A Data-Driven Method for Direct Estimation of Global 8-Day 500-m Ecosystem Water Use Efficiency

Abstract

Accurately quantifying ecosystem water use efficiency (WUE) is essential for advancing our understanding of carbon and water exchanges between the land surface and atmosphere. Routinely, WUE is estimated by first predicting gross primary production (GPP) and evapotranspiration (ET) and then calculating WUE as the ratio of GPP to ET. However, this approach can lead to amplified errors in WUE estimates due to uncertainties in GPP and ET predictions. Here, we proposed a novel random forest (RF)-based WUE estimation model, referred to as the DRF model, which directly predicts WUE as the targeted variable to improve WUE estimation. The DRF model was trained using a combination of remote sensing (RS), meteorological reanalysis, and digital elevation model (DEM) datasets, along with in situ WUE observations at 261 global flux tower sites from the FLUXNET2015 and AmeriFlux FLUXNET datasets. Moreover, the DRF model was intercompared with the routine WUE estimation method using the RF model (the IRF model) as well as the widely used Moderate-Resolution Imaging Spectroradiometer (MODIS) and Penman-Monteith–Leuning version 2 (PMLv2) products in WUE estimation. Our results demonstrated that the DRF model well-reproduced 8-day in situ WUE, with the root-mean-square error (RMSE) of 1.07 g C kg−1 H2O, the coefficient of determination ( $R^{2}$ ) of 0.59, and the mean bias error (Bias) of 0.00 g C kg−1 H2O, and showed significant improvement over the IRF model with the RMSE of 1.20 g C kg−1 H2O, $R^{2}$ of 0.50, and Bias of −0.09 g C kg−1 H2O. Moreover, the DRF model considerably outperformed the MODIS product (RMSE =1.93 g C kg−1 H2O, $R^{2} =0.01$ , and Bias $= -0.49$ g C kg−1 H2O) and the PMLv2 product (RMSE =1.70 g C kg−1 H2O, $R^{2} =0.22$ , and Bias =0.25 g C kg−1 H2O). Finally, the DRF model better captured seasonal fluctuations of in situ WUE than the other three models/products. Our study indicates that the DRF model is a promising alternative to routine WUE estimation methods and has the potential to produce more accurate global WUE estimates in future studies.