Atmospheric correction of APEX hyperspectral data

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Atmospheric correction plays a crucial role among the processing steps applied to remotely sensed hyperspectral data. Atmospheric correction comprises a group of procedures needed to remove atmospheric effects from observed spectra, i.e. the transformation from at-sensor radiances to at-surface radiances or reflectances. In this paper we present the different steps in the atmospheric correction process for APEX hyperspectral data as applied by the Central Data Processing Center (CDPC) at the Flemish Institute for Technological Research (VITO, Mol, Belgium). The MODerate resolution atmospheric TRANsmission program (MODTRAN) is used to determine the source of radiation and for applying the actual atmospheric correction. As part of the overall correction process, supporting algorithms are provided in order to derive MODTRAN configuration parameters and to account for specific effects, e.g. correction for adjacency effects, haze and shadow correction, and topographic BRDF correction. The methods and theory underlying these corrections and an example of an application are presented.

Berk, A, Andersonm GP, Bernstein, L, Acharya, P, Dothe, H, Matthew, M, Adler-Golden, S, Chetwynd Jr, J, Richtsmeier, S, Brian Pukall, B, Allred, C, Jeong, L & Hoke, M 1999, MODTRAN4 radiative transfer modeling for atmospheric correction, Proceedings SPIE 3756, Optical Spectroscopic Techniques and Instrumentation for Atmospheric and Space Research III, DOI:10.1117/12.366388 [October 20, 1999].

Brando, VE & Dekker, AG 2003, ‛Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality’. IEEE Transactions on Geoscience and Remote Sensing, vol. 41, pp. 1378−1387.

Brando V, Anstee J, Wettle M, Dekker A, Phinn, S & Roelfsema, C 2009, ‛A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data’, Remote Sensing of Environment, vol. 113, pp. 755−770.

COLIBRI interactive toolbox for hyperspectral data, 2015. Available from: .

De Haan, JF & Kokke, JMM 1996, Remote sensing algorithm development Toolkit: 1. Operationalization of atmospheric correction methods for tidal and inland waters, BCRS Report.

(DLR 2015) DLR website, see

Gao B & Goetz, AFH 1990, ‛Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data’, Journal of Geophysical Research, vol. 95, no. D4, pp. 3549−3564.

Gao, BC, Montes, MJ, Ahmad, Z. & Davis, CO 2000, ‛Atmospheric correction algorithm for hyperspectral remote sensing of ocean color from space’, Applied Optics, vol. 39, 887−896.

Gao, BC, Montes, MJ & Davis, CO 2004, ‛Refinement of wavelength calibrations of hyperspectral imaging data using a spectrummatching technique’, Remote Sensing of Environment, vol. 90, no. 4, pp. 424−433.

Gao, BC, Davis, CO & Goetz, AFH 2006, ‛A review of atmospheric correction techniques for hyperspectral remote sensing of land surfaces and ocean color’, Proceedings of IGARSS 2006.

Gao, BC, Montes, MJ, Davis, CO &. Goetz, AFH 2009, ‛Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean’, Remote Sensing of Environment, vol. 113, pp 17-24.

Giardino, C, Brando, V, Dekker, AG. Strombeck, N & Candiani, G 2007, ‛Assessment of water quality in Lake Garda (Italy) using Hyperion’, Remote Sensing of Environment, vol. 109, no 2, pp. 183−195.

Heege, T, Kisselev, V, Miksa, S, Pinnel, N & Häse, C 2005, ‛Mapping aquatic systems with a physically based process chain’, SPIE Ocean Optics, Fremantle, Australia.

Liang, S, Fang, H & Chen, M 2001, ‛Atmospheric correction of Landsat ETM+land surface imagery - Part 1: Methods, IEEE’, Transactions on Geoscience and Remote Sensing, vol. 39, pp. 2490-2498.

Montes, MJ, Gao, BC & Davis, CO 2004, NRL atmospheric correction algorithms for oceans: Tafkaa user’s guide, NRL/ MR/7230--04-8760, Washington, DC

Richter, R 1998, ‛Correction of satellite imagery over mountainous terrain’, Applied Optics, vol. 37, pp. 4004-4015.

Richter, R, Kellenberger, T & Kaufmann, H 2009, ‛Comparison of topographic correction methods’, Remote Sensing, vol. 1, pp. 184-196.

Richter, R, Schläpfer, D & Müller, A 2006, ‛An automatic atmospheric correction algorithm or visible/NIR imagery’, International Journal of Remote Sensing, vol. 27, no. 10, pp. 2077-2085.

Rodger, A & Lynch, MJ 2001, ‛Determining atmospheric column water vapour in the 0.4-2.5 μm spectral region,’ Proceedings of the JPL-NASA AVIRIS Workshop 2001, Pasadena, California.

Schläpfer, D, Biesemans, J, Hueni, A & Meuleman, K 2008, ‛Evaluation of the atmospheric correction procedure for the APEX level 2/3 processor’, Proceedings of SPIE, 7107(710709):12.

Schläpfer, D & Richter, R 2011, ‛Spectral polishing of high resolution imaging spectroscopy data’, 7th SIG-IS Workshop on Imaging Spectroscopy, Edinburgh, Scotland.

Verhoef, W & Bach, H 2003, ‛Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models’, Remote Sensing of Environment, vol. 87, pp. 23-41.

Miscellanea Geographica

Regional Studies on Development

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