Thesis (M.S. in Meteorology)--Naval Postgraduate School, June 1992. / Thesis Advisors: Durkee, Philip A. ; Wash, Carlyle H. "June 1992." Description based on title screen as viewed on March 10, 2009. Includes bibliographical references (p. 71-72). Also available in print.

Thesis (Ph. D.)--University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 15, 2007) Vita. Includes bibliographical references.

Remote sensing of land surface temperature (LST) is a complex task. From a satellite-based perspective the radiative properties of the land surface and the atmosphere are inextricably linked. Knowledge of both is required if one is to accurately measure the temperature of the land surface from a space-borne platform. In practice, most satellite-based sensors designed to measure LST over the surface of the Earth are polar orbiting. They scan swaths of the order of 2000 km, utilizing zenith angles of observation of up to 60°. As such, satellite viewing geometry is important when comparing estimates of LST between different overpasses of the same point on the Earth's surface. In the case of the atmosphere, the optical path length through which the surfaceleaving radiance propagates increases with increasing zenith angle of observation. A longer optical path may in turn alter the relative contributions which molecular absorption and emission processes make to the radiance measured at the satellite sensor. A means of estimating the magnitudes of these radiative components in relation to the viewing geometry of the satellite needs to be developed if their impacts on the at-sensor radiance are to be accurately accounted for. The problem of accurately describing radiative transfer between the surface and the satellite sensor is further complicated by the fact that the surface-leaving radiance itself may also vary with sensor viewing geometry. Physical properties of the surface such as emissivity are known to vary as the zenith angle of observation changes. The proportions of sunlit and shaded areas with the field-of-view of the sensor may also change with viewing geometry depending on the type of cover (eg vegetation), further impacting the surface emissivity. / Investigation of the change in surface-leaving radiance as the zenith angle of observation varies is then also important in developing a better understanding of the radiative interaction between the land surface and the atmosphere. The work in this study investigates the atmospheric impacts using surface brightness temperature measurements from the ATSR-2 satellite sensor in combination with atmospheric profile data from radiosondes and estimates of the downwelling sky radiance made by a ground-based radiometer. A line-by-line radiative transfer model is used to model the angular impacts of the atmosphere upon the surfaceleaving radiance. Results from the modelling work show that if the magnitude of the upwelling and downwelling sky radiance and atmospheric transmittance are accurately known then the surface-emitted radiance and hence the LST may be retrieved with negligible error. Guided by the outcomes of the modelling work an atmospheric correction term is derived which accounts for absorption and emission by the atmosphere, and is based on the viewing geometry of the satellite sensor and atmospheric properties characteristic of a semi-arid field site near Alice Springs in the Northern Territory (Central Australia). Ground-based angular measurements of surface brightness temperature made by a scanning, self calibrating radiometer situated at this field site are then used to investigate how the surface-leaving radiance varies over a range of zenith angles comparable to that of the ATSR-2 satellite sensor. / Well defined cycles in the angular dependence of surface brightness temperature were observed on both diumal and seasonal timescales in these data. The observed cycles in surface brightness temperature are explained in terms of the interaction between the downwelling sky radiance and the angular dependence of the surface emissivity. The angular surface brightness temperature and surface emissivity information is then applied to derive an LST estimate of high accuracy (approx. 1 K at night and 1-2 K during the day), suitable for the validation of satellite-derived LST measurements. Finally, the atmospheric and land surface components of this work are combined to describe surface-atmosphere interaction at the field site. Algorithms are derived for the satellite retrieval of LST for the nadir and forward viewing geometries of the ATSR-2 sensor, based upon the cycles in the angular dependence of surface brightness temperature observed in situ and the atmospheric correction term developed from the modelling of radiative transfer in the atmosphere. A qualitative assessment of the performance of these algorithms indicates they may obtain comparable accuracy to existing dual angle algorithms (approx. 1.5 K) in the ideal case and an accuracy of 3-4 K in practice, which is limited by knowledge of atmospheric properties (eg downwelling sky radiance and atmospheric transmittance), and the surface emissivity. There are, however, strong prospects of enhanced performance given better estimates of these physical quantities, and if coefficients within the retrieval algorithms are determined over a wider range of observation zenith angles in the future.

Thesis (Ph.D.)--University of Central Florida, 2009. / Adviser: W. Linwood Jones. Includes bibliographical references (p. 129-132).

Thesis (M.S.)--Brigham Young University. Dept. of Mechanical Engineering, 2003. / Includes bibliographical references (p. 87-89).

With global warming on the rise and the urge for conserving our natural resources, it becomes very important that proper steps are taken to protect our natural resources and utilize them efficiently. Forest fires are one of the many issues on the charts towards protection of natural resources. The catastrophic aftermaths caused by forest fires are known to all. The causes for these fires could be known/unknown natural causes or human intervention. Remote sensing techniques use the electromagnetic radiation in the RF/Microwave region, emitted from an object. The amount of energy emitted from an object depends on its present conditions, primarily its temperature and its emissivity. The sensing devices used in such measurements are classified into active and passive sensors. Herein, passive radiometry is used to investigate a model for the propagation of subsurface radiation from underground forest fires through upper ground layers of soil till the land-air interface. Passive radiometry involves capturing the radiation incident on a radiometer antenna aperture directly or deflected from several objects. The energy emitted from sources above 0K is collected and is compared with the calibration standards to estimate the physical quantity under test. Detecting forest fires is one of the potential applications of passive radiometry investigated here.

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.

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.

The Aquarius/SAC-D joint international science mission, between the National Aeronautics and Space Administration (NASA) of United States and the Argentine Space Agency (Comision Nacional de Actividades Espaciales, CONAE), was launched on a polarorbiting satellite on June 10, 2011. This mission of discovery will provide measurements of the global sea surface salinity, which contributes to understanding climatic changes in the global water cycle and how these variations influence the general ocean circulation. The Microwave Radiometer (MWR), a three channel Dicke radiometer operating at 23.8 GHz H-Pol and 36.5 GHz V-& H-Pol provided by CONAE, will complement Aquarius (NASA’s L-band radiometer/scatterometer) by providing simultaneous spatially collocated environmental measurements such as water vapor, cloud liquid water, surface wind speed, rain rate and sea ice concentration. This dissertation focuses on the overall radiometric calibration of MWR instrument. Which means establishing a transfer function that relates the instrument output to the antenna brightness temperature (Tb). To achieve this goal, the dissertation describes a microwave radiative transfer model of the instrument and validates it using the laboratory and thermal-vacuum test data. This involves estimation of the losses and physical temperature profile in the path from the receiver to each antenna feed-horn for all the receivers. As the pre-launch laboratory tests can only provide a simulated environment which is very different from the operational environment in space, an on-orbit calibration of the instrument is very important. Inter-satellite radiometric cross-calibration of MWR using the Naval Research iii Laboratory’s multi-frequency polarimetric microwave radiometer, WindSat, on board the Coriolis satellite is also an important part of this dissertation. Cross-calibration between two different satellite instruments require normalization of Tb’s to account for the frequency and incidence angle difference between the instruments. Also inter-satellite calibration helps to determine accurate antenna pattern correction coefficients and other small instrument biases.