Applying SPOT data to estimate the aerosol optical depth and air quality

Liu, Gin Rong; Chen, A. J.; Lin, Tang Huang; Kuo, Tsung Hua

doi:10.1016/s1364-8152(01)00047-0

Cited by 24 publications

(9 citation statements)

References 11 publications

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“…They finally determined a d value of five by analyzing the distribution pattern of the structure function value in the d range of one to ten. Through frequency statistics of d from one to ten, Liu et al [14] found that the structure function value was negatively correlated with the change of d when it was more than five. They removed the structure function value of d when it was more than five, and then used the d mean method to retrieve the aerosol optical thickness.…”

Section: Introductionmentioning

confidence: 99%

A Geostatistics-Based Method to Determine the Pixel Distance in a Structure Function Model for Aerosol Optical Depth Inversion

Zhou

Zhang

et al. 2017

Atmosphere

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Inversion of aerosol optical depth (AOD) over bright land surface by optical remote sensing is particularly challenging because surface reflectance dominates the satellite signal. A structure function method is suitable and can effectively solve aerosol optical depth inversion in high reflectance areas. How to select d (the pixel distance between two pixels) value is one of the key problems with the structure function method. We present a method based on geostatistics, where variogram theory is referenced, to determine the pixel distance in structure function model for aerosol optical depth inversion. This method was validated by the Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km resolution level 1B data from Beijing, China. The results indicate that the relationship between variogram and d in four different directions can be fitted by exponential function, of which correlation coefficients are all above 0.9. Compared with the MODIS aerosol product (MOD 04 product), the inversion AOD has higher accuracy, with an absolute error of −0.00187 instead of −0.00854 and a relative error of 0.99% instead of 4.35%, based on AERosol RObotic NETwork (AERONET) observations. For validation, we applied this new method separately to both Beijing-Tianjin-Hebei region and MODIS 500 m resolution images for region and resolution validation. The results show that inversion AOD has higher accuracy and more efficient pixels.

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Section: Introductionmentioning

confidence: 99%

A Geostatistics-Based Method to Determine the Pixel Distance in a Structure Function Model for Aerosol Optical Depth Inversion

Zhou

Zhang

et al. 2017

Atmosphere

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“…This method estimates AOD based on the differences in reflectance between two adjacent pixels. The structure function method has been applied in various bright areas with high accuracy [21,22]. However, to provide a reference structure function value, a clear image close to the object image is required.…”

Section: Introductionmentioning

confidence: 99%

Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images

et al. 2015

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Abstract:Conventional methods for Aerosol Optical Depth (AOD) retrieval are limited to areas with low reflectance such as water or vegetated areas because the satellite signals from the aerosols in these areas are more obvious than those in areas with higher reflectance such as urban and sandy areas. Land Surface Reflectance (LSR) is the key parameter that must be estimated accurately. Most current methods used to estimate AOD are applicable only in areas with low reflectance. It has historically been difficult to estimate the LSR for bright surfaces because of their complex structure and high reflectance. This paper provides a method for estimating LSR for AOD retrieval in bright areas, and the method is applied to AOD retrieval for Landsat 8 Operational Land Imager (OLI) images at 500 m spatial resolution. A LSR database was constructed with the MODerate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product (MOD09A1), and this database was also used to estimate the LSR of Landsat 8 OLI images. The AOD retrieved from the Landsat 8 OLI images was validated using the AOD measurements from four AErosol RObotic NETwork (AERONET) stations located in areas with bright surfaces. The MODIS AOD product (MOD04) was also compared with the retrieved AOD. The results demonstrate that the AOD retrieved with the new algorithm is highly consistent with the AOD derived from ground measurements, and its precision is better than that of MOD04 AOD products over bright areas.

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“…AOT is determined from the atmospheric transmission based on the ratio of the transmission among several images [2]. SFM has been confirmed to show good capability in retrieving AOD distribution over arid regions from either high-resolution Landsat TM images [1,3] or low-resolution NOAA AVHRR images [2,3]. This paper attempts to retrieve AOT of bright areas using SFM.…”

Section: Introductionmentioning

confidence: 99%