Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

Polarimetric Synthetic Aperture Radar, Principles and Application

Demonstrates the benefits of the usage of fully polarimetric synthetic aperture radar data in applications of Earth remote sensing, with educational and development purposes. Includes numerous up-to-date examples with real data from spaceborne platforms and possibility to use a software to support lecture practicals. Reviews theoretical principles in an intuitive way for each application topic. Covers in depth five application domains (forests, agriculture, cryosphere, urban, and oceans), with reference also to hazard monitorin

Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review

This paper is focused on considering the effects of speckle noise on the eigen decomposition of the co- herency matrix. Based on a perturbation analysis of the matrix, it is possible to obtain an analytical expression for the mean value of the eigenvalues and the eigenvectors, as well as for the Entropy, the Anisotroopy and the dif- ferent a angles. The analytical expressions are compared against simulated polarimetric SAR data, demonstrating the correctness of the different expressions.Peer ReviewedPostprint (published version

Polarimetric information content of Sentinel-1 for land cover mapping: An experimental case study using quad-pol data synthesized from complementary repeat-pass acquisitions

This work presents a technique to merge two Sentinel-1 image products of complementary polarimetric information (HH/HV and VH/VV) to derive pseudo-polarimetric features, such as polarimetric covariance, but also model-based and eigenvalue-based decompositions and an unsupervised Wishart classification of scattering types. The images were acquired within a 6-day period over Southern Germany and have been processed to mimic an actual quad-pol product. This was analyzed statistically, visually and within several classification processes to get an understanding of how well such a dataset depicts scattering mechanisms and other polarimetric features as inputs for land use and land cover mapping. A systematic comparison with the original dual-polarization product showed an increase in information content and largely feasible polarimetric features. Yet, especially the average Alpha angle was found to be biased and too high for some of the compared surfaces. Despite these inaccuracies, the polarimetric features turned out to improve potential land cover mapping as compared with backscatter intensities and dual-polarization features of the input products alone. Among the most significant variables related to land use and cover reported by an independent dataset, Entropy, the co-polarization ratio and the C22 element of the covariance matrix generated the strongest impact on the class separability, although misclassifications between physically related classes remain. Yet, the findings are encouraging concerning further investigation of the polarimetric potential to combine repeat-pass acquisitions of Sentinel-1 for a better description of more specific types of land cover

Spatial Modeling of Compact Polarimetric Synthetic Aperture Radar Imagery

The RADARSAT Constellation Mission (RCM) utilizes compact polarimetric (CP) mode to provide data with varying resolutions, supporting a wide range of applications including oil spill detection, sea ice mapping, and land cover analysis. However, the complexity and variability of CP data, influenced by factors such as weather conditions and satellite infrastructure, introduce signature ambiguity. This ambiguity poses challenges in accurate object classification, reducing discriminability and increasing uncertainty. To address these challenges, this thesis introduces tailored spatial models in CP SAR imagery through the utilization of machine learning techniques. Firstly, to enhance oil spill monitoring, a novel conditional random field (CRF) is introduced. The CRF model leverages the statistical properties of CP SAR data and exploits similarities in labels and features among neighboring pixels to effectively model spatial interactions. By mitigating the impact of speckle noise and accurately distinguishing oil spill candidates from oil-free water, the CRF model achieves successful results even in scenarios where the availability of labeled samples is limited. This highlights the capability of CRF in handling situations with a scarcity of training data. Secondly, to improve the accuracy of sea ice mapping, a region-based automated classification methodology is developed. This methodology incorporates learned features, spatial context, and statistical properties from various SAR modes, resulting in enhanced classification accuracy and improved algorithmic efficiency. Thirdly, the presence of a high degree of heterogeneity in target distribution presents an additional challenge in land cover mapping tasks, further compounded by signature ambiguity. To address this, a novel transformer model is proposed. The transformer model incorporates both fine- and coarse-grained spatial dependencies between pixels and leverages different levels of features to enhance the accuracy of land cover type detection. The proposed approaches have undergone extensive experimentation in various remote sensing tasks, validating their effectiveness. By introducing tailored spatial models and innovative algorithms, this thesis successfully addresses the inherent complexity and variability of CP data, thereby ensuring the accuracy and reliability of diverse applications in the field of remote sensing

Synthetic aperture radar remote sensing for landfill monitoring

Despite today’s intensive efforts directed at the recycling and recovery of solid wastes, the controlled disposal of refuse into land remains an important and necessary means of effective waste management. The work presented in this thesis investigates the use of Synthetic Aperture Radar (SAR) data to monitor solid waste landfills. The end-users’ interests vary from detecting the presence of a landfill to more specifically monitoring on-site operations and environmental conditions. Following a general literature review on the application of Earth Observation data for landfill monitoring, the identified research objectives are to: 1) assess whether SAR data can support the identification of landfill sites by distinguishing them from other disturbed areas which present similar optical spectral signatures, and 2) assess the possibility of correlating SAR data with onsite operational procedures. Data acquired for the research are: ground observations and measurements examining the spatial, temporal and biophysical characteristics of a landfill that can influence SAR data; historical and new programmed SAR scenes obtained from the ESA ERS-1 and -2 satellites and from Envisat ASAR instrument; ground based SAR (GB-SAR) acquisitions; simulations based on the RT2 backscatter model; additional space-based and airborne optical data to support the analysis and discussion. The examination of both the SAR amplitude spatial structure and the temporal decorrelation of these sites shows that there are three key characteristics that can distinguish them from other disturbed areas with similar optical spectral signatures: the presence of anisotropic features that strongly affect the SAR backscatter; the fact that the coherence magnitude images of these sites are characterised by large decorrelated areas with transient attributes; and their distinctive positive topography. The analysis highlights that one single-polarisation acquisition can hardly provide correct land-cover information, and consequently knowledge on land-use. The research demonstrates the key value of merging together complementary information derived from both the space and time dimensions, achieving fairly accurate land-use classification results. The research also provides an appreciation of the applicability of the developed techniques in an operational framework. These can suffer a number of limitations if a landfill site is located in a particular environment, and/or if meteorological conditions can significantly affect the radar signal, and/or unusual landfilling procedures are applied by the operators. Concluding remarks on the end-users needs point out that there are a number of aspects, ranging from practical and managerial matters to legal and technical issues, that often discourage the utilisation of EO data by new potential users.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation

This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector

Elevation and Deformation Extraction from TomoSAR

3D SAR tomography (TomoSAR) and 4D SAR differential tomography (Diff-TomoSAR) exploit multi-baseline SAR data stacks to provide an essential innovation of SAR Interferometry for many applications, sensing complex scenes with multiple scatterers mapped into the same SAR pixel cell. However, these are still influenced by DEM uncertainty, temporal decorrelation, orbital, tropospheric and ionospheric phase distortion and height blurring. In this thesis, these techniques are explored. As part of this exploration, the systematic procedures for DEM generation, DEM quality assessment, DEM quality improvement and DEM applications are first studied. Besides, this thesis focuses on the whole cycle of systematic methods for 3D & 4D TomoSAR imaging for height and deformation retrieval, from the problem formation phase, through the development of methods to testing on real SAR data. After DEM generation introduction from spaceborne bistatic InSAR (TanDEM-X) and airborne photogrammetry (Bluesky), a new DEM co-registration method with line feature validation (river network line, ridgeline, valley line, crater boundary feature and so on) is developed and demonstrated to assist the study of a wide area DEM data quality. This DEM co-registration method aligns two DEMs irrespective of the linear distortion model, which improves the quality of DEM vertical comparison accuracy significantly and is suitable and helpful for DEM quality assessment. A systematic TomoSAR algorithm and method have been established, tested, analysed and demonstrated for various applications (urban buildings, bridges, dams) to achieve better 3D & 4D tomographic SAR imaging results. These include applying Cosmo-Skymed X band single-polarisation data over the Zipingpu dam, Dujiangyan, Sichuan, China, to map topography; and using ALOS L band data in the San Francisco Bay region to map urban building and bridge. A new ionospheric correction method based on the tile method employing IGS TEC data, a split-spectrum and an ionospheric model via least squares are developed to correct ionospheric distortion to improve the accuracy of 3D & 4D tomographic SAR imaging. Meanwhile, a pixel by pixel orbit baseline estimation method is developed to address the research gaps of baseline estimation for 3D & 4D spaceborne SAR tomography imaging. Moreover, a SAR tomography imaging algorithm and a differential tomography four-dimensional SAR imaging algorithm based on compressive sensing, SAR interferometry phase (InSAR) calibration reference to DEM with DEM error correction, a new phase error calibration and compensation algorithm, based on PS, SVD, PGA, weighted least squares and minimum entropy, are developed to obtain accurate 3D & 4D tomographic SAR imaging results. The new baseline estimation method and consequent TomoSAR processing results showed that an accurate baseline estimation is essential to build up the TomoSAR model. After baseline estimation, phase calibration experiments (via FFT and Capon method) indicate that a phase calibration step is indispensable for TomoSAR imaging, which eventually influences the inversion results. A super-resolution reconstruction CS based study demonstrates X band data with the CS method does not fit for forest reconstruction but works for reconstruction of large civil engineering structures such as dams and urban buildings. Meanwhile, the L band data with FFT, Capon and the CS method are shown to work for the reconstruction of large manmade structures (such as bridges) and urban buildings

Design Options For Low Cost, Low Power Microsatellite Based SAR.

This research aims at providing a system design that reduces the mass and cost of spaceborne Synthetic Aperture Radar (SAR) missions by a factor of two compared to current (TecSAR - 300 kg, ~ £ 127 M) or planned (NovaSAR-S — 400 kg, ~ £ 50 M) mission. This would enable the cost of a SAR constellation to approach that of the current optical constellation such as Disaster Monitoring Constellation (DMC). This research has identified that the mission cost can be reduced significantly by: focusing on a narrow range of applications (forestry and disasters monitoring); ensuring the final design has a compact stowage volume, which facilitates a shared launch; and building the payload around available platforms, rather than the platform around the payload. The central idea of the research has been to operate the SAR at a low instantaneous power level—a practical proposition for a micro-satellite based SAR. The use of a simple parabolic reflector with a single horn at L-band means that a single, reliable and efficient Solid State Power Amplifier (SSPA) can be used to lower the overall system cost, and to minimise the impact on the spacecraft power system. A detailed analysis of basic pulsed (~ 5 - 10 % duty cycle) and Continuous Wave (CW) SAR (100 % duty cycle) payloads has shown their inability to fit directly into existing microsatellite buses without involving major changes, or employing more than one platform. To circumvent the problems of pulsed and CW techniques, two approaches have been formulated. The first shows that a CW SAR can be implemented in a mono-static way with a single antenna on a single platform. In this technique, the SAR works in an Interrupted CW (ICW) mode, but these interruptions introduce periodic gaps in the raw data. On processing, these gapped data result in artefacts in the reconstructed images. By applying data based statistical estimation techniques to “fill in the gaps” in the simulated raw SAR data, this research has shown the possibility of minimising the effects of these artefacts. However, once the same techniques are applied to the real SAR data (in this case derived from RADARSAT-1), the artefacts are shown to be problematic. Because of this the ICW SAR design technique it is—set aside. The second shows that an extended chirp mode pulsed (ECMP) SAR (~ 20 - 54 % duty cycle) can be designed with a lowered peak power level which enables a single SSPA to feed a parabolic Cassegrain antenna. The detailed analysis shows the feasibility of developing a microsatellite based SAR design at a comparable price to those of optical missions

Comparing synthetic aperture radar and LiDAR for above-ground biomass estimation in Glen Affric, Scotland

Quantifying above-ground biomass (AGB) and carbon sequestration has been a significant focus of attention within the UNFCCC and Kyoto Protocol for improvement of national carbon accounting systems (IPCC, 2007; UNFCCC, 2011). A multitude of research has been carried out in relatively flat and homogeneous forests (Ranson & Sun, 1994; Beaudoin et al.,1994; Kurvonen et al., 1999; Austin et al., 2003; Dimitris et al., 2005), yet forests in the highlands, which generally form heterogeneous forest cover and sparse woodlands with mountainous terrain have been largely neglected in AGB studies (Cloude et al., 2001; 2008; Lumsdon et al., 2005; 2008; Erxue et al., 2009, Tan et al., 2010; 2011a; 2011b; 2011c; 2011d). Since mountain forests constitute approximately 28% of the total global forest area (Price and Butt, 2000), a better understanding of the slope effects is of primary importance in AGB estimation. The main objective of this research is to estimate AGB in the aforementioned forest in Glen Affric, Scotland using both SAR and LiDAR data. Two types of Synthetic Aperture Radar (SAR) data were used in this research: TerraSAR-X, operating at X-band and ALOS PALSAR, operating at L-band, both are fully polarimetric. The former data was acquired on 13 April 2010 and of the latter, two scenes were acquired on 17 April 2007 and 08 June 2009. Airborne LiDAR data were acquired on 09 June 2007. Two field measurement campaigns were carried out, one of which was done from winter 2006 to spring 2007 where physical parameters of trees in 170 circular plots were measured by the Forestry Commission team. Another intensive fieldwork was organised by myself with the help of my fellow colleagues and it comprised of tree measurement in two transects of 200m x 50m at a relatively flat and dense plantation forest and 400m x 50m at hilly and sparse semi-natural forest. AGB is estimated for both the transects to investigate the effectiveness of the proposed method at plot-level. This thesis evaluates the capability of polarimetric Synthetic Aperture Radar data for AGB estimation by investigating the relationship between the SAR backscattering coefficient and AGB and also the relationship between the decomposed scattering mechanisms and AGB. Due to the terrain and heterogeneous nature of the forests, the result from the backscatter-AGB analysis show that these forests present a challenge for simple AGB estimation. As an alternative, polarimetric techniques were applied to the problem by decomposing the backscattering information into scattering mechanisms based on the approach by Yamaguchi (2005; 2006), which are then regressed to the field measured AGB. Of the two data sets, ALOS PALSAR demonstrates a better estimation capacity for AGB estimation than TerraSAR-X. The AGB estimated results from SAR data are compared with AGB derived from LiDAR data. Since tree height is often correlated with AGB (Onge et al., 2008; Gang et al., 2010), the effectiveness of the tree height retrieval from LiDAR is evaluated as an indicator of AGB. Tree delineation was performed before AGB of individual trees were calculated allometrically. Results were validated by comparison to the fieldwork data. The amount of overestimation varies across the different canopy conditions. These results give some indication of when to use LiDAR or SAR to retrieve forest AGB. LiDAR is able to estimate AGB with good accuracy and the R2 value obtained is 0.97 with RMSE of 14.81 ton/ha. The R2 and RMSE obtained for TerraSAR-X are 0.41 and 28.5 ton/ha, respectively while for ALOS PALSAR data are 0.70 and 23.6 ton/ha, respectively. While airborne LiDAR data with very accurate height measurement and consequent three-dimensional (3D) stand profiles which allows investigation into the relationship between height, number density and AGB, it's limited to small coverage area, or large areas but at large cost. ALOS PALSAR, on the other hand, can cover big coverage area but it provide a lower resolution, hence, lower estimation accuracy