Snowpatches and satellites

Overview

For many years, the extent of snow cover in the Scottish Highlands has been recorded and documented by groups of ground-based observers from a volunteer community of ‘citizen scientists’. This winter snow cover melts in the spring and summer months, leaving behind remnant snowpatches, some of which have shown themselves to be ‘perennial’, lasting until the subsequent winter snowfalls (Cameron et al. 2023). The numbers of snowpatch ‘survivals’ have been recorded and there is much debate as to what this data might mean in terms of climate variability, both in terms of how this may impact the upland environment in Scotland in the future (Cameron 2021; Rivington et al. 2019), but also the past, in terms of when true glacial ice may have existed in the Scottish mountains (Were there glaciers in the mountains of Scotland as recently as the mid-19th century?) (Harrison et al. 2022).

The existing research (in the domain of both established scientific research institutes and external volunteers) has focused on modelling and ground-based observations (Using GIS techniques to analyse and model the topographical environment and dependencies of long-lasting snowpatch locations in the Scottish mountains) (Spencer et al. 2014; Rivington et al. 2019), but the potential for utilising Earth Observation (EO) data, imagery, analysis techniques and resources for this from satellite platforms has not been fully explored in the context of the Scottish Highlands. EO can be used to analyse and map snow cover in mountain environments and has demonstrated its effectiveness in this regard using synthetic-aperture radar (SAR) and multispectral imaging (Koehler et al. 2022).

The snowpatches in the Scottish Highlands exist in a wider, global context, both physically (The Scottish mountains: on the glacial ‘knife-edge’) and in terms of approaches to environmental management and research. The seventeen Sustainable Development Goals (SDGs) from the United Nations Framework Convention on Climate Change (UNFCCC) are interlinked and have many mutual dependencies (Kavvada et al., 2020), but some of the goals are directly related to the upland mountain environments of the earth’s surface, and in particular, snow and ice features in these environments such as glaciers, icefields, snow cover and snowpatches.

Snow and ice cover on the earth are important environmental features for the SDGs. They have been identified as an Essential Climate Variable (ECV) within the Global Climate Observing System (GCOS) (Dietz et al., 2015; Bayat et al., 2021).

Snow and ice features can also be considered as part of the cryosphere of the earth, and as such the Intergovernmental Panel on Climate Change (IPCC) has explicitly identified this as a crucial component in the task of meeting many of the targets of several different SDGs (Pörtner et al., 2019).

Using EO and satellite-based remote sensing (RS) are essential for this because “Continuous, long-term, and large-scale ground-based data collection on snow cover dynamics…is particularly difficult in fragmented and inaccessible high-altitude mountain areas” (Koehler et al., 2022:2). Snow cover can vary significantly within short time spans and often extends over vast areas. EO techniques have the ability to overcome these difficulties.

Sustainable Development Goals

Table 1 shows the details of the two most relevant SDG targets and indicators related to snow and ice features in the global upland and mountain environment, from SDG 6 (Clean Water and Sanitation) and SDG 15 (Life on Land). There are also strong links to SDG 8 (Decent Work and Economic Growth) and SDG 13 (Climate Action) (Kavvada et al., 2020).

Table 1. Relevant goals, targets and indicators.

Snow cover on the earth’s surface directly impacts climate due to its high albedo and reflection of sunlight, affecting the regional and global energy balance. It also affects local habitats, ecosystems and biodiversity of plants and animals. Snow cover that has a duration throughout the year, as exists in some mountain environments, has a prominent role in influencing these systems. Snow and ice features in mountains also act as an important freshwater resource for some areas of the world, providing the means for electricity generation, agriculture and drinking water (Koehler et al., 2022).

The economy and trade of mountainous areas, as well as the lifestyles of people living in these areas, are impacted by snow and ice features. Examples of this are the skiing industry and mountain-related tourism in the Scottish Highlands and the European Alps (Harrison et al., 2001; Koehler et al., 2022).

Techniques

There are several EO techniques that can be used to observe and analyse snow cover globally.

An important source of data for analysing snow cover is the Global SnowPack (GSP). This is a dataset of snow cover across the globe with a temporal resolution of daily observations at a point on the earth’s surface (with a timeseries starting in the year 2000), and a spatial resolution of 500m. The data is obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Earth Observing System Terra spacecraft platform (Hall et al., 1995; Dietz et al., 2015). This instrument operates in spectral bands of visible/optical wavelengths of light and is thus affected by cloud cover and darkness (particularly in the polar regions). Another optical wavelength instrument that has been used to analyse snow cover is the Advanced Very High Resolution Radiometer (AVHRR) instrument carried on various EO platforms such as the National Oceanic and Atmospheric Administration (NOAA) family of polar orbiting satellites.

These EO sensors and instruments have relatively low spatial resolution. Better spatial resolution of snow cover data (using similar optical wavelength spectral bands) is available from the NASA/USGS Landsat and Copernicus Programme Sentinel-2 platforms (30m and 10-60m respectively), although with lower temporal resolution (Koehler et al., 2022).

To overcome these limitations of observing global snow cover, another EO technology can be used, synthetic aperture radar (SAR), on platforms like Copernicus Programme Sentinel-1 and Advanced Land Observing Satellite-2 (ALOS-2) PALSAR-2. The active sensing approach of SAR produces data independent from clouds and illumination conditions. SAR also offers many advantages over technologies that utilise optical/multispectral imagery to observe snow cover, including better spatial resolution and the ability to penetrate the surface snowpack as well as cloud cover (using polarisation of the signal). A disadvantage of SAR is the lower temporal resolution (>5 days) and a smaller timeseries of data due to the relative newness of the technology and satellite platforms (Tsai et al., 2019a).

Various data analysis methods are also used to overcome the limitations of cloud cover and darkness with optical wavelength measurements as well. One method is to fill in gaps in the data by using interpolation combined with spectral indices (such as the Normalised Difference Water Index or NDWI) to classify snow and ice features (Dietz et al., 2015; Koehler et al., 2022). Another method is to use Machine Learning (ML) approaches and algorithms, combining optical wavelength data with SAR data, as well as using land cover and Digital Elevation Model (DEM) data (Tsai et al., 2019b; Cresson, 2020).

A further step is the validation of data gathered from EO platforms using ‘ground-truth’ on-site data measurements of snow and ice features such as depth and extent. This step is one requirement in the characterisation of global snow cover as an ECV (Bayat et al., 2021). An example is the Copernicus High Resolution Snow & Ice Monitoring Service which uses Sentinel-2 data and weather station snow depth measurements from various countries (Barrou Dumont et al., 2021).

Conclusions

The importance and relevance of snow and ice features in the mountainous regions of the earth’s surface and their impact on the achievement of the SDGs is clear. Snowpatches in the Scottish Highlands are a small part of this bigger picture. The importance of using EO techniques and data, and their superiority in terms of effectiveness when compared to other forms of observations, with this specific focus, is also clear.

There are still limitations to overcome however, primarily in generating timeseries of datasets of a sufficient length with sufficient temporal and spatial resolution, to allow deep and accurate analysis, classification and forecasting techniques. The current research environment is rich in potential in this respect and offers good scope for future results.

Achieving the SDGs has many difficulties and barriers, which are covered in detail in Kavadda et al. (2020), but in the context of using EO techniques to measure global snow cover significant progress in this area is likely.

References

Barrou Dumont, Z., Gascoin, S., Hagolle, O., Ablain, M., Jugier, R., Salgues, G., Marti, F., Dupuis, A., Dumont, M. and Morin, S. (2021) Brief communication: evaluation of the snow cover detection in the Copernicus High Resolution Snow & Ice Monitoring Service. The Cryosphere, 15(10): 4975-4980.

Bayat, B., Camacho, F., Nickeson, J., Cosh, M., Bolten, J., Vereecken, H. and Montzka, C. (2021) Toward operational validation systems for global satellite-based terrestrial essential climate variables. International Journal of Applied Earth Observation and Geoinformation, 95.102240.

Cameron, I. (2021) The Vanishing Ice. Vertebrate Publishing.

Cameron, I., Fyffe, B. and Kish, A. (2023) No Scottish snow patches survive until winter 2022/23. Weather, 78(4): 101-103.

Cresson, R. (2020) Deep Learning for Remote Sensing Images with Open Source Software. CRC Press.

Dietz, A.J., Kuenzer, C. and Dech, S. (2015) Global SnowPack: a new set of snow cover parameters for studying status and dynamics of the planetary snow cover extent. Remote sensing letters, 6(11): 844-853.

Hall, D.K., Riggs, G.A. and Salomonson, V.V. (1995) Mapping global snow cover using moderate resolution imaging spectroradiometer (MODIS) data. Glaciological Data: 33-36.

Harrison, S., Rowan, A.V., Dye, A.R., Plummer, M.A. and Anderson, K. (2022) Late Holocene glaciers in western Scotland? Geografiska Annaler: Series A, Physical Geography, 104(2): 57-69.

Harrison, S.J., Winterbottom, S.J. and Johnson, R.C. (2001) A preliminary assessment of the socio-economic and environmental impacts of recent changes in winter snow cover in Scotland. Scottish Geographical Journal, 117(4): 297-312.

Kavvada, A., Metternicht, G., Kerblat, F., Mudau, N., Haldorson, M., Laldaparsad, S., Friedl, L., Held, A. and Chuvieco, E. (2020) Towards delivering on the sustainable development goals using earth observations. Remote Sensing of Environment, 247:111930.

Koehler, J., Bauer, A., Dietz, A.J. and Kuenzer, C. (2022) Towards forecasting future snow cover dynamics in the European Alps – The potential of long optical remote-sensing time series. Remote Sensing, 14(18): 4461.

Pörtner, H.O., Roberts, D.C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A. and Petzold, J. (2019) IPCC special report on the ocean and cryosphere in a changing climate. IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland, 1(3):1-755.

Rivington, M., Spencer, M., Gimona, A., Artz, R., Wardell-Johnson, D. and Ball, J. (2019) Snow Cover and Climate Change in the Cairngorms National Park: Summary Assessment. ClimateXChange, Edinburgh.

Spencer, M., Essery, R., Chambers, L. and Hogg, S. (2014) The historical snow survey of Great Britain: digitised data for Scotland. Scottish Geographical Journal, 130(4): 252-265.

Tsai, Y.L.S., Dietz, A., Oppelt, N. and Kuenzer, C. (2019a) Remote sensing of snow cover using spaceborne SAR: A review. Remote Sensing, 11(12):1456.

Tsai, Y.L.S., Dietz, A., Oppelt, N. and Kuenzer, C. (2019b) Wet and dry snow detection using Sentinel-1 SAR data for mountainous areas with a machine learning technique. Remote Sensing, 11(8): 895.