RADARSAT-2 Synthetic Aperture Radar Research Articles

Ground deformation due to natural resource extraction in the Western Canada Sedimentary Basin

The study links multiple natural resource extraction activities in the Western Canada Sedimentary Basin (WCSB) with observations of ground deformation. Sentinel-1, RADARSAT-2, and RADARSAT Constellation Mission (RCM) Synthetic Aperture Radar (SAR) data acquired as early as 2015 were used to compute deformation maps and ground deformation time series. Ground deformation produced by induced earthquakes (2015 Mw 4.6 in British Columbia and 2022/23 Mw 5.1/4.6 in Alberta; and 2017/18 Mw 5.0/4.2 slow slip events in British Columbia) were mapped with individual interferograms. Long-lasting ground deformation due to Steam-Assisted Gravity Drainage (SAGD) and Cyclic Steam Stimulation (CSS) enhanced oil recovery and coal and potash underground mining were mapped with the Multidimensional Small Baseline Subset (MSBAS) time series method. When both ascending and descending data were available, MSBAS was used to compute two-dimensional (2D) deformation time series and rates. It was observed that seismic and aseismic ground deformation due to hydraulic fracturing and saltwater injection in WCSB had a similar surface expression (paired anomaly), spatial extent (5-10 km) and orientation (striking NW-SE), suggesting that triggering processes might be technogenic. In the case of hydraulic fracturing, ground deformation appeared soon after the beginning of the operation, while not with saltwater injection, as it took years. The source mechanism of the 2015 Mw 4.6 earthquake was obtained through inverse modelling of the two descending interferograms. The rectangular source model corresponded to a fault at 1.98 ± 0.09 km depth, with 126° ± 7° strike, 14° ± 2° dip, 72° ± 7° rake, 1.61 ± 0.24 km width, 2.54 ± 0.23 km length, and 0.19 ± 0.07 m slip. In the case of in-situ enhanced oil recovery, ground deformation varied significantly depending on the technique. Continuous uplift observed at the SAGD site had a rate of only about 0.05 m/year, while reversible uplift and subsidence at CSS sites exceeded 0.2 m/month, even though wells in both cases were located at approximately similar depths (380 m for SAGD vs 520 m for CSS). In the case of underground mining, the spatial extent of subsidence depended on the extraction depth (200-500 m for coal vs 1000 m for potash). When a mining operation was conducted in an area with topographic relief, horizontal deformation was observed in addition to subsidence. Various observed deformation signals may precede more consequential deformation events.

A novel semi-empirical model for crop leaf area index retrieval using SAR co- and cross-polarizations

The retrieval of continuous leaf area index (LAI) in space and time from remote sensing is beneficial for cropland monitoring and management. Synthetic Aperture Radar (SAR) with the advantages of all-weather operation and fine spatial resolutions has been utilized in various agricultural applications. Although the water cloud model (WCM) has been extensively used for LAI estimation over croplands, it is modified for application to crop canopies with large gaps. The requirement of prior information on soil moisture is also a hinderance for the model application. In this study, WCM is theoretically modified to consider the correlation of active microwave propagation through the canopy in downward and upward directions. Such a modification is particularly important for sparse vegetation with large gaps between crop plants in the early stage of the growing season. A parameter with explicit physical meaning, i.e. the full-vegetation backscattering coefficient, was defined to simplify our model scheme and make the model applicable for the whole growing season. In addition, a physics-based SAR data processing scheme is developed to entangle the influences of LAI and soil moisture on SAR backscatter by taking advantage of the multiple polarizations of RADARSAT-2 (R2) SAR data. In this way, LAI was estimated using modified WCM without the prior knowledge of soil moisture. To evaluate LAI retrieved from the R2 datasets (R2 LAI), ground-based LAI measurements were made at the experimental area of SMAPVEX16-MB in Canada with twenty approximately 800 m х 800 m plots in soybeans and corn. R2 LAI was well correlated to these ground-based LAI (n = 15, R2 = 0.63, RMSE = 0.99 m2·m−2 for soybeans; n = 5, R2 = 0.66, RMSE = 1.20 m2·m−2 for corn). LAI was also retrieved from optical data acquired by Sentinel-2/MSI (S2), denoted as S2 LAI. The R2 LAI and S2 LAI are well correlated and achieved coefficients of determination (R2) of 0.76 and 0.71 and root mean square errors (RMSE) of 1.1 and 1.4 m2·m−2 for soybeans and corn, respectively. The seasonal variations of R2 LAI and S2 LAI are generally similar except at the end of the growing season when S2 LAI is considerably larger than R2 LAI. S2 LAI followed the trend of the reduction in leaf chlorophyll content, while R2 LAI reduced only slightly due to the decrease in leaf water content near the end the growing season. R2 LAI represents the total standing leaf area useful for surface energy balance estimation, while S2 LAI responds to green leaf area useful for crop productivity modeling.

Crop Classification Based on GDSSM-CNN Using Multi-Temporal RADARSAT-2 SAR with Limited Labeled Data

Crop classification is an important part of crop management and yield estimation. In recent years, neural networks have made great progress in synthetic aperture radar (SAR) crop classification. However, the insufficient number of labeled samples limits the classification performance of neural networks. In order to solve this problem, a new crop classification method combining geodesic distance spectral similarity measurement and a one-dimensional convolutional neural network (GDSSM-CNN) is proposed in this study. The method consisted of: (1) the geodesic distance spectral similarity method (GDSSM) for obtaining similarity and (2) the one-dimensional convolutional neural network model for crop classification. Thereinto, a large number of training data are extracted by GDSSM and the generalized volume scattering model which is based on radar vegetation index (GRVI), and then classified by 1D-CNN. In order to prove the effectiveness of the GDSSM-CNN method, the GDSSM method and 1D-CNN method are compared in the case of a limited sample. In terms of evaluation and verification of methods, the GDSSM-CNN method has the highest accuracy, with an accuracy rate of 91.2%, which is 19.94% and 23.91% higher than the GDSSM method and the 1D-CNN method, respectively. In general, the GDSSM-CNN method uses a small number of ground measurement samples, and it uses the rich polarity information in multi-temporal fully polarized SAR data to obtain a large number of training samples, which can quickly improve the accuracy of classification in a short time, which has more new inspiration for crop classification.

Numerical Simulation of SAR Image for Sea Surface

Based on the simulated signal, a numerical simulation method of synthetic aperture radar (SAR) imaging for time-varying sea surfaces is proposed, which is helpful to study the SAR imaging mechanism of time-varying sea surfaces so as to better extract ocean wave parameters from SAR images. Not only are the modulation of ocean waves, speckle noise, and temporal decorrelation of the small-scale waves considered, but the velocity bunching (VB) effect caused by the motion of large-scale waves is also effectively added to the simulation of the SAR echo signal. To verify the reliability of the simulation method, the simulated SAR images using the parameters of the RADARSAT-2 SAR, the corresponding wind wave information measured by an in-situ buoy, and the reanalysis wave spectra have been compared with the actual RADARSAT-2 SAR images. The comparisons demonstrate that the characteristics of simulated SAR images, such as the intensity distribution and the image spectra, are consistent with those of actual RADARSAT-2 SAR images. Based on the numerical simulation method proposed by us, SAR images of ocean waves for different marine environments and radar platform parameters are simulated. The imaging results indicate that the texture feature of the wind waves would be severely damaged due to the VB effect, while the texture of swells in the simulated SAR images may not be damaged or even becomes clearer. From the simulated SAR image spectrum, it can be found that the azimuth wavenumber is cut off when the VB effect is considered in the simulation process, and the azimuth cut-off wavelength increases with the range-to-velocity ratio.

IDUDL: Incremental Double Unsupervised Deep Learning Model for Marine Aquaculture SAR Images Segmentation

Marine aquaculture is an important natural resource exploration that requires rational planning to avoid environmental damage. Synthetic Aperture Radar (SAR) images are essential in remote sensing to monitor the marine ecological environment. Unsupervised methods provide an adequate solution to avoid the cost of training sample collection. However, unsupervised methods often struggle to discover effective semantic information and incrementally utilize newly acquired data. To address these challenges, this article presents an incremental double unsupervised deep learning (IDUDL) model, which is specially designed to characterize unlabeled marine aquaculture and achieve the results semantically. Based on the idea of alternately generating and updating pseudo-labels, the proposed IDUDL model defines the double neural networks comprised of the feature extraction network (FEN) and the fully convolutional semantic segmentation network (FCSSN). A patch estimation (PE) is proposed to generate pseudo-labels with aquaculture semantic information based on the features extracted by the FEN network. Then, the aquaculture extraction results are obtained by the FCSSN with generated pseudo-labels. After that, the pseudo-labels and extraction results are updated in turns until the pseudo-labels are stable. In addition, due to the unique structure of double neural networks, newly acquired marine aquaculture SAR images can also be added to the pre-trained FCSSN and followed pseudo-labels updated based on the FEN and PE part, which can achieve new data incremental learning without retrained the whole IDUDL model. Experiments demonstrate the effectiveness of the proposed approach based on two different ways of the marine aquaculture including raft and cage types, which consist of GaoFen-3 (GF-3) and RADARSAT-2 SAR images from the Dalian and Ningde areas, respectively. The incremental experiments are also designed to verify the generalization of the IDUDL model for new obtained marine aquaculture SAR images. The code of this work will be available at https://github. com/fjc1575/Marine-Aquaculture/tree/main/IDUDL for the sake of reproducibility.

Assessment of the Available Historic RADARSAT-2 Synthetic Aperture Radar Data Prior to the Manefay Slide at the Bingham Canyon Mine Using Modern InSAR Techniques

Assessment of the Available Historic RADARSAT-2 Synthetic Aperture Radar Data Prior to the Manefay Slide at the Bingham Canyon Mine Using Modern InSAR Techniques

A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index

The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m−2 and mean absolute error (MAE) of 0.51 m2m−2. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m−2 and MAE of 0.61 m2m−2) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m−2 and MAE of 0.30 m2m−2). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance.

A Phase Optimization Method for DS-InSAR Based on SKP Decomposition From Quad-Polarized Data

A novel distributed scatterer interferometric synthetic aperture radar (DS-InSAR) method is presented in which the sum of Kronecker product (SKP) decomposition method is applied to DS candidates. Unlike existing polarimetric optimization methods, the proposed method considers polarimetric and interferometric coherence information simultaneously, resulting in separation of the polarimetric scattering process for each target and the maximum diversity for the corresponding phase center locations. Physical reliability of the phase optimization solution is thereby ensured. The performance of the novel method is evaluated using 30 quad-polarized C-band Radarsat-2 synthetic aperture radar (SAR) images over Kilauea Volcano, Hawaii. The proposed method provides a higher density of measurement scatterer (MS) points and a higher quality of DS interferometric phase with a temporally stable phase center than single-polarization (HH) method and quad-polarization exhaustive search polarimetric optimization (ESPO) method. Thus, the proposed method shows good performance in phase quality improvement and point density increasement.

A fast, edge-preserving, distance-regularized model with bilateral filtering for oil spill segmentation of SAR images

Marine oil spills are among the most significant sources of marine pollution. Synthetic aperture radar (SAR) has been used to improve oil spill observations because of its advantages in oil spill detection and identification. However, speckle noise, weak boundaries, and intensity inhomogeneity often exist in the oil spill regions of SAR imagery, which will seriously affect the accurate identification of oil spills. To enhance marine oil spill segmentation of SAR images, a fast, edge-preserving framework based on the distance-regularized level set evolution (DRLSE) model was proposed. Specifically, a bilateral filter penalty term is designed and incorporated into the DRLSE energy function (BF-DRLSE) to preserve the edges of oil spills, and an adaptive initial box boundary was selected for the DRLSE model to reduce the operation time complexity. Two sets of RadarSat-2 SAR data were used to test the proposed method. The experimental results indicate that the bilateral filtering scheme incorporated into the energy function during level set evolution improved the stability of level set evolution. Compared with other methods, the proposed improved BF-DRLSE algorithm displayed a higher overall segmentation accuracy (97.83%). In addition, using an appropriate initial box boundary for the DRLSE method accelerated the global search process, improved the accuracy of oil spill segmentation, and reduced computational time. Therefore, the results suggest that the proposed framework is effective and applicable for marine oil spill segmentation.

Estimating Tropical Cyclone Wind Structure and Intensity From Spaceborne Radiometer and Synthetic Aperture Radar

We present a relatively simple method to estimate tropical cyclone (TC) surface wind structure (34-, 50-, and 64-kt wind radii) and intensity [maximum wind speed (MWS)] from wind fields acquired from the L-band SMAP radiometer and C-band Sentinel-1A/B and RADARSAT-2 synthetic aperture radar (SAR) between 2015 and 2020. The radiometer and SAR-derived wind radii and MWS are systematically compared with the best-track estimates. The root-mean-square errors (RMSEs) of R34, R50, and R64 are 31.2, 21.8, and 17.0 nmi (1 nmi = 1.852 km) for radiometer, and 21.7, 16.5, and 16.3 nmi for SAR, respectively. These error values are smaller than the averaged best-track uncertainty estimates for the three wind radii. Compared with the best-track reports, the bias and RMSE for the MWS estimates are -0.2 m/s and 5.8 m/s for radiometer, and 4.4 m/s and 9.1 m/s for SAR, respectively. These results are for the wind speeds in the range of 17-80 m/s. For the two typical TCs (Lionrock and Noru) in the Northwest Pacific Ocean, our results show that a combination of the radiometer and SAR wind data acquired within a very short time interval has the potential to simultaneously obtain reasonable measurements of the wind radii and intensity parameters. Moreover, for a TC with a long lifecycle, such as Typhoon Noru, we demonstrate that the high-resolution and multitemporal synergistic observations from SAR and radiometer are valuable for studying fine-scale features of the wind field and characteristics of wind asymmetry associated with intensity change, as well as the evolution of TC surface wind structure and intensity.

Retrieving Soil Moisture Over Soybean Fields During Growing Season Through Polarimetric Decomposition

Soil moisture ( Mv ) estimation and monitoring over agricultural areas using Synthetic Aperture Radar (SAR) are often affected by vegetation cover during the growing season. Volume scattering and vegetation attenuation can complicate the received SAR backscatter signal when microwave interacts with the vegetation canopy. To address the existing problems, this article employed the model-based polarimetric decomposition method considering the two-way attenuation to remove the volume scattering and vegetation attenuation. A deorientation process of SAR data was applied to remove the influence of randomly distributed target orientation angles before the polarimetric decomposition. To parameterize the two-way attenuation, Radar Vegetation Index derived from the SAR intensity images was adopted. The Dubois model was used to describe backscattering from the underlying bare soil. Since the soil roughness parameters are difficult to measure under vegetation cover, the optimum surface roughness method was used to parameterize the Dubois model. This soil moisture retrieval algorithm was applied to the polarimetric multitemporal RADARSAT-2 SAR data over soybean fields. The validation indicates the root-mean-square error of 9.2 vol.% and 8.2 vol.% at HH and VV polarization, respectively, over the entire soybean growing period, suggesting that the proposed method is capable of reducing the effect of vegetation cover for soil moisture monitoring over the soybean field.

Estimation of Deformation Intensity above a Flooded Potash Mine Near Berezniki (Perm Krai, Russia) with SAR Interferometry

In this study we used RADARSAT-2 and Sentinel-1 Synthetic Aperture Radar data for measuring subsidence above a flooded potash mine, which is almost entirely located within the city of Berezniki (Perm Krai, Russia), population 150,000. This area has experienced very fast subsidence since October 2006 when the integrity of the Berezniki-1 mine was compromised, resulting in water intrusion, subsequent flooding and closure of the mine. Due to the ongoing dissolution of carnallite, subsidence in this region is expected to continue in the foreseeable future. In addition to rapid subsidence, at least five sinkholes have formed in the region, with the largest being 440 × 320 m. We observed ground subsidence during the period October 2011–April 2014 (RADARSAT-2) with a vertical rate up to 14 cm/year and horizontal rate up to 10 cm/year; during the period July 2016–June 2020 (Sentinel-1) with a vertical rate up to 17 cm/year. Our results were validated by precise leveling, with a coefficient of correlation of 0.75. Subsidence faster than 17 cm/year observed by precise leveling was not resolvable with Differential Interferometric Synthetic Aperture Radar (DInSAR). Our results show the complementary nature of ground-based and space-borne measurement techniques. The precise leveling captures subsidence along profile lines with high precision but lower temporal resolution, while DInSAR captures subsidence with high spatial and temporal resolutions but with lower precision. DInSAR is also significantly affected by decorrelation outside of urban areas. An important advantage of our methodology is the ability to measure the horizontal east component of the ground deformation when both, ascending and descending, data are available. This measurement directly characterizes the level of anthropogenic load on buildings and infrastructure. We recommend continuing monitoring subsidence using both measurement techniques, which can also be complemented by continuous Global Navigation Satellite System (GNSS).

An assessment of temporal RADARSAT-2 SAR data for crop classification using KPCA based support vector machine

Crop discrimination with synthetic aperture radar (SAR) data primarily depends on the characterization of crop geometry using radar backscatter response. Differences in phenological development of crops lead to dissimilar temporal signatures of backscatter intensities, which may influence the separability of the crop classes. This principle leads to multi-date classification approach. In this work, kernel principal component (KPCA) is adopted for feature selection from multi-date datasets, and the selected features are used for classification using support vector machine (SVM) classifier. The classification is investigated for both the KPCA-based SVM and only SVM approaches using quad-pol C-band RADARSAT-2 data acquired over the test site in Vijayawada, India. KPCA-based SVM classification shows an overall accuracy of 89%, which is better than 82% obtained using the SVM-based classification. The proposed methodology effectively incorporates the temporal crop information during classification.

Texture Classification-Based NLM PolSAR Filter

In this letter, a texture classification-based nonlocal means polarimetric SAR (NLM PolSAR) filter is introduced and named as texture classification-based filter (TBF). In this process, a classification algorithm that identifies the data into textural variations and heterogeneity due to speckle noise is presented. Also, a similarity metric is derived to estimate the patch similarity of K-distributed covariance matrices. Hence, a filter is proposed that processes the classified data suitably using NLM patch-based filters with either K- or Wishart distribution patch similarity metrics. The filtering performance of TBF is being analyzed on full-pol single-look RADARSAT-2 and four-look Airborne Synthetic Aperture Radar (AIRSAR) data.

Is Radar Phase Information Useful for Sea Ice Detection in the Marginal Ice Zone?

With continuing sea ice reductions in the Arctic, dynamic physical and ecological processes have more active roles compared to the ice-locked, isolated Arctic Ocean of previous decades. To better understand these changes, observations of high-resolution sea ice conditions are needed. Remote sensing is a useful tool for observations in the harsh Arctic environment. For unsupervised ice detection, we demonstrate the promising value of radar phase difference from polarimetric radar measurements in this study, based on full polarimetric complex RADARSAT-2 SAR images in the marginal ice zone. It is demonstrated that the phase difference from co-polarized and cross-polarized synthetic aperture radar (SAR) images show promising capability for high resolution sea ice discrimination from open water. In particular, the phase difference shows superior potential for the detection of frazil ice compared to the traditional methodology based on the radar intensity ratio. The relationship between phase difference and radar incidence angle is also analyzed, as well as the potential influence of high sea state. The new methodology provides an additional tool for ice detection. In order to make the best use of this tool, directions for further studies are discussed for operational ice detection and possible ice classification.

Development of an Automated Tool for Delineation of Flood Footprints from SAR Imagery for Rapid Disaster Response: A Case Study

Floods are one of the most common natural disasters. In recent times, microwave synthetic aperture radar (SAR) satellite images have been used widely for mapping flood affected areas due to its all-weather capability and acquisition during day and night. Here, in this paper, an automated algorithm is proposed to delineate flood extent from SAR images without any human intervention. The algorithm consists of pre-processing steps like applying orbit file, calibrate to sigma naught, speckle filtering, terrain correction and linear to decibel conversion. The water layer is delineated using multi-segmentation and Otsu’s thresholding technique. Further, flood layer is extracted by postprocessing steps using majority filter, applying permanent water body mask and eliminating hill shadows. The algorithm is tested on a significant number of satellite images covering floods in India, which are having diverse terrain and flooding patterns. In this paper, the steps involved in delineating flood from SAR image of VH polarization from SENTINEL-1 satellite covering chronic flood-prone stretch of part of Ganga River in Bihar state is presented. Accuracy assessment is carried out with the flood layer derived from RADARSAT SAR HV polarized data acquired on the same day and an accuracy of about 96% is obtained. The total processing time taken for the extraction of the flood layer is 9 min. This automation process is beneficial for the generation of rapid flood inundation maps, with high accuracy, which is helpful for flood monitoring and relief management during a disaster.

The Study of Ocean Wave and Wave Power Observations by Synthetic Aperture Radar in Nearshore Waters

Wan, Y.; Zheng, C.W.; Zhang, J.; Dai, Y.; Li, L.; Qu, X., and Zhang, X., 2020. The Study of Ocean Wave and Wave Power Observations by Synthetic Aperture Radar in Nearshore Waters. In: Zheng, C.W.; Wang, Q.; Zhan, C., and Yang, S.B. (eds.),Air-Sea Interaction and Coastal Environments of the Maritime Polar Silk Roads.Journal of Coastal Research, Special Issue No. 99, pp. 9-15. Coconut Creek (Florida), ISSN 0749-0208.Based on 2 RADARSAT-2 SAR (Synthetic Aperture Radar) SLC (Single Look Complex) data and the cross-spectrum method, the distributions of significant wave heights and mean wave periods of ocean wave were inverted, and the distributions of the wave power density, which can represent the wave power, were calculated. Some comparisons between the results of the SAR and those of the ECMWF (European Center for Medium-Range Weather Forecasts) dataset were carried out, and the observation ability for the distribution characteristics of wave and wave power by the SAR was studied in detail. The results show that compared with buoys, which are single-point observations, and wave models, which are simulated observations, the SAR is an ideal tool for extensive wave observations in nearshore waters. The advantage of SAR observations is that some fine structure and microcosmic changes in wave and wave power can be found, which helps us understand the internal structure and transformation patterns of wave better. Therefore, the SAR showed extremely extensive application prospects in the field of wave observations. The contributions in this paper will provide some references for researchers aiming for ocean wave detection by remote sensing and promote the application level of SAR in the detection of wave and wave power.

Mineral Oil Slicks Identification Using Dual Co-polarized Radarsat-2 and TerraSAR-X SAR Imagery

This study is devoted to a generalization of C-band Radarsat-2 and X-band TerraSAR-X synthetic aperture radar (SAR) data in the form of a diagram serving to easily identify mineral oil slicks (crude oil and emulsions) and separate them from the other oil slicks. The diagram is based on the multi-polarization parameter called Resonant to Non-resonant signal Damping (RND) introduced by Ivonin et al. in 2016, which is related to the ratio between damping within the slick of the short waves and wave breakings. SAR images acquired in the North Sea during oil-on-water exercises in 2011–2012 containing three types of oil spills (crude oil, emulsion, and plant oil) were used. The analysis was performed under moderate sea conditions (wind speeds of 2–6 m/s and sea wave heights of less than 2 m), the incidence angles of 27°–49°, and the signal-to-noise ratio (SNR) of −3 to 11 dB within slicks. On the diagram plane, created by the RND parameter and the Bragg wave number, the mineral oil samples form a well-outlined zone, called a mineral oil zone. For C-band data, the plant oil samples were clearly distinguished from the mineral oils in the diagram. Determination of the confidence level for the detection of mineral oils versus plant oil was proposed using the mineral oil zone boundaries. The mineral oil data with SNR within slicks better than 2 dB lay within this zone with a confidence level better than 65%. The plant oil data with the same SNR lay outside this zone with a confidence level of better than 80%. For mineral oil with SNR of −3 dB, the confidence level is 55%.

Modelling of early winter snow density using fully polarimetric C-band SAR data in the Indian Himalayas

The seasonal snow cover contributes significantly to the water resource in the Indian Himalayas, and snow density is one of the vital parameters in the determination of the hydrological potential of snow. The application of conventional methods for snow density retrieval using fully polarimetric SAR data is constrained by the properties of snow primarily due to the melt and frost cycles, as compared to fresh dry snow. The surface component of backscatter is significant in case of melt and frost. In the conventional decomposition based methods, the surface component is not considered for the inversion of permittivity, as proposed in this study. In this paper, we also propose a methodology for the estimation of snow density using bi-temporal fully polarimetric C-band RADARSAT-2 synthetic aperture radar (SAR) data. We utilize the relation between the differential modified Mueller matrix components, the attenuation constants, and the Fresnel transmission coefficients. The permittivity of snow is derived using the inversion of the Fresnel transmission coefficients which is used to determine the snow density using a state of the art empirical relation. The snow density estimates from the proposed method are compared with other methods based on coherency matrix decomposition and evaluated against in-situ measurements collected during a field campaign carried out in Dhundi in the state of Himachal Pradesh in India. The snow density estimates using the proposed method are observed to correlate with the in-situ measurements and were also found to be better than the decomposition based methods.

In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data

Accurate spatio-temporal classification of crops is of prime importance for in-season crop monitoring. Synthetic Aperture Radar (SAR) data provides diverse physical information about crop morphology. In the present work, we propose a day-wise and a time-series approach for crop classification using full-polarimetric SAR data. In this context, the 4 × 4 real Kennaugh matrix representation of a full-polarimetric SAR data is utilized, which can provide valuable information about various morphological and dielectric attributes of a scatterer. The elements of the Kennaugh matrix are used as the parameters for the classification of crop types using the random forest and the extreme gradient boosting classifiers.The time-series approach uses data patterns throughout the whole growth period, while the day-wise approach analyzes the PolSAR data from each acquisition into a single data stack for training and validation. The main advantage of this approach is the possibility of generating an intermediate crop map, whenever a SAR acquisition is available for any particular day. Besides, the day-wise approach has the least climatic influence as compared to the time series approach. However, as time-series data retains the crop growth signature in the entire growth cycle, the classification accuracy is usually higher than the day-wise data.Within the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative, in situ measurements collected over the Canadian and Indian test sites and C-band full-polarimetric RADARSAT-2 data are used for the training and validation of the classifiers. Besides, the sensitivity of the Kennaugh matrix elements to crop morphology is apparent in this study. The overall classification accuracies of 87.75% and 80.41% are achieved for the time-series data over the Indian and Canadian test sites, respectively. However, for the day-wise data, a ∼6% decrease in the overall accuracy is observed for both the classifiers.