Estimation of PMx Concentrations from Landsat 8 OLI Images Based on a Multilayer Perceptron Neural Network

The estimation of PMx (incl. PM10 and PM2.5) concentrations using satellite observations is of great significance for detecting environmental issues in many urban areas of north China. Recently, aerosol optical depth (AOD) data have been being used to estimate the PMx concentrations by implementing linear and/or nonlinear regression analysis methods. However, a lot of relevant research based on AOD published so far have demonstrated some limitations in estimating the spatial distribution of PMx concentrations with respect to estimation accuracy and spatial resolution. In this research, the Google Earth Engine (GEE) platform is employed to obtain the band reflectance (BR) data of a large number of Landsat 8 Operational Land Imager (OLI) remote sensing images. Combined with the meteorological, time parameter and the latitude and longitude zone (LLZ) method proposed in this article, a new BR (band reflectance)-PMx (incl. PM10 and PM2.5) model based on a multilayer perceptron neural network is constructed for the estimation of PMx concentrations directly from Landsat 8 OLI remote sensing images. This research used Beijing, China as the test area and the conducted experiments demonstrated that the BR-PMx model achieved satisfactory performances for the PMx-concentration estimations. The coefficient of determination (R-2) of the BR-PM2.5 and BR-PM10 models reached 0.795 and 0.773, respectively, and the root mean square error (RMSE) reached 20.09 mu g/m(3) and 31.27 mu g/m(3). Meanwhile, the estimation results have been compared with the results calculated by Kriging interpolation at the same time point, and the spatial distribution is consistent. Therefore, it can be concluded that the proposed BR-PMx model provides a new promising method for acquiring accurate PMx concentrations for various cities of China.