Análise da relação dinâmica entre índices espectrais e a produtividade do cafeeiro pós-poda.

The study monitored ten plots using a productivity time series (2012–2024) and annual images from the Landsat 8 satellite. Through the Google Earth Engine platform, the NDVI, SAVI, and NDWI indices were calculated from the central pixel of each plot and correlated with yield data. The results indicate that the recepa pruning intensified and inverted the crop's biennial bearing cycle. An abrupt drop in spectral indices was observed in the year prior to pruning, demonstrating predictive potential for management. The vegetative recovery, measured by the indices, peaked in 2020, with most plots exhibiting a one-year lag between maximum vegetative vigor and peak production. The multiple regression models showed high explanatory power (R² > 0.80) only during the years of greatest heterogeneity between plots (2018–2020), the period corresponding to post-pruning recovery. However, strong collinearity between the indices and the small sample size limited statistical robustness. It was observed that the relationship between the spectral response and coffee productivity is dynamic and sensitive to management interventions and the plant's physiology. This invalidates the hypothesis of a universal and stable regression model for yield estimation in different phases of the coffee cycle, highlighting that the applicability of remote sensing for this purpose depends on the crop's phenological context.