Estimating Atmospheric Turbidity from SPOT and GMS-5 Data
Gin-Rong Liu, Tang-Huang Lin, A.J. Chen
Center for Space and Remote Sensing Research
National Central University, Chung-Lu, Taiwan 320
Tel:(886)-3-4227151 ext 7620, FaxL996)-3-4255535
E-mail: grliu@csrsr.ncu.edu.tw
Keywords: Aerosol optical depth, Atmospheric turbidity, Structure function.
Abstract
The atmospheric turbidity is one very important factor in the air pollution measurements and monitoring with remotely sensed data, especially in visible bands. The scattering effects of atmospheric molecular and aerosols in varying atmospheric turbidity conditions can influent the original spectral information of remotely sensed data strongly. From another point of view, the atmospheric turbidity can be estimate by evaluating the information variation induced by the scattering effects. Tanre et al. proposed the Structure Function (SF) to estimate the atmospheric optical depth in 1988. Their study result showed that the Aerosol Optical Depth (AOD) can be assessed with Landsat TM data by assuming the landcovers are same in the set of multi-temporal TM images. In this study, the SF method is improved for appliying in Taiwan area. Owing to the rather rough terrain and complex landuse properties in Taiwan area, this study used higher spatial resolution SPOT data and hourly GMS-5 data, to derive the AOD. The result shows the improvements in this study can get satisfying result. The result reveals we can derive these satellite data for the monitoring of hourly air pollution and air quality variation.
Introductions
The air pollution index (PSI) contains the effects of SO2, NO2, CO, O3 and turbidity, etc. Since turbidity is one of the main effects of the air pollution, effective monitoring of the aerosol and suspend sediments (S.S.) of the atmosphere becomes very important for the air pollution control. Because of having the advantages of winder Range, higher temporal resolution and data consistent property than the traditional measured data, applying satellite observed data to monitor the air pollution condition becomes one alternative way. In this study the aerosol optical depth (AOD) had been estimated from kinds of satellite data around Taiwan area, and demonstrated to show its application in air pollution monitoring.
Because aerosol parameters are not easy to estimate, methods of aerosol parameter retrievals has been discussed by many research terms. For example, an image-based of retrieved aerosol characteristics by the Dense Dark Vegetation (DDV) method to calibrate atmospheric effect of image of image itself was present by Liu et al. in 1996. But the DDV method applied in this study showed larger error when there are few or none DDV pixels in the test images. By assuming the ground reflectance is constant, variations of satellite signal may be attributed to variations of the atmospheric optical properties. Based upon this, another method of multi-temporal aerosol parameter retrievals by a reference ground measurement was accomplished by Tanre et al. in 1988. The single-directional structure function (SF) is defined for deriving AOD from Landsat TM data. Holben also got good results by applying SF method to NOAA AVHRR data in 1992.
In this study, an improvement of SF method is applied to SPOT and GMS-5 data. It also showed reasonable results can be gotten. The high spatial resolution SPOT data can provide more detailed information of atmosphere on local area, like industrial or manufacturer locations, and the GMS-5 data can hourly provide the distribution and variation information of atmosphere around the wise area. If these kinds of satellite data can be routinely applied, effective monitoring of the air pollution from satellite observation can be accomplished easily. This is the main aim of this study.
