Cryospheric Sensing Using GNSS-R - GNSS Remote Sensing: Theory, Methods and Applications - page 255

Fig. 11.3 Theoretical ( lines ) and measured ( squares ) elevation plots for a reservoir covered by
a 0.3 cm thick snow layer on top of 12.7-, 26.7-, 39.4-, 54.7-, and 68.7-cm-thick ice layers with
h = 50.5 cm for the GPS satellite PRN 10 ( Jacobson 2010 )

pack near Barrow, Alaska, USA. The correlation was quite consistent for forward-
scattered GPS returns and RADARSAT backscattered measurements. This behavior
of the reflected signal showed clearly the sensitivity to ice condition, indicating
that the GPS reflected signals can well determine the ice status and features. The
potential of sensing sea ice from low Earth orbit was explored using two signals
from two different ice concentrations by Gleason ( 2010 ). This result showed some
agreement between GPS measurements and estimates of ice concentration from the
Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-
E) instrument, but due to the small amount of data no conclusions could be made.

11.2.2 Observations from Ground GNSS-R

The change in snow depth is also monitored using the corresponding multi-path
modulation of the ground GPS signal. The tested results for two spring 2009
snowstorms in Colorado showed strong agreement between GPS snow depth
estimates, field measurements, and nearby ultrasonic snow depth sensors ( Larson
et al. 2009 ). In addition, Fig. 11.3 shows the measurement and theoretical results for
a 0.3 cm thick snow layer on top of 12.7-, 26.7-, 39.4-, 54.7-, and 68.7-cm-thick ice
layer from the GPS satellite PRN 10 ( Jacobson 2010 ). It has shown that a theoretical
ice thickness of 39.4 cm agrees best with the measurements with regards to the first
deep fade shape.