{
  "abstract": "Accurately deriving remote sensing reflectance (Rrs) from satellite imagery in optically complex inland and coastal waters is critical for water quality monitoring and management. This study presents AQUAVERSE-AC, the atmospheric correction component of the AQUAVERSE (Aquatic inversion scheme for remote sensing of fresh and coastal waters) platform, an innovative machine learning-based atmospheric correction (AC) framework, that leverages Mixture Density Networks (MDNs) to retrieve high-quality Rrs from multispectral data acquired by Landsat-8/9 (OLI) and Sentinel-2A/B (MSI) satellites. By coupling a well-validated ocean-atmosphere radiative transfer (RT) model with extensive simulated and in situ datasets, AQUAVERSE-AC compensates for aerosol scattering and atmospheric absorption across diverse environmental and imaging conditions. The model’s performance was evaluated through a comprehensive matchup analysis against AERONET-OC and GLORIA datasets, wherein AQUAVERSE-AC generally produced lower median symmetric accuracy (ε) compared to the state-of-the-art AC processors. ε ranged from 15% to 26% for key spectral bands (coastal-blue to 705 nm red-edge band), representing a 20%–100% improvement in accuracy compared to alternative methods. Notably, AQUAVERSE-AC exhibited robust performance in the blue spectrum within the evaluated matchups. The results, including generated Rrs maps, were benchmarked against outputs from other widely used AC processors (ACOLITE, POLYMER, SeaDAS, OC-SMART), indicating AQUAVERSE-AC’s competitive performance across diverse aquatic environments, while also providing comprehensive uncertainty estimates. These uncertainty metrics, inherently generated by the MDN framework, provide critical confidence indicators for downstream applications. These results demonstrate the potential of AQUAVERSE-AC as an AC framework for OLI and MSI observations of inland and coastal waters, while further validation is needed to establish its performance across additional regions, atmospheric conditions, and diverse optically complex aquatic systems.",
  "abstract_shown": true,
  "affiliations": [
    "NASA Goddard Space Flight Center, Greenbelt, MD, United States",
    "Science Systems and Applications Inc. (SSAI), Lanham, MD, United States",
    "Earth Observation and Geoinformatics Division, National Institute for Space Research, Sao Jose Dos Campos, Brazil",
    "Department of Physics, University of Maryland Baltimore County, Baltimore, MD, United States",
    "Geo-Sensing and Imaging Consultancy, Trivandrum, Kerala, India",
    "Department of ECE, Chennai Institute of Technology, Chennai, India"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 15,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Akash Ashapure"
    },
    {
      "affs": [
        1
      ],
      "name": "Akash Ashapure"
    },
    {
      "affs": [
        0
      ],
      "name": "Brandon Smith"
    },
    {
      "affs": [
        1
      ],
      "name": "Brandon Smith"
    },
    {
      "affs": [
        0
      ],
      "name": "Ryan E. O’Shea"
    },
    {
      "affs": [
        1
      ],
      "name": "Ryan E. O’Shea"
    },
    {
      "affs": [
        0
      ],
      "name": "William Wainwright"
    },
    {
      "affs": [
        1
      ],
      "name": "William Wainwright"
    },
    {
      "affs": [
        0
      ],
      "name": "Arun M. Saranathan"
    },
    {
      "affs": [
        1
      ],
      "name": "Arun M. Saranathan"
    },
    {
      "affs": [
        2
      ],
      "name": "Daniel Andrade Maciel"
    },
    {
      "affs": [
        3
      ],
      "name": "Peng-Wang Zhai"
    },
    {
      "affs": [
        4
      ],
      "name": "Sundarabalan V. Balasubramanian"
    },
    {
      "affs": [
        5
      ],
      "name": "Sundarabalan V. Balasubramanian"
    },
    {
      "affs": [
        1
      ],
      "name": "Sakib Kabir"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/00ncxdgbxj45w.bib",
  "cited_by": 3,
  "doi": "10.3389/frsen.2026.1839175",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/frsen.2026.1839175/full",
  "id": "00ncxdgbxj45w",
  "issns": [
    "2673-6187"
  ],
  "landing_url": "https://doi.org/10.3389/frsen.2026.1839175",
  "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": [
    [
      "lcc",
      "QC801-809"
    ],
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      "lcc",
      "QC851-999"
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      "openalex",
      "1910|Marine Plankton and Biogeochemistry"
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  "openalex": "W4405978345",
  "pdf_url": null,
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  "pmid": null,
  "published": "2026-11-04",
  "publisher": "Frontiers Media S.A.",
  "ris": "https://research.draconitic.com/paper/00ncxdgbxj45w.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T14:21:01Z",
      "native_id": "540592f8123f4770a94ef197141bb862",
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      "url": "https://doi.org/10.3389/frsen.2026.1839175"
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  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:1910|Marine Plankton and Biogeochemistry",
      "subfield": "oceanography"
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  ],
  "title": "AQUAVERSE-AC: a machine learning-based atmospheric correction framework for inland and coastal waters",
  "title_text": "AQUAVERSE-AC: a machine learning-based atmospheric correction framework for inland and coastal waters",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T14:20:30Z",
  "url": "https://research.draconitic.com/paper/00ncxdgbxj45w",
  "venue": "Frontiers in Remote Sensing",
  "versions": []
}