  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.
