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Acta Geophysica

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1895-7455
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Volume 63, Issue 6 (Dec 2015)

Issues

Relating Hyperspectral Airborne Data to Ground Measurements in a Complex and Discontinuous Canopy

Javier F. Calleja
  • Corresponding author
  • Department of Physics, University of Oviedo, Polytechnic School in Mieres, Mieres, Spain
  • Email:
/ Christine Hellmann
  • University of Bielefeld, Experimental and Systems Ecology, Bielefeld, Germany
  • University of Bayreuth, Agroecosystem Research, BAYCEER, Bayreuth, Germany
  • Email:
/ Gorka Mendiguren
  • Institute of Economy, Geography and Demography, CCHS-CSIC, Madrid, Spain
  • Email:
/ Suvarna Punalekar
  • University of Reading, Department of Geography and Environmental Science, Reading, United Kingdom
  • Email:
/ Juanjo Peón
  • Area of Cartographic, Geodesic and Photogrammetric Engineering, University of Oviedo, Polytechnic School in Mieres, Mieres, Spain
  • Email:
/ Alasdair MacArthur
  • NERC Field Spectroscopy Facility, School of Geosciences, University of Edinburgh, Scotland
  • Email:
/ Luis Alonso
  • Image Processing Laboratory, University of Valencia, Paterna, Valencia, Spain
  • Email:
Published Online: 2015-12-30 | DOI: https://doi.org/10.1515/acgeo-2015-0036

Abstract

The work described in this paper is aimed at validating hyperspectral airborne reflectance data collected during the Regional Experiments For Land-atmosphere EXchanges (REFLEX) campaign. Ground reflectance data measured in a vineyard were compared with airborne reflectance data. A sampling strategy and subsequent ground data processing had to be devised so as to capture a representative spectral sample of this complex crop. A linear model between airborne and ground data was tried and statistically tested. Results reveal a sound correspondence between ground and airborne reflectance data (R2 > 0.97), validating the atmospheric correction of the latter.

Keywords: hyperspectral remote sensing; AHS; validation; reflectance field spectrometry

References

  • Anderson, K., J.L. Dungan, and A. MacArthur (2011), On the reproducibility of field-measured reflectance factors in the context of vegetation studies, Remote Sens. Environ. 115, 8, 1893-1905, DOI: 10.1016/j.rse.2011.03.012. [Crossref]

  • Beisl, U., G. Strub, and C. Dickerhof (2000), Validation of hyperspectral imaging data from the Barrax test site with BRDF ground measurements in the reflective wavelength range. In: 2nd EARSeL Workshop on Imaging Spectroscopy, 11-13 July 2000, Enschede, The Netherlands.

  • Blackburn, G.A. (2007), Hyperspectral remote sensing of plant pigments, J. Exp. Bot. 58, 4, 855-867, DOI: 10.1093/jxb/erl123. [Crossref] [Web of Science]

  • Cho, M.A., I. Sobhan, A.K. Skidmore, and J. de Leeuw (2008), Discriminating species using hyperspectral indices at leaf and canopy scales, Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 37, B7, 369-376.

  • Darvishzadeh, R., C. Atzberger, A. Skidmore, and M. Schlerf (2010), Retrieval of vegetation biochemicals using a radiative transfer model and hyperspectral data. In: W. Wagner, and B. Székely (eds.), TC VII Symposium - 100 Years ISPRS - Advancing Remote Sensing Science, 5-7 July 2010, Vienna, Austria, 171-175.

  • de Miguel, E., M. Jiménez, I. Pérez, O.G. de la Cámara, F. Muñoz, and J.A. Gómez- Sánchez (2015), AHS and CASI processing for the REFLEX remote sensing campaign: methods and results, Acta Geophys. 63, 6, 1485-1498, DOI: 10.1515/acgeo-2015-0031 (this issue). [Crossref]

  • Delalieux, S., B. Somers, W.W. Verstraeten, J.A.N. van Aardt, W. Keulemans, and P. Coppin (2009), Hyperspectral indices to diagnose leaf biotic stress of apple plants, considering leaf phenology, Int. J. Remote Sens. 30, 8, 1887-1912, DOI: 10.1080/01431160802541556. [Web of Science] [Crossref]

  • Dobrowski, S.Z., J.C. Pushnik, P.J. Zarco-Tejada, and S.L. Ustin (2005), Simple reflectance indices track heat and water stress-induced changes in steady-state chlorophyll fluorescence at the canopy scale, Remote Sens. Environ. 97, 3, 403-414, DOI: 10.1016/j.rse.2005.05.006. [Crossref]

  • Govender, M., P.J. Dye, I.M. Weiersbye, E.T.F. Witkowski, and F. Ahmed (2009), Review of commonly used remote sensing and ground-based technologies to measure plant water stress, Water SA 35, 5, 741-752, DOI: 10.4314/wsa. v35i5.49201. [Crossref]

  • Guanter, L., V. Estellés, and J. Moreno (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, DOI: 10.1016/j.rse.2006.12.005.

  • Haboudane, D., J.R. Miller, E. Pattey, P.J. Zarco-Tejada, and I.B. Strachan (2004), Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture, Remote Sens. Environ. 90, 3, 337-352, DOI: 10.1016/j.rse.2003.12.013. [Crossref]

  • Johnson, L.F. (2003), Temporal stability of an NDVI-LAI relationship in a Napa Valley vineyard, Aust. J. Grape Wine Res. 9, 2, 96-101, DOI: 10.1111/j. 1755-0238.2003.tb00258.x. [Crossref]

  • Martínez, B., F.J. García-Haro, and F. Camacho-de Coca (2009), Derivation of highresolution leaf area index maps in support of validation activities: Application to the cropland Barrax site, Agr. Forest Meteorol. 149, 1, 130-145, DOI: 10.1016/j.agrformet.2008.07.014. [Crossref] [Web of Science]

  • Osório, J., M.L. Osório, and A. Romano (2012), Reflectance indices as nondestructive indicators of the physiological status of Ceratonia siliqua seedlings under varying moisture and temperature regimes, Funct. Plant Biol. 39, 7, 588-597, DOI: 10.1071/FP11284. [Crossref] [Web of Science]

  • Quintano, C., A. Fernández-Manso, Y.E. Shimabukuro, and G. Pereira (2012), Spectral unmixing, Int. J. Remote Sens. 33, 17, 5307-5340, DOI: 10.1080/01431161.2012.661095. [Crossref]

  • Richter, R., and D. Schläpfer (2011). Atmospheric/topographic correction for airborne imagery. DLR Report, DLR-IB 565-02/11, Wessling, Germany.

  • Schaepman-Strub, G., M.E. Schaepman, T.H. Painter, S. Dangel, and J.V. Martonchik (2006), Reflectance quantities in optical remote sensing-definitions and case studies, Remote Sens. Environ. 103, 1, 27-42, DOI: 10.1016/ j.rse.2006.03.002. [Crossref]

  • Schmid, T., M. Koch, J. Gumuzzio, and P.M. Mather (2004), A spectral library for a semi-arid wetland and its application to studies of wetland degradation using hyperspectral and multispectral data, Int. J. Remote Sens. 25, 13, 2485-2496, DOI: 10.1080/0143116031000117001. [Crossref]

  • Sobrino, J.A., J.C. Jiménez-Muñoz, P.J. Zarco-Tejada, G. Sepulcre-Cantó, and E. de Miguel (2006), Land surface temperature derived from airborne hyperspectral scanner thermal infrared data, Remote Sens. Environ. 102, 1-2, 99-115, DOI: 10.1016/j.rse.2006.02.001. [Crossref]

  • Timmermans, W., C. van der Tol, J. Timmermans, M. Ucer, X. Chen, L. Alonso, J. Moreno, A. Carrara, R. Lopez, F. de la Cruz Tercero, H.L. Corcoles, E. de Miguel, J.A.G. Sanchez, I. Pérez, B. Franch, J.-C.J. Munoz, D. Skokovic, J. Sobrino, G. Soria, A. MacArthur, L. Vescovo, I. Reusen, A. Andreu, A. Burkart, C. Cilia, S. Contreras, C. Corbari, J.F. Calleja, R. Guzinski, C. Hellmann, I. Herrmann, G. Kerr, A.-L. Lazar, B. Leutner, G. Mendiguren, S. Nasilowska, H. Nieto, J. Pachego-Labrador, S. Pulanekar, R. Raj, A. Schikling, B. Siegmann, S. von Bueren, and Z. Su (2015), An overview of the Regional Experiments for Land-atmosphere Exchanges 2012 (REFLEX 2012) Campaign, Acta Geophys. 63, 6, 1465-1484, DOI: 10.2478/s11600-014-0254-1 (this issue). [Crossref]

  • Yao, Y., N. Wei, Y. Chen, Y. He, and P. Tang (2010), Soil moisture monitoring using hyper-spectral remote sensing technology. In: Q. Luo (ed.), 2010 2nd IITA Int. Conf. on Geoscience and Remote Sensing (IITA-GRS), 28-31 August 2010, Qingdao, China, IEEE, 373-376, DOI: 10.1109/IITA-GRS.2010.5604219. [Crossref]

  • Zarco-Tejada, P.J., J.R. Miller, J. Harron, B. Hu, T.L. Noland, N. Goel, G.H. Mohammed, and P. Sampson (2004), Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies, Remote Sens. Environ. 89, 2, 189-199, DOI: 10.1016/j.rse.2002.06.002. [Crossref]

  • Zarco-Tejada, P.J., A. Berjón, R. López-Lozano, J.R. Miller, P. Martín, V. Cachorro, M.R. González, and A. de Frutos (2005), Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy, Remote Sens. Environ. 99, 3, 271-287, DOI: 10.1016/j.rse.2005.09.002. [Crossref]

About the article

Received: 2014-01-30

Revised: 2014-12-15

Accepted: 2015-01-05

Published Online: 2015-12-30

Published in Print: 2015-12-01


Citation Information: Acta Geophysica, ISSN (Online) 1895-7455, DOI: https://doi.org/10.1515/acgeo-2015-0036. Export Citation

© 2016. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

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