{
  "abstract": "With drought and extreme weather events increasing in frequency, it has become crucial to understand the relation between weather and biome responses. The semi-arid Caatinga biome of Brazil is highly sensitive to climatic variability, and particularly to changes in precipitation and drought frequency. This study explores a remote-sensing workflow to estimate green vegetation cover and assess its relationship with precipitation using satellite imagery. Monthly satellite composites were processed to distinguish green and non-green vegetation, comparing the efficacy between a convolutional neural network (CNN) and a python classification method. We match the computational measurements of green vegetation cover with monthly precipitation data from the four nearest weather stations. Green cover analysis shows a lagged positive relationship with precipitation, with wetter months generally exhibiting greater green vegetation coverage. The study demonstrates the potential of combining satellite imagery, CNN classification and climatic data for longterm remote ecosystem monitoring.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Aishling R. T. O’Neill"
    },
    {
      "name": "Merakai Maťusová"
    },
    {
      "name": "José Irigon de Irigon"
    },
    {
      "name": "Juliane Simmchen",
      "orcid": "0000-0001-9073-9770"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1v5a5dq16yycy.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010679/v1",
  "fulltext_url": null,
  "id": "1v5a5dq16yycy",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010679/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2303|Remote Sensing in Agriculture"
    ]
  ],
  "openalex": "W7223061491",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/1v5a5dq16yycy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.26434/chemrxiv.15010679",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010679/v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2303|Remote Sensing in Agriculture",
      "subfield": "ecology"
    }
  ],
  "title": "Vegetation-Rainfall Relationship within the Caatinga Biome",
  "title_text": "Vegetation-Rainfall Relationship within the Caatinga Biome",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:24Z",
  "url": "https://research.draconitic.com/paper/1v5a5dq16yycy",
  "venue": "ChemRxiv",
  "versions": []
}