Skip to main content
SpringerLink
Log in

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

  • Published:
Surveys in Geophysics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Adapted from Thompson et al. (2018a). Red, green, and blue lines indicate visible wavelengths

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Adapted from Thompson et al. (2016b)

Similar content being viewed by others

References

  • 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

  • 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):10689–10714

    Article  Google Scholar 

  • Berk A et al (2016a) Algorithm theoretic basis document (ATBD) for next generation MODTRAN®. Spectral Sciences Inc, Burlington

    Google Scholar 

  • 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-685

  • 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 668–678

  • 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, 1:53

  • 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 1298

  • Cannizzaro JP, Carder KL (2006) Estimating chlorophyll a concentrations from remote-sensing reflectance in optically shallow waters. Remote Sens Environ 101(2006):13–24

    Article  Google Scholar 

  • Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62(3):241–252

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 18–47

  • 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):9734–9739

    Article  Google Scholar 

  • Fraser RS, Kaufman YJ (1985) The relative importance of aerosol scattering and absorption in remote sensing. IEEE J Geosci Remote Sens GE-23:525–633

    Google Scholar 

  • Frouin Robert, Pelletier Bruno (2015) Bayesian methodology for inverting satellite ocean-color data. Remote Sens Environ 159:332–360

    Article  Google Scholar 

  • Fu Q, Liou KN (1992) On the correlated k-distribution method for radiative transfer in nonhomogeneous atmospheres. J Atmos Sci 49(22):2139–2156

    Article  Google Scholar 

  • 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):3549–3564

    Article  Google Scholar 

  • 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):155–162

    Article  Google Scholar 

  • 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 52:4231–4237

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Gao B-C, Heidebrecht KB, Goetz AFH (1993) Derivation of scaled surface reflectances from AVIRIS data. Remote Sens Environ 44:165–178

    Article  Google Scholar 

  • 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 103:32169–32176

    Article  Google Scholar 

  • 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):887–896

    Article  Google Scholar 

  • 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 40:1659–1668

    Article  Google Scholar 

  • Gao B-C, Montes MJ, Davis CO (2004) Refinement of wavelength calibrations of hyperspectral imaging data using a spectrum-matching technique. Remote Sens Environ 90:424–433

    Article  Google Scholar 

  • 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–S24

    Article  Google Scholar 

  • Goetz AFH, Kindel BC, Ferri M, Qu Z (2003) HATCH: results from simulated radiances, AVIRIS and Hyperion. IEEE TGRS 41:1215–1222

    Google Scholar 

  • 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, NV

  • 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 2–11

  • 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):W10402

    Article  Google Scholar 

  • Guanter L, Richter R, Moreno J (2006) Spectral calibration of hyperspectral imagery using atmospheric absorption features. Appl Opt 45:2360–2370

    Article  Google Scholar 

  • 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):54–65

    Article  Google Scholar 

  • 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):2898–2913

    Article  Google Scholar 

  • 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):2668–2691

    Article  Google Scholar 

  • 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):304–319

    Article  Google Scholar 

  • 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 37:724–738

    Article  Google Scholar 

  • 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):1739–1748

    Article  Google Scholar 

  • Kaufman YJ, Sendra C (1988) Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery. Int J Remote Sens 9(8):1357–1381

    Article  Google Scholar 

  • 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 35:1286–1298

    Article  Google Scholar 

  • Kobayashi S, Sanga-Ngoie K (2008) The integrated radiometric correction of optical remote sensing imageries. Int J Remote Sens 29:5957–5985

    Article  Google Scholar 

  • 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–S91

    Article  Google Scholar 

  • 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 24:31–51

    Article  Google Scholar 

  • 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, CA

  • Kuhlmann G, Hueni A, Damm A, Brunner D (2016) An algorithm for in-flight spectral calibration of imaging spectrometers. Remote Sens 8:1017

    Article  Google Scholar 

  • 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):6329–6338

    Article  Google Scholar 

  • 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):3831–3843

    Article  Google Scholar 

  • Li A, Wang Q, Bian J, Lei G (2015) An improved physics-based model for topographic correction of Landsat TM images. Remote Sens 7:6296–6319

    Article  Google Scholar 

  • 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 30:4295–4318

    Article  Google Scholar 

  • Makarau A, Richter R, Müller R, Reinartz P (2014) Haze detection and removal in remotely sensed multispectral imagery. IEEE TGRS 52:5895–5905

    Google Scholar 

  • Makarau A, Richter R, Schläpfer D, Reinartz P (2016) Combined haze and cirrus removal for mulispectral imagery. IEEE GRSL 13:379–383

    Google Scholar 

  • Makarau A, Richter R, Schläpfer D, Reinartz P (2017) APDA water vapor retrieval validation for Sentinel-2 imagery. IEEE GRSL 14:227–231

    Google Scholar 

  • 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 157–163

  • Mouroulis P, Green RO, Chrien TG (2000) Design of pushbroom imaging spectrometers for optimum recovery of spectroscopic and spatial information. Appl Opt 39:2210–2220

    Article  Google Scholar 

  • 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):219–228

    Article  Google Scholar 

  • 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):310–325

    Article  Google Scholar 

  • 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 167:269–280

    Article  Google Scholar 

  • 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 51:111707–111708

    Article  Google Scholar 

  • 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):2211–2225

    Article  Google Scholar 

  • Reinersman PN, Carder KL, Chen RF (1998) Satellite-sensor calibration verification with the cloud-shadow method. Appl Opt 37:5541–5549

    Article  Google Scholar 

  • Riano D, Chuvieco E, Salas J, Aguado I (2003) Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types. IEEE TGRS 41:1056–1061

    Google Scholar 

  • Richter R (1998) Correction of satellite imagery over mountainous terrain. Appl Opt 37:4004–4015

    Article  Google Scholar 

  • Richter R, Schlaepfer D (2002) Geo-atmospheric processing of airborne imaging spectrometry data, Part 2: atmospheric/topographic correction. Int J Remote Sens 23(13):2631–2649

    Article  Google Scholar 

  • Richter R, Kellenberger T, Kaufmann H (2009) Comparison of topographic correction methods. Remote Sens 1:184–196

    Article  Google Scholar 

  • Richter R, Schläpfer D, Müller A (2011) Operational atmospheric correction for imaging spectrometers accounting for the smile effect. IEEE TGRS 49:1772–1780

    Google Scholar 

  • Richter R, Heege T, Kiselev V, Schläpfer D (2014) Correction of ozone influence on TOA radiance. Int J Remote Sens 35:8044–8056

    Article  Google Scholar 

  • 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, CA

  • 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 21–30

  • Rodgers CD (2000) Inverse methods for atmospheric sounding: theory and practice. World Scientific, Singapore

    Book  Google Scholar 

  • Sandmeier S, Itten KI (1997) A physically-based model to correct atmospheric and illumination effects in optical satellite data of rugged terrain. IEEE TGRS 35:708–717

    Google Scholar 

  • 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):27–42

    Article  Google Scholar 

  • 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

  • Schläpfer D, Borel CC, Keller J, Itten KI (1998) Atmospheric precorrected differential absorption technique to retrieve columnar water vapor. Remote Sens Environ 65(3):353–366

    Article  Google Scholar 

  • 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

  • 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):1855–1864

    Article  Google Scholar 

  • Shepherd JD, Dymond JR (2003) Correcting satellite imagery for the variance of reflectance and illumination with topography. Int J Remote Sens 24:3503–3514

    Article  Google Scholar 

  • Soenen SA, Peddle DR, Coburn CA (2005) SCS + C: a modified sun-canopy-sensor topographic correction in forested terrain2. IEEE TGRS 43:2148–2159

    Google Scholar 

  • SRTM (2018) Shuttle radar topography mission digital elevation model. https://lta.cr.usgs.gov/SRTM1Arc. Last access 17 May 2018

  • 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):2502–2509

    Article  Google Scholar 

  • 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 10:1665–1688

    Article  Google Scholar 

  • 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):709–728

    Article  Google Scholar 

  • Tanré D, Herman M, Deschamps PY (1981) Influence of the background contribution upon space measurements of ground reflectances. Appl Opt 20:3676–3684

    Article  Google Scholar 

  • Tanré D, Herman M, Deschamps PY (1983) Influence of the atmosphere on space measurements of directional properties. Appl Opt 22(5):733–741

    Article  Google Scholar 

  • Teillet PM, Guindon B, Goodenough DG (1982) On the slope-aspect correction of multispectral scanner data. Can J Remote Sens 8:84–106

    Article  Google Scholar 

  • 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 167:64–77

    Article  Google Scholar 

  • 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 8:4383–4397

    Article  Google Scholar 

  • 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 42:4116–4123

    Article  Google Scholar 

  • 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 43:6571–6578

    Article  Google Scholar 

  • 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):9174–9190

    Article  Google Scholar 

  • 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 204:850–860

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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):3833–3850

    Article  Google Scholar 

  • United States National Bureau of Standards, Nicodemus FE (1977) Geometrical considerations and nomenclature for reflectance, vol 160. US Department of Commerce, National Bureau of Standards

  • Ustin SL, Roberts DA, Gamon JA, Asner GP, Green RO (2004) Using imaging spectroscopy to study ecosystem processes and properties. Bioscience 54(6):523–534

    Article  Google Scholar 

  • 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 102:17131–17141

    Article  Google Scholar 

  • 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 35:675–686

    Article  Google Scholar 

  • 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):616–627

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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):173–187

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA, 91109, USA

    David R. Thompson

  2. GFZ German Research Centre for Geosciences, Helmholtz Centre Potsdam, Telegrafenberg, Building A 17, Room 20.22, 14473, Potsdam, Germany

    Luis Guanter

  3. Spectral Sciences, Inc., 4 Fourth Ave., Burlington, MA, 01803, USA

    Alexander Berk

  4. Naval Research Laboratory, 4555 Overlook Ave., Washington, DC, 20375, USA

    Bo-Cai Gao

  5. German Aerospace Center (DLR), Münchener Straße 20, 82234, Weßling, Germany

    Rudolf Richter

  6. ReSe Applications LLC, Langeggweg 3, 9500, Wil, Switzerland

    Daniel Schläpfer

  7. NASA/Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD, 20771, USA

    Kurtis J. Thome

Authors
  1. David R. Thompson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luis Guanter
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexander Berk
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bo-Cai Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rudolf Richter
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Daniel Schläpfer
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kurtis J. Thome
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David R. Thompson.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thompson, D.R., Guanter, L., Berk, A. et al. Retrieval of Atmospheric Parameters and Surface Reflectance from Visible and Shortwave Infrared Imaging Spectroscopy Data. Surv Geophys 40, 333–360 (2019). https://doi.org/10.1007/s10712-018-9488-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10712-018-9488-9

Keywords

Search

Navigation