[Assessment of chlorophyll content using a new vegetation index based on multi-angular hyperspectral image data]

Area of Science:

• Agricultural Remote Sensing
• Plant Physiology
• Hyperspectral Imaging

Background:

• Accurate chlorophyll estimation is vital for crop growth monitoring, disease assessment, and yield prediction.
• Traditional vegetation indices often suffer from saturation and leaf area index (LAI) effects, limiting their accuracy.

Purpose of the Study:

• To develop a novel vegetation index for accurate chlorophyll estimation in maize using multi-angular hyperspectral data.
• To reduce the influence of LAI and vegetation index saturation on chlorophyll content assessment.

Main Methods:

• Acquired multi-angular hyperspectral imagery of maize canopies during different growth stages.
• Calculated the hot-dark-spot (HDS) index using simulated and measured data.
• Modified the TCARI (Triangular Chlorophyll Index) with the HDS index to create HD-TCARI.
• Validated HD-TCARI against ground-measured chlorophyll content and LAI.

Main Results:

• HD-TCARI effectively reduced the impact of LAI on chlorophyll estimation (R² between 26.88%-28.72% for high chlorophyll content).
• HD-TCARI demonstrated resistance to vegetation index saturation at high chlorophyll concentrations.
• The linear correlation between HD-TCARI and chlorophyll content improved by 9% compared to TCARI across varying LAI.
• Ground validation showed HD-TCARI achieved a higher correlation (R² = 66.74%) with chlorophyll content than TCARI (R² = 39.92%).

Conclusions:

• The proposed HD-TCARI vegetation index, utilizing multi-angular hyperspectral data, shows significant potential for accurate chlorophyll estimation in maize.
• HD-TCARI offers improved performance over traditional indices by mitigating LAI effects and saturation issues.
• This method enhances the reliability of remote sensing for precision agriculture applications.