Mapping perennial crops in tropical regions remains challenging due to high spectral complexity, frequent cloud cover, and phenological overlap between different types of vegetation. This study evaluated the potential of the Harmonized Landsat-Sentinel (HLS) dataset to identify perennial crops in the municipality of Jacupiranga, São Paulo, Brazil, an area representative of the Atlantic Forest mosaic. A hierarchical classification was applied using the Random Forest (RF) algorithm on temporal compositions of 2024 NDVI, NDWI, and BSI indices, structured into three analytical levels: (1) natural vegetation versus anthropic areas, (2) perennial crops versus other uses, and (3) banana versus peach palm. Accuracies ranged from 0.86 to 0.98 and F1 ranked between 0.86 and 0.95. The most influential variables were concentrated in the transition months between the dry and rainy seasons, highlighting the relevance of canopy moisture and vegetative vigor in class discrimination. The final maps indicate that approximately 25% of Jacupiranga is agricultural land, of which 43 km² corresponds to perennial crops, with 80% occupied by banana plantations. The results demonstrate the potential of HLS open data to generate accurate multiscale mapping of perennial crops in complex tropical landscapes, supporting digital agriculture and sustainable management in family farming regions.