Improved Aerosol Optical Depth and Ångstrom Exponent Retrieval Over Land From MODIS Based on the Non-Lambertian Forward Model

Yang, Leiku, Xue, Yong, Guang, Jie, Kazemian, Hassan, Zhang, Jiahua and Li, Chi (2014) Improved Aerosol Optical Depth and Ångstrom Exponent Retrieval Over Land From MODIS Based on the Non-Lambertian Forward Model. IEEE geoscience and remote sensing letters, 11 (9). pp. 1629-1633. ISSN 1545-598X

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Abstract / Description

In this letter, an improved algorithm for aerosol retrieval is presented by employing the non-Lambertian forward model (forward model) (NL_FM) in the Moderate Resolution Imaging Spectroradiometer (MODIS) dark target (DT) algorithm to reduce the uncertainties induced when using the Lambertian FM (L_FM). This new algorithm was applied to MODIS measurements of the whole year of 2008 over Eastern China. By comparing the results with that of AERONET, we found that the accuracy of the aerosol optical depth (AOD) retrieval was improved with the regression plots concentrating around the 1 : 1 line and two-thirds falling within the expected error (EE) envelope EE = ±0.05 ± 0.1τ (from 53.6% with L_FM to 68.7% with NL_FM at band 0.55 μm). Surprisingly, more accurate retrieval of the AOD demonstrated significantly improved the Ångstrom exponent (AE) retrieval, which is related to particle size parameters. The regression plots tended to concentrate around the 1 : 1 line, and many more fell within the EE = ±0.4 from 53.6% with L_FM to 80.9% with NL_FM. These results demonstrate that including the NL_FM in the MODIS DT algorithm has the potential to significantly improve both AOD and AE retrievals with respect to AERONET in comparison to the L_FM used in the current MODIS operational retrievals.

Item Type: Article
Uncontrolled Keywords: Aerosol optical depth, Ångstrom exponent (AE), bidirectional reflectance distribution functions (BRDF), dark target (DT), Moderate Resolution Imaging Spectroradiometer (MODIS), non-Lambertian forward model (FM) (NL_FM)
Subjects: 000 Computer science, information & general works
Department: School of Computing and Digital Media
Depositing User: Bal Virdee
Date Deposited: 20 Feb 2019 08:45
Last Modified: 21 Feb 2019 16:00
URI: http://repository.londonmet.ac.uk/id/eprint/4632

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