New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery

Area of Science:

• Agricultural remote sensing
• Plant pathology
• Geospatial analysis

Background:

• Yellow rust significantly reduces winter wheat yield and quality.
• Accurate monitoring of yellow rust is crucial for agricultural management.
• Remote sensing offers a scalable solution for crop disease detection.

Purpose of the Study:

• To assess Sentinel-2 MSI's capability for discriminating winter wheat yellow rust severity.
• To develop and validate a new spectral index for yellow rust detection.
• To evaluate the index's performance at both canopy and regional scales.

Main Methods:

• Simulated Sentinel-2 bands derived from in situ hyperspectral data.
• Random Forest (RF) method to identify sensitive spectral bands (B4, B5, B7).
• Development of the Red Edge Disease Stress Index (REDSI) and optimal threshold mapping.

Main Results:

• REDSI achieved 84.1% accuracy at the canopy scale and 85.2% at the regional scale.
• REDSI outperformed other spectral indices for yellow rust discrimination.
• Validated mapping using Sentinel-2 data demonstrated REDSI's regional applicability.

Conclusions:

• Sentinel-2 MSI is suitable for yellow rust discrimination.
• The proposed REDSI index demonstrates robustness for detecting yellow rust at various scales.
• Remote sensing provides valuable scientific guidance for crop disease monitoring and management.