Abstract
Water vapor content in the atmosphere is very significant for atmospheric correction of optical remote sensing data. Nowadays, the common atmospheric correction models use a single value of the average water vapor content of the study area to perform atmospheric correction. As the distribution of water vapor content varies greatly with time and space, it is obviously inaccurate to represent the total water vapor conditions of the whole area by just reading the average water vapor content. In this study, we altered the 6S sources so that it could read the water vapor content image which was retrieved from MODIS 1 km data. Atmospheric correction was implemented for the band 1 of MODIS 500 m data pixel-by-pixel using the improved 6S model. In comparison with the traditional 6S model, this improved 6S model is more reasonable in atmospheric correction, for it considers the spatial distribution of the water vapor content retrieved from MODIS data in the near infrared to define the atmospheric conditions for simulating the atmospheric radiative transfer. The results corrected by the improved 6S model showed more reasonable in pixel spatial distribution and closer histogram with the original image than those by traditional 6S model.
Highlights
Since the first Earth’s digital image was acquired, scientists have shown great interest in atmospheric correction [1]
In comparison with the traditional 6S model, this improved 6S model is more reasonable in atmospheric correction, for it considers the spatial distribution of the water vapor content retrieved from MODIS data in the near infrared to define the atmospheric conditions for simulating the atmospheric radiative transfer
We can infer that the right image which was corrected by the improved 6S model is more reasonable in the spatial distribution than the left image, especially in the ocean area
Summary
Since the first Earth’s digital image was acquired, scientists have shown great interest in atmospheric correction [1]. The temperature, the pressure, the water vapor and ozone content and the aerosol optical depth are the necessary parameters, because the program will perform atmospheric correction by using them to simulate the transmission conditions of the solar radiation in the atmosphere. It seems very hard to obtain the atmospheric parameters synchronously with the image, especially the retrieval of the water vapor content and the aerosol optical depth (AOD), because the distribution of water vapor and aerosol varies greatly with time and space [4]. The remote sensing image covers vast with a large space span, especially the low resolution images, single value of the average water vapor content cannot satisfy with the accurate description of the atmospheric conditions of the whole area.
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