TY - JOUR
T1 - Monitoring the atmospheric aerosol optical depth with SPOT data in complex terrain
AU - Lin, T. H.
AU - Chen, A. J.
AU - Liu, G. R.
AU - Kuo, T. H.
PY - 2002/2/20
Y1 - 2002/2/20
N2 - Two methods-the structure function method and the dispersion coefficient method, both based on the contrast reduction method-are utilized with accompanying sunphotometer observation data and shown to cause a significant reduction in the errors produced when retrieving the aerosol optical depth. When applying the structure function method on the multitemporal SPOT HRV data in estimating the atmospheric aerosol optical depth, the result is affected significantly by the observed geometry, landuse change, non-lambertian surface and terrain effect. Thus, an improved version is proposed in this paper to reduce these effects. Instead of using a single direction method, we expanded it into a multidirectional method that produced a more complete picture of the structure function. In addition, an 'optimal distance index' was further introduced to truncate the abnormal part of the structure functions in some cases. The result shows that the mean error can be reduced from 19% to 6.5% when compared with ground measurements. In addition, the dispersion coefficient method was also examined and improved with a linear regression to correct its bias. Results show than the mean error decreases from 85% to 24%.
AB - Two methods-the structure function method and the dispersion coefficient method, both based on the contrast reduction method-are utilized with accompanying sunphotometer observation data and shown to cause a significant reduction in the errors produced when retrieving the aerosol optical depth. When applying the structure function method on the multitemporal SPOT HRV data in estimating the atmospheric aerosol optical depth, the result is affected significantly by the observed geometry, landuse change, non-lambertian surface and terrain effect. Thus, an improved version is proposed in this paper to reduce these effects. Instead of using a single direction method, we expanded it into a multidirectional method that produced a more complete picture of the structure function. In addition, an 'optimal distance index' was further introduced to truncate the abnormal part of the structure functions in some cases. The result shows that the mean error can be reduced from 19% to 6.5% when compared with ground measurements. In addition, the dispersion coefficient method was also examined and improved with a linear regression to correct its bias. Results show than the mean error decreases from 85% to 24%.
UR - http://www.scopus.com/inward/record.url?scp=0037138469&partnerID=8YFLogxK
U2 - 10.1080/01431160110069827
DO - 10.1080/01431160110069827
M3 - 期刊論文
AN - SCOPUS:0037138469
SN - 0143-1161
VL - 23
SP - 647
EP - 659
JO - International Journal of Remote Sensing
JF - International Journal of Remote Sensing
IS - 4
ER -