Improved aerosol optical depth and ångstrom exponent retrieval over land From MODIS based on the non-lambertian forward model

Journal article


Leiku, Yang, Xue, Yong, Guang, Jie, Hassan, Kazemian, 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. https://doi.org/10.1109/LGRS.2014.2303317
AuthorsLeiku, Yang, Xue, Yong, Guang, Jie, Hassan, Kazemian, Zhang, Jiahua and Li, Chi
Abstract

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.

KeywordsRemote sensing; Aerosol optical depth; Atmospheric radiation; Particle size; Regression analysis
Year2014
JournalIEEE Geoscience and Remote Sensing Letters
PublisherIEEE
ISSN1545598X
15580571
Digital Object Identifier (DOI)https://doi.org/10.1109/LGRS.2014.2303317
Web address (URL)http://hdl.handle.net/10545/621926
http://creativecommons.org/licenses/by/4.0/
hdl:10545/621926
Publication dates28 Feb 2014
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Deposited30 Oct 2017, 10:11
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Archived with thanks to IEEE Geoscience and Remote Sensing Letters

ContributorsBeijing Normal University, Institute of Remote Sensing and Digital Earth and London Metropolitan University
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