This paper uses the Bloch sphere formalism to introduce a quantum-inspired representation of full polarimetric Synthetic Aperture Radar (SAR) targets. By mapping SAR target vectors to qubit states in an orthonormal trihedral-dihedral basis, we demonstrate that scattering mechanisms can be effectively modeled as quantum states. Our method constructs a real 4D Stokes-like vector from the expectation values of Pauli spin operators, providing a geometrically interpretable Bloch vector. This vector precisely locates target states on the unit sphere, enabling intuitive visualization of scattering behavior for polarimetric analysis. The proposed qubit representation enhances interpretability and paves the way for quantum-computational processing of polarimetric SAR data in remote sensing applications.

Detecting rice transplantation dates is crucial for understanding its effect on grain yield and water consumption at regional scales. Traditionally, identifying the rice transplantation phase using dual-polarized (dual-pol) synthetic aperture radar (SAR) data has relied on backscatter intensity due to its characteristic low values during the flooding stage. This study leverages a recently proposed dual-pol radar surface index (DpRSI) to analyze the spatiotemporal dynamics of the rice transplantation phases. Using this index, we propose an unsupervised framework to identify rice transplantation dates. The framework is evaluated using ground-truth (GT) data over rice-cultivated regions in Vijayawada, India, during the kharif season 2018, demonstrating its effectiveness in detecting shifts in transplantation dates over a large spatial extent.

Synthetic aperture radar (SAR) data classification has gained significant research interest, as accurate land-cover information is vital in a wide range of planning and management activities. While classification algorithms for full-polarimetric (full-pol) SAR data are typically based on the statistical or physical characteristics of the scattering mechanism from targets, classification of co-cross polarization (VV-VH or HH-HV) dual-polarimetric (dual-pol) SAR data has traditionally relied on backscatter intensity information due to its limited polarimetric information. Several studies also employ the dual-pol entropy/alpha decomposition parameters, establishing a conventional framework for supervised and unsupervised classification of dual-pol SAR data. However, it is essential to note that the conventional approach cannot differentiate between certain elementary targets, leading to misclassification among diverse land-cover targets. To address this limitation, we introduce an unsupervised clustering technique for dual-pol Sentinel-1 SAR data utilizing the conventional entropy parameter alongside a dual-pol target characteristic parameter that discriminates between various land-cover targets, including “dihedral-like” (buildings, etc.) and “surface-like” (water bodies, etc.) targets in a dual-pol scene. Thus, the proposed clustering scheme, which applies to both single look complex (SLC) and ground range detected (GRD) SAR, categorizes it into eight clusters, each representing specific target characteristics. We adopted two strategies to assess the proposed clustering scheme: 1) cluster zones obtained for diverse land-cover targets spanning continents and 2) temporal changes in cluster zones over rice-cultivated fields at various growth stages. The proposed approach effectively discriminates diverse land-cover targets and distinct growth stages of rice.

This paper explores four distinct target descriptors derived from full-polarimetric Synthetic Aperture Radar (SAR) data. Initially, we define a 2 × 1 real positive target vector by leveraging the mean and standard deviation of complex eigenvalues extracted from the 2 × 2 Sinclair matrix. This vector is the basis for two innovative parameters: 1) the scattering-type parameter, and 2) the scattering asymmetry parameter. Furthermore, we introduce the scattering purity and complexity parameters derived from the mean and standard deviation of the real positive eigenvalues of a Hermitian positive semi-definite 3 × 3 coherency (covariance) matrix. We highlight the efficacy of these parameters by conducting an experimental analysis with several canonical targets. Subsequently, we investigate their performance by thoroughly examining Radarsat-2 full-polarimetric SAR data.

Crop residue burning (CRB) is a common post-harvest practice in India, where approx. 150 metric tonnes of crop residue are burned annually. CRB has adverse consequences: air quality degradation with pollutants affecting respiratory health and soil structure. Remote sensing data are employed to spatially assess air quality degradation, providing a comprehensive understanding of CRB’s spatial and temporal dynamics and environmental implications. This study introduces a new multi-band vegetation index using Sentinel-2 satellite imagery to map crop residue burning. We then assess its impact on air quality with parameters like aerosol, particulate matter (PM2.5), carbon monoxide, and nitrogen oxides from satellites and ground stations.

Synthetic Aperture Radar (SAR) data, particularly for Asian countries, are valuable in monitoring crops. Scattering information extracted from full-polarimetric (FP) SAR data offers exceptional sensitivity to crop water content and geometrical properties. Thus, they are widely used for continuous crop monitoring throughout their growth stages. However, many of these methods are limited to FP SAR data. This study introduces a new approach to obtain scattering power components from SLC and GRD dual-polarimetric (DP) SAR data. We found the scattering powers obtained from the dual-pol SAR data to be sensitive to changes in the crop morphology as it progresses to advanced growth stages.

Characterizing targets with dual-polarimetric (dual-pol) Synthetic Aperture Radar (SAR) data has traditionally relied only on backscatter intensity. However, the limitations of conventional dual-pol parameters, such as the inability to differentiate between orthogonal targets like dihedral and trihedral structures, result in misclassification among diverse targets. This study proposes an innovative target characteristic parameter derived from both dual-pol single-look complex (SLC) and ground range detected (GRD) SAR data that effectively distinguishes between "dihedral-like" and "surface-like" targets, enabling improved characterization of diverse land cover targets.

Characterization and classification of natural and human-made targets using polarimetric Synthetic Aperture Radar (SAR) data have been widely explored for diverse applications. The Cloude and Pottier scattering-type parameter α has become a standard tool for target characterization and classification. However, it fails to discriminate between all canonical targets. In response to this limitation, this research introduces an alternative scattering-type parameter capable of distinguishing all canonical targets. To achieve this, we first define a real 2 × 1 vector comprising two roll-invariant descriptors derived from the 2 × 2 complex scattering matrix. Subsequently, we calculate the Euclidean norm of this vector relative to a reference real vector associated with a standard dipole scatterer. We leverage this computed Euclidean norm as a key element in formulating our alternate scattering-type parameter. The alternate scattering-type parameter is formulated to effectively characterize a wide spectrum of targets, encompassing both coherent and incoherent. We systematically evaluate the performance of our proposed alternate scattering-type parameter against the well-established Cloude-Pottier α parameter for a diverse set of targets. Additionally, we introduce a target classification framework for dominant scatterers utilizing the vector with two roll-invariant descriptors. To validate our approach, we conducted experiments utilizing two full-polarimetric Earth observation datasets acquired in the C- and L- bands and one full-polarimetric Lunar dataset in the L-band. These datasets were selected to showcase and validate the efficacy of both the alternate scattering-type parameter and the target classification framework.

Effective monitoring of wetlands plays a pivotal role in comprehending and managing these ecologically vital ecosystems. This study assesses the potential of C-band synthetic aperture radar (SAR) imagery in compact polarization (CP) mode, utilizing the RADARSAT Constellation Mission (RCM), for wetland characterization. We introduce the compact-polarimetric signature (CPS) as a novel descriptor to delineate wetlands, including bog, fen, and marsh classes. In addition, we propose an alternative decomposition technique ($\mu -\chi $) to segment the total power into three components: odd-bounce scattering$(P_{s})$, double-bounce scattering$(P_{d})$, and random scattering$(P_{v})$. For our evaluation, we selected a test site in New Brunswick, Canada, and acquired a series of RCM datasets covering this region. The time-series CPS plots yield valuable insights, elucidating the scattering mechanisms of different wetland classes. Notably, these plots reveal that during the active season, characterized by changing vegetation structures, the scattered waves exhibit variations, leading to changes in received power and the purity parameter ($\mu $). Furthermore, the observed variations in the proposed power components demonstrate a significant discriminatory capacity among wetlands. The$P_{s}$,$P_{d}$, and$P_{v}$components effectively distinguish bog, fen, and marsh classes, respectively, capturing the unique characteristics of each wetland type. These findings carry considerable potential for advancing wetland characterization through the RCM CP-SAR mission. The improved discriminative ability among different wetland classes is a valuable contribution to the broader field of wetland ecology and management. This advancement potentially empowers precise wetland classification, facilitating well-informed decision-making in wetland preservation and resource allocation. The applications of these findings extend to ecosystem monitoring, environmental impact assessments, and the long-term evaluation of wetland health. Eventually, this contributes to developing more effective wetland conservation and management strategies.

Target characterization parameters are pivotal in accurately identifying and assessing diverse land cover targets in radar polarimetry. While full-polarimetric (full-pol) synthetic aperture radar (SAR) data offer numerous parameters, characterizing targets with HH-HV or VV-VH dual-polarimetric (dual-pol) SAR data has traditionally relied on backscatter intensity alone due to limited polarimetric information, which leads to ambiguities in characterizing diverse land cover targets. In response to this limitation, this study introduces a novel target characteristic parameter$\overline {\alpha }_{(k)}$derived from dual-pol single-look complex (SLC) and ground range detected (GRD) SAR data that are capable of discriminating between “dihedral-like” (buildings, bridges, ships, and so on) and “surface-like” (water bodies, bare fields, runways, and so on) targets, by employing a data-driven approach. We first derive a set of normalized descriptors independently of SLC and GRD SAR data to formulate two indices that characterize “dihedral-like” and “surface-like” targets. Using the two indices, we derive the dual-pol target characteristic parameter, providing a novel perspective on the intricate nature of radar responses from diverse land cover targets acquired by dual-pol SAR sensors. Furthermore, we employ this parameter to extract three scattering power components: “dihedral-like” ($P_{d-l}$), unpolarized ($P_{u}$), and “surface-like” ($P_{s-l}$) from both dual-pol SLC and GRD SAR data. We assess the proposed target characteristic parameter and scattering power components using Sentinel-1 images acquired over diverse land cover targets spanning six continents. This novel approach enables improved global land cover characterization with operational SAR missions such as Sentinel-1 and upcoming NASA-ISRO SAR (NISAR) missions.

This study contributes to advancing the understanding and utilization of compact polarimetry (CP) RCM Synthetic Aperture Radar (SAR) data for enhanced crop characterization and mapping. The received wave polarization signature captures the explicit variation of the received power with a fixed transmit polarization and varying received polarization bases. This information is then suitably utilized for improved discrimination among multiple crop types. Furthermore, the study explores using multi-date polarimetric features extracted from RCM imagery to achieve more accurate and detailed crop mapping results. By incorporating information from multiple acquisition dates, the multi-date polarimetric features illustrate excellent potential in capturing temporal variations in crop characteristics, leading to enhanced crop mapping accuracy. The implications and findings from this study could be essential in demonstrating the role of RCM data in agricultural applications.

Built-up area (BA) mapping is vital for understanding the effect of the urban regions on the environment, thereby supporting sustainable development. This study proposes a new dual-pol radar built-up area index (DpRBI) to detect the BA using Sentinel-1 SAR data. The DpRBI formulation is based on the three Stokes vector elements of the scattered wave derived from the 2 × 2 covariance matrix C2. This study uses the Sentinel-1 SAR data sets over Milan, Italy and Barcelona, Spain to map the BA using DpRBI. The overall accuracy of the BA extracted using the proposed technique was found to be 84.19% and 87.95% over Milan and Barcelona, respectively. It is noteworthy that even relatively small low-density BA is precisely classified using the proposed built-up index.

In contrast to the widely used van Zyl received wave polarimetric signature, the Touzi scattered wave signature in term of the total power ($S$0), and the degree of polarization ($p$) is also helpful for target characterization. Although, the van Zyl polarimetric signature includes the contribution of So, and p, the explicit consideration of the two scattered wave parameters (i.e., independent of the received wave polarization basis) can provide additional information about the target. Hence, in this study, we have used both the van Zyl received, and Touzi scattered wave information to characterize scattering from Paddy at a particular phenological stage with C- and L-band full polarimetric SAR data.