Special issue on remote sensing of snow and its applications

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2019-06-01

Author

Arslan, Ali Nadir
Akyürek, Sevda Zuhal

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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. The main objectives of this Special Issue, Remote Sensing of Snow and Its Applications in Geosciences are to present a wide range of topics such as (1) remote sensing techniques and methods for snow, (2) modeling, retrieval algorithms, and in-situ measurements of snow parameters, (3) multi-source and multi-sensor remote sensing of snow, (4) remote sensing and model integrated approaches of snow, and (5) applications where remotely sensed snow information is used for weather forecasting, flooding, avalanche, water management, traffic, health and sport, agriculture and forestry, climate scenarios, etc. It is very important to understand (a) differences and similarities, (b) representativeness and applicability, (c) accuracy and sources of error in measuring of snow both in-situ and remote sensing and assimilating snow into hydrological, land surface, meteorological, and climate models. This Special Issue contains nine articles and covers some of the topics we listed above.

Subject Keywords

Remote sensing, Snow parameters, Spatial and temporal variability of snow, Snow hydrology, Integration of remote sensing with models

URI

https://hdl.handle.net/11511/48546

Journal

Geosciences (Switzerland)

DOI

https://doi.org/10.3390/geosciences9060277

Collections

Department of Civil Engineering, Article

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Evaluation of the consistency of station-based soil moisture measurements with hydrological model and remote sensing observations over Turkey Bulut, Burak; Yılmaz, Mustafa Tuğrul; Department of Civil Engineering (2015) Soil moisture is a critical parameter for many subjects like climate, drought, water and energy balance, weather prediction; yet the number of studies involving soil moisture has been limited in Turkey. Soil moisture parameter can be obtained using several different methods. Among the values obtained via different methods, station-based observations have the greatest potential to provide the most accurate soil moisture information, even though station based observations have the representativeness errors ov...
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...
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 ...
ESTIMATION OF SUBPIXEL SNOW-COVERED AREA BY NONPARAMETRIC REGRESSION SPLINES 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...
Comment on "Catchment flow estimation using Artifical Neural Networks in the mountainous Euphrates basin" by AG Yilmaz, MA Imteaz, G. Jenkins (J. Hydrol. 410 (2011) 134-140) ŞENSOY ŞORMAN, AYNUR; Sorman, A. Unal; ŞORMAN, ALİ ARDA (2012-08-06) The studies conducted in the Euphrates Basin draws special attention due to its high snow potential and hydropolitical condition. Snow and hydrometeorological instrumentation has been set up for real time monitoring and data collection in the Upper Euphrates Basin over the past decade. Hydrological modeling studies using satellite snow products have been carried out in the basin for real time runoff forecasting. Moreover, the Upper Euphrates Basin is a pilot basin for several national and international proj...

Citation Formats

A. N. Arslan and S. Z. Akyürek, “Special issue on remote sensing of snow and its applications,” Geosciences (Switzerland), pp. 0–0, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48546.