Modelling of the canopy conductance of potted cherry trees based on an artificial neural network

Canopy conductance ( G c ) is a very important parameter for the simulation of regional transpiration and water transport in the Soil–Plant–Atmosphere continuum system. However, the determination of G c is a complicated and nonlinear process, and so far G c has not been directly measured by an experimental approach. An artificial neural network (ANN) is ideally suited for studying a complicated, nonlinear and uncertain process. Thus, it is very meaningful to assess the feasibility of predicting G c based on an ANN model. In this study, the value of G c was back-calculated from the Penman–Monteith model as the “measured G c ” using sap flow data, and this value was compared with the simulated G c value from the ANN and multiple regression (MRL) models based on various combinations of vapor pressure deficit ( V PD ), photosynthetic active radiation ( PAR ), air temperature ( T a ) and air humidity with the cross-validation method. The data were divided into part A (from 13 April to 28 June) and part B (from 29 June to 20 August), the data for group A represented the data of part A were used to train and the data of part B were used to test, and group B had the similar meaning. The results showed that the ANN model had a higher accuracy, and the performance was better than that of the MRL model under different radiation levels. Mean relative errors were all less than 15%, and were respectively 10.56% for group A and 14.18% for group B. The environment factor order rank affecting the model accuracy was V PD > PAR > T a > RH , and the three input variables V PD , PAR and T a were the optimal combination.