TY - JOUR
T1 - Improving the SMAC atmospheric correction code by analysis of Meteosat Second Generation NDVI and surface reflectance data
AU - Proud, Simon Richard
AU - Rasmussen, M.O.
AU - Fensholt, R.
AU - Sandholt, I.
AU - Shisanya, C.
AU - Mutero, W.
AU - Mbow, C.
AU - Anyamba, A.
PY - 2010/8
Y1 - 2010/8
N2 - In order to obtain high quality data, the correction of atmospheric perturbations acting upon land surface reflectance measurements recorded by a space-based sensor is an important topic within remote sensing. For many years the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model and the Simplified Method for Atmospheric Correction (SMAC) codes have been used for this atmospheric correction, but previous studies have shown that in a number of situations the quality of correction provided by the SMAC is low. This paper describes a method designed to improve the quality of the SMAC atmospheric correction algorithm through a slight increase in its computational complexity. Data gathered from the SEVIRI aboard Meteosat Second Generation (MSG) is used to validate the additions to SMAC, both by comparison to simulated data corrected using the highly accurate 6S method and by comparison to in-situ and 6S corrected SEVIRI data gathered for two field sites in Africa. The additions to the SMAC are found to greatly increase the quality of atmospheric correction performed, as well as broaden the range of atmospheric conditions under which the SMAC can be applied. When examining the Normalised Difference Vegetation Index (NDVI), the relative difference between SMAC and in-situ values decreases by 1.5% with the improvements in place. Similarly, the mean relative difference between SMAC and 6S reflectance values decreases by a mean of 13, 14.5 and 8.5% for Channels 1, 2 and 3 respectively. Furthermore, the processing speed of the SMAC is found to remain largely unaffected, with only a small increase in the time taken to process a full SEVIRI scene. Whilst the method described within this paper is only applicable to SEVIRI data, a similar approach can be applied to other data sources than SEVIRI, and should result in a similar accuracy improvement no matter which instrument supplies the original data.
AB - In order to obtain high quality data, the correction of atmospheric perturbations acting upon land surface reflectance measurements recorded by a space-based sensor is an important topic within remote sensing. For many years the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model and the Simplified Method for Atmospheric Correction (SMAC) codes have been used for this atmospheric correction, but previous studies have shown that in a number of situations the quality of correction provided by the SMAC is low. This paper describes a method designed to improve the quality of the SMAC atmospheric correction algorithm through a slight increase in its computational complexity. Data gathered from the SEVIRI aboard Meteosat Second Generation (MSG) is used to validate the additions to SMAC, both by comparison to simulated data corrected using the highly accurate 6S method and by comparison to in-situ and 6S corrected SEVIRI data gathered for two field sites in Africa. The additions to the SMAC are found to greatly increase the quality of atmospheric correction performed, as well as broaden the range of atmospheric conditions under which the SMAC can be applied. When examining the Normalised Difference Vegetation Index (NDVI), the relative difference between SMAC and in-situ values decreases by 1.5% with the improvements in place. Similarly, the mean relative difference between SMAC and 6S reflectance values decreases by a mean of 13, 14.5 and 8.5% for Channels 1, 2 and 3 respectively. Furthermore, the processing speed of the SMAC is found to remain largely unaffected, with only a small increase in the time taken to process a full SEVIRI scene. Whilst the method described within this paper is only applicable to SEVIRI data, a similar approach can be applied to other data sources than SEVIRI, and should result in a similar accuracy improvement no matter which instrument supplies the original data.
UR - http://www.scopus.com/inward/record.url?scp=77955320565&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2010.02.020
DO - 10.1016/j.rse.2010.02.020
M3 - Journal article
AN - SCOPUS:77955320565
SN - 0034-4257
VL - 114
SP - 1687
EP - 1698
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
IS - 8
ER -