Abstract
Fractional snow cover (FSC) is an important parameter to estimate snow water equivalent (SWE) and surface albedo important to climatic and hydrological applications. The presence of forest creates challenges to retrieve FSC accurately from satellite data, as forest canopy can block the sensor’s view of snow cover. In addition to the challenge related to presence of forest, in situ data of FSC—necessary for algorithm development and validation—are very limited. This paper investigates the estimation of FSC using digital imagery to overcome the obstacle caused by forest canopy, and the possibility to use this imagery in the validation of FSC derived from satellite data. FSC is calculated here using an algorithm based on defining a threshold value according to the histogram of an image, to classify a pixel as snow-covered or snow-free. Images from the MONIMET camera network, producing a continuous image series in Finland, are used in the analysis of FSC. The results obtained from automated image analysis of snow cover are compared with reference data estimated by visual inspection of same images. The results show the applicability and usefulness of digital imagery in the estimation of fractional snow cover in forested areas, with a Root Mean Squared Error (RMSE) in the range of 0.1–0.3 (with the full range of 0–1).
Highlights
Snow cover is an essential climate variable directly affecting the Earth’s energy balance, due to its high albedo
We conclude that snow cover could be analyzed with consumer grade cameras
The results showed that the tested snow algorithm is able to estimate fractional snow cover with high R-squared and low RMSE values
Summary
Snow cover is an essential climate variable directly affecting the Earth’s energy balance, due to its high albedo. Snow cover has a number of important physical properties that exert an influence on global and regional energy, water supply, and carbon cycles. Proper description and assimilation of snow cover information into hydrological, land surface, meteorological, and climate models, are critical to address the impact of snow on various phenomena, to predict local snow water resources, and to warn about snow-related natural hazards. Boreal forest occupies about 17 percent of the Earth’s land surface area in a circumpolar belt in the far northern hemisphere. The presence of forest in seasonally snow-covered regions, especially in the northern hemisphere, creates great challenges for the accurate FSC retrieval from satellites, as forest canopy can block sensor’s view of snow cover, either almost totally, or at least partially. Many studies have been conducted to overcome the presence of forest [8,9,10,11,12,13]
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