Direct Estimation of Ecosystem Water Use Efficiency Using the Random Forest Machine Learning Model

Accurate quantification of ecosystem water use efficiency (eWUE), defined as the ratio of gross primary production (GPP) and evapotranspiration (ET), is vital to deepen our understanding of global water and carbon cycles. However, the influence of varying abiotic and biotic factors on GPP and ET is still not thoroughly understood, and thus accurate estimation of GPP and ET is still challenging, which may introduce uncertainties into eWUE. Here, we applied the random forest (RF) machine learning model to directly estimate the 8-day observed eWUE collected from the 197 globally distributed flux sites involved in the FLUXNET2015 dataset. Additionally, the RF model was also intercompared with the widely used Moderate Resolution Imaging Spectroradiometer (MODIS) and Penman-Monteith-Leuning version 2 (PMLv2) products. Our results show that the RF model could well reproduce the 8-day observed eWUE, as indicated by the root mean square error (RMSE) = 1.01 g C Kg
^-1
H2O, the coefficient of determination (R
²
) = 0.66, and the mean prediction error (Bias) = 0.00 g C Kg
^-1
H2O. More importantly, the RF model showed considerable improvements over the MODIS and PMLv2 products in simulating 8-day eWUE, with decreasing the RMSE by 1.03 g C Kg
^-1
H2O and 0.86 g C Kg
^-1
H2O, increasing the R
²
by 0.65 and 0.49, and reducing the Bias by 0.64 g C Kg
^-1
H2O and 0.32 g C Kg
^-1
H2O, respectively. This study indicates a promising avenue for using machine learning models to simulate eWUE directly.