Integrating Uav-Derived VARI, GNDVI, NDVI, and NDRE for phenological rice growth analysis
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Author |
Asmala Ahmad, Mohd Yazid Abu Sari, Mohd Nasran Hasan, Wan Mohd Yaakob Wan Bejuri, Hamzah Sakidin, Suliadi Firdaus Sufahani, Abd Rahman Mat Amin and Abd Wahid Rasib
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e-ISSN |
1819-6608 |
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On Pages
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1572-1580
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Volume No. |
20
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Issue No. |
18
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Issue Date |
December 30, 2025
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DOI |
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Keywords |
vegetation indices, UAV, rice, remote sensing, precision agriculture.
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Abstract
This study investigates the effectiveness of UAV-based multispectral vegetation indices, namely Visible Atmospherically Resistant Index (VARI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Red Edge (NDRE) for temporal and spatial monitoring of rice crop growth over a 117-day cultivation period. UAV imagery was collected at nine key growth stages and processed to derive the indices. Quantitative analysis revealed that NDVI, GNDVI, and VARI were highly correlated (r = 0.77-0.93), reflecting strong sensitivity to canopy greenness and chlorophyll content, whereas NDRE exhibited low correlation with the other indices (r = -0.20 to 0.01), indicating its complementary role in detecting subtle physiological changes during reproductive and ripening phases. These findings demonstrate that integrating multiple indices enhances phenological monitoring, offering a robust, high-resolution, and cost-effective approach for precision rice agriculture.
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