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Question: Can satellite time series be used to identify tree and grass green-up dates in a semi-arid savanna system, and are there predictable environmental cues for green-up for each life form?

Location: Acacia nigrescens/Combretum apiculatum savanna, Kruger National Park, South Africa (25 degrees S, 31 degrees E).

Methods: Remotely-sensed data from the MODIS sensor were used to provide a five year record of greenness (NDVI) between 2000 and 2005. The seasonal and inter-annual patterns of leaf display of trees and grasses were described, using additional ecological information to separate the greening signal of each life form from the satellite time series. Linking this data to daily meteorological and soil moisture data allowed the cues responsible for leaf flush in trees and grasses to be identified and a predictive model of savanna leaf-out was developed. This was tested on a 22-year NDVI dataset from the Advanced Very High Resolution Radiometer. A day length cue for tree green-up predicted 86% of the green-ups with an accuracy better than one month. A soil moisture and day length cue for grass green-up predicted 73% of the green-ups with an accuracy better than a month, and 82% within 45 days. This accuracy could be improved if the temporal resolution of the satellite data was shortened from the current two weeks.

Conclusions: The data show that at a landscape scale savanna trees have a less variable phenological cycle (within and between years) than grasses. Realistic biophysical models of savanna systems need to take this into account. Using climatic data to predict these dynamics is a feasible approach.