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

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

Assessment of a power law relationship between P-band SAR backscatter and aboveground biomass and its implications for BIOMASS mission performance

This paper presents an analysis of a logarithmic relationship between P-band cross-polarized backscatter from synthetic aperture radar (SAR) and aboveground biomass (AGB) across different forest types based on multiple airborne datasets. It is found that the logarithmic function provides a statistically significant fit to the observed relationship between HV backscatter and AGB. While the coefficient of determination varies between datasets, the slopes, and intercepts of many of the models are not significantly different, especially when similar AGB ranges are assessed. Pooled boreal and pooled tropical data have slopes that are not significantly different, but they have different intercepts. Using the power law formulation of the logarithmic relation allows estimation of both the equivalent number of looks (ENL) needed to retrieve AGB with a given uncertainty and the sensitivity of the AGB inversion. The campaign data indicates that boreal forests require a larger ENL than tropical forests to achieve a specified relative accuracy. The ENL can be increased by multichannel filtering, but ascending and descending images will need to be combined to meet the performance requirements of the BIOMASS mission. The analysis also indicates that the relative change in AGB associated with a given backscatter change depends only on the magnitude of the change and the exponent of the power law, and further implies that to achieve a relative AGB accuracy of 20% or better, residual errors from radiometric distortions produced by the system and environmental effects must not exceed 0.43 dB in tropical and 0.39 dB in boreal forests

ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research

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

Evaluation of the potential of ALOS PALSAR L-band quadpol radar data for the retrieval of growing stock volume in Siberia

Because of the massive wood trade, illegal logging and severe damages due to fires, insects and pollution, it is necessary to monitor Siberian forests on a large-scale, frequently and accurately. One possible solution is to use synthetic aperture radar (SAR) remote sensing technique, in particular by combining polarimetric technique. In order to evaluate the potentiality of ALOS PALSAR L-band full polarimetric radar for estimation of GSV, a number of polarimetric parameters are investigated to characterise the polarisation response of forest cover. Regardless of the weather conditions, a high correlation (R=-0.87) is achieved between polarimetric coherence and GSV. The coherence in sparse forest is always higher than in dense forest. The coherence level and the dynamic range strongly depends on the weather conditions. The four-component polarimetric decomposition method has been applied to the ALOS PALSAR L-band data to compare the decomposition powers with forest growing stock volume (GSV). Double-bounce and volume scattering powers show significant correlation with GSV. The correlation between polarimetric decomposition parameters and GSV is enhanced if the ratio of ground-to-volume scattering is used instead of considering polarimetric decomposition powers separately. Two empirical models have been developed that describe the ALOS PALSAR L-band polarimetric coherence and ground-to-volume scattering ratio as a function of GSV. The models are inverted to retrieve the GSV for Siberian forests. The best RMSE of 38 m³/ha and R²=0.73 is obtained based on polarimetric coherence. On the other hand, using the ratio of ground-to-volume scattering the best retrieval accuracy of 44 m³/ha and R²=0.62 is achieved. The best retrieval results for both cases are observed under unfrozen condition. Saturation effects for estimated GSV versus ground-truth GSV are not observed up to 250 m³/ha

Physics-based Modeling for High-fidelity Radar Retrievals.

Knowledge of soil moisture on a global scale is crucial for understanding the Earth's water, energy, and carbon cycles. This dissertation is motivated by the need for accurate soil moisture estimates and focuses on the improvement of soil moisture retrieval based on active remote sensing over vegetated areas. It addresses important, but often neglected, aspects in radar imaging: effects related to the ionosphere, multispecies vegetation (heterogeneity at pixel level), and heterogeneity at landscape level. The first contribution is the development of a generalized radar scattering model as an advancement of current radar modeling techniques for vegetated areas at fine-scale pixel level. It consists of realistic representations of multispecies and subsurface soil layer modeling, and includes terrain topography. This modeling improvement allows greater applicability to different land cover types and higher soil moisture retrieval accuracy. Most coarse-scale satellite pixels (km-scale or coarser) contain highly heterogeneous scenes with fine-scale (100 m or finer) variability of soil moisture, soil texture, topography, and vegetation cover. The second contribution is the development of spatial scaling techniques to investigate effects of landscape-level heterogeneity on radar scattering signatures. Using the above radar forward scattering model, which assumes homogeneity over fine scales, tailor-made models are derived for the contribution of fine-scale heterogeneity to the coarse-scale satellite pixel for effective soil moisture retrieval. Finally, the third contribution is the development of a self-contained calibration technique based on an end-to-end radar system model. The model includes ionospheric effects allowing the use of spaceborne radar signals for accurate soil moisture retrieval from lower frequencies, such as L- and P-band. These combined contributions will greatly increase the usability of low-frequency spaceborne radar data for soil moisture retrieval: ionospheric effects are mitigated, landscape level heterogeneity is resolved, and fine-scale scenes are better modeled. These contributions ultimately allow improved fidelity in soil moisture retrieval and are immediately applicable in current missions such as the ongoing AirMOSS mission that observes root-zone soil moisture with a P-band radar at fine-scale resolution (100 m), and NASA's upcoming SMAP spaceborne mission, which will assess surface soil moisture with an L-band radar and radiometer at km-scale resolution (3 km).PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107290/1/mburgin_1.pd

Quantitative Estimation of Surface Soil Moisture in Agricultural Landscapes using Spaceborne Synthetic Aperture Radar Imaging at Different Frequencies and Polarizations

Soil moisture and its distribution in space and time plays an important role in the surface energy balance at the soil-atmosphere interface. It is a key variable influencing the partitioning of solar energy into latent and sensible heat flux as well as the partitioning of precipitation into runoff and percolation. Due to their large spatial variability, estimation of spatial patterns of soil moisture from field measurements is difficult and not feasible for large scale analyses. In the past decades, Synthetic Aperture Radar (SAR) remote sensing has proven its potential to quantitatively estimate near surface soil moisture at high spatial resolutions. Since the knowledge of the basic SAR concepts is important to understand the impact of different natural terrain features on the quantitative estimation of soil moisture and other surface parameters, the fundamental principles of synthetic aperture radar imaging are discussed. Also the two spaceborne SAR missions whose data was used in this study, the ENVISAT of the European Space Agency (ESA) and the ALOS of the Japanese Aerospace Exploration Agency (JAXA), are introduced. Subsequently, the two essential surface properties in the field of radar remote sensing, surface soil moisture and surface roughness are defined, and the established methods of their measurement are described. The in situ data used in this study, as well as the research area, the River Rur catchment, with the individual test sites where the data was collected between 2007 and 2010, are specified. On this basis, the important scattering theories in radar polarimetry are discussed and their application is demonstrated using novel polarimetric ALOS/PALSAR data. A critical review of different classical approaches to invert soil moisture from SAR imaging is provided. Five prevalent models have been chosen with the aim to provide an overview of the evolution of ideas and techniques in the field of soil moisture estimation from active microwave data. As the core of this work, a new semi-empirical model for the inversion of surface soil moisture from dual polarimetric L-band SAR data is introduced. This novel approach utilizes advanced polarimetric decomposition techniques to correct for the disturbing effects from surface roughness and vegetation on the soil moisture retrieval without the use of a priori knowledge. The land use specific algorithms for bare soil, grassland, sugar beet, and winter wheat allow quantitative estimations with accuracies in the order of 4 Vol.-%. Application of remotely sensed soil moisture patterns is demonstrated on the basis of mesoscale SAR data by investigating the variability of soil moisture patterns at different spatial scales ranging from field scale to catchment scale. The results show that the variability of surface soil moisture decreases with increasing wetness states at all scales. Finally, the conclusions from this dissertational research are summarized and future perspectives on how to extend the proposed model by means of improved ground based measurements and upcoming advances in sensor technology are discussed. The results obtained in this thesis lead to the conclusion that state-of-the-art spaceborne dual polarimetric L-band SAR systems are not only suitable to accurately retrieve surface soil moisture contents of bare as well as of vegetated agricultural fields and grassland, but for the first time also allow investigating within-field spatial heterogeneities from space

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