Correction of Ionosphere for InSAR by the Combination of Differential TEC Estimators

Low frequency spaceborne SAR configurations are favoured for global forest mapping applications and D-InSAR applications over natural terrain. Several missions have been scheduled to be launched / or proposed to be implemented in the next years: JAXA’s ALOS-II (L-band), NASA’s Destyni (L-band), DLR’s Tandem-L (L-band) and ESA’s BIOMASS (P-band) are some of them. A common challenge for all these missions is to control / compensate the disturbances induced by the ionosphere. At these lower frequencies the ionosphere effects several components of the SAR measurements performed: It delays the group velocity of the transmitting / receiving pulses, advances their phase(s) and rotates their polarisation state. Accordingly, it distorts not only intensity but also polarimetric, interferometric and polarimetric interferometric observation spaces. The total electron content (TEC) is the most decisive parameter in the characterisation of the ionosphere. It is defined as the integrated electron number density per unit volume along the direction of propagation. Most of the free electrons are distributed within a relatively narrow altitude range allowing modelling the ionosphere as a thin layer at a fixed altitude. In this case the ionosphere can be characterised by a 2-D scalar field of TEC [1], [2]. Depending now on the SAR configuration and its observation space different correction approaches are possible leading to a wide range of calibration algorithms. In this paper we propose a concept towards the generalisation of ionospheric calibration methodology by integrating a number of individual approaches / algorithms. In this sense, a novel generic correction schema based on a combined (and improved) estimation of the 2-D TEC field (or the associated differential TEC field in the interferometric case) from a set of individual data based TEC and/or TEC gradient estimates is introduced and discussed. As a special case a combined 2-D (differential) TEC field estimator based on (differential) TEC estimated from Faraday rotation measurements and (differential) TEC gradients obtained from the estimation of azimuth/range (differential) shifts is presented. Both observations are independent, allowing establishing an inverse problem for the (differential) TEC estimation. Geophysical knowledge as the anisotropic nature of the TEC distribution can be incorporated as a priori information in the “combined” (differential) TEC estimator. The performance of the proposed approach is tested using ALOS quad-pol interferometric data sets over several test sites in Alaska. The achieved estimates are characterised by a significantly improved performance: While the FR based estimator suffers from the random granular deviation pattern of TEC after conversion, the proposed combined estimator effectively is free of such artefacts. Emphasis is given in the role of polarisation in the TEC estimation procedure [3] and on the calibration of Pol-InSAR data. References [1] Franz J. Meyer and Jeremy Nicoll, “Prediction, detection, and correction of Faraday rotation in full-polarimetric L-band SAR data”, IEEE Trans. Geosci. And Remote Sensing, 46(10), Oct., 3076-3086, 2008 [2] Xiaoqing Pi, Anthony Freeman, Bruce Champman, Paul Rosen, and Zhenhong Li, “Imaging ionospheric inhomogeneities using spaceborne synthetic aperture radar”, Jour. of Geophysical Research, 116, A04303, 2011 [3] Jun Su Kim, Konstantinos Papathanassiou, Shaun Quegan and Neil Rogers, “Estimation and correction of scintillation effects on spaceborne P-band SAR images”, in Proceedings of IGARSS2012, 23-27. Jul., 201

The Impact of System Effects on Estimates of Faraday Rotation From Synthetic Aperture Radar Measurements

Radio waves traversing the Earth's ionosphere suffer from Faraday rotation with noticeable effects on measurements from lower frequency space-based radars, but these effects can be easily corrected given estimates of the Faraday rotation angle, i.e., Ω. Several methods to derive Ω from polarimetric measurements are known, but they are affected by system distortions (crosstalk and channel imbalance) and noise. A first-order analysis for the most robust Faraday rotation estimator leads to a differentiable expression for the bias in the estimate of Ω in terms of the amplitudes and phases of the distortion terms and the covariance properties of the target. The analysis applies equally to L-band and P-band. We derive conditions on the amplitudes and phases of the distortion terms that yield the maximum bias and a compact expression for its value for the important case where Ω = 0. Exact simulations confirm the accuracy of the first-order analysis and verify its predictions. Conditions on the distortion amplitudes that yield a given maximum bias are derived numerically, and the maximum bias is shown to be insensitive to the amplitude of the channel imbalance terms. These results are important not just for correcting polarimetric data but also for assessing the accuracy of the estimates of the total electron content derived from Faraday rotation

Estimation of Forest Biomass and Faraday Rotation using Ultra High Frequency Synthetic Aperture Radar

Synthetic Aperture Radar (SAR) data in the Ultra High Frequency (UHF; 300 MHz – 3 GHz)) band have been shown to be strongly dependent of forest biomass, which is a poorly estimated variable in the global carbon cycle. In this thesis UHF-band SAR data from the fairly flat hemiboreal test site Remningstorp in southern Sweden were analysed. The data were collected on several occasions with different moisture conditions during the spring of 2007. Regression models for biomass estimation on stand level (0.5-9 ha) were developed for each date on which SAR data were acquired. For L-band (centre frequency 1.3 GHz) the best estimation model was based on HV-polarized backscatter, giving a root mean squared error (rmse) between 31% and 46% of the mean biomass. For P-band (centre frequency 340 MHz), regression models including HH, HV or HH and HV backscatter gave an rmse between 18% and 27%. Little or no saturation effects were observed up to 290 t/ha for P-band. A model based on physical-optics has been developed and was used to predict HH-polarized SAR data with frequencies from 20 MHz to 500 MHz from a set of vertical trunks standing on an undulating ground surface. The model shows that ground topography is a critical issue in SAR imaging for these frequencies. A regression model for biomass estimation which includes a correction for ground slope was developed using multi-polarized P-band SAR data from Remningstorp as well as from the boreal test site Krycklan in northern Sweden. The latter test site has pronounced topographic variability. It was shown that the model was able to partly compensate for moisture variability, and that the model gave an rmse of 22-33% when trained using data from Krycklan and evaluated using data from Remningstorp. Regression modelling based on P-band backscatter was also used to estimate biomass change using data acquired in Remningstorp during the spring 2007 and during the fall 2010. The results show that biomass change can be measured with an rmse of about 15% or 20 tons/ha. This suggests that not only deforestation, but also forest growth and degradation (e.g. thinning) can be measured using P-band SAR data. The thesis also includes result on Faraday rotation, which is an ionospheric effect which can have a significant impact on spaceborne UHF-band SAR images. Faraday rotation angles are estimated in spaceborne L-band SAR data. Estimates based on distributed targets and calibration targets with high signal to clutter ratios are found to be in very good agreement. Moreover, a strong correlation with independent measurements of Total Electron Content is found, further validating the estimates

The Interaction Between Faraday Rotation and System Effects in Synthetic Aperture Radar Measurements of Backscatter and Biomass

For long-wavelength space-based radars, such as the P-band radar on the recently selected European Space Agency BIOMASS mission, system distortions (crosstalk and channel imbalance), Faraday rotation, and system noise all combine to degrade the measurements. A first-order analysis of these effects on the measurements of the polarimetric scattering matrix is used to derive differentiable expressions for the errors in the polarimetric backscattering coefficients in the presence of Faraday rotation. Both the amplitudes and phases of the distortion terms are shown to be important in determining the errors and their maximum values. Exact simulations confirm the accuracy and predictions of the first-order analysis. Under an assumed power-law relation between σhv and the biomass, the system distortions and noise are converted into biomass estimation errors, and it is shown that the magnitude of the deviation of the channel imbalance from unity must be 4-5 dB less than the crosstalk, or it will dominate the error in the biomass. For uncalibrated data and midrange values of biomass, the crosstalk must be less than -24 dB if the maximum possible error in the biomass is to be within 20% of its true value. A less stringent condition applies if the amplitudes and phases of the distortion terms are considered random since errors near the maximum possible are very unlikely. For lower values of the biomass, the noise becomes increasingly important because the σhv signal-to-noise ratio is smaller

Ionospheric correction of interferometric SAR data with application to the cryospheric sciences

Thesis (Ph.D.) University of Alaska Fairbanks, 2018The ionosphere has been identified as an important error source for spaceborne Synthetic Aperture Radar (SAR) data and SAR Interferometry (InSAR), especially for low frequency SAR missions, operating, e.g., at L-band or P-band. Developing effective algorithms for the correction of ionospheric effects is still a developing and active topic of remote sensing research. The focus of this thesis is to develop robust and accurate techniques for ionospheric correction of SAR and InSAR data and evaluate the benefit of these techniques for cryospheric research fields such as glacier ice velocity tracking and permafrost deformation monitoring. As both topics are mostly concerned with high latitude areas where the ionosphere is often active and characterized by turbulence, ionospheric correction is particularly relevant for these applications. After an introduction to the research topic in Chapter 1, Chapter 2 will discuss open issues in ionospheric correction including processing issues related to baseline-induced spectrum shifts. The effect of large baseline on split spectrum InSAR technique has been thoroughly evaluated and effective solutions for compensating this effect are proposed. In addition, a multiple sub-band approach is proposed for increasing the algorithm robustness and accuracy. Selected case studies are shown with the purpose of demonstrating the performance of the developed algorithm. In Chapter 3, the developed ionospheric correction technology is applied to optimize InSAR-based ice velocity measurements over the big ice sheets in Greenland and the Antarctic. Selected case studies are presented to demonstrate and validate the effectiveness of the proposed correction algorithms for ice velocity applications. It is shown that the ionosphere signal can be larger than the actual glacier motion signal in the interior of Greenland and Antarctic, emphasizing the necessity for operational ionospheric correction. The case studies also show that the accuracy of ice velocity estimates was significantly improved once the developed ionospheric correction techniques were integrated into the data processing flow. We demonstrate that the proposed ionosphere correction outperforms the traditionally-used approaches such as the averaging of multi-temporal data and the removal of obviously affected data sets. For instance, it is shown that about one hundred multi-temporal ice velocity estimates would need to be averaged to achieve the estimation accuracy of a single ionosphere-corrected measurement. In Chapter 4, we evaluate the necessity and benefit of ionospheric-correction for L-band InSAR-based permafrost research. In permafrost zones, InSAR-based surface deformation measurements are used together with geophysical models to estimate permafrost parameters such as active layer thickness, soil ice content, and permafrost degradation. Accurate error correction is needed to avoid biases in the estimated parameters and their co-variance properties. Through statistical analyses of a large number of L-band InSAR data sets over Alaska, we show that ionospheric signal distortions, at different levels of magnitude, are present in almost every InSAR dataset acquired in permafrost-affected regions. We analyze the ionospheric correction performance that can be achieved in permafrost zones by statistically analyzing correction results for large number of InSAR data. We also investigate the impact of ionospheric correction on the performance of the two main InSAR approaches that are used in permafrost zones: (1) we show the importance of ionospheric correction for permafrost deformation estimation from discrete InSAR observations; (2) we demonstrate that ionospheric correction leads to significant improvements in the accuracy of time-series InSAR-based permafrost products. Chapter 5 summarizes the work conducted in this dissertation and proposes next steps in this field of research

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

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

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

The BIOMASS level 2 prototype processor : design and experimental results of above-ground biomass estimation

BIOMASS is ESA’s seventh Earth Explorer mission, scheduled for launch in 2022. The satellite will be the first P-band SAR sensor in space and will be operated in fully polarimetric interferometric and tomographic modes. The mission aim is to map forest above-ground biomass (AGB), forest height (FH) and severe forest disturbance (FD) globally with a particular focus on tropical forests. This paper presents the algorithms developed to estimate these biophysical parameters from the BIOMASS level 1 SAR measurements and their implementation in the BIOMASS level 2 prototype processor with a focus on the AGB product. The AGB product retrieval uses a physically-based inversion model, using ground-canceled level 1 data as input. The FH product retrieval applies a classical PolInSAR inversion, based on the Random Volume over Ground Model (RVOG). The FD product will provide an indication of where significant changes occurred within the forest, based on the statistical properties of SAR data. We test the AGB retrieval using modified airborne P-Band data from the AfriSAR and TropiSAR campaigns together with reference data from LiDAR-based AGB maps and plot-based ground measurements. For AGB estimation based on data from a single heading, comparison with reference data yields relative Root Mean Square Difference (RMSD) values mostly between 20% and 30%. Combining different headings in the estimation process significantly improves the AGB retrieval to slightly less than 20%. The experimental results indicate that the implemented retrieval scheme provides robust results that are within mission requirements

Estimation of the Degree of Polarization in Polarimetric SAR Imagery : Principles and Applications

Les radars à synthèse d’ouverture (RSO) polarimétriques sont devenus incontournables dans le domaine de la télédétection, grâce à leur zone de couverture étendue, ainsi que leur capacité à acquérir des données dans n’importe quelles conditions atmosphériques de jour comme de nuit. Au cours des trois dernières décennies, plusieurs RSO polarimétriques ont été utilisés portant une variété de modes d’imagerie, tels que la polarisation unique, la polarisation double et également des modes dits pleinement polarimétriques. Grâce aux recherches récentes, d’autres modes alternatifs, tels que la polarisation hybride et compacte, ont été proposés pour les futures missions RSOs. Toutefois, un débat anime la communauté de la télédétection quant à l’utilité des modes alternatifs et quant au compromis entre la polarimétrie double et la polarimétrie totale. Cette thèse contribue à ce débat en analysant et comparant ces différents modes d’imagerie RSO dans une variété d’applications, avec un accent particulier sur la surveillance maritime (la détection des navires et de marées noires). Pour nos comparaisons, nous considérons un paramètre fondamental, appelé le degré de polarisation (DoP). Ce paramètre scalaire a été reconnu comme l’un des paramètres les plus pertinents pour caractériser les ondes électromagnétiques partiellement polarisées. A l’aide d’une analyse statistique détaillée sur les images polarimétriques RSO, nous proposons des estimateurs efficaces du DoP pour les systèmes d’imagerie cohérente et incohérente. Ainsi, nous étendons la notion de DoP aux différents modes d’imagerie polarimétrique hybride et compacte. Cette étude comparative réalisée dans différents contextes d’application dégage des propriétés permettant de guider le choix parmi les différents modes polarimétriques. Les expériences sont effectuées sur les données polarimétriques provenant du satellite Canadian RADARSAT-2 et le RSO aéroporté Américain AirSAR, couvrant divers types de terrains tels que l’urbain, la végétation et l’océan. Par ailleurs nous réalisons une étude détaillée sur les potentiels du DoP pour la détection et la reconnaissance des marées noires basée sur les acquisitions récentes d’UAVSAR, couvrant la catastrophe de Deepwater Horizon dans le golfe du Mexique. ABSTRACT : Polarimetric Synthetic Aperture Radar (SAR) systems have become highly fruitful thanks to their wide area coverage and day and night all-weather capabilities. Several polarimetric SARs have been flown over the last few decades with a variety of polarimetric SAR imaging modes; traditional ones are linear singleand dual-pol modes. More sophisticated ones are full-pol modes. Other alternative modes, such as hybrid and compact dual-pol, have also been recently proposed for future SAR missions. The discussion is vivid across the remote sensing society about both the utility of such alternative modes, and also the trade-off between dual and full polarimetry. This thesis contributes to that discussion by analyzing and comparing different polarimetric SAR modes in a variety of geoscience applications, with a particular focus on maritime monitoring and surveillance. For our comparisons, we make use of a fundamental, physically related discriminator called the Degree of Polarization (DoP). This scalar parameter has been recognized as one of the most important parameters characterizing a partially polarized electromagnetic wave. Based on a detailed statistical analysis of polarimetric SAR images, we propose efficient estimators of the DoP for both coherent and in-coherent SAR systems. We extend the DoP concept to different hybrid and compact SAR modes and compare the achieved performance with different full-pol methods. We perform a detailed study of vessel detection and oil-spill recognition, based on linear and hybrid/compact dual-pol DoP, using recent data from the Deepwater Horizon oil-spill, acquired by the National Aeronautics and Space Administration (NASA)/Jet Propulsion Laboratory (JPL) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). Extensive experiments are also performed over various terrain types, such as urban, vegetation, and ocean, using the data acquired by the Canadian RADARSAT-2 and the NASA/JPL Airborne SAR (AirSAR) system