Journals Articles Conferences News About CLP
Submit a paper Email Alert
Search
Search
Articles
Journals
News
Advanced Search
Top Searches
2D MaterialsTransformation opticsQuantum PhotonicsSmall lasersSemiconductor UV PhotonicsSupersymmetric microring laser arrays

Acta Optica Sinica, Volume. 39, Issue 5, 0528005(2019)

Improved Dynamic Threshold Cloud Detection Algorithm for Suomi-NPP Visible Infrared Imaging Radiometer

Yulei Chi1, Lin Sun1、*, and Jing Wei2
Author Affiliations
  • 1 College of Geomatics, Shandong University of Science and Technology, Qingdao, Shandong 266590, China
  • 2 College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
    • show less
    AbstractGet PDF(in Chinese)
    Figures&Tables (10)
    Equations (0)
    References (0)
    Cited By (3)
    Tools
    Paper Information
    Topics
    Figures & Tables(10)
    Spectral response curves of VIIRS and MODIS sensors

    Fig. 1. Spectral response curves of VIIRS and MODIS sensors

    Download full size
    Land surface reflectance of VIIRS and MODIS sensors in different channels. (a) Blue band; (b) green band; (c) red band; (d) near infrared band

    Fig. 2. Land surface reflectance of VIIRS and MODIS sensors in different channels. (a) Blue band; (b) green band; (c) red band; (d) near infrared band

    Download full size
    Distributions of land surface reflectance and brightness temperature for different surface features. (a) Reflectance and brightness temperature; (b) brightness temperature difference

    Fig. 3. Distributions of land surface reflectance and brightness temperature for different surface features. (a) Reflectance and brightness temperature; (b) brightness temperature difference

    Download full size
    Three-dimensional distributions of brightness temperature for different surface features

    Fig. 4. Three-dimensional distributions of brightness temperature for different surface features

    Download full size
    Flow chart of I-DTCDA algorithm for VIIRS data

    Fig. 5. Flow chart of I-DTCDA algorithm for VIIRS data

    Download full size
    Comparison of cloud detection results between UDTCDA and I-DTCDA algorithms for different surface features. (a) Desert, 2014-09-24; (b) desert ,2014-08-12; (c) bare land, 2014-07-09; (d) bare land,2014-06-23; (e) water, 2014-03-20; (f) water,2014-04-23; (g) snow,2014-06-06; (h) snow, 2014-06-20

    Fig. 6. Comparison of cloud detection results between UDTCDA and I-DTCDA algorithms for different surface features. (a) Desert, 2014-09-24; (b) desert ,2014-08-12; (c) bare land, 2014-07-09; (d) bare land,2014-06-23; (e) water, 2014-03-20; (f) water,2014-04-23; (g) snow,2014-06-06; (h) snow, 2014-06-20

    Download full size
    Comparisonof cloud detection results between I-DTCDA and VCM algorithms for different surface features. (a) Desert, 2014-06-04; (b) desert, 2014-07-15; (c) bare land, 2014-05-17; (d) bare land, 2014-07-29; (e) water, 2014-10-23; (f) water, 2014-11-13; (g) snow, 2014-02-06; (h) snow, 2014-11-18

    Fig. 7. Comparisonof cloud detection results between I-DTCDA and VCM algorithms for different surface features. (a) Desert, 2014-06-04; (b) desert, 2014-07-15; (c) bare land, 2014-05-17; (d) bare land, 2014-07-29; (e) water, 2014-10-23; (f) water, 2014-11-13; (g) snow, 2014-02-06; (h) snow, 2014-11-18

    Download full size
    Comparison in overall accuracy between detected and true cloud concentrations for different cloud detection algorithms

    Fig. 8. Comparison in overall accuracy between detected and true cloud concentrations for different cloud detection algorithms

    Download full size

    • Table 1. Parameters of cloud-detection bands for VIIRS

      View table

      Table 1. Parameters of cloud-detection bands for VIIRS

      BandChannelWavelength range /μmCenter wavelength /μm
      M3Blue0.478-0.4980.488
      M4Green0.545-0.5650.555
      M5Red0.662-0.6820.672
      M7Near infrared0.846-0.8850.865
      M10Short-wave infrared1.580-1.6401.610
      M15Thermal infrared10.263-11.26310.763
      M16Thermal infrared11.538-12.48812.013

    • Table 2. Evaluation results of thin cloud detection accuracy

      View table

      Table 2. Evaluation results of thin cloud detection accuracy

      Surface typeAlgorithmHOA /%HCE /%HOE /%HKappa
      UDTCDA84.5612.9136.950.714
      DesertVCM86.3411.4115.610.725
      I-DTCDA91.896.9211.140.805
      UDTCDA90.961.7314.070.753
      Bare landVCM87.078.514.070.739
      I-DTCDA93.572.979.850.809
      UDTCDA88.674.4115.270.756
      WaterVCM90.825.3512.530.768
      I-DTCDA95.581.844.770.868
      UDTCDA86.1715.236.890.721
      SnowVCM82.5429.876.360.703
      I-DTCDA91.8817.334.330.801
      UDTCDA87.728.5718.30.736
      MeanVCM86.6913.7812.140.734
      I-DTCDA93.237.277.520.821
    Tools

    Get Citation

    Copy Citation Text

    Yulei Chi, Lin Sun, Jing Wei. Improved Dynamic Threshold Cloud Detection Algorithm for Suomi-NPP Visible Infrared Imaging Radiometer[J]. Acta Optica Sinica, 2019, 39(5): 0528005

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Set citation alerts for article
    Save article for my favorites
    Paper Information

    Category: Remote Sensing and Sensors

    Received: Oct. 29, 2018

    Accepted: Feb. 19, 2019

    Published Online: May. 10, 2019

    The Author Email:

    DOI:10.3788/AOS201939.0528005

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology