Correlation analysis of NDVI with remote sensing–based vegetation indices for sesame crop monitoring

Abstract

Accurate assessment of crop growth using remote sensing is crucial for precision agriculture and sustainable crop management. The Normalized Difference Vegetation Index (NDVI) is widely used to assess vegetation vigor; however, its effectiveness can vary depending on soil background, canopy structure, and crop growth stage. In the present study, NDVI was correlated with several remote sensing–based vegetation indices, including the Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), Difference Vegetation Index (DVI), Green Difference Vegetation Index (GDVI), Transformed Difference Vegetation Index (TDVI), and Renormalized Difference Vegetation Index (RDVI), to evaluate their suitability for monitoring sesame crop growth. Regression analysis revealed positive relationships between NDVI and all selected vegetation indices, with coefficients of determination (R²) ranging from 0.580 to 0.845. The strongest correlation was observed between NDVI and DVI (R² = 0.845), followed closely by EVI (R² = 0.841) and SAVI (R² = 0.802), indicating their high sensitivity to vegetation vigor and biomass. LAI also showed a strong association with NDVI (R² = 0.788), supporting the use of NDVI as a reliable indicator of leaf area development in sesame. Moderate to strong correlations were recorded for GDVI (R² = 0.748) and TDVI (R² = 0.745), whereas the weakest relationship was observed with RDVI (R² = 0.580).