Revista de Teledetección (Jul 2022)

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,
  • Miriam Wahbi,
  • Mohamed Ait Kazzi,
  • Otmane Yazidi Alaoui,
  • Hakim Boulaassal,
  • Mustapha Maatouk,
  • Mohamed Najib Zaghloul,
  • Omar El Kharki

DOI
https://doi.org/10.4995/raet.2022.17426
Journal volume & issue
no. 60
pp. 47 – 69

Abstract

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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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