Hyperspectral Estimation Models of Winter Wheat Chlorophyll Content Under Elevated CO₂

Area of Science:

• Agricultural Science
• Plant Physiology
• Remote Sensing

Background:

• Chlorophyll content is a key indicator of winter wheat health.
• Elevated CO2 levels may alter the relationship between spectral reflectance and chlorophyll.
• Understanding this relationship is crucial for crop monitoring.

Purpose of the Study:

• To investigate if elevated CO2 affects the spectral reflectance-chlorophyll content relationship in winter wheat.
• To evaluate hyperspectral measurement's efficacy in estimating chlorophyll under varying CO2 conditions.
• To develop models for chlorophyll estimation using spectral data.

Main Methods:

• Open-top chambers were used to create ambient (aCO2) and elevated (eCO2) CO2 environments.
• Winter wheat spectral reflectance was measured under both conditions.
• Estimation models were built using red edge position, sensitive bands, and spectral indices (e.g., DVI).

Main Results:

• A strong correlation exists between winter wheat chlorophyll content and canopy spectral characteristics.
• Chlorophyll content was accurately estimated using sensitive spectral bands and DVI under both aCO2 and eCO2.
• Model accuracy varied slightly between ambient and elevated CO2 conditions.

Conclusions:

• Hyperspectral measurements are effective for estimating winter wheat chlorophyll content under both ambient and elevated CO2.
• This technique offers a valuable tool for monitoring plant physiology and growth in changing CO2 environments.
• Spectral-based chlorophyll estimation remains robust despite CO2 level variations.