Our poster was presented on the first IAG’s conference-workshop on “Geodesy for Climate Research” which is currently (March 29-31, 2021) being held online. The idea of this poster, as can be understood from the title, was on examining the temperature effect on GNSS reflectometry over lake ice.

During the freezing season of 2019-2020, a GNSS-R experiment was conducted at a mid-latitude frozen lake called MacDonald Lake to examine the ability of GNSS interferometric reflectometry for lake ice remote sensing. The initial plan was analyzing GPS signals reflected from the lake to monitor ice formation and generate an ice thickness chart for those dates. However, due to natural features of temperate lake ice covers, such as warm temperatures and mid-winter melt/refreeze events, ice-thickness estimation using GNSS-R was not as straightforward as other routine GNSS altimetry applications conducted before over snow fields or sea surfaces. Meanwhile, the footprints of other lake ice features, such as slush and wet snow, were appeared on the recorded GNSS-R data.

Among those side-parameters, temperature was chosen for this poster. One may think that, by the word temperature effect, I am talking about the tropospheric effects on GNSS signals, such as delay and bending effects, which do matter and have been evaluated for near surface GNSS-R receivers, but I do not mean that. Except for the tropospheric effect, and also the electrical aspects of GNSS receivers, temperature does not directly impact reflected GNSS signals. The point is that temperature causes mid-winter melt/refreeze events in lake ice and, by implication, changes the roughness patterns of reflective layers. Therefore, roughness is the key driver affecting the reflected signals, which is itself affected by temperature variations.

To be specific, for this experiment, we used the SNR-analysis approach in which daily SNR files were extracted and their frequency were calculated to retrieve the antenna heights above reflective surfaces, which could be the ice-water interface, slush layers, or top of the wet snow pack. More details can be found in Chapters 2 and 4 of my master’s thesis. Meanwhile, the power spectrum related to those frequencies showed to be varying over the time, i.e., as shown in Fig 2 in the poster, the SNR amplitude values were not necessarily the same among different dates. Plotting this ups and downs against daily temperatures, as shown in Fig 3 of the poster, we see a harmony, which can be meaningful.

Around Dec 10-15, the freeze-up event happened and a large spike appeared; afterwards, the SNR power values dwindled to almost a third or less and that sharp spike never showed up again. This might be explained by saying when a thin layer of ice appears at the beginning of the freeze-up period, a calm ice/water surface appears whose roughness is much less than that of the lake water in the absence of the ice just before the freeze-up. So, it makes sense if we say the surface became very reflective and a huge reflection happened towards the GNSS antenna. This explanation has been also used in previous GNSS reflectometry studies, e.g. Qingyun Yan‘s research items on sea ice remote sensing through investigating DDMs. However, this reflectivity does not last for a long time as roughness conditions of the reflective layer(s) changes due to lake ice evolution, precipitation, wet layers appearance, and so on. Nevertheless, an agreement can be seen between temperature variations and SNR spectrum vales, so that when the temperature is close to or above zero, small jumps can be seen in SNR amplitudes. To better illustrate this co-variation, Fig 4 is plotted in the poster to illustrate SNR jumps against temperature.

As shown there, when temperature rises, red lines appear in “dAmplitude” values meaning that there are downward jumps in the SNR spectrum. It is not always true, but it mostly happens. Besides, a delay may be seen in this co-variability, which may be because of the time needed for melt/refreeze events to proceed. Meanwhile, one of our co-authors, Justin, who is currently doing his Ph.D. on lake ice remote sensing with the University of Waterloo, raised questions about my explanation and said “as shown in Fig 3, an increase in temperature mostly causes an increase in SNR amplitudes”. This statement retracts from the validity of the poster idea. “This is also agree with what we have in the physics of remote sensing”, Justin added “where wet layers appeared due to melt event leads to a higher dielectric constant and results in stronger returns”.

Justin’s criticism elicited my response by referring to bi-static radar properties where receivers and transmitters are not collocated; so an increase in dielectric constant does not necessarily bring about an increase in reflection strength towards the antenna. Instead, roughness has the main effect even if it happens at very small scales, as recently shown in Milad Asgarimehr‘s studies on the effect of small-scale roughness changes on GNSS signals reflected off oceans. However, my explanation may not look satisfying as those warm days are only some occasions; so, significant trends yet to be derived from this new idea with a short time series. It should be noted that Justin justly mentioned to the refreeze event as a concomitant of the melt event and its significant impact on the roughness, because I hadn’t included the refreeze event and only mentioned to the melt event in the first draft of the poster.

The bottom line is that climate change may show it’s effects on GNSS signals reflected off frozen lakes; however, further investigations are required for achieving to a firm conclusion. Longer time series may be required for future studies to extract more meaningful trends and correlations.