Abstract:
It is important to closely monitor the state of the world’s
wetlands, as climate change and human encroachment in a
rapid global urbanization trend threaten to cause large-scale
wetland collapse. Because wetlands are often difficult to
observe in situ, remote sensing is the only viable way to
map wetland extent globally. However, current remote
sensing methods suffer limitations in capturing wetland
extent, and more importantly, wetland dynamics at
appropriate spatial and temporal scales. GNSSReflectometry
could help fill the current observation gap, as
experimental data show that ground-reflected GNSS signals
are very sensitive to changes in inundated areas.
Furthermore, because this technique only requires a custom
developed receiver and antenna system, a constellation of
such instruments can potentially be launched at relatively
low cost, providing global observations at sub-daily
intervals. One challenge remains, however, which is
quantitatively formulating the geophysical product of
reflections over the land surface in various states of
inundation. Here, we use a novel reflection dataset, derived
from the SMAP radar receiver, to elucidate the sensitivity of
reflections to small land surface features and their seasonal
variations. Additionally, we quantify the dynamic range of
reflections over both open and closed wetlands, and suggest
an algorithm for wetland type classification.
