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.

通过引入时间维度和深度卷积层，本文使用部分卷积神经网络模型提高遥感图像的数据填补效果，该模型在使用空气质量模型模拟的数据集上表现出更好的一致性和准确性。

基于深度可分离卷积神经网络的TROPOMI NO2柱状体时空估计