ArticleScientific reports2026
K-means clustering applied to vegetation indices for mapping cultivated areas using high-resolution Moroccan Mohammed VI satellite imagery.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study presents a pixel-based unsupervised classification approach for mapping cultivated land using high-resolution imagery from the Moroccan Mohammed VI satellite. The proposed method integrates the K-means clustering algorithm with spectral features derived from vegetation indices, particularly the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI), together with the Near-Infrared (NIR) band. The output is a classified map composed of three classes: background, bare soil, and crop-dominated areas. The method was evaluated over a 175-hectare agricultural region in northern Morocco and achieved a relative error of 1.41%, significantly outperforming NIR threshold-based classification (7.2% error), NDVI-based classification (6.95%), and standard K-means classification using spectral bands only (5.47%). The results demonstrate the effectiveness of combining vegetation indices with unsupervised clustering and highlight the potential of the high-resolution satellite imagery for field-scale agricultural mapping, precision irrigation support, and sustainable land management.
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