Vegetation-Rainfall Relationship within the Caatinga Biome

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.

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BibTeX RIS

BibTeX

@misc{oneill2026vegetationrainfall,
  title = {{Vegetation-Rainfall Relationship within the Caatinga Biome}},
  author = {Aishling R. T. O’Neill and Merakai Maťusová and José Irigon de Irigon and Juliane Simmchen},
  howpublished = {ChemRxiv},
  year = {2026},
  month = oct,
  doi = {10.26434/chemrxiv.15010679/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010679/v1},
  note = {CC BY},
}

RIS

TY  - UNPB
TI  - Vegetation-Rainfall Relationship within the Caatinga Biome
AU  - Aishling R. T. O’Neill
AU  - Merakai Maťusová
AU  - José Irigon de Irigon
AU  - Juliane Simmchen
PY  - 2026
DA  - 2026/10/19
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15010679/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010679/v1
AB  - 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.
ER  -

Abstract

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