【Objective】 Forest canopy density is related to stand age and vegetation growth status. In the remote sensing estimation of regional forest net primary productivity, combining forest canopy density may substantially improve the estimation accuracy. 【Method】 In this study, Shaoguan City in Guangdong Province was used as the case study area; the Landsat-8 OLI images and the fixed sample plot data of 357 forest resources continuous inventory in 2017 were collected as the main information sources. Using four models of random forest the multiple linear regression, artificial neural network and K-nearest neighbor classification methods, and forest canopy density, the selection of modeling variables, the evaluation of model accuracy, and the creation of a spatial map of net primary productivity of regional forest vegetation were performed. 【Result】 The results showed that among the characteristic variables, red band (B4), normalized vegetation index (NDVI), ratio vegetation index (RVI), leaf area index (LAI), soil vegetation factor of tassel cap transformation, texture characteristics, and terrain characteristics played important roles in the prediction of vegetation net primary productivity. After adding the forest canopy density factor into the inversion model, the accuracy of the four remote sensing estimation models was substantially improved. The performance comparison of the four remote sensing-based models indicated that the random forest model had the highest accuracy, followed by the multiple linear regression model and artificial neural network model; the K-nearest neighbor classification model had the lowest accuracy. The average net primary productivity of forests in the study area was 10.689 t/(hm²·a), and the area of high forest net primary productivity (≥18 t/(hm²·a)) accounted for 19.61% of the total area in the study area, which was mainly distributed in the northwest region at higher altitudes. 【Conclusion】 In this study, at the regional scale, the model prediction accuracy of forest net primary productivity can be effectively improved by combining canopy density.