Abstract
As a basis for the quantitative application of satellite remote sensing, surface reflectance can be retrieved through atmospheric correction methods. Currently, most studies have focused on developing or comparing atmospheric correction methods. However, few studies have quantitatively analyzed the effects of input parameters in an atmospheric correction method on retrieved surface reflectance. In this study, we evaluated the effects of the calibration coefficient, aerosol optical depth (AOD), aerosol type, and satellite zenith angle over four typical surfaces using wide field-of-view sensor four data of the Gao Fen-1 satellite. The results showed that (1) the relative errors of shrub, corn, grass, and soil reflectance increased as the calibration coefficient error increased; (2) the calibration coefficient, AOD, aerosol type, and satellite zenith angle affected corn reflectance retrieval the most, whereas they had the smallest effect on soil reflectance retrieval; and (3) the accuracy of the satellite zenith angle on the retrieved surface reflectance was the least pronounced, whereas the accuracy of aerosol type was the most pronounced.
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Acknowledgements
This work was supported in part by the National Key R&D Program of China (Grant No. 2017YFB0502800), Beijing Natural Science Foundation (Grant Number: 8184087), and the open Fund of State Key Laboratory of Remote Sensing Science (Grant No. OFSLRSS201715). The authors would like to thank CCRSDA for providing GF-1 satellite WFV4 imagery and NASA for providing MOD09 product.
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Appendices
Appendix A: The Characteristics of GF-1 Satellite WFV4 Camera
See Table 4.
Appendix B: The Geographic Locations of Surfaces
See Table 5.
Appendix C: The Effects of Sun Zenith Angle Accuracy on Surface Reflectance Retrieval
See Fig. 8.
Appendix D: The Effects of Azimuth Angle Accuracy on Surface Reflectance Retrieval
See Fig. 9.
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Guo, H., Gu, X., Bao, F. et al. Analysis of Surface Reflectance Retrieval Over Four Typical Surfaces Via Gaofen-1 Satellite WFV4 Imagery. J Indian Soc Remote Sens 48, 709–720 (2020). https://doi.org/10.1007/s12524-019-01068-5
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DOI: https://doi.org/10.1007/s12524-019-01068-5