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Atmospheric Corrections

Chapter

Abstract

As the EM radiation passes through the atmosphere, it undergoes modification in intensity due to atmospheric interaction, viz. selective scattering, absorption and emission. The main objective of atmospheric corrections is to retrieve the realistic surface reflectance or emittance values of a target from remotely sensed image data. In the solar reflection region, atmospheric scattering is the dominant cause of path radiance. In the thermal infrared region, atmospheric window is used to minimize the effect of atmospheric emission. Different types of procedures are used for atmospheric correction that include empirical-statistical procedures and radiative transfer modelling.

References

  1. Aspinall RJ, Marcus WA, Boardman JW (2002) Considerations in collecting, processing, and analysing high spatial resolution hyperspectral data for environmental investigations. J Geograph Syst 4:15–29CrossRefGoogle Scholar
  2. Ben-Dor E, Kindel B, Goetz AFH (2004) Quality assessment of several methods to recover surface reflectance using synthetic imaging spectroscopy data. Remote Sens Environ 90:389–404CrossRefGoogle Scholar
  3. Berk A, Anderson G, Acharya P, Shettle E (2008) MODTRAN 5.2.0.0 user’s manual. Air Force Geophysics Laboratory, Hanscom, AFB, MA, USGoogle Scholar
  4. Berk A, Bernstein LS, Robertson DC (1989) MODTRAN: a moderate resolution model for LOWTRAN7. Tech Rep GL-TR-89-0122, Geophysics Laboratory, Bedford, MassGoogle Scholar
  5. Berk A, Conforti P, Kennett R, Perkins T, Hawes F, van den Bosch J (2014) MODTRAN6: a major upgrade of the MODTRAN radiative transfer code. Proceedings SPIE 9088, algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery XX, 90880H (13 June 2014). doi: 10.1117/12.2050433
  6. Chavez PS Jr (1988) An improved dark object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens Environ 24:459–479CrossRefGoogle Scholar
  7. Chavez PS Jr (1996) Image-based atmospheric corrections revisited and improved. Photogramm Eng Remote Sens 62:1025–1036Google Scholar
  8. Crippen RE (1987) The regression intersection method of adjusting image data for band ratioing. Int J Remote Sens 9:767–776CrossRefGoogle Scholar
  9. Farrand WH, Singer RB, Merényi E (1994) Retrieval of apparent surface reflectance from AVIRIS data—a comparison of empirical line, radiative-transfer and spectral mixture methods. Remote Sens Environ 47(3):311–321CrossRefGoogle Scholar
  10. Gao BC, Heidebrecht KB, Goetz AFH (1993) Derivation of scaled surface reflectances from AVIRIS data. Remote Sens Environ 44:165–178CrossRefGoogle Scholar
  11. Gao BC, Montes MJ, Davis OC, Goetz AFH (2009) Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean. Remote Sens Environ 113:S17–S24CrossRefGoogle Scholar
  12. Gordon HR (1978) Removal of atmospheric effects from the satellite imagery of the oceans. Appl Opt 17:1631–1636Google Scholar
  13. Hadjimitsis DG (2009) Aerosol optical thickness (AOT) retrieval over land using satellite image-based algorithm. Air Qual Atmos Health 2:89–97CrossRefGoogle Scholar
  14. Kruse FA (1988) Use of airborne imaging spectrometer data to map minerals associated with hydrothermally altered rocks in the northern Grapevine Mountains, Nevada and California. Remote Sens Environ 24:31–51CrossRefGoogle Scholar
  15. Rahman H, Dedieu G (1994) SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum. Int J Remote Sens 15:123–143CrossRefGoogle Scholar
  16. Richter R, Schläpfer D (2016) Atmospheric/topographic correction for airborne imagery. ATCOR-4 User Guide, Version 7.1.0, Nov 2016Google Scholar
  17. Roberts DA, Yamaguchi Y, Lyon R (1986) Comparison of various techniques for calibration of AIS data. In: Vane G, Goetz AFH (eds) Proceedings of the 2nd airborne imaging spectrometer data analysis workshop, JPL Publication, vol 86–35, pp 21−30, Jet Propulsion Lab, Pasadena, CAGoogle Scholar
  18. Schott JR, Salvaggio C, Volchok WJ (1988) Radiometric scene normalization using pseudoinvariant features. Remote Sens Environ 26(1):1–14CrossRefGoogle Scholar
  19. Schroeder TA, Cohen WB, Song C, Canty MJ, Yang Z (2006) Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon. Remote Sens Environ 103:16–26CrossRefGoogle Scholar
  20. Smith GM, Milton EJ (1999) The use of the empirical line method to calibrate remotely sensed data to reflectance. Int J Rem Sens 20(13):2653–2662CrossRefGoogle Scholar
  21. Tanre D et al (1990) Description of a computer code to simulate the satellite signal in the solar spectrum: the 5S code. Int J Rem Sensing 11:659–668CrossRefGoogle Scholar
  22. Vermote EF, Tanre D, Denze JL, Herman M, Morcette JJ (1997) Second simulation of the satellite signal in the solar spectrum 6S: an overview. IEEE Trans Geosci Remote Sens 35(3):675–686CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  1. 1.Formerly Professor, Earth Resources Technology, Department of Earth SciencesIndian Institute of Technology RoorkeeRoorkeeIndia

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