Tree–grass phenology information improves light use efficiency modelling of gross primary productivity for an Australian tropical savanna
- 1School of Earth, Atmosphere and Environment, Monash University, Clayton, VIC, 3800, Australia
- 2School of Earth and Environment, University of Western Australia, Crawley, WA, 6009, Australia
- 3Department of Environmental Sciences, University of Sydney, Eveleigh, NSW, 2015, Australia
- 4Terrestrial Ecosystem Research Network Ecosystem Modelling and Scaling Infrastructure, University of Sydney, Eveleigh, NSW, 2015, Australia
- 5School of Environment, Research Institute for the Environment and Livelihoods, Charles Darwin University, Casuarina, NT, 0909, Australia
Abstract. The coexistence of trees and grasses in savanna ecosystems results in marked phenological 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–bust seasonal pattern of productivity that follows the wet–dry seasonal rainfall cycle. As the climate changes into the 21st century, modification to rainfall and temperature regimes in savannas is highly likely. There is a need to link phenology cycles of different species with productivity to understand how the tree–grass relationship may shift in response to climate change. This study investigated the relationship between productivity and phenology for trees and grasses in an Australian tropical savanna. Productivity, estimated from overstory (tree) and understory (grass) eddy covariance flux tower estimates of gross primary productivity (GPP), was compared against 2 years of repeat time-lapse digital photography (phenocams). We explored the phenology–productivity relationship at the ecosystem scale using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and flux tower GPP. These data were obtained from the Howard Springs OzFlux/Fluxnet site (AU-How) in northern Australia. Two greenness indices were calculated from the phenocam images: the green chromatic coordinate (GCC) and excess green index (ExG). These indices captured 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 = 0.14 to 0.23). The MODIS enhanced vegetation index (EVI) correlated well with GPP at the ecosystem scale (r2 = 0.70). Lastly, we used GCC and EVI to parameterise a light use efficiency (LUE) model and found it to improve the estimates of GPP for the overstory, understory and ecosystem. We conclude that phenology is an important parameter to consider in estimating GPP from LUE models in savannas and that phenocams can provide important insights into the phenological variability of trees and grasses.