Spatiotemporal Estimation of TROPOMI NO2 Column with Depthwise Partial Convolutional Neural Network

TL;DR

This paper introduces a depthwise partial convolutional neural network that effectively imputes missing satellite NO2 data by incorporating temporal information, outperforming traditional methods and previous models.

Contribution

The novel depthwise PCNN model integrates temporal dimensions into spatial imputation, significantly improving accuracy in remote sensing data with extensive missing regions.

Findings

Achieved an index of agreement of 0.82, outperforming baseline models.

Demonstrated over 95% accuracy in imputing data with more than 95% missing.

Outperformed conventional imputation methods by 3-15%.

Abstract

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 (TCDNO2) to impute TROPOspheric Monitoring Instrument TCDNO2. The depthwise PCNN model achieves an index of agreement of 0.82 and outperforms the default PCNN models, with and without temporal dimensionality of data, and conventional data imputation methods such as inverse distance weighting by 3-11% and 8-15% in the index of agreement and correlation, respectively. The model demonstrates more consistency in the reconstruction of TROPOspheric Monitoring Instrument tropospheric column density of NO2 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.