【Objective】Canopy fuel moisture parameters play a crucial role in assessing the physiological status of forest plants and fire risk. This study primarily explores methods for estimating vegetation canopy fuel moisture characteristic parameters using field hyperspectral data, providing a new approach for rapidly and non-destructively acquiring vegetation canopy moisture characteristic information to assess forest fuel fire risk.【Method】For canopy samples from four forest types, including Pinus massoniana, P. taeda, Camphora officinarum and Photinia serratifolia, spectral reflectance data, leaf area, fresh weight, dry weight, and leaf area index (LAI) were obtained through field sampling and laboratory experiments. Correlations between LAI and moisture characteristic variables (FMC, EWT, FMC_C and EWT_C) were analyzed. Partial least squares regression (PLSR) and artificial neural networks (ANN) were used to predict and estimate the moisture characteristic parameters FMC_C and EWT_C.【Result】The correlation between LAI and FMC_C (R²=0.86) was significantly higher than that between LAI and EWT_C(R²=0.04), while the correlation between LAI and EWT (R²=0.52) was slightly higher than that between LAI and FMC (R²=0.50). The estimation and validation processes for the aggregated samples of FMC_C and EWT_C across the four forest stand types showed that the coefficient of determination for the PLSR method was higher than that for the ANN, while the root mean square error was lower. In the estimation of EWT_C and FMC_C values for canopy fuel moisture characteristic variables in the four different forest types, the mean R² value was 0.73 and the mean RMSEcv was 0.22 when using the PLSR method. P. massoniana showed the best performance (FMC_C: R²=0.80, RMSE_CV=0.02; EWT_C: R²=0.74, RMSE_CV=0.19). When using the ANN method, the mean R² value was 0.76 and the mean RMSEcv was 0.20. The best performance was observed for the estimation of FMC_C in P. massoniana (R_{CV}^2=0.91, RMSE_CV=0.12) and EWT_C in P. taeda (R_{CV}^2=0.90, RMSE_CV=0.01) using ANN-2 as input.【Conclusion】Both EWT_C and FMC_C can be accurately predicted from reflectance spectra. As a mass-based variable, FMC_C can achieve higher estimation accuracy than the area-based variable EWT_C through both PLSR and ANN methods. Plant mass may play a more significant role than plant area in determining spectral emissivity, indicating that fuel mass factors have a greater impact on the inversion of spectral reflectance moisture characteristics, while leaf area has a relatively smaller influence. Overall, the hyperspectral data of forest stand canopy obtained through field measurements are effective in estimating fuel moisture characteristic parameters, and can provide valuable support for forest fuel fire risk prediction, forecasting, and level assessment.