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Discussion papers
https://doi.org/10.5194/acp-2018-1339
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2018-1339
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 25 Jan 2019

Research article | 25 Jan 2019

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Atmospheric Chemistry and Physics (ACP).

Evaluation and comparison of MAIAC, DT and DB aerosol products over China

Ning Liu1, Bin Zou1,2, Huihui Feng1,2, Yuqi Tang1, and Yu Liang1 Ning Liu et al.
  • 1School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China
  • 2Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Central South University), Ministry of Education, Changsha, 410083, China

Abstract. A new Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm has been applied in Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and recently provides globally high spatial resolution Aerosol Optical Depth (AOD) products at 1 km. Meanwhile, several improvements are modified in classical Dark Target (DT) and Deep Blue (DB) aerosol retrieval algorithms in MODIS collection 6.1 products. However, validation and comparison for MAIAC, DT and DB algorithms is still lacking in China. In this paper, a comprehensive assessment and comparison of AOD products at 550 nm wavelength based three aerosol retrieval algorithms in MODIS sensor using ground-truth measurements from Aerosol Robotic Network (AERONET) sites over China during 2000 to 2017 is presented. In general, after quality assurance (QA) filter, the coefficient of determination (R2=0.854), correlation coefficient (R=0.929), root-mean-square error (RMSE=0.178), mean bias (Bias=0.019) and the fraction fall within expected error (Within_EE=67.10 %, EE=±(0.05+0.15×AOD)) results for MAIAC algorithm show better accuracy than those from DT and DB algorithms. While the R2, R, RMSE, Bias and Within_EE of DT algorithm are 0.817, 0.930, 0.192, 0.077, 55.36 %, respectively, those corresponding statistics for DB algorithm are 0.827, 0.921, 0.190, 0.018, 63.32 %. Moreover, the spatiotemporal completeness for MAIAC (29.69 %) product is also better than DT (8.00 %) and DB (19.50 %) products after QA filter. In addition, the land type dependence characteristic, view geometry dependence, spatiotemporal retrieval accuracy and spatial variation pattern difference for three products are also analyzed in details.

Ning Liu et al.
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Short summary
The first systematic validation and comparison of MAIAC, DT and DB AOD products across China during 2000 to 2017 is presented. In this process, the overall accuracy, land type dependence, view geometry dependence, spatiotemporal retrieval accuracy, spatial variation pattern and spatiotemporal completeness of three products are analyzed in details. In general, MAIAC achieves higher accuracy than DT and DB, but DT or DB may perform better than MAIAC under some specific land cover types or seasons.
The first systematic validation and comparison of MAIAC, DT and DB AOD products across China...
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