Abstract

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.

Degree

College and Department

Ira A. Fulton College of Engineering and Technology; Electrical and Computer Engineering

Rights

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