Estimation Model for Cotton Canopy Structure Parameters Based on Spectral Vegetation Index.

Area of Science:

• Agricultural Science
• Remote Sensing
• Plant Physiology

Background:

• Spectral vegetation indices are crucial for assessing vegetation characteristics.
• Remote sensing offers a non-destructive method for detailed spectral analysis.

Purpose of the Study:

• To investigate the relationship between cotton yield and canopy spectral indices.
• To develop accurate yield estimation models for cotton using spectral data.

Main Methods:

• Collected spectral reflectance data using an ASD FieldSpec Pro VNIR 2500 spectrometer.
• Developed six prediction models using spectral vegetation indices (NDVI, RVI) to estimate Leaf Area Index (LAI) and biomass.
• Utilized power and exponential function models for estimation.

Main Results:

• Normalized Difference Vegetation Index (NDVI) demonstrated strong predictive capacity for Leaf Area Index (LAI) using a power function model (R² = 0.8184).
• Ratio Vegetation Index (RVI) achieved high correlation for fresh biomass estimation (R² = 0.8851) via a power function model.
• An exponential function model provided precise dry biomass estimation (R² = 0.8456).

Conclusions:

• Spectral remote sensing shows significant potential for accurately predicting cotton canopy structural parameters and biomass.
• This research provides valuable insights for precision cotton planting and field management.
• Optimized agricultural practices and enhanced vegetation health monitoring are achievable through integrated spectral analysis and remote sensing.