{
  "abstract": "Accurate and timely crop yield estimation is fundamental for global food security, agricultural policy, and farm management. The Copernicus Sentinel-2 constellation has catalyzed a paradigm shift in Earth observation for agriculture, enabling field and sub-field scale monitoring. This review synthesizes recent advances in crop yield estimation that leverage Sentinel-2 data. A dominant theme is the transition from regional-scale to high-resolution field-level assessments, driven by three approaches: (i) empirical models using vegetation indices coupled with machine and deep learning (e.g., Random Forest, Convolutional Neural Networks); (ii) integration of process-based crop growth models (e.g., WOFOST, SAFY) through data assimilation of Sentinel-2 derived biophysical variables such as Leaf Area Index; and (iii) data fusion of Sentinel-2 with Sentinel-1 Synthetic Aperture Radar to overcome cloud cover. The synthesis shows that Sentinel-2-based frameworks can explain a large fraction of within-field yield variability, while performance remains constrained by limited ground-truth data, cloud gaps, and model transferability. Looking ahead, knowledge-guided models, self-supervised foundation-model pre-training, lightweight edge workflows, improved ground observations, and multi-sensor fusion are key pathways toward robust, operational decision-support tools for precision agriculture.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biological and Agricultural Engineering, University of California, Davis, CA 95616, USA"
  ],
  "article_type": "review",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mohammadreza Narimani",
      "orcid": "0009-0001-7302-405X"
    },
    {
      "affs": [
        0
      ],
      "name": "Alireza Pourreza",
      "orcid": "0000-0002-8237-1985"
    },
    {
      "affs": [
        0
      ],
      "name": "Ali Moghimi",
      "orcid": "0000-0002-9249-8365"
    },
    {
      "affs": [
        0
      ],
      "name": "Parastoo Farajpoor",
      "orcid": "0009-0003-1457-2683"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1wae3dg5r3zd7.bib",
  "cited_by": 9,
  "doi": "10.1016/j.atech.2026.102405",
  "fulltext_url": null,
  "id": "1wae3dg5r3zd7",
  "issns": [
    "2772-3755"
  ],
  "landing_url": "https://doi.org/10.1016/j.atech.2026.102405",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2303|Remote Sensing in Agriculture"
    ]
  ],
  "openalex": "W7169573541",
  "pdf_url": "https://www.sciencedirect.com/science/article/pii/S2772375526006301/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-17",
  "publisher": "Elsevier BV",
  "ris": "https://research.draconitic.com/paper/1wae3dg5r3zd7.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T13:21:11Z",
      "native_id": "W7169573541",
      "source": "openalex",
      "url": "https://doi.org/10.1016/j.atech.2026.102405"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2303|Remote Sensing in Agriculture",
      "subfield": "ecology"
    }
  ],
  "title": "Sentinel-2 for crop yield estimation: A systematic review",
  "title_text": "Sentinel-2 for crop yield estimation: A systematic review",
  "type": "peer-reviewed",
  "updated_at": "2026-11-10T13:21:18Z",
  "url": "https://research.draconitic.com/paper/1wae3dg5r3zd7",
  "venue": "Smart Agricultural Technology",
  "versions": []
}