Atmospheric Correction Methods for GF-1 WFV1 Data in Hazy Weather

  • Zheng Wang
  • Junshi Xia
  • Lihui Wang
  • Zhihua Mao
  • Qun Zeng
  • Liqiao Tian
  • Liangliang Shi
Research Article
  • 121 Downloads

Abstract

Increasing hazy weather in the eastern area of China limits the potential application of high-resolution satellite data and poses a huge challenge for the atmospheric correction of remote sensing images. Consequently, it is necessary to find the most suitable atmospheric correction method under hazy condition. In this study, five kinds of atmospheric correction models, including 6S, COST, FLAASH, QUAC, and ATCOR2, are applied to the GaoFen-1 Wild Field Camera (GF-1 WFV1) data in the eastern area of China, and examined by both quantitative and qualitative analyses using the measured spectrum data. Experimental results indicated that ATCOR2 achieves the best performance among the atmospheric correction methods qualitatively and quantitatively. Hence, specifically for the study area and GF-1 WFV1 dataset, ATCOR2 is the most suitable atmospheric correction approach under hazy in the eastern area of China.

Keywords

Hazy Atmospheric correction GF-1 WFV1 

Notes

Acknowledgements

The authors would like to thank the reviewers, the editors, and Dr. Jike Chen for their highly constructive comments and remarks. The authors would like to thank China Resources Satellite Application Center for providing the GF-1 WFV1 data. This study was supported by the National Key Research and Development Program of China (2016YFC1400901), the High Resolution Earth Observation Systems of National Science and Technology Major Projects (41-Y20A31-9003-15/17), the National Science Foundation of China (41476156, 41621064), and the Public Science and Technology Research Funds Projects of Ocean (201005030).

References

  1. Bernstein, L. S., Adler-Golden, S. M., Sundberg, R. L., Levine, R. Y., Perkins, T. C., & Berk, A., et al. (2005). A new method for atmospheric correction and aerosol optical property retrieval for VIS-SWIR multi- and hyperspectral imaging sensors: QUAC (QUick atmospheric correction). Paper presented at the geoscience and remote sensing symposium, 2005. IGARSS ‘05. Proceedings. 2005 IEEE International.Google Scholar
  2. Chavez, P. S. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote sensing of environment, 24(3), 459–479.Google Scholar
  3. Chavez, P. S. (1996). Image-based atmospheric corrections-revisited and improved. Photogrammetric Engineering and Remote Sensing, 62(9), 1025–1035.Google Scholar
  4. Cooley, T., Anderson, G. P., Felde, G. W., Hoke, M. L., Ratkowski, A. J., & Chetwynd, J. H., et al. (2002). FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation. Paper presented at the geoscience and remote sensing symposium, 2002. IGARSS ‘02. 2002 IEEE International.Google Scholar
  5. Felde, G. W., Anderson, G. P., Cooley, T. W., Matthew, M. W., Adler-Golden, S. M., & Berk, A., et al. (2003). Analysis of Hyperion data with the FLAASH atmospheric correction algorithm. In IEEE international symposium on geoscience and remote sensing (IGARSS) (pp. 90–92).Google Scholar
  6. Gao, B., Montes, M. J., Davis, C. O., & Goetz, A. F. H. (2009). Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean. Remote Sensing of Environment, 113, S17–S24.CrossRefGoogle Scholar
  7. Ghulam, A., Qin, Q., Zhu, L., & Abdrahman, P. (2004). Satellite remote sensing of groundwater: Quantitative modelling and uncertainty reduction using 6S atmospheric simulations. International Journal of Remote Sensing, 25(23), 5509–5524.CrossRefGoogle Scholar
  8. Guo, H. (2014). Evaluation of four dark object atmospheric correction methods based on ZY-3 CCD Data. Spectroscopy & Spectral Analysis, 34(8), 2203–2207.Google Scholar
  9. Guo, Y., & Zeng, F. (2012). Atmospheric correction comparison of SPOT-5 image based on model FLAASH and model QUAC. In: ISPRSInternational archives of the photogrammetry, remote sensing and spatial information sciences (Vol. 39, No. b7, pp. 7–11).Google Scholar
  10. Han, X. Q., Yi, S. U., Jing, L. I., Zhang, Y., Liu, J., & Gao, W. M. (2012). Atmospheric correction and verification of the SPOT remote sensing image in coastal zones. Geographical Research, 31(11), 2007–2016.Google Scholar
  11. He, G., Xiao, P., Feng, X., Zhang, X., Wang, Z., & Chen, N. (2015). Extracting snow cover in mountain areas based on SAR and optical data. IEEE Geoscience and Remote Sensing Letters, 12(5), 1136–1140.CrossRefGoogle Scholar
  12. Karpouzli, E., & Malthus, T. (2003). The empirical line method for the atmospheric correction of IKONOS imagery. International Journal of Remote Sensing, 24(5), 1143–1150.CrossRefGoogle Scholar
  13. Li, Z., Mei, L. Y., Hua, Z. S., & Long, G. Y. (2015). Remote sensing monitoring of Taihu Lake water quality by using GF-1 satellite WFV data. Remote Sensing for Land and Resources, 27(1), 113–120.Google Scholar
  14. Liang, S., Fang, H., & Chen, M. (2001). Atmospheric correction of Landsat ETM + land surface imagery. I. Methods. IEEE Transactions on Geoscience and Remote Sensing, 39(11), 2490–2498.CrossRefGoogle Scholar
  15. Liang, S., Fang, H., & Chen, M. (2002). Atmospheric correction of Landsat ETM + land surface imagery: II. Validation and applications. IEEE Transactions on Geoscience & Remote Sensing, 40(12), 1–10.Google Scholar
  16. Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2002). Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research. International Journal of Remote Sensing, 23(13), 2651–2671.CrossRefGoogle Scholar
  17. Manakos, I., Manevski, K., & Kalaitzidis, C. (2011). Comparison between FLAASH & ATCOR atmospheric correction modules on the basis of WorldView-2 imagery and in situ spectral radiometric measurements. In 7th EARSeL SIG imaging spectroscopy workshop.Google Scholar
  18. Matthew, M. W., Adler-Golden, S. M., Berk, A., & Felde, G. (2003). Atmospheric correction of spectral imagery: Evaluation of the FLAASH algorithm with AVIRIS data. In Aerosense (Vol. 5093, pp. 157–163). International Society for Optics and Photonics.Google Scholar
  19. Peng, N. (2008). Atmospheric correction of QuickBird-2 imagery for turbid water coastal areas using MODIS data. Acta Optica Sinica, 28(5), 817–821.CrossRefGoogle Scholar
  20. Richter, R. (1990). A fast atmospheric correction algorithm applied to landsat TM images. International Journal Remote Sensing, 11, 159–166. Google Scholar
  21. Richter, R. (1996). Atmospheric correction of satellite data with haze removal including a haze/clear transition region. Computers & Geosciences, 22(6), 675–681.CrossRefGoogle Scholar
  22. Richter, R. (1997). Correction of atmospheric and topographic effects for high spatial resolution satellite imagery. International Journal of Remote Sensing, 18(5), 1099–1111.CrossRefGoogle Scholar
  23. Tanré, D., Deroo, C., Duhaut, P., Herman, M., Morcrette, J. J., Perbos, J., et al. (1990). Technical note Description of a computer code to simulate the satellite signal in the solar spectrum: The 5S code. International Journal of Remote Sensing, 11(4), 659–668.CrossRefGoogle Scholar
  24. Vermote, E. F., Tanre, D., Deuze, J. L., Herman, M., & Morcette, J. J. (1997). Second simulation of the satellite signal in the solar spectrum, 6S: An overview. IEEE Transactions on Geoscience and Remote Sensing, 35(3), 675–686.CrossRefGoogle Scholar
  25. Wang, X. F., Mao, Z. H., & Chen, J. Y. (2011). Atmospheric correction of the SPOT satellite data of the coastal zones. Journal of Marine Sciences, 29(1), 68–72.Google Scholar
  26. Wang, Z. T., Wang, H. M., Qing, L. I., Zhao, S. H., Shen-Shen, L. I., Chen, L. F., et al. (2014). A quickly atmospheric correction method for HJ-1 CCD with deep blue algorithm. Spectroscopy & Spectral Analysis, 34(3), 729–734.Google Scholar
  27. Wu, J., Wang, D., & Bauer, M. E. (2005). Image-based atmospheric correction of QuickBird imagery of Minnesota cropland. Remote Sensing of Environment, 99(3), 315–325.CrossRefGoogle Scholar
  28. Wu, M., Huang, W., Zheng, N., & Wang, C. (2015). Combining HJ CCD, GF-1 WFV and MODIS data to generate daily high spatial resolution synthetic data for environmental process monitoring. International Journal of Environmental Research & Public Health, 12(8), 9920–9937. Google Scholar
  29. Qi, X. Y., & Tian, Q. J. (2005). The advances in the study of atmospheric correction for optical remote sensing. Remote Sensing for Land & Resources, 17(4), 1–6.Google Scholar
  30. Yang, Y. L., Zhao, N., & Cheng, X. Q. (2015). Atmospheric correction and evaluation of SPOT6 satellite image based on FLAASH model. Modern Surveying & Mapping, (2), 3–6.Google Scholar
  31. Yao, W., Li, Z. J., Yao, G., Wu, J. F., & Jiang, D. L. (2011). Atmospheric correction model for Landsat images. Transactions of Atmospheric Sciences, 34(2):251–256.Google Scholar

Copyright information

© Indian Society of Remote Sensing 2017

Authors and Affiliations

  • Zheng Wang
    • 1
    • 2
    • 3
  • Junshi Xia
    • 4
  • Lihui Wang
    • 5
  • Zhihua Mao
    • 2
    • 3
  • Qun Zeng
    • 6
    • 7
  • Liqiao Tian
    • 8
  • Liangliang Shi
    • 9
  1. 1.School of Geographic and Oceanographic SciencesNanjing UniversityNanjingChina
  2. 2.Collaborative Innovation Center for the South China Sea StudiesNanjing UniversityNanjingChina
  3. 3.States Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of OceanographyState Oceanic AdministrationHangzhouChina
  4. 4.Research Center for Advanced Science and TechnologyThe University of TokyoTokyoJapan
  5. 5.Institute of Geodesy and GeophysicsChinese Academy of SciencesWuhanChina
  6. 6.Editorial Department of Journal of Central China Normal UniversityWuhanChina
  7. 7.The College of Urban and Environmental SciencesCentral China Normal UniversityWuhanChina
  8. 8.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote SensingWuhan UniversityWuhanChina
  9. 9.Ocean CollegeZhejiang UniversityHangzhouChina

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