Remote Sensing Inversion of Leaf Maximum Carboxylation Rate Based on a Mechanistic Photosynthetic Model

The maximum carboxylation rate (
$V_{\mathrm {cmax}}$
) of a leaf is a rate-limiting step in photosynthesis. A quantitative spatial distribution of
$V_{\mathrm {cmax}}$
is necessary to predictively understand forest gross primary production (GPP). In this study, we propose a new method that combines hyperspectral data with the Farquhar photosynthetic mechanistic model to estimate
$V_{\mathrm {cmax}}$
. First, the photochemical reflectance index (PRI) of a leaf was calculated, and then a model of PRI and sunlit leaf light-use efficiency (LUE) was constructed to estimate sunlit leaf LUE. Next, sunlit leaf LUE and absorbed photosynthetically active radiation (APAR) were used to estimate the GPP of the sunlit leaf. Finally,
$V_{\mathrm {cmax}}$
was deduced based on the Farquhar mechanistic model by using the GPP of sunlit leaves, and differences in
$V_{\mathrm {cmax}}$
were analyzed across various types of vegetation. The results showed that the coefficient of determination between
$V_{\mathrm {cmax}}$
estimated using the mechanistic model and the mean
$V_{\mathrm {cmax}}$
measured at the sampling sites was 0.82, with a root mean square error of
$4.27~\mu $
mol
${}\cdot {}\text{m}^{\mathrm {-2}} {}\cdot {}\text{s}^{\mathrm {-1}}$
. Using a forest-type map of the Mao’er Mountain region as a reference, statistical analysis showed that coniferous forest showed the highest
$V_{\mathrm {cmax}}$
, followed by coniferous–broadleaf mixed forest, while broad-leaved forest showed the lowest
$V_{\mathrm {cmax}}$
. The proposed method can generate regional
$V_{\mathrm {cmax}}$
distribution maps efficiently.