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-GeorgeEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 865)


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.


Sentinel-2 LAI PROSAIL Image processing chain Gaussian process 



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.


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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
    Email author
  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

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