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Nutrient phenology of evergreen subtropical forests of southern Africa is poorly understood. Foliar nitrogen (N) and phosphorous (P) forms key components of photosynthesis and are vulnerable to global change stressors. Remote sensing techniques can potentially map and monitor nutrient phenology, yet models to predict across species, seasons and climatic regions are deficient. This study evaluates the capability of various models, developed from leaf spectra of selected spectral regions and seasons, to predict nutrient concentration across season and species. Seasonal differences in foliar N and P were assessed using a one-way ANalysis Of VAriance (ANOVA). The relationship between leaf spectra and nutrients was assessed using linear regressions between the foliar nutrients and spectral indices. The predictive capability of three models was compared using root mean square error (RMSE) values …