Development of an NDRE-Based Nitrogen Uptake Estimation Model for Rice Using Sentinel-2 Imagery - Muhammad Rizki Dhani Nurdiyanto; Delvi Yanti (AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment), 2026)
Nitrogen availability is a critical factor influencing rice growth and productivity. Conventional methods such as Kjeldahl and SPAD are limited in spatial coverage, time efficiency, and operational costs. This study aims to develop a model for estimating rice nitrogen uptake from Sentinel-2 satellite imagery using the Normalised Difference Red Edge (NDRE) index. This study was conducted in Koto Tangah Regency, Padang City, Indonesia, at three rice growth stages: 7–12, 27–32, and 47–52 days after planting (DAP). NDRE values were derived from Sentinel-2 image processing, while actual leaf nitrogen content was measured using SPAD readings calibrated by the Kjeldahl method. The relationship between NDRE and leaf nitrogen content was modelled using linear, exponential, and quadratic regression. The results showed a significant relationship between NDRE and leaf nitrogen content at all growth stages, with a positive coefficient of determination (R²). The linear regression model performed better than the other tested models for estimating nitrogen uptake from Sentinel-2 imagery across all observed growth phases. The NSE values for the First Period (7-12 DAP) were 0.70, the Second Period (27-32 DAP) were 0.62, and the Third Period (47-52 DAP) were 0.74. A positive NSE value approaching 1 indicates improved model performance, allowing the model to continue representing the general trend of the relationships among the analysed variables. Contribution to Sustainable Development Goals (SDGs):SDG 2: Zero HungerSDG 12: Responsible Consumption and ProductionSDG 13: Climate ActionSDG 15: Life on LandSDG 9: Industry, Innovation and Infrastructure