Next Article in Journal
Wind Direction Signatures in GNSS-R Observables from Space
Next Article in Special Issue
Evaluating the Performance of the SCOPE Model in Simulating Canopy Solar-Induced Chlorophyll Fluorescence
Previous Article in Journal
Learning a Dilated Residual Network for SAR Image Despeckling
Previous Article in Special Issue
Estimating Surface Downward Shortwave Radiation over China Based on the Gradient Boosting Decision Tree Method
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle

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

1, 2,*, 3,* and 1,4
College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
Satellite Environment Center, Ministry of Environmental Protection of China, Beijing 100094, China
Geomatics College, Shandong University of Science and Technology, Qingdao 266590, China
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;
Received: 28 November 2017 / Revised: 11 January 2018 / Accepted: 26 January 2018 / Published: 29 January 2018
PDF [5099 KB, uploaded 29 January 2018]


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

Graphical abstract

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).
Printed Edition Available!
A printed edition of this Special Issue is available here.

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top