Advancing coffee management mapping through multisensor data and multistep ensemble learning.

PARREIRAS, T. C.; BOLFE, E. L.; FURUYA, D. E. G.

Advancing coffee management mapping through multisensor data and multistep ensemble learning.

Autores

PARREIRAS, T. C.

BOLFE, E. L.

FURUYA, D. E. G.

Descripción

Despite the advances, accurately identifying recently renovated and skeletonized coffee areas remains a challenge, as their altered canopy structure and reduced vigor produce spectral signatures similar to those of fallow or non-coffee areas. To address these limitations, upcoming research will focus on leveraging a space-time hybrid approach with deep learning and surface phenology modeling. Specifically, we plan to implement a workflow combining the spatial detail of Sentinel-2 with the temporal continuity of HLS.