Fine-scale Gross Primary Productivity for Murray-Darling Basin wetlands

A seasonal basin–wide layer of terrestrial Gross Primary Productivity (GPP) of water-dependent vegetation has been developed at monthly timestep and 20-meter resolution. The layer was created using an innovative approach that combines a process-based radiative transfer model, Sentinel-2, machine learning and weather observations (Wolanin 2019). The product provides an enhanced ability to assess changes in floodplain terrestrial productivity over space and time at a Basin scale. Terrestrial GPP is highly variable across space and time in the Basin with inundation, rainfall and temperature (season) all influencing observed patterns. Generation of a Basin wide terrestrial GPP product represents the first step in creating a Basin scale remotely–sensed estimation of floodplain litter load. The layer can be used to inform both potential benefits (e.g., additional carbon for food web support) of watering actions and risks (e.g., areas that may pose hypoxic blackwater risks when inundated).