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In order to explore an effective analytical model and method for estimating photosynthetic parameters of Cinnamomum camphora (Linn.) Presl by using unmanned aerial vehicle (UAV) multispectral technology, taking Cinnamomum camphora (Linn.) Presl as the research object, its canopy six-band spectral reflectance was obtained through a multispectral camera carried by UAV, and its net photosynthetic rate (Pn), intercellular carbon dioxide concentration (Ci), stomatal conductance (Gs) and transpiration rate (Tr) were simultaneously measured. The optimal index factor (OIF) was used to screen the combination of spectral reflectance and vegetation index as independent variables. Partial least squares method (PLS), back propagation neural network (BPNN), and random forest (RF) were used to construct estimation models for the independent variables and photosynthetic parameters, and the accuracy of each estimation model was analyzed and compared. The results showed that there was a close relationship between photosynthetic parameters and leaf reflectance in the red edge band 2 (center wavelength 750nm) and near infrared band (center wavelength 840nm) of Cinnamomum camphora L. The combination of red edge band 2, enhanced vegetation index 2 (EVI2), and red edge chlorophyll index (CI rededge) had the highest OIF value of 0.0126, which can be used as the best combination of model independent variables. The optimal models for the four photosynthetic parameters Pn, Ci, Gs, and Tr were all BPNN, with the modeling set decision factors R2 of 0.85, 0.81, 0.80, and 0.82, and the root mean square error (RMSE) of 0.85μmol/(m2·s), 16.23μmol/mol, 0.03mol/(m2·s) and 0.37mmol/(m2·s). The relative analytical error (RPD) were 2.59, 2.33, 2.28, and 2.37, respectively. The R2 of the validation set was 0.81, 0.73, 0.83, 0.76, and the RMSE was 1.46μmol/(m2·s), 18.37μmol/mol, 0.03mol/(m2·s) and 0.67mmol/(m2·s), with RPD of 1.39, 1.86, 2.67, and 1.20, respectively. The research results can provide a theoretical basis for the estimation of photosynthetic parameters of Cinnamomum camphora in dwarf forests using UAV multispectral remote sensing, and provide technical support for rapid monitoring of the growth status of economic plants in large areas.