The conductance-photosynthesis (g_s-A) model is widely used for estimating the transpiration rate (ET_c). The stomatal conductance slope (g₁) in the g_s-A model is crucial, and is usually parameterized with a PFT-specific g₁. However, because there is seasonal variation in g₁, the scheme of constantizing g₁ results in large potential uncertainties in ET_c estimation. Therefore, optimizing the parameterized scheme of g₁ is key for improving the estimation of ET_c. Previous studies have shown that the dynamic parameterization of g₁ using remote sensing-based Leaf Area Index (LAI) can effectively improve the accuracy of the estimated ET_c in deciduous forests. However, it is still not clear whether this method is applicable to evergreen forests, in which the canopy shows slight seasonal variability. In addition, the First-Principles Theory indicates that g₁ can be expressed as a function of temperature. Hence, whether temperature can be used to simulate g₁ with a better performance than that when using LAI requires further investigations. In view of the above questions, six FLUXNET evergreen forest sites were selected to investigate the relationships between g₁, and LAI and air temperature, and the ET_c simulation results under the two parameterization schemes were compared. Results showed that g₁ varied with both LAI and temperature at each site, showing a significant negative correlation, while the results of the dynamic parameterization scheme (DYN) were better than those of the constant g₁ scheme (FIX). This showed that the simulation of g₁ using LAI is still effective in evergreen forests, but the explanation of temperature on g₁ (R²=0.45±0.12) is stronger than that of LAI (R²=0.28±0.23). Further comparing the g_s and ET_c obtained using the two schemes, we found that the scheme based on temperature performed better than that of LAI at the daily scale, with the Root Mean Square Error (RMSE) of the ET_c estimation being significantly reduced (26.0%±24.4%). This study emphasizes the feasibility of the temperature-based parameterized scheme of g₁ and helps to fundamentally improve the simulation of g_s and ET_c in evergreen forest landscapes.