Standard

Building a model for predicting the water level in a river using remote sensing data from open sources. / Kolesnikov, Alexey; Kikin, Pavel; Nungesser, Anastasia.

In: E3S Web of Conferences, Vol. 223, 02006, 21.12.2020.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Kolesnikov, A, Kikin, P & Nungesser, A 2020, 'Building a model for predicting the water level in a river using remote sensing data from open sources', E3S Web of Conferences, vol. 223, 02006. https://doi.org/10.1051/e3sconf/202022302006

APA

Kolesnikov, A., Kikin, P., & Nungesser, A. (2020). Building a model for predicting the water level in a river using remote sensing data from open sources. E3S Web of Conferences, 223, [02006]. https://doi.org/10.1051/e3sconf/202022302006

Vancouver

Kolesnikov A, Kikin P, Nungesser A. Building a model for predicting the water level in a river using remote sensing data from open sources. E3S Web of Conferences. 2020 Dec 21;223. 02006. https://doi.org/10.1051/e3sconf/202022302006

Author

Kolesnikov, Alexey ; Kikin, Pavel ; Nungesser, Anastasia. / Building a model for predicting the water level in a river using remote sensing data from open sources. In: E3S Web of Conferences. 2020 ; Vol. 223.

BibTeX

@article{2ff08e980aa34e2bbc298a28a434a201,
title = "Building a model for predicting the water level in a river using remote sensing data from open sources",
abstract = "The article discusses the possibility of predicting the water surface area of a river (and based on these values, the calculation of the water level) based on only open data of remote sensing. The area and depth of snow cover, the intensity of precipitation according to Landsat and Sentinel data and the monitoring indicators MODIS, Copernicus, REMSS are used as initial parameters. For the selected parameters, the degree of influence on the final forecast was assessed. ",
author = "Alexey Kolesnikov and Pavel Kikin and Anastasia Nungesser",
note = "Publisher Copyright: {\textcopyright} The Authors, published by EDP Sciences, 2020. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2020 Regional Problems of Earth Remote Sensing, RPERS 2020 ; Conference date: 29-09-2020 Through 02-10-2020",
year = "2020",
month = dec,
day = "21",
doi = "10.1051/e3sconf/202022302006",
language = "English",
volume = "223",
journal = "E3S Web of Conferences",
issn = "2555-0403",
publisher = "EDP Sciences",

}

RIS

TY - JOUR

T1 - Building a model for predicting the water level in a river using remote sensing data from open sources

AU - Kolesnikov, Alexey

AU - Kikin, Pavel

AU - Nungesser, Anastasia

N1 - Publisher Copyright: © The Authors, published by EDP Sciences, 2020. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/12/21

Y1 - 2020/12/21

N2 - The article discusses the possibility of predicting the water surface area of a river (and based on these values, the calculation of the water level) based on only open data of remote sensing. The area and depth of snow cover, the intensity of precipitation according to Landsat and Sentinel data and the monitoring indicators MODIS, Copernicus, REMSS are used as initial parameters. For the selected parameters, the degree of influence on the final forecast was assessed.

AB - The article discusses the possibility of predicting the water surface area of a river (and based on these values, the calculation of the water level) based on only open data of remote sensing. The area and depth of snow cover, the intensity of precipitation according to Landsat and Sentinel data and the monitoring indicators MODIS, Copernicus, REMSS are used as initial parameters. For the selected parameters, the degree of influence on the final forecast was assessed.

UR - http://www.scopus.com/inward/record.url?scp=85098892036&partnerID=8YFLogxK

U2 - 10.1051/e3sconf/202022302006

DO - 10.1051/e3sconf/202022302006

M3 - Conference article

AN - SCOPUS:85098892036

VL - 223

JO - E3S Web of Conferences

JF - E3S Web of Conferences

SN - 2555-0403

M1 - 02006

T2 - 2020 Regional Problems of Earth Remote Sensing, RPERS 2020

Y2 - 29 September 2020 through 2 October 2020

ER -

ID: 76286526