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• Precipitation is the dominant factor affecting soil water content. • Evapotranspiration is the dominant factor affecting vegetation growth. • The impact of meteorologic on soil moisture and vegetation decreases from temperate to tropical. • LST/Tair negatively affects soil moisture and vegetation on a small scale. Long-term meteorological drought can cause soil moisture deficiency and hinder vegetation growth, however, the dominant driving force leading to alteration of soil water content (SWC) and vegetation growth condition is still not clearly defined due to the natural coupling between meteorological elements. Here, we decoupled the relationship between precipitation, evaporation and temperature to explore the main driver of impacting SWC and solar-induced chlorophyll fluorescence (SIF) in China by means of binning, partial correlation analysis and structural equation modelling. We found that precipitation led to a positive change in SWC nationally (nonstandardized path coefficient (NSPC) = 0.75, P < 0.001), while its influence gradually decreased from temperate to tropical climate areas. Precipitation had a positive effect on SIF, except in tropical areas (NSPC = 0.7, P < 0.001). The results of the three research methods indicated that the primary factor influencing SIF was evaporation (NSPC = 0.69, P < 0.001), with a significant correlation between SIF and evaporation in approximately 75 % of the country's area. The binning analysis revealed that SIF initially increased and then decreased as evaporation increased. Partial correlation results showed that temperature had a negative impact on both SWC and SIF, while binning results showed that temperature had no significant impact on SWC, and structural equation modelling result showed temperature had relatively less impact on SWC (NSPC = -0.21, P < 0.05) and SIF (NSPC = -0.19, P < 0.01) than precipitation and evaporation. In addition, we found the dominant drivers' influence weakened from temperate to tropical climate areas, which revealed by the area percentage of partial correlation between evaporation and SIF, precipitation and SWC. The same distribution pattern can be seen from NSPC of evaporation and SIF, NSPC of precipitation and SWC. [ABSTRACT FROM AUTHOR]

• This study proposed a semimechanistic SIF-based ET0 estimation model. • The correlation of SIF with ET0 is improved by multiplying the term of VPD0.5. • SIF-based model can accurately estimate ET0 and shows a better performance than the traditional meteorological model. 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 (g w) 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. [ABSTRACT FROM AUTHOR]