A novel hybrid modelling framework for GPP estimation: Integrating a multispectral surface reflectance based V simulator into the process-based model.

Terrestrial gross primary productivity (GPP) is the key element in the carbon cycle process. Accurate GPP estimation hinges on the maximum carboxylation rate (V). The high uncertainty in deriving ecosystem-level V has long hampered efforts toward the performance of the GPP model. Recently studies suggest the strong relationship between spectral reflectance and V. We proposed the multispectral surface reflectance-driven V simulator using the fully connected deep neural network and built the hybrid modelling framework for GPP estimation by integrating the data-driven V simulator in the process-based model. The performance of hybrid GPP model was evaluated at 95 flux sites. The result shows that the multispectral surface reflectance-driven V simulator acquires the satisfactory estimation, with correlation coefficient (R), root mean square error (RMSE) and median absolute percentage error (MdAPE) ranging from 0.34 to 0.80, 14 to 43 μmol m s and 21 % to 66 % across different land cover types, respectively. The hybrid framework generates good GPP estimates with R, RMSE and MdAPE varying from 0.76 to 0.89, 1.79 to 6.16 μmol m s and 27 % to 90 %, respectively. Compared with EVI-driven method, the multispectral surface reflectance significantly improves the V and GPP estimates, with MdAPE declining by 0.6 %-18 % and 1 % to 21 %, respectively. The Shapley value analysis reveals that red (620-670 nm), near-infrared (841-876 nm) and shortwave infrared (1628-1652 nm and 2105-2155 nm) are the key bands for V estimation. This study highlights the potential of multispectral surface reflectance for quantifying ecosystem-level V. The new hybrid framework fully extracts the information of all available spectral bands using deep learning to reduce parameter uncertainty while maintains the description of photosynthetic process to ensure its physical reasonability. It can serve as a powerful tool for accurate global GPP estimation.