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

Piazzi, Gaia
Tanis, Cemal Melih
Simsek, Burak
Puca, Silvia
Toniazzo, Alexander
Takala, Matias
Akyürek, Sevda Zuhal
Gabellani, Simone
Arslan, Ali Nadir
Information on snow properties is of critical relevance for a wide range of scientific studies and operational applications, mainly for hydrological purposes. However, the ground-based monitoring of snow dynamics is a challenging task, especially over complex topography and under harsh environmental conditions. Remote sensing is a powerful resource providing snow observations at a large scale. This study addresses the potential of using Sentinel-2 high-resolution imagery to assess moderate-resolution snow products, namely H10-Snow detection (SN-OBS-1) and H12-Effective snow cover (SN-OBS-3) supplied by the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) project of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). With the aim of investigating the reliability of reference data, the consistency of Sentinel-2 observations is evaluated against both in-situ snow measurements and webcam digital imagery. The study area encompasses three different regions, located in Finland, the Italian Alps and Turkey, to comprehensively analyze the selected satellite products over both mountainous and flat areas having different snow seasonality. The results over the winter seasons 2016/17 and 2017/18 show a satisfying agreement between Sentinel-2 data and ground-based observations, both in terms of snow extent and fractional snow cover. H-SAF products prove to be consistent with the high-resolution imagery, especially over flat areas. Indeed, while vegetation only slightly affects the detection of snow cover, the complex topography more strongly impacts product performances.


Determination of snow water equivalent over eastern part of Turkey using passive microwave data
Beşer, Özgür; Şorman, Ali Ünal; Department of Civil Engineering (2011)
The assimilation process to produce daily Snow Water Equivalent (SWE) maps is modified by using Helsinki University of Technology (HUT) snow emission model and AMSR-E passive microwave data. The characteristics of HUT emission model is analyzed in-depth and discussed with respects to the extinction coefficient function. A new extinction coefficient function for the HUT model is proposed for snow over mountainous areas. Performance of the modified model is checked against original and other modified cases ag...
KUTER, SEMİH; Akyürek, Sevda Zuhal; Weber, G. -W. (Copernicus GmbH; 2016-10-17)
Measurement of the areal extent of snow cover with high accuracy plays an important role in hydrological and climate modeling. Remotely-sensed data acquired by earth-observing satellites offer great advantages for timely monitoring of snow cover. However, the main obstacle is the tradeoff between temporal and spatial resolution of satellite imageries. Soft or subpixel classification of low or moderate resolution satellite images is a preferred technique to overcome this problem. The most frequently employed...
Comparison of MODIS-derived land surface temperature with air temperature measurements
Georgiou, Andreas; Akcit, Nuhcan (2017-03-23)
Air surface temperature is an important parameter for a wide range of applications such as agriculture, hydrology and climate change studies. Air temperature data is usually obtained from measurements made in meteorological stations, providing only limited information about spatial patterns over wide areas. The use of remote sensing data can help overcome this problem, particularly in areas with low station density, having the potential to improve the estimation of air surface temperature at both regional a...
Special issue on remote sensing of snow and its applications
Arslan, Ali Nadir; Akyürek, Sevda Zuhal (2019-06-01)
Snow cover is an essential climate variable directly affecting the Earth's energy balance. Snow cover has a number of important physical properties that exert an influence on global and regional energy, water, and carbon cycles. Remote sensing provides a good understanding of snow cover and enable snow cover information to be assimilated into hydrological, land surface, meteorological, and climate models for predicting snowmelt runoff, snow water resources, and to warn about snow-related natural hazards. Th...
Fractional Snow Cover Mapping by Artificial Neural Networks and Support Vector Machines
ÇİFTÇİ, BORA BERKAY; KUTER, SEMİH; Akyürek, Sevda Zuhal; WEBER, GERHARD WİEHELM (Copernicus GmbH; 2017-10-15)
Snow is an important land cover whose distribution over space and time plays a significant role in various environmental processes. Hence, snow cover mapping with high accuracy is necessary to have a real understanding for present and future climate, water cycle, and ecological changes. This study aims to investigate and compare the design and use of artificial neural networks (ANNs) and support vector machines (SVMs) algorithms for fractional snow cover (FSC) mapping from satellite data. ANN and SVM models...
Citation Formats
G. Piazzi et al., “Cross-Country Assessment of H-SAF Snow Products by Sentinel-2 Imagery Validated against In-Situ Observations and Webcam Photography,” GEOSCIENCES, pp. 0–0, 2019, Accessed: 00, 2020. [Online]. Available: