MODIS aerosol optical depth retrieval based on random forest approach

Despite significant improvement in Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrieval, high-resolution-high-accuracy AOD retrieval remains a challenging task. This study utilises machine learning for AOD retrieval of MODIS data. The global long-time-series data of AERONET sites and corresponding MODIS data in time and space were used as sample training data for aerosol retrieval via use of the random forest (RF) approach. The accuracy and stability of retrieval were ensured by processing AERONET site data, performing time-space matching between different data types, and determining related parameters in the RF model. MODIS data use bands 1-7 of the top-of-atmosphere reflectance (TOA) - extending from the visible to near-infrared radiation spectra - along with the corresponding observation geometry data, global land surface satellite (GLASS) albedo dataset, and normalised difference vegetation index (NDVI) data. The proposed method facilitates realisation of high-accuracy aerosol retrieval. Furthermore, significant enhancement in the efficiency of aerosol inversion is an added advantage.

Automated summary

This study utilized machine learning for AOD retrieval of MODIS data, achieving high-accuracy aerosol detection by using AERONET site data and MODIS data as training sample data. The proposed method facilitates realisation of high-accuracy aerosol retrieval and significantly enhances the efficiency of aerosol inversion.