Landsat-8 Satellite and Plan Position Indicator Lidar Observations for Retrieving Aerosol Optical Properties in the Lower Troposphere

  • Aminuddin J
  • Purbantoro B
  • Lagrosas N
  • et al.
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Abstract

Observation of optical properties of atmospheric aerosols, especially their behavior near the surface level, is indispensable for better understanding of atmospheric environmental conditions. Concurrent observations of ground-based instruments and satellite-borne sensors are useful for attaining improved accuracy in the observation of relatively wide area. In the present paper, aerosol parameters in the lower troposphere are monitored using a plan position indicator (PPI) lidar, ground-sampling instruments (a nephelometer, an aethalometer, and optical particle counters), as well as a sunphotometer. The purpose of these observations is to retrieve the aerosol extinction coefficient (AEC) and aerosol optical thickness (AOT) simultaneously at the overpass time of Landsat-8 satellite. The PPI lidar, operated at 349 nm, provides nearly horizontal distribution of AEC in the lower part of the atmospheric boundary layer. For solving the lidar equation, the boundary condition and lidar ratio are determined from the data of ground sampling instruments. The value of AOT, on the other hand, is derived from sunphotometer, and used to analyze the visible band imagery of Landsat-8 satellite. The radiative transfer calculation is conducted using the MODTRAN code with the original aerosol type that has been determined from the ground sampling data coupled with the Mie scattering calculation. Reasonable agreement is found between the spatial distribution of AEC from the PPI lidar and that of AOT from the blue band (band 2) of Landsat-8. The influence of AOT on the values of apparent surface reflectance is also discussed.

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Aminuddin, J., Purbantoro, B., Lagrosas, N., Manago, N., & Kuze, H. (2018). Landsat-8 Satellite and Plan Position Indicator Lidar Observations for Retrieving Aerosol Optical Properties in the Lower Troposphere. Advances in Remote Sensing, 07(03), 183–202. https://doi.org/10.4236/ars.2018.73013

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