Digging Through the Trash: An Investigation into Hidden Sources of Information in GNSS-R Observations

Clara Christabel Chew, Margaret Scott, Ian Colwell

Interpreting remote sensing data sometimes feels like a dark art. We aim to assign a single meaning to ambiguous signals that were actually affected by a slew of unique conditions. And NASA's Cyclone Global Navigation Satellite System (CYGNSS) data are no exception. When we observe an increase in CYGNSS surface reflectivity, we most often attribute it to either an increase in soil moisture or in inundation extent. Yet distinguishing between these two surface conditions is impossible to do with reflectivity observations alone. Here, we propose a way forward to better understand the primary driver affecting data from CYGNSS and other GNSS-Reflectometry satellites. We present a new metric created using information contained within the surface reflection that is often ignored or discarded during processing, which we label 'little gamma.' We discuss whether this new metric and other such 'coherency metrics' are actually measuring coherency, which is assumed to be indicative of the surface roughness, or whether the story is, in fact, more complicated. Our findings suggest that the dielectric constant of the diffuse component can significantly change a coherency metric with no corresponding change in roughness, and this aspect should be considered when interpreting such a metric. Finally, we present evidence that combining little gamma with surface reflectivity can help us distinguish a change in the observation that is due to soil moisture versus surface inundation, lessening the ambiguity currently plaguing the interpretation of surface reflectivity.