoring of woodland resources is necessary, since woodland is vital for the natural environment and human survival. The intent of this paper is to propose a fusion scheme for woodland extraction with different frequency (P- and X-band) polarimetric synthetic aperture radar (PolSAR) and interferometric SAR (InSAR) data. In the study area of Hanjietou, China, a supervised complex Wishart classifier based on the initial polarimetric feature analysis was first applied to the PolSAR data and achieved an overall accuracy of 88%. An unsupervised classification based on elevation threshold segmentation was then applied to the InSAR data, with an overall accuracy of 90%. After Dempster-Shafer (D-S) evidence theory fusion processing for the PolSAR and InSAR classification results, the overall accuracy of fusion result reached 95%. It was found the proposed fusion method facilitates the reduction of polarimetric and interferometric SAR classification errors, and is suitable for the extraction of large areas of land cover with a uniform texture and height. The woodland extraction accuracy of the study area was sufficiently high (producer’s accuracy of 96% and user’s accuracy of 96%) enough that the woodland map generated from the fusion result can meet the demands of forest resource mapping and monitoring.

PolInSAR Coherence Region Modeling and Inversion: The Best Normal Matrix Approximation Solution

In this paper, we address the theoretical aspects of coherence region modeling and its inversion methods for polarimetric interferometric synthetic aperture radar (PolInSAR). Instead of only considering the geometrical shape of the coherence region, we focus on directly modeling the whitened interferometric cross-correlation matrix of the PolInSAR data. In particular, we consider three classes of generic scattering models that respectively contain single, double, and triple phase centers within one resolution cell. We then demonstrate, for each class, that the modeled whitened interferometric cross-correlation matrix is normal. Based on this property, we propose, for each case, efficient algorithms to obtain the best normal matrix approximation solution for model inversion from the observed whitened interferometric cross-correlation matrix. In addition, we show how a simple and effective criterion can be designed from the derived solutions for model validation. Finally, we verify the solutions with both simulated and real data. The results prove the proposed method to be a very promising tool for PolInSAR applications.

Monitoring Tropical Forest Degradation in Betampona Nature Reserve, Madagascar Using Multisource Remote Sensing Data Fusion

This paper demonstrates how animal and plant species diversity in the Betampona Nature Reserve (BNR), Madagascar has become threatened through forest degradation and the introduction of invasive species over the last two decades. First, land-use changes and agricultural activities were analyzed using Landsat and IKONOS-2 data from 1990 to 2010. Then, a decision tree algorithm was developed to map under canopy invasive plant species using high resolution optical stereo imaging, land-use classification, and characterizing plant growth using Interferometric Synthetic Aperture Radar (InSAR) and polarimetric InSAR observations from Phased Array type L-band Synthetic Aperture Radar (PALSAR). Next, causal association between land use, climate change, and spatial and temporal dynamics of invasive plant species distribution was explored using satellite derived and in situ climate variables, changes in drought regimes, and tropical cyclones. Results showed that the region experienced intense land-use changes characterized by significant increase in agricultural lands at the cost of primary forest and other land-cover types. Encroachment by habitat-altering invasive plants from 2005 to 2012 within the reserve was obvious, and were probably attributable to illegal logging, erosion of the reserve boundary from anthropogenic activities and cyclone damage as well as shifts in drought regimes. The spatial extent of guava ( Psidium cattleianum ) has increased from 5.6% of the reserve in 2005 to 7.9% in 2012, a 55-ha increase over less than 7 years. Madagascar cardamom ( Aframomum angustifolium ) has increased by 1.7% and Molucca raspberry ( Rubus moluccanus ) by 2.3%, respectively.

POLINSAR Coherence-Based Regression Analysis of Forest Biomass Using RADARSAT-2 Datasets

Abstract. Forests play a pivotal role in synchronizing earth’s carbon cycle by absorbing carbon from the atmosphere and storing it in the form of biomass. Researchers today are trying to understand the climatic variations, especially those occurring due to destruction of forest and its corresponding biomass loss. Hence, quantification of various forest parameters such as biomass is imperative for evaluating the carbon. The objective of this research was to exploit the potential of C-band Radarsat-2 Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) technique for analysing the relationship between complex coherence and field-estimated aboveground biomass. Association between the backscatter and the aboveground biomass was also established in the process. To serve our objective, Radarsat-2 interferometric pair dated 4th March, 2013 (master image) and 28th March, 2013 (slave image) were procured for the Barkot Reserve Forest region of Dehradun, India. Field sampling was done for 30 plots (31.62 m x 31.62 m) and stem diameter and tree height were measured in each plot. The study emphasized on the application of POLINSAR coherence instead of using conventional method of relying on backscatter values for retrieving forest biomass. Coherence matrices were utilized for generating complex coherence values for different polarization channels and were regressed against field estimated aboveground biomass. Results indicated a negative linear relationship between complex coherence and aboveground biomass with the cross – polarized coherence showing the highest R2 value of 0.71. Further, the backscatter mechanism when studied with respect to aboveground biomass indicated a positive linear relationship between backscatter values and field estimated aboveground biomass with R2 value of 0.45 and 0.61 for slave and master image respectively. The results suggest that PolInSAR technique, in combination with different modelling approaches, can be adopted for estimating forest biomass.

Performance of Building Height Estimation Using High-Resolution PolInSAR Images

This paper investigates the use of polarimetry to improve the estimation of the height of buildings in high-resolution synthetic aperture radar (SAR) images. Polarimetric coherence optimization techniques solve the problem of layover effects in urban scenes by allowing a phase separation of the scatterers sharing the same resolution cell. Bare soil elevation estimation is also improved by the polarimetric phase diversity. First, we present an analysis of the statistical modeling of the generalized coherence set. A building height estimation method is then derived from this analysis. Finally, the method is tested and quantitatively validated over an X-band polarimetric interferometric SAR (PolInSAR) airborne image acquired in a single-pass mode, containing a set of 140 different buildings with ground truth.

Invariant Contrast Parameters of PolInSAR Homogenous RVoG Model

It has been shown that the Cramer-Rao bound (CRB) can be helpful to characterize vegetation and ground height estimations based on the homogenous random volume over ground (RVoG) model and polarimetric interferometric SAR techniques. However, this model is a function of 20 unknown parameters, which makes the performance analysis a tedious task. We show that the group invariance property of the RVoG model can greatly reduce the complexity of the analysis since the CRB of the vegetation and ground heights only depends on four unknown parameters instead of 20. Furthermore, for the considered situations analyzed in this letter, only three of these four parameters have a nonnegligible influence and can be interpreted as contrast parameters.

Assessment and Estimation of the RVoG Model in Polarimetric SAR Interferometry

This paper investigates the validity of the random-volume-over-ground (RVoG) scattering model assumption for forest scattering on polarimetric interferometric synthetic aperture radar (PolInSAR) data. The model makes some assumptions about the data and the structure of coherency matrices, namely, the equality of the polarimetric covariance matrices and the affine equivalence of the contracted polarimetric interferometric covariance matrix with a Hermitian matrix. The proposed methodology is divided into two main steps. First, invertible affine transforms (ATs) are studied and proposed as a tool to operate with coherence regions. Based on this analysis, the concept of the trace matrix is introduced as its rank depends on the RVoG model assumption validity. Then, with the objective to consider the effects of speckle noise, we consider a maximum-likelihood (ML) framework, on the hypothesis of data distributed according to the complex Gaussian distribution. Hence, we define the ML estimator (MLE) of the PolInSAR coherency matrix according to the RVoG model assumption and the generalized likelihood ratio test of the model. The validity tests and the MLE are analyzed in terms of simulated and real PolInSAR data, considering P-band and L-band data over tropical and boreal forests.

Multi-mode PolInSAR experiments using an airborne X-band SAR system in China

In this paper, we describe our polarimetric interferometric synthetic aperture radar (PolInSAR) experiments with high-resolution X-band data acquired by a multi-mode airborne SAR system over an area of Linshui in southern China. First, we introduce our latest multi-mode X-band airborne imaging radar system (Multi-Mode-XSAR), which integrates three operation modes of bistatic, ping-pong, and mixed. Then, the Multi-Mode-XSAR data set and the corresponding ground measurements in test areas are briefly described. Considering the characteristics of the Multi-Mode-XSAR imagery, a dual-baseline polarimetric interferometry (DPI) method is proposed in this article. The proposed method guarantees a high coherence on the full polarimetric data and combines the benefits of short and long baselines to facilitate the phase unwrapping and promote height sensitivity. Our PolInSAR experiment results demonstrate that the DPI method is capable of generating DSM with higher accuracy than other multi-baseline (MB) methods and the Multi-Mode-XSAR imagery has great potential in PolInSAR applications.

Forest height estimation from mountain forest areas using general model–based decomposition for polarimetric interferometric synthetic aperture radar images

The estimation of forest parameters over mountain forest areas using polarimetric interferometric synthetic aperture radar (PolInSAR) images is one of the greatest interests in remote sensing applications. For mountain forest areas, scattering mechanisms are strongly affected by the ground topography variations. Most of the previous studies in modeling microwave backscattering signatures of forest area have been carried out over relatively flat areas. Therefore, a new algorithm for the forest height estimation from mountain forest areas using the general model–based decomposition (GMBD) for PolInSAR image is proposed. This algorithm enables the retrieval of not only the forest parameters, but also the magnitude associated with each mechanism. In addition, general double- and single-bounce scattering models are proposed to fit for the cross-polarization and off-diagonal term by separating their independent orientation angle, which remains unachieved in the previous model-based decompositions. The efficiency of the proposed approach is demonstrated with simulated data from PolSARProSim software and ALOS-PALSAR spaceborne PolInSAR datasets over the Kalimantan areas, Indonesia. Experimental results indicate that forest height could be effectively estimated by GMBD.