Novel biophysical crop model and earth observation calibration methods

The use of either satellite-based earth observation (EO) or simulation models of crops offers significant opportunities to refine decision support tools to the field and sub-field level. Such refinement offers advantages and savings for farmers by reducing the need for direct field measurements while enhancing the accuracy and resolution of decision-making. However, using EO data or simulation models by themselves is hampered by two challenges. First and regarding EO, saturation issues in metrics such as the Normalized Difference Vegetation Index (NDVI) make them difficult to directly convert to a crop performance metric (e.g., cover, leaf area, biomass, yield). Second, and regarding simulation models, their calibration procedure relies on ground data from direct crop measurements. A potential solution to overcome these challenges is to couple biophysical crop models with EO where the EO data informs the models, while the models fill gaps in data caused through both image saturation and interruption. This project will investigate and develop this solution for applications in the Australian Broadacre cropping industries. 

This PhD Scholarship offers the opportunity to work alongside leasing agricultural systems modelers in the University of Southern Queensland, Centre for Sustainable Agricultural Systems, as well as their international collaborators.