C-band Polarimetric SAR Data Research Articles

Retrieval of Surface Soil Moisture over Wheat Fields during Growing Season Using C-Band Polarimetric SAR Data

Accurate estimation and regular monitoring of soil moisture is very important for many agricultural, hydrological, or climatological applications. Our objective was to evaluate potential contributions of polarimetry to soil moisture estimation during crop growing cycles using RADARSAT-2 C-band images. The research focused on wheat field data collected during Soil Moisture Active Passive Validation Experiment (SMAPVEX12) conducted in 2012 in Manitoba (Canada). A sensitivity analysis was performed to select the most relevant non-polarimetric and polarimetric variables extracted from RADARSAT-2, and statistical models were developed to estimate soil moisture. In fine, three models were developed and validated: a non-polarimetric model based on cross-polarized backscattering coefficient σHV0; a polarimetric mixed model using six polarimetric and non-polarimetric retained variables after the sensitivity analysis; and a simplified polarimetric mixed model considering only the phase difference (ϕHH−VV) and the co-polarized backscattering coefficient σHH0. The validation reveals significant positive contributions of polarimetry. It shows that the non-polarimetric model has a much larger error (RMSE = 0.098 m3/m3) and explains only 19% of observed soil moisture variation compared to the polarimetric mixed model, which has an error of 0.087 m3/m3, with an explained variance of 44%. The simplified model has the lowest error (0.074 m3/m3) and explains 53.5% of soil moisture variation.

Snow cover characterization using C-band polarimetric SAR in parts of the Himalaya

Assessment of the snow cover state in the initiation zone of an avalanche is essential for the snow avalanche hazard mapping in the mountainous region. Seasonal snow cover is very dynamic in the Himalaya and changes its state from dry to moist or wet according to temperature during the progression of winter. In this study, a methodology based on decomposition techniques using polarimetric synthetic aperture radar (SAR) data in conjunction with field snow wetness profiles is adapted for characterization of snow cover into dry and moist or wet snow in the Himalayan region. C-band Radarsat-2 full-polarization SAR data is utilized for polarimetric descriptors generation by applying various in-coherent target decomposition techniques, viz., Yamaguchi 4-component, Freeman Durden 3-component and Wishart Unsupervised (H-α). The generated polarimetric descriptors, viz., surface, volume, double-bounce and helix scattering are utilized in the supervised classification and Entropy (H) and mean scattering angle (α) for understanding the underlying scattering process happening within the snowpack. These decomposition models although describes various physical scattering mechanisms but are inadequate to provide quantitative estimates of underlying physical properties (as in our case changes in snowpack wetness). A supervised classification based on support vector machine (SVM) is used to characterize snow cover with changing conditions of snow wetness from dry to moist or wet. Three data sets of snow-accumulation and snow-melt period of winter 2015 are processed and analyzed along with field snow wetness (% by volume) profiles. A total 47 Nos. of snow wetness profiles of the snowpack are collected using snow fork instrument in synchronous with satellite passes at well distributed, identifiable field locations and are used in the training (34 Nos.) and testing (13 Nos.) of the SVM model. Snow being heterogeneous material varies metamorphically within snowpack. Based on changing weather conditions and sun exposure duration, it results into different scattering behavior in microwave region of the electromagnetic spectrum. It has been qualitatively observed from target decomposition results that polarimetric descriptors generated using Yamaguchi 4-component scattering decomposition have better captured the increase in the surface scattering (during February and March imagery, due to wet snow conditions of snow cover) from volume scattering (during January imagery, due to dry or moist snow conditions) from seasonal snowpack over the acquisition period from January to March 2015. Further to understand and comprehend the scattering process in the snowpack metamorphism with time, Wishart unsupervised (H-α) classification has been applied on all Radarsat-2 datasets. It was observed in the Himalayan region that during 29 Jan 2015 under dry snow conditions, anisotropic and multiple scattering processes dominates and during period between 22 Feb 2015 and 18 Mar 2015 under moist or wet snow conditions the dipole and Bragg type scattering are dominating. A confusion matrix has been generated for the accuracy assessment of the SVM based classification results, which shows overall accuracy ∼ 79.6 % (for 29 Jan 2015), 72.4 % (for 22 Feb 2015) and 74.5 % (for 18 Mar 2015), respectively with the testing samples. The C-band polarimetric SAR data shown its capability for snow cover type characterization and can be applied for operational applications in the avalanche zones of the snow bound terrain.

Assessment of the X- and C-Band Polarimetric SAR Data for Plastic-Mulched Farmland Classification

We present a classification of plastic-mulched farmland (PMF) and other land cover types using full polarimetric RADARSAT-2 data and dual polarimetric (HH, VV) TerraSAR-X data, acquired from a test site in Hebei, China, where the main land covers include PMF, bare soil, winter wheat, urban areas and water. The main objectives were to evaluate the outcome of using high-resolution TerraSAR-X data for classifying PMF and other land covers and to compare classification accuracies based on different synthetic aperture radar bands and polarization parameters. Initially, different polarimetric indices were calculated, while polarimetric decomposition methods were used to obtain the polarimetric decomposition components. Using these polarimetric components as input, the random forest supervised classification algorithm was applied in the classification experiments. Our results show that in this study full-polarimetric RADARSAT-2 data produced the most accurate overall classification (94.81%), indicating that full polarization is vital to distinguishing PMF from other land cover types. Dual polarimetric data had similar levels of classification error for PMF and bare soil, yielding mapping accuracies of 53.28% and 59.48% (TerraSAR-X), and 59.56% and 57.1% (RADARSAT-2), respectively. We found that Shannon entropy made the greatest contribution to accuracy in all three experiments, suggesting that it has great potential to improve agricultural land use classifications based on remote sensing.

Joint estimation of Plant Area Index (PAI) and wet biomass in wheat and soybean from C-band polarimetric SAR data

Retrieval of the Plant Area Index (PAI) and wet biomass from polarimetric SAR (PolSAR) data is of paramount importance for in-season monitoring of crop growth. Notably, the joint estimation of biophysical parameters might be effective instead of an individual parameter due to their inherent relationships (possibly nonlinear). The semi-empirical water cloud model (WCM) can be suitably utilized to estimate biophysical parameters from PolSAR data. Nevertheless, instability problems could occur during the model inversion process using traditional inversion approaches. Iterative optimization (IO) can have difficulty in finding the global minima while look up table (LUT) searches have a lower generalization capability. These challenges reduce the transferability of IO and LUT search inversions in computational efficiency and seldom account for the inter-correlation among the parameters. Alternatively, a machine learning regression technique with a regularization routine may provide a stable and optimum solution for ill-posed problems related to the inversion of the WCM. In the present work, the crop biophysical parameters viz. PAI and wet biomass are estimated simultaneously using the multi-target Random Forest Regression (MTRFR) technique. The accuracy of the retrieval method is analyzed using the in-situ measurements and quad-pol RADARSAT-2 data acquired during the SMAPVEX16 campaign over Manitoba, Canada. The inversion process is tested with different polarization combinations of SAR data for wheat and soybean. The validation used ground measured biophysical parameters for various crops, indicating promising results with a correlation coefficient (r) in the range of 0.6–0.8. In addition, the relationship between PAI and wet biomass using the multi-target and single output model is also assessed based on in-situ measurements. The results confirm that the inter-correlation between biophysical parameters is well preserved in the MTRFR based joint inversion technique for both wheat and soybean.

Scattering Mechanism Based Snow Cover Mapping Using RADARSAT-2 C-Band Polarimetric SAR Data

Monitoring of snow cover over large areal extent requires the use of remotely sensed satellite data. Conventional snow cover mapping algorithms using SAR data have seldom utilized target scattering information for land cover characterization. In this paper, an approach is proposed for snow cover mapping that utilizes the Touzi eigenvalue-eigenvector-based decomposition parameters viz., the scattering helicities $(\tau _{m}; m = 1, 2)$ , the scattering magnitude $(\alpha _{s})$ , and the entropy $(H)$ . The seasonal variation of these parameters is used as an indicator of scattering mechanism dominance in the proposed algorithm. The C -band RADARSAT-2 (FQ20) winter–summer image pairs over the Manali-Dhundi region of Himachal Pradesh, India, located in the western Hindu-Kush Himalayas are used in this paper. The proposed methodology is able to delineate the snow-covered areas suitably. The temporal extent of the cover is also quantified and validated with field campaigns and weather data. Furthermore, the results obtained from the proposed methodology are compared with NDSI-based snow cover maps derived from LANDSAT-8 images.

An Efficient Polarimetric SAR Calibration Algorithm Using Corner Reflectors

ABSTRACTFully polarimetric synthetic aperture radar (SAR) has been widely used in applications, such as ground parameter inversion and classification. However, to accurately decipher the information contained in the polarimetric characteristics of an illuminated scene, the cross talks in cross-polarimetric channels and imbalances among co-pol channels must be removed from the measured polarimetric responses. Therefore, it is important to improve the polarimetric SAR calibration technique to reduce the distortions between various channels and calibrate the polarimetric data. In this article, we propose an efficient polarimetric SAR calibration method that uses known calibration point targets to remove the uncertainties among observed polarized responses. We first construct a common scattering model of natural targets organized by matrix products. Next, we reorder the square matrices in vector form. Finally, we construct a set of analytical equations based on the relations between theoretical and measured scattering matrices and derive the full elements of the distortion matrix by solving these equations. This algorithm determines the calibration matrix from corner reflectors with known scattering matrices, and it is efficient and easy to understand. Finally, we validate the algorithm with airborne C-band polarimetric SAR data.

Mapping Plant Functional Types in Floodplain Wetlands: An Analysis of C-Band Polarimetric SAR Data from RADARSAT-2

The inclusion of functional approaches on wetland characterizations and on biodiversity assessments improves our understanding of ecosystem functioning. In the Lower Paraná River floodplain, we assessed the ability of C-band polarimetric SAR data of contrasting incidence angles to discriminate wetland areas dominated by different plant functional types (PFTs). Unsupervised H/α and H/A/α Wishart classifications were implemented on two RADARSAT-2 images differing in their incidence angles (FQ24 and FQ08). Obtained classes were assigned to the information classes (open water, bare soil and PFTs) by a priori labeling criteria that involved the expected interaction mechanisms between SAR signal and PFTs as well as the relative values of H and α. The product obtained with the shallow incidence angle scene had a higher accuracy than the one obtained with the steep incidence angle product (61.5% vs. 46.2%). We show how a systematic analysis of the H/A/α space can be used to improve the knowledge about the radar polarimetric response of herbaceous vegetation. The map obtained provides novel ecologically relevant information about plant strategies dominating the floodplain. Since the obtained classes can be interpreted in terms of their functional features, the approach is a valuable tool for predicting vegetation response to floods, anthropic impacts and climate change.

Mean-Shift-Based Speckle Filtering of Polarimetric SAR Data

The mean shift algorithm, which uses a moving window and utilizes both spatial and range information contained in an image, is widely employed in digital image filtering and segmentation. However, because of the large dynamic range of synthetic aperture radar (SAR) images, applying the conventional mean shift algorithm directly to SAR image filtering will not produce meaningful results. This paper proposes an adaptive variable asymmetric bandwidth selection approach to be used in a newly derived generalized mean shift algorithm. The proposed mean shift algorithm is very versatile and can be used for SAR and polarimetric SAR (PolSAR) image filtering directly without any preprocessing steps. Monte Carlo-simulated PolSAR data are used to demonstrate the effectiveness of the proposed algorithm in speckle filtering by comparing it with other filters. Experimental Synthetic Aperture Radar (ESAR) L-band and Radarsat-2 C-band PolSAR data are used to evaluate its ability to preserve the polarimetric information of PolSAR data. The effects of initial value estimating and multilook processing on the filtered results are discussed at the end of this paper.

ANALYSIS OF TEMPORAL POLARIZATION PHASE DIFFERENCE FOR MAJOR CROPS IN INDIA

A polarimetric radar system measures the complete scattering matrix of a target in the backscattered fleld that includes magnitudes of linearly polarized scattering amplitudes and the co- polarised and cross-polarised phase angles. Apart from backscattering intensity, the co-polarization phase difierence (CPD) calculated from polarimetric synthetic aperture radar (SAR) data produces important information about target physical, geometrical and dielectric properties. In the present work, the distribution of CPD in C-band polarimetric SAR data corresponding to major kharif and rabi crops (denoting the monsoon and the winter season) and other land cover features have been studied over Central State Farm, Hisar, Haryana. The probability density functions (PDF) of CPD have been compared with dominant scattering contributions from these targets as obtained from polarimetric target decompositions. The results show that crops and other land cover features show characteristic CPD distributions, which relates well with crop physical and geometrical properties. An intuition of the rate of growth and plant vigour is indicative from the temporal PDF pattern.

Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks

Abstract. The purpose of this study was to develop an approach to estimate soil surface parameters from C-band polarimetric SAR data in the case of bare agricultural soils. An inversion technique based on multi-layer perceptron (MLP) neural networks was introduced. The neural networks were trained and validated on a noisy simulated dataset generated from the Integral Equation Model (IEM) on a wide range of surface roughness and soil moisture, as it is encountered in agricultural contexts for bare soils. The performances of neural networks in retrieving soil moisture and surface roughness were tested for several inversion cases using or not using a-priori knowledge on soil parameters. The inversion approach was then validated using RADARSAT-2 images in polarimetric mode. The introduction of expert knowledge on the soil moisture (dry to wet soils or very wet soils) improves the soil moisture estimates, whereas the precision on the surface roughness estimation remains unchanged. Moreover, the use of polarimetric parameters α1 and anisotropy were used to improve the soil parameters estimates. These parameters provide to neural networks the probable ranges of soil moisture (lower or higher than 0.30 cm3 cm−3) and surface roughness (root mean square surface height lower or higher than 1.0 cm). Soil moisture can be retrieved correctly from C-band SAR data by using the neural networks technique. Soil moisture errors were estimated at about 0.098 cm3 cm−3 without a-priori information on soil parameters and 0.065 cm3 cm−3 (RMSE) applying a-priori information on the soil moisture. The retrieval of surface roughness is possible only for low and medium values (lower than 2 cm). Results show that the precision on the soil roughness estimates was about 0.7 cm. For surface roughness lower than 2 cm, the precision on the soil roughness is better with an RMSE about 0.5 cm. The use of polarimetric parameters improves only slightly the soil parameters estimates.

Laplacian Eigenmaps-Based Polarimetric Dimensionality Reduction for SAR Image Classification

In this paper, we propose a novel scheme of polarimetric synthetic aperture radar (PolSAR) image classification. We apply Laplacian eigenmaps (LE), a nonlinear dimensionality reduction (NDR) technique, to a high-dimensional polarimetric feature representation for PolSAR land-cover classification. A wide variety of polarimetric signatures are chosen to construct a high-dimensional polarimetric manifold which can be mapped into the most compact low-dimensional structure by manifold-based dimensionality reduction techniques. This NDR technique is employed to obtain a low-dimensional intrinsic feature vector by the LE algorithm, which is beneficial to PolSAR land-cover classification owing to its local preserving property. The effectiveness of our PolSAR land-cover classification scheme with LE intrinsic feature vector is demonstrated with the RadarSat-2 C-band PolSAR data set and the 38th Research Institute of China Electronics Technology Group Corporation X-band PolInSAR data set. The performance of our method is measured by the separability in the feature space and the accuracy of classification. Comparisons on the feature space show that the LE intrinsic feature vector is more separable than different original feature vectors. Our LE intrinsic feature vector also improves the classification accuracy.

Remote Sensing of Ocean Waves by Polarimetric SAR

Abstract A new method to measure ocean wave slope spectra using fully polarimetric synthetic aperture radar (POLSAR) data was developed without the need for a complex hydrodynamic modulation transform function. There is no explicit use of a hydrodynamic modulation transfer function. This function is not clearly known and is based on hydrodynamic assumptions. The method is different from those developed by Schuler and colleagues or Pottier but complements their methods. The results estimated from NASA Jet Propulsion Laboratory (JPL) Airborne Synthetic Aperture Radar (AIRSAR) C-band polarimetric SAR data show that the ocean wavelength, wave direction, and significant wave height are in agreement with buoy measurements. The proposed method can be employed by future satellite missions such as RADARSAT-2.

Mapping deforestation and land use in amazon rainforest by using SIR-C imagery

In this paper, the potential use of spaceborne polarimetric synthetic aperture radar (SAR) data in mapping landcover types and monitoring deforestation in tropics is studied. Here, the emphasis is placed on several clearing practices and forest regeneration that can be characterized by using the sensitivity of SAR channels to vegetation biomass and canopy structure. A supervised Bayesian classifier designed for SAR signal statistics is employed to separate five classes: primary forest, secondary forest, pasture-crops, quebradao, and disturbed forest. The L- and C-band polarimetric SAR data acquired during the shuttle imaging radar-C (SIR-C)/X-SAR space-shuttle mission in 1994 are used as input data to the classifier. The results are verified by field observation and comparison with the Landsat data acquired in August of 1994. The SAR data can delineate these five classes with approximately 72% accuracy. The confusion arises when separating old secondary forests from primary forest and the young ones from pasture-crops. It is shown that Landsat and SAR data carry complementary information about the vegetation structure that, when used in synergism, may increase the classification accuracy over secondary forest regrowth. When the number of land-cover types was reduced to three classes including primary forest, pasture-crops, and regrowth-disturbed forest, the accuracy of classification increased to 87%. A dimensionality analysis of the classifier showed that the accuracy can be further improved to 92% by reducing the feature space to L-band HH and HV channels. Comparison of SIR-C data acquired in April (wet period) and October (dry period) indicates that multi-temporal data can be used for monitoring deforestation; however, the data acquired during the wet season are not suitable for accurate land-cover classification.

Crop classification from C-band polarimetric radar data

Abstract Synthetic aperture radar “SAR” systems are an attractive source of information for agricultural crop classification applications, particularly in regions where cloud cover is a problem. The accuracy with which crops can be classified is dependent on a range of sensor properties, including the SAR operating configuration. This paper focuses on the effect of one aspect of the SAR operating configuration, polarization, on crop classification accuracy using uncalibrated C-band polarimetric SAR data. Conventional like- and cro spolarized configurations and polarimetric coefficients “the pedestal and variation coefficients” were used as discriminating variables in classifications of agricultural crops. Two approaches to classification were investigated, a discriminant analysis and an artif cial neural network and results from a set of training classifications are presented. The results show that the polarimetric coefficients used provided a high level of inter-class discrimination and that a nine-class...

Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution

Abstract Multi-look polarimetric SAR (synthetic aperture radar) data can be represented either in Mueller matrix form or in complex covariance matrix form. The latter has a complex Wishart distribution. A maximum likelihood classifier to segment polarimetric SAR data according to terrain types has been developed based on the Wishart distribution. This algorithm can also be applied to multifrequency multi-look polarimetric SAR data, as well as 10 SAR data containing only intensity information. A procedure is then developed for unsupervised classification. The classification error is assessed by using Monte Carlo simulation of multilook polarimetric SAR data, owing to the lack of ground truth for each pixel. Comparisons of classification errors using the training sets and single-look data are also made. Applications of this algorithm are demonstrated with NASA/JPL P-, L- and C-band polarimetric SAR data.