Dual-polarimetric Synthetic Aperture Radar Research Articles

A Comprehensive Evaluation of Dual-Polarimetric Sentinel-1 SAR Data for Monitoring Key Phenological Stages of Winter Wheat

Large-scale crop phenology monitoring is critical for agronomic planning and yield prediction applications. Synthetic Aperture Radar (SAR) remote sensing is well-suited for crop growth monitoring due to its nearly all-weather observation capability. Yet, the capability of the dual-polarimetric SAR data for wheat phenology estimation has not been thoroughly investigated. Here, we conducted a comprehensive evaluation of Sentinel-1 SAR polarimetric parameters’ sensibilities on winter wheat’s key phenophases while considering the incidence angle. We extracted 12 polarimetric parameters based on the covariance matrix and a dual-pol-version H-α decomposition. All parameters were evaluated by their temporal profile and feature importance score of Gini impurity with a decremental random forest classification process. A final wheat phenology classification model was built using the best indicator combination. The result shows that the Normalized Shannon Entropy (NSE), Degree of Linear Polarization (DoLP), and Stokes Parameter g2 were the three most important indicators, while the Span, Average Alpha (α2¯), and Backscatter Coefficient σVH0 were the three least important features in discriminating wheat phenology for all three incidence angle groups. The smaller-incidence angle (30–35°) SAR images are better suited for estimating wheat phenology. The combination of NSE, DoLP, and two Stokes Parameters (g2 and g0) constitutes the most effective indicator ensemble. For all eight key phenophases, the average Precision and Recall scores were above 0.8. This study highlighted the potential of dual-polarimetric SAR data for wheat phenology estimation. The feature importance evaluation results provide a reference for future phenology estimation studies using dual-polarimetric SAR data in choosing better-informed indicators.

Spectral Analysis to Explore Gypsum Deposits and Its Association with Palaeochannels and Lithology Using PRISMA, Sentinel 2, and Dual-Polarimetric SAR Data in Bikaner, Rajasthan, Bharat

Spectral Analysis to Explore Gypsum Deposits and Its Association with Palaeochannels and Lithology Using PRISMA, Sentinel 2, and Dual-Polarimetric SAR Data in Bikaner, Rajasthan, Bharat

Deep-learning-based marine aquaculture zone extractions from dual-polarimetric SAR imagery

Deep-learning-based marine aquaculture zone extractions from dual-polarimetric SAR imagery

Soil permittivity estimation over vegetative fields using dual polarimetric SAR data

Soil permittivity estimation using Synthetic Aperture Radar (SAR) data under the vegetation cover has been an extensive research domain. In particular, the vegetation canopy over the soil layer complicates the interaction of microwave signals with the underneath soil layer and the vegetation canopy geometry. Nevertheless, the sensitivity of the microwave signal with the target dielectric and geometric properties makes it possible to retrieve the target permittivity from the SAR data. This work proposes a methodology for retrieving soil permittivity using dual-polarimetric (HH-HV or VV-VH) SAR data. We first derive the 2 × 2 covariance matrix (CXX-XYXB) for the extended Bragg (X-Bragg) surface scattering for both HH-HV and VV-VH configurations. The X-Bragg covariance information inherently captures the soil surface condition. Finally, we utilize the existing scattering power factorization framework to retrieve soil permittivity. We have validated the efficacy of the proposed technique using the simulated NISAR product from the L-band UAVSAR data over Manitoba, Canada, and the C- and L- band ESAR data over DEMMIN, Germany. The proposed technique achieves the soil permittivity within a Root Mean Square Error (RMSE) range of 2 to 6 for HH-HV and VV-VH configurations.

Optimizing Soil Moisture Retrieval: Utilizing Compact Polarimetric Features with Advanced Machine Learning Techniques

Soil moisture plays a crucial role in various environmental processes and is essential for agricultural management, hydrological modeling, and climate studies. Synthetic Aperture Radar (SAR) remote sensing presents significant potential for estimating soil moisture due to its ability to operate in all weather conditions and provide day-and-night imaging capabilities. Among the SAR configurations, the Compact Polarimetric (CP) mode has gained increasing interest as it relaxes system constraints, improves coverage, and enhances target information compared to conventional dual polarimetric SAR systems. This paper introduces a novel approach for soil moisture retrieval utilizing machine learning algorithms and CP SAR features. The CP SAR features are derived from a series of RADARSAT Constellation Mission (RCM) CP SAR imagery acquired over Canadian experimental sites equipped with Real-Time In Situ Soil Monitoring for Agriculture (RISMA) stations. This study employs a diverse dataset of compact polarimetric SAR features and corresponding ground truth soil moisture measurements for training and validation purposes. The results of our study achieved a Root Mean Square Error (RMSE) of 6.88% with a coefficient of determination R2 equal to 0.60, which corresponds to a correlation R between true and predicted soil moisture values of 0.75, using optimized Ensemble Learning Regression (ELR) with a decision-tree-based model. These results improved, yielding an RMSE of 5.67% and an R2 equal to 0.73 (R = 0.85), using an optimized Gaussian Process Regression (GPR) model.

An improved volume power approach to estimate LAI from optimized dual-polarized SAR decomposition

ABSTRACT Synthetic Aperture Radar (SAR) imagery has proven to be a valuable tool for monitoring the Earth’s surface, particularly vegetation. However, many studies using SAR have focused solely on backscattering sensitivity without considering the structural and physical properties of the vegetation being analysed. This study proposes a new approach for vegetation monitoring using the Volume Power (VP) analysis technique. The proposed method aims to improve the accuracy of VP derived from the Freeman-Durden (FD) decomposition technique for dual polarimetric SAR data. To increase the sensitivity of vegetation scattering in SAR analysis, this study modified the VP using Depolarized Volume Power (DVP) and Anisotropic Volume Power (AVP). The first modification, DVP, was achieved by incorporating the degree of polarization () in the analysis. The second modification, AVP, is achieved by considering the anisotropic scattering properties of vegetation. The modified VP is then used to estimate Leaf Area Index (LAI) using an empirical relationship between LAI and the modified VPs. The accuracy of the LAI estimation is evaluated using ground truth measurements. The results demonstrate that the proposed method provides more accurate LAI estimates than traditional methods. The approach also shows improved sensitivity to vegetation scattering compared to the original VP from the FD, indicating the effectiveness of the degree of polarization and anisotropic scattering in reducing the impact of unwanted scattering mechanisms from the surface. The accuracy (R 2) between in-situ LAI and the LAI retrieved from AVP and DVP was 0.85 and 0.82, whereas for FDVP, it was 0.74. These findings highlight the potential of the proposed approach for improving the accuracy of VP estimation in dual-polarimetric SAR data and enabling more accurate and efficient estimation of LAI.

On the ability of dual-polarimetric SAR measurements to observe lava flows under different volcanic environments

On the ability of dual-polarimetric SAR measurements to observe lava flows under different volcanic environments

Vegetation descriptors from Sentinel-1 SAR data for crop growth monitoring

Synthetic aperture radar (SAR) remote sensing technology has the advantage of all-weather observation and can acquire time-series images with crop growth period, which has great potential for applications such as crop phenology analysis. However, available studies primarily focus on conducting statistical and crop growth analyses based on the polarization or backscatter intensities of SAR images, and the exploration of polarization scattering information in SAR images is not sufficient. To comprehensively reflect the polarization characteristics and scattering mechanisms of crop at different growth stages, we propose a new method for extracting vegetation descriptors from Sentinel-1 dual-polarimetric SAR data. The method combines the backscattering intensity and polarization decomposition information to construct a normalized index q, which is used to generate three vegetation descriptors: the co-pol purity parameter (mcp), the pseudo-scattering angle (θcp), and the pseudo-scattering entropy (Hcp). Further, a novel unsupervised clustering framework, founded on Hcp and θcp, has been proposed. This framework establishes six zones (named as Z1 to Z6) representing distinct physical scattering mechanisms, and by statistically sampling point data, it can determine the growth stage of the crops For validating the performance of the proposed vegetation descriptors and clustering framework, we conducted a three-year experiment using four crops from two publicly available datasets, namely wheat and canola from the Carman in Canada (Test site-1), corn and soybeans from Iowa in the United States (Test site-2). The experimental results indicate that mcp,θcp, and Hcp exhibit regular changes at different growth stages of crops from planting to maturity, with mcp and θcp gradually decreasing while Hcp gradually increasing. Within the entire phenology window, θcp changes by approximately 42°, while both mcp and θcp varies by about 0.9, and the sampling points shift from the Z2 to the Z5 zone. The vegetation descriptors are highly sensitive to the growth status of crops, and the clustering framework can also effectively respond to different growth stages of vegetation. Furthermore, the vegetation descriptors and clustering framework proposed in this study have the potential for extended application to different crop types and other polarimetric SAR data sources.

A Deep Feature Fusion Method for Complex Ground Object Classification in the Land Cover Ecosystem Using ZY1-02D and Sentinel-1A

Despite the successful application of multimodal deep learning (MDL) methods for land use/land cover (LULC) classification tasks, their fusion capacity has not yet been substantially examined for hyperspectral and synthetic aperture radar (SAR) data. Hyperspectral and SAR data have recently been widely used in land cover classification. However, the speckle noise of SAR and the heterogeneity with the imaging mechanism of hyperspectral data have hindered the application of MDL methods for integrating hyperspectral and SAR data. Accordingly, we proposed a deep feature fusion method called Refine-EndNet that combines a dynamic filter network (DFN), an attention mechanism (AM), and an encoder–decoder framework (EndNet). The proposed method is specifically designed for hyperspectral and SAR data and adopts an intra-group and inter-group feature fusion strategy. In intra-group feature fusion, the spectral information of hyperspectral data is integrated by fully connected neural networks in the feature dimension. The fusion filter generation network (FFGN) suppresses the presence of speckle noise and the influence of heterogeneity between multimodal data. In inter-group feature fusion, the fusion weight generation network (FWGN) further optimizes complementary information and improves fusion capacity. Experimental results from ZY1-02D satellite hyperspectral data and Sentinel-1A dual-polarimetric SAR data illustrate that the proposed method outperforms the conventional feature-level image fusion (FLIF) and MDL methods, such as S2ENet, FusAtNet, and EndNets, both visually and numerically. We first attempt to investigate the potentials of ZY1-02D satellite hyperspectral data affected by thick clouds, combined with SAR data for complex ground object classification in the land cover ecosystem.

Application of H/ᾱ Decomposition to Limited and Dual-Polarimetric 3D SAR Data of Civilian Vehicles

Application of <i>H</i>/ᾱ Decomposition to Limited and Dual-Polarimetric 3D SAR Data of Civilian Vehicles

A Full Tensor Decomposition Network for Crop Classification with Polarization Extension

The multisource data fusion technique has been proven to perform better in crop classification. However, traditional fusion methods simply stack the original source data and their corresponding features, which can be only regarded as a superficial fusion method rather than deep fusion. This paper proposes a pixel-level fusion method for multispectral data and dual polarimetric synthetic aperture radar (PolSAR) data based on the polarization extension, which yields synthetic quad PolSAR data. Then we can generate high-dimensional features by means of various polarization decomposition schemes. High-dimensional features usually cause the curse of the dimensionality problem. To overcome this drawback in crop classification using the end-to-end network, we propose a simple network, namely the full tensor decomposition network (FTDN), where the feature extraction in the hidden layer is accomplished by tensor transformation. The number of parameters of the FTDN is considerably fewer than that of traditional neural networks. Moreover, the FTDN admits higher classification accuracy by making full use of structural information of PolSAR data. The experimental results demonstrate the effectiveness of the fusion method and the FTDN model.

Forest Height Retrieval Based on the Dual PolInSAR Images

A new algorithm for forest height estimation based on dual polarimetric interferometric SAR data is presented in this study. The main objective is to consider the efficiency of the dual-polarization data compared to the full polarimetric images with respect to forest height retrieval. Accordingly, the forest height estimation based on the random volume over the ground model is examined using a geometrical procedure named the three-stage method. An exhaustive search polarization optimization technique is also applied to improve the results by employing the efficiency of all the polarization bases based on the four-dimensional lexicographic PolInSAR vector. The repeat-pass experimental SAR (ESAR) images, which include both L- and P-band full polarimetric data, are employed for the accuracy assessment of the dual PolInSAR data and the newly proposed method for forest height estimation. The experimental results on the L-band PolInSAR data show the ability of the dual PolInSAR data for forest height estimation with an average root mean square error (RMSE) of 4.97 m against Lidar data based on the conventional three-stage method. Additionally, the proposed method results in an accuracy of 2.95 m for forest height estimation, indicating its high potential for tree height retrieval.

Polarimetric SAR pixel offset tracking for large-gradient landslide displacement mapping

Interferometry and pixel offset tracking (POT) are two major technologies to measure ground deformation with repeat-pass synthetic aperture radar (SAR) observations. The SAR POT method can tolerate a much larger deformation gradient than interferometric SAR (InSAR), and thus it has been widely applied in monitoring earthquakes, volcanoes, landslides, glaciers, etc. However, there is an intrinsic trade-off between matching accuracy and detail preserving in terms of matching window size for the traditional SAR POT method using only single-polarization intensity. Taking advantage of polarimetric scattering properties, this study develops a novel POT method, named PCM-POT, towards mitigating the dependence of offset estimations on matching window size. It employs a generalized likelihood ratio test (GLRT) upon polarimetric covariance matrices (PCMs) to evaluate the similarity of two candidate pixels and estimate high-resolution surface displacements with high accuracy. The effectiveness and advantage of PCM-POT are demonstrated by an experiment of retrieving the displacements of the Slumgullion landslide from a pair of full-polarimetric UAVSAR images. Comparisons with the traditional POT indicate that using polarimetric scattering properties can reduce the uncertainty in offset estimations over heterogeneous sliding areas. Further experiments suggest that PCM-POT is also suitable for dual-polarimetric satellite SAR data.

Identification of saline landscapes from an integrated SVM approach from a novel 3-D classification schema using Sentinel-1 dual-polarized SAR data

This study presented an integrated SVM classification method that investigated the relationship between intrinsic scattering attributes of the surface features and the conductivity characteristics of the soil in the newly proposed two- and three-dimensional classification (2D and 3D) schema to classify the saline landscape. The study was conducted in the Vellore district, Tamil Nadu, which is composed of heterogeneous saline landforms due to the active geological processes and anthropogenic activities. The intensity products of Sentinel-1 data (VV + VH) of C-band frequency were employed in the present study. The SVM with linear kernel has outperformed the other models, namely random forest (RF), naive Bayes (NB), and K-Nearest Neighbour (k−NN), in performing the broad level of classification from the 2D classification schema (SVMOA = 84.2%, RFOA = 80.4%, k-NNOA = 78.8%, NBOA = 68.4%). The soil EC values derived from the dielectric loss measurements (R2 = 0.79, ρ = 0.018, and α=95%) were used to introduce the integrated SVM approach in the 3D schema to further breakdown the mapped classes into non-saline (NS) (soil EC ≤ 2 ds/cm), slightly saline (SS) (soil EC = 2.1 to 4 ds/cm) and moderately saline (MS) (soil EC = 4.1 to 8 ds/cm) categories. The overall performance of the integrated SVM approach implemented for the 3D classification schema (F1 = 0.80) was found to be satisfactory, but with an associated uncertainty majorly from MS (Precision = 0.52, F1 = 0.69), SS (Recall = 0.09, F1 = 0.15), and NS waterbodies (Recall = 0.18, F1 = 0.29) as shown in the Precision-Recall graph (AUCPR3D = 0.62). However, with the promising performance level demonstrated for the other nine classes such as NS, SS, and MS wet soil (F1 = 0.92, 0.92, 0.96), healthy plants (F1 = 0.83), salt-tolerant plants under SS and MS conditions (F1 = 0.83, 0.88), and waterlogged vegetation under NS, SS, and MS conditions (F1 = 0.82, 0.83, 0.83), the proposed classification scheme becomes an effective method to map saline and non-saline features from dual-polarimetric SAR data.

Integration of Optical and SAR Imagery for Dual PolSAR Features Optimization and Land Cover Mapping

Integrating optical and synthetic aperture radar (SAR) data at a feature level has proven to be a powerful method for land cover mapping. However, blurring effects and resolution loss induced by the despeckling procedure can degrade the estimation accuracy of SAR features and therefore introduce artifacts into data fusion. This article presents a method to solve this problem using SAR features estimation driven by both optical and dual polarimetric SAR images. The incentive behind this method is that compared to a low signal-to-noise ratio (SNR) SAR image, labeling sample from the optical image with higher SNR and spectral resolution can enhance sample homogeneity. The homogeneous sample following the same population can ensure SAR feature estimation accuracy and simultaneously preserve the spatial resolution. The optimum SAR features, together with optical images, are then integrated using the machine learning algorithm to obtain a classification map. We further fully evaluate the influence of SAR features estimation on fusion classification. The effectiveness of the proposed method is validated by moderate resolution Sentinel-1/2 datasets over the Saint Louis area, where abundant land cover types and rich texture present a relative challenge for classification. The quantitative analysis indicates that the proposed method can increase around 5%–10% in overall accuracy than the state-of-the-art methods. The results confirm the necessity of SAR features with high quality for land cover classification, especially over the textural region.

A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm

A high-resolution soil moisture prediction method has recently gained its importance in various fields such as forestry, agricultural and land management. However, accurate, robust and non- cost prohibitive spatially monitoring of soil moisture is challenging. In this research, a new approach involving the use of advance machine learning (ML) models, and multi-sensor data fusion including Sentinel-1(S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and ALOS Global Digital Surface Model (ALOS DSM) to predict precisely soil moisture at 10 m spatial resolution across research areas in Australia. The total of 52 predictor variables generated from S1, S2 and ALOS DSM data fusion, including vegetation indices, soil indices, water index, SAR transformation indices, ALOS DSM derived indices like digital model elevation (DEM), slope, and topographic wetness index (TWI). The field soil data from Western Australia was employed. The performance capability of extreme gradient boosting regression (XGBR) together with the genetic algorithm (GA) optimizer for features selection and optimization for soil moisture prediction in bare lands was examined and compared with various scenarios and ML models. The proposed model (the XGBR-GA model) with 21 optimal features obtained from GA was yielded the highest performance (R2 = 0. 891; RMSE = 0.875%) compared to random forest regression (RFR), support vector machine (SVM), and CatBoost gradient boosting regression (CBR). Conclusively, the new approach using the XGBR-GA with features from combination of reliable free-of-charge remotely sensed data from Sentinel and ALOS imagery can effectively estimate the spatial variability of soil moisture. The described framework can further support precision agriculture and drought resilience programs via water use efficiency and smart irrigation management for crop production.

Capability assessment of Sentinel-1 data for estimation of snow hydrological potential in the Khanabad watershed in the Hindu Kush Himalayas of Afghanistan

The water resource in arid countries like Afghanistan is mainly dependent upon the rivers driven by snowmelt runoff. The Khanabad watershed in Afghanistan contributes significantly to the river network system in this country. This study investigates the role of freely available Sentinel-1 dual polarimetric and interferometric SAR data to determine snow hydrological potential in the Khanabad watershed. The objectives of this study are to assess the Snow Water Equivalent (SWE) estimates derived from the Sentinel-1 data with respect to field observations and ERA5-Reanalysis data for February and March 2019. The snow depth and snow density estimates are derived using the interferometric and polarimetric methods to determine the SWE. The snow depth is determined using differential interferometry with snow-free scenes and snow-covered scenes acquired by Sentinel-1 data. The density is empirically modeled using the permittivity estimates derived from bi-temporal VV-channel Sentinel-1 data corresponding to snow-covered scenes. The comparison between the SWE results observed from Sentinel-1 data with respect to field observations showed high agreement determined by the overall correlation of 0.877 and a Root Mean Square Error (RMSE) of 37 mm. The higher RMSE was attributed to the uncertainties introduced by the presence of liquid water content in the March 2019 dataset.

Methodology for groundnut discrimination based on timeseries of dual-polarimetric SAR parameters

The groundnut, also known as peanut, is a legume crop that is classified as both a grain legume and an oil crop (due to its high oil content). About 85% of the total groundnut in India is sown in the kharif season under rainfed conditions. This makes it extremely difficult to apply the optical remote sensing methodologies for groundnut discrimination and acreage estimation. We develop a remote sensing strategy based on timeseries synthetic aperture radar (SAR) imagery, which is immune to the rainy conditions of the observation period. Further, our work is distinct from the traditional multitemporal SAR backscatter analysis as it incorporates polarimetric parameters such as the alpha angle (α) and the polarimetric radar vegetation index to aid discrimination. These parameters were shortlisted on the basis of maximal sensitivity to groundnut growth stages. Random forest classifier, an ensemble learning-based classifier, is used to carryout the crop classification using these parameters. This approach is contrasted with SAR backscatter-based analysis (using the same RF classifier) and with a Euclidean distance (ED)-based model matching method (with the same polarimetric parameters). A detailed comparative analysis of classification accuracy of groundnut discrimination is carried out using extensive ground truth data over the agricultural fields of Junagadh district in the Gujarat state of Western India. The backscatter-based analysis with RF achieves 86.2% overall accuracy in discriminating groundnut (with the competing cotton crop), whereas polarimetric parameters with ED model matching achieves 76%. Using the proposed method, with dual-pol parameters derived out of Sentinel-1 SAR data, we report 92.7% overall accuracy with a kappa coefficient of 0.81. The major insight is that combining timeseries polarimetric information together with a good machine learning classifier improves the classification accuracy of short crops compared to nonpolarimetric approach.

Assessment of rice crop biophysical parameters using Sentinel-1 C-band SAR data

Evaluation of crop growth parameters is crucial to understand the crop phenology and its relation with Synthetic Aperture Radar (SAR) scattering mechanism. The study explores the potential of dual polarization Sentinel–1 backscatter parameters to investigate its sensitivity with various biophysical parameters of paddy crop. Temporal profile of crop growth parameters such as fresh biomass, dry biomass, vegetation water content (VWC) and plant height of paddy crop are assessed with respect to SAR parameters such as VV, VH, VH/VV and Radar Vegetation Index (RVI). Temporal Sentinel–1A SAR data during June to November 2018 was considered for this study. Regression analysis using linear and logarithmic models reveal that the sensitivity of Sentinel–1A backscatter parameters with crop biophysical parameters is best when the crop is at early vegetation stage i.e. between tillering to booting stages with maximum R2 value of 0.68 for liner regression with VH and 0.69 for multiple regression for crop height followed by other biophysical parameters. At later vegetation stage i.e. ripening stage maximum R2 value of 0.52 for liner regression with VH and multiple regression for crop biomass is observed. There is no significant correlation for peak vegetation stage. The results are indicative for the potential use of dual-polarimetric SAR parameters as input parameters for multiple regression models to improve the prediction of biophysical parameters at different phenolgical stages.

Soil moisture retrieval over croplands using dual-pol L-band GRD SAR data

Vegetation cover significantly influences the hydrometeorological processes of land surfaces. The heterogeneity of vegetation cover makes these processes more complex and impacts the interaction between water held in the soil matrix and vegetation cover. The backscatter measured by Synthetic Aperture Radar (SAR) is sensitive to target dielectric and morphological properties. In addition, SAR acquisitions are weather-independent, providing a major advantage over optical imaging during periods of cloud cover. Most often, vegetation properties are measured using vegetation indices, including simple ratios of backscatter intensities. However, this approach can miss the scattered wave purity from vegetation targets. The scattered wave characteristic is an essential parameter as it is susceptible to the complex random structure of vegetation. In this study, we propose a novel dual-polarimetric SAR vegetation descriptor based on the co-pol purity component of the wave. We use this descriptor within a semi-empirical vegetation model to estimate soil moisture. It is noteworthy that this proposed method to retrieve soil moisture uses only the dual-polarimetric Ground Range Detected (GRD) SAR product, i.e., only backscatter intensities. Therefore, the proposed method has the potential for operational scale monitoring applications at a global scale. This study validated different crop types (viz. canola, oats, forage, maize, soybeans, sugar beet, wheat, winter wheat, winter barley) over two test sites using airborne SAR data. The Root Mean Square Error (RMSE) values for soil moisture are within the range of 4.3% to 7.7% with the Pearson correlation coefficient r greater than 0.6. This novel descriptor will facilitate the operational use of dual-polarimetric GRD SAR data for vegetation monitoring and soil moisture estimation.The code to generate DpRVIc in Google Earth Engine is available at: https://github.com/Narayana-Rao/dual_pol_descriptors.