Correlation of RGB Drone Indices and Sentinel‑2 in Rice (Oryza sativa L.) at UBPC Las Nuevas, Sancti Spíritus
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Abstract
Rice production in Cuba faces a significant gap between national demand and domestic supply, aggravated by technological and input limitations. In this context, the present study aimed to evaluate the correlation between spectral indices derived from RGB orthophotography captured with a Phantom 4 drone and multispectral indices obtained from Sentinel‑2 L2A images, in rice (Oryza sativa L.) at the seedling stage, in plot 6 of UBPC Las Nuevas, during the dry season of 2020. The methodology included the use of QGIS 3.40 for image processing, calculation of spectral indices (NDVI, GNDVI, NDRE, VARI, ExG, CIVE), and statistical analysis using Pearson’s correlation coefficient in Statgraphics. The results showed significant correlations between RGB indices and satellite indices: VARI-NDVI (r ≈ 0.74), ExG-GNDVI (r ≈ 0.67), and CIVE-NDRE (r ≈ -0.63). Field validation confirmed the homogeneity of the seedling stage and the absence of water stress, in agreement with the spectral values obtained. It is concluded that RGB indices can be used as reliable proxies for multiscale agricultural monitoring under resource‑limited conditions. It is recommended to extend the analysis to time series, incorporate new indices such as SAVI and BSI, and strengthen technical training in tools such as QGIS and Statgraphics to improve agricultural management in Cuba.
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