High Spatial Resolution PM2.5 Retrieval Using MODIS and Ground Observation Station Data Based on Ensemble Random Forest

Abstract

Limited by the number of ground observation stations, PM2.5 retrieval from the remote sensing data is an effective complement to conventional ground observations and is a current research hotspot. The general principle behind the remote sensing retrieval of PM2.5 is to first retrieve the aerosol optical depth (AOD) and calculate the PM2.5 via the AOD-based statistical relationships. This method is likely to cause error propagation, which leads to instability in the retrieval model. In this paper, we propose a PM2.5 remote sensing retrieval method via an ensemble random forest machine learning method to directly establish the relationship between the moderate-resolution imaging spectroradiometer (MODIS) images and ground observational PM2.5 to avoid retrieval errors from the atmospheric aerosol optical depths and obtain PM2.5 retrieval results with higher precision and spatial resolution. The proposed method first uses a random forest to train and validate the MODIS images and ground observation station PM2.5 data; then, an optimal multi-model group, according to the determination coefficient R-square (R 2 ) index, is selected. Finally, the optimal multi-model group is used on the whole MODIS image to obtain the PM2.5 retrieval result for the whole area. In an attempt to use machine learning technology to retrieve PM2.5, the experiments selected a substantial amount of MODIS image data during four seasons in Guangdong Province for validation and compared three performance indicators (R 2 , RMSE, and correlation coefficient (CC) to verify the superiority of the proposed algorithm.