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Retrieval of high-resolution aerosol optical depth using Landsat 8 OLI data over Beijing

  • role： First author第一作者
  • Affiliation：

    College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

    Joint Center for Global Change Studies(JCGCS), Beijing 100875, China

  • Email：tian_xp@mail.bnu.edu.cn
  • Introduction：田信鹏(1990—　)，男，博士研究生，研究方向为大气定量遥感。E-mail：tian_xp@mail.bnu.edu.cn
Xinpeng TIAN12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Geomatics College, Shandong University of Science and Technology, Qingdao 266590, China

  • Email：sunlin6@126.com
  • Introduction：孙林(1975—　)，男，教授，研究方向为定量遥感、大气校正及气溶胶遥感。E-mail：sunlin6@126.com
Lin SUN3*,  
  • Affiliation：

    College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

    Joint Center for Global Change Studies(JCGCS), Beijing 100875, China

Qiang LIU12,  
  • Affiliation：

    College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

Xiuhong LI1

Resümee

Satellite remote sensing has been widely used to retrieve Aerosol Optical Depth (AOD), which is an indicator of air quality and radiative forcing. The Dark Target (DT) algorithm is applied to low reflectance areas, such as vegetated or water areas, and the Deep Blue (DB) algorithm is adopted over bright-reflecting source regions. However, the spatial resolutions of the AOD products obtained with the DT and DB algorithms are relatively low, and the distribution details for urban areas are poor. In this study, a modified retrieval algorithm is proposed for the retrieval of AOD at a spatial resolution of 500 m over Beijing, China, on the basis of Landsat 8 OLI data. The key points are the accurate estimation of surface reflectance and the reasonable assumptions of the aerosol model. We developed a new algorithm to improve the accuracy of land surface reflectance for urban areas. A monthly Minimum Land Surface Reflectance (MLSR) database for China was established using Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance products. The construction of this database was based on the principle of minimum synthesis technique. The conversion model from the blue band reflectance of MODIS to Landsat 8 OLI was established using the ASTER spectrum database to compensate for the difference between the spectral settings of the two sensors. AErosol RObotic NETwork (AERONET) data were adopted to determine aerosol optical properties, such as Ångström exponent, complex refractive index, Single Scattering Albedo (SSA), and asymmetric factor (g). The AODs retrieved from the 36 OLI images were validated with the AERONET data and the NASA/MODIS Collection 6 aerosol products at a 10 km resolution. For this purpose, the DT and DB (DT/DB) algorithms were combined. Results indicated that the proposed algorithm accurately retrieved AODs over the Beijing area and that the retrieved aerosol distribution contained more spatial details and variability than the DT/DB AOD products. Ground-based AERONET observations from four sites (Beijing, Xianghe, Beijing_CAMS, and Beijing_RADI) were used to validate the retrieved AODs. The results from the proposed algorithm demonstrated the highest accuracy, with an average correlation coefficient (R) and Root-Mean-Square Error (RMSE) of approximately 0.963 and 0.156, respectively. Approximately 85.3% of the points fell within the expected error (EE) ± (0.05+0.15 AOD) envelope defined by NASA. The DT/DB AODs showed a small R (0.962) and slightly large RMSE (0.158), with 75.8% of the collocations falling within the EE. An aerosol retrieval algorithm for Landsat 8 OLI 500 m data was proposed in this study. The assumptions in this algorithm are as follows: the variation of surface reflectance needs to be small for a month, and the SSA and g are regionally constant for a particular day. The method can also be used to achieve AOD inversion of other terrestrial observation satellite data. However, according to the uncertainty analysis, the proposed algorithm has some limitations that must be addressed. (1) Errors may arise from the use of constant SSA and g values for the day of retrieval. (2) The MLSR database approach was sometimes unsuccessful over snow surfaces, particularly when seasonal changes such as the snow melting in March and the accumulation of snow in Novembers are significant. These factors will be explored in our future studies.

Schlüsselwort

aerosol optical depth;spectrum conversion;surface reflectance database;Landsat 8 OLI;AERONET

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