Detection and monitoring of land use and land

cover changes, obtained through accurate remote sensing data,

are of vital importance due to the impact of climate change and

the need to implement the principles of sustainable development

in modern society. In this context, this paper aimed to evaluate

the capacity of SAR data (Sentinel-1) for hierarchical

classification in heterogeneous landscapes. The methodology

consisted of the steps of SAR vegetation indices calculation,

dimensionality reduction of variables analysis, reduction of

sample noise, and classification process based on satellite image

time series and deep learning. The results demonstrated a

decrease in classification accuracy when considering scenarios

with more specific thematic classes, starting from three

hierarchical levels (C1 Scenario) to seven hierarchical levels (C3

Scenario), achieving global accuracy values of 0.975, 0.928, and

0.902, respectively. In addition, the similar volumetric

backscattering behavior of different vegetative canopies in SAR

data was a key parameter for the results obtained in this work.