Advertisement

Automating an Image Processing Chain of the Sentinel-2 Satellite

  • Rodrigo Rodriguez-Ramirez
  • María Guadalupe Sánchez
  • Juan Pablo Rivera-Caicedo
  • Daniel Fajardo-Delgado
  • Himer Avila-George
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 865)

Abstract

In this paper, a chain of satellite image processing using free software libraries is proposed, to estimate biophysical parameters using data from the Sentinel-2 satellite. In particular, the processing chain proposed allows atmospheric correction, resampling and spatial cropping of satellite images. To evaluate the functionality of the developed processing chain, the sugarcane cultivation of the Mexican region of Jalisco is introduced as a case study; from the selected scene, the leaf area index (LAI) is estimated using a model based on the Gaussian Process Regression technique, which is trained employing synthetic reflectance data created utilizing the PROSAIL radiative transfer model.

Keywords

Sentinel-2 LAI PROSAIL Image processing chain Gaussian process 

Notes

Acknowledgments

The first author thanks CONACYT for the scholarship granted to carry out your postgraduate studies. The other authors thank Dr. Jochem Verrelst from the Image Processing Laboratory of the University of Valencia, Spain, for giving access to the ARTMO tool.

References

  1. 1.
    FAO: Anuario Estadistico de la FAO 2014 - La Alimentación y la Agricultura en América Latina y el Caribe. Organización de las Naciones Unidas para la Alimentación y la Agricultura, Santiago de Chile (2014)Google Scholar
  2. 2.
    European Space Agency: United space in Europe. https://www.esa.int/ESA
  3. 3.
    Svendsen, D.H., Martino, L., Campos-Taberner, M., García-Haro, F.J., Camps-Valls, G.: Joint Gaussian processes for biophysical parameter retrieval. IEEE Trans. Geosci. Remote Sens. 56(3), 1718–1727 (2018)Google Scholar
  4. 4.
    Baret, F., Buis, S.: Estimating canopy characteristics from remote sensing observations: review of methods and associated problems. In: Advances in Land Remote Sensing: System, Modeling, Inversion and Application, pp. 173–201. Springer Netherlands, Amsterdam (2008)Google Scholar
  5. 5.
    Verrelst, J., Camps-Valls, G., Muñoz-Marí, J., Rivera, J.P., Veroustraete, F., Clevers, J.G., Moreno, J.: Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – a review. ISPRS J. Photogramm. Remote Sens. 108(1), 273–290 (2015)Google Scholar
  6. 6.
    Fernandes, R., Weiss, M., Camacho, F., Berthelot, B., Baret, F., Duca, R.: Development and assessment of leaf area index algorithms for the Sentinel-2 multispectral imager. In: IEEE Geoscience and Remote Sensing Symposium (2014)Google Scholar
  7. 7.
    Jacquemoud, S., Bacour, C., Poilve, H., Frangi, J.-P.: Comparison of four radiative transfer models to simulate plant canopies reflectance: direct and inverse mode. Remote. Sens. Environ. 74(3), 471–481 (2000)Google Scholar
  8. 8.
    Liang, S.: Quantitative Remote Sensing of Land Surfaces. Wiley, Hoboken (2005)Google Scholar
  9. 9.
    Jacquemoud, S., Baret, F.: PROSPECT: a model of leaf optical properties spectra. Remote Sens. Environ. 34(2), 75–91 (1990)Google Scholar
  10. 10.
    Verhoef, W., Bach, H.: Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data. Remote Sens. Environ. 109(2), 166–182 (2007)Google Scholar
  11. 11.
    Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner, G.P., François, C., Ustin, S.L.: PROSPECT + SAIL models: a review of use for vegetation characterization. Remote Sens. Environ. 113, S56–S66 (2009)Google Scholar
  12. 12.
    Stein, M.: Large sample properties of simulations using Latin hypercube sampling. Technometrics 29(2), 143–151 (1987)Google Scholar
  13. 13.
    Verrelst, J., Rivera, J., Alonso, L., Moreno, J.: ARTMO: an automated radiative transfer models operator toolbox for automated retrieval of biophysical parameters through model inversion. In: EARSeL 7th SIG-Imaging Spectroscopy Workshop, Edinburgh, UK (2011)Google Scholar
  14. 14.
    Combal, B., Baret, F., Weiss, M., Trubuil, A., Macé, D., Pragnère, A., Myneni, R., Knyazikhin, Y., Wang, L.: Retrieval of canopy biophysical variables from bidirectional reflectance: using prior information to solve the ill-posed inverse problem. Remote Sens. Environ. 84(1), 1–15 (2003)Google Scholar
  15. 15.
    Verrelst, J., Rivera, J.P., Veroustraete, F., Muñoz-Marí, J., Clevers, J.G., Camps-Valls, G., Moreno, J.: Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods – a comparison. ISPRS. J. Photogramm. Remote. Sens. 108(1), 260–272 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rodrigo Rodriguez-Ramirez
    • 1
  • María Guadalupe Sánchez
    • 1
  • Juan Pablo Rivera-Caicedo
    • 2
  • Daniel Fajardo-Delgado
    • 1
  • Himer Avila-George
    • 3
  1. 1.TecNM, Instituto Tecnológico de Ciudad GuzmánCiudad GuzmánMexico
  2. 2.Cátedras CONACyT, Universidad Autónoma de NayaritTepicMexico
  3. 3.Centro Universitario de los Valles, Universidad de GuadalajaraAmecaMexico

Personalised recommendations