Snow monitoring in Scotland using satellite data


In this novel research, we want to improve the management and protection of regions prone to flash floods as a result of rapid snow melt. We will be using satellite images, ground cameras and radars, and drone data.

You will be working principally on a test site in the Cairngorms, Scotland. The Allt a’Mharcaidh catchment, including the mountains of Sgoran Dubh Mor (1111 m) and Meall Buidhe (976 m) have been monitored as part of the UK Environmental Change Network (ECN) since 1998. The ECN generates a range of high frequency physical, biogeochemical and biological measurements that are made in close proximity. These long-term data comprise measurements of ecosystem variables that drive and respond to environmental change. As part of this suite of measurements, snow monitoring by fixed-point repeat photography has taken place at the Allt a’Mharcaidh site since 2002.

Snow cover represents a storage of fresh water that is then released when the temperature rises in Spring. The melting of the snow can cause substantial run off that can produce flush floods. These flush floods are really hard to predict since it is difficult to predict the moment when the snow will start melting and the amount of water that is stored in the snow cover. Additionally, increased unpredictability of weather patterns complicate even further the forecasting, since the time snow is accumulated and the time of subsequent spring melt are highly variable[e.g. Andrews et al 2016].

In this project we will use satellite data together with ground instruments to demonstrate internationally the benefits of monitoring of snow cover. We will make a large use of satellite Synthetic Aperture Radar (SAR). SAR is able to obtain images of the environment from space using microwaves and it has been demonstrated to be particularly sensitive at detecting snow melt [Marin et al 2020]. Additionally it has strong potentials to evaluate the Snow Water Equivalent (SWE) which is possibly the most important parameter when predicting the quantity of run off [Tsang et al 2021]. SAR allows us to acquire images independent of weather condition and solar illumination, which is ideal to be used during Scottish winter. We will also use a cutting edge radar technology called polarimetry interferometric (Pol-InSAR). The advantage of Pol-InSAR is that we can use the polarisation and interferometric information of the radar echo to obtain more images and therefore more information about objects in the scene [ESA-PolSAR]. Beside using satellite data we will deploy ground instruments including cameras and snow depth probes to train and tune the retrieval algorithms. We will be carrying out extensive experiments in snow using a ground radar which can simulate the images obtained from satellites. Finally, we will make use of the emerging technology of UAV (“drone”) based observation for field validation and rapid local assessment using low cost aircraft.

A strong motivation for using satellite images is that we entered a new era of freely available satellite data (e.g. the ESA Sentinel constellation missions [ESA-Sentinel]). We are experiencing a rapid growth of activities in the Space industry and the Earth Observation sector. When paired to the exponentially growing sector of unmanned aerial monitoring, this opportunity not only supports businesses activities but also provides many state of the art tools to the environmental management community.
The development work will be accompanied by large fieldwork in Scotland. We will be using cameras and snow depth probes, but also a ground radar. It is expected that several fieldwork trips will be organised during the Winter and early Spring season. If successful, the results will be included in the products provide by the Scotland’s International Environment Centre (lead by Stirling University) to the Scottish Environmental Protection Agency (SEPA) flood management strategy.


Deliverables: In this project, we will set up a series of methodologies that, starting from images acquired from space, will be able to provide weekly update of SWE and snow cover.
Novelty: Pol-InSAR is a cutting edge technology and is very useful to retrieve physical parameters of vegetation and soil [ESA-PolSAR]. However, we are in urgent need of a controlled experiment on the ground, which will allow a much better understanding of the satellite signal over snow. Additionally, the research work carried out in this project puts the management at the core of the project, developing mechanisms that use satellite observation for leading actions.

Data (satellite): Archived Pol-InSAR data are already available. Future acquisitions will be carried out synchronised to fieldwork. The datasets used will include at least the following satellite missions: ALOS-2 (Japanese Space Agency); Sentinel-1 (European Space Agency).

Data (ground): We will be using a ground radar built in the Stirling radar lab (based on a VNA architecture) to acquire images that emulates satellites. This can be tuned at different frequencies and acquire quad-polarimetric and interferometric data. It can be easily transported and installed on a tripod. It is especially interesting to perform experiment when snow is progressively removed, or it has suffered metamorphosis with appearance of ice layers. We will also use camera to monitor mountain peaks. These are very valuable especially to cover areas that cannot easily be seen by satellites due to the viewing geometry, and provide part of the ground truthing. A stream gauging station on the catchment outflow will provide further data on excess run-off during periods of snow-melt, whilst an in-situ weather station will provide data on the local climate.

Data (drone): We will be using our DJI Phantom-4 drone to collect aerial images of the peatlands during fieldwork.

Algorithm development: In this project we will develop algorithms that exploit weekly available Pol-InSAR images combined with sparse ground measurements to monitor snow cover and SWE to improve flood forecast.
1) We will monitor SWE by applying scattering models and change detectors. One of the methodologies will be based on the use of optimisations of polarimetric data [Marino et al 2014].
2) AI. To fuse all the different data sources we will use a data driven approach employing Deep Learning. In order to avoid issues with the size of training data for the model, we will use Transfer Learning.

Project Timeline

Year 1

Preparing a literature review on the topics: SAR, drone imaging, snow. Fieldwork. Start working on ground measurements and monitor of SWE with Pol-InSAR. Attending international training events. Expected submission of a journal paper on monitoring snow in Scotland with Pol-InSAR.

Year 2

Monitor snow cover, SWE and melting time using fusion of data. Expected submission of a journal paper on fusing different data sources Pol-InSAR and drone data.

Year 3

Refine the AI methods and extrapolate quantity of run off using the extracted parameters. This will eventually produce the SIEC processing stuck for providing early warning of flush floods. Starting writing the thesis chapters. Expected submission of journal paper on sustainability assessment.

Year 3.5

Complete thesis, submission and viva.

& Skills

This is a multi-disciplinary project including topics related to (a) satellite Earth Observation; (b) drone surveys; (c) physical models (electromagnetic scattering); (d) data analysis; (e) snow, floods; (f) programming.

The successful candidate will have the opportunity to gain valuable skills in the context of: (a) analysing and processing satellite and drone images using Python; (b) planning and accomplishing ground radar and drone campaigns; (c) developing analytical and empirical models to measure biophysical parameters of the environment; (d) using Geographical Information Systems (GIS) software.

The training will also include the attendance of major international training events such as the training on polarimetric SAR data, provide by ESA in Italy

References & further reading

[Andrews et al 2016] Andrews, C., Ives, S. and Dick, J. (2016), Long-term observations of increasing snow cover in the western Cairngorms. Weather, 71: 178-181.[ESA-PolSAR]:[ESA-Sentinel]:[Marin et al 2020] Marin, C., Bertoldi, G., Premier, V., Callegari, M., Brida, C., Hürkamp, K., Tschiersch, J., Zebisch, M., and Notarnicola, C.: Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions, The Cryosphere, 14, 935–956,, 2020.[Marino et al 2014]: Marino, A. and Hajnsek, I. (2014). “A change detector based on an optimization with polarimetric SAR imagery”. IEEE TGRS, 52(8).[Tsang et al 2021] Tsang, L., Durand, M., Derksen, C., Barros, A. P., Kang, D.-H., Lievens, H., Marshall, H.-P., Zhu, J., Johnson, J., King, J., Lemmetyinen, J., Sandells, M., Rutter, N., Siqueira, P., Nolin, A., Osmanoglu, B., Vuyovich, C., Kim, E. J., Taylor, D., Merkouriadi, I., Brucker, L., Navari, M., Dumont, M., Kelly, R., Kim, R. S., Liao, T.-H., and Xu, X.: Review Article: Global Monitoring of Snow Water Equivalent using High Frequency Radar Remote Sensing, The Cryosphere Discuss. [preprint],, in review, 2021.

Further Information

This project is in the framework of the Scotland’s International Environment Centre and it will feed in the Scottish Environmental Protection Agency (SEPA) flood management strategy.

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