Retrieval of total and fine mode aerosol optical depth by an improved MODIS Dark Target algorithm

Xin Su; Lunche Wang; Xuan Gui; Leiku Yang; Lei Li; Ming Zhang; Wenmin Qin; Minghui Tao; Shaoqiang Wang; Lizhe Wang

doi:10.1016/j.envint.2022.107343

Retrieval of total and fine mode aerosol optical depth by an improved MODIS Dark Target algorithm

Environ Int. 2022 Aug:166:107343. doi: 10.1016/j.envint.2022.107343. Epub 2022 Jun 11.

Authors

Xin Su¹, Lunche Wang², Xuan Gui¹, Leiku Yang³, Lei Li⁴, Ming Zhang¹, Wenmin Qin¹, Minghui Tao¹, Shaoqiang Wang⁵, Lizhe Wang⁶

Affiliations

¹ Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China.
² Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China. Electronic address: wang@cug.edu.cn.
³ School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China.
⁴ State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, Beijing, China.
⁵ Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China.
⁶ School of Computer Science, China University of Geosciences, Wuhan 430074, China; Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China.

PMID: 35716506
DOI: 10.1016/j.envint.2022.107343

Abstract

Total and fine mode aerosol optical depth (AODT and AODF), as well as the fine mode fraction (FMF = AODF/AODT), are critical variables for climate change and atmospheric environment studies. The retrievals with high accuracy from satellite observations, particularly FMF and AODF over land, remain challenging. This study aims to improve the Moderate-resolution Imaging Spectro-radiometer (MODIS) land dark target (DT) algorithm for retrieving AODT, AODF, and FMF on a global scale. Based on the fact that the underestimated surface reflectance (SR) could overestimate the AODT and underestimate the aerosol size parameter in the DT algorithm, two robust schemes were developed to improve SR determination: the first (NEW1 DT) used the top of the atmosphere reflectance instead of SR at 2.12 µm; the second (NEW2 DT) used eleven-year MODIS data to establish a monthly spectral SR relationship model (2.12-0.47 and 2.12-0.65 µm) database at pixel-by-pixel scale. Then a novel lookup table approach based on the physical process was proposed to retrieve the AODF and FMF. The new MODIS AODT, FMF, and AODF were compared to AERosol RObotic NETwork (AERONET) retrievals. Results showed that the root mean square error (RMSE) was 0.096-0.103, 0.098-0.099, and 0.167-0.180 for the new AODTs, AODFs, and FMFs, respectively, which were better than that of the Collection 6.1 (C6.1) DT (0.117, 0.235, and 0.426) in the validation by global AERONET sites. From the validation results, NEW2 DT provided better AODT and coarse mode AOD retrievals, while NEW1 DT had better AODF and FMF performances. The spatial patterns of AODF, FMF, and AODC of the new DT algorithms were comparable to those of the Polarization and Directionality of the Earth's Reflectances aerosol product. Hence, the new algorithms have the potential to provide global AODT, FMF, and AODF products over land to the scientific community with high accuracy using long-term MODIS data.

Keywords: Dark Target algorithm; Fine mode aerosol optical depth; Fine mode fraction; Land General Aerosol algorithm; Surface reflectance.