GPS World, October 2009

Algorithms Methods INNOVATION 80 60 40 20 0 20 40 60 80 SNR 40 30 20 10 40 0 30 60s periods 30 20 10 0 60 90s periods 66 79 500 1000 1500 2000 2500 3000 3500 Normalized power x100 Time seconds past hour 155 FIGURE 5 Example SNR profile from MKEA top panel as a function of time in linear amplitude units after direct signal contributions have been removed The bottom panels show wavelet power at different periods colored lines which are averaged together to form the wavelet power over 30 60 and 60 90 seconds period bands of interest heavy black lines rocky these cinder cones generate strong multipath refl ections The sloped hillsides can be broken into a set of discrete refl ectors at different distances creating multipath oscillations at different frequencies over each satellite pass For a more in depth discussion of MKEA multipath and other power spectral map examples see Mapping the GPS Multipath Environment Using the Signal to Noise Ratio SNR listed in Further Reading Soil Moisture Manuel Martin Neira is credited with introducing the idea in 1993 that refl ected GPS signals could be used for scientifi c studies Since then GPS refl ection studies for ocean altimetry and winds soil moisture and snow sensing have all been discussed in the literature These studies typically use an antenna pointed to optimize Earth refl ections and specifi cally designed to track refl ected LHCP signals This means that antennas designed to suppress ground refl ections such as those used by the geophysical geodetic and surveying communities are not used Motivated by our studies showing that multipath effects could clearly be seen in geodetic quality data collected with multipath suppressing antennas we proposed that these same GPS data could be used to extract a multipath parameter that would correlate with changes in the refl ectance of the ground surface In our initial study we used data from an existing IGS GPS site in Tashkent Uzbekistan and concentrated on SNR refl ectance changes caused by rain and subsequent drying of the soil While the correlation between the SNR data and precipitation models was strong we 33 39 47 56 lacked proper ground instrumentation to demonstrate that we were measuring true soil moisture changes Subsequently together with other colleagues we carried out an experiment designed to more rigorously demonstrate the link between GPS SNR and soil moisture Specifi cally we were interested in using GPS refl ection parameters to determine the soils volumetric water content the fraction of the total volume of soil that is occupied by water an important input to climate and meteorological models Traditional soil moisture sensors water content refl ectometers were buried in the ground at multiple depths 25 and 75 centimeters at a site just south of the University of Colorado in Boulder Precipitation data were also collected Using a fi xed frequency Equation 7 was used to model the SNR data and estimate an amplitude and phase www gpsworld com October 2009 GPS World 37