Machine learning and remote sensing-based modeling of the optimal stomatal behavior of crops

Abstract

The slope (g1) of stomatal conductance to photosynthesis is an important parameter in the optimal stomatal behavior theory-based stomatal conductance model of Medlyn et al. (2011). Although studies have modelled the spatial variations in g1, disclosing its variations over environmental gradients and different plant functional types. However, the above methods are still not accurate enough on a global scale, as they do not consider the temporal variations in g1. To address this issue we used the Ensemble Kalman Filter (EnKF) to assimilate tower-based gross primary productivity (GPP) and latent heat flux (LE) of 17 cropland flux sites into a remote sensing (RS)-based evapotranspiration-photosynthesis coupled model, termed SCOPES-Crop, to derive the temporal variations in g1 for C3 and C4 crops. We also used the feedforward artificial neural network (FANN) along with RS variables to model g1. Results showed g1 to rise rapidly in spring and summer, and then decline in autumn. The value of g1 reached the lowest value and remained stable in wintertime. FANN-based modeling of g1 showed R(RMSE) = 0.81 (1.94 kPa0.5) and 0.90 (0.70 kPa0.5) for C3 and C4 Crops, respectively, for the testing dataset. The estimates of GPP and LE using FANN-derived g1 at the 17 flux sites were improved as compared to that using fixed g1. The mean values of site-level R(RMSE) for GPP and LE simulated using FANN-derived g1 are 0.92 (1.8 gC m−2 d-2) and 0.85 (22.5 W m−2), respectively. Our results revealed notable seasonal variations in g1, indicating the importance of considering the temporal variations in g1 in evapotranspiration-photosynthesis coupled model. The FANN along with RS variables showed great potential of representing the g1 variations.