Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on hyperspectral imagery

Area of Science:

• Plant physiology
• Remote sensing
• Biophysical modeling

Background:

• Quantifying vegetation physiological condition relies on understanding light interaction with canopies.
• Chlorophyll fluorescence (CF) and biochemical properties influence spectral reflectance.

Purpose of the Study:

• To demonstrate that remote sensing can detect chlorophyll fluorescence (CF) effects.
• To estimate CF and chlorophyll a + b (Ca + b) content using coupled fluorescence-reflectance-transmittance (FRT) and PROSPECT leaf models.
• To develop a novel index for detecting vegetation stress.

Main Methods:

• Simulated reflectance using FRT and PROSPECT models.
• Conducted laboratory measurements of spectral reflectance at leaf and canopy levels.
• Inverted the FRT-PROSPECT model to estimate CF, Ca + b, and fluorescence parameters.
• Analyzed derivative reflectance (DR) and developed a derivative chlorophyll index (DCI).

Main Results:

• CF effects on spectral reflectance were detectable by remote sensing.
• The coupled FRT-PROSPECT model accurately estimated CF, Ca + b, and fluorescence parameters (e.g., Fv/Fm, F'm, Ft, delta F/F'm).
• A double peak in DR correlated with increased CF and Ca + b.
• Airborne imagery confirmed double peaks in canopy DR in stressed sugar maple sites.
• The developed DCI effectively detected vegetation stress.

Conclusions:

• Remote sensing, coupled with biophysical models, can quantify vegetation physiological status.
• The FRT-PROSPECT model inversion is a reliable method for estimating leaf biochemical and fluorescence properties.
• The DCI shows promise for non-invasive vegetation stress detection.