Spatiotemporal estimation of TROPOMI NO2 column with depthwise partial convolutional neural network

• Published in: Neural computing & applications 2023-07, Vol.35 (21), p.15667-15678
• Main Authors: Lops, Yannic, Ghahremanloo, Masoud, Pouyaei, Arman, Choi, Yunsoo, Jung, Jia, Mousavinezhad, Seyedali, Salman, Ahmed Khan, Hammond, Davyda
• Format: Article
• Language: English
• Subjects: Air pollution; Air quality; Artificial Intelligence; Artificial neural networks; Cloud cover; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Density; Emission analysis; Environmental monitoring; Image Processing and Computer Vision; Impact analysis; Missing data; Neural networks; Nitrogen dioxide; Numerical models; Original Article; Outdoor air quality; Probability and Statistics in Computer Science; Remote sensing
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-023-08558-1

Satellite-derived measurements are negatively impacted by cloud cover and surface reflectivity. These biases must be discarded and significantly increase the amount of missing data within remote sensing images. This paper expands the application of a partial convolutional neural network (PCNN) to incorporate depthwise convolution layers, conferring temporal dimensionality to the imputation process. The addition of a temporal dimension to the imputation process adds a state of successive existence within the dataset, which spatial imputation cannot capture. The depthwise convolution process enables the PCNN to independently convolve the data for each channel. The deep learning system is trained with the Community Multiscale Air Quality model-simulated tropospheric column density of Nitrogen Dioxide (TCDNO 2 ) to impute TROPOspheric Monitoring Instrument TCDNO 2 . The depthwise PCNN model achieves an index of agreement of 0.88 and outperforms the default PCNN models, with and without temporal dimensionality of data, and conventional data imputation methods such as inverse distance weighting by 4–7 and 10–15% in the index of agreement and correlation, respectively. The model demonstrates more consistency in the reconstruction of TROPOspheric Monitoring Instrument tropospheric column density of NO 2 images. The model has also demonstrated the accurate imputation of remote sensing images with over 95% of the data missing. PCNN enables the accurate imputation of remote sensing data with large regions of missing data and will benefit future researchers conducting data assimilation for numerical models, emission studies, and human health impact analyses from air pollution.