Abstract

Remote sensing techniques are continuously being developed to extract physical information about the Earth’s surface. Over the years, space-borne and airborne sensors have been used for the characterization of surface sediments. Geophysical properties of a sediment surface such as its density, grain size, surface roughness, and moisture content can influence the angular dependence of spectral signatures, specifically the Bidirectional Reflectance Distribution Function (BRDF). Models based on radiative transfer equations can relate the angular dependence of the reflectance to these geophysical variables. Extraction of these parameters can provide a better understanding of the Earth’s surface, and play a vital role in various environmental modeling processes. In this work, we focused on retrieving two of these geophysical properties of earth sediments, the bulk density and the soil moisture content (SMC), using directional hyperspectral reflectance. We proposed a modification to the radiative transfer model developed by Hapke to retrieve sediment bulk density. The model was verified under controlled experiments within a laboratory setting, followed by retrieval of the sediment density from different remote sensing platforms: airborne, space-borne and a ground-based imaging sensor. The SMC was characterized using the physics based multilayer radiative transfer model of soil reflectance or MARMIT. The MARMIT model was again validated from experiments performed in our controlled laboratory setting using several different soil samples across the United States; followed by applying the model in mapping SMC from imagery data collected by an Unmanned Aerial System (UAS) based hyperspectral sensor.

Publication Date

8-6-2019

Document Type

Dissertation

Student Type

Graduate

Degree Name

Imaging Science (Ph.D.)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

Charles Bachmann

Advisor/Committee Member

David Ross

Advisor/Committee Member

Aaron Gerace

Comments

This dissertation has been embargoed. The full-text will be available on or around 8/28/2020.

Campus

RIT – Main Campus

Available for download on Thursday, August 27, 2020

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