Raspberry Pi for GNSS-IR; A review on a recently-published paper

A new study has been recently published, in which a low-cost GPS antenna connected to a Raspberry Pi system has been employed to conduct a GPS-IR experiment for water level monitoring. In this paper, the authors have described the advantages of this system; however, there are some hidden points that I am going to flesh them out.

Makan Karegar, who is currently a researcher with the University of Bonn, is known for his innovative studies in various areas of GPS applications, ranged from gravimetric modelling to COVID monitroing. In his new paper, Makan, along with three other well-known reflectometrists (do we ever have such a word? reflectometrist?) analyzed the potential of low-cost GPS antennas attached to a Raspberry Pi microcomputer for water level monitoring. This system, which is called RPR (Raspberry Pi Reflectometer), was installed next to a river in Germany co-located with a guage for an interferometric reflectometry experience (you may watch this animiation of mine to see how GPS-IR works for the height estimation), and showed an acceptable RMSE (1.5 to 3 cm). Makan also properly mentioned how the advanteges of this system outweigh its limitations in both technical and financial ways. However, I would like to add three points based on experiences that I had in the past in working with a similar RPR. If you’ve ever been the reader of this blog, you probabely remember some of my experiences with RPR for soil moisture monitoring. I also had an unsucessful experience of using RPR for lake ice thickness and extent monitoring, which I will elaborate why it failed. So, I thought that I may be eligible to put some comments on this newly-published and, of course, valuable paper.

Firstly, the authors have emphasized the affordable price of this system compared to other GPS reflectometry sensors. That’s correct. Compared to what RPR gives, its cost (~$200) is reasonable; but it’s not THAT cheaper. The authors have mentioned that a very high-quality geodetic GPS receiver is higher than $10,000, which is correct, but one doesn’t need such an instrument for GPS-IR purposes. As an example, in 2019, I conducted a GPS-IR experiment using a brand-new EMLIS RS+ GNSS unit, which cost ~$700 (tax and shipping included). Besides, we didn’t have to develop any code to extract the navigation data since the unit simply gave us RINEX files, which could be directly used as an input for the gnssrefl software. More details about our experiment can be found in my M.Sc. thesis.

Secondly, the whole RPR system, as tabulated by the authors, is indeed low-cost. Therefore, one can connect an extra antenna with the left-hand polarization at a very low price too. The details of such a dual-polarized instrumentation has been provdied in Gary Chan‘s M.Sc. thesis, and we use the same system for our soil mositure experiment, as well as that failed lake ice research. The benefit of this designation, compared to the only-one-antenna one, is obvious for reflectometrists; no need to discriminate the direct and reflected signals, chance to receive reflections from satellites with higher elevation angles, and opportunity for comparing GPS-IR and GPS-R.

Thirdly, RPR is fragile, and that’s why our lake ice experiment failed: the RPR may work in normal temperture, but it can breaks in very cold conditions. To be more specific and clear, we had planned to install our RPR to a tower located on the shore of a lake in central Ontario to collect reflections from lake ice surface. Although, we’d tested our system in a lab fridge at -80 C to make sure it would work at very cold conditions, its USB connections broke after installation. So, before conducting any experiment, the tempeature variation of the river, or any other study site, should be taken into consideration in order to plan for any possible freezing sitatuons. The authors have wisely employed a heat sink pack to cool the system in the summer; so, it would be reasonable to think about an opposite component for the witertime as well.

Let’s look forward to seeing a network of RPRs soon, which, as suggested by the authors, is indeed technically and financially doable.

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 AltuntaƟ, 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.