References
Borshch SV et al (2013) Visualization of the hydrological
situation in the basins of large rivers using GIS
technologies. In: Proceedings of the Hydrometeorological Research Center of the Russian Federation
349:47–62
Chang A et al (1985) Snow water equivalent determination by microwave radiometry. Cold Reg Sci Technol
5:259–267
Chang A et al (1987) Nimbus7 SMMR derived global
snow cover parameters. Ann Glaciol 39–44
Gan TY, Kalinga O, Purushottam S (2009) Comparison of
snow water equivalent retrieved from SSM/I passive
microwave data using artificial neural network, projection pursuit and nonlinear regressions. Remote Sens
Environ 113(5):919–927
Global snow monitoring for climate research – design
justification file (2010) European space agency study
contract report 21703/08/I-EC deliverable. 1(7):246
Golovko VA (2001) Neural networks: training, organization and application. Book 4. Moscow, p 256
Horritt MS (2006) A methodology for the validation of
uncertain flood inundation models. J Hydrol 326
(1):153–165
Hollinger JP, Pierce JL, Poes GA (1990) SSM/I Instrument evaluation. IEEE Trans Geos Remote Sens
28:781–790
Karionov YuI (2010) Estimation of the accuracy of the
SRTM heights. Geoprofi 1:48–51
Kitaev LM, Titkova TB (2010) Estimation of snow
storage using satellite information 1:76–80
Luojus K et al (2010) Snow Water Equivalent (SWE) product guide. GlobSnow Consortium. https://www.
globsnow.info/swe/GlobSnow_SWE_product_
readme_v1.0a.pdf
Mitnik LM, Mitnik ML (2005) Calibration and validation
as prerequisite components of satellite microwave
radiometer measurements from Meteor-M No. 2 series
satellites. Current problems in remote sensing of the
Earth from Space 2:244–249
Nosenko GA, Dolgikh NA, Nosenko OA (2005) On the
feasibility of practical implementation of existing
algorithms
for
reconstructing
snow
cover
characteristics from microwave survey data from
space for monitoring water resources. Physical fundamentals, methods and technologies for monitoring the
environment, potentially dangerous phenomena and
objects: collection of Scientific Works. Moscow.
II:150–156
Nosenko OA, Nosenko GA (2007) Snow cover of the
European part of Russia in the microwave range
(AMSR-E и SSM/I). Current problems in remote
sensing of the Earth from Space 2(4):97–103
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning
internal representations by error propagation. Parallel
Distrib Process 1:318–362
Semmens KA et al (2013) Early snowmelt events:
detection, distribution, and significance in a major
sub-arctic watershed. Environ Res Lett 8(1):014020
Sharkov EA (2004) Passive microwave remote sensing of
the Earth: in the past, at present and future plans.
Current problems in remote sensing of the Earth from
Space 1(1):70–80
Stiles WH, Ulaby FT (1980) The active and passive
microwave response to snow parameters. J Geophys
Research 85:1037–1044
Tedesco M et al (2004) Artificial neural network-based
techniques for the retrieval of SWE and snow depth
from SSM/I data. Remote Sens Environ 90(1):76–85
Tong J et al (2010) Testing snow water equivalent
retrieval algorithms for passive microwave remote
sensing in an alpine watershed of western Canada.
Canadian J Remote Sens 36(sup1):S74–S86
Vladimirov VA et al (2012) Forecasting the level of
spring floods and monitoring flood zones based on
GIS technologies and artificial intelligence systems.
Civ Prot Strategy Probl Res 2:519–540
Volckak AA, Kostiuk DA, Petrov DO (2013) Approach
to the calculation of the flood zone for the river
network. Information Technologies and Systems
(ITS). Minsk 338–339
Volckak AA, Petrov DO, Kostiuk DA (2016) Estimation
of the snow water equivalent according to the passive
microwave scanning of the Earth surface using
artificial neural networks for the Russian Federation
territory. The Snow and Ice 56(1):43–51
250
A. A. Volchak et al.
Borshch SV et al (2013) Visualization of the hydrological
situation in the basins of large rivers using GIS
technologies. In: Proceedings of the Hydrometeorological Research Center of the Russian Federation
349:47–62
Chang A et al (1985) Snow water equivalent determination by microwave radiometry. Cold Reg Sci Technol
5:259–267
Chang A et al (1987) Nimbus7 SMMR derived global
snow cover parameters. Ann Glaciol 39–44
Gan TY, Kalinga O, Purushottam S (2009) Comparison of
snow water equivalent retrieved from SSM/I passive
microwave data using artificial neural network, projection pursuit and nonlinear regressions. Remote Sens
Environ 113(5):919–927
Global snow monitoring for climate research – design
justification file (2010) European space agency study
contract report 21703/08/I-EC deliverable. 1(7):246
Golovko VA (2001) Neural networks: training, organization and application. Book 4. Moscow, p 256
Horritt MS (2006) A methodology for the validation of
uncertain flood inundation models. J Hydrol 326
(1):153–165
Hollinger JP, Pierce JL, Poes GA (1990) SSM/I Instrument evaluation. IEEE Trans Geos Remote Sens
28:781–790
Karionov YuI (2010) Estimation of the accuracy of the
SRTM heights. Geoprofi 1:48–51
Kitaev LM, Titkova TB (2010) Estimation of snow
storage using satellite information 1:76–80
Luojus K et al (2010) Snow Water Equivalent (SWE) product guide. GlobSnow Consortium. https://www.
globsnow.info/swe/GlobSnow_SWE_product_
readme_v1.0a.pdf
Mitnik LM, Mitnik ML (2005) Calibration and validation
as prerequisite components of satellite microwave
radiometer measurements from Meteor-M No. 2 series
satellites. Current problems in remote sensing of the
Earth from Space 2:244–249
Nosenko GA, Dolgikh NA, Nosenko OA (2005) On the
feasibility of practical implementation of existing
algorithms
for
reconstructing
snow
cover
characteristics from microwave survey data from
space for monitoring water resources. Physical fundamentals, methods and technologies for monitoring the
environment, potentially dangerous phenomena and
objects: collection of Scientific Works. Moscow.
II:150–156
Nosenko OA, Nosenko GA (2007) Snow cover of the
European part of Russia in the microwave range
(AMSR-E и SSM/I). Current problems in remote
sensing of the Earth from Space 2(4):97–103
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning
internal representations by error propagation. Parallel
Distrib Process 1:318–362
Semmens KA et al (2013) Early snowmelt events:
detection, distribution, and significance in a major
sub-arctic watershed. Environ Res Lett 8(1):014020
Sharkov EA (2004) Passive microwave remote sensing of
the Earth: in the past, at present and future plans.
Current problems in remote sensing of the Earth from
Space 1(1):70–80
Stiles WH, Ulaby FT (1980) The active and passive
microwave response to snow parameters. J Geophys
Research 85:1037–1044
Tedesco M et al (2004) Artificial neural network-based
techniques for the retrieval of SWE and snow depth
from SSM/I data. Remote Sens Environ 90(1):76–85
Tong J et al (2010) Testing snow water equivalent
retrieval algorithms for passive microwave remote
sensing in an alpine watershed of western Canada.
Canadian J Remote Sens 36(sup1):S74–S86
Vladimirov VA et al (2012) Forecasting the level of
spring floods and monitoring flood zones based on
GIS technologies and artificial intelligence systems.
Civ Prot Strategy Probl Res 2:519–540
Volckak AA, Kostiuk DA, Petrov DO (2013) Approach
to the calculation of the flood zone for the river
network. Information Technologies and Systems
(ITS). Minsk 338–339
Volckak AA, Petrov DO, Kostiuk DA (2016) Estimation
of the snow water equivalent according to the passive
microwave scanning of the Earth surface using
artificial neural networks for the Russian Federation
territory. The Snow and Ice 56(1):43–51
250
A. A. Volchak et al.