Estimation of summer maize evapotranspiration using MARS model in the semi-arid region of northwest China

Abstract Evapotranspiration (ET) is a crucial parameter of agricultural water management, and its accurate estimation is of great significance to implement precision irrigation and optimal allocation of regional water resources. This study investigated the efficiency of the multivariate adaptive regression splines (MARS) model for estimating daily ET of summer maize under twelve input combinations, including complete and incomplete meteorological factors and crop growth indicators for the period 2011–2013. The performance of the MARS model was compared with the empirical Priestley-Taylor (P-T), Shuttleworth-Wallace (S-W) and Two-Patch (T-P) models as well as the back-propagation neural networks (BPNN) model. The optimal MARS and BPNN models achieved better ET prediction than the optimal empirical models at each growth stage. The MARS model was superior to the BPNN model at all growth stages in terms of mean absolute error: seedling emergence to tasseling (0.8476 mm/d vs. 0.9833 mm/d), tasseling to grouting (0.6777 mm/d vs. 0.7162 mm/d), grouting to harvest (0.3342 mm/d vs. 0.4336 mm/d) and the entire growth period (0.8752 mm/d vs. 0.9577 mm/d). Generally, the MARS model outperformed the BPNN model and empirical models at all growth stages, indicating that the optimal MARS models accurately modeled the complex nonlinear relationships between ET and the meteorological factors and crop growth indicators. The MARS model is thus highly recommended for estimating ET during the entire growth period of summer maize in the semi-arid regions when lack of adequate meteorological factors or crop growth indicators.