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A novel hybrid sand and dust storm detection method using MODIS data on GEE platform

Wei Wang, Alim Samat, Jilili Abuduwaili, Yongxiao Ge, Philippe De Maeyer (UGent) and Tim Van de Voorde (UGent)
(2022) EUROPEAN JOURNAL OF REMOTE SENSING. 55(1). p.420-428
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Abstract
Accurate sand and dust storm (SDS) detection is important for assessing SDS disaster risk. Machine learning (ML) based SDS detection approaches have been widely used in recent years due to their higher accuracy and better detection results. However, this approach usually requires manual annotation of numerous training samples that are, in practice, laborious and time-consuming. To overcome this challenge, we propose a novel hybrid SDS detection method that combines the support vector machine (SVM) algorithm implemented on the Google Earth Engine (GEE) cloud computing platform with a spectral index to aid the automatic labelling of training samples. Based on 8 SDS events captured by MODIS over Arid Central Asia (ACA), the effectiveness and accuracy of this method were assessed and compared to traditional approaches. The experimental results indicate that the proposed method can distinguish between mixed pixels (thin cloud and land surface) and SDS pixels and that it minimizes misdetection more effectively. This method achieved more than 98% training accuracy and validation accuracy in SDS detection.
Keywords
Applied Mathematics, Atmospheric Science, Computers in Earth Sciences, General Environmental Science, word, sand and dust storm detection, support vector machine (SVM), Multi-Band Snow Cloud Index (MBSCI), Google Earth Engine (GEE), MODIS, INDEX, WATER

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MLA
Wang, Wei, et al. “A Novel Hybrid Sand and Dust Storm Detection Method Using MODIS Data on GEE Platform.” EUROPEAN JOURNAL OF REMOTE SENSING, vol. 55, no. 1, 2022, pp. 420–28, doi:10.1080/22797254.2022.2093278.
APA
Wang, W., Samat, A., Abuduwaili, J., Ge, Y., De Maeyer, P., & Van de Voorde, T. (2022). A novel hybrid sand and dust storm detection method using MODIS data on GEE platform. EUROPEAN JOURNAL OF REMOTE SENSING, 55(1), 420–428. https://doi.org/10.1080/22797254.2022.2093278
Chicago author-date
Wang, Wei, Alim Samat, Jilili Abuduwaili, Yongxiao Ge, Philippe De Maeyer, and Tim Van de Voorde. 2022. “A Novel Hybrid Sand and Dust Storm Detection Method Using MODIS Data on GEE Platform.” EUROPEAN JOURNAL OF REMOTE SENSING 55 (1): 420–28. https://doi.org/10.1080/22797254.2022.2093278.
Chicago author-date (all authors)
Wang, Wei, Alim Samat, Jilili Abuduwaili, Yongxiao Ge, Philippe De Maeyer, and Tim Van de Voorde. 2022. “A Novel Hybrid Sand and Dust Storm Detection Method Using MODIS Data on GEE Platform.” EUROPEAN JOURNAL OF REMOTE SENSING 55 (1): 420–428. doi:10.1080/22797254.2022.2093278.
Vancouver
1.
Wang W, Samat A, Abuduwaili J, Ge Y, De Maeyer P, Van de Voorde T. A novel hybrid sand and dust storm detection method using MODIS data on GEE platform. EUROPEAN JOURNAL OF REMOTE SENSING. 2022;55(1):420–8.
IEEE
[1]
W. Wang, A. Samat, J. Abuduwaili, Y. Ge, P. De Maeyer, and T. Van de Voorde, “A novel hybrid sand and dust storm detection method using MODIS data on GEE platform,” EUROPEAN JOURNAL OF REMOTE SENSING, vol. 55, no. 1, pp. 420–428, 2022.
@article{8761888,
  abstract     = {{Accurate sand and dust storm (SDS) detection is important for assessing SDS disaster risk. Machine learning (ML) based SDS detection approaches have been widely used in recent years due to their higher accuracy and better detection results. However, this approach usually requires manual annotation of numerous training samples that are, in practice, laborious and time-consuming. To overcome this challenge, we propose a novel hybrid SDS detection method that combines the support vector machine (SVM) algorithm implemented on the Google Earth Engine (GEE) cloud computing platform with a spectral index to aid the automatic labelling of training samples. Based on 8 SDS events captured by MODIS over Arid Central Asia (ACA), the effectiveness and accuracy of this method were assessed and compared to traditional approaches. The experimental results indicate that the proposed method can distinguish between mixed pixels (thin cloud and land surface) and SDS pixels and that it minimizes misdetection more effectively. This method achieved more than 98% training accuracy and validation accuracy in SDS detection.}},
  author       = {{Wang, Wei and Samat, Alim and Abuduwaili, Jilili and Ge, Yongxiao and De Maeyer, Philippe and Van de Voorde, Tim}},
  issn         = {{2279-7254}},
  journal      = {{EUROPEAN JOURNAL OF REMOTE SENSING}},
  keywords     = {{Applied Mathematics,Atmospheric Science,Computers in Earth Sciences,General Environmental Science,word,sand and dust storm detection,support vector machine (SVM),Multi-Band Snow Cloud Index (MBSCI),Google Earth Engine (GEE),MODIS,INDEX,WATER}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{420--428}},
  title        = {{A novel hybrid sand and dust storm detection method using MODIS data on GEE platform}},
  url          = {{http://doi.org/10.1080/22797254.2022.2093278}},
  volume       = {{55}},
  year         = {{2022}},
}
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