Hyperspectral imagery combined with machine learning enables accurate large-scale mapping of plant traits but struggles with uncertainty when facing unfamiliar environmental conditions. This study introduces a distance-based method that measures dissimilarities between new and training data to reliably quantify uncertainty. Results show it effectively identifies uncertain predictions, greatly improving the reliability of global vegetation monitoring compared to traditional methods.