Search

The potential of single date fully polarimetric SAR data was brought out in discriminating different land covers at level 3 (LULC classification system NRSC) by deriving various polarimetric parameters. Various coherent and incoherent decompositions viz. Krogager, Freeman–Durden, Yamaguchi, Van Zyl, H-A-Alpha were analyzed to evaluate its potential for crop identification. Freeman-3 decomposition overestimates the volume component predominantly for high biomass crops but is sensitive across variable range of biomass. Van Zyl decomposition was not found suitable for crop studies as it overestimates surface scattering by underestimating volume scattering from canopy structure. The differencing method using various decomposition conveyed dynamic response for various crops over the individual response. The Entropy-Alpha scatter plot exhibits its capability in discriminating various land covers. Further, polarimetric signature resulted in unique response for various land covers that can be exploited for their identification. High pedestal heights were observed for high biomass crops and vice-versa. [ABSTRACT FROM AUTHOR]

In this feasibility study discriminating oil slicks and newly formed sea ice using synthetic aperture radar (SAR) imagery is investigated, using imagery from the L-band high-resolution uninhabited aerial vehicle synthetic aperture radar (UAVSAR) airborne and the satellite C-band RADARSAT-2 (RS-2) systems. To determine the separability of these two varying but similar appearing low backscatter ocean surfaces, multipolarization features are utilized from both SAR datasets. The discrimination is evaluated using the Kolmogorov-Smirnov separability test. All imagery was obtained during several sea ice campaigns in the Arctic Ocean and separate oil spill campaigns in Norway and the Gulf of Mexico, with each campaign collecting in situ observations. We observe that the polarization difference (VV-HH) reliably separates the mineral oil slicks and newly formed sea ice areas using UAVSAR images, due to the low noise floor and subsequent high signal-to-noise ratio (SNR) radiometric performance of the airborne system. The comparably higher noise floor and related lower SNR hampers the separability in the RS-2 images. Simulated noise floors were generated by adding white Gaussian noise to the UAVSAR data, which show that discrimination between the two low backscatter phenomena using multipolarization features is possible, provided that both datasets are still well above the noise floor. The pixel resolution has a limited effect on the separability. The results of this study provide an approach to distinguish oil slicks from newly formed sea ice, which might be of special interest should an oil spill occur within the marginal ice zone.

This work introduces a novel method that combines machine learning (ML) techniques with dual-polarimetric (dual-pol) synthetic aperture radar (SAR) observations for estimating quad-polarimetric (quad-pol) parameters, which are presumed to contain geophysical sea ice information. In the training phase, the output parameters are generated from quad-pol observations obtained by Radarsat-2 (RS2), and the corresponding input data consist of features obtained from overlapping dual-pol Sentinel-1 (S1) data. Then, two, well-recognized ML methods are studied to learn the functional relationship between the output and input data. These ML approaches are the Gaussian process regression (GPR) and neural network (NN) for regression models. The goal is to use the aforementioned ML techniques to generate Arctic sea ice information from freely available dual-pol observations acquired by S1, which can, in general, only be generated from quad-pol data. Eight overlapping RS2 and S1 scenes were used to train and test the GPR and NN models. Statistical regression performance measures were computed to evaluate the strength of the ML regression methods. Then, two scenes were selected for further evaluation, where overlapping optical images were available as well. This allowed the visual interpretation of the maps estimated by the ML models. Finally, one of the methods was tested on an entire S1 scene to perform prediction on areas outside of the RS2 and S1 overlap. Our results indicate that the studied ML techniques can be utilized to increase the information retrieval capacity of the wide swath dual-pol S1 imagery while embedding physical properties in the methodology. [ABSTRACT FROM AUTHOR]

Accurate estimation of crop biophysical and biochemical parameters during crop growing seasons is essential for improving site-specific management and yield estimation. The potential ability of fully polarimetric synthetic aperture radar (SAR) data in estimating above-ground biomass of oilseed rape was investigated in this study. The temporal profile of different scattering intensity and polarimetric features during the entire growing season was identified with ground measurements. A polarimetric feature, relying on the polarimetric decomposition method, was put forward to estimate the biomass of oilseed rape. Validation results revealed great potential with a determination coefficient (R2) of 0.85, root mean squared error (RMSE) of 41.6 g/m2, and relative error (RE) of 28.5% for dry biomass, and an R2 of 0.76, RMSE of 527.4 g/m2 and RE of 28.6% for fresh biomass. Moreover, the use of full polarization SAR data was compared with single and dual polarization SAR data. The results suggest that when full polarization SAR data is available, a simpler model, higher saturation point and better accuracy can be achieved in biomass estimation of oilseed rape, which highlights the importance and value of polarimetry information in quantitative crop monitoring. This study provides guidelines for in-season monitoring of crop growth parameters with SAR data, which further improves crop monitoring capability in adverse weather conditions. [ABSTRACT FROM AUTHOR]

The role of global sensitivity analysis (GSA) is to quantify and rank the most influential features for biophysical variable estimation. In this letter, an approximation model, called high-dimensional model representation (HDMR), is utilized to develop a regression method in conjunction with a GSA in the context of determining key input drivers in the estimation of crop biophysical variables from polarimetric synthetic aperture radar data. A multitemporal Radarsat-2 data set is used for the retrieval of three biophysical variables of barley: leaf area index, normalized difference vegetation index, and Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie stage. The HDMR technique is first adopted to estimate a regression model with all available polarimetric features for each biophysical parameter, and sensitivity indices of each feature are then derived to explain the original space with a smaller number of features in which a final regression model is established. To evaluate the applicability of this methodology, root-mean square and coefficient of determination were performed under different amounts of samples. Results highlight that HDMR can be used effectively in biophysical variable estimation for not only reducing computational cost but also for providing a robust regression. [ABSTRACT FROM AUTHOR]

Abstract This study proposes a two-component (surface and volume) C-band polarimetric decomposition to retrieve soil moisture over agricultural fields covered by different crop types. RADARSAT-2 data analysis shows that vegetation attenuation on the surface scattering component is stronger for the narrow-leaf (wheat) than broad-leaf crops (canola, corn and soybean). Thus, the vegetation attenuation factor is integrated into the proposed C-band polarimetric decomposition to better account for soil contribution over the whole crop phenological season. After removing the volume scattering component from the full coherency matrix, the surface scattering component was simulated by the Oh soil scattering model, instead of conventional Bragg or X-Bragg model, which is not physically valid at C-band due to limited roughness constraint condition. The proposed retrieval algorithm was applied to the RADARSAT-2 time series acquired during the Soil Moisture Active Passive Validation Experiment in 2012 (SMAPVEX12). Results indicate that during the crop growth, the volume scattering power of wheat shows weak temporal variation, while it increases for corn, soybean and canola. The sensitivity of the ground scattering component to soil moisture is enhanced due to the removal of the volume scattering from the full signature. The retrieved soil moisture was validated using the soil moisture ground measurements during the SMAPVEX12. The validations indicate correlation coefficients from 0.63 to 0.76, and RMSEs from 0.058 to 0.074 m3/m3 for the entire phenological period of SMAPVEX12 campaign. The negligible dihedral scattering component (1.6–9.2%) at C-band greatly reduces the complexity of the soil moisture retrieval from the ground component. Highlights • A two-component C-band model-based polarimetric decomposition is developed. • Surface scattering component is simulated by Oh model instead of X-Bragg model. • A vegetation attenuation factor is included in the surface scattering component. • Soil moisture is retrieved for the entire phenological stages during SMAPVEX12. [ABSTRACT FROM AUTHOR]

Classification of mangrove species using satellite images is important for investigating the spatial distribution of mangroves at community and species levels on local, regional and global scales. Hence, studies of mangrove deforestation and reforestation are imperative to support the conservation of mangrove forests. However, accurate discrimination of mangrove species remains challenging due to many factors such as data resolution, species number and spectral confusion between species. In this study, three different combinations of datasets were designed fromWorldview-3 and Radarsat-2 data to classify four mangrove species, Kandelia obovate (KO), Avicennia marina (AM), Acanthus ilicifolius (AI) and Aegiceras corniculatum (AC). Then, the Rotation Forest (RoF) method was employed to classify the four mangrove species. Results indicated the benefits of dual polarimetric SAR data with an improvement of accuracy by 2-3%, which can be useful for more accurate large-scale mapping of mangrove species. Moreover, the difficulty of classifying different mangrove species, in order of increasing difficulty, was identified as KO < AM < AI < AC. Dual polarimetric SAR data are recognized to improve the classification of AI and AC species. Although this improvement is not remarkable, it is consistent for all three methods. The improvement can be particularly important for large-scale mapping of mangrove forest at the species level. These findings also provide useful guidance for future studies using multi-source satellite data for mangrove monitoring and conservation. [ABSTRACT FROM AUTHOR]

In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The classification accuracies as well as the backscatter and reflectance characteristics were analyzed using reference data collected during three field work campaigns and include in situ data and high resolution airborne photography. The optical data offered an acceptable initial accuracy for the land cover classification. The overall accuracy was increased by the combination of PolSAR and optical data and was up to 71% for unsupervised (Landsat 8 and TerraSAR-X) and up to 87% for supervised classification (Landsat 8 and Radarsat-2) for five tundra land cover types. The decomposition features of the dual and quad-polarized data showed a high sensitivity for the non-vegetated substrate (dominant surface scattering) and wetland vegetation (dominant double bounce and volume scattering). These classes had high potential to be automatically detected with unsupervised classification techniques.

Growth parameters like biomass, leaf area index (LAI) and stem height play an import role for crop monitoring and yield prediction. Compact polarimetric (CP) SAR has shown great potential and similar performance to fully-polarimetric (FP) SAR in crop mapping and phenology retrieval, but its potential in growth parameters inversion has not been fully explored. In this paper, a time series of images of CP SAR was simulated from five FP SAR data gathered during the entire growth season of rape. CP response of 27 parameters, relying on Stokes parameters and their child parameters, decomposition parameters and backscattering coefficients, were extracted and investigated as a function of days after sowing (DAS) during the whole rape growth cycle to interpret their sensitivity to each growth parameter. Then, random forest (RF) was chosen as an automatic approach for the growth parameters inversion method, and its results were compared with traditional single-parameter regression models. Most of the CP parameters showed high sensitivity with growth parameters and great potential for growth parameters inversion. Among all of the regression models, the quadratic regression model showed the best performance for all of the growth parameters inversion, the best result for biomass inversion was the third component of the Stokes parameters (g3) with R2 of 0.765 and RMSE of 73.20 g/m2. For LAI and stem height was one of the Stokes child parameters, the circular polarization ratio (Uc), with R2 of 0.857 and 0.923 and RMSE of 0.66 and 18.71 cm, respectively. RF showed the highest accuracy and smallest RMSE for all of three growth parameters inversion; R2 for biomass, LAI and stem height were 0.93, 0.96 and 0.95, respectively; RMSE were 46.24 g/m2, 0.25 and 13.5 cm, respectively. However, there are also some CP parameters, which showed low sensitivity to growth parameters, that had high importance for RF inversion. The results confirmed the potential of CP data and the RF method in growth parameters inversion, but they also confirmed that it was difficult to give a physical interpretation for the RF inversion model. [ABSTRACT FROM AUTHOR]

In this study, polarimetric Synthetic Aperture Radar (PolSAR) data at X-, C- and L-Bands, acquired by the satellites: TerraSAR-X (2011), Radarsat-2 (2011), ALOS (2010) and ALOS-2 (2016), were used to characterize the tundra land cover of a test site located close to the town of Tuktoyaktuk, NWT, Canada. Using available in situ ground data collected in 2010 and 2012, we investigate PolSAR scattering characteristics of common tundra land cover classes at X-, C- and L-Bands. Several decomposition features of quad-, co-, and cross-polarized data were compared, the correlation between them was investigated, and the class separability offered by their different feature spaces was analyzed. Certain PolSAR features at each wavelength were sensitive to the land cover and exhibited distinct scattering characteristics. Use of shorter wavelength imagery (X and C) was beneficial for the characterization of wetland and tundra vegetation, while L-Band data highlighted differences of the bare ground classes better. The Kennaugh Matrix decomposition applied in this study provided a unified framework to store, process, and analyze all data consistently, and the matrix offered a favorable feature space for class separation. Of all elements of the quad-polarized Kennaugh Matrix, the intensity based elements K0, K1, K2, K3 and K4 were found to be most valuable for class discrimination. These elements contributed to better class separation as indicated by an increase of the separability metrics squared Jefferys Matusita Distance and Transformed Divergence. The increase in separability was up to 57% for Radarsat-2 and up to 18% for ALOS-2 data. [ABSTRACT FROM AUTHOR]

Use of polarimetric SAR data for offshore pollution monitoring is relatively new and shows great potential for operational offshore platform monitoring. This paper describes the development of an automated oil spill detection chain for operational purposes based on C-band (RADARSAT-2) and X-band (TerraSAR-X) fully polarimetric images, wherein we use polarimetric features to characterize oil spills and look-alikes. Numbers of near coincident TerraSAR-X and RADARSAT-2 images have been acquired over offshore platforms. Ten polarimetric feature parameters were extracted from different types of oil and ‘look-alike’ spots and divided into training and validation dataset. Extracted features were then used to develop a pixel based Artificial Neural Network classifier. Mutual information contents among extracted features were assessed and feature parameters were ranked according to their ability to discriminate between oil spill and look-alike spots. Polarimetric features such as Scattering Diversity , Surface Scattering Fraction and Span proved to be most suitable for operational services. [ABSTRACT FROM AUTHOR]

Rice growth monitoring is very important as rice is one of the staple crops of the world. Rice variables as quantitative indicators of rice growth are critical for farming management and yield estimation, and synthetic aperture radar (SAR) has great advantages for monitoring rice variables due to its all-weather observation capability. In this study, eight temporal RADARSAT-2 full-polarimetric SAR images were acquired during rice growth cycle and a modified water cloud model (MWCM) was proposed, in which the heterogeneity of the rice canopy in the horizontal direction and its phenological changes were considered when the double-bounce scattering between the rice canopy and the underlying surface was firstly considered as well. Then, three scattering components from an improved polarimetric decomposition were coupled with the MWCM, instead of the backscattering coefficients. Using a genetic algorithm, eight rice variables were estimated, such as the leaf area index (LAI), rice height (h), and the fresh and dry biomass of ears (Fe and De). The accuracy validation showed the MWCM was suitable for the estimation of rice variables during the whole growth season. The validation results showed that the MWCM could predict the temporal behaviors of the rice variables well during the growth cycle (R2 > 0.8). Compared with the original water cloud model (WCM), the relative errors of rice variables with the MWCM were much smaller, especially in the vegetation phase (approximately 15% smaller). Finally, it was discussed that the MWCM could be used, theoretically, for extensive applications since the empirical coefficients in the MWCM were determined in general cases, but more applications of the MWCM are necessary in future work. [ABSTRACT FROM AUTHOR]

Many research studies have investigated surface parameter inversion for bare soils. This paper attempts to take into account the agricultural fields with crop residues and fields under low vegetation cover in addition to bare soil fields. An integrated surface parameter inversion scheme (ISPIS) is proposed to invert surface parameters in these agricultural fields based on the analysis of H-\alpha parameters at the early crop growing stages, in which the calibrated integral equation model (CIEM) is adopted to invert surface parameters for bare soils, and an adaptive two-component decomposition combined with the CIEM and a simplified adaptive volume scattering model is developed for fields with crop residues and under low vegetation cover. Fully polarimetric RADARSAT-2 data with ground truth collected on April 29 and May 9 in 2013 and from May to June in 2014 are used for validation. Compared with other methods, the derived volumetric soil moisture (MV) and surface roughness (KS) of all agricultural fields are consistent with verifiable observations with the lowest overall root mean square error: 6.12 [vol.%] and 0.48, respectively, when all sample sites are considered. [ABSTRACT FROM AUTHOR]

C-band linear and polarimetric parameters derived from synthesis and decomposition of Radarsat-2 SAR data are analyzed and evaluated with respect to snow covers of variable thickness on smooth first year sea ice (SFYI). The study is conducted for two selected temperature conditions: 1) cold (− 7.9 °C) and 2) warm (− 0.4 °C). First, the polarimetric SAR response from the snow cover at cold conditions is analyzed with an objective to investigate the sensitivity of linear and polarimetric backscatter to snow thickness. Second, the changes in these sensitivities are examined as a function of change in surface air temperature (from − 7.9 °C to − 0.4 °C) through the changes manifested in snow geophysical properties. The dependence of linear and polarimetric backscatter response on radar incidence angle for different snow thicknesses is also evaluated. Our results demonstrate strong to weak relationships between the polarimetric parameters and snow thickness under cold conditions but virtually no relationship under warm conditions. The strength of this sensitivity varies with parameter type, radar incidence angle and snow geophysical properties. Under the cold conditions, fourteen polarimetric parameters demonstrate a positive relationship and nine demonstrate a negative relationship with snow thickness. This parameter sensitivity to snow thickness indicates an increase in volume or mixed scattering component with increase in snow thickness. The cause of this is primarily an increased microwave interaction with a greater number of dry snow grains, and/or more interaction with increased brine volumes in the brine-wetted snow and upper sea ice layer. A comparison of polarimetric signatures at warm conditions to that at cold conditions shows both an increase and a decrease in response for all snow thicknesses. For thin or medium snow cover, every time a polarimetric parameter response increases or decreases as a consequence of an increase in temperature there is always an opposite response in the same polarimetric parameter for thick snow cover. This is primarily due to the masking effect of moist-snow, likely caused by reduced penetration depth resulting in dominant surface scattering from the air/moist snow interface, and/or the dry snow–brine-wetted snow interface, as opposed to the snow–ice interface. Normal probability density functions (PDFs) employed to determine the snow thickness separation capability of polarimetric parameters show near overlapping signatures of all snow classes under warm conditions, suggesting that snow thickness estimation using snow thermodynamics at warm conditions is not possible. [ABSTRACT FROM AUTHOR]

This study proposes a new three-component method for timely detection of land cover changes using polarimetric synthetic aperture radar (PolSAR) images. The three components are object-oriented image analysis (OOIA), change vector analysis (CVA), and post-classification comparison (PCC). First, two PolSAR images acquired over the same area at different dates are segmented hierarchically to delineate land parcels (image objects). Then, parcel-based CVA is performed with the coherency matrices of the PolSAR data to detect changed parcels. Finally, PCC based on a parcel-based classification algorithm integrating polarimetric decomposition, decision tree algorithms, and support vector machines is used to determine the type of change for the changed parcels. Compared with conventional PCC based on the widely used Wishart supervised classification, the three-component method achieves much higher accuracy for land cover change detection with PolSAR images. The contribution of each component is evaluated by excluding it from the method. The integration of OOIA in the method greatly reduces the false alarms caused by speckle noise in PolSAR images as well as improves the accuracy of PolSAR image classification. CVA contributes to the method by significantly reducing the effect of the classification errors on the change detection. The use of PCC in the method not only identifies different types of land cover change but also reduces the false alarms introduced by the change in the environment. The three-component method is validated in land development detection, which is important to many developing countries that are confronting a growing problem of unauthorized construction land expansion. The results show that the three-component method is effective in detecting land developments with PolSAR images. [ABSTRACT FROM AUTHOR]

The feasibility of monitoring lodging of wheat fields by exploiting fully polarimetric C-band radar images has been investigated in this paper. A set of backscattering intensity features and polarimetric features, derived by target decomposition techniques, was extracted from 5 consecutive Radarsat-2 images. The temporal evolutions of these features of lodging wheat fields were investigated as a function of DAS (day after sowing) during the entire growing season. The temporal behavior was compared between typical lodging fields and normal fields in different growing stages. It was found that polarimetric feature from synthetic aperture radar (SAR) data was very sensitive to wheat lodging. Then a method called polarimetric index, availing the sensitivity of polarimetry to the structure, was put forward to monitor wheat lodging. The method was validated by two sets of in situ data collected in Shangkuli Farmland area, Inner Mongolia, China, at heading and ripe stages of spring wheat. Almost all the lodging fields were successfully distinguished from normal fields. Furthermore, the result revealed that the polarimetric index can reflect the intrinsic feature of lodging wheat with good anti-inference ability such as wheat growth difference. While optical sensors relied on its spectral features to monitor crop lodging, the proposed method based on radar data utilized polarimetric features to monitor crop lodging. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel unsupervised change detection scheme for multilook polarimetric synthetic aperture radar (PolSAR) images using heterogeneous clutter models. First, a multilook product model is introduced to describe the heterogeneous clutter for multilook PolSAR data, and a corresponding covariance matrix estimation method is derived. Based on this model, a new similarity measure is proposed to quantify the degree of evolution between the statistical characteristics of multitemporal fully PolSAR images. Compared with the classical similarity measure of Wishart distribution, both the structure of the covariance matrix and the power information of PolSAR clutter are considered in the proposed similarity measure. A Kittler and Illingworth (K&I) minimum-error threshold segmentation method is applied to extract the changed areas. Both the simulated PolSAR data set and two three-look Radarsat-2 fully polarimetric images of Suzhou, China, acquired on April 9, 2009 and June 15, 2010, are used for our experiment. The results demonstrate that the proposed change detection method can give a much higher detection rate and a lower false alarm rate than the method using the Wishart similarity measure. [ABSTRACT FROM PUBLISHER]

Rice growth monitoring is very important as rice is one of the staple crops of the world. Rice variables as quantitative indicators of rice growth are critical for farming management and yield estimation, and synthetic aperture radar (SAR) has great advantages for monitoring rice variables due to its all-weather observation capability. In this study, eight temporal RADARSAT-2 full-polarimetric SAR images were acquired during rice growth cycle and a modified water cloud model (MWCM) was proposed, in which the heterogeneity of the rice canopy in the horizontal direction and its phenological changes were considered when the double-bounce scattering between the rice canopy and the underlying surface was firstly considered as well. Then, three scattering components from an improved polarimetric decomposition were coupled with the MWCM, instead of the backscattering coefficients. Using a genetic algorithm, eight rice variables were estimated, such as the leaf area index (LAI), rice height (h), and the fresh and dry biomass of ears (Fe and De). The accuracy validation showed the MWCM was suitable for the estimation of rice variables during the whole growth season. The validation results showed that the MWCM could predict the temporal behaviors of the rice variables well during the growth cycle (R2 > 0.8). Compared with the original water cloud model (WCM), the relative errors of rice variables with the MWCM were much smaller, especially in the vegetation phase (approximately 15% smaller). Finally, it was discussed that the MWCM could be used, theoretically, for extensive applications since the empirical coefficients in the MWCM were determined in general cases, but more applications of the MWCM are necessary in future work.

In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The classification accuracies as well as the backscatter and reflectance characteristics were analyzed using reference data collected during three field work campaigns and include in situ data and high resolution airborne photography. The optical data offered an acceptable initial accuracy for the land cover classification. The overall accuracy was increased by the combination of PolSAR and optical data and was up to 71% for unsupervised (Landsat 8 and TerraSAR-X) and up to 87% for supervised classification (Landsat 8 and Radarsat-2) for five tundra land cover types. The decomposition features of the dual and quad-polarized data showed a high sensitivity for the non-vegetated substrate (dominant surface scattering) and wetland vegetation (dominant double bounce and volume scattering). These classes had high potential to be automatically detected with unsupervised classification techniques. [ABSTRACT FROM AUTHOR]

Ortho-rectification using digital terrain models is a key issue for full polarimetric complex synthetic aperture radar (SAR) data because resampling the complex data can corrupt the polarimetric phase, mainly in terrain with relief. Two methods for ortho-rectification of the complex SAR data can be applied: the polarimetric processing is performed before (image-space method) or after (ground-space method) the geometric processing. This research evaluated the impact of the digital elevation models (DEMs), which are generally available to users (topographic DEM and ASTER GDEM V2). The two methods were applied to three Radarsat-2 fine-quad data acquired with different look angles over a hilly relief study site. Quantitative evaluations between the two approaches as a function of different geometric and radiometric parameters were, thus, performed to evaluate the impact during the ortho-rectification. The results demonstrated that the look angles and the terrain slopes can potentially corrupt the single-look polarimetric complex SAR data during its ortho-rectification with the ground-space method, mainly at the layover limit. However, advice is provided to reduce these impacts to an acceptable level. [ABSTRACT FROM PUBLISHER]

The aim of this study is to assess the sensitivity to buried archaeological structures of C- and L-band Synthetic Aperture Radar (SAR) in various polarisations. In particular, single and dual polarised data from the Phased Array type L-band SAR (PALSAR) sensor on-board the Advanced Land Observing Satellite (ALOS) is used, together with quadruple polarised (quad pol) data from the SAR sensor on Radarsat-2. The study region includes an isolated area of open fields in the eastern outskirts of Rome where buried structures are documented to exist. Processing of the SAR data involved multitemporal averaging, analysis of target decompositions, study of the polarimetric signatures over areas of suspected buried structures and changes of the polarimetric bases in an attempt to enhance their visibility. Various ancillary datasets were obtained for the analysis, including geological and lithological charts, meteorological data, Digital Elevation Models (DEMs), optical imagery and an archaeological chart. For the Radarsat-2 data analysis, results show that the technique of identifying the polarimetric bases that yield greatest backscatter over anomaly features, and subsequently changing the polarimetric bases of the time series, succeeded in highlighting features of interest in the study area. It appeared possible that some of the features could correspond with structures documented on the reference archaeological chart, but there was not a clear match between the chart and the results of the Radarsat-2 analysis. A similar conclusion was reached for the PALSAR data analysis. For the PALSAR data, the volcanic nature of the soil may have hindered the visibility of traces of buried features. Given the limitations of the accuracy of the archaeological chart and the spatial resolution of both the SAR datasets, further validation would be required to draw any precise conclusions on the sensitivity of the SAR data to buried structures. Such a validation could include geophysical prospection or excavation. [ABSTRACT FROM AUTHOR]

In this paper, a study of polarimetric optimization techniques in the frame of differential synthetic aperture radar (SAR) interferometry (DInSAR) is considered. Historically, DInSAR techniques have been limited to the single-polarimetric case, mainly due to the unavailability of fully polarimetric data. Lately, the launch of satellites with polarimetric capabilities, such as the Advanced Land Observing Satellite (ALOS), RADARSAT-2, or TerraSAR-X, allowed merging polarimetric and interferometric techniques to improve the pixels' phase quality and, thus, the density and the reliability of the final DInSAR results. The relationship between the polarimetrically optimized coherence or amplitude dispersion maps and the final DInSAR results is carefully analyzed, using both orbital and ground-based SAR fully polarimetric data. DInSAR processing using polarimetric optimization techniques in the pixel selection process is compared with the classical single-polarimetric approach, achieving up to a threefold increase of the number of pixel candidates in the coherence case and up to a factor of seven in the amplitude dispersion case. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) data are used extensively for terrain classification applying SAR features from various target decompositions and certain textural features. However, one source of information has so far been neglected from PolSAR classification: Color. It is a common practice to visualize PolSAR data by color coding methods and thus, it is possible to extract powerful color features from such pseudocolor images so as to provide additional data for a superior terrain classification. In this paper, we first review previous attempts for PolSAR classifications using various feature combinations and then we introduce and perform in-depth investigation of the application of color features over the Pauli color-coded images besides SAR and texture features. We run an extensive set of comparative evaluations using 24 different feature set combinations over three images of the Flevoland- and the San Francisco Bay region from the RADARSAT-2 and the AIRSAR systems operating in C- and L-bands, respectively. We then consider support vector machines and random forests classifier topologies to test and evaluate the role of color features over the classification performance. The classification results show that the additional color features introduce a new level of discrimination and provide noteworthy improvement in classification performance (compared with the traditionally employed PolSAR and texture features) within the application of land use and land cover classification. [ABSTRACT FROM PUBLISHER]

The monitoring of soil freezing and thawing states over large areas is very challenging on ground. In order to investigate the potential and the limitations of space-borne SAR polarimetry at C-band for soil state survey, analyses were conducted on an entire winter time series of fully polarimetric RADARSAT-2 data from 2011/2012 to identify freezing as well as thawing states within the soil. The polarimetric data were acquired over the Sodankylä test site in Finland together with in situ measurements of the soil and the snow cover. The analyses indicate clearly that the dynamics of the polarimetric entropy and mean scattering alpha angle are directly correlated to soil freezing and thawing states, even under distinct dry snow cover. First modeling attempts using the Extended Bragg soil scattering model justify the observed trends, which indicate surface-like scattering during frozen soil conditions and multiple/volume scattering for thawed soils. Hence, these first investigations at C-band foster motivation to work towards a robust polarimetric detection of soil freezing and thawing states as well as their transition phase. [ABSTRACT FROM AUTHOR]

In previous works, a general framework to exploit polarimetric diversity to optimize the results of persistent scatterers interferometry (PSI) was presented, but tested only with dual-pol data. In this paper, the performance of these algorithms is assessed using fully polarimetric data, acquired by the Radarsat-2 satellite over the urban area of Barcelona, Spain. In addition, two new highly efficient polarimetric optimization methods, mean intensity polarimetric optimization and joint diagonalization-based polarimetric optimization, are introduced and evaluated. Given the variety of dual-pol configurations provided by current polarimetric satellites, such as TerraSAR-X and Radarsat-2, and the upcoming launch of Sentinel-1, ALOS-2, and Radarsat Constellation Mission, a study has been also carried out to determine the best performing dual-pol configurations for polarimetric PSI. Subsidence maps of the area of study are computed for single-pol, dual-pol, and full-pol data, which show the increase in pixel density with valid deformation results as more polarimetric information is made available. In particular, for full-pol data we get an increase of up to 2.5 times more pixels for coherence-based PSI techniques (degraded resolution), and over four times more for amplitude-based approaches (full resolution), in comparison with single-pol data. Both higher density and quality of pixels yield better results in terms of coverage and accuracy. [ABSTRACT FROM PUBLISHER]

• Polynomial Chaos Expansion regression is suitable for estimation of crop biophysical parameters. • Correlated input vectors prevent reliable Global Sensitivity Analysis with Sobol's method. • Relationship between polynomial coefficients and Sobol sensitivity indices is an advantage. • Interactions between features affect regression of biophysical parameters in different level. Data-driven machine learning regression methods are easy to implement and applicable to a wide-range of data in biophysical parameter estimation and have become a common approach in the remote sensing field. Among the regression methods, polynomial chaos expansion (PCE) is one of the reliable and interesting ones due to its tight relationship with uncertainty quantification. One of the advantages of PCE is that global sensitivity analysis (GSA) with Sobol's method can be analytically computed from polynomial coefficients if the input space is statistically independent. However, most of the phenomena include dependent features either statistically or physically. Though the physical independence is provided between inputs, they must be statistically uncorrelated. Therefore, an independent and uncorrelated input space must be created before the regression analysis. In this paper, we performed PCE-based regression analysis for the estimation of biophysical parameters of crops. The study was conducted in the experimental fields of field pea, barley, canola, and oat of the AgriSAR2009 campaign. The input parameters of the regression model were formed by creating polarimetric features derived from RADARSAT-2 imagery. The estimated biophysical parameters were based on the discrete in situ measurements of leaf area index (LAI) and normalized difference vegetation index (NDVI), scattered semi-randomly in each crop field. We implemented neighbourhood component analysis (NCA) to create an independent and uncorrelated input space by eliminating correlations. Finally, we investigated the importance of features, which drive the PCE-based regression models applying GSA with Sobol's method. Besides the individual effects of each feature, their interactions were found significant. [ABSTRACT FROM AUTHOR]

ABSTRACT The aim of the present work was to compare Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) with RADARSAT-2 C-band satellite in order to identify the most suitable method for the detection of ground anomalies due to the presence of shallow underground archaeological structures. This comparison was performed over two UNESCO archaeological sites: Djebel Barkal (northern province of Meroe, Sudan) and Samarra (130 km north of Bagdad, Iraq). The choice of these two cultural sites was motivated by their position in a desert area (especially the Djebel Barkal site) where soil moisture has smaller impact on radar wave penetration, thus not compromising data interpretation. The largest difference between the satellites is the spatial resolution, around 20 m for the PALSAR sensor and 9 m for the RADARSAT-2 satellite. Given the importance of spatial resolution for archaeological purposes, that difference is balanced by the wave band utilized, which can be translated in differences in soil penetration. However, the quite superficial remains in both the sites give different but complementary responses. Polarimetric SAR data were analysed with PolSARpro software, and all the products were then compared with archaeological maps and available optical satellite images, used as a basis for the georeferencing process. This work constitutes a fundamental step in archaeological research toward a correct interpretation of all the scattering mechanisms above or around ancient structures. Additional information, such as meteorological conditions where available and archaeological maps (that can be consulted from the UNESCO website), was used as a support for interpretation of the archaeological areas; in the case of the Iraqi city of Samarra and of the archaeological area of Gebel Barkal, ground-truth surveys in situ are not allowed for political reasons. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The objective of this study was to analyze the potential of the C-band polarimetric synthetic aperture radar (SAR) parameters for the soil surface characterization of bare agricultural soils. RADARSAT-2 data and simulations using the integral equation model were analyzed to evaluate the polarimetric SAR parameters' sensitivities to the soil moisture and surface roughness. The results showed that the polarimetric parameters in the C-band were not very relevant to the characterization of the soil surface over bare agricultural areas. Low dynamics were often observed between the polarimetric parameters and both the soil moisture content and the soil surface roughness. These low dynamics do not allow for the accurate estimation of the soil parameters, but they could augment the standard inversion approaches to improve the estimation of these soil parameters. The polarimetric parameter \alpha1 could be used to detect very moist soils ( > 30%), while the anisotropy could be used to separate the smooth soils. [ABSTRACT FROM PUBLISHER]

Oil spills on the sea surface are observed relatively often. Pollution due to either accidents or deliberate oily discharges from ships represents a serious threat to the marine environment. Operational oil spill monitoring is currently done using a combination of satellite monitoring and aircraft surveillance. The combined use of satellite-based synthetic aperture radar (SAR) images and aircraft surveillance flights is a cost-effective way to monitor oil spills in large ocean areas and catch the polluters. SAR images enable covering large areas, but aircraft observations are needed to prosecute the polluter, and in certain cases to verity the oil spill. Traditionally, oil spill detection is based on single polarization SAR images. Oil spills can be discriminated from look-alikes based on a set of features describing the contrast, shape, homogeneity, source, and surroundings of the slick. Good performance is reported for single-polarization oil spill detection, but in certain cases the oil slicks cannot be discriminated from biogenic films. In the recent years, a number of studies have shown that polarimetric SAR can improve the discrimination between oil slicks and biogenic films. Several features computed from dual-pol or quad-pol images have been proposed. These include both quad-pol features like polarimetric entropy and anisotropy, mean scattering angle, polarimetric span, conformity coefficient, as well as the dual-pol features standard deviation of the copolarized phase difference and the copolarized correlation coefficient. As dual-pol SAR imagery is now available on a regular basis from Cosmo Skymed and TerraSAR-X, and quad-pol data are available from RADARSAT-2, polarimetric SAR can now be utilized on a more regular basis. Optical data from sensors like Aqua MODIS and ENVISAT MERIS can be a useful supplement under certain cloud-free conditions. [ABSTRACT FROM AUTHOR]

The interpretation of SAR images of the sea surface is difficult, due to the complexity of the geophysics and of the interaction mechanisms between electromagnetic and sea waves. The determination of the wind direction is crucial for the evaluation of the wind speed, but its retrieval is still an open issue. One of the few methods able to extract the sea surface wind from SAR data only has been developed and extensively applied to Envisat ASAR images in the past years, using the two-dimensional wavelet transform to detect the backscatter signature related to locally coherent wind cells. A preliminary analysis on the applicability of this method to RADARSAT-2 fully polarimetric images has been conducted to verify if polarimetry may improve the detection of backscatter imprints related to the wind direction. [ABSTRACT FROM AUTHOR]

The new full polarimetric modes of Radarsat-2 were evaluated for digital elevation model (DEM) generation using stereo-radargrammetry with Toutin's three-dimensional physical model. The fusion of the four polarimetric bands was thus addressed to improve the resulting DEMs. The stereo-radargrammetric DEMs were evaluated with 20 cm accuracy Lidar elevation data. Results on a test site north of Quebec City, Canada showed a good accuracy: 5 m and 6-7 m (1σ) for the horizontal positioning and the elevation (on bare soils), respectively. However, the best results were obtained when the four polarimetric bands were fused with the restoration method and the second best results (around 5% worse) were obtained with the SPAN method (total power of the four intensity bands). [ABSTRACT FROM AUTHOR]

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)