Snow is a crucial environmental factor that responds dynamically to climate change, particularly in mountainous areas. Earth observation (EO) data tracks snow cover at various spatial resolutions. Long-term remote sensing operations can create multi-decadal time series and identify long-term patterns.
Study: Towards Forecasting Future Snow Cover Dynamics in the European Alps—The Potential of Long Optical Remote-Sensing Time Series. Image Credit: Andrei Stepanov/Shutterstock.com
A recent study published in Remote Sensing investigates the potential of remote sensing time series to anticipate the snow line elevation in the European Alps. The researchers produced snow line elevation time series using data from the entire Landsat archive in 43 Alpine catchments.
Influence of Snow Covers Dynamics on Climate Change and Environment
Snow is essential for the environment, society, and economy in many world areas. Snow cover directly influences climate change due to its high albedo. Snow also has a significant impact on habitats, ensuring biodiversity.
The length of the snow cover is an effective predictor of species distribution, while the snowmelt timing affects plant phenology and production. Habitat changes brought on by climate change-induced snow cover dynamics increase environmental temperature.
Importance of Snow Cover Monitoring and Estimation
It is crucial for planners, tourism organizations, and other stakeholders in the European Alps to have accurate predictions of future snow cover and snow line elevation dynamics. High-altitude snow cover is a significant source of freshwater, enabling agriculture and energy production. Snow provides the foundation for the tourism-based economies of several locations in Austria, Germany, France, Italy, and Switzerland, particularly in the European Alps.
Alpine ski tourism during winter can be severely impacted by decreasing snow cover durations and a progressively lowering snow line elevation. Collecting environmental data over extensive timescales and broad spatial scales depends heavily on space-borne Earth Observation (EO).
Current Techniques and Products for Monitoring Snow Cover Dynamics
The formation of land surface data products from lengthy RS time series is made more accessible by recent breakthroughs in remote sensing. The Global Snow Pack (GSP), a snow cover monitoring product based on MODIS, allows snow cover studies at a 500 m resolution. Based on data from the Advanced High-Resolution Radiometer AVHRR), even longer time series of snow cover dynamics have been created. The snow-related climatology and phenology investigation is particularly well suited for both sensors because of their extremely high temporal resolutions.
Synthetic Aperture Radar (SAR) is a particularly promising technique for monitoring snow cover as its detection capabilities are independent of cloud cover and lighting conditions. SAR data is ideally suited for constructing time series with no gaps. Its capacity to recognize moist snow makes it easier to analyze the onset of seasonal snow melt.
Landsat is another product for monitoring snow cover and makes it easier to produce optical time series. Although data produced by Landsat has a lesser temporal resolution (16 days) than MODIS or AVHRR, the far greater spatial resolution enables thorough snow mapping even in complicated topography of high mountain ranges.
Development of Long RS Time Series to Investigate Snow Cover Dynamics
Most current projections of future snow cover are produced using regional climate models (RCM) or general circulation models (GCM) with relatively coarse geographical resolution. Koehler et al. developed the long RS time series to investigate historical snow cover dynamics and trends. Long RS time series are easier to produce due to reduced complexity, m when produced from ARD, and they can provide a general indication of future dynamics on various spatial scales.
Research Findings
Snow cover in mountainous areas is a metric heavily influenced by climate change. In this study, Koehler et al. established the foundation for a snow line elevation forecasting system based on long time series of EO data. The researchers created the first-ever Alpine-wide snow line elevation dataset by generating monthly snow line elevation time series for each of the 43 Alpine river catchments from EO data spanning 1985 to 2021.
The capacity of seven forecasting algorithms to model and predict future snow line elevation based only on historical observations was assessed using this dataset as an input. The researchers projected future SLE time series up to 2029 using a technique that integrated the best-performing forecasts in each catchment. The anticipated median snow line elevation level movement kept the long-term trend sign in the vast majority of catchments.
Random Forest Telescope (0.76, 268 m) and seasonal ARIMA produced the best results with a Nash-Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolute Error (MAE) of 258 m. Significant gains in suggested snow line elevation monitoring can be predicted in the future by incorporating external predictor factors in a multi-variate modeling strategy.
Reference
Koehler, J., Bauer, A., Dietz, A. J., & 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. https://www.mdpi.com/2072-4292/14/18/4461/htm
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