Current Science

Open Access Open Access  Restricted Access Subscription Access

An Empirical Comparison of Calibration and Validation Methodologies for Airborne Imaging Spectroscopy


Affiliations
1 Space Applications Centre (ISRO), Ahmedabad 380 015, India
2 Jet Propulsion Laboratory, California Institute of Technology, United States
3 Physical Research Laboratory, Ahmedabad 380 009, India
 

To date, a large number of existing applications in India have used multi-band observations from airborne and spaceborne platforms. New sensors are providing additional capabilities thanks to special aerial missions with the compact airborne spectrographic imager (CASI), the short-wave infrared (SWIR) full spectrum imager (SFSI) and the National Aeronautics and Space Administration’s (NASA’s) Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG). Opportunities to exploit quantitative spectroscopic signatures and high spatial resolution have garnered great interest among the scientific community, and the success of these missions will rely on accurate calibration. Here we focus on a vicarious calibration experiment conducted for the AVIRIS-NG India campaign. We discuss initial validation results, with descriptions of in situ and remote calibration and measurement protocols, geometric processing with precise position and attitude data, and atmospheric simulations used to validate the remote measurement. A partnership between Indian Space Research Organisation (ISRO) and NASA investigators proved a unique opportunity to assess the empirical variability in results, indicating their sensitivity to modelling choices and assumptions. The vicarious calibration exercise uses multiple radiative transfer models, including MODTRAN 6.0 and a new version of the 6S radiative transfer code, viz. 6SV2.1, which is capable of accounting for polarization.

Keywords

Hyperspectral Measurements, Radiative Transfer, Reflectance, Vicarious Calibration.
User
Notifications
Font Size

  • Naughton, D. et al., Absolute radiometric calibration of the rapid eye multispectral imager using the reflectance-based vicarious calibration method. J. Appl. Remote Sensing, 2011, 5(1), 053544; https://doi.org/10.1117/1.3613950.

  • Slater, P. N. et al., Reflectance-based and radiance-based methods for the in-flight absolute calibration of multispectral sensors. Remote Sensing Environ., 1987, 22, 11–37.

  • Thome, K. J., Absolute radiometric calibration of Landsat 7 ETM+ using the reflectance-based method. Remote Sensing Environ., 2001, 78, 27–38.

  • Thome, K. J., Arai, K., Tsuchida, S. and Biggar, S. F., Vicarious calibration of ASTER via the reflectance-based approach. IEEE Trans. Geosci. Remote Sensing, 2008, 46, 3285–3295.

  • Dinguirard, M. and Slater, P. N., Calibration of spacemultispectral imaging sensors: a review. Remote Sensing Environ., 1999, 68, 194–205.

  • Green, R. O., Eastwood, M. L. and Satrure, C. M., Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sensing Environ., 1998, 65, 227–248.

  • Thompson, D. R., Natraj, V., Green, R. O., Helmlinger, M., Gao, B.-C. and Eastwood, M. L., Optimal estimation for imaging spectrometer atmospheric correction. Remote Sensing Environ., 2018, 216, 355–373.

  • Berk, A., Conforti, P., Kennett, R., Perkins, T., Hawes, F. and van den Bosch, J., MODTRAN6: a major upgrade of the MODTRAN radiative transfer codes. Proc. SPIE 9088, 2014, 7; doi:10.1117/12.2050433.

  • Kotchenova, S. Y. and Vermote, E. F., Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part II: Homogeneous Lambertian and anisotropic surfaces, Appl. Opt., 2007, 46, 4455–4464.

  • Sridhar, V. N., Mehta, K. B., Prajapati, R. P., Babu, K. N., Suthar, N. M. and Shukla, A. K., Absolute vicarious calibration of OCM2 and AWiFS sensors using a reflectance based method over land sites in the Rann of Kutch, Gujarat. Int. J. Remote Sensing, 2013, 34, 5690–5708.

  • Patel, P. N., Dumka, U. C., Kaskaoutis, D. G., Babu, K. N. and Mathur, A. K., Optical and radiative properties of aerosols over Desalpar, a remote site in western India: source identification, modification processes and aerosol type discrimination. Sci. Total Environ., 2017, 575, 612–627.

  • Thompson, D. R., Boardman, J. W., Eastwood, M. L., Green, R. O., Haag, J. M. and Gorp, B. V., Imaging spectrometer stray spectral response: in-flight characterization, correction and validation. Remote Sensing Environ., 2018, 204, 850–860.

  • Thompson, D. R. et al., Real-time remote detection and measurement for airborne imaging spectroscopy: a case study with methane. Atmosph. Meas. Tech., 2015, 8, 4383–4397.

  • Pagnutti, M., Ryan, R. E., Kelly, M., Holekamp, K., Zanoni, V., Thome, K. and Schiller, S., Radiometric characterization of IKONOS multispectralimagery. Remote Sensing Environ., 2003, 88, 53–68.

  • Thome, K., Biggar, S. and Choi, H. J., Vicarious calibration of Terra ASTER, MISR, and MODIS. Proc. SPIE 5542, 2004; doi:10.1117/12.559942

  • Schott, J. R., Remote Sensing: The Image Chain Approach, Oxford University Press, 2007, p. 665.

  • Thome, K., Helder, D., Aaron, D. and Dewald, J., Landsat-5 TM and Landsat-7 ETM+ absolute radiometric calibration using the reflectance-based method. IEEE Trans. Geosci. Remote Sensing, 2004, 42, 2777–2785.

  • Helmlinger, M., Eastwood, M., Green, R. P. and Thompson, D. R., Solar-similar near-infrared suppressed ‘blue’ calibration source. In IEEE Aerospace Conference, Big Sky, USA, MT, 2016; doi:10.1109/AERO.2016.7500714.

  • Biggar, S. F., Slater, P. N. and Gellman, D. I., Uncertainties in the in flight calibration of sensors with reference to measured ground sites in the 0.4–1.1 μm range. Remote Sensing Environ., 1994, 48, 245–252.


Abstract Views: 332

PDF Views: 123




  • An Empirical Comparison of Calibration and Validation Methodologies for Airborne Imaging Spectroscopy

Abstract Views: 332  |  PDF Views: 123

Authors

K. N. Babu
Space Applications Centre (ISRO), Ahmedabad 380 015, India
A. K. Mathur
Space Applications Centre (ISRO), Ahmedabad 380 015, India
David R. Thompson
Jet Propulsion Laboratory, California Institute of Technology, United States
Robert O. Green
Jet Propulsion Laboratory, California Institute of Technology, United States
Piyushkumar N. Patel
Physical Research Laboratory, Ahmedabad 380 009, India
R. P. Prajapati
Space Applications Centre (ISRO), Ahmedabad 380 015, India
Brian D. Bue
Jet Propulsion Laboratory, California Institute of Technology, United States
Sven Geier
Jet Propulsion Laboratory, California Institute of Technology, United States
Michael L. Eastwood
Jet Propulsion Laboratory, California Institute of Technology, United States
Mark C. Helmlinger
Jet Propulsion Laboratory, California Institute of Technology, United States

Abstract


To date, a large number of existing applications in India have used multi-band observations from airborne and spaceborne platforms. New sensors are providing additional capabilities thanks to special aerial missions with the compact airborne spectrographic imager (CASI), the short-wave infrared (SWIR) full spectrum imager (SFSI) and the National Aeronautics and Space Administration’s (NASA’s) Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG). Opportunities to exploit quantitative spectroscopic signatures and high spatial resolution have garnered great interest among the scientific community, and the success of these missions will rely on accurate calibration. Here we focus on a vicarious calibration experiment conducted for the AVIRIS-NG India campaign. We discuss initial validation results, with descriptions of in situ and remote calibration and measurement protocols, geometric processing with precise position and attitude data, and atmospheric simulations used to validate the remote measurement. A partnership between Indian Space Research Organisation (ISRO) and NASA investigators proved a unique opportunity to assess the empirical variability in results, indicating their sensitivity to modelling choices and assumptions. The vicarious calibration exercise uses multiple radiative transfer models, including MODTRAN 6.0 and a new version of the 6S radiative transfer code, viz. 6SV2.1, which is capable of accounting for polarization.

Keywords


Hyperspectral Measurements, Radiative Transfer, Reflectance, Vicarious Calibration.

References





DOI: https://doi.org/10.18520/cs%2Fv116%2Fi7%2F1101-1107