Development of S-NPP VIIRS global surface type classification map using support vector machines

Rui Zhang, Chengquan Huang, Xiwu Zhan, Huiran Jin, Xiao Peng Song

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


With the launch of the Joint Polar Satellite System (JPSS)/Suomi National Polar-orbiting Partnership (S-NPP) satellite in October 2011, many of the terrestrial remote sensing products generated from Moderate Resolution Imaging Spectroradiometer (MODIS), such as the global land cover map, have been inherited and expanded into the JPSS/S-NPP mission using the new Visible Infrared Imaging Radiometer Suite (VIIRS) data. In this study, an improved algorithm including the use of a new classifier support vector machines (SVM) classifier was proposed to produce VIIRS surface type maps. In addition to the new classification algorithm, a new post-processing strategy involving the use of new ancillary data to refine the classification output is implemented. As a result, the new global International Geosphere-Biosphere Programme (IGBP) map based on the 2014 VIIRS surface reflectance data was generated with a 78.5 ± 0.6% overall classification accuracy. The new map was compared to a previously delivered VIIRS surface type map, and to the MODIS land cover product. Validation results including the error matrix, overall accuracy, and the user’s and producer’s accuracy suggest the new global surface type map provides similar classification accuracy compared to the old VIIRS surface type map, with higher accuracy achieved in agricultural types.

Original languageEnglish (US)
Pages (from-to)212-232
Number of pages21
JournalInternational Journal of Digital Earth
Issue number2
StatePublished - Feb 1 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • General Earth and Planetary Sciences


  • Global surface type
  • SVM
  • land cover


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