Vegetation-Rainfall Relationship within the Caatinga Biome
Year: 2026
Type: preprint
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.
Source: ChemRxiv
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDGs: Climate action, Life on land
Open Access status: green
OpenAlex ID: W7223061491