资源环境科技发展态势分析平台

Satellite-derived aerosol optical depth is negatively impacted by cloud cover and surface reflectivity. As these issues lead to biases, they need to be discarded, which significantly increases the amount of missing data within an image. This paper presents a unique application of the partial convolutional neural network (partial CNN) for imputing missing data from the Geostationary Ocean Color Imager (GOCI) by training the partial CNN model with the Community Multiscale Air Quality model simulated AOD. The partial CNN model outperforms various models and algorithms for imputing GOCI images with a significant amount of missing data (45% of the dataset has at least 80% missing pixels) and distance to the nearest known pixel within the GOCI image. Once trained, the model requires significantly less processing time and fewer resources than the other models and methods. The model allows the accurate imputation of remote sensing images within significant amounts of missing data.