Reflectance and chlorophyll fluorescence-based retrieval of photosynthetic parameters improves the estimation of subtropical forest productivity

Abstract

Forest ecosystems play a significant role in climate change mitigation and uptake a larger amount of atmospheric CO2 than other terrestrial ecosystems via photosynthesis process in form of gross primary production (GPP). The photosynthesis or GPP is largely determined by the photosynthetic capacity of vegetation (i.e., maximum rate of carboxylation, Vcmax) in ecosystem models. However, considerable uncertainties of Vcmax estimates may limit our potential to address scientific issues of GPP related to the increasing emission of atmospheric CO2. Recently, solar-induced chlorophyll fluorescence (SIF) signals have been used as a proxy for resolving photosynthesis. In this study, the biochemical and structural parameters were retrieved from hyperspectral reflectance and fluorescence quantum efficiency (FQEs) was retrieved from ground-based SIF. Then, retrieved parameters were incorporated into the Soil Canopy Observation Photosynthesis and Energy (SCOPE) model to explore the potential of ground-based SIF to track Vcmax variability for a subtropical evergreen mixed forest. Then, SIF-derived Vcmax was used to parameterize Boreal Ecosystem Production Simulator (BEPS) model to simulate the GPP. With retrieved vegetative parameters and FQEs, the ground-based SIF was strongly correlated with the model-based SIF simulation at O2-B and O2-A bands, demonstrating that the coefficient of determination (R2) improved from 0.15 (constant values) to 0.60 (retrieved values) for SIFB and from 0.79 to 0.94 for SIFA simulation. Using SIF-derived Vcmax, the R2 value of simulated GPP against eddy covariance-based measurements substantially increased from 0.18 (constant Vcmax) to 0.38 (SIF-derived Vcmax) for dry season and from 0.56 to 0.67 for wet season respectively. The utilization of SIF-derived Vcmax with its corrected temperature response function reduced the relative error in annual GPP simulations by 24.9%. Our results support the significant references toward reducing unbiased SIF simulation and highlighting the potential of ground SIF in deriving Vcmax at the site scale for defining forest management options.