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Satellite radiometers are used to remotely measure properties of the Earth's surface. Radiometers enable wide spatial coverage and daily temporal coverage. Radiometer measurements are used in a wide array of applications, including freeze/thaw states inference, vegetation index calculations, rainfall estimation, and soil moisture estimation. Resolution enhancement of these radiometer measurements enable finer details to be resolved and improve our understanding of Earth. The Soil Moisture Active Passive (SMAP) radiometer was launched in April 2014 with a goal to produce high resolution soil moisture estimates. However, due to hardware failure of the radar channels, prepared algorithms could no longer be used. Current algorithms utilize a narrow spatial and temporal overlap between the SMAP radiometer and the SENTINEL-1 radar to produce high resolution soil moisture estimates that are spatially and temporally limited. This thesis explores the use of resolution enhancing algorithms to produce high resolution soil moisture estimates without the spatial coverage limitations caused by using multiple sensors. Two main approaches are considered: calculating the iterative update in brightness temperature and calculating the update in soil moisture. The best performing algorithm is the Soil Moisture Image Reconstruction (SMIR) algorithm that is a variation of the Radiometer form of the Scatterometer Image Reconstruction (rSIR) algorithm that has been adapted to operate in parameter space. This algorithm utilizes a novel soil moisture measurement response function (SMRF) in the reconstruction. It matches or exceeds the performance of other algorithms and allows for wide spatial coverage.

Scatterometry and radiometry are used to obtain measurements of Earth properties with extensive spatial coverage at daily or near-daily temporal resolution. Their measurements are used in many climate studies and weather applications, such as iceberg tracking, ocean wind estimation, and volumetric soil moisture measurements. The spatial resolution of these data products ranges from a few kilometers to tens of kilometers. Techniques to enhance the spatial resolution of these products help reveal finer scale features, but come at the cost of increased noise. This thesis explores the application of neural networks as a possible method to handle the noise and uncertainty in enhanced resolution scatterometer and radiometer data products. The specific sensors discussed are the Advanced Scatterometer (ASCAT) and its Ultrahigh Resolution (UHR) winds, and the Soil Moisture Active Passive (SMAP) radiometer and its soil moisture measurements. ASCAT UHR winds have already been validated in previous studies [1], but inherent ambiguity in the wind retrieval model couples with higher noise levels to decrease overall accuracy. Neural networks are tested as an alternate modeling method to possibly improve the accuracy compared with the current method. It is found that the feed forward neural networks tested are able to accurately estimate winds in most calculations, but struggle with the same ambiguity that occurs in the current model. The neural networks handle this ambiguity inconsistently, which results in worse overall network performance compared to the current wind retrieval method. For the SMAP soil moisture measurements, the radiometer form of the Scatterometer Image Reconstruction algorithm is validated as a method to enhance resolution. While the increased noise at higher resolution does worsen overall accuracy, the performance remains within about 0.04 cm^3 cm^−3 RMSE of a validated soil moisture product, suggesting that fine scale features revealed as resolution is enhanced are accurate. Corrections to the soil moisture extraction model used in these tests could further improve these results. Neural networks are then applied and compared with the theory-based approach to extract soil moisture from the brightness temperature measurements, and are found to give slightly more accurate results than the theoretical model, though with somewhat higher error variance.

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A 94.8 GHz interferometric imaging system utilizing aperture synthesis and tomography is developed for the Center for Advanced Sensor and Communication Antennas. Whereas typical interferometer designs employ multiple antennas to synthesize an aperture for image reconstruction, this unique interferometer will reproduce a scene's brightness temperature with only two antennas. To achieve this, the aperture synthesis is done with one antenna remaining stationary while the second antenna is moved at discrete increments along two controlled tracks. The two signals received by the antennas are cross-correlated to produce measured visibility function samples. The visibility samples reconstruct the scene brightness temperature through an inverse Fourier transform relationship.

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.

Three problems at the forefront of microwave radiometry are solved using probability theory and inverse problem formulations which are heavily based in probability theory. Probability theory is able to capture information about random phenomena, while inverse problem theory processes that information. The use of these theories results in more accurate estimates and assessments of estimate error than is possible with previous, non-probabilistic approaches. The benefits of probabilistic approaches are expounded and demonstrated. The first problem to be solved is a derivation of the error that remains after using a method which corrects radiometric measurements for polarization rotation. Yueh [1] proposed a method of using the third Stokes parameter TU to correct brightness temperatures such as Tv and Th for polarization rotation. This work presents an extended error analysis of Yueh's method. In order to carry out the analysis, a forward model of polarization rotation is developed which accounts for the random nature of thermal radiation, receiver noise, and (to first order) calibration. Analytic formulas are then derived and validated for bias, variance, and root-mean-square error (RMSE) as functions of scene and radiometer parameters. Examination of the formulas reveals that: 1) natural TU from planetary surface radiation, of the magnitude expected on Earth at L-band, has a negligible effect on correction for polarization rotation; 2) RMSE is a function of rotation angle Ω, but the value of Ω which minimizes RMSE is not known prior to instrument fabrication; and 3) if residual calibration errors can be sufficiently reduced via postlaunch calibration, then Yueh's method reduces the error incurred by polarization rotation to negligibility. The second problem addressed in this dissertation is optimal estimation of calibration parameters in microwave radiometers. Algebraic methods for internal calibration of a certain class of polarimetric microwave radiometers are presented by Piepmeier [2]. This dissertation demonstrates that Bayesian estimation of the calibration parameters decreases the RMSE of the estimates by a factor of two as compared with algebraic estimation. This improvement is obtained by using knowledge of the noise structure of the measurements and by utilizing all of the information provided by the measurements. Furthermore, it is demonstrated that much significant information is contained in the covariance information between the calibration parameters. This information can be preserved and conveyed by reporting a multidimensional pdf for the parameters rather than merely the means and variances of those parameters. The proposed method is also extended to estimate several hardware parameters of interest in system calibration. The final portion of this dissertation demonstrates the advantages of a probabilistic approach in an empirical situation. A recent inverse problem formulation, sketched in [3], is founded on probability theory and is sufficiently general that it can be applied in empirical situations. This dissertation applies that formulation to the retrieval of Antarctic air temperature from satellite measurements of microwave brightness temperature. The new method is contrasted with the curve-fitting approach which is the previous state-of-the-art. The adaptibility of the new method not only results in improved estimation but is also capable of producing useful estimates of air temperature in areas where the previous method fails due to the occurence of melt events.

AMSR-E is a space-borne radiometer which measures Earth microwave emissions or brightness temperatures (Tb) over a wide swath. AMSR-E data and images are useful in mapping valuable Earth-surface and atmospheric phenomena. A modified version of the Scatterometer Image Reconstruction (SIR) algorithm creates Tb images from the collected data. SIR is an iterative algorithm with tuning parameters to optimize the reconstruction for the instrument and channel. It requires an approximate aperture function for each channel to be effective. This thesis presents a simulator-based optimization of SIR iteration and aperture function threshold parameters for each AMSR-E channel. A comparison of actual Tb images generated using the optimal and sub-optimal values is included. Tuned parameters produce images with sharper transitions between regions of low and high Tb for lower-frequency channels. For higher-frequency channels, the severity of artifacts due to temporal Tb variation of the input measurements decreases and coverage gaps are eliminated after tuning. A two-parameter Gaussian-like bell model is currently assumed in image reconstruction to approximate the AMSR-E aperture function. This paper presents a method of estimating the effective AMSR-E aperture function using Tb measurements and geographical information. The estimate is used as an input for image reconstruction. The resulting Tb images are compared with those produced with the previous Gaussian approximation. Results support the estimates found in this paper for channels 1h, 1v, and 2h. Images processed using the old or new aperture functions for all channels differed by a fraction of a Kelvin over spatially smooth regions.

Remote sensing is the process of gathering and analyzing information about the earth's ocean, land and atmosphere using electromagnetic "wireless" techniques. Mathematical models, known as Radiative Transfer Models (RTM), are developed to calculate the observed radiance (brightness temperature) seen by the remote sensor. The RTM calculated brightness temperature is a function of fourteen environmental parameters, including atmospheric profiles of temperature, pressure and moisture, sea surface temperature, and cloud liquid water. Input parameters to the RTM model include data from NOAA Centers for Environmental Prediction (NCEP), Reynolds weekly Sea Surface Temperature and National Ocean Data Center (NODC) WOA98 Ocean Salinity and special sensor microwave/imager (SSM/I) cloud liquid water. The calculated brightness temperatures are compared to collocated measurements from the WindSat satellite. The objective of this thesis is to fine tune the RadTb model, using simultaneous environmental parameters and measured brightness temperature from the well-calibrated WindSat radiometer. The model will be evaluated at four microwave frequencies (6.8 GHz, 10.7 GHz, 18.7 GHz, and 37.0 GHz) looking off- nadir for global radiance measurement.

This thesis presents the development and validation of the Hurricane Imaging Retrieval Algorithm (HIRA) for the measurement of oceanic surface wind speed and rain rate in hurricanes. The HIRA is designed to process airborne microwave brightness temperatures from the NOAA, Stepped Frequency Microwave Radiometer (SFMR), which routinely collects data during NOAA hurricane hunter aircraft flights. SFMR measures wind speeds and rain rates at nadir only, but HIRA will soon be integrated with an improved surface wind speed model for expanded utilization with next generation microwave hurricane imagers, such as the Hurricane Imaging Radiometer (HIRad). HIRad will expand the nadir only measurements of SFMR to allow the measurement of hurricane surface winds and rain over a wide swath Results for the validation of HIRA retrievals are presented using SFMR brightness temperature data for 22 aircraft flights in 5 hurricanes during 2003-2005. Direct comparisons with the standard NOAA SFMR empirical algorithm provided excellent results for wind speeds up to 70 m/s. and rain rates up to 50 mm/hr.