Satellite-based identification of aerosol particle species using a 2D-space aerosol classification model

摘要

The classification of aerosol types using remote sensing tools is crucial for improving our knowledge on the impacts of aerosol species on global and regional climate. Ground-based classification methods have proven useful though they are limited by discontinuous spatial observations of aerosol optical properties. As such, there is a need to develop methods in aerosol species identification using satellite data. In this study, a 2D-space model suitable for MODIS AOD bands is developed to distinguish aerosol particle species based on aerosol optical depth (AOD) and aerosol relative optical depth (AROD). The model can be used to classify five aerosol types, including biomass burning (BB: A0D(470) >= 0.45, AROD(660/470) < 0.58), urban industry (UI: AOD(470) >= 0.45, 0.68 > AROD(660)/470 >= 0.58), sub-continental (SB: AOD(470) >= 0.45, 0.91 > AROD(660/470) >= 0.68), desert dust (DD: AROD(660/470) > 0.91) and continental (CO: AOD(470) < 0.45, AROD(660/470) < 0.91). AROD values corresponding to different types of aerosols are calculated through Mie scattering theory using the aerosol particle size distribution parameters and spectral complex refractive index. The accuracy and applicability of the model are verified in three steps: 1) we compare aerosol discrimination results of model with ground-based data and the local atmospheric conditions over different regions, 2) we compare aerosol identification results from Moderate Resolution Imaging Spectroradiometer (MODIS) AOD retrieval and ground-based data, and 3) we test our model to validate its use in individual cases studies and regional observations. Our results demonstrate that aerosol species classification for each study region is consistent with the characteristics of their atmospheric conditions, and the classification results using MODIS AOD retrieval are similar to the ground-based observation results. The differences in MODIS versus AERONET AOD, caused by MODIS observation errors, is the major contributor to the differences between ground-based and satellite-based recognition results. Our three-steps validation results indicate that the 2D-space model developed in this paper can accurately identify different types of aerosols and has strong applicability. This provides a basis for the use of satellite data for continuous spatial identification of aerosol type, which greatly promotes research in related fields.