Doctor of Philosophy, The Ohio State University, 2012, Electrical and Computer Engineering

Range-resolved interferometric phase and Doppler spectra are two subjects of interest with regard to the retrieval of sea surface height profiles from coherent marine radar measurements. The studies of this dissertation attempt to improve understanding of the properties and associated measurement errors of these quantities through the use of numerically simulated low-grazing-angle backscatter data. In the first part of the dissertation, studies of the interferometric phase are described. Backscattered fields computed using the method of moments for one dimensional ocean-like surface profiles are used to examine statistical properties of the single-look interferometric phase estimator, in order to investigate the applicability of standard expectations for height retrieval accuracy in this problem. The results show that shadowing and multipath propagation effects cause errors in interferometric phase estimation beyond those caused by speckle effects alone. In addition, the decorrelation between the fields received at two antennas is found to be impacted by shadowing and multipath propagation effects, making standard models for this quantity less applicable as well. These results show that modeling the expected performance of interferometric sea surface height retrieval approaches at low grazing angles is difficult. The second part of the dissertation involves studies of the range-resolved Doppler spectra at low-grazing-angles. Backscattered fields are computed for a single realization of a one-dimensional ocean-like surface profile as the realization evolves in time. Transformation into the range-Doppler domain enables examination of properties of the resulting Doppler spectra (for both HH and VV polarizations) and their relationship to properties of the surface profile. In general, a strong correspondence between the long wave orbital velocity of the surface and the Doppler centroid frequency is observed for visible portions of the surface, as well as some evidence (open full item for complete abstract)

Committee: Joel Johnson (Advisor); Robert Burkholder (Committee Member); Fernando Teixeira (Committee Member) Subjects: Electrical Engineering

Master of Science, The Ohio State University, 2005, Graduate School

Committee: Not Provided (Other) Subjects:

Doctor of Philosophy, The Ohio State University, 2021, Electrical and Computer Engineering

Sea ice is one of the most important components of Earth's cryosphere that regulates heat flow between the ocean and atmosphere, impacts water cycles and oceanic currents due to salt transport during the melt and freeze seasons, provides a natural habitat for many life forms, and affects global transportation. Remote sensing of sea ice has become more important than ever given the substantial reduction in sea ice extent in the Arctic in recent decades. A major advantage of remote sensing for sea ice studies is the continuous monitoring available from space that eliminates the need for expensive and difficult in-situ measurements. Although satellite-borne passive microwave radiometer observations of sea ice have been performed since the 1970s, significant challenges remain. Radiometer measurements at frequencies greater than approximately 19 GHz can detect sea ice concentration and extent but are impacted by clouds. L-band radiometers operating at 1.4 GHz have been shown to successfully estimate sea ice thicknesses up to ~1.5 m; however, thickness sensing performance is limited by the penetration depth in sea ice at this signal frequency. Radiometers operating at lower frequencies have not been used on satellites to date due to the strong man-made radio frequency interference (RFI) present at frequencies less than 1.4 GHz. The Ultra-Wideband Software-defined Microwave Radiometer (UWBRAD) was developed at The Ohio State University to observe thermal emissions in the presence of RFI. The instrument performs RFI detection and mitigation algorithms at multiple frequency channels from 0.5 – 2.0 GHz. As a result, UWBRAD is capable of retrieving sea ice thickness for ice thicknesses greater than 1.5 m due to its lower operating frequencies. In addition, the measurement of sea ice thermal emissions over the 0.5-2 GHz spectrum reveals additional information on ice characteristics and can allow the simultaneous retrieval of sea ice thickness and salinity. This dissertati (open full item for complete abstract)

Committee: Joel T. Johnson (Advisor); Robert Lee (Committee Member); Caglar Yardim (Committee Member); Kenneth C. Jezek (Committee Member) Subjects: Electrical Engineering; Electromagnetics; Remote Sensing

Doctor of Philosophy, The Ohio State University, 2017, Geodetic Science and Surveying

Snow accumulation on the ground changes the energy balance between the land and the atmosphere, and stores winter precipitation for water supplies in many parts of the world. In practice, the snow water equivalent (SWE), defined as the equivalent depth of liquid water when snow completely melts, is difficult to map in cold regions except via remote sensing techniques. The microwave remote sensing (MWRS) has been used for SWE estimation since the 1980s based on the interactions of microwave radiation with snow crystals. In this study, physically based radiative transfer (RT) models and the Bayesian-based Markov Chain Monte Carlo (MCMC) approach were applied to develop a high-accuracy SWE retrieval algorithm. The models and the algorithms were tested using ground-based snowpit and microwave measurements. Two widely-used snow RT models were fully-compared in the aspects of snow micro-structure assumptions, volume scattering theories and the RT equation resolution. The Microwave Emission Model of Layered Snowpacks (MEMLS) based on the Improved Born Approximation (IBA) was shown to be an adequate observation model to estimate SWE using the multi-frequency brightness temperature (TB) at 10.65 to 90 GHz. The prior information is from a set of globally-available datasets, and the performance is tested for local prior information derived from historical ground measurements. The retrieval algorithm was later adapted for active microwave SWE retrieval using the backscattering coefficient at 10.2 to 16.7 GHz. Results showed that MEMLS-IBA can simulate the measured microwave signals with a 10-K accuracy for TB and a 1-dB accuracy for the backscattering coefficient. The passive microwave retrieval algorithm achieved an accuracy of 30-mm for shallow snow, with two-layer snow properties estimated. However, the active microwave retrieval algorithm reproduced similar accuracy only in the synthetic experiment using 1-layer snow property estimates. Future improvement requires a better (open full item for complete abstract)

Committee: Michael Durand (Advisor); Che-Kwan Shum (Committee Member); Ian Howat (Committee Member); Joel Johnson (Committee Member); Barbara Wyslouzi (Committee Member) Subjects: Earth; Geography; Hydrologic Sciences

Doctor of Philosophy, The Ohio State University, 2015, Geodetic Science and Surveying

Snow and its water equivalent plays a vital role in global water and energy balances, with particular relevance in mountainous areas with arid and semi-arid climate regimes. Spaceborne passive microwave (PM) remote sensing measurements are attractive for snowpack characterization due to their continuous global coverage and historical record; over 30 years of research has been invested in the development of methods to characterize large-scale snow water resources from PM-based measurements. Historically, use of PM data for snowpack characterization in montane enviroments has been obstructed by the complex subpixel variability of snow properties within the PM measurement footprint. The main subpixel effects can be grouped as: the effect of snow microstructure (e.g. snow grain size) and stratigraphy on snow microwave emission, vegetation attenuation of PM measurements, and the sensitivity PM brightness temperature (Tb) observation to the variability of different subpixel properties at spaceborne measurement scales. This dissertation is focused on a systematic examination of these issues, which thus far have prevented the widespread integration of snow water equivalent (SWE) retrieval methods. It is meant to further our comprehension of the underlying processes at work in these rugged, remote, a hydrologically important areas. The role that snow microstructure plays in the PM retrievals of SWE is examined first. Traditional estimates of grain size are subjective and prone to error. Objective techniques to characterize grain size are described and implemented, including near infrared (NIR), stereology, and autocorrelation based approaches. Results from an intensive Colorado field study in which independent estimates of grain size and their modeled brightness temperature (Tb) emission are evaluated against PM Tb observations are included. The coarse resolution of the passive microwave measurements provides additional challenges when trying to resolve snow states via (open full item for complete abstract)

Committee: Michael Durand (Advisor); Howat Ian (Committee Member); Alsdorf Doug (Committee Member) Subjects: Hydrologic Sciences; Hydrology

Master of Science, The Ohio State University, 2011, Geodetic Science and Surveying

Snow is an important component of hydrology and climate at both local and global scales. In-situ snowpack measurements provide accurate, reliable data on snowpack properties, but represent only a point measurement of the spatially variable snow cover, lacking spatial continuity. Spaceborne passive microwave remote sensing (PM) measurements are attractive for snowpack characterization due to their continuous global coverage, but a drawback of coarse spatial resolution. In this paper, a footprint based method is developed to improve the PM snow measurements by extracting more information on snow properties. Several experiments carried out in Kern River Basin, Sierra Nevada, USA show PM data processed via the new method contain significant snowpack information, especially information on snow water equivalent (SWE) and melt timing, which are two most important snow properties. When compared with the traditionally used PM dataset, the newly processed data show three times more sensitivity to in-situ SWE, and a 9.6% increase in the correlation coefficient between SWE and PM measurements: both indicate the new data processing method has the capability to improve the PM data's sensitivity to snow.

Committee: Michael Durand PhD (Committee Chair); Alan Saalfeld PhD (Committee Member) Subjects: Civil Engineering; Earth; Geographic Information Science; Geophysical

Doctor of Philosophy, The Ohio State University, 2007, Electrical Engineering

The Small Perturbation Method (SPM) is a low frequency approximation to the electromagnetic scattering from rough surfaces. The theory involves a small height expansion in conjunction with a perturbation series expansion of the unknown scattering coefficients. Recently, an arbitrary order, iterative solution procedure has been derived for SPM: kernels at any order are expressed as a summation over lower order kernels in an iterative fashion. Such a form is very useful, because it allows evaluation of the field statistical moments in a direct manner, when considering stochastic surfaces. In this dissertation, this procedure is extended to the two layer (two rough surfaces on top of each other) problem and the complete solution is given. Utilizing this formulation, the second and fourth order bi-static scattering coefficients for two rough surfaces characterized by two uncorrelated Gaussian Random Processes (GRP) are obtained. The effects of upper and lower roughnessesand the interaction effect in the total fourth order cross section can be identified in the theory. Studies on the ratio of the interaction effect to the total cross section are presented for example cases, investigating the relative importance of interactions among surfaces. Results show the interaction term contributes most to the cross-pol cross sections when surfaces are close to each other at near grazing incidence. In addition, the previously developed arbitrary order SPM solution for the single layer problem is utilized to derive the fourth order term in the small slope approximation (SSA) of thermal emission from the sea surface. It is shown that this term has the form of a four-fold integration over a product of two sea spectra for a Gaussian random process sea, thereby describing emission “interaction” effects among pairs of sea waves. Interaction effects between “long” and “short” waves are considered, both through numerical and approximate evaluations of the fourth order theory. The approxima (open full item for complete abstract)

Committee: Joel Johnson (Advisor) Subjects: