Estimation of Leaf Photosynthetic Capacity From Leaf Chlorophyll Content and Leaf Age in a Subtropical Evergreen Coniferous Plantation

Abstract

Photosynthetic rate is a key source of uncertainty in the modeling of the terrestrial carbon cycle. Recent studies have utilized leaf chlorophyll content (Chl) as a proxy for leaf photosynthetic capacity in croplands and deciduous forests, with little investigation into this relationship for other plant function types and for different leaf ages. In this study, we evaluated the relationship between the maximum rate of carboxylation (Vcmax25) and the maximum electron transport capacity (Jcmax25) at 25 °C with both leaf nitrogen and Chl from different leaf ages (current and previous year) in Masson's pine (Pinus massoniana Lamb.) and slash pine (Pinus elliottii Engelm.) species in a subtropical evergreen coniferous forest. Our results showed small changes in leaf nitrogen over the growing season. In contrast, Vcmax25, Jmax25, and Chl displayed larger seasonal variations. Vcmax25 was more related to leaf Chl than leaf nitrogen in both previous year's and current year's leaves, likely due to the variable partitioning of leaf nitrogen between and within photosynthetic and nonphotosynthetic fractions. Leaf Chl and month after budding (MAB) were the main predictors for Vcmax25 based on the random forest regression analysis. These findings highlighted the problem in using leaf nitrogen as a proxy for Vcmax25 where there is a dynamic nitrogen investment (i.e., with leaf ontogenesis, or between different species) and illustrated the value of using leaf Chl (as retrievable from remotely sensing) and MAB to constrain Vcmax25 in process‐based models to improve the simulation of photosynthetic rates in evergreen coniferous forests.

Estimating photosynthetic capacity from optimized Rubisco–chlorophyll relationships among vegetation types and under global change

“…In this way, leaf Chl is a proxy for photosynthetic nitrogen, in that plants optimize the allocation of leaf nitrogen between Chl and Rub to maximize photosynthetic rates under given resource availability (Hikosaka andHirose 1997, Prentice et al 2014). The empirical relationship between leaf Chl content and Vcmax 25 may provide a new way to map Vcmax 25 on a large scale because leaf Chl content could be retrieved from satellite imagery at a relatively high level of accuracy (Xu et al 2019, Croft et al 2020. However, the investigations in crops and other natural ecosystems illustrated that the coefficients of this relationship vary with different vegetation types obviously (Wang et al 2020, Qian et al 2021.…”

“…The empirical relationship between leaf Chl content and Vcmax 25 may provide a new way to map Vcmax 25 on a large scale because leaf Chl content could be retrieved from satellite imagery at a relatively high level of accuracy (Xu et al 2019, Croft et al 2020. However, the investigations in crops and other natural ecosystems illustrated that the coefficients of this relationship vary with different vegetation types obviously (Wang et al 2020, Qian et al 2021. This poses a challenge for mapping Vcmax 25 on a large scale when adopting Vcmax 25 -Chl relationship.…”

Assessment of Satellite Chlorophyll-Based Leaf Maximum Carboxylation Rate (Vcmax) Using Flux Observations at Crop and Grass Sites

“…Even for C3 vegetation types, different studies have established different Cab-Vcmax relationships, for which there are many influencing factors, such as vegetation types, leaf-water stress status, leaf age, etc. [12,13,17]. Therefore, a comprehensive comparative analysis and selection of appropriate functional relationships can help with obtaining accurate values of leaf Vcmax.…”

“…Nevertheless, it is difficult to accurately retrieve the nitrogen content from remote sensing data as the relevant spectral response bands are easily influenced by the foliar water content, cellular structure scattering and atmospheric water vapor [10,11]. Chlorophyll, at the light-harvesting apparatus, plays a crucial role in photosynthesis [12]. In recent years, many researchers have theoretically and experimentally demonstrated a good correlation between Vcmax and the leaf chlorophyll content (Cab) [13][14][15][16].…”

Estimación de variables esenciales de la vegetación en un ecosistema de dehesa utilizando factores de reflectividad simulados estacionalmente

“…Además, cambios en el metabolismo del tejido foliar producidos por alteraciones nutricionales, factores climáticos o enfermedades, producen la degradación del contenido de pigmentos (Alonso et al, 2002;Haldimann et al, 2008), por lo que han sido ampliamente utilizados para la estimación de daños en la vegetación. Los contenidos en clorofilas a y b y carotenoides son rasgos de la vegetación ampliamente utilizados para la parametrización de modelos RTM como PROSPECT (Jacquemoud et al, 2009) por su influencia decisiva en la capacidad fotosintética foliar Wang et al, 2020). La determinación de pigmentos en laboratorio es laboriosa y se ha realizado tradicionalmente mediante extracciones con disolventes orgánicos de diferente polaridad seguida de una determinación espectrofotométrica (Lichtenthaler y Buschmann, 2001;Ritchie, 2008) o por cromatografía líquida (De las Rivas et al, 1989).…”