High Resolution Land Cover Mapping and Crop Classification in the Loukkos Watershed (Northern Morocco): An Approach Using SAR Sentinel-1 Time Series


  • El Mortaji Nizar Abdelmalek Essaadi University
  • Miriam Wahbi Abdelmalek Essaadi University image/svg+xml
  • Mohamed Ait Kazzi Abdelmalek Essaadi University
  • Otmane Yazidi Alaoui Abdelmalek Essaadi University
  • Hakim Boulaassal Abdelmalek Essaadi University
  • Mustapha Maatouk Abdelmalek Essaadi University
  • Mohamed Najib Zaghloul Abdelmalek Essaadi University
  • Omar El Kharki Abdelmalek Essaadi University




Land cover, Sentinel-1, Crop classification, Time series, Loukkos watershed


Remote  sensing  has  become  more  and  more  a  reliable  tool  for  mapping  land  cover  and  monitoring  cropland. Much of the work done in this field uses optical remote sensing data. In Morocco, active remote sensing data remain under-exploited despite their importance in monitoring spatial and temporal dynamics of land cover and crops even during cloudy weather. This study aims to explore the potential of C-band Sentinel-1 data in the production of a high-resolution land cover mapping and crop classification within the irrigated Loukkos watershed agricultural landscape in northern Morocco. The work was achieved by using 33 dual-polarized images in vertical-vertical  (VV)  and  vertical-horizontal  (VH)  polarizations.  The  images  were  acquired  in  ascending  orbits  between  April 16 and October 25, 2020, with the purpose to track the backscattering behavior of the main crops and other land  cover  classes  in  the  study  area.  The  results  showed  that  the  backscatter  increased  with  the  phenological  development  of  the  monitored  crops  (rice,  watermelon,  peanuts,  and  winter  crops),  strongly  for  the  VH  and  VV  bands, and slightly for the VH/VV ratio. The other classes (water, built-up, forest, fruit trees, permanent vegetation, greenhouses, and bare lands) did not show significant variation during this period. Based on the backscattering analysis and the field data, a supervised classification was carried out, using the Random Forest Classifier (RF) algorithm.  Results  showed  that  radiometric  characteristics  and  6  days’  time  resolution  covered  by  Sentinel-1  constellation gave a high classification accuracy by dual-polarization with Radar Ratio (VH/VV) or Radar Vegetation Index and textural features (between 74.07% and 75.19%). Accordingly, this study proves that the Sentinel-1 data provide useful information and a high potential for multi-temporal analyses of crop monitoring, and reliable land cover mapping which could be a practical source of information for various purposes in order to undertake food security issues.


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Author Biographies

El Mortaji Nizar, Abdelmalek Essaadi University


Miriam Wahbi, Abdelmalek Essaadi University


Mohamed Ait Kazzi, Abdelmalek Essaadi University


Hakim Boulaassal, Abdelmalek Essaadi University


Mustapha Maatouk, Abdelmalek Essaadi University


Mohamed Najib Zaghloul, Abdelmalek Essaadi University


Omar El Kharki, Abdelmalek Essaadi University



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