High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area
Currently, the accurate estimation of the maximum snow water equivalent (SWE) in mountainous areas is an important topic. In this study, in order to improve the accuracy and spatial resolution of SWE reconstruction in alpine regions, the Sentinel-2(MSI) and Landsat 8(OLI) satellite data with the spa...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/3/460 |
| _version_ | 1819289408751271936 |
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| author | Mingyu Liu Chuan Xiong Jinmei Pan Tianxing Wang Jiancheng Shi Ninglian Wang |
| author_facet | Mingyu Liu Chuan Xiong Jinmei Pan Tianxing Wang Jiancheng Shi Ninglian Wang |
| author_sort | Mingyu Liu |
| collection | DOAJ |
| description | Currently, the accurate estimation of the maximum snow water equivalent (SWE) in mountainous areas is an important topic. In this study, in order to improve the accuracy and spatial resolution of SWE reconstruction in alpine regions, the Sentinel-2(MSI) and Landsat 8(OLI) satellite data with the spatial resolution of tens of meters are used instead of the Moderate-resolution Imaging Spectroradiometer (MODIS) data so that the pixel mixing problem is avoided. Meanwhile, geostationary satellite-based and topographic-corrected incoming shortwave radiation is used in the restricted degree-day model to improve the accuracy of radiation inputs. The seasonal maximum SWE accumulation of a river basin in the winter season of 2017−2018 is estimated. The spatial and temporal characteristics of SWE at a fine spatial and temporal resolution are then analyzed. And the results of reconstruction model with different input parameters are compared. The results showed that the average maximum SWE of the study area in 2017−2018 was 377.83 mm and the accuracy of snow cover, air temperature and the radiation parameters all affects the maximum SWE distribution on magnitude, elevation and aspect. Although the accuracy of other forcing parameters still needs to be improved, the estimation of the local maximum snow water equivalent in mountainous areas benefits from the application of high-resolution Sentinel-2 and Landsat 8 data. The joint usage of high-resolution remote sensing data from different satellites can greatly improve the temporal and spatial resolution of snow cover and the spatial resolution of SWE estimation. This method can provide more accurate and detailed SWE for hydrological models, which is of great significance to hydrology and water resources research. |
| first_indexed | 2024-12-24T03:06:23Z |
| format | Article |
| id | doaj.art-54384af69e48454084f48cb16db585d5 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-24T03:06:23Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-54384af69e48454084f48cb16db585d52022-12-21T17:17:59ZengMDPI AGRemote Sensing2072-42922020-02-0112346010.3390/rs12030460rs12030460High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous AreaMingyu Liu0Chuan Xiong1Jinmei Pan2Tianxing Wang3Jiancheng Shi4Ninglian Wang5College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaCollege of Urban and Environmental Sciences, Northwest University, Xi’an 710127, ChinaCurrently, the accurate estimation of the maximum snow water equivalent (SWE) in mountainous areas is an important topic. In this study, in order to improve the accuracy and spatial resolution of SWE reconstruction in alpine regions, the Sentinel-2(MSI) and Landsat 8(OLI) satellite data with the spatial resolution of tens of meters are used instead of the Moderate-resolution Imaging Spectroradiometer (MODIS) data so that the pixel mixing problem is avoided. Meanwhile, geostationary satellite-based and topographic-corrected incoming shortwave radiation is used in the restricted degree-day model to improve the accuracy of radiation inputs. The seasonal maximum SWE accumulation of a river basin in the winter season of 2017−2018 is estimated. The spatial and temporal characteristics of SWE at a fine spatial and temporal resolution are then analyzed. And the results of reconstruction model with different input parameters are compared. The results showed that the average maximum SWE of the study area in 2017−2018 was 377.83 mm and the accuracy of snow cover, air temperature and the radiation parameters all affects the maximum SWE distribution on magnitude, elevation and aspect. Although the accuracy of other forcing parameters still needs to be improved, the estimation of the local maximum snow water equivalent in mountainous areas benefits from the application of high-resolution Sentinel-2 and Landsat 8 data. The joint usage of high-resolution remote sensing data from different satellites can greatly improve the temporal and spatial resolution of snow cover and the spatial resolution of SWE estimation. This method can provide more accurate and detailed SWE for hydrological models, which is of great significance to hydrology and water resources research.https://www.mdpi.com/2072-4292/12/3/460snow water equivalenthigh resolutionreconstructionlandsat 8sentinel-2mountainous areapixel mixing problem |
| spellingShingle | Mingyu Liu Chuan Xiong Jinmei Pan Tianxing Wang Jiancheng Shi Ninglian Wang High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area Remote Sensing snow water equivalent high resolution reconstruction landsat 8 sentinel-2 mountainous area pixel mixing problem |
| title | High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area |
| title_full | High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area |
| title_fullStr | High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area |
| title_full_unstemmed | High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area |
| title_short | High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area |
| title_sort | high resolution reconstruction of the maximum snow water equivalent based on remote sensing data in a mountainous area |
| topic | snow water equivalent high resolution reconstruction landsat 8 sentinel-2 mountainous area pixel mixing problem |
| url | https://www.mdpi.com/2072-4292/12/3/460 |
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