{
  "abstract": "Accurate estimation of Global Horizontal Irradiance (GHI) is essential for solar energy applications, climate modeling, and various geophysical processes. Traditional satellite-based methods rely on the pixel-centric Independent Pixel Approximation (IPA), which treats each pixel as radiatively isolated from its neighbors. This pixel-centric approach presents challenges, which are amplified by the higher spatial resolutions of third-generation geostationary satellites. In this study, we evaluate deep-learning models that explicitly incorporate spatial context through multispectral image patches of varying sizes ( 3 × 3 to 31 × 31 pixels) from GOES-16 imagery to improve satellite-based GHI estimation and address IPA shortcomings. We compare two architectures—a Fully Connected Network (FCN) and a convolutional-based model—against a state-of-the-art physical retrieval method (PSM3), using in-situ GHI measurements from 31 stations across North America. Our results show that spatial-context-aware deep learning models outperform PSM3 across most metrics, especially under cloudy and partially clear conditions, yielding improved performance, stability, and reduced bias. The best-performing model achieves a 14.6% lower root mean squared error and a 17.1% lower mean absolute error compared to PSM3 on a year-long test set composed of 6 test stations. However, our current implementation of deep learning models still struggles to consistently outperform PSM3 bias under clear-sky conditions at certain sites. By quantifying how image size and architecture jointly shape the performance and generalization of our models, this work underscores the potential of spatial-context-aware deep learning for satellite-based all-sky GHI retrieval.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Vadim Becquet"
    },
    {
      "name": "Philippe Blanc"
    },
    {
      "name": "Yves-Marie Saint-Drenan"
    },
    {
      "name": "Yehia Eissa"
    },
    {
      "name": "Hadrien Verbois"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/490kbrarc2c01.bib",
  "cited_by": 0,
  "doi": "10.1016/j.solener.2026.114844",
  "fulltext_url": null,
  "id": "490kbrarc2c01",
  "issns": [
    "0038-092X",
    "1471-1257"
  ],
  "landing_url": "https://minesparis-psl.hal.science/hal-05742494v1",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.phys"
    ],
    [
      "hal",
      "0.sdu"
    ],
    [
      "hal",
      "0.spi"
    ],
    [
      "hal",
      "1.phys.phys"
    ],
    [
      "hal",
      "1.sdu.ocean"
    ],
    [
      "hal",
      "2.phys.phys.phys-ao-ph"
    ],
    [
      "openalex",
      "1702|Solar Radiation and Photovoltaics"
    ]
  ],
  "openalex": "W7171546282",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/490kbrarc2c01.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:31Z",
      "native_id": "hal-05742494",
      "source": "hal",
      "url": "https://minesparis-psl.hal.science/hal-05742494v1"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "hal:2.phys.phys.phys-ao-ph",
      "subfield": "atmospheric"
    },
    {
      "field": "earth",
      "origin": "hal:1.sdu.ocean",
      "subfield": "oceanography"
    },
    {
      "field": "physics",
      "origin": "hal:0.phys",
      "subfield": "general"
    }
  ],
  "title": "Spatial context importance in deep learning for global horizontal irradiance estimation from satellite imagery",
  "title_text": "Spatial context importance in deep learning for global horizontal irradiance estimation from satellite imagery",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:31Z",
  "url": "https://research.draconitic.com/paper/490kbrarc2c01",
  "venue": "Solar Energy",
  "versions": []
}