Detecting changes in reflected Global Navigation Satellite System signals over land using a spaceborne receiver: Results from the TechDemoSat Mission

Abstract:

Spaceborne GPS and GNSS receivers can be used to retrieve information about changes on the Earth’s surface. Both experimental and modeling efforts have shown that these receivers can detect changes in reflected GNSS signals that are indicative of changes in sea state. Numerous studies using GNSS receivers flown on aircraft have also shown that the reflected signals are sensitive to changes in soil moisture and vegetation cover. However, the only analysis of the detection of GNSS reflected signals over land using spaceborne receivers has been limited to the small amount of data recorded nearly 10 years ago by the UK-DMC satellite.

Last year’s launch of the TechDemoSat (TDS) satellite, carrying an instrument similar to that planned for NASA’s CYGNSS mission, represents an enormous opportunity to investigate the potential of using spaceborne GNSS receivers to sense changes in the land surface, including soil moisture and flood-inundated areas. With a revisit time of only a few hours, the observations from the CYGNSS constellation could provide data with a temporal resolution that would be unmatched by traditional remote sensing satellites.

Here, we present data collected over land by the receiver onboard TDS and report its sensitivity to changes in surface roughness, vegetation parameters, and open water (lakes and rivers), as well as standing water beneath vegetation (marshes and wetlands). In particular, we investigate how the normalized peak power of the delay-Doppler maps that are recorded by the receiver is affected by changes in the land surface. Preliminary results indicate that the signal is strongly affected by changes in topography. However, once this effect is removed using digital elevation models, the influence of rivers, lakes, and wetlands on the signal is clearly seen. Examples of large signal changes coming from areas of likely-saturated ground lend credence to the idea that these data could also be sensitive to changes in surface soil moisture.