The goal of achieving higher water use efficiency in irrigated agriculture hinges on the ability of irrigators to apply the right amount of water at the right time and right location to a crop. In large-scale field agriculture the challenge is to have access to data of the right granularity to optimize this decision-making process. _x000D_ Here, we present temporal and spatial data collected at irrigated fields of the Lethbridge Polytechnic Research and Demonstration Farm. These datasets include high-frequency timeseries of volumetric water content, matric potential, and evapotranspiration; high-resolution maps of brightness temperature (as a proxy of volumetric water content); and low-frequency / low-resolution data series of manual soil moisture assessment and estimated crop water use._x000D_ We compare error margins of the different datasets to identify where relevant gains in irrigation scheduling can be made. We also evaluate the precision and error margins of the datasets to those of irrigation equipment to see what is achievable at the field scale._x000D_ These analyses form a roadmap for work needed to advance data driven irrigation management.
