Abstract: This research aims to evaluate the canopy-resistance models in the forest evapotranspiration (ET) estimates across various stages of canopy development. A systematic analysis was also conducted to compare the performances of different models after simulation. The principal influencing factors of canopy resistance and the underlying mechanisms were determined to clarify the impact on the estimation of ET. Nine frequently used canopy-resistance models were selected after optimization. The data source was the eddy-covariance flux observation that collected from 2021 to 2023 at the Wudang Flux Observation Station (Wudang Station), which was represented a typical forest ecosystem in the Hanjiang River Basin. Initially, the canopy resistance was calculated using these observational data. Subsequently, the Penman-Monteith (P-M) model was incorporated to estimate the ET of the forest ecosystem. A comparison was also carried out on the canopy resistance and ET of the forest ecosystem during different stages of canopy development. Specifically, the canopy resistance was simulated by each of the nine canopy-resistance models. While the forest ET was estimated in each stage of canopy development. Additionally, the main influencing factors of canopy resistance were determined after comparison. When considering the entire growing season, the Stannard model (ST) demonstrated the highest score 0.97 in the Global Performance Indicator (GPI), indicating the overall superior performance among all the models. The performance of the nine models was ranked in the descending order of the Stannard (ST) > Katerji - Perrier (KP) > Jarvis (JA) > Leuning (RL) > Massman (MA) > García - Santos (GA) > Farias (FA) > Ball - Berry - Leuning (BBL) > Todorovic (TD). The better performance of the Stannard model was attributed to the complex interplay of environmental variables. There were the direct factors (such as solar radiation and wind speed) and the indirect effects of soil moisture on the canopy resistance. The Stannard model can be expected to provide more accurate simulations of canopy resistance, thereby leading to a more reliable estimation of forest ET. Furthermore, the JA model emerged as the top-performing model in the low-coverage stage. The reason was that the leaf area index and air temperature were considered the two most crucial influencing factors on the canopy resistance. Among them, the leaf area index was used to determine the surface area available for transpiration. Additionally, there was some influence of air temperature on the stomatal conductance, which in turn affected the canopy resistance. For example, the stomata tended to open wider, as the temperature increased, thus reducing the canopy resistance for the high transpiration rates. The JA model successfully captured these dynamics, resulting in a more accurate simulation of canopy resistance and ET during the low-coverage stage. The KP model was used to integrate the dominant influencing factor of canopy resistance and the vapor pressure deficit. Specifically, the driving force was also represented for the water vapor diffusion from the leaf surface to the atmosphere. The KP model was used to simulate the relationship between vapor pressure deficit and canopy resistance, which was closely approximated to the actual canopy resistance. As a result, the highest accuracy of ET estimate by the KP model was achieved in the high-coverage stage among all the models. The vapor pressure deficit also shared a more pronounced impact on the water-loss process. The KP model was highly suitable for the ET estimates under such conditions. In conclusion, it is highly recommended to utilize the KP model in the high-canopy-coverage scenarios. Conversely, it is advisable to prioritize the canopy resistance that is estimated by the JA and P–M models for the low-canopy-coverage situations. The more accurate estimation of forest latent heat can greatly contribute to the forest water-energy balance in resource management, climate change research, and hydrological modeling.