RapidScat is a pencil-beam wind scatterometer which operated from September 2014 to August 2016. Mounted aboard the International Space Station (ISS), RapidScat experiences significant altitude and attitude variations over its dataset. These variations need to be properly accounted for to ensure accurate calibration and to produce high resolution scatterometer images. Both the antenna pose and the one-way antenna pattern need to be validated. The spatial response function (SRF) is the two-way antenna pattern for a scatterometer combined with the processing and filtering done in the radar system electronics, and is dominated by the two-way pattern. To verify the pointing of the RapidScat antenna, the RapidScat SRF is estimated using on-orbit data. A rank reduced least squares estimate is used, which was developed previously for the Oceansat-2 (OSCAT) scatterometer [1]. This algorithm uses a small, isolated island as a delta function to sample the SRF. The island used is Rarotonga Island of the Cook Islands. The previously developed algorithm is updated to estimate the SRF in terms of beam azimuth and elevation angle rather than in kilometers on the ground. The angle-based coordinate system promotes greater understanding of how the SRF responds to biases and errors in antenna geometry. The estimation process is simulated to verify its accuracy by calculating the SRF for several thousand measurements in the region of Rarotonga. The calculated SRFs are multiplied by a corresponding synthetically created surface and integrated to yield simulated backscatter measurements, with added white noise. The SRF estimation algorithm is then performed. The results of the simulation show that the SRF estimation process yields a close estimate of the original SRF. The antenna pointing is validated by introducing a fixed offset in azimuth angle into the simulation and observing that the SRF is correspondingly shifted in the azimuth-elevation grid. The SRF computed from real data shows that there is an azimuth rotation angle bias of about 0.263 degrees for the inner beam and about 0.244 degrees for the outer beam. Since the SRF is dominated by the two-way antenna pattern, it can be modeled as the product of two identical one-way antenna patterns which are slightly offset from each other due to antenna rotation during the transmit/receive cycle. A method is developed based on this model to derive the one-way antenna pattern from the estimated SRF. Using a Taylor series expansion the one-way antenna pattern is computed from the SRF. The derived pattern recovers the SRF with small error, but there is significant error in the inferred one-way pattern when compared to the pre-launch estimated RapidScat one-way antenna pattern.

Originally designed for wind velocity estimation over the ocean, scatterometers have since been applied to climate studies of the Earth's cryosphere and bioshere. As an integral part of climatological studies of the planet, the NASA Scatterometer Climate Record Pathfinder (SCP) supplies scatterometer-based products designed to aid researchers in climatological studies of the planet. In this thesis, necessary steps are taken to facilitate data from the Oceansat-2 Ku-band scatterometer (OSCAT) to be used in extending the Ku-band SCP dataset of conically scanning pencil-beam scatterometers begun by the Seawinds scatterometer flown on the QuikSCAT mission 1999-2009. As a standard SCP product, a temporal resolution enhancement technique for the scatterometer image reconstruction (SIR) algorithm is applied to OSCAT data. A relative cross-calibration method is developed to ensure consistency amongst datasets of conically scanning pencil-beam scatterometers in the SCP data time series. By application of the method, both raw data and SIR image data of OSCAT is cross-calibrated with QuikSCAT. To enable creation of SCP products requiring knowledge of the spatial response function (SRF) with OSCAT data, a method of estimating the SRF of pencil-beam scatterometers is developed. The estimation method employs rank-reduced least-squares to invert the radar equation using measurements over islands. A simulation is performed to validate the efficacy of the method and provide optimum choice of island size and number of singular values used in rank-reduced least-squares. The utility of the SRF estimates is demonstrated by applying an estimate of the OSCAT SRF to SIR image construction with OSCAT data.

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.