Snow Cover Area (SCA), defined as the areal extent of snow-covered ground, is an important parameter for snowmelt runoff modelling. Snow cover is one of the most widely used parameters in snow monitoring. By considering the advantages and disadvantages of each remote sensing observation method, the most favourable solution for studying snow cover currently remains the synergistic use of multi-sensor satellite data.
Seasonal variability of Snow Cover Area (SCA) is of great interest for applications related to environmental monitoring, stream flow prediction, avalanche risk assessments, flood predictions, among others.
Monitoring of the snow pack from space has reached a mature stage in the past years. Different techniques have been developed and proven to be efficient in measuring snow related variables. Optical sensors onboard satellites offer daily coverage over almost any part of the globe. This makes them very attractive when looking for regular coverage even considering they have limitations in terms of cloud cover and sun illumination. On the other hand, Synthetic Aperture Radar (SAR) sensors do not have such limitations, although they can only directly detect the presence of snow when it is in wet conditions. Dry snow in mountain areas can be then inferred using physically based rules, so the combined use of these two types of sensors (optical+SAR) can complement the limitations that each of them has, giving a better and more complete estimation of SCA.
To apply this strategy, Star2Earth has developed an algorithm that makes use of optical and SAR data. The retrieval from optical data is based on a technique that gives as output the fraction of snow cover within each resolution cell (500 m x 500 m). This is suitable in mountains areas where the variation of the snow line can be considerable within an extension of 500 m.
The algorithm is fully automated and able to update the monitored area as soon as a new image is available, giving as output maps of the snow cover area and wet snow coverage (see the figure below). Moreover, for each of the pixels in the SCA map, the time from the last update is given as a quality index.