Atmospheric correction is the basis for quantitative analysis of satellite remote sensing images, such as monitoring land surface changes. However, precise atmospheric correction is still challenging. Landsat 8 is a satellite used for surface monitoring launched by the United States National Aeronautics and Space Administration (NASA) in 2013, with good spatial resolution. Rich spectral information. In this paper, an improved dense dark vegetation(DDV)aerosol retrieval algorithm is developed, and the retrieved AOD map will be used to process the aerosol impact factor of remote sensing images. Atmospheric correction is performed based on a lookup table generated by the 6SV model. Validation with the Land Surface Reflectance Code (LaSRC) algorithm produced atmospheric correction images, correction images of the paper algorithm showed a good agreement with high correlation(Correlation R exceeds 95%). Meanwhile, a reliable software prototype system for processing atmospheric correction on Landsat 8 OLI images was developed. This system is based on C++ language and can perform atmospheric correction automatically, low-latency, and accuracy. The data products corrected by the software prototype are helpful for the widespread application of remote sensing data in emergency response, environmental monitoring, and national defense.
As so far, studies based on remote sensing to explore ozone column concentration keep a watchful eye on the stratosphere or troposphere, while few focus on the near-surface, though it directly correlative to human health. In this paper, the regional near-surface total column ozone was inversed based on the moderate-resolution imaging spectroradiometer (MODIS) for its extraordinary spatial resolution. First, the statistical synthetic regression algorithm was utilized to retrieve the first guess. A nonlinear physical iterative method was then employed to acquire final ozone profiles. Finally, after creating a unique database, the ozone column concentration was obtained by using the multivariable linear regression model. Compared with the measurements of ground monitoring sites, the retrieval results were over 95% accurate and its distribution consists with the actual situation. The method proposed in this paper can be applied to monitor air pollution.
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