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In-situ snow measurements conducted by European institutions for operational, research, and energy business applications were surveyed in the framework of the European Cooperation in Science and Technology (COST) Action ES1404, called “A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology, and numerical weather prediction”. Here we present the results of this survey, which was answered by 125 participants from 99 operational and research institutions, belonging to 38 European countries. The typologies of environments where the snow measurements are performed range from mountain to low elevated plains, including forests, bogs, tundra, urban areas, glaciers, lake ice, and sea ice. Of the respondents, 93% measure snow macrophysical parameters, such as snow presence, snow depth (HS), snow water equivalent (SWE), and snow density. These describe the bulk characteristics of the whole snowpack or of a snow layer, and they are the primary snow properties that are needed for most operational applications (such as hydrological monitoring, avalanche forecast, and weather forecast). In most cases, these measurements are done with manual methods, although for snow presence, HS, and SWE, automatized methods are also applied by some respondents. Parameters characterizing precipitating and suspended snow (such as the height of new snow, precipitation intensity, flux of drifting/blowing snow, and particle size distribution), some of which are crucial for the operational services, are measured by 74% of the respondents. Parameters characterizing the snow microstructural properties (such as the snow grain size and shape, and specific surface area), the snow electromagnetic properties (such as albedo, brightness temperature, and backscatter), and the snow composition (such as impurities and isotopes) are measured by 41%, 26%, and 13% of the respondents, respectively, mostly for research applications. The results of this survey are discussed from the perspective of the need of enhancing the efficiency and coverage of the in-situ observational network applying automatic and cheap measurement methods. Moreover, recommendations for the enhancement and harmonization of the observational network and measurement practices are provided.

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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).

Ecosystems' potential to provide services, e.g. to sequester carbon, is largely driven by the phenological cycle of vegetation. Timing of phenological events is required for understanding and predicting the influence of climate change on ecosystems and to support analyses of ecosystem functioning. Analyses of conventional camera time series mounted near vegetation has been suggested as a means of monitoring phenological events and supporting wider monitoring of phenological cycle of biomes that is frequently done with satellite earth observation (EO). Especially in the boreal biome, sparsely scattered deciduous trees amongst conifer-dominant forests pose a problem for EO techniques as species phenological signal mix, and render EO data difficult to interpret. Therefore, deriving phenological information from on the ground measurements would provide valuable reference data for earth observed phenology products in a larger scale. Keeping this in mind, we established a network of digital cameras for automated monitoring of phenological activity of vegetation in the boreal ecosystems of Finland. Cameras were mounted at 14 sites, each site having 1-3 cameras. In this study, we used data from 12 sites to investigate how well networked cameras can detect the phenological development of birches (Betula spp.) along a latitudinal gradient. Birches typically appear in small quantities within the dominant species. We tested whether the small, scattered birch image elements allow a reliable extraction of colour indices and the temporal changes therein. We compared automatically derived phenological dates from these birch image elements both to visually determined dates from the same image time series and to independent observations recorded in the phenological monitoring network covering the same region. Automatically extracted season start dates, which were based on the change of green colour fraction in spring, corresponded well with the visually interpreted start of the season, and also to the budburst dates observed in the field. Red colour fraction turned out to be superior to the green colour-based indices in predicting leaf yellowing and fall. The latitudinal gradients derived using automated phenological date extraction corresponded well with the gradients estimated from the 3 phenological field observations. We conclude that small and scattered birch image elements allow reliable extraction of key phenological dates for the season start and end of deciduous species studied here, providing thus important species-specific data for model validation and for explaining the temporal variation in EO phenology products.