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Surveys in Geophysics

, Volume 40, Issue 3, pp 333–360 | Cite as

Retrieval of Atmospheric Parameters and Surface Reflectance from Visible and Shortwave Infrared Imaging Spectroscopy Data

  • David R. ThompsonEmail author
  • Luis Guanter
  • Alexander Berk
  • Bo-Cai Gao
  • Rudolf Richter
  • Daniel Schläpfer
  • Kurtis J. Thome
Article

Abstract

Remote imaging spectroscopy in the 0.4–2.5-μm visible and shortwave infrared (VSWIR) range captures the majority of solar-reflected energy and enables a wide range of earth surface studies. This spectral range is also influenced by atmospheric effects including absorption from atmospheric gases and aerosols, Rayleigh scattering, and particle scattering. Globally consistent surface measurements must compensate for these atmospheric effects. This article reviews the physical and mathematical foundations of modern VSWIR atmospheric retrieval, focusing on imaging spectrometers. We assess sensitivity of the retrieval to errors in atmospheric state estimation. Finally, we describe some promising avenues of future research to support the next generation of orbital imaging spectrometers.

Keywords

Imaging spectroscopy Atmospheric correction Hyperspectral imaging Surface reflectance 

Notes

Acknowledgements

We acknowledge the critical support and facilitation of the International Space Science Institute (ISSI), Bern, Switzerland. A portion of this research was performed at the Jet Propulsion Laboratory, California Institute of Technology. AVIRIS-C and AVIRIS-NG are supported by National Aeronautics and Space Administration Earth Science, Science Mission Directorate. U.S. Federal Government support acknowledged. Copyright 2018. All Rights Reserved.

References

  1. ASTER (2018) The advanced spaceborne thermal emission and reflection radiometer global digital elevation map. https://asterweb.jpl.nasa.gov/gdem.asp. Last access 17 May 2018
  2. Bachmann M, Makarau A, Segl K, Richter R (2015) Estimating the influence of spectral and radiometric calibration uncertainties on EnMAP data products—examples for ground reflectance retrieval and vegetation indices. Remote Sens 7(8):10689–10714Google Scholar
  3. Berk A et al (2016a) Algorithm theoretic basis document (ATBD) for next generation MODTRAN®. Spectral Sciences Inc, BurlingtonGoogle Scholar
  4. Berk A, van den Bosch J, Hawes F, Perkins T, Conforti PF, Anderson GP, Kennett RG, Acharya PK (2016b). MODTRAN®6.0.0 user’s manual (revision 5). Spectral Sciences, Inc., Burlington. SSI-TR-685Google Scholar
  5. Bernstein LS, Adler-Golden SM, Sundberg RL, Levine RY, Perkins TC, Berk A, Ratkowski AJ, Felde G, Hoke ML (2005) Validation of the QUick Atmospheric Correction (QUAC) algorithm for VNIR-SWIR multi-and hyperspectral imagery. In: Defense and security. International Society for Optics and Photonics, pp 668–678Google Scholar
  6. Boardman JW (1998) Post-ATREM polishing of AVIRIS apparent reflectance data using EFFORT: a lesson in accuracy versus precision. In: Summaries of the 7th JPL airborne earth science workshop, JPL Publication 97–21, 1:53Google Scholar
  7. Bruegge CJ, Conel JE, Margolis JS, Green RO, Toon GC, Carrere V, Holm RG, Hoover G (1990) In-situ atmospheric water-vapor retrieval in support of AVIRIS validation. In: Proceedings of SPIE - The International Society for Optical Engineering, vol 1298Google Scholar
  8. Cannizzaro JP, Carder KL (2006) Estimating chlorophyll a concentrations from remote-sensing reflectance in optically shallow waters. Remote Sens Environ 101(2006):13–24Google Scholar
  9. Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62(3):241–252Google Scholar
  10. Clark RN, Swayze GA, Livo KE, Kokaly RF, Sutley SJ, Dalton JB, McDougal RR, Gent CA (2003) Imaging spectroscopy: earth and planetary remote sensing with the USGS Tetracorder and expert systems. J Geophys Res 108:5131.  https://doi.org/10.1029/2002JE001847,E12 Google Scholar
  11. Conel JE, Green RO, Vane G, Bruegge CJ, Alley RE (1987) AIS-2 radiometry and a comparison of methods for the recovery of ground reflectance. In: Vane G (ed) Proceedings of the 3rd airborne imaging spectrometer data analysis workshop, JPL Publ. volume 87–30 Jet Propulsion Laboratory, Pasadena, CA, pp 18–47Google Scholar
  12. Frankenberg C, Thorpe A, Thompson DR, Hulley G, Kort E, Vance N, Borchard J, Krings T, Gerilowski K, Sweeney C, Conley S, Bue B, Aubrey A, Hook S, Green RO (2016) Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners region. Proc Natl Acade Sci 113(35):9734–9739Google Scholar
  13. Fraser RS, Kaufman YJ (1985) The relative importance of aerosol scattering and absorption in remote sensing. IEEE J Geosci Remote Sens GE-23:525–633Google Scholar
  14. Frouin Robert, Pelletier Bruno (2015) Bayesian methodology for inverting satellite ocean-color data. Remote Sens Environ 159:332–360Google Scholar
  15. Fu Q, Liou KN (1992) On the correlated k-distribution method for radiative transfer in nonhomogeneous atmospheres. J Atmos Sci 49(22):2139–2156Google Scholar
  16. Gao B-C, Goetz AF (1990) Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data. J Geophys Res Atmos 95(D4):3549–3564Google Scholar
  17. Gao B-C, Goetzt AF (1995) Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data. Remote Sens Environ 52(3):155–162Google Scholar
  18. Gao B-C, Kaufman YJ (1995) Selection of the 1.375-µm MODIS channel for remote sensing of cirrus clouds and stratospheric aerosols from space. J Atmos Sci 52:4231–4237Google Scholar
  19. Gao B-C, Liu M (2013) A fast smoothing algorithm for post-processing of surface reflectance spectra retrieved from airborne imaging spectrometer data. Sensors 13:13879–13891.  https://doi.org/10.3390/s131013879 Google Scholar
  20. Gao B-C, Heidebrecht KB, Goetz AFH (1993) Derivation of scaled surface reflectances from AVIRIS data. Remote Sens Environ 44:165–178Google Scholar
  21. Gao B-C et al (1998) Correction of thin cirrus path radiance in the 0.4–1.0 µm spectral region using the sensitive 1.375-µm cirrus detecting channel. J Geophys Res 103:32169–32176Google Scholar
  22. Gao B-C, Montes MJ, Ahmad Z, Davis CO (2000) Atmospheric correction algorithm for hyperspectral remote sensing of ocean color from space. Appl Opt 39(6):887–896Google Scholar
  23. Gao B-C, Yang P, Han W, Li R-R, Wiscombe WJ (2002) An algorithm using visible and 1.38-micron channels to retrieve cirrus cloud reflectances from aircraft and satellite data. IEEE Trans. Geosci. Remote Sensing 40:1659–1668Google Scholar
  24. Gao B-C, Montes MJ, Davis CO (2004) Refinement of wavelength calibrations of hyperspectral imaging data using a spectrum-matching technique. Remote Sens Environ 90:424–433Google Scholar
  25. Gao B-C, Montes MJ, Davis CO, Goetz AF (2009) Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean. Remote Sens Environ 113:S17–S24Google Scholar
  26. Goetz AFH, Kindel BC, Ferri M, Qu Z (2003) HATCH: results from simulated radiances, AVIRIS and Hyperion. IEEE TGRS 41:1215–1222Google Scholar
  27. Green RO, Carrere V, Conel JE (1989) Measurement of atmospheric water vapor using the Airborne Visible/Infrared Imaging Spectrometer. In: Proceedings of the ASPRS conference on image processing, Reno, NVGoogle Scholar
  28. Green RO, Conel JE, Roberts DA (1993) Estimation of aerosol optical depth, pressure elevation, water vapor, and calculation of apparent surface reflectance from radiance measured by the airborne visible/infrared imaging spectrometer (AVIRIS) using a radiative transfer code. Optical Engineering and Photonics in Aerospace Sensing. International Society for Optics and Photonics. pp 2–11Google Scholar
  29. Green RO, Painter TH, Roberts DA, Dozier J (2006) Measuring the expressed abundance of the three phases of water with an imaging spectrometer over melting snow. Water Resour Res 42(10):W10402Google Scholar
  30. Guanter L, Richter R, Moreno J (2006) Spectral calibration of hyperspectral imagery using atmospheric absorption features. Appl Opt 45:2360–2370Google Scholar
  31. Guanter L, Estellés V, Moreno J (2007) Spectral calibration and atmospheric correction of ultra-fine spectral and spatial resolution remote sensing data. Application to CASI-1500 data. Remote Sens Environ 109(1):54–65Google Scholar
  32. Guanter L, Gómez-Chova L, Moreno J (2008) Coupled retrieval of aerosol optical thickness, columnar water vapor and surface reflectance maps from ENVISAT/MERIS data over land. Remote Sens Environ 112(6):2898–2913Google Scholar
  33. Hagolle O, Huc M, Villa Pascual D, Dedieu G (2015) A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of FormoSat-2, LandSat, VENμS and Sentinel-2 images. Remote Sens 7(3):2668–2691Google Scholar
  34. Herold M, Roberts DA, Gardner ME, Dennison PE (2004) Spectrometry for urban area remote sensing—development and analysis of a spectral library from 350 to 2400 nm. Remote Sens Environ 91(3):304–319Google Scholar
  35. Hu B, Lucht W, Strahler AH (1999) The interrrelationship of atmospheric correction of reflectances and surface BRDF retrieval: a sensitivity study. IEEE Trans Geosci Remote Sens 37:724–738Google Scholar
  36. Jensen DJ, Simard M, Cavanaugh KC, Thompson DR (2018) Imaging spectroscopy BRDF correction for mapping Louisiana’s coastal ecosystems. IEEE Trans Geosci Remote Sens 56(3):1739–1748Google Scholar
  37. Kaufman YJ, Sendra C (1988) Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery. Int J Remote Sens 9(8):1357–1381Google Scholar
  38. Kaufman YJ, Wald A, Remer LA, Gao B-C, Li RR, Flynn L (1997) The MODIS 2.1-µm channel—correlation with visible reflectance for use in remote sensing of aerosol. IEEE Trans Geosci Remote Sens 35:1286–1298Google Scholar
  39. Kobayashi S, Sanga-Ngoie K (2008) The integrated radiometric correction of optical remote sensing imageries. Int J Remote Sens 29:5957–5985Google Scholar
  40. Kokaly RF, Asner GP, Ollinger SV, Martin ME, Wessman CA (2009) Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sens Environ 113:S78–S91Google Scholar
  41. 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 24:31–51Google Scholar
  42. Kruse FA (2004) Comparison of ATREM, ACORN, and FLAASH atmospheric corrections using low-altitude AVIRIS data of Boulder, CO. In: Summaries of 13th JPL Airborne Geoscience Workshop, Jet Propulsion Laboratory, Pasadena, CAGoogle Scholar
  43. Kuhlmann G, Hueni A, Damm A, Brunner D (2016) An algorithm for in-flight spectral calibration of imaging spectrometers. Remote Sens 8:1017Google Scholar
  44. Lee Z, Carder KL, Mobley CD, Steward RG, Patch JS (1998) Hyperspectral remote sensing for shallow waters. I. A semianalytical model. Appl Opt 37(27):6329–6338Google Scholar
  45. Lee Z, Carder KL, Mobley CD, Steward RG, Patch JS (1999) Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization. Appl Opt 38(18):3831–3843Google Scholar
  46. Li A, Wang Q, Bian J, Lei G (2015) An improved physics-based model for topographic correction of Landsat TM images. Remote Sens 7:6296–6319Google Scholar
  47. Loyola DG, Coldewey-Egbers RM, Dameris M, Gamy H, Stenke A, Van Roozendael M, Lerot C, Balis D, Koukouli M (2009) Global long-term-monitoring of the ozone layer—a prerequisite for predictions. Int J Remote Sens 30:4295–4318Google Scholar
  48. Makarau A, Richter R, Müller R, Reinartz P (2014) Haze detection and removal in remotely sensed multispectral imagery. IEEE TGRS 52:5895–5905Google Scholar
  49. Makarau A, Richter R, Schläpfer D, Reinartz P (2016) Combined haze and cirrus removal for mulispectral imagery. IEEE GRSL 13:379–383Google Scholar
  50. Makarau A, Richter R, Schläpfer D, Reinartz P (2017) APDA water vapor retrieval validation for Sentinel-2 imagery. IEEE GRSL 14:227–231Google Scholar
  51. Matthew MW, Adler-Golden SM, Berk A, Felde G, Anderson GP, Gorodetzky D, Paswaters S, Shippert M (2002) Atmospheric correction of spectral imagery: evaluation of the FLAASH algorithm with AVIRIS data. In: Proceedings of the 31st applied imagery pattern recognition workshop. IEEE, pp 157–163Google Scholar
  52. Mouroulis P, Green RO, Chrien TG (2000) Design of pushbroom imaging spectrometers for optimum recovery of spectroscopic and spatial information. Appl Opt 39:2210–2220Google Scholar
  53. Mumby PJ, Skirving W, Strong AE, Hardy JT, LeDrew EF, Hochberg EJ et al (2004) Remote sensing of coral reefs and their physical environment. Mar Pollut Bull 48(3):219–228Google Scholar
  54. Nagler PL, Inoue Y, Glenn EP, Russ AL, Daughtry CST (2003) Cellulose absorption index (CAI) to quantify mixed soil–plant litter scenes. Remote Sens Environ 87(2):310–325Google Scholar
  55. Palacios SL, Kudela RM, Guild LS, Negrey KH, Torres-Perez J, Broughton J (2015) Remote sensing of phytoplankton functional types in the coastal ocean from the HyspIRI Preparatory Flight Campaign. Remote Sens Environ 167:269–280Google Scholar
  56. Perkins T, Adler-Golden S, Matthew MW, Berk A, Bernstein LS, Lee J, Fox M (2012) Speed and accuracy improvements in FLAASH atmospheric correction of hyperspectral imagery. Opt Eng 51:111707–111708Google Scholar
  57. Popp C, Brunner D, Damm A, Van Roozendael M, Fayt C, Buchmann B (2012) High-resolution NO2 remote sensing from the Airborne Prism EXperiment (APEX) imaging spectrometer. Atmos Meas Tech 5(9):2211–2225Google Scholar
  58. Reinersman PN, Carder KL, Chen RF (1998) Satellite-sensor calibration verification with the cloud-shadow method. Appl Opt 37:5541–5549Google Scholar
  59. Riano D, Chuvieco E, Salas J, Aguado I (2003) Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types. IEEE TGRS 41:1056–1061Google Scholar
  60. Richter R (1998) Correction of satellite imagery over mountainous terrain. Appl Opt 37:4004–4015Google Scholar
  61. Richter R, Schlaepfer D (2002) Geo-atmospheric processing of airborne imaging spectrometry data, Part 2: atmospheric/topographic correction. Int J Remote Sens 23(13):2631–2649Google Scholar
  62. Richter R, Kellenberger T, Kaufmann H (2009) Comparison of topographic correction methods. Remote Sens 1:184–196Google Scholar
  63. Richter R, Schläpfer D, Müller A (2011) Operational atmospheric correction for imaging spectrometers accounting for the smile effect. IEEE TGRS 49:1772–1780Google Scholar
  64. Richter R, Heege T, Kiselev V, Schläpfer D (2014) Correction of ozone influence on TOA radiance. Int J Remote Sens 35:8044–8056Google Scholar
  65. 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 86-35, 21–30, Jet Propulsion Lab, Pasadena, CAGoogle Scholar
  66. 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, Jet Propulsion Lab, Pasadena, CA, pp 21–30Google Scholar
  67. Rodgers CD (2000) Inverse methods for atmospheric sounding: theory and practice. World Scientific, SingaporeGoogle Scholar
  68. Sandmeier S, Itten KI (1997) A physically-based model to correct atmospheric and illumination effects in optical satellite data of rugged terrain. IEEE TGRS 35:708–717Google Scholar
  69. Schaepman-Strub G, Schaepman ME, Painter TH, Dangel S, Martonchik JV (2006) Reflectance quantities in optical remote sensing—definitions and case studies. Remote Sens Environ 103(1):27–42Google Scholar
  70. Schläpfer D, Richter R (2011) Spectral polishing of high resolution imaging spectroscopy data. In: Proceedings of the 7th SIG-IS workshop on imaging spectroscopy, Edinburgh, UK, p 7. http://www.daniel-schlaepfer.ch/pdf/Schlaepfer_IS2011_polish.pdf
  71. Schläpfer D, Borel CC, Keller J, Itten KI (1998) Atmospheric precorrected differential absorption technique to retrieve columnar water vapor. Remote Sens Environ 65(3):353–366Google Scholar
  72. Schläpfer D, Richter R, Damm A (2013) Correction of shadowing in imaging spectroscopy data by quantification of the proportion of diffuse illumination. In: Presented at the 8th EARSeL SIG-IS Workshop on Imaging Spectroscopy, Nantes, FR, pp 10. http://www.daniel-schlaepfer.ch/pdf/Schlaepfer_Earsel2013_Shadow.pdf
  73. Schläpfer D, Richter R, Feingersh T (2015) Operational BRDF effects correction for wide-field-of-view optical scanners (BREFCOR). IEEE Trans Geosci Remote Sens 53(4):1855–1864Google Scholar
  74. Shepherd JD, Dymond JR (2003) Correcting satellite imagery for the variance of reflectance and illumination with topography. Int J Remote Sens 24:3503–3514Google Scholar
  75. Soenen SA, Peddle DR, Coburn CA (2005) SCS + C: a modified sun-canopy-sensor topographic correction in forested terrain2. IEEE TGRS 43:2148–2159Google Scholar
  76. SRTM (2018) Shuttle radar topography mission digital elevation model. https://lta.cr.usgs.gov/SRTM1Arc. Last access 17 May 2018
  77. Stamnes K, Tsay SC, Wiscombe W, Jayaweera K (1988) Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media. Appl Opt 27(12):2502–2509Google Scholar
  78. Tack F, Merlaud A, Iordache M-D, Danckaert T, Yu H, Fayt C, Meuleman K, Deutsch F, Fierens F, Van Roozendael M (2017) High-resolution mapping of the NO2 spatial distribution over Belgian urban areas based on airborne APEX remote sensing. Atmos Meas Tech 10:1665–1688Google Scholar
  79. Tan I, Storelvmo T (2016) Sensitivity study on the influence of cloud microphysical parameters on mixed-phase cloud thermodynamic phase partitioning in CAM5. J Atmos Sci 73(2):709–728Google Scholar
  80. Tanré D, Herman M, Deschamps PY (1981) Influence of the background contribution upon space measurements of ground reflectances. Appl Opt 20:3676–3684Google Scholar
  81. Tanré D, Herman M, Deschamps PY (1983) Influence of the atmosphere on space measurements of directional properties. Appl Opt 22(5):733–741Google Scholar
  82. Teillet PM, Guindon B, Goodenough DG (1982) On the slope-aspect correction of multispectral scanner data. Can J Remote Sens 8:84–106Google Scholar
  83. Thompson DR, Gao BC, Green RO, Roberts DA, Dennison PE, Lundeen SR (2015a) Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign. Remote Sens Environ 167:64–77Google Scholar
  84. Thompson DR, Leifer I, Bovensmann H, Eastwood M, Fladeland M, Frankenberg C, Gerilowski K, Green RO, Kratwurst S, Krings T, Luna B, Thorpe A (2015b) Real-time remote detection and measurement for airborne imaging spectroscopy: a case study with methane. Atmos Meas Tech 8:4383–4397Google Scholar
  85. Thompson DR, Siedel F, Gao B-C, Gierach M, Kudela R, Green RO, Mouroulis P (2015c) Optimizing solar irradiance for coastal spectroscopy. Geophys Res Lett 42:4116–4123Google Scholar
  86. Thompson DR, Thorpe AK, Frankenberg C, Green RO, Duren R, Hollstein A, Guanter L, Middleton E, Ong L, Ungar S (2016a) Orbital measurement of the Aliso Canyon CH4 super-emitter. Geophys Res Lett 43:6571–6578Google Scholar
  87. Thompson DR, McCubbin I, Gao B-C, Green RO, Matthews AA, Mei F, Meyer K, Platnick S, Schmid B, Tomlinson J, Wilcox E (2016b) Measuring cloud thermodynamic phase with shortwave infrared imaging spectroscopy. J Geophys Res Atmos 121(15):9174–9190Google Scholar
  88. Thompson DR, Boardman JW, Eastwood ML, Green RO, Haag JM, Mouroulis P, Van Gorp BE (2018a) Imaging spectrometer stray spectral response: in-flight characterization, correction, and validation. Remote Sens Environ 204:850–860Google Scholar
  89. Thompson DR, Kahn BH, Green RO, Chien SA, Middleton EM, Tran DQ (2018b) Global spectroscopic survey of cloud thermodynamic phase at high spatial resolution, 2005–2015. Atmos Meas Tech 11:1019–1030.  https://doi.org/10.5194/amt-11-1019-2018 Google Scholar
  90. Thompson DR, Natraj V, Green RO, Helmlinger MC, Gao B-C, Eastwood ML (2018c) Optimal estimation for imaging spectrometer atmospheric correction. Remote Sens Environ.  https://doi.org/10.1016/j.rse.2018.07.003 Google Scholar
  91. Thorpe AK, Frankenberg C, Thompson DR, Duren RM, Aubrey AD, Bue BD, Green RO, Gerilowski K, Krings T, Borchardt J, Kort EA, Sweeney C, Conley S, Roberts DA, Dennison PE (2017) Airborne DOAS retrievals of methane, carbon dioxide, and water vapor concentrations at high spatial resolution: application to AVIRIS-NG. Atmos Meas Tech 10(10):3833–3850Google Scholar
  92. United States National Bureau of Standards, Nicodemus FE (1977) Geometrical considerations and nomenclature for reflectance, vol 160. US Department of Commerce, National Bureau of StandardsGoogle Scholar
  93. Ustin SL, Roberts DA, Gamon JA, Asner GP, Green RO (2004) Using imaging spectroscopy to study ecosystem processes and properties. Bioscience 54(6):523–534Google Scholar
  94. Vermote EF, El-Saleous N, Justice CO, Kaufman YJ, Privette JL, Remer L, Roger JC, Tanré D (1997a) Atmospheric correction of visible to middle infrared EOS-MODIS data over land surface: background, operational algorithm and validation. J Geophys Res 102:17131–17141Google Scholar
  95. Vermote EF, Tanré D, Deuzé JL, Herman M, Morcrette JJ (1997b) Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Trans Geosci Remote Sens 35:675–686Google Scholar
  96. Weyermann J, Damm A, Kneubuhler M, Schaepman ME (2014) Correction of reflectance anisotropy effects of vegetation on airborne spectroscopy data and derived products. IEEE Trans Geosci Remote Sens 52(1):616–627Google Scholar
  97. Yamamoto H, Tsuchida S, Yoshioka H (2008) A study on ASTER/MODIS radiometric and atmospheric correction. IGARSS.  https://doi.org/10.1109/IGARS.2008.4779982 Google Scholar
  98. Zhang Y, Guindon B, Cihlar J (2002) An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images. Remote Sens Environ 82(2–3):173–187Google Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA
  2. 2.GFZ German Research Centre for Geosciences, Helmholtz Centre PotsdamPotsdamGermany
  3. 3.Spectral Sciences, Inc.BurlingtonUSA
  4. 4.Naval Research LaboratoryWashingtonUSA
  5. 5.German Aerospace Center (DLR)WeßlingGermany
  6. 6.ReSe Applications LLCWilSwitzerland
  7. 7.NASA/Goddard Space Flight CenterGreenbeltUSA

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