Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia Academic Article


  • Journal of Maps


  • Land cover–land use (LCLU) classification tasks can take advantage of the fusion of radar andoptical remote sensing data, leading generally to increase mapping accuracy. Here wepropose a methodological approach to fuse information from the new European SpaceAgency Sentinel-1 and Sentinel-2 imagery for accurate land cover mapping of a portion ofthe Lower Magdalena region, Colombia. Data pre-processing was carried out using theEuropean Space Agency’s Sentinel Application Platform and the SEN2COR toolboxes. LCLUclassification was performed following an object-based and spectral classification approach,exploiting also vegetation indices. A comparison of classification performance using threecommonly used classification algorithms was performed. The radar and visible-near infraredintegrated dataset classified with a Support Vector Machine algorithm produce the mostaccurate LCLU map, showing an overall classification accuracy of 88.75%, and a Kappacoefficient of 0.86. The proposed mapping approach has the main advantages of combiningthe all-weather capability of the radar sensor, spectrally rich information in the visible-nearinfrared spectrum, with the short revisit period of both satellites. The mapping resultsrepresent an important step toward future tasks of aboveground biomass and carbonestimation in the region.

publication date

  • 2017/8/24


  • 13


  • Colombia
  • aboveground biomass
  • carbon
  • comparison
  • imagery
  • land cover
  • land use
  • land use classification
  • near infrared
  • performance
  • radar
  • remote sensing
  • sensor
  • support vector machine
  • vegetation index
  • weather

number of pages

  • 8

start page

  • 718

end page

  • 726