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.

Application of GNSS-R for wetlands mapping using the Android phone device and u-blox GNSS module

Helicopters and low-cost Android GPS receivers can be a promissing tool for wetland remote sensing. Lazar Jeftic, the R&D specialist at Vojvodjanska privredna avijacija, Ciklonizacija Logistika – Novi Sad, AP Vojvodina, Republic of Serbia has more for us

Wetlands are areas that are inundated or saturated either permanently or intermittently and support vegetation types that are adapted to saturated conditions. Mapping the wetlands is very important in identifying mosquito breeding sites along the river floodplain areas on different water levels. The GNSS-R as passive remote sensing technique can improve the mapping of wetlands in combination with the RGB and CIR cameras and provide better image interpretation and overall results. L-band signals transmitted from the GNSS constellations are less obscured by high biomass vegetation which can be found in the floodplain areas.

The Android phone device uses the custom adapted GNSS-R Logger application based on the open source code from Google GNSSLogger App. The application is installed on phone with Android 11 operating system version, which is covered with the PCB plate for blocking the direct signal reception. The first test was conducted together with the RGB camera image acquisition from the helicopter with Above Ground Level of 250 meters over a wetland area consisting of various vegetation type covers. GNSS-R Logger app recorded raw GNSS data at L1 frequency bands of GPS and GLONASS satellites. The second test was also conducted from the helicopter with Above Ground Level of 400 meters over the two rivers.

The further tests will also include the GNSS-R receiver based on u-blox 7 GNSS module with the laptop computer as a data logger, and also taking into consideration the distribution of GNSS satellites before the flight. The improvements will also include the new GNSS receivers with capability for receiving at L2 and L5 frequency bands, and defining the appropriate permanent position for GNSS-R devices on helicopter Bell 206B-3.

Prototype GNSS-R devices. Android phone device and u-blox GNSS module.

The First test returns the following results:

C/N0 carrier-to-noise density of GPS 26 satellite with an elevation of 25 degrees. The red and orange dots with higher values represent the pass over the clear water and wetland. The image is an RGB orthophoto image with a spatial resolution of 9cm/pixel.

and the second test gives us these figures:

C/N0 carrier-to-noise density of GPS 18 satellite with an elevation of 80 degrees 29.07.2022. The  red high values represent the pass over the water. The pass over the little island in the river can be seen with short low values signal appearing. The background image is RGB Sentinel 2 image 23.07.2022.

C/N0 carrier-to-noise density of GPS 18 satellite with an elevation of 80 degrees 29.07.2022. The  red high values represent the pass over the river. The background image is RGB Sentinel 2 image 23.07.2022.

GIRAS; a GNSS-IR Analysis Software

Recently, a GNSS-IR analysis software has been published online offering a handy graphical user interface, which tends to become a popular alternative to existing software packages and tools. This MATLAB-based open-source software package is GIRAS.

GNSS-IR has become a robust method to extract the characteristic environmental features of reflected surfaces, in which, fluctuations in the strength of the signal received by the GNSS receiver are analyzed. A short video was created and posted here showing how fluctuations in reflected SNR relates to altimetric applications. Frequency, amplitude, and phase values that provide the most appropriate model of the changes in signal strength are used as GNSS-IR metrics. Although there are several different software packages currently available to find these metrics, they may be insufficient in some cases.

Newly, a MATLAB-based, open-source GNSS-IR analysis software (GIRAS), developed by Cemali Altuntas and Nursu Tunalioglu from Yildiz Technical University, Turkey, has been published online in GPS Solutions. With its graphical user interface and capabilities, GIRAS is a good alternative to existing software packages and tools. GIRAS has three main modules and five sub-modules. The first main module is “Read & convert files”. This module reads raw GNSS data and stores the necessary observations in the MATLAB environment. RINEX version 2 and RINEX version 3 observation files are supported. Both broadcast ephemeris and precise ephemeris (sp3) files can be used. Multi-GNSS (GPS, GLONASS, Galileo, Beidou) sp3 files are supported.

The second main module is “Pre-analysis” which has three submodules: (1) SNR & dSNR data, (2) Sky view, (3) FFZ. Here, the user can plot the SNR and dSNR data for different polynomial degrees for each satellite and signal type. The sky view can be plotted as including the selected satellite systems. The first Fresnel zones (FFZs) can be displayed on the plot screen and Google Earth. The user also can export all graphs as MATLAB figures.

The third main module is “Analysis”, and it has two submodules: (1) Make estimations, (2) Improve estimations. Here, GNSS-IR metrics can be estimated using several options included. In addition, filtering and outlier analysis of the results can be performed. Analysis results can be saved in both TXT and MAT formats.

GIRAS enables the selection of azimuthal and elevation angle masks with multiple range selections for either short-term or long-term GNSS data collected, which may help to predict future models for climatological studies. The software is also suitable for multi-GNSS analysis. The quality-control modules are in a way of multiple choice, in where the researchers can apply options for their aims of use. The output files are well-designed for users in a defined format for further computations.

GIRAS seems to be widely used in future GNSS-IR work, thanks to its functional GUI and many options included.

Improving GNSS-R ability in mean sea level measurement; a new paper on Turkish Mediterranean coast

GNSS antennas are designed to suppress the reflected signal before it reaches the antenna. However, some of the reflected signals interfere with the direct signal, leading to a multipath effect in the GNSS observations. Reflected signals are used in many remote sensing applications. The determination of sea level variations is also included in these applications.

Cansu Beşel, a researcher from Karadeniz Technical University, Department of Geomatics Engineering, Turkey, has recently published a paper determining the sea level variations along the Turkish Mediterranean coast using GNSS-R. For this purpose, the monitoring sea level was assessed by using the MERS station located in Erdemli, Mersin, on the Mediterranean coast of Turkey. In this paper, the dominant multipath frequency in the SNR data is derived with two methods presented: the Lomb-Scargle periodogram (LSP) and the LSP with the Moving Average (MA) filtering.

Time series of the sea level heights derived from LSP analysis can contain irregular variations. Thus, reasonable results cannot be generated for sea level heights. This study uses the moving average filtering method to eliminate these irregular variations. First, GNSS-R sea level heights were retrieved using the LSP method. Then, these results were used as input data in the MA filtering method and reconstruct the sea level heights. The GNSS-derived sea level observations were computed by the LSP and LSP with MA method and were compared with nearby tide gauge records. Figure below shows the comparison of GNSS-R retrievals and tide gauge records using LSP and LSP with MA method.

Sea level at MERS station estimated from GNSS-R and sea level recorded by Erdemli tide gauge. GPS- L1 signal is shown in the top, GPS-L5 signal is shown in the bottom.

LSP with MA method indicated improvement in GNSS-R derived sea level, decreasing the RMSE and standard deviation. The results of the LSP and the LSP with MA method are presented in the table below. The GNSS-R-derived sea level observations and sea level records from a nearby tide gauge show similar trends. It should be noted that the performance of the SNR data at the MERS site could be improved with a higher sampling rate and different quality control metrics.

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.

Importance of Effective Isotropic Radiative Power (EIRP) for GNSS-R Lake Ice Remote Sensing

GNSS-R applications for lake ice remote sensing is still under study, and there are many vague points in this subject, especially for those like me who are “encore vert” in this topic. Among various corrections and calibrations must be made on GNSS-R data, EIRP looks decisive; however, a genius solution has been recently proposed for that: dynamic calibration.

Postdoctoral researcher with the University of Michigan Tianlin Wang, who is known with his insightful research items on microwave instrumentation of GNSS-R systems, has recently published a paper expanding the concept and application of the dynamic calibration of EIRP for GNSS-R remote sensing. This research item, which has been conducted in co-authorship with a number of GNSS-R giants, such as Dr. Chris Ruf and Dr. Scott Gleason, is in the following of a conference paper that had been presented at IGARSS 2019. In this research, authors have discussed the necessity of various calibrations should be applied to normalized bistatic radar cross section (NBRCS) measurements as it is a key to obtain physical insights into the mechanism of GNSS-R scattering from reflective surfaces. Among those calibrations, the one applied to EIRP has been discussed as one of the most challenging as it has to overcome multiple major challenges including variations in the transmit power, inaccuracies in antenna gain measurements, and the flex transmit power. Being mentioned as the most important motivation of this research, the latter challenge, i.e., flex transmit power, will cause changes in CYGNSS level-1 NBRCS values, which may limit the mission’s potential by flagging out up to 37% of observations. In this dynamic calibration method, direct GPS signals are used to estimate the EIRP in the direction of specular reflection point, and by implication, calibrate the NRBCS.

It is my understanding that this correction is crucial specially for CYGNSS as it is classified among conventional GNSS-R approaches (vs. interferometric GNSS-R), in which the narrower bandwidth of the public codes (for more information on iGNSS-R and cGNSS-R, and their application in ice altimetry, see this paper). Moreover, the calibration quality of NBRCS could be extremely decisive as the roughness of the reflective surface increases. Roughness can be really challenging in lake ice GNSS-R remote sensing as I have mentioned here and here. Moreover, under this LinkedIn post, which was about the same topic, two GNSS-R researchers (Ben and Lucy) discussed various sources of uncertainty in CYGNSS remote sensing specifically for lake ice studies. Tianlin’s paper seems to open a new way to improve CYGNSS ability in lake ice remote sensing.

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.

Can Small Changes in Water Surface Topography be Detected by GNSS Reflectometry?

Precipitation detection over ocean using various radar remote sensing techniques have been fully understood and tested for several decades. But in terms of GNSS Reflectometry, researchers had been skeptical about its potential as a rain detector. Recently, a groundbreaking paper has been published by Dr. Milad Asgarimehr and his colleagues addressing this question cropping up in another paper published three years ago.

Having done his Ph.D. with Technische Universität Berlin, Berlin, Germany, Milad is currently doing his postdoctoral research on improving GNSS-R observations with AI at the German Research Centre for Geosciences (GFZ). Although the GNSS-R capability of rain detection had been doubted at the beginning, Milad’s recent study suggested that precipitation affects the power of reflected GNSS signals, which can be critical in GNSS-R experiments as we prefer to seek for unaffected data.

Regardless of microwave remote sensing, the effect of rain on ocean waves and currents has been simulated in laboratory experiments. As an example, this study designed a circulating wind-wave tank to simulate rain drops effects on ocean surface waves showing that ring waves caused by rain drops can amplify the surface roughness at centimetre scales although it can attenuate gravity waves over ocean surfaces. However, these laboratory studies could not fully explain the mechanism of rain effects on ocean surfaces in real environment.

In this recent study, Milad tested the rain effect by analyzing polarimetric GNSS observation at a GFZ coastal GNSS-R station at Onsala Space Observatory in Sweden equipped with two side-looking antennas at two different polarizations (RHCP and LHCP). Among multiple results obtained from this experiment, including sea surface salinity changes due to rain events, the key one was discerning rain drops in the power of RHCP and LHCP reflections over a calm sea. Analyzing the I/Q components of the reflected signals, authors has shown that the received signal power at a low-wind-speed condition (<5 m/s) is reduced due to the diffuse scattering caused by rain-drop-made ring-waves over sea surface.

Reviewing this paper, I came up with this question if we could study ice bottom roughness using GNSS-R tools as either a ground-based or a satellite payload sensor. As discussed in a previous post, mid-winter warm temperature may cause changes in ice roughness, and this variation can be significant enough to be discerned by reflected GNSS signals. In addition, the water underneath the ice is so calm that satisfies the weak diffuse scattering regime condition, similar to the low-wind-speed condition Milad suggested in his paper. To see if GNSS-R has the potential to be used as a roughness indicator, the same steps can be taken as Milad has; however, a very big challenge is ahead of us in lake ice remote sensing: multi-layer scattering! In most GNSS-R studies, the reflection is assumed to be from a single reflective layer, e.g, sea water surface, but in lake ice, as shown in my master’s thesis, there are usually multiple reflective layers unless we experience consecutive days with a cold temperature so we can neglect undesired L-band reflections from layers rather than the ice-water interface.

To be specific, our Haliburton experience, which has been explained in Chapter 4 of my master’s thesis, clearly reflects the temperature effect in the power retrieval process (see this post). But aside from the temperature effect, Milad’s paper shows that GNSS-R is able to detect topography changes in small amplitudes. In other words, if the temperature is cold enough to avoid wet layers, ice bottom roughness may change the power of reflected signals as GNSS-R has well shown the potential to discern the rain-caused roughness in the order of ~5 to 50 mm. However, the other big challenge we may face is ancillary data about real ice bottom roughness, which seemingly necessitates another lengthy fieldwork. Dr. Grant Gunn has recently conducted a research on freshwater ice roughness and capacity (will be bulished by Jun 2021) summerizing multiple methods of ice roughness retrieval and explaining in-situ measurements they have done for this purpose in the Straits of Mackinac region of the Great Lakes between Michigan’s upper and lower peninsulas.

CYGNSS and Spatial Interpolation; a Review

“Why talk to others when you can talk to yourself?” This is how Clara Chew captioned her fantastic YouTube video in LinkedIn to start suggesting a spatial interpolation method for CYGNSS data.

First of all, I should say what a great idea form one of my most inspiring and favorite researchers. Establishing a YouTube channel to talk to ourselves about GNSS reflectometry is what we’d need, and to be honest, that’s the main encouragement for myself to kick off this website. Although I am currently the only writer here, but this platform is going to become an online magazine for GNSS-R researchers to freely read and write others’ opinions. It’s yet to be widely advertised, but the core idea is the same: let’s just talk to ourselves : )

About the video, let’s start from the end; this method is not going to be used for every application. “If you are trying to do some sort of analysis where you need exact knowledge of reflectivity at a certain point,” Clara clarifies “you might not want to use this method”. It sounds very true for my current research focusing on Qinghai Lake, Tibet Plateau, where I restricted Fresnel Zones to only the central region of the lake to make sure that I am not receiving reflections from the surrounding lands or even the shore lines. So, no interpolation is required at any level. But aside from the Qinghai’s research, it’s been a few months that I’ve been thinking about expanding my CYGNSS explorations to a wider region, say central desert of Iran, and meanwhile, one of my greatest concerns has been how to do data interpolation over a large area. Clara has also mentioned it somewhere in her video that “trust me, it won’t be too frustrating”, but it is for me :)) because I haven’t started yet.

But when I’m saying my concern is on how to do it, I wouldn’t say that choosing the interpolation method is not a big concern, as it really is, but it is not the biggest issue for me, because I had practiced a method years ago. The story took place in 2013-2015, when I was working on geodynamics of the Earth, and my specific research at that time was calculating strain tensors. I found out that routine methods for interpolating crustal movement values, e.g. IDW and Spline, are way off the road. I spent couple of months on that, and realized that “deterministic interpolation methods” do not perfectly work for those problems which require “geo-statistical interpolation methods”, such as Kriging. That was the point.

In short, despite deterministic methods which only consider distance as the effective parameter in weighting procedure, geo-statistical interpolation methods also take the correlation into account as a weighting function. This is exactly what Clara suggested: “correlation coefficients“. In Kriging, for example, a covariance matrix is created among node points based on statistical constraints, and then, that matrix plays a key role in establishing the weighting function. The same idea can be seen in another powerful interpolation method called Least-Square Collocation, which is widely used in geoid problems and potential field anomaly interpolations.

I employed Ordinary and Universal Kriging interpolation techniques for estimating strain matrix components, and improved the accuracy up to 70%. As Clara has remained the discussion open at the end of her video, I would invite her to a collaboration in order to explore the ability of geo-statistical methods of interpolation in CYGNSS cases.

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.