High-resolution daily AOD estimated to full coverage using the random forest model approach in the Beijing-Tianjin-Hebei region

Publication date: Available online 31 January 2019

Source: Atmospheric Environment

Author(s): Chen Zhao, Zhaorong Liu, Qing Wang, Jie Ban, Nancy Xi Chen, Tiantian Li

Abstract

Remote sensing is an effective means of observing and detecting global aerosol distribution and changes over time, which impact human health and climate change. However, aerosol optical depth (AOD) always has low spatial coverage, which not only affects the analysis of AOD but also harms many relevant applications of the data, such as utilization to estimate PM_2.5. In our study, we utilize the random forest model, which is an effective ensemble learning method, to estimate the gaps of Moderate Resolution Imaging Spectroradiometer (MODIS) AOD data with a spatial resolution of 0.01°×0.01° in a typical contaminated region of Beijing-Tianjin-Hebei during 2010-2016. Our model performs accurately in that the results of R² testing exceed 0.9 and the final estimated AOD coverage achieves 100%. The average value of the AOD is 0.44 (0.41-0.47 by year) over the study period. The simulation values of AOD have an obvious seasonal distribution, with the highest AOD in summer. The AOD estimations in the southern region are higher than those in the northern region. Aerosol Robotic Network (AERONET) AOD observations are compared with MODIS AOD (R²=0.44) and AOD estimations (R²=0.36). We analyze and screen each of the variables to compute their contributions. Specifically, the elevation and 2-meter dew point are the most important in modeling the AOD, while road data, snowfall depth and snowfall have the least impact on modeling the AOD. Practical applications of AOD data include estimating the various impacts of PM_2.5 concentrations on health based on the AOD observations in China's typically polluted areas that have cloud influence. We compare two measurement ranges that will most accurately model and fill the AOD data missing in areas. After careful consideration, we determine that our preferred range is 0-2.

Graphical abstract

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