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The coexistence of trees and grasses in savanna ecosystems results in markedphenological dynamics that vary spatially and temporally with climate.Australian savannas comprise a complex variety of life forms and phenologies,from evergreen trees to annual/perennial grasses, producing a boom–bustseasonal pattern of productivity that follows the wet–dry seasonal rainfallcycle. As the climate changes into the 21st century, modification torainfall and temperature regimes in savannas is highly likely. There is aneed to link phenology cycles of different species with productivity tounderstand how the tree–grass relationship may shift in response to climatechange. This study investigated the relationship between productivity andphenology for trees and grasses in an Australian tropical savanna.Productivity, estimated from overstory (tree) and understory (grass) eddycovariance flux tower estimates of gross primary productivity (GPP), wascompared against 2 years of repeat time-lapse digital photography(phenocams). We explored the phenology–productivity relationship at theecosystem scale using Moderate Resolution Imaging Spectroradiometer (MODIS)vegetation indices and flux tower GPP. These data were obtained from theHoward Springs OzFlux/Fluxnet site (AU-How) in northern Australia. Twogreenness indices were calculated from the phenocam images: the greenchromatic coordinate (GCC) and excess green index (ExG). These indicescaptured the temporal dynamics of the understory (grass) and overstory(trees) phenology and were correlated well with tower GPP for understory(r2 =  0.65 to 0.72) but less so for the overstory (r2 =  0.14to 0.23). The MODIS enhanced vegetation index (EVI) correlated well with GPPat the ecosystem scale (r2 =  0.70). Lastly, we used GCC and EVI toparameterise a light use efficiency (LUE) model and found it to improve theestimates of GPP for the overstory, understory and ecosystem. We concludethat phenology is an important parameter to consider in estimating GPP fromLUE models in savannas and that phenocams can provide important insights intothe phenological variability of trees and grasses

Tree–grass phenology information improves light use efficiency modelling of gross primary productivity for an Australian tropical savanna