CNN-Based NO2 Estimation from Sentinel-5P Data: A Proof-of-Concept

This work deals with the estimation of the tropospheric vertical column density of nitrogen dioxide from Sentinel-5P radiance data using convolutional neural networks. The current processing chain to retrieve this information from Sentinel-5P data requires a complex, computationally demanding, physical modeling that involves the use of additional side information such as meteorological variables, which are not always available. Therefore, in this proof-of-concept study, we explored the feasibility of an estimation exclusively using radiance data from Sentinel-5P, leveraging on the powerful representational capacity of deep neural networks. Preliminary results are very promising encouraging further investigation.