Abstract
The relation between the fraction of snow cover and the spectral behavior of the surface is a critical issue that must be approached in order to retrieve the snow cover extent from remotely sensed data. Ground-based cameras are an important source of datasets for the preparation of long time series concerning the snow cover. This study investigates the support provided by terrestrial photography for the estimation of a site-specific threshold to discriminate the snow cover. The case study is located in the Italian Alps (Falcade, Italy). The images taken over a ten-year period were analyzed using an automated snow-not-snow detection algorithm based on Spectral Similarity. The performance of the Spectral Similarity approach was initially investigated comparing the results with different supervised methods on a training dataset, and subsequently through automated procedures on the entire dataset. Finally, the integration with satellite snow products explored the opportunity offered by terrestrial photography for calibrating and validating satellite-based data over a decade.
Highlights
Snow cover is an important component of the cryosphere that plays a key role for climate dynamics and the resources availability: the seasonality of the snow cover influences, weather patterns, hydropower generation, agriculture, forestry, tourism, and aquatic ecosystems [1,2,3]
The Fractional Snow Cover (FSC) estimated by terrestrial photography will be compared to the output obtained by remotely sensed data
Comparison between Supervised and Automated Classifiers. This first part of the analysis includes two steps: one dedicated to the orthorectification of the panoramic view observed by the webcam; the other focused on the image classification performed considering the color components associated with a RGB color space
Summary
Snow cover is an important component of the cryosphere that plays a key role for climate dynamics and the resources availability: the seasonality of the snow cover influences, weather patterns, hydropower generation, agriculture, forestry, tourism, and aquatic ecosystems [1,2,3]. Two different aspects must be considered for the enhancement of the final output: time and spatial resolutions Both components, using remotely sensed data, are connected to each other, since the higher the spatial resolution (below hundreds of meters), the lower the revisit time interval (more than 1 week) [4]. The state-of-the-art snow products concerning the snow extent are remotely sensed and they are based mainly on multispectral optical sensors. They can investigate the snow cover and give information about the size and the shape of snow grains [5]; the presence of impurity soot; the age of the snow; and the presence of depth hoars.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
