Validation and Comparison Among Different VIIRS Cloud Mask Products

  • Yulei ChiEmail author
  • Tianlong Zhang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1228)


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.


Cloud detection UDTCDA VCM I-DTCDA Evaluation 


  1. 1.
    Goodman, A.H., Henderson-Sellers, A.: Cloud detection and analysis: a review of recent progress. Atmos. Res. 21(3–4), 203–228 (1988)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 5.
    Revision, J.: Joint Polar Satellite System (JPSS) Ground Project 474–00061 (2012)Google Scholar
  6. 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. 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)CrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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. 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. 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)CrossRefGoogle Scholar
  12. 12.
    Wei, W., Wenbin, Z.Q.: Effects of spectral response differences on NDVI. Remote Sens. Inf. 4, 91–98 (2015)Google Scholar
  13. 13.
    Johnson, E.C.: A parallel decomposition algorithm for constrained nonlinear optimization. Rensselaer Polytechnic Institute (2001)Google Scholar
  14. 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)CrossRefGoogle Scholar
  15. 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)CrossRefGoogle Scholar
  16. 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)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.College GeomaticsShandong University of Science and TechnologyQingdaoChina
  2. 2.University of the Chinese Academy of SciencesBeijingChina

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