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Validation and Comparison Among Different VIIRS Cloud Mask Products

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Book cover Geoinformatics in Sustainable Ecosystem and Society (GSES 2019, GeoAI 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1228))

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Abstract

Universal Dynamic Threshold Cloud Detection Algorithm (UDTCDA) is a recently proposed cloud detection algorithm for remote sensing image with the support of a prior land surface reflectance database. In the visible and near infrared bands, the overall accuracy of cloud detection is still low due to the similar spectral characteristics of some bright surfaces and clouds. Aimed at this problem, this paper proposes an improved VIIRS dynamic threshold cloud detection algorithm (I-DTCDA) on the basis of the characteristics of multi-channels, wide coverage and short revisit period for visible infrared Imaging Radiometer (VIIRS) satellite. The distribution characteristics of thin clouds and other surface features from visible to thermal infrared bands under different spatial and temporal conditions were analyzed, and the detection model of thin clouds and ice/snow was improved. VCM cloud products which used the reflectance and brightness temperature of 16 bands from visible to thermal infrared channels were adopted. The pixel cloud phase logic was established to improve the accuracy of cloud detection. The accuracy of three kinds of cloud detection algorithms was evaluated by visual interpretation. The results illustrated that the I-DTCDA algorithm can identify the clouds over different surface features in remote sensing images with higher precision. What’s more, the thin cloud detection results and overall accuracy of I-DTCDA algorithm are higher than those of UDTCDA and VCM algorithms.

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References

  1. Goodman, A.H., Henderson-Sellers, A.: Cloud detection and analysis: a review of recent progress. Atmos. Res. 21(3–4), 203–228 (1988)

    Article  Google Scholar 

  2. Jedlovec, G.J., Haines, S.L., Lafontaine, F.J.: Spatial and temporal varying thresholds for cloud detection in GOES imagery. IEEE Trans. Geosci. Remote Sens. 46(6), 1705–1717 (2008)

    Article  Google Scholar 

  3. Zhu, Z., Wang, S., Woodcock, C.E.: Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images. Remote Sens. Environ. 159, 269–277 (2015)

    Article  Google Scholar 

  4. Wu, W., Luo, J., Hu, X.: A thin-cloud mask method for remote sensing images based on sparse dark pixel region detection. Remote Sens. 10(4), 617 (2018)

    Article  Google Scholar 

  5. Revision, J.: Joint Polar Satellite System (JPSS) Ground Project 474–00061 (2012)

    Google Scholar 

  6. Piper, M., Bahr, T.: A rapid cloud mask algorithm for Suomi NPP VIIRS imagery EDRS. In: The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-7/W3, pp. 237–242 (2015)

    Google Scholar 

  7. Parmes, E., Rauste, Y., Molinier, M.: Automatic cloud and shadow detection in optical satellite imagery without using thermal bands—application to Suomi NPP VIIRS images over Fennoscandia. Remote Sens. 9(8), 806 (2017)

    Article  Google Scholar 

  8. Sun, L., Wei, J., Wang, J.: A universal dynamic threshold cloud detection algorithm (UDTCDA) supported by a prior surface reflectance database. J. Geophys. Res. Atmos. 121(12), 7172–7196 (2018)

    Article  Google Scholar 

  9. Godin, R.: Joint polar satellite system (JPSS) VIIRS cloud mask (VCM) algorithm theoretical basis document (ATBD). Joint Polar Satellite System (JPSS) Ground Project Code, 474, 474-00033 (2014)

    Google Scholar 

  10. Kopp, T.J., Thomas, W., Heidinger, A.K.: The VIIRS cloud mask: progress in the first year of S-NPP toward a common cloud detection scheme. J. Geophys. Res.: Atmos. 119(5), 2441–2456 (2014)

    Google Scholar 

  11. Yulei, C., Lin, S., Jing, W.: Improved dynamic threshold cloud detection algorithm for Suomi-NPP visible infrared imaging radiometer. Acta Opt. Sin. 39(5), 0528005 (2019)

    Article  Google Scholar 

  12. Wei, W., Wenbin, Z.Q.: Effects of spectral response differences on NDVI. Remote Sens. Inf. 4, 91–98 (2015)

    Google Scholar 

  13. Johnson, E.C.: A parallel decomposition algorithm for constrained nonlinear optimization. Rensselaer Polytechnic Institute (2001)

    Google Scholar 

  14. Vermote, E.F., Tanre, D., Deuze, J.L.: Second simulation of the satellite signal in the solar spectrum, 6 s: an overview. IEEE Trans. Geosci. Remote Sens. 35(3), 675–686 (2002)

    Article  Google Scholar 

  15. Salomonson, V.V., Appel, I.: Estimating fractional snow cover from MODIS using the normalized difference snow index. Remote Sens. Environ. 89(3), 351–360 (2004)

    Article  Google Scholar 

  16. Hutchison, K.D., Iisager, B.D., Mahoney, R.L.: Enhanced snow and ice identification with the VIIRS cloud mask algorithm. Remote Sens. Lett. 4(9), 929–936 (2013)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. College Geomatics, Shandong University of Science and Technology, Qingdao, 266590, China

    Yulei Chi

  2. University of the Chinese Academy of Sciences, Beijing, 100049, China

    Tianlong Zhang

Authors
  1. Yulei Chi
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  2. Tianlong Zhang
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Corresponding author

Correspondence to Yulei Chi .

Editor information

Editors and Affiliations

  1. Eastern Michigan University, Ypsilanti, MI, USA

    Yichun Xie

  2. Guangzhou Institute of Geography, Guangzhou, China

    Yong Li

  3. Guangzhou Institute of Geography, Guangzhou, China

    Ji Yang

  4. Guangzhou Institute of Geography, Guangzhou, China

    Jianhui Xu

  5. Guangzhou Institute of Geography, Guangzhou, China

    Yingbin Deng

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Chi, Y., Zhang, T. (2020). Validation and Comparison Among Different VIIRS Cloud Mask Products. In: Xie, Y., Li, Y., Yang, J., Xu, J., Deng, Y. (eds) Geoinformatics in Sustainable Ecosystem and Society. GSES GeoAI 2019 2019. Communications in Computer and Information Science, vol 1228. Springer, Singapore. https://doi.org/10.1007/978-981-15-6106-1_6

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  • DOI: https://doi.org/10.1007/978-981-15-6106-1_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-6105-4

  • Online ISBN: 978-981-15-6106-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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