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Carbon satellites are an important observation tool for analyzing ground carbon emission. From the perspective of the Earth’s scale, the spatiotemporal sparse characteristics of raw data observed from carbon satellite requires the accurate interpolation of data, and based on only this work, people predict future carbon emission trends and formulate appropriate management and conservation strategies. The existing research work has not fully considered the close correlation between data and seasons, as well as the characteristics accumulated over a long time scale. In this paper, firstly, by employing extreme random forests and auxiliary data, we reconstruct a daily average CO2 dataset at a resolution of 0.25°, and achieve a validated determination coefficient of 0.92. Secondly, introducing technologies such as Time Convolutional Networks (TCN), Channel Attention Mechanism (CAM), and Long Short-Term Memory networks (LSTM), we conduct atmospheric CO2 concentration interpolation and predictions. When conducting predictive analysis for the Yangtze River Delta region, we train the model by using quarterly data from 2016 to 2020; the correlation coefficient in summer is 0.94, and in winter it is 0.91. These experimental data indicate that compared to other algorithms, this algorithm has a significantly better performance.
Hu et al. (Sat,) studied this question.