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Special issue on remote sensing of snow and its applications
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Date
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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Analysis of future climate change impacts on snow distribution over mountainous watersheds in Northern California by means of a physically-based snow distribution model
Ishida, K.; Ercan, Ali; Trinh, T.; Kavvas, M. L.; Ohara, N.; Carr, K.; Anderson, M. L. (2018-12-01)
The impacts of climate change on snow distribution through the 21st century were investigated over three mountainous watersheds in Northern California by means of a physically-based snow distribution model. The future climate conditions during a 90-year future period from water year 2010 to 2100 were obtained from 13 future climate projection realizations from two GCMs (ECHAM5 and CCSM3) based on four SRES scenarios (A1B, A1FI, A2, and B1). The 13 future climate projection realizations were dynamically down...
Impacts of climate change on snow accumulation and melting processes over mountainous regions in Northern California during the 21st century
Ishida, K.; Ohara, N.; Ercan, Ali; Jang, S.; Trinh, T.; Kavvas, M. L.; Carr, K.; Anderson, M. L. (2019-10-01)
A point-location-based analysis of future climate change impacts on snow accumulation and melting processes was conducted over three study watersheds in Northern California during a 90-year future period by means of snow regime projections. The snow regime projections were obtained by means of a physically-based snow model with dynamically downscaled future climate projections. Then, atmospheric and snow-related variables, and their interrelations during the 21st century were investigated to reveal future c...
Assessment of random forest method in pixel-based snow cover classification in Alpine region, Tatra mountains and Kaçkar mountains
Aksu, Cansu; Akyürek, Sevda Zuhal; Department of Geodetic and Geographical Information Technologies (2022-11-21)
For most countries in the Northern Hemisphere, the amount of usable water throughout the year is roughly determined by the amount of snow. Climate change and increasing demand on drinking and industrial water due to population growth make the monitoring of snow cover even more crucial than it was in the past. Today, to observe the amount of snow cover, different algorithms are being used on remote sensing data for classification of snow, aside from in-situ data collection techniques. This study presents the...
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 ...
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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.