LSDSSIMR: Large-Scale Dust Storm Database Based on Satellite Images and Meteorological Reanalysis Data
In recent years, dust storms have occurred frequently, significantly affecting people's daily lives. Therefore, the detection, monitoring, and early warning of dust storms have a great social significance. Previous methods have mainly been based on atmospheric motion to build physical mod...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10287393/ |
| _version_ | 1797330388945731584 |
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| author | Cong Bai Zhipeng Cai Xiaomei Yin Jinglin Zhang |
| author_facet | Cong Bai Zhipeng Cai Xiaomei Yin Jinglin Zhang |
| author_sort | Cong Bai |
| collection | DOAJ |
| description | In recent years, dust storms have occurred frequently, significantly affecting people's daily lives. Therefore, the detection, monitoring, and early warning of dust storms have a great social significance. Previous methods have mainly been based on atmospheric motion to build physical models for weather forecasting. Although there are many meteorological applications based on deep learning, to the best of authors' knowledge, there is no dust storm database with a high spatiotemporal resolution, which is essential for deep learning methods. Since meteorological satellites can observe the Earth's atmosphere from a spatial perspective at a large scale, in this article, a dust storm database is constructed using multichannel and dust label data from the Fengyun-4 A geosynchronous orbiting satellite, namely the large-scale dust storm database based on satellite images and meteorological reanalysis data (LSDSSIMR), with a temporal resolution of 15 min and a spatial resolution of 4 km from March to May of each year during 2020–2022. Meteorological reanalysis data are added to LSDSSIMR for spatiotemporal prediction methods. Each data file is stored in an HDF5 format, and the final LSDSSIMR contains nearly 5400 HDF5 files. Moreover, some traditional dust detection methods based on spectral analysis are executed as a benchmark. |
| first_indexed | 2024-03-08T07:19:09Z |
| format | Article |
| id | doaj.art-2d2cc8e54c48446899a7ddf57816e744 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-08T07:19:09Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-2d2cc8e54c48446899a7ddf57816e7442024-02-03T00:01:06ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-0116101211013110.1109/JSTARS.2023.332578310287393LSDSSIMR: Large-Scale Dust Storm Database Based on Satellite Images and Meteorological Reanalysis DataCong Bai0https://orcid.org/0000-0002-6177-3862Zhipeng Cai1https://orcid.org/0009-0009-4670-8817Xiaomei Yin2Jinglin Zhang3https://orcid.org/0000-0003-1618-8493College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, ChinaCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, ChinaBeijing Weather Forecast Center, Beijing, ChinaSchool of Control Science and Engineering, Shandong University, Jinan, ChinaIn recent years, dust storms have occurred frequently, significantly affecting people's daily lives. Therefore, the detection, monitoring, and early warning of dust storms have a great social significance. Previous methods have mainly been based on atmospheric motion to build physical models for weather forecasting. Although there are many meteorological applications based on deep learning, to the best of authors' knowledge, there is no dust storm database with a high spatiotemporal resolution, which is essential for deep learning methods. Since meteorological satellites can observe the Earth's atmosphere from a spatial perspective at a large scale, in this article, a dust storm database is constructed using multichannel and dust label data from the Fengyun-4 A geosynchronous orbiting satellite, namely the large-scale dust storm database based on satellite images and meteorological reanalysis data (LSDSSIMR), with a temporal resolution of 15 min and a spatial resolution of 4 km from March to May of each year during 2020–2022. Meteorological reanalysis data are added to LSDSSIMR for spatiotemporal prediction methods. Each data file is stored in an HDF5 format, and the final LSDSSIMR contains nearly 5400 HDF5 files. Moreover, some traditional dust detection methods based on spectral analysis are executed as a benchmark.https://ieeexplore.ieee.org/document/10287393/Databasedust stormFengyun-4 A (FY-4A)meteorological reanalysis |
| spellingShingle | Cong Bai Zhipeng Cai Xiaomei Yin Jinglin Zhang LSDSSIMR: Large-Scale Dust Storm Database Based on Satellite Images and Meteorological Reanalysis Data IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Database dust storm Fengyun-4 A (FY-4A) meteorological reanalysis |
| title | LSDSSIMR: Large-Scale Dust Storm Database Based on Satellite Images and Meteorological Reanalysis Data |
| title_full | LSDSSIMR: Large-Scale Dust Storm Database Based on Satellite Images and Meteorological Reanalysis Data |
| title_fullStr | LSDSSIMR: Large-Scale Dust Storm Database Based on Satellite Images and Meteorological Reanalysis Data |
| title_full_unstemmed | LSDSSIMR: Large-Scale Dust Storm Database Based on Satellite Images and Meteorological Reanalysis Data |
| title_short | LSDSSIMR: Large-Scale Dust Storm Database Based on Satellite Images and Meteorological Reanalysis Data |
| title_sort | lsdssimr large scale dust storm database based on satellite images and meteorological reanalysis data |
| topic | Database dust storm Fengyun-4 A (FY-4A) meteorological reanalysis |
| url | https://ieeexplore.ieee.org/document/10287393/ |
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