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Gross primary productivity (GPP) is a critical flux determining the quantity of carbon entering an ecosystem. Thus, studying GPP at larger spatial and longer time scales is necessary to identifying locations of potential sinks or sources of carbon. This study employs remote sensing techniques to estimate savanna GPP in the Northern Territory (NT), Australia using MODIS (Moderate Resolution Imaging Spectro-radiometer) data. A light use efficiency algorithm was used with inputs from fraction of absorbed photosynthetically active radiation (fPAR) from the latest (Collection 5) MODIS product, regional specific climate data and field based light use efficiency (LUE) to estimate GPP for the entire savanna region in NT from 2000 to 2007. Results showed that GPP estimated with this approach captured the magnitude of GPP quite well with only 6% error compared to flux tower based GPP. The estimated GPP was then used to describe the spatial and temporal variations across the NT savanna region. The estimated GPP captured the spatial patterns reasonably well with closed forest having six times more GPP than Acacia vegetation. Whilst, in terms of inter-annual variability, arid ecosystems had higher variation (>20%) in GPP than forests (<10%) and this was associated with large variations in rainfall (>30% for arid vegetation versus 19% for forest). These findings are similar to other studies elsewhere and prove that a simple remote sensing based LUE models (with reliable meteorological data) can effectively capture the magnitude and patterns of GPP in savanna ecosystem in northern Australia.