The potential of NIRvP in estimating evapotranspiration

Abstract

Accurate estimation of regional-scale evapotranspiration (ET) and reference evapotranspiration (ET_o) is crucial for scientific and rational water resource management, agricultural irrigation decision-making, and ecosystem monitoring. Currently, the estimation of ET_o or ET still mainly relies on observational data from surface meteorological stations or flux towers. However, due to the complexity of parameters and models, as well as the uneven distribution of observation stations, there is significant uncertainty in the estimation of ET_o or ET. We have developed a semi-empirical model based on Fick's law and the optimal stomatal behavior model by combining vegetation photosynthesis indicators and meteorological parameters. We compared the potential of using solar-induced chlorophyll fluorescence (SIF), near-infrared reflectance of vegetation (NIRv), and the product of NIRv and photosynthetically active radiation (NIRvP) to estimate ET_o and ET. The results indicate that NIRvP×VPD^0.5 (the 0.5th power of vapor pressure deficit) has an advantage in estimating ET_o. Additionally, in nonlinear models, the accuracy of estimating ET using NIRvP and VPD^0.5 surpasses that of using SIF. We also revealed that temperature and atmospheric pressure are the main factors mediating the relationship between NIRvP×VPD^0.5 and ET_o, as well as between NIRvP and ET. The research results lay the foundation for providing more accurate and reliable methods for estimating vegetation evapotranspiration.