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Miscellanea Geographica

Regional Studies on Development

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CiteScore 2016: 0.40

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Source Normalized Impact per Paper (SNIP) 2016: 0.404

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2084-6118
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Radiative Transfer Model parametrization for simulating the reflectance of meadow vegetation

Anna M. Jarocińska
Published Online: 2014-06-17 | DOI: https://doi.org/10.2478/mgrsd-2014-0001

Abstract

Natural vegetation is complex and its reflectance is not easy to model. The aim of this study was to adjust the Radiative Transfer Model parameters for modelling the reflectance of heterogeneous meadows and evaluate its accuracy dependent on the vegetation characteristics. PROSAIL input parameters and reference spectra were collected during field measurements. Two different datasets were created: in the first, the input parameters were modelled using only field measurements; in the second, three input parameters were adjusted to minimize the differences between modelled and measured spectra. Reflectance was modelled using two datasets and then verified based on field reflectance using the RMSE. The average RMSE for the first dataset was equal to 0.1058, the second was 0.0362. The accuracy of the simulated spectra was analysed dependent on the value of the biophysical parameters. Better results were obtained for meadows with higher biomass value, greater LAI and lower water content.

Keywords: Meadows; spectral reflectance; Radiative Transfer Model; PROSAIL

References

  • Ceccato, P, Flasse, S, Tarantola, S, Jacquemoud, S & Grégorie, JM 2001, ‘Detecing vegetation leaf water content using reflectance on the optical domain’, Remote Sensing of Environment, vol. 77, pp. 22-33.Google Scholar

  • Clevers, JGPW, Kooistra, L & Schaepman, ME 2010, ‘Estimating canopy water content using hyperspectral remote sensing data’, International Journal of Applied Earth Observation and Geoinformation, vol. 12, pp. 119-125.Google Scholar

  • Damarez, V & Gastellu-Etchegorry, JP 2000, ‘A modeling approach for studying forest chlorophyll content’, Remote Sensing of Environment, vol. 71, pp. 226-238.Google Scholar

  • Darvishzadeh R, Skidmore, A, Schlerf, M & Atzberger, C 2008, ‘Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in heterogeneous grassland’, Remote Sensing of Environment, vol. 112, pp. 2592-2604.Web of ScienceGoogle Scholar

  • Darvishzadeh, R, Atzberger, C, Skidmore, A & Schlerf, M 2011, ‘Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models’, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66, no. 6, pp. 894-906.Web of ScienceGoogle Scholar

  • Feret, J, Frençois, C, Asner, GP, Gitelson, AA, Martin, RE, Bidel, LPR, Ustin, S, le Maire, G & Jacquemoud, S 2008, ‘PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments’, Remote Sensing of Environment, vol. 112, pp. 3030-3043.Web of ScienceGoogle Scholar

  • Haboudane, D, Miller, JR, Pattey, E, Zarco-Tejada, P & Strachan, IB 2004, ‘Hyperspectral vegetation indices and novel algorithms for predicting green LAI for crop canopies: Modeling and validation in the context of precision agriculture’, Remote Sensing of Environment, vol 90, pp. 337-352.Google Scholar

  • Jacquemoud, S & Baret, F 1990, ‘PROSPECT: A model of leaf optical properties spectra’, Remote sensing of environment, vol. 34, pp. 75-91.Google Scholar

  • Jacquemoud, S 1993, ‘Inversion of the PROSPECT+SAIL canopy reflectance model from AVIRIS equvalent spectra: Theoretical study’, Remote sensing of environment, vol. 44, pp. 281-292.Google Scholar

  • Jacquemoud, S, Verhoef, W, Baret, F, Bacour, C, Zarco- Tejada, PJ, Asner, GP, François, H & Ustin, SL 2009, ‘PROSPECT+SAIL models: A review of use for vegetation characterization’, Remote sensing of environment, vol. 113, pp. 56-66.Google Scholar

  • Jarocińska, A 2011, ‘The comparison of the spectrum modelling of different kinds of meadows’ in Proceedings of 31st EARSeL Symosium, Prague, 30 May - 2 June 2011, Remote sensing and geoinformation not only for scientific cooperation, ed L Halounová, pp. 144-151.Google Scholar

  • Jarocińska, A 2012, ‘Ocena skuteczności modeli transferu promieniowania w badaniach stanu roślinności łąk’, Teledetekcja Środowiska, vol. 48.Google Scholar

  • Jensen, JR, 1983, ‘Biophysical remote sensing - Review Article’, Annals of the asssociations of American geographers, vol. 73, pp. 111-132.Google Scholar

  • Kucharski, L 2009, Trwałe użytki zielone w programie rolnośrodowiskowym, Biblioteczka Programu Rolnośrodowiskowego 2007-2013. Ministerstwo Rolnictwa i Rozwoju Wsi, Warsaw.Google Scholar

  • Kumar, L, Schmidt, K, Dury, S & Skidmore, A 2006, ‘Imaging spectrometry and vegetation science’ in Imaging Spectrometry. Basic principles and Prospective Applications, eds FD van der Meer & S M de Jong’, Springer, pp. 111-155.Google Scholar

  • Nawara, Z 2006, Rośliny łąkowe, Publisher MULTICO, Warsaw.Google Scholar

  • PROSPECT+SAIL=PROSAIL, 2013. Available from: <http://teledetection.ipgp.jussieu.fr/prosail/>. [28 November 2013] Verhoef, W & Bach, H 2007, ‘Coupled soil-leaf-conopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data’, Remote Sensing of Environment, vol. 109, pp. 166-182.Web of ScienceGoogle Scholar

  • Verhoef, W 1984, ‘Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model’, Remote Sensing of Environment, vol. 16, pp. 125-141.Google Scholar

  • Verhoef, W, Jia, L, Xiao, Q & Su, Z 2007, ‘Unified Optical-Thermal Four-Stream Radiative Transfer Theory for Homogeneous Vegetation Canopies’, IEEE Transactions on geoscience and remote sensing, vol. 45, pp. 1808-1822.Web of ScienceGoogle Scholar

  • Zhang, N & Zhao, Y 2009, ‘Estimating leaf area index by inversion of reflectance model for semiarid natural grasslands’, Science in China Series D: Earth Sciences, vol. 52, no. 1, pp. 66-84.Web of ScienceGoogle Scholar

About the article

Received: 2013-07-31

Accepted: 2013-12-19

Published Online: 2014-06-17

Published in Print: 2014-06-01


Citation Information: Miscellanea Geographica, ISSN (Online) 2084-6118, DOI: https://doi.org/10.2478/mgrsd-2014-0001.

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© by Anna M. Jarocińska. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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