Remotely sensed vegetation indices for crop nutrition mapping

Area of Science:

• Agricultural Remote Sensing
• Plant Physiology
• Geospatial Analysis

Background:

• Spectral reflectance and vegetation indices are established tools for monitoring plant nutrition.
• Remote sensing offers rapid, non-destructive crop nutrition assessment, increasingly vital with advancements in UAVs and IoT.
• Accurate crop nutrition diagnosis via remote sensing can optimize fertilization, reduce costs, and prevent yield loss.

Purpose of the Study:

• To evaluate vegetation indices for crop nutrition mapping using satellite imagery.
• To assess the performance of various spectral indices for estimating crop nutrient content.
• To determine optimal timing and spectral bands for accurate crop nutrition assessment.

Main Methods:

• Utilized satellite images over two years across two distinct regions (Ray and Karaj).
• Selected eight reference points per farm annually for model development and accuracy validation.
• Calculated ten spectral vegetation indices using mid-season and end-of-season data.

Main Results:

• Transformed Chlorophyll Absorption in Reflectance Index (TCARI) showed the highest R² (0.83).
• Modified Chlorophyll Absorption in Reflectance Index (MCARI) yielded the lowest RMSE (12.47 kg ha⁻¹).
• TCARI and MCARI demonstrated strong performance in crop nutrition mapping.

Conclusions:

• TCARI and MCARI indices enable the creation of highly accurate crop nutrition maps.
• Mid-season data incorporating near infra-red and red-edge bands outperformed end-of-season indices.
• Remote sensing, particularly with red-edge bands, is a powerful tool for precision crop nutrition management.