International Journal on Science and Technology

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Evaluating NDVI, GNDVI, EVI, and SAVI for Accurate Crop Classification: A Case Study in Mahalaxmi Kheda Village

Author(s) Ms. Shivani Somnath Bhosle, Dr. Prapti Devidas Deshmukh
Country India
Abstract Accurate crop identification is crucial for effective agricultural monitoring, resource planning, and sustainable farming. This study evaluates the performance of four vegetation indices—Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Enhanced Vegetation Index (EVI), and Soil-Adjusted Vegetation Index (SAVI)—derived from Harmonized Landsat and Sentinel-2 (HLS) imagery for crop classification in Mahalaxmikheda village, Gangapur Tehsil, Chh. Sambhajinagar District, India. The indices were calculated for three different dates during the crop-growing season and combined into a multitemporal dataset. The random Forest (RF) algorithm was employed for supervised classification, and the results were validated against ground truth data collected through field visits. The accuracy assessment revealed that EVI achieved the highest overall accuracy (97.97%) and kappa coefficient (0.97), followed by SAVI (97.35% and 0.96), GNDVI (94.84% and 0.92), and NDVI (90.90% and 0.87), respectively. The superior performance of EVI and SAVI can be attributed to their ability to minimize soil background effects and atmospheric influences, thereby capturing crop-specific variations more effectively. The integration of the ground truth data further strengthened the classification results by reducing errors and improving the reliability of the RF model. These findings suggest that EVI and SAVI are more robust indices for crop identification in heterogeneous agricultural landscapes than NDVI and GNDVI are. This study highlights the potential of these indices to enhance precision agriculture applications, such as crop monitoring, management, and yield estimation, contributing to more sustainable agricultural practices.
Keywords Crop identification, Vegetation indices, NDVI, GNDVI, EVI, SAVI, Random Forest
Published In Volume 16, Issue 4, October-December 2025
Published On 2025-12-01
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