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

The spectral vegetation indices derived from remote sensing data provide a detailed spectral analysis for assessing vegetation characteristics. This study investigated the relationship between cotton yield and canopy spectral indices to develop yield estimation models. Spectral reflectance data were collected at various growth stages using an ASD FieldSpec Pro VNIR 2500 spectrometer. Six prediction models were developed using spectral vegetation indices, including the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI), to estimate the Leaf Area Index (LAI) and above-ground biomass. For LAI estimation using the NDVI, the power function model (y = 10.083x) demonstrated higher precision, with a multiple correlation coefficient of R² = 0.8184 and the smallest root mean square error (RMSE = 0.3613). These results confirm the strong predictive capacity of NDVI for LAI, with the power function model offering the best estimation accuracy. In estimating above-ground biomass using RVI, the power function model of y = 6.5218x achieved the higher correlation (R² = 0.8851) for fresh biomass with an RMSE of 0.1033, making it the most accurate. For dry biomass, the exponential function model (y = 9.1565 × 10) was the most precise, achieving an R² value of 0.8456 and the lowest RMSE value of 0.0076. These findings highlight the potential of spectral remote sensing for accurately predicting cotton canopy structural parameters and biomass weights. By integrating spectral analysis techniques with remote sensing, this research offers valuable insights for precision cotton planting and field management, enabling optimized agricultural practices and enhanced vegetation health monitoring.