Abstract
Earth's future carbon balance and regional carbon exchange dynamics are inextricably linked to plant photosynthesis. Spectral vegetation indices are widely used as proxies for vegetation greenness and to estimate state variables such as vegetation cover and leaf area index. However, the capacity of green leaves to take up carbon can change throughout the season. We quantify photosynthetic capacity as the maximum rate of RuBP carboxylation (Vcmax) and regeneration (Jmax). Vcmax and Jmax vary within-season due to interactions between ontogenetic processes and meteorological variables. Remote sensing-based estimation of Vcmax and Jmax using leaf reflectance spectra is promising, but temporal variation in relationships between these key determinants of photosynthetic capacity, leaf reflectance spectra, and the models that link these variables has not been evaluated. To address this issue, we studied hybrid poplar (Populus spp.) during a 7-week mid-summer period to quantify seasonally-dynamic relationships between Vcmax, Jmax, and leaf spectra. We compared in situ estimates of Vcmax and Jmax from gas exchange measurements to estimates of Vcmax and Jmax derived from partial least squares regression (PLSR) and fresh-leaf reflectance spectroscopy. PLSR models were robust despite dynamic temporal variation in Vcmax and Jmax throughout the study period. Within-population variation in plant stress modestly reduced PLSR model predictive capacity. Hyperspectral vegetation indices were well-correlated to Vcmax and Jmax, including the widely-used Normalized Difference Vegetation Index. Our results show that hyperspectral estimation of plant physiological traits using PLSR may be robust to temporal variation. Additionally, hyperspectral vegetation indices may be sufficient to detect temporal changes in photosynthetic capacity in contexts similar to those studied here. Overall, our results highlight the potential for hyperspectral remote sensing to estimate determinants of photosynthetic capacity during periods with dynamic temporal variations related to seasonality and plant stress, thereby improving estimates of plant productivity and characterization of the associated carbon budget.
Highlights
Photosynthesis by land plants plays a critical role in regional and global carbon balance [1,2,3]
Our results show that hypers can predict photosynthetic parameters across time, suggesting that these hyperspectral remote sensing techniques have great potential to constrain model estimates of plant function
Relationships between leaf reflectance spectra and photosynthetic capacity were robust throughout a 7-week period with dynamic change in photosynthetic capacity
Summary
Photosynthesis by land plants plays a critical role in regional and global carbon balance [1,2,3]. Terrestrial carbon uptake by plants varies annually and seasonally based on climate conditions [5,6,7,8,9], water and nutrient availability [10,11], and plant physiological properties such as water use efficiency [12] and photosynthetic capacity [13]. Spatial and temporal variation in plant photosynthesis can be estimated using remote sensing-derived spectral indices. Many terrestrial biosphere models use static values of determinants of photosynthetic capacity [27]; there is some evidence that allowing photosynthetic capacity to vary temporally could improve representation of carbon dynamics [28]. Spectral methods that capture dynamic temporal changes in photosynthetic capacity could yield more accurate estimates of vegetation productivity and associated carbon uptake
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