SIF-Constrained Optimization of SCOPE Vegetation Parameters for Gross Primary Productivity Estimation

Solar-induced chlorophyll fluorescence (SIF) provides a new pathway for the accurate estimation of gross primary productivity (GPP) by directly probing vegetation photosynthetic functioning, but mechanistic estimation is still constrained by the difficulty of obtaining the seasonal dynamics of key physiological parameters. Using maize, a representative C4 crop, as an experimental case, this study integrated long-term tower-based SIF observations with the SCOPE model to propose a cross-variable constraint strategy, in which key vegetation parameters were optimized against in situ SIF observations and independently validated against eddy covariance-derived GPP. Specifically, key sensitive parameters were first identified via Sobol global sensitivity analysis. A double-baseline asymmetric Gaussian phenology model was then constructed to characterize the seasonal dynamics of Vcmax, while Cab was derived from Vcmax using a smoothed piecewise-linear empirical relationship. A two-stage Bayesian optimization scheme was applied to optimize the parameters, which were subsequently used to run the SCOPE model and reconstruct continuous SIF and GPP time series. The results showed that the estimated daily SIF and GPP agreed well with ground observations, yielding R2 values of 0.86 and 0.74, respectively. Furthermore, the upscaled results correlated strongly with satellite products (MODIS: R = 0.94, R2 = −2.69; PML: R = 0.95, R2 = 0.81). This study demonstrates the feasibility of using SIF observations to constrain SCOPE vegetation parameters for improved GPP estimation, providing a parameterization basis and methodological support for mechanistic remote sensing monitoring of vegetation productivity.