Automated Webcam Monitoring of Fractional Snow Cover in Northern Boreal Conditions

Abstract

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).

Cross-Country Assessment of H-SAF Snow Products by Sentinel-2 Imagery Validated against In-Situ Observations and Webcam Photography

“…Several webcam networks are currently operational worldwide, such as the European phenology camera network (EUROPhen) [26], the PhenoCam Network [27], and MONIMET camera network [24]. Recently, a growing interest aims at using webcam photography to detect snow cover from digital images to monitor its variability in space and time, even though the use of these observations is restricted to limited spatial scales [12,[28][29][30][31][32][33][34].Remote sensing represents a suited and powerful tool to monitor snow properties at larger scales and to overcome the gradual decrease of the representativeness of the gauging network with the increasing altitude. Under specific conditions (e.g., day-time, absence of cloudiness) [35], the snow cover detection is relatively straightforward through satellite-based optical observations, because of the high albedo of snow with respect to most land surfaces and the higher near-infrared reflectance of most clouds compared to snow-covered surfaces [36,37].…”

“…Lacking any available in-situ reference data, a common approach relies on a cross-sensor comparison among different satellite-derived snow products by assuming one of the analyzed datasets as the reference truth [55][56][57][58]. This approach is even necessary when assessing the accuracy of satellite-derived products of fractional snow cover (FSC) requiring spatially distributed observations of reference [33]. Even though currently there is no agreed-upon methodology to perform a cross-sensor comparison, the most commonly used approach assumes the high-resolution satellite imagery as the reference effective dataset to assess Geosciences 2019, 9, 129 3 of 30 moderate-resolution remotely-sensed observations, since it is supposed to provide the most reliable information on the actual snow cover [59].…”

Comparison of MODIS and Model-Derived Snow-Covered Areas: Impact of Land Use and Solar Illumination Conditions

“…This opens interesting opportunities to take advantage of different sources of reference data, ranging from high-resolution satellite data (e.g. LANDSAT or Sentinel-2) to snow cover retrievals of digitaly imagery, which can provide extremely detailed information at the local scale, [51,52].…”

Webcam network and image database for studies of phenological changes of vegetation and snow cover in Finland, image time series from 2014 to 2016

“…Colour changes in deciduous vegetation and snow cover over the season are distinctive enough to be clearly depicted behind the day-to-day variation due to clouds and irradiation (Peltoniemi et al, 2018;Arslan et al, 2017), but in wetlands and coniferous trees these differences can be more obscure.…”