{
  "abstract": "Monitoring essential climate variables (ECV) from optical remote sensing remains challenging due to the ill-posed nature of biophysical variable retrieval and the limitations of radiative transfer models (RTMs). The recently proposed PROSAIL-VAE framework addresses some of these issues by embedding physical modeling within a variational autoencoder (VAE). Unaddressed RTM deficiencies -- including oversimplified soil spectra, un-modeled canopy clumping, and signal saturation -- introduce systematic residuals that are assumed negligible in most hybrid retrieval frameworks. We adapt a residual physics-guided VAE that explicitly accounts for RTM shortcomings via a refinement network and extended latent space, constrained to balance physical and data-driven contributions. The framework has two outputs: improved retrieval and learned residuals that can be interpreted in physical terms. On two independent in-situ datasets, retrieval improvements over the operational BVNET processor (Leaf Area Index (LAI) RMSE \\(19\\%\\) and \\(5.2\\%\\); Canopy Chlorophyll Content (CCC) \\(47.9\\%\\)) demonstrate the behavior of the framework. A non-trivial CCC degradation of 7.7% relative to the baseline PROSAIL-VAE emerges as a trade-off of reconstruction-driven optimization, where chlorophyll's weak spectral signature becomes entangled with structural and soil parameters. We further show that this degradation is partially mitigated by the extended latent space and decorrelation constraints. Spectral and spatial analysis of the learned residuals reveals that they concentrate in regimes where PROSAIL is known to be deficient -- particularly soil reflectance modeling and signal saturation -- suggesting that residual modeling can serve both as a retrieval enhancement and as a diagnostic probe of model inadequacy, while acknowledging that a fully validated diagnostic framework remains an open challenge.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Kevin de Sousa"
    },
    {
      "name": "Jordi Inglada"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/27a5yb1tr3r2s.bib",
  "cited_by": 0,
  "doi": null,
  "fulltext_url": null,
  "id": "27a5yb1tr3r2s",
  "issns": [],
  "landing_url": "https://hal.science/hal-05740441v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sde"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://hal.science/hal-05740441/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/27a5yb1tr3r2s.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:29Z",
      "native_id": "hal-05740441",
      "source": "hal",
      "url": "https://hal.science/hal-05740441v1"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "hal:0.sde",
      "subfield": "environmental-science"
    }
  ],
  "title": "Accounting for Radiative Transfer Model Errors in Physics-Guided VAEs for Biophysical Variable Retrieval",
  "title_text": "Accounting for Radiative Transfer Model Errors in Physics-Guided VAEs for Biophysical Variable Retrieval",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:29Z",
  "url": "https://research.draconitic.com/paper/27a5yb1tr3r2s",
  "venue": "HAL",
  "versions": []
}