Journal of Agronomy and Field Innovations

Background: Oryza sativa L. (rice) is the world's leading staple cereal crop, feeding over 3.5 billion people. Inefficient nitrogen fertilizer management in rice production accounts for significant environmental losses, economic costs, and contributes to greenhouse gas emissions. Sentinel-2 multispectral satellite imagery offers real-time crop monitoring capabilities for precision nutrient management.
Objective: To evaluate the effectiveness of Sentinel-2-derived vegetation indices for monitoring crop nitrogen status and predicting grain yield in rice, enabling precision-based variable-rate nitrogen application.
Methods: A randomized complete block design experiment with four nitrogen treatment levels (0, 100, 150, and 200 kg ha⁻¹) was conducted in rice. Sentinel-2 imagery was acquired at key phenological stages and processed to derive normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE), green normalized difference vegetation index (GNDVI), and chlorophyll index red-edge (CIred-edge). Ground truth measurements included leaf nitrogen concentration, soil-plant analysis development (SPAD) chlorophyll values, plant height, leaf area index, aboveground biomass, and grain yield. Linear regression analysis established relationships between vegetation indices and nitrogen status indicators (R² = 0.89–0.94).
Results: Red-edge indices (NDRE and CIred-edge) demonstrated superior correlation with leaf nitrogen concentration (r = 0.91, P < 0.001) compared to conventional NDVI (r = 0.78, P < 0.001). Sentinel-2 imagery successfully identified nitrogen-deficient zones, enabling variable-rate fertilizer application that reduced nitrogen inputs by 18% while maintaining yields. Grain yield prediction models achieved R² = 0.92 and RMSE = 285 kg ha⁻¹.
Conclusion: Sentinel-2 multispectral imagery provides a practical, cost-effective tool for real-time nitrogen status monitoring in rice, enhancing nitrogen use efficiency and supporting sustainable crop intensification. Integration with GIS-based decision-support systems offers significant potential for precision nutrient management at farm and regional scales.