New Regression Method to Merge Different MODIS Aerosol Products Based on NDVI Datasets

Weiwei Xu,Lixin Wu,Wei Wang

doi:10.3390/atmos10060303

Abstract

The moderate resolution and imaging spectroradiometer (MODIS) level 2 operational aerosol products that are based on the dark target (DT) method over vegetated regions and the enhanced deep blue (DB) algorithms over bright pixels provide daily global aerosol optical depth (AOD). However, increasing the data coverage by merging the DT and DB merged AOD product has recently become the focus of research. Therefore, this study aims to improve the merged AOD performance by introducing a new regression method (DTBRG), depending on the normalized difference vegetation index values when DT and DB AOD are valid. The DTBRG AOD is validated on a global scale while using aerosol robot network AOD measurements. Merged AOD550s from the MODIS official method and Bilal’s customized methods are evaluated for the same period for comparison. The inter-comparison of merged AOD550s from different methods with an equal number of coincident observations demonstrates that the DTBRG method performs better than the MODIS official algorithm with increased expected error (83% versus 76%), R (0.92 versus 0.90), and decreased bias (−0.001 versus 0.012). Therefore, it can be operationally used for global merged aerosol retrievals.

Highlights

Aerosols are generally solid or liquid particles that are suspended in the atmosphere; they can be classified into anthropogenic and natural according to how they were generated, namely, dust, mist, smog, volcanic ash, haze, and air pollution particles [1,2,3]
A new regression method (DTBRG) that is dependent on the normalized difference vegetation index (NDVI) values is introduced to reduce the error in the Moderate resolution imaging spectroradiometer (MODIS) Collection 6 (C6).1 combined aerosol optical depth (AOD) products (DTBC61) at 550 nm
The DTBRG is a regression method that is based on the NDVI that explores the multiple linear regression relationship between dark target (DT), deep blue (DB), and aerosol robot network (AERONET) AOD, as well as the relationship between the regression coefficient and the MODIS NDVI product (MYD13C1)

Summary

Introduction

Aerosols are generally solid or liquid particles that are suspended in the atmosphere; they can be classified into anthropogenic and natural according to how they were generated, namely, dust, mist, smog, volcanic ash, haze, and air pollution particles [1,2,3]. Aerosols have significant effects on earth’s climate, human health, and global energy budget [4,5,6,7]. They have a significant effect on the earth’s radiation budget, because they can affect the microphysical properties and the lifespan of clouds [8,9,10]. Moderate resolution imaging spectroradiometer (MODIS), which is an important passive sensor aboard Aqua (launched 2002) and Terra (launched 1999) satellites, monitors the global climate characteristics (including aerosols) twice daily [17,18,19]. The MODIS aerosol products include dark target (DT) land (DT-land), DT-ocean, and deep blue (DB) products for the observations of aerosol optical depth (AOD) [17,20]

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Conclusion