Snow cover variability has a significant impact on climate and the environment and is of great socioeconomic importance for the European Alps. Terrestrial photography offers a high potential to monitor snow cover variability, but its application is often limited to small catchment scales. Here, we present a semiautomatic procedure to derive snow cover maps from publicly available webcam images in the Swiss Alps and propose a procedure for the georectification and snow classification of such images. In order to avoid the effort of manually setting ground control points (GCPs) for each webcam, we implement a novel registration approach that automatically resolves camera parameters (camera orientation; principal point; field of view, FOV) by using an estimate of the webcams' positions and a high-resolution digital elevation model (DEM). Furthermore, we propose an automatic image-to-image alignment to correct small changes in camera orientation and compare and analyze two recent snow classification methods. The resulting snow cover maps indicate whether a DEM grid is snow-covered, snow-free, or not visible from webcams' positions. GCPs are used to evaluate our novel automatic image registration approach. The evaluation reveals a root mean square error (RMSE) of 14.1 m for standard lens webcams (FOV<48 ^∘) and a RMSE of 36.3 m for wide-angle lens webcams (FOV≥48 ^∘). In addition, we discuss projection uncertainties caused by the mapping of low-resolution webcam images onto the high-resolution DEM. Overall, our results highlight the potential of our method to build up a webcam-based snow cover monitoring network.

Snow cover is of high relevance for the Earth’s climate system, and its variability plays a key role in alpine hydrology, ecology, and socioeconomic systems. Measurements obtained by optical satellite remote sensing are an essential source for quantifying snow cover variability from a local to global scale. However, the temporal resolution of such measurements is often affected by persistent cloud coverage, limiting the application of high resolution snow cover mapping. In this study, we derive the regional snow line elevation in an alpine catchment area using public webcams. We compare our results to the snow line information derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 snow cover products and find our results to be in good agreement therewith. Between October 2017 and the end of June 2018, snow lines derived from webcams lie on average 55.8 m below and 33.7 m above MODIS snow lines using a normalized-difference snow index (NDSI) of 0.4 and 0.1, respectively, and are on average 53.1 m below snow lines derived from Sentinel-2. We further analyze the superior temporal resolution of webcam-based snow cover information and demonstrate its effectiveness in filling temporal gaps in satellite-based measurements caused by cloud cover. Our findings show the ability of webcam-based snow line elevation retrieval to complement and improve satellite-based measurements.

Snow cover variability has a significant impact on climate and environment and is of great socio-economic importance for the European Alps. Terrestrial photography offers a high potential to monitor snow cover variability, but its application is often limited to the small catchment scale. Here, we present a semi-automatic procedure to derive snow cover maps from arbitrary webcam images. We use freely available webcam images of the Swiss Alps and propose a procedure for the georectification and snow classification of such images. In order to avoid the effort of manually setting ground control points (GCPs) for each webcam, we implement a new registration approach that automatically resolves camera parameters (camera orientation, principal point, field of view (FOV)) by using an estimate of the webcams position and a high-resolution digital elevation model (DEM). Furthermore, two recent snow classification methods are compared and analyzed. The resulting snow cover maps have the same spatial resolution as the DEM and indicate whether a grid cell is snow-covered, snow-free, or not visible from webcams' positions. GCPs were used to evaluate our novel automatic image registration approach. The evaluation reveals in a root mean square error (RMSE) of 14.1 m for standard lens webcams (FOV

Publicly available webcam images offer an enormous potential to study the variability of snow cover on a high spatio-temporal scale. Such cameras allow detailed analyses of snow cover on steep slopes due to their oblique view on the mountains and can provide snow cover information even under cloudy weather conditions. Our webcam-based snow cover monitoring network comprises several hundreds of webcams and enables to gather snow cover information over a large area with a minimum amount of manual user input. This information can serve as a reference for improved validation of satellite-based approaches, as well as complement satellite-based snow cover retrieval, in particular under cloudy weather conditions. Here, we present a framework to estimate the regional snow line elevation in the Swiss Alps. The snow line elevation is an important indicator of snow cover in mountainous regions and can be used, for example, as an input for hydrological modeling or to study the seasonality of river discharge. We compare and combine snow line retrieval from Sentinel-2 snow cover maps and webcam-based snow cover information to analyze regional differences in the spring snowmelt period. Since cloud cover is an important factor that affects the quality of satellite-based snow cover products, the combination with snow information from webcams can improve the accuracy and can fill temporal gaps, especially during recurrent cloud cover. Furthermore, we present a method to detect cloud cover in webcam images and discuss limitations of webcam-based snow cover monitoring.