Articles | Volume 16, issue 18
https://doi.org/10.5194/amt-16-4165-2023
https://doi.org/10.5194/amt-16-4165-2023
Research article
 | 
19 Sep 2023
Research article |  | 19 Sep 2023

Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives

Hadrien Verbois, Yves-Marie Saint-Drenan, Vadim Becquet, Benoit Gschwind, and Philippe Blanc

Related authors

Validating physical and semi-empirical satellite-based irradiance retrievals using high- and low-accuracy radiometric observations in a monsoon-influenced continental climate
Yun Chen, Dazhi Yang, Chunlin Huang, Hongrong Shi, Adam Jensen, Xiang'ao Xia, Yves-Marie Saint-Drenan, Christian Gueymard, Martin Mayer, and Yanbo Shen
EGUsphere, https://doi.org/10.5194/egusphere-2025-3414,https://doi.org/10.5194/egusphere-2025-3414, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Validating global horizontal irradiance retrievals from Meteosat SEVIRI at increased spatial resolution against a dense network of ground-based observations
Job I. Wiltink, Hartwig Deneke, Yves-Marie Saint-Drenan, Chiel C. van Heerwaarden, and Jan Fokke Meirink
Atmos. Meas. Tech., 17, 6003–6024, https://doi.org/10.5194/amt-17-6003-2024,https://doi.org/10.5194/amt-17-6003-2024, 2024
Short summary
An alternative cloud index for estimating downwelling surface solar irradiance from various satellite imagers in the framework of a Heliosat-V method
Benoît Tournadre, Benoît Gschwind, Yves-Marie Saint-Drenan, Xuemei Chen, Rodrigo Amaro E Silva, and Philippe Blanc
Atmos. Meas. Tech., 15, 3683–3704, https://doi.org/10.5194/amt-15-3683-2022,https://doi.org/10.5194/amt-15-3683-2022, 2022
Short summary

Cited articles

Ball, J. E., Anderson, D. T., and Chan, C. S.: Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community, J. Appl. Remote Sens., 11, 042609, https://doi.org/10.1117/1.jrs.11.042609, 2017.​​​​​​​ a
Blanc, P. and Wald, L.: The SG2 algorithm for a fast and accurate computation of the position of the Sun for multi-decadal time period, Sol. Energy, 86, 3072–3083, https://doi.org/10.1016/j.solener.2012.07.018, 2012. a, b
Blanc, P., Gschwind, B., Lefèvre, M., and Wald, L.: The HelioClim project: Surface solar irradiance data for climate applications, Remote Sens., 3, 343–361, https://doi.org/10.3390/rs3020343, 2011a. a
Blanc, P., Gschwind, B., Lefèvre, M., and Wald, L.: The HelioClim project: Surface solar irradiance data for climate applications, Remote Sens., 3, 343–361, https://doi.org/10.3390/rs3020343, 2011b. a
Blanc, P., Gschwind, B., Lefevre, M., and Wald, L.: Twelve monthly maps of ground Albedo parameters derived from MODIS data sets, International Geoscience and Remote Sensing Symposium (IGARSS), 13–18 July 2014, Quebec City, QC, Canada, IEEE, 3270–3272, https://doi.org/10.1109/IGARSS.2014.6947177, 2014.​​​​​​​ a, b
Download
Short summary
Solar surface irradiance (SSI) estimations inferred from satellite images are essential to gain a comprehensive understanding of the solar resource, which is crucial in many fields. This study examines the recent data-driven methods for inferring SSI from satellite images and explores their strengths and weaknesses. The results suggest that while these methods show great promise, they sometimes dramatically underperform and should probably be used in conjunction with physical approaches.
Share