Fractional Snow Cover Mapping by Artificial Neural Networks and Support Vector Machines

Akyürek, Sevda Zuhal
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 with different model building settings are trained by using Moderate Resolution Imaging Spectroradiometer surface reflectance values of bands 1-7, normalized difference snow index and normalized difference vegetation index as predictor variables. Reference FSC maps are generated from higher spatial resolution Landsat ETM+ binary snow cover maps. Results on the independent test data set indicate that the developed ANN model with hyperbolic tangent transfer function in the output layer and the SVM model with radial basis function kernel produce high FSC mapping accuracies with the corresponding values of R = 0.93 and R = 0.92, respectively.


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...
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...
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; KUTER, SEMİH; Simsek, Burak; Puca, Silvia; Toniazzo, Alexander; Takala, Matias; Akyürek, Sevda Zuhal; Gabellani, Simone; Arslan, Ali Nadir (2019-03-15)
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 p...
A machine learning-based accuracy enhancement on EUMETSAT H-SAF H35 effective snow-covered area product
Kuter, Semih; Bolat, Kenan; Akyürek, Sevda Zuhal (2022-04-01)
Snow is a major element of the cryosphere with significant impact on the Earth's water cycle and global energy budget. Acquiring consistent and long time series data on the spatial extent of snow cover doubtlessly plays a key role in our understanding and modeling of the current and future environmental dynamics. Remote sensing offers a powerful tool for continuous retrieval of snow cover information by utilizing snow's contrasting reflectance characteristics at optical wavelengths. The pre-operational H35 ...
Analysis of Relations between Solar Activity, Cosmic Rays and the Earth Climate using Machine Learning Techniques
Belen, Bükem; Leloğlu, Uğur Murat; Demirköz, Melahat Bilge; Department of Earth System Science (2021-9-7)
The Earth's climate is part of a complicated system that can be affected by many different parameters, both internal and external. Important external forces on the climate are galactic cosmic rays (GCR) and the Sun. Some research has already been conducted to investigate the relationship between the climate and external forces such as the GCR and solar activity. However, the relations are quite complicated and buried into almost chaotic meteorological measurements. This thesis looks deeper into the interact...
Citation Formats
B. B. ÇİFTÇİ, S. KUTER, S. Z. Akyürek, and G. W. WEBER, “Fractional Snow Cover Mapping by Artificial Neural Networks and Support Vector Machines,” 2017, Accessed: 00, 2020. [Online]. Available: