Enhanced-Resolution Processing and Applications of the ASCAT Scatterometer

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Enhanced-Resolution Processing and Applications of the ASCAT Scatterometer Enhanced-Resolution Processing and Applications of the ASCAT Scatterometer Richard D. Lindsley A dissertation submitted to the faculty of Brigham Young University in partial fulfillment of the requirements for the degree of Doctor of Philosophy David G. Long, Chair Neal K. Bangerter Brian D. Jeffs Dah-Jye Lee Karl F. Warnick Department of Electrical and Computer Engineering Brigham Young University December 2015 Copyright c 2015 Richard D. Lindsley All Rights Reserved ABSTRACT Enhanced-Resolution Processing and Applications of the ASCAT Scatterometer Richard D. Lindsley Department of Electrical and Computer Engineering, BYU Doctor of Philosophy The ASCAT scatterometer measures the Earth surface microwave radar backscatter in order to estimate the near-surface winds over the oceans. While the spatial resolution of the conventional applications is sufficient for many purposes, other geoscience applications benefit from an improved spatial resolution. Specialized algorithms may be applied to the scatterometer data in order to reconstruct the radar backscatter on a high-resolution grid. Image reconstruction requires the spatial response function (SRF) of each measurement, which is not reported with the measurement data. To address this need, I precisely model the SRF incorporating (1) the antenna beam response, (2) the processing performed onboard ASCAT before telemetering to the ground, and (3) the Doppler shift induced by a satellite orbiting the rotating Earth. I also develop a simple parameterized model of the SRF to reduce computational complexity. The accuracy of both models is validated. Image reconstruction of the ASCAT data is performed using the modeled SRF. I discuss the spatial resolution of the reconstructed ASCAT images and consider the first- and second-order statistics of the reconstructed data. Optimum values for the parameters of the reconstruction algorithms are also considered. The reconstructed radar backscatter data may be used for enhanced-resolution wind retrieval and for geoscience applications. In this dissertation, the reconstructed backscatter data is used to map the surface extent of the 2010 Deepwater Horizon oil spill and in a study to quantify the azimuth angle anisotropy of backscatter in East Antarctica. Near-coastal ocean wind retrieval is also explored in this dissertation. Because near- coastal ocean measurements of backscatter may be “contaminated” from nearby land and introduce errors to wind retrieval, they must be discarded. The modeled SRF is used to quantify the land contamination, enabling enhanced-resolution wind retrieval much closer to the coasts. The near-coastal winds are validated against buoy measurements. Keywords: ASCAT, scatterometer, spatial response function, wind retrieval, image recon- struction ACKNOWLEDGMENTS First, I thank my advisor, Dr. Long. Thank you for years of support, instruction, and guidance. I appreciate your feedback in directing and improving my research and writing, and I am happy you introduced me to the exciting fields of radar and remote sensing. I also thank my associates at EUMETSAT, especially Craig Anderson, Julia Figa- Saldaña, and Julian Wilson. Your technical expertise was critical in refining and correcting my research, and I enjoyed your warm hospitality during my stay in Darmstadt. Finally, I am most thankful to my wife, Whitney. Even though I was perpetually “almost done,” you’ve supported and encouraged me through many long hours and late nights. Table of Contents List of Tables . viii List of Figures . ix 1 Introduction . 1 1.1 Overview . 1 1.2 Summary of Results . 2 1.3 Outline . 4 2 Background . 6 2.1 Scatterometry . 6 2.2 ASCAT . 10 2.3 Backscatter Sampling and Reconstruction . 12 2.3.1 Gridding . 14 2.3.2 Full Reconstruction . 15 2.3.3 Partial Reconstruction . 16 2.4 Applications of Enhanced-Resolution Backscatter . 18 2.5 Contamination in Ocean Measurements . 19 2.5.1 Land Contamination . 19 2.5.2 Oil Contamination . 20 2.6 Summary . 21 iv 3 ASCAT Spatial Response Function . 23 3.1 Modeled SRF . 24 3.1.1 Antenna Beam Response . 24 3.1.2 Range-Doppler Processing . 24 3.1.3 Along-Track Pulse Averaging . 28 3.1.4 Cumulative Effect . 28 3.2 Parameterized Estimate . 29 3.3 SRF Validation . 36 3.4 Conclusion . 37 4 Enhanced Resolution Backscatter Reconstruction . 39 4.1 Spatial Resolution . 39 4.2 Reconstruction Parameters . 43 4.3 Reconstruction Results . 46 4.4 Reconstruction Statistics . 49 4.5 Spectral Analysis . 55 4.6 Conclusion . 60 5 Near-Coastal Enhanced-Resolution Wind Retrieval . 62 5.1 Ultra-High Resolution Winds . 62 5.2 ASCAT Land Fraction . 64 5.3 Land Contamination Removal . 65 5.4 Near-Coastal UHR Validation . 71 5.5 Summary . 79 6 Applications of Enhanced Resolution Backscatter Images . 81 v 6.1 Deepwater Horizon Oil Spill . 81 6.1.1 Method . 82 6.1.2 Results . 85 6.1.3 Conclusion . 93 6.2 Azimuth Angle Modulation . 96 6.2.1 Backscatter Model . 97 6.2.2 Model Parameter Estimation . 98 6.2.3 Results and Discussion . 100 6.2.4 Conclusion . 108 7 Conclusion . 110 7.1 Summary . 110 7.2 Contributions . 111 7.3 Publications . 114 7.4 Future Research . 114 7.5 Final Remarks . 116 Bibliography . 117 A Background in Sampling and Reconstruction . 125 A.1 Sampling Theory for Scatterometers . 125 A.1.1 Regular Sampling and Reconstruction . 125 A.1.2 Irregular Sampling . 126 A.1.3 Aperture-Filtered Irregular Sampling . 127 A.1.4 Two-Dimensional Signals . 128 A.1.5 Discrete Signals . 128 vi A.1.6 Signal Bandlimit . 129 A.1.7 Aperture Bandlimit . 129 A.2 Block AART . 130 B ASCAT FFT Bin Response . 132 B.1 Rect Window Response . 132 B.2 ASCAT Onboard Processing . 135 B.3 Windowed FFT Bin Response . 135 B.4 Summary . 135 C Reconstruction Image Statistics . 139 C.1 Confidence Intervals . 140 C.2 Measurement Noise . 141 C.2.1 Predicted Values . 141 C.2.2 Sample Values . 143 C.2.3 Validation . 143 C.3 True Signal Uncertainty . 145 C.4 Signal and Noise Uncertainty . 146 C.4.1 Predicted Values . 146 C.4.2 Sample Values . 147 C.4.3 Validation . 148 C.5 Conclusion . 151 vii List of Tables 3.1 Discriminator frequency parameters for Eq. (3.1). 26 3.2 The parameters for a sample ASCAT measurement used for Fig. 3.4. 29 3.3 The angles β and ϕ for the six ASCAT beams. β is the angle between along- beam and along-track, and ϕ is the angle between along-track and northing. The angle φ is the azimuth angle reported in the L1B data. 31 4.1 The average δ and corresponding spatial resolution from Fig. 4.1 for equatorial (< 50◦) and for polar (> 55◦) latitudes. 43 5.1 LCR compass simulation input values, for a total of 10 × 8 × 10 × 7 = 5600 input combinations. 68 6.1 The estimated azimuth modulation magnitude and phase coefficients for the data shown in Fig. 6.11. The mk terms are in dB and the ϕk terms are in degrees. ..
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