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A Generic Model to Estimate Ozone Concentration From Landsat 8 Satellite Data Based on Machine Learning Technique

As ground-level O3 has become one of the major pollutants affecting air quality in recent years, monitoring and revealing the spatial distribution pattern of O3 are of great significance to study air-pollution characteristics. Based on the multilayer backpropagation neural network, one O3 estimation model is proposed to obtain the continuous spatial distribution of O3 concentrations, where the Landsat 8 images, meteorological parameters, and air quality data have been integrated together as the input for the model training and validation. In order to enhance the estimation accuracy, the proposed model has been optimized with respect to the influencing factors and spatial extent. In the test areas of Beijing, Tianjin, and Shijiazhuang of China, the proposed O3 estimation model has demonstrated quite satisfactory performance—with the average coefficient of determination (R2) larger than 0.90 and root-mean-square error smaller than 19.0 μg/m3. It is worth mentioning that all the data employed in this research are freely available and can be applied nationwide in the mainland of China. Taking advantage of the generic nature and the positive O3 estimation results with high accuracy and spatial resolution, the proposed model can be expected to be a new way for studying air-pollution characteristics and, thus, support the decision making for environmental governance.