Inversion of rough-surface parameters via polarimetric synthetic aperture radar data

Abstract

I apply my method to a set of experimentally collected SAR data, finding reasonable agreement with results reported in the literature.

Synthetic aperture radar (SAR) is a form of remote sensing capable of providing high-resolution images, day or night, independent of weather. Monostatic SAR involves a moving, airborne or spaceborne antenna that periodically transmits electromagnetic pulses and measures the resulting backscattered signal. Specific applications include planetary exploration, climate change research, environmental monitoring, land-use monitoring, and terrain mapping, as well as military and agricultural applications.

In general, the problem of Earth remote sensing is to extract as much information as possible about some region of interest via some remotely sensed signal. In this dissertation, I focus on rough-surface scattering. In particular, I use monostatic, polarimetric SAR data to estimate three parameters characterizing the rough surface under investigation: the (frequency-dependent) complex-valued permittivity, the correlation length, and the root-mean-square (RMS) height. The permittivity is of interest, for example, in estimating soil moisture content in an agricultural context and in the improvement of numerical weather predictions and climate simulations. The correlation length and RMS height are of interest, for example, in monitoring soil erosion, which has implications in agricultural land management.