We use a time-series Random Forest model taking samples from 2022 to classify yearly date cubes up to 2015. The classification achieved an agreement of 89.20% with the reference map for 2022. Over the years, the agreement showed a cumulative decline of 2%. Our results suggest that the transferability of a machine-learning model is highly correlated with a set of highly representative training samples.