Using the modis sensor for snow cover modeling and the assessment of drought effects on snow cover in a mountainous area
Aghelpour, Pouya ; Guan, Yiqing ; Bahrami‐pichaghchi, Hadigheh ; Mohammadi, Babak LU
; Kisi, Ozgur
and Zhang, Danrong
(2020)
In Remote Sensing
12(20).
p.1-22
- Abstract
Snow is one of the essential factors in hydrology, freshwater resources, irrigation, travel, pastimes, floods, avalanches, and vegetation. In this study, the snow cover of the northern and southern slopes of Alborz Mountains in Iran was investigated by considering two issues: (1) Estimating the snow cover area and the (2) effects of droughts on snow cover. The snow cover data were monitored by images obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The meteorological data (including the precipitation, minimum and maximum temperature, global solar radiation, relative humidity, and wind velocity) were prepared by a combination of National Centers for Environmental Prediction‐Climate Forecast System... (More)
Snow is one of the essential factors in hydrology, freshwater resources, irrigation, travel, pastimes, floods, avalanches, and vegetation. In this study, the snow cover of the northern and southern slopes of Alborz Mountains in Iran was investigated by considering two issues: (1) Estimating the snow cover area and the (2) effects of droughts on snow cover. The snow cover data were monitored by images obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The meteorological data (including the precipitation, minimum and maximum temperature, global solar radiation, relative humidity, and wind velocity) were prepared by a combination of National Centers for Environmental Prediction‐Climate Forecast System Reanalysis (NCEP‐CFSR) points and meteorological stations. The data scale was monthly and belonged to the 2000–2014 period. In the first part of the study, snow cover estimation was conducted by Multiple Linear Regression (MLR), Least Square Support Vector Machine (LSSVM), Group Method of Data Handling (GMDH), Multilayer Perceptron (MLP), and MLP with Grey Wolf Optimization (MLP‐ GWO) models. The most accurate estimations were produced by the MLP‐GWO and GMDH models. The models produced better snow cover estimations for the northern slope compared to the southern slope. The GWO improved the MLP’s accuracy by 10.7%. In the second part, seven drought indices, including the Palmer Drought Severity Index (PDSI), Bahlme–Mooley Drought Index (BMDI), Standardized Precipitation Index (SPI), Multivariate Standardized Precipitation Index (MSPI), Modified Standardized Precipitation Index (SPImod), Joint Deficit Index (JDI), and Standardized Precipitation‐Evapotranspiration Index (SPEI) were calculated for both slopes. The results showed that the effects of a drought event on the snow cover area would remain up to 5 (or 6) months in the region. The highest impact of drought appears after two months in the snow cover area, and the drought index most related to snow cover variations is the 2–month time window of SPI (SPI2). The results of both subjects were promising and the methods can be examined in other snowy areas of the world.
(Less)
- author
- Aghelpour, Pouya
; Guan, Yiqing
; Bahrami‐pichaghchi, Hadigheh
; Mohammadi, Babak
LU
; Kisi, Ozgur
and Zhang, Danrong
- publishing date
- 2020-10-02
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Bio‐inspired optimization algorithm, Machine learning, MODIS data, Snow cover area, Snow modeling
- in
- Remote Sensing
- volume
- 12
- issue
- 20
- article number
- 3437
- pages
- 22 pages
- publisher
- MDPI AG
- external identifiers
-
- scopus:85092779836
- ISSN
- 2072-4292
- DOI
- 10.3390/rs12203437
- language
- English
- LU publication?
- no
- id
- 3f0c86cc-dfca-4c8b-a1d8-6e26ed75dee5
- date added to LUP
- 2020-12-30 05:09:52
- date last changed
- 2022-04-26 22:52:49
@article{3f0c86cc-dfca-4c8b-a1d8-6e26ed75dee5,
abstract = {{<p>Snow is one of the essential factors in hydrology, freshwater resources, irrigation, travel, pastimes, floods, avalanches, and vegetation. In this study, the snow cover of the northern and southern slopes of Alborz Mountains in Iran was investigated by considering two issues: (1) Estimating the snow cover area and the (2) effects of droughts on snow cover. The snow cover data were monitored by images obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The meteorological data (including the precipitation, minimum and maximum temperature, global solar radiation, relative humidity, and wind velocity) were prepared by a combination of National Centers for Environmental Prediction‐Climate Forecast System Reanalysis (NCEP‐CFSR) points and meteorological stations. The data scale was monthly and belonged to the 2000–2014 period. In the first part of the study, snow cover estimation was conducted by Multiple Linear Regression (MLR), Least Square Support Vector Machine (LSSVM), Group Method of Data Handling (GMDH), Multilayer Perceptron (MLP), and MLP with Grey Wolf Optimization (MLP‐ GWO) models. The most accurate estimations were produced by the MLP‐GWO and GMDH models. The models produced better snow cover estimations for the northern slope compared to the southern slope. The GWO improved the MLP’s accuracy by 10.7%. In the second part, seven drought indices, including the Palmer Drought Severity Index (PDSI), Bahlme–Mooley Drought Index (BMDI), Standardized Precipitation Index (SPI), Multivariate Standardized Precipitation Index (MSPI), Modified Standardized Precipitation Index (SPImod), Joint Deficit Index (JDI), and Standardized Precipitation‐Evapotranspiration Index (SPEI) were calculated for both slopes. The results showed that the effects of a drought event on the snow cover area would remain up to 5 (or 6) months in the region. The highest impact of drought appears after two months in the snow cover area, and the drought index most related to snow cover variations is the 2–month time window of SPI (SPI2). The results of both subjects were promising and the methods can be examined in other snowy areas of the world.</p>}},
author = {{Aghelpour, Pouya and Guan, Yiqing and Bahrami‐pichaghchi, Hadigheh and Mohammadi, Babak and Kisi, Ozgur and Zhang, Danrong}},
issn = {{2072-4292}},
keywords = {{Bio‐inspired optimization algorithm; Machine learning; MODIS data; Snow cover area; Snow modeling}},
language = {{eng}},
month = {{10}},
number = {{20}},
pages = {{1--22}},
publisher = {{MDPI AG}},
series = {{Remote Sensing}},
title = {{Using the modis sensor for snow cover modeling and the assessment of drought effects on snow cover in a mountainous area}},
url = {{http://dx.doi.org/10.3390/rs12203437}},
doi = {{10.3390/rs12203437}},
volume = {{12}},
year = {{2020}},
}