Abstract: For estimating aerosol optical thickness, many constructed attributes from remote sensing data generally are used as inputs for a regression model. However, there might be noisy and correlated attributes in heterogeneous high dimensional inputs. They lead to the loss of estimation accuracy and reduction of robustness. To solve the problem, this paper proposed a feature selection approach by integrating Least Absolute Shrinkage Selection Operator (LASSO) and remote sensing priori knowledge. We validated our approach based on the spatial temporal synchronization data between MODIS and 197 global observation sites in the aerosol robotic network during April 2, 2009 and April 1, 2011. Back propagation neural networks are used as regression models. The experimental results showed that the proposed feature selection approach can group remote sensing attributes according to remote sensing knowledge and effectively select informative features from groups gradually by an iterative LASSO procedure. In this way, the proposed approach significantly improves the estimation accuracy of aerosol optical thickness.