GNSS-Scatterometry Challenges in Lake Ice Remote Sensing

Delay-Doppler Maps (DDMs) have already shown promising potentials in the sea ice remote sensing, including ice recognition and sea ice thickness estimation. However, this kind of products is yet to be tested for lake ice remote sensing, which looks more challenging than that of sea ice, especially for mid-latitude ones.

Generally speaking, two main approaches have been established so far for GNSS-R remote sensing: altimetric and scatterometric approaches. Although these two approaches are somehow connected to each other, the methodologies can be way apart. Accordingly, ice remote sensing using GNSS-R can be, and has been, conducted in both ways.

Among many scatterometric-based research items for ice remote sensing, Qingyun Yan has successfully carried out and published several papers on sea ice remote sensing by analyzing DDMs obtained from TechDemoSat-1 (TDS-1) satellite over northern regions. He has also introduced the feasibility of sea ice thickness measurement by extracting brightness temperature values from DDMs reflectivity observations. However, no GNSS scatterometry experiments have been yet done over lake ice to examine the GNSS-R capability of lake ice remote sensing by means of DDMs.

As part of an experiment that I’m currently involved in regarding the DDMs potential of lake ice remote sensing, I started exploring challenges that one may face in lake ice remote sensing using scatterometric data. Lake ice covers, especially mid-latitude ones, as discussed in some previous posts of this blog (e.g., this one), are, in general, highly influenced by mid-winter warmings and melt/refreeze events, which significantly change the reflectivity properties of the lake ice reflective layer(s). Therefore, I came up with two main challenges as follows.

Firstly, in GNSS scatterometry, it’s usually assumed that we’re dealing with a single scattering regime, i.e., only one surface contributes to the reflection. This assumption in mid-latitude frozen lakes is improper because, as mentioned above as well as in my master’s thesis, mid-winter warming events leads to the appearance of some wet layers inside and over the ice column causing interferences in reflections. In other words, depending on the water content of each contributing layer, which controls the dielectric permittivity of overlaying layers, these layers may cause a multi-reflection regime, which differs the scenario from what has been defined in the GNSS-R tutorial.

secondly, melt/refreeze events during the freezing season change the ice-water interface roughness significantly, which may change the amount of the reflected power received by the satellites. In the literature, some theoretical models, such as Kirchhoff Approximation and Integral Equation Method, are applied to simplify assumptions regarding model the roughness of reflective surfaces. However, ice-water interfaces at frozen lakes are less likely to be adopted with those models; therefore, non-specular reflections are very likely to happen from lake ice towards GNSS-R receivers.

Furthermore, topography and EIRP effects have been discussed here an here, respectively; however, we’re actively exploring for other potential sources of inconsistencies. Feel free to reach us out and contribute if you have ideas on that.

How to Fund Smartphone-Based GNSS-R Observations for Lake Ice Remote Sensing; Cryptocurrencies may Help!

I got surprised to see this post, which was about smartphones potential for lake ice thickness measurement, has been circulated a lot and gained many views from different countries around the world. However, talking about this ideas through one-to-one discussions with my colleagues and friends, a question cropped up, which made me to write this post: “How would we encourage non-specialist individuals to dedicate their phones, even for a short period of time they’re spending over lake ice, for our GNSS-Reflectometry goal regarding lake ice thickness measurement?”

I have heard a lot from people inside academia saying that environmental issues are somehow apart from financial affairs. In their point of view, when we get through environmental issues/crises, it doesn’t make sense if one talks about costs and economic feasibility, but instead, people should sincerely dedicate what they have, or even don’t, to tackle the issues. Dead Wrong! A commercialized business plan should be, I believe, the first step for turning any research\academic solutions into a practical subject.

I came up with this challenge too, when I was thinking about the idea I proposed in the previous post. In our scenario, individuals go over lake ice covers to spend their leisure time or even to do their routine jobs by, for instance, ice fishing. Now, we’d like to ask them to put their smartphones on the ice surface in a certain short distance off themselves and leave their smartphones there for couple of hours to measure the ice thickness by running our proposed app. How would we make it beneficial for them?

I know that education is a very important and effective way to raise public awareness about harms caused by the downward trends of lake ice duration, which are obviously shown by multiple environmental studies. But I believe that public awareness could not be enough to encourage individuals to devote their smartphones for this purpose; a stronger inspiration would be required.

This figure was used in my master’s thesis, without any technical analyses; originally derived from CLIMo, a lake ice model developed by Dr. Claude Duguay

Cryptocurrencies have already become a very hot topic in financial affairs, and a fast-growing number of businesses and concepts are being defined or re-developed using blockchain technologies. Aside from businesses, scientific activities have also shown potentials to be benefited and funded by endowments placed on the blockchain. Moreover, cryptocurrencies have suggested an opportunity to better protect the integrity and provenance of scientific data. In addition, ENV Finance has introduced itself as a cryptocurrency project aiming to bring environment and finance together; you can read about their project here. Seemingly, we’re not the pioneers of the “cryptoscience” (such an odd word), but we have some examples to refer.

As a result, people who are helping the environment by their smartphones equipped with a GNSS-R app may be awarded by tokens assigned by sponsors. Furthermore, a very recent cryptocurrency has been developed by a group of Stanford’s scientists enabling smartphones to mine blocks. This cryptocurrency, which is called Pi Network, may give us hints on how to encourage smartphone users to measure ice thickness and mine coins at the same time.

I am not a blockchain specialist; but even if I were, any comments, guides, and critiques would still be welcome.

GNSS Reflectometry Using Smartphones; A Potential for Lake Ice Thickness Monitoring

SNR-based GNSS-Reflectometry has shown an enormous potential for ground-based altimetry purposes as the frequency of SNR oscillations is directly connected to antenna height from the reflective surface. This technique, which is usually called in the literature as GNSS Interferometric Reflectometry, has been already tested for retrieving snow depth, monitoring water level changes, and measuring lake ice thickness. The considerable advantage of this technique can be listed as the low costs of equipment since a simple standard GNSS receiver would be enough for running a GNSS-IR experiment.

Recently, Cemali Altuntaş, a researcher from Yildiz Technical University, Turkey, has evaluated the potential of android smartphones for the GNSS-IR experiment and antenna height measurement. This well-structured research, which is going to be published in a couple of weeks by Digital Sensor Processing, Elsevier, analyzed the SNR-based retrieved heights obtained from a single-frequency GNSS receiver embbeded in a Xiaomi Mi 8 Lite smartphone, compared with those recorded by a Trimble NetR9 geodetic receiver, and then validated with in-situ measurements. Results show a stunning performance by the smartphone unit compared to the geodetic receiver in terms of height residuals.

As I am not an electrical specialist, I have no clue on why a low-cost GNSS receiver designed for daily routine jobs shows better results in comparison with a standard geodetic receiver, which has been always used for highly professional geodetic observations. Although the positioning precision of Xiaomi Mi 8 Lite has been tested and shown to get close and be comparable to geodetic receivers, but in terms of SNR-based reflectometry, I would be grateful if a specialist could make it a bit clear whether it is related to the noise amount, antenna gain, or any other possible reasons.

Considering the advantageous potentials of smartphone GNSS receivers for reflectometry purposes, one may expand the mobile-based GNSS-IR to a wider range of remote sensing applications, especially those requireing massive data, which can be collected by non-specialists individuals. As an example, GNSS-IR has shown a promising potential for the freshwater ice thickness measurement using a single geodetic receiver. In a study, we placed a GNSS antenna on the ice surface allowing it to receive reflected GNSS signals from the ice-water interface to measure lake ice thickness. Lake ice is known as a very common place for many people who live in cold regions, such as Canada, to spend hours over for, for instance, ice fishing. Therefore, smartphone-based GNSS-IR may offer a new opportunity for individuals allowing them to just leave their smartphones in a short distance away from themselves on the ice surface during their on-ice activities in order to collect reflected GNSS signals from the lake ice. This contribution may be aggregated by a central server to provide a wide coverage of lake ice thickness data with a demonstrated accuracy in our paper.

Temperature Effect on Reflected GNSS Signals from Mid‐Latitude Lake Ice; Review and Discussion

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.