How (actually) will the CYGNSS satellites interpret ocean roughness by comparing direct and reflected signals from GPS satellites? I'm interested in an explanation of how the received signals will be compared to extract a number for ocean roughness.
I stumbled upon a pretty good explanation by accident. The Taiwan National Science and Technology Council's main web page is currently featuring an image of the Triton (Formosat 7R) satellite and links to this Taiwan Space Agency (TASA, formerly NSPO)'s page which shows the following graphics (click for larger):
The 2nd image shows a rough illustration of intensity vs time for a given signal from a GPS satellite; first there's a sharp peak labeled "direct signal" which would be line-of-sight from a GPS satellite to any GNSS-Reflectometry (GNSS-R) satellite, be it CYGNSS or Triton or others.
This is followed by a delayed peak centered at the "specular point" when a signal reflected off of a smooth, "shiny" ocean would arrive.
The diagram shows two effects related to sea surface roughness due to wind
- a reduction in the prompt reflected intensity from the specular point
- presence of a variable amount of delayed reflected intensity from any other areas on the ocean surface, which via Fermat's principle or principle of least time will always be delayed relative to the specular signal.
A helpful analogy is the reflection of sunlight off of a body of water seen from some height; either one you are standing next to, or from space. Smooth seas will show a small, self-contained reflection of the Sun, choppy seas will show reflected light from a much larger patch.
While our eyes use a lens and retina to convert Fermat's least time to a spatial image corresponding to angle, the non-imaging satellite uses a more direct measurement of time to differentiate the concentrated reflection near the specular point from diffuse, off-specular reflection from choppy areas further away.
Of course they will use more complicated signal processing to do that. I found this nice document GNSS-R 遙測技術的工程應用 (found here (google: "GNSS-R Engineering Application of Telemetry Technology")
Putting the relevant section into google translate and doing a little cleanup:
GNSS-R Application of sea surface wind farm and roughness.
The route of the GPS reflection signal is longer than that of direct-firing signals, and it will generate delays. In addition, the impact of the roughness of the reflection point and the scattering of the reflection signal scattering. Under the action of wind, the sea surface causes the waves to change the roughness of the sea surface. The larger the wind speed, the greater the roughness of the sea surface, and the greater the degree of changes in the impact of the reflected signal; the slower the wind, the smoother the waveform2, as shown in Figure 6.
Zavorotny, Voronovich, and Sensing17 theoretically modeled describes the global positioning system (GPS) signal and the relationship between the power and the sea wind field. Then El Fouhaily, Thompson,
Linstrom, and Sensing18 further improve the model. Clarizia, Ruf,
Jales, Gommenginger, and sensing17 proposed minimum
Variance (MV) Wind Speed Estimator, using 5 different observations
Point to establish GNSS-R Delay-DopPler Maps (DDMS), and push further to estimate the sea wind farm. Foti et al. 20. The secondary shift remote test display has good consistency with the measured data (Figure 7).
2Katzberg, S.J. and Garrison Jr, J.L. (1996). Utilizing GPS to determine ionospheric delay over the ocean.
17Zavorotny, V.U., Voronovich, A.G., and Sensing, R. (2000). Scattering of GPS signals from the ocean with wind remote sensing application. IEEE Transactions on Geoscience and Remote Sensing, 38(2), 951-964. IEEE, Researchgate and University of Michigan
18Elfouhaily, T., Thompson, D.R., Linstrom, L., and Sensing, R. (2002). Delay-Doppler analysis of bistatically reflected signals from the ocean surface: theory and application. IEEE Transactions on Geoscience and Remote Sensing, 40(3), 560-573. IEEE
19Clarizia, M.P., Ruf, C.S., Jales, P., Gommenginger, C., and Sensing, R. (2014). Spaceborne GNSS-R minimum variance wind speed estimator. IEEE Transactions on Geoscience and Remote Sensing, 52(11), 6829-6843.
20Foti, G., Gommenginger, C., Jales, P., Unwin, M., Shaw, A., Robertson, C., and Rosello, J. (2015). Spaceborne GNSS reflectometry for ocean winds: First results from the UK TechDemoSat‐1 mission. Geophysical Research Letters, 42(13), 5435-5441.
From here is an illustration of how the time delay and doppler shift provide some information about the location of a reflected component relative to the specular point.
As far as I can tell, the technique involves three parts
- develop a complete theoretical framework to predict intensity as a function of incoming/outgoing directions for each of a large number of different wave shapes associated with wind speed (and unfortunately history, waves build up and die down over time, only the smallest ones are short-term proxies)
- measure as much as you can that's related, using existing satellites and demonstrators
- compare the results to "ground truth" or "water truth" measurements of wind speeds
- say to yourself "oh, it's not so good of a fit"
- come up with some kind of multivariate model with several parameters that helps to improve the poor correlation between measured wind speeds and algorithms operating on recorded GNSS-R data.
- say it works well, mission success!
Fig. 2. Configuration of glistening, annulus, and Doppler zones.