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.
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Notes
- 1.
ARTMO http://ipl.uv.es/artmo/.
- 2.
- 3.
RSGISLib: https://www.rsgislib.org/.
- 4.
OpenCV: https://opencv.org/.
- 5.
Numpy: http://www.numpy.org/.
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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.
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Rodriguez-Ramirez, R., Sánchez, M.G., Rivera-Caicedo, J.P., Fajardo-Delgado, D., Avila-George, H. (2019). Automating an Image Processing Chain of the Sentinel-2 Satellite. In: Mejia, J., Muñoz, M., Rocha, Á., Peña, A., Pérez-Cisneros, M. (eds) Trends and Applications in Software Engineering. CIMPS 2018. Advances in Intelligent Systems and Computing, vol 865. Springer, Cham. https://doi.org/10.1007/978-3-030-01171-0_20
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