RapidScat 2.5 km ultrahigh resolution (UHR) wind estimation is introduced and validated it in near-coastal regions. In addition, this thesis applies direction interval retrieval techniques and develops a new wind processing method to enhance the performance of RapidScat UHR wind estimation in the nadir region. The new algorithm is validated with L2B wind estimates, Numerical Weather Prediction (NWP) wind products, and buoy measurements. The wind processing improvements produce more spatially consistent UHR winds that compare well with the wind products mentioned above. Hawaii regional climate model (HRCM), QuikSCAT, and ASCAT wind estimates are compared in the lee of the Big Island with the goal of understanding UHR scatterometer wind retrieval capabilities in this area. UHR wind vectors better resolve fine resolution wind speed features compared to L2B, but still do not resolve the expected wind direction features. A comparison of scatterometer measured σ 0 and HRCM and NWP predicted σ 0 suggests that scatterometers can detect a reverse flow in the lee of the island. Differences between scatterometer measured σ 0 and HRCM predicted σ 0 indicate error in the placement of key reverse flow features by the model. Coarse initialization fields and a large fixed size median filter window are also shown to impede UHR wind retrieval in this area.

Two surface-based, portable, S-band and C-band polarimetric scatterometers intended for in situ measurements of both terrain and the ocean’s surface are presented. The scatterometers' layout, hardware design, measurement accuracy, calibration, and signal processing concepts are described. To augment in situ geophysical observations, researchers have often employed in situ scatterometers for validating satellite-based retrievals and also for their innate ability to monitor geophysical variations of localized regions with fine temporal resolution. Backscatter measurement variability due to system effects is presented, providing the fundamental basis for the quantitative analysis of data. Sample polarimetric retrievals are presented for asphalt pavement and grass.

The standard L2B ocean wind product from the Advanced Scatterometer (ASCAT) is retrieved as a 25 km product on a 12.5 km grid. Ultra-high resolution (UHR) processing allows ASCAT wind retrieval on a high-resolution 1.25 km grid. Ideally, such a high-resolution sample grid provides wind information down to a 2.5 km scale, allowing better analysis of winds with high spatial variability such as those in near-coastal regions and storms. Though the wind field is sampled on a finer grid, the actual data resolution needs to be validated. This thesis provides an analysis and validation of ASCAT UHR wind estimates in order to determine the improvement in resolution compared to the L2B product. This is done using analysis tools such as statistics, the power spectrum, and derivative fields, and through comparison to other high-resolution data such as synthetic aperture radar (SAR). The improvement of UHR wind retrieval is also explored by reducing ambiguity selection errors and correcting for contamination of wind vectors near land. Results confirm that ASCAT UHR winds contain high-resolution information that is not present in the L2B product. The resolution improvement is difficult to quantify due to a lack of truth data. Nevertheless, there is evidence to suggest that the resolution is improved by at least a factor of three to 10 km, and perhaps down to 3 or 4 km. It is found through comparison of UHR and SAR winds that (1) both products have common fine-scale features, (2) their comparative statistics are similar to that of L2B and SAR, suggesting that the high resolution content agrees just as well as the low resolution content because the comparison is performed at a finer scale (3) both products have derivative fields that match well, (4) the UHR product benefits from high-resolution direction information, and (5) the UHR product matches better the expected spectral properties of ocean winds. For the UHR processing improvement methods, the model-based improvement of UHR ambiguity selection allows obvious ambiguity errors to be found and corrected, increases the self-consistency of the wind field, and causes the spectrum to better follow a power law at high wavenumbers. The removal of land-contamination from near-coastal wind vectors allows accurate wind retrieval much closer to land and greater visibility of high-resolution wind features near the coast.

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.

Spaceborne scatterometer instruments are important tools for the remote sensing of the Earth's environment. In addition to the primary goal of measuring ocean winds, data from scatterometers have proven useful in the study of a variety of land and cryopshere processes as well. Several satellites carrying scatterometers have flown in the last two decades. These previous systems have been "fan-beam" scatterometers, where multiple antennas placed in fixed positions are used. The fan-beam scatterometer approach, however, has disadvantages which limit its utility for future missions. An alternate approach, the conically-scanning "pencil-beam" scatterometer technique, alleviates many of the problems encountered with earlier systems and provides additional measurement capability. Due to these advantages, the pencil-beam approach has been selected by NASA as the basis for future scatterometer missions. Whereas the fan-beam approach is mature and well understood, there is need for a fundamental study of the unique aspects of the pencil-beam technique. In this dissertation, a comprehensive treatment of the design issues associated with pencil-beam scatterometers is presented. A new methodology is established for evaluating and optimizing the performance of conically-scanning radar systems. Employing this methodology, key results are developed and used in the design of the SeaWinds instrument - NASA's first pencil-beam scatterometer. Further, the theoretical framework presented in this study is used to propose new scatterometer techniques which will significantly improve the spatial resolution and measurement accuracy of future instruments.

The SeaWinds Calibration Ground Station (CGS) is a passive ground station used to receive and sample transmissions from the SeaWinds scatterometer. During post processing, the received transmissions are characterized in order to verify proper instrument operation and to eliminate error in satellite telemetry and in data products generated from processing SeaWinds data. Sources of instrument error include uncertainties in transmitted power, pulse timing, and carrier frequency drift. Identifying these errors prevents their propagation to data products. A key aspect of this analysis involves accurately estimating the parameters of the SeaWinds transmissions. As better parameter estimates are researched and developed, the scatterometer can be more finely calibrated and better characterized, allowing improved accuracy of environmental measurements. This work explores several methods to estimate SeaWinds frequency parameters by parametrically modeling the signal as a series of linear FM pulses. Improved frequency estimates are obtained by transforming the signal into appropriate signal spaces. These methods are compared and their tradeoffs revealed. SNR regions are assigned to each method to mark appropriate performance bounds, and improvements over previous SeaWinds data analysis methods are shown. Finally, recent estimates of SeaWinds parameters are disclosed. This analysis helps to advance the level to which future scatterometer instruments may be calibrated, providing the potential for more accurate scatterometer data products.

Scatterometers, such as SeaWinds, can provide wide coverage of ocean surface winds. They estimate near-surface wind vectors by relating measured radar backscatter to a geophysical model function. However, SeaWinds measurements are also sensitive to rain, and conventional wind retrieval degrades in rainy conditions. An algorithm that exploits SeaWinds' sensitivity to both wind and rain has be developed. This algorithm, termed simultaneous wind/rain retrieval, retrieves both wind vectors and rain rates for a given ocean area. Instantaneous results of simultaneous wind/rain retrieval in Hurricane events is analyzed through comparison with the NEXRAD ground-based radar system. This comparison allows validation of retrieved rains. Additionally, conditions that affect the accuracy of SeaWinds wind/rain observations are evaluated. It is shown that, when thresholded, the rains retrieved by SeaWinds give an adequate rain flag. The comparisons of SeaWinds and NEXRAD rain estimates facilitate construction of a model to simulate variability in the SeaWinds rain estimates. The model is used to show that rain estimates are unbiased, though with significant variability. The variability is likely to be primarily driven by the noise inherent to the SeaWinds system.

This dissertation focuses on relating spaceborne microwave data to the geophysical characteristics of the Sahara desert and the Amazon vegetation. Radar and radiometric responses of the Saharan ergs are related to geophysical properties of sand formations and near surface winds. The spatial and temporal variability of the Amazon vegetation is studied using multi-frequency and multi-polarization data. The Sahara desert includes large expanses of sand dunes called ergs that are constantly reshaped by prevailing winds. Radar backscatter measurements observed at various incidence and azimuth angles from the NASA Scatterometer (NSCAT), the ERS scatterometer (ESCAT), the SeaWinds scatterometer aboard QuikScat (QSCAT), and the Precipitation Radar (TRMM-PR) aboard the Tropical Rain Monitoring Mission (TRMM) are used to model the backscatter response from sand dunes. Backscatter incidence and azimuth angle variation depends upon the slopes and orientations of the dune slopes. Sand dunes are modeled as a composite of tilted rough facets, which are characterized by a probability distribution of tilt. The small ripples are modeled as cosinusoidal surface waves that contribute to the return signal at Bragg angles. The backscatter response is high at look angles equal to the mean tilts of the rough facets and is lower elsewhere. The modeled backscatter response is similar to NSCAT and ESCAT observations. Backscatter also varies spatially and reflects the spatial inhomogeneity of the sand surface. A model incorporating the backscatter azimuth modulation and spatial inhomogeneity is proposed. The maxima of the azimuth modulation at 33 degrees incidence angle reflect the orientation of the slip-sides on the sand surface. These slip-side orientations are consistent with the European Centre for Medium-Range Weather Forecasts wind directions spatially and temporally. Radiometric emissions from the ergs have strong dependence on the surface geometry. The radiometric temperature (Tb) of ergs is modeled as the weighted sum of the Tb from all the composite tilted rough facets. The dual polarization Tb measurements at 19 GHz and 37 GHz from the Special Sensor Microwave Imager (SSM/I) aboard the Defense Meteorological Satellite Program and the Tropical Rainfall Measuring Mission Microwave Imager are used to analyze the radiometric response of erg surfaces and compared to the model results. It is found that longitudinal and transverse dune fields are differentiable based on their polarization difference azimuth modulation, which reflects type and orientation of dune facets. Polarization difference at 19 GHz and 37 GHz provide consistent results. In the Amazon, backscatter measurements from Seasat A scatterometer (SASS), ESCAT, NSCAT, QSCAT and TRMM-PR; and Tb measurements from SSM/I are used to study the multi-spectral microwave response of vegetation. Backscatter versus incidence angle signatures of data combined from scatterometers and the precipitation radar depend upon vegetation density. The multi-frequency signatures of backscatter and Tb provide unique responses for different vegetation densities. Backscatter and Tb spatial inhomogeneity is related to spatial geophysical characteristics. Temporal variability of the Amazon basin is studied using C-band ERS data and a Ku-band time series formed by SASS, NSCAT and QSCAT data. Although the central Amazon forest represents an area of very stable radar backscatter measurements, portions of the southern region exhibit backscatter changes over the past two decades.

Using co-located space-borne satellite (TRMM PR, ESCAT on ERS 1/2) measurements, and numerical predicted wind fields (ECMWF), the sensitivity of C-band backscatter measurement to rain is evaluated. It is demonstrated that C-band radar backscatter can be significantly altered by rain surface perturbation, an effect that has been previously neglected. A low-order wind/rain backscatter model is developed that has inputs of surface rain rate, incidence angle, wind speed, wind direction, and azimuth angle. The wind/rain backscatter model is accurate enough for describing the total backscatter in raining areas with relatively low variance. Rain has a more significant impact on measurements at high incidence angles than at low incidence angles. Using three distinct regimes, the conditions for which wind, rain, and both wind and rain can be retrieved from scatterometer backscatter measurements are determined. The effects of rain on ESCAT wind-only retrieval are evaluated. The additional scattering from rain causes estimated wind speeds to be biased high and estimated wind directions to be biased toward the along-track direction in heavy rains. To compensate for rain-induced backscatter, we develop a simultaneous wind/rain retrieval method (SWRR), which simultaneously estimates wind and rain from ESCAT backscatter measurements with an incidence angle of over 40 degrees. The performance of SWRR under typical wind/rain conditions is evaluated through simulation and validation with collocated TRMM PR and ECMWF data sets. SWRR is shown to significantly improve wind velocity estimates and the SWRR-estimated rain rate has relatively high accuracy in moderate to heavy rain cases. RADARSAT-1 ScanSAR SWA images of Hurricane Katrina are used to retrieve surface wind vectors over the ocean. Collocated H*wind wind directions are used as the wind direction estimate and the wind speed is derived from SAR backscatter measurements by inversion of a C-band HH-polarization Geophysical Model Function (GMF) that is derived from the VV-polarization GMF, CMOD5, using a polarization ratio model. Because existing polarization models do not fit the ScanSAR SWA data well, a recalibration model is proposed to recalibrate the ScanSAR SWA images. Validated with collocated H*wind wind speed estimates, the mean difference between SAR-retrieved and H*wind speed is small and the root mean square (RMS) error is below 4 m/s. Rain effects on the ScanSAR measurements are analyzed for three different incidence angle ranges using collocated ground-based Doppler weather radar (NEXRAD) rain measurements. Compared with the scatterometer-derived model, the rain-induced backscatter observed by the ScanSAR at incidence angles 44 to 45.7 degrees is consistent with the scatterometer-derived model when the polarization difference between HH and VV polarizations is considered.

The Greenland ice sheet is an area of great interest to the scientific community due to its role as an important bellwether for the global climate. Satellite-borne scatterometers are particularly well-suited to studying temporal changes in the Greenland ice sheet because of their high spatial coverage, frequent sampling, and sensitivity to the presence of liquid water. The dry snow zone is the largest component of the Greenland ice sheet and is identified as the region that experiences negligible annual melt. Due to the lack of melt in the dry snow zone, backscatter was previously assumed to be relatively constant over time in this region. However, this thesis shows that a small seasonal variation in backscatter is present in QuikSCAT data in the dry snow zone. Understanding the cause of this seasonal variability is important to verify the accuracy of QuikSCAT measurements, to better understand the ice sheet conditions, and to improve future scatterometer calibration efforts that may use ice sheets as calibration targets.This thesis provides a study of the temporal behavior of backscatter in the dry snow zone of the Greenland ice sheet focusing on seasonal variation. Spatial averaging of backscatter and the Karhunen-Lo`eve transform are used to identify and study the dominant patterns in annual backscatter behavior. Several QuikSCAT instrumental parameters are tested as possible causes of seasonal variation in backscatter in the dry snow zone to verify the accuracy of QuikSCAT products. None of the tested parameters are found to be related to seasonal variation. Further evidence is given that suggests that the cause of the seasonal variation is geophysical and several geophysical factors are tested. Temperature is found to be highly related to dry snow backscatter and therefore may be driving the seasonal variation in backscatter in the dry snow zone.