A new fast atmospheric correction method for Landsat 8 images

Kangzhuo Yang; Yunping Chen; Yue Yang; Wangyao Shen

doi:10.1117/12.2606576

24 November 2021 A new fast atmospheric correction method for Landsat 8 images

Kangzhuo Yang, Yunping Chen, Yue Yang, Wangyao Shen

Proceedings Volume 12064, AOPC 2021: Optical Spectroscopy and Imaging; 120640I (2021) https://doi.org/10.1117/12.2606576
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China

Abstract

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.

Citation Download Citation

Kangzhuo Yang, Yunping Chen, Yue Yang, and Wangyao Shen "A new fast atmospheric correction method for Landsat 8 images", Proc. SPIE 12064, AOPC 2021: Optical Spectroscopy and Imaging, 120640I (24 November 2021); https://doi.org/10.1117/12.2606576

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