Beyond greenness: Detecting temporal changes in photosynthetic capacity with hyperspectral reflectance data

Area of Science:

• Plant Physiology and Photosynthesis
• Remote Sensing and Spectroscopy
• Ecology and Carbon Cycling

Background:

• Plant photosynthetic capacity, crucial for Earth's carbon balance, is often estimated using spectral vegetation indices.
• Key determinants of photosynthetic capacity, maximum rate of RuBP carboxylation (Vcmax) and regeneration (Jmax), vary seasonally due to plant development and environmental factors.
• The temporal dynamics of relationships between leaf spectral properties and these physiological traits remain underexplored for remote sensing applications.

Purpose of the Study:

• To quantify seasonally-dynamic relationships between Vcmax, Jmax, and leaf reflectance spectra in hybrid poplar.
• To evaluate the robustness of remote sensing models (Partial Least Squares Regression - PLSR) for estimating Vcmax and Jmax under temporal variation.
• To assess the utility of hyperspectral vegetation indices for detecting changes in photosynthetic capacity.

Main Methods:

• In situ measurements of Vcmax and Jmax using gas exchange.
• Leaf reflectance spectroscopy and Partial Least Squares Regression (PLSR) modeling.
• Analysis of data collected over a 7-week mid-summer period on hybrid poplar.

Main Results:

• PLSR models demonstrated robustness in estimating Vcmax and Jmax despite significant within-season temporal variations.
• Plant stress within populations moderately impacted the predictive accuracy of PLSR models.
• Hyperspectral vegetation indices, including the Normalized Difference Vegetation Index (NDVI), showed strong correlations with Vcmax and Jmax.

Conclusions:

• Hyperspectral remote sensing, particularly using PLSR, shows potential for robust estimation of plant physiological traits, even with dynamic temporal changes.
• Hyperspectral vegetation indices can effectively monitor temporal shifts in photosynthetic capacity.
• These findings support the use of hyperspectral remote sensing for improved plant productivity and carbon budget estimations in dynamic environments.