Crop type classification and spatial mapping in River Nile and Northern State, Sudan, using Sentinel-2 satellite data and field observation


  • Emad H. E. Yasin Faculty of Forestry, University of Khartoum, Sudan and Institute of Geomatics and Civil Engineering, University of Sopron, Hungary
  • Mahir M. Sharif Faculty of Computer Science and Information Technology, Omdurman Islamic University, Sudan
  • Mahadi Y. A. Yahia Faculty of Agriculture, Omdurman Islamic University, Sudan
  • Aladdin Y. Othman Faculty of Computer Science and Information Technology, Omdurman Islamic University, Sudan
  • Ashraf O. Ibrahim Faculty of Computer Science and Information Technology, Alzaiem Alazhari University, Khartoum North 13311, Sudan
  • Manal A. Kheiry Faculty of Geographical Science and Environmental, University of Khartoum, Khartoum, 11115, Sudan
  • Mazin Musa Faculty of Mathematical Sciences and Statistics, Al Neelain University, Khartoum 11121, Sudan



crop type classification, NDVI , remote sensing, Sentinel-2, spatial mapping, winter cropping patterns


Maintaining productive farmland necessitates precise crop mapping and identification. While satellite remote sensing makes it possible to generate such maps, there are still issues to resolve, such as how to choose input data and the best classifier algorithm, especially in areas with scarce field data. Accurate assessments of the land used for farming are a crucial part of national food supply and production accounting in many African countries, and to this end, remote sensing tools are being increasingly put to use. The aim of this study was to assess the potentiality of Sentinel-2 to distinguish and discriminate crop species in the study area and constraints on accurately mapping cropping patterns in the winter season in River Nile and Northern State, Sudan. The research utilized Sentinel-2 Normalized Different Vegetation Index (NDVI) at 10 m resolution, unsupervised and supervised classification method with ground sample and accuracy assessment. The results of the study found that the signatures of grain sorghum, wheat, okra, Vicia faba, alfalfa, corn, haricot, onion, potato, tomato, lupine, tree cover, and garlic have clear distinctions, permitting an overall accuracy of 87.38%, with trees cover, onion, wheat, potato, garlic, alfalfa, tomato, lupine and Vicia faba achieving more than 87% accuracy. Major mislabeling problems occurred primarily in irrigated areas for grain sorghum, okra, corn, and haricot, in wooded areas comprised of small parcels of land. The research found that high-resolution temporal images combined with ground data had potential and utility for mapping cropland at the field scale in the winter.


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How to Cite

Yasin, E. H. E., Sharif , M. M., Yahia, M. Y. A., Othman, A. Y., Ibrahim, A. O., Kheiry, M. A., & Musa, M. (2024). Crop type classification and spatial mapping in River Nile and Northern State, Sudan, using Sentinel-2 satellite data and field observation. Journal of Degraded and Mining Lands Management, 11(3), 5997–6007.



Research Article