Application of the ESTARFM algorithm for fusing Sentinel-2 and MODIS NDSI series in the eastern Qilian Mountains
Study region: Eastern Qilian Mountains, China Study focus: It is important to monitor snow cover via high-spatiotemporal-resolution remote sensing data. However, there is a trade-off between the high/low spatial resolution and low/high temporal resolution of available sensors. In this study, an enha...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S221458182400452X |
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| author | Hui Guo Xiaoyan Wang Zhiqi Ouyang Siyong Chen Tao Che Zhaojun Zheng |
| author_facet | Hui Guo Xiaoyan Wang Zhiqi Ouyang Siyong Chen Tao Che Zhaojun Zheng |
| author_sort | Hui Guo |
| collection | DOAJ |
| description | Study region: Eastern Qilian Mountains, China Study focus: It is important to monitor snow cover via high-spatiotemporal-resolution remote sensing data. However, there is a trade-off between the high/low spatial resolution and low/high temporal resolution of available sensors. In this study, an enhanced spatiotemporal adaptive reflectance fusion model (ESTARFM) based on the Google Earth Engine (GEE) was used to resolve this limitation. We fused MODIS and Sentinel-2 normalized difference snow index (NDSI) to generate daily 10-m snow cover data for the eastern Qilian Mountains from November 1, 2021, to May 1, 2022. We then evaluated the accuracy of the fusion images and compared the differences in snow cover distributions with different resolutions. New hydrological insights: Most fusion studies have focused on algorithm development and applied these algorithms to vegetation phenology. Our findings show that using ESTARFM could increase the accuracy of snow cover monitoring in mountainous areas. The snow cover areas in the MODIS and fusion images exhibit similar changes, while the fusion images provide more detailed and reliable snow distributions. Overall, ESTARFM implemented on GEE can generate reliable and accurate NDSI series, which have advantages over traditional singular datasets, and fusion images created from these series are suitable for describing the snow cover variations in regional-scale areas. This study provides valuable insights for improving the simulation of snowmelt runoff in regional hydrological models. |
| format | Article |
| id | doaj-art-d00f743bb8e74e4d837222e95eaef1b5 |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-d00f743bb8e74e4d837222e95eaef1b52025-01-22T05:42:02ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-02-0157102103Application of the ESTARFM algorithm for fusing Sentinel-2 and MODIS NDSI series in the eastern Qilian MountainsHui Guo0Xiaoyan Wang1Zhiqi Ouyang2Siyong Chen3Tao Che4Zhaojun Zheng5College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu 730000, ChinaCollege of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu 730000, China; Correspondence to: No. 222, South Tianshui Road, Lanzhou 730000, ChinaCollege of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu 730000, ChinaSchool of Geography and Ocean Science, Nanjing University, Nanjing 210046, ChinaNorthwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaNational Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, ChinaStudy region: Eastern Qilian Mountains, China Study focus: It is important to monitor snow cover via high-spatiotemporal-resolution remote sensing data. However, there is a trade-off between the high/low spatial resolution and low/high temporal resolution of available sensors. In this study, an enhanced spatiotemporal adaptive reflectance fusion model (ESTARFM) based on the Google Earth Engine (GEE) was used to resolve this limitation. We fused MODIS and Sentinel-2 normalized difference snow index (NDSI) to generate daily 10-m snow cover data for the eastern Qilian Mountains from November 1, 2021, to May 1, 2022. We then evaluated the accuracy of the fusion images and compared the differences in snow cover distributions with different resolutions. New hydrological insights: Most fusion studies have focused on algorithm development and applied these algorithms to vegetation phenology. Our findings show that using ESTARFM could increase the accuracy of snow cover monitoring in mountainous areas. The snow cover areas in the MODIS and fusion images exhibit similar changes, while the fusion images provide more detailed and reliable snow distributions. Overall, ESTARFM implemented on GEE can generate reliable and accurate NDSI series, which have advantages over traditional singular datasets, and fusion images created from these series are suitable for describing the snow cover variations in regional-scale areas. This study provides valuable insights for improving the simulation of snowmelt runoff in regional hydrological models.http://www.sciencedirect.com/science/article/pii/S221458182400452XSentinel-2MODISNDSIFusionGoogle Earth EngineSnow cover variation |
| spellingShingle | Hui Guo Xiaoyan Wang Zhiqi Ouyang Siyong Chen Tao Che Zhaojun Zheng Application of the ESTARFM algorithm for fusing Sentinel-2 and MODIS NDSI series in the eastern Qilian Mountains Journal of Hydrology: Regional Studies Sentinel-2 MODIS NDSI Fusion Google Earth Engine Snow cover variation |
| title | Application of the ESTARFM algorithm for fusing Sentinel-2 and MODIS NDSI series in the eastern Qilian Mountains |
| title_full | Application of the ESTARFM algorithm for fusing Sentinel-2 and MODIS NDSI series in the eastern Qilian Mountains |
| title_fullStr | Application of the ESTARFM algorithm for fusing Sentinel-2 and MODIS NDSI series in the eastern Qilian Mountains |
| title_full_unstemmed | Application of the ESTARFM algorithm for fusing Sentinel-2 and MODIS NDSI series in the eastern Qilian Mountains |
| title_short | Application of the ESTARFM algorithm for fusing Sentinel-2 and MODIS NDSI series in the eastern Qilian Mountains |
| title_sort | application of the estarfm algorithm for fusing sentinel 2 and modis ndsi series in the eastern qilian mountains |
| topic | Sentinel-2 MODIS NDSI Fusion Google Earth Engine Snow cover variation |
| url | http://www.sciencedirect.com/science/article/pii/S221458182400452X |
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