A water stress factor based on normalized difference water index substantially improved the accuracy of light use...

High-accuracy simulation of gross primary productivity (GPP) is crucial for the monitoring and evaluation of the ecosystem services and the adaptive management of grassland. The light use efficiency (LUE) model is one of the most widely-used methods to simulate GPP, given its simple structure and low input requirements. Current LUE models are less applicable to grasslands than other vegetation types and have lower overall estimation accuracy in arid and semi-arid regions. A grassland-specific light use efficiency model (GRASS-LUE), which optimizes three important parameters (the fraction of absorbed photosynthetically active radiation FPAR, optimum temperature T_opt and water stress factor f(W)), has been developed to improve the accuracy of GPP simulation for grasslands along aridity gradients. GPP simulated by the GRASS-LUE agreed well with the eddy covariance (EC) GPP estimates for grasslands along the aridity gradient at 8-day (coefficient of determination (R²) = 0.85, Bias = -0.67 gC m^-2 day^-1), monthly (R² = 0.88, Bias = -22.33 gC m^-2 month^-1) and annual time scales (R² = 0.95, Bias = -118.91 gC m^-2 year^-1). Compared with five state-of-the-art GPP products (PML, MOD17, rEC-LUE, VPM and BESS), GRASS-LUE had the best and most stable performance in reproducing EC GPP, especially for semi-arid grassland, with the highest global performance indicator (GPI) value. Sensitivity tests further revealed that: 1) modifying f(W) to be based on the Normalized Difference Water Index (NDWI) substantially improved the model accuracy for arid and semi-arid grasslands and 2) using the minimum of temperature and water stress factors (i.e., min⁡(f(W),f(T))) to represent environmental stress in GRASS-LUE was better than that from the multiplication of temperature and water stress factors (i.e., f(W)×f(T)).