Scaling solar-induced chlorophyll fluorescence by using VPD0.5 improves the simulation of reference crop evapotranspiration in the arid and semiarid regions of northern China

Abstract

Accurately estimating reference crop evapotranspiration (ET0) is crucial for decision-making regarding irrigation, improving farmland water use efficiency, planning water resources, and simulating the global water cycle. However, because traditional ET0 estimation models often require a large number of meteorological parameters as input variables, their application potential is limited, especially when simulating arid and semiarid regions where meteorological data are lacking. Therefore, it is still very challenging to estimate ET0 at large scales in arid and semiarid areas. In this study, we first proposed a semi-mechanistic model for estimating ET0 by deriving the relationship between canopy conductance to water vapor (gw) and solar-induced chlorophyll fluorescence (SIF) based on Fick's law and the light use efficiency (LUE) model, combining theories on optimal stomatal behaviour. Second, the relationship between SIF × VPD0.5 (VPD0.5: 0.5-power of the vapor pressure deficit) and ET0 in the model was verified at the site scale. On this basis, the semi-mechanistic model was applied to three machine learning models to simulate ET0. Four statistical indicators, including the coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (NRMSE) and mean absolute error (MAE), were used to verify the model performance and screen out the optimal model. Finally, the optimal model was compared with five traditional empirical models. The results showed that (1) the relationship between ET0 and the product of SIF and the 0.5-power vapor pressure deficit (SIF × VPD0.5) was closer than the relationship between ET0 and SIF at the site scale. (2) Compared to the case in which SIF was used as the only input variable, three machine learning models performed better when SIF was scaled using VPD0.5 as the input variable, and the random forest regression (RFR) model in which SIF × VPD0.5 was the input variable was the optimal model (ET0opt). (3) The total mean value of ET0 at 22 sites simulated by ET0opt is 15.46 mm/4 days. The total mean ET0 value for the 22 sites calculated by the five traditional empirical models vary from 10.70 mm/4 days to 20.96 mm/4 days, while our results were in this range and close to their mean value. Our study provides a valuable reference basis for large-scale ET0 estimations using remote sensing in arid and semiarid areas.