Moderate Resolution Satellites Monitor Vegetation Phenology Accurately in African Savannas: A Comparison with Ground Data from Phenocams

Reliable monitoring of leaf phenology across environmental gradients is essential for understanding ecosystem functioning and climate responses, yet existing approaches in African savannas are constrained by either sparse ground-based observations or the limited resolution of satellite products. We evaluated the ability of MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite), and Sentinel-2-derived vegetation indices (Normalised-Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Green Chromatic Coordinate (GCC)) to track phenocam-derived phenology across 10 plots along a water-availability gradient in Africa. Satellite vegetation index time series were smoothed using generalized additive models and compared with phenocam-derived GCC, including estimates of transition dates. All satellite datasets estimated longer growing seasons than phenocams, associated with earlier start-of-season (SOS) and later end-of-season (EOS) estimates. MODIS NDVI and VIIRS NDVI most accurately estimated transition dates (SOS root-mean-squared error (RMSE), a standard measure of model accuracy, of 10 and 11 days, respectively; EOS RMSE of 27 days in both datasets). MODIS GCC and VIIRS EVI followed with high estimated transition date accuracy (SOS RMSE of 11 days; EOS RMSE of 32 days, in both datasets). VIIRS EVI and Sentinel-2 (S2) GCC most accurately tracked phenocam temporal fluctuations (r = 0.83 ± 0.08 and 0.83 ± 0.10, respectively), while MODIS GCC and MODIS NDVI also showed high correlations (r = 0.78 ± 0.12 and 0.78 ± 0.15). These MODIS products tracked intraseasonal fluctuations more closely than the VIIRS and S2 datasets. We consider MODIS NDVI the most accurate dataset overall, given its accuracy in estimating SOS and EOS, visible tracking of intraseasonal fluctuations, and high temporal correlation with phenocam phenology. These results support using MODIS and VIIRS for large-scale phenology monitoring and upscaling phenocam observations in African savannas.