Forest canopy closure is a crucial parameter in forest resource inventory that plays a significant role in evaluating and monitoring the stability of forest ecosystems. With the continuous development of remote sensing technology， the estimation of large-scale forest canopy closure using multi-source remote sensing data has become a hot research topic. In this study， a regression model was constructed using machine learning algorithms based on laser point cloud data and multi-source optical remote sensing data to estimate forest canopy closure in large forested areas. Firstly， the dependent variable of the regression model， which is the true values of forest canopy closure， was calculated from the Airborne Laser Scanning （ALS） point cloud data. Secondly， 18 independent variables， such as vegetation indices and texture， were extracted from Sentinel-2 MSI， Landsat-8 OLI， and Sentinel-1 SAR images. Then， taking 14 forest plots in Guangxi as an example， the impacts of different independent variable combinations on forest canopy closure inversion were experimentally analyzed using two machine learning models， Random Forest Regression （RFR） and Support Vector Regression （SVR）. Finally， the best variable combination and machine learning method were selected to map the forest canopy closure in Guangxi. The experimental results showed that RFR performed better than SVR， and the S2+S1 combination had the highest accuracy， with a correlation coefficient R² of 0.703， Root Mean Square Error （RMSE） of 0.19， and Mean Absolute Error （MAE） of 0.13. Additionally， polarization features can significantly improve the inversion accuracy of forest canopy closure.