Vegetation Sensing Using GPS-Interferometric Reflectometry: Theoretical Effects of Canopy Parameters on Signal-to-Noise Ratio Data

Abstract

The potential to use GPS signal-to-noise ratio (SNR) data to estimate changes in vegetation surrounding a ground-based antenna is evaluated. A 1-D plane-stratified model that simulates the response of GPS SNR data to changes in both soil moisture and vegetation is presented. The model is validated against observations of SNR data from four field sites with varying vegetation cover. Validation shows that the average correlation between modeled and observed SNR data is higher than the average correlation between concurrent SNR observations from different satellite tracks at a site. The model also reproduces variations in the SNR metrics amplitude, phase, and effective reflector height over a range of vegetation wet weights from 0 to 4 $\hbox{kg}\cdot\hbox{m}^{-2}$ , with $r^{2}$ values of 0.79, 0.84, and 0.62, respectively. Model simulations indicate that the amplitude of SNR oscillations may be used to estimate vegetation amount when vegetation wet weight is below 1.5 $\hbox{kg}\cdot\hbox{m}^{-2}$ . When vegetation wet weight exceeds 1.5 $\hbox{kg}\cdot\hbox{m}^{-2}$ , the sensitivity of amplitude to changes in vegetation amount decreases. Phase of SNR oscillations also varies consistently with vegetation up to 1.5 $\hbox{kg}\cdot\hbox{m}^{-2}$ . However, phase is also very sensitive to soil moisture variations, thus limiting its utility for estimating vegetation. Effective reflector height is not a consistent indicator of vegetation change. Beyond 1.5 $\hbox{kg}\cdot\hbox{m}^{-2}$ , the constant frequency assumption used to characterize SNR fluctuations does not adequately describe observed data. A more complex approach than the standard SNR metrics used here is required to extend GPS-Interferometric Reflectometry sensing to thicker canopies.