Satellite Image Processing for Retrieving Historical Solar Irradiance Data Within the Mexican Territory

  • Juan M. Callejas-Cornejo
  • Manuel I. Peña-CruzEmail author
  • Luis M. Valentín-Coronado
Conference paper
Part of the Springer Proceedings in Physics book series (SPPHY, volume 233)


Solar resource assessment is a key subject for the viability and implementation of solar power plants. Solar sensors measure irradiance locally, but high cost of these specialized sensors makes their implementation through all the Mexican territory difficult. Satellite images provide a valuable tool for the determination of solar irradiance through image processing. In this work, an algorithm for the estimation of global solar irradiance based on determining the cloud index through pixel analysis is implemented for the central region of Mexico.


  1. 1.
    M. Valdés-Barrón, D. Riveros-Rosas, C. Arancibia-Bulnes, R. Bonifaz, The solar resource assessment in Mexico: state of the art. Energy Procedia. 57, 1299–1308 (2014). Scholar
  2. 2.
    D. Riveros-Rosas, C. Arancibia-Bulnes, R. Bonifaz, M.A. Medina, R. Peon, M. Valdes, Analysis of a solarimetric database for Mexico and comparison with the CSR model. Renew. Energy 75, 21–29 (2015). Scholar
  3. 3.
  4. 4.
    L. Diabaté, G. Moussu, L. Wald, Description of an operational tool for determining global solar radiation at ground using geostationary satellite images. Sol. Energy 42(3), 201–207 (1989). ElsevierADSCrossRefGoogle Scholar
  5. 5.
    R. Perez, Time specific irradiances derived from geostationary satellite images. J. Sol. Energy Eng. 124(1) (2002).

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Juan M. Callejas-Cornejo
    • 1
  • Manuel I. Peña-Cruz
    • 2
    Email author
  • Luis M. Valentín-Coronado
    • 2
  1. 1.PICYT—Centro de Investigaciones en Optica A. C., Unidad AguascalientesAguascalientes, AgsMexico
  2. 2.CONACYT—Centro de Investigaciones en Optica A. C., Unidad AguascalientesAguascalientes, AgsMexico

Personalised recommendations