Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
Dust detection is essential for environmental protection, climate change assessment, and human health issues. Based on the Fengyun-4A (FY-4A)/Advance Geostationary Radiation Imager (AGRI) images, this paper aimed to examine the performances of two classic dust detection algorithms (i.e., the brightn...
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/3/1365 |
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| author | Lu Yang Lu She Yahui Che Xingwei He Chen Yang Zixian Feng |
| author_facet | Lu Yang Lu She Yahui Che Xingwei He Chen Yang Zixian Feng |
| author_sort | Lu Yang |
| collection | DOAJ |
| description | Dust detection is essential for environmental protection, climate change assessment, and human health issues. Based on the Fengyun-4A (FY-4A)/Advance Geostationary Radiation Imager (AGRI) images, this paper aimed to examine the performances of two classic dust detection algorithms (i.e., the brightness temperature difference (BTD) and normalized difference dust index (NDDI) thresholding algorithms) as well as two dust products (i.e., the infrared differential dust index (IDDI) and Dust Score products (DST) developed by the China Meteorological Administration). Results show that a threshold below −0.4 for BTD (11–12 µm) is appropriate for dust identification over China and that there is no fixed threshold for NDDI due to its limitations in distinguishing dust from bare ground. The IDDI and DST products presented similar results, where they are capable of detecting dust over all study areas only for daytime. A validation of these four dust detection algorithms has also been conducted with ground-based particulate matter (PM10) concentration measurements for the spring (March to May) of 2021. Results show that the average probability of correct detection (POCD) for BTD, NDDI, IDDI, and DST were 56.15%, 39.39%, 48.22%, and 46.75%, respectively. Overall, BTD performed the best on dust detection over China with its relative higher accuracy followed by IDDI and DST in the spring of 2021. A single threshold for NDDI led to a lower accuracy than those for others. Additionally, we integrated the BTD and IDDI algorithms for verification. The POFD after integration was only 56.17%, and the fusion algorithm had certain advantages over the single algorithm verification. |
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| id | doaj.art-07893678d2c745c5b3e6eab3e5ae8de9 |
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| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:52:39Z |
| publishDate | 2023-01-01 |
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| spelling | doaj.art-07893678d2c745c5b3e6eab3e5ae8de92023-11-16T16:04:22ZengMDPI AGApplied Sciences2076-34172023-01-01133136510.3390/app13031365Analysis of Dust Detection Algorithms Based on FY-4A Satellite DataLu Yang0Lu She1Yahui Che2Xingwei He3Chen Yang4Zixian Feng5School of Geography and Planning, Ningxia University, Yinchuan 750021, ChinaSchool of Geography and Planning, Ningxia University, Yinchuan 750021, ChinaSchool of Engineering and Built Environment, Griffith University, Kessels Road, Brisbane, QLD 4111, AustraliaKey Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, ChinaSchool of Geography and Planning, Ningxia University, Yinchuan 750021, ChinaSchool of Geography and Planning, Ningxia University, Yinchuan 750021, ChinaDust detection is essential for environmental protection, climate change assessment, and human health issues. Based on the Fengyun-4A (FY-4A)/Advance Geostationary Radiation Imager (AGRI) images, this paper aimed to examine the performances of two classic dust detection algorithms (i.e., the brightness temperature difference (BTD) and normalized difference dust index (NDDI) thresholding algorithms) as well as two dust products (i.e., the infrared differential dust index (IDDI) and Dust Score products (DST) developed by the China Meteorological Administration). Results show that a threshold below −0.4 for BTD (11–12 µm) is appropriate for dust identification over China and that there is no fixed threshold for NDDI due to its limitations in distinguishing dust from bare ground. The IDDI and DST products presented similar results, where they are capable of detecting dust over all study areas only for daytime. A validation of these four dust detection algorithms has also been conducted with ground-based particulate matter (PM10) concentration measurements for the spring (March to May) of 2021. Results show that the average probability of correct detection (POCD) for BTD, NDDI, IDDI, and DST were 56.15%, 39.39%, 48.22%, and 46.75%, respectively. Overall, BTD performed the best on dust detection over China with its relative higher accuracy followed by IDDI and DST in the spring of 2021. A single threshold for NDDI led to a lower accuracy than those for others. Additionally, we integrated the BTD and IDDI algorithms for verification. The POFD after integration was only 56.17%, and the fusion algorithm had certain advantages over the single algorithm verification.https://www.mdpi.com/2076-3417/13/3/1365dust detectionFY-4ABTDNDDIIDDIDST |
| spellingShingle | Lu Yang Lu She Yahui Che Xingwei He Chen Yang Zixian Feng Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data Applied Sciences dust detection FY-4A BTD NDDI IDDI DST |
| title | Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data |
| title_full | Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data |
| title_fullStr | Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data |
| title_full_unstemmed | Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data |
| title_short | Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data |
| title_sort | analysis of dust detection algorithms based on fy 4a satellite data |
| topic | dust detection FY-4A BTD NDDI IDDI DST |
| url | https://www.mdpi.com/2076-3417/13/3/1365 |
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