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Preliminary validation of GF-1/GF-2 surface reflectance products over land using VNIR atmospheric correction method
Author(s): Jiafei Xu; Zhengchao Chen; Hao Zhang; Bing Zhang; Tao Liu
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Paper Abstract

The surface reflectance is an essential parameter for the quantitative applications using remote sensing satellite data; therefore, it is of great importance for the scientific community to produce standard surface reflectance products using an operational running algorithm and system. There have been various medium- to high-resolution satellites in China, yet there is still a lack of relevant surface reflectance products and systems. In this paper, high-resolution GF-1/GF-2 data from the year 2014 and 2017 were utilized for retrieval of surface reflectance products over land by using an operational atmospheric correction algorithm, adaptive to most multispectral satellites with visible and near-infrared bands (VNIR), namely, the VNIR approach. This method was based on the Second Simulation of a Satellite Signal in the Solar Spectrum, Vector (6SV) code and the look-up tables (LUTs). The surface reflectance products over land were validated against the ground-based atmospherically corrected reflectance over Beijing-Tianjin-Hebei regions and middle and lower regions of the Yangtze River in China. The preliminary validation results showed that the surface reflectance products agreed quiet well with the ground-based corrected reflectance, with the linear regression fitting coefficients being 1.09– 1.03, the correlation coefficients of R2 being 0.97–0.99, and the Root Mean Square Error (RMSE) being 0.01. Simultaneously, the mean reflectance normalized residuals between the surface reflectance products and the ground-based corrected reflectance were 19.7 %, 13.5 %, 8.7 %, and 6.6 %, respectively, indicating that the surface reflectance products over land derived from VNIR atmospheric correction approach had a good accuracy.

Paper Details

Date Published: 9 October 2019
PDF: 10 pages
Proc. SPIE 11152, Remote Sensing of Clouds and the Atmosphere XXIV, 111521I (9 October 2019); doi: 10.1117/12.2535792
Show Author Affiliations
Jiafei Xu, Institute of Remote Sensing and Digital Earth (China)
Univ. of Chinese Academy of Sciences (China)
Zhengchao Chen, Institute of Remote Sensing and Digital Earth (China)
Hao Zhang, Institute of Remote Sensing and Digital Earth (China)
Bing Zhang, Institute of Remote Sensing and Digital Earth (China)
Univ. of Chinese Academy of Sciences (China)
Tao Liu, Institute of Remote Sensing and Digital Earth (China)
China Univ. of Mining and Technology (China)


Published in SPIE Proceedings Vol. 11152:
Remote Sensing of Clouds and the Atmosphere XXIV
Adolfo Comerón; Evgueni I. Kassianov; Klaus Schäfer; Richard H. Picard; Konradin Weber; Upendra N. Singh, Editor(s)

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