Abstract: In order to achieve rapid and non-destructive monitoring of plants health, the Suaeda salsa in the Yellow River Delta wetland was used as the research object, and the chlorophyll fluorescence parameter Fv/Fm in 123 sample plots and an unmanned aerial vehicle (UAV) hyperspectral image of Suaeda salsa wetland were measured. Based on this, the correlation analysis between some vegetation index such as ratio, difference, and normalized difference vegetation index and the chlorophyll fluorescence parameter Fv/Fm were carried out. The inversion models of Fv/Fm by using statistical regression and machine learning algorithms were constructed and evaluated by R² and RMSE. The results showed that the Fv/Fm was highly correlated with vegetation index of D₄₅₀/D₇₅₆, D₆₁₂−D₈₄₉ and (D₅₁₇−D₈₅₁)/(D₅₁₇+D₈₅₁). Among statistical regression models, the model accuracy based on Fv/Fm and vegetation index (D₅₁₇−D₈₅₁)/(D₅₁₇+D₈₅₁) were the highest (R²=0.606, RMSE=0.006), and the R² and RMSE between predicted and measured values were 0.739 and 0.724, respectively. Among machine learning models, the model accuracy based on Fv/Fm and vegetation index (D₆₁₂−D₈₄₉) were the highest. The R² and RMSE of inversion model based on BP neural network algorithm were 0.821 and 0.261, respectively, while the R² and RMSE of inversion model based on the PSO-DELM algorithm were 0.982 and 0.140, respectively. This study not only provides an important reference for estimating the chlorophyll fluorescence parameters using the UAV hyperspectral imagery, but also provides technical support for estimating the chlorophyll fluorescence parameters at the macro scale.