This study presents a novel approach for the detection of contrails in satellite imagery using a convolutional neural network (CNN). Contrails are important to monitor because their contribution to climate change is uncertain and complex. Contrails are found to have a net warming effect because the clouds prevent terrestrial (longwave) radiation from escaping the atmosphere. Globally, this warming effect is greater than the cooling effect the clouds have in the reduction of solar (shortwave) radiation reaching the surface during the daytime. The detection of contrails in satellite imagery is challenging due to their similarity to natural clouds. In this study, a certain type of CNN, U-Net, is used to perform image segmentation in satellite imagery to detect contrails. U-Net can accurately detect contrails with an overall probability of detection of 0.51, a false alarm ratio of 0.46 and a F1 score of 0.52. These results demonstrate the effectiveness of using a U-Net for the detection of contrails in satellite imagery and could be applied to large-scale monitoring of contrail formation to measure their impact on climate change.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.