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Retrieval of Aerosol Optical Depth in the Arid or Semiarid Region of Northern Xinjiang, China

1, 2,*, 3,* and 1,4
1
College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
2
Satellite Environment Center, Ministry of Environmental Protection of China, Beijing 100094, China
3
Geomatics College, Shandong University of Science and Technology, Qingdao 266590, China
4
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 197; https://doi.org/10.3390/rs10020197
Received: 28 November 2017 / Revised: 11 January 2018 / Accepted: 26 January 2018 / Published: 29 January 2018
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Abstract

Satellite remote sensing has been widely used to retrieve aerosol optical depth (AOD), which is an indicator of air quality as well as radiative forcing. The dark target (DT) algorithm is applied to low reflectance areas, such as dense vegetation, and the deep blue (DB) algorithm is adopted for bright-reflecting regions. However, both DT and DB algorithms ignore the effect of surface bidirectional reflectance. This paper provides a method for AOD retrieval in arid or semiarid areas, in which the key points are the accurate estimation of surface reflectance and reasonable assumptions of the aerosol model. To reduce the uncertainty in surface reflectance, a minimum land surface reflectance database at the spatial resolution of 500 m for each month was constructed based on the moderate-resolution imaging spectroradiometer (MODIS) surface reflectance product. Furthermore, a bidirectional reflectance distribution function (BRDF) correction model was adopted to compensate for the effect of surface reflectance anisotropy. The aerosol parameters, including AOD, single scattering albedo, asymmetric factor, Ångström exponent and complex refractive index, are determined based on the observation of two sunphotometers installed in northern Xinjiang from July to August 2014. The AOD retrieved from the MODIS images was validated with ground-based measurements and the Terra-MODIS aerosol product (MOD04). The 500 m AOD retrieved from the MODIS showed high consistency with ground-based AOD measurements, with an average correlation coefficient of ~0.928, root mean square error (RMSE) of ~0.042, mean absolute error (MAE) of ~0.032, and the percentage falling within the expected error (EE) of the collocations is higher than that for the MOD04 DB product. The results demonstrate that the new AOD algorithm is more suitable to represent aerosol conditions over Xinjiang than the DB standard product. View Full-Text
Keywords: BRDF; aerosol; MODIS; sunphotometer; arid/semiarid BRDF; aerosol; MODIS; sunphotometer; arid/semiarid
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Tian, X.; Liu, S.; Sun, L.; Liu, Q. Retrieval of Aerosol Optical Depth in the Arid or Semiarid Region of Northern Xinjiang, China. Remote Sens. 2018, 10, 197.

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