In desert cities, securing sufficient water supply to meet the needs of both existing population and future growth is a complex problem with few easy solutions. Grass lawns are a major driver of water consumption and accurate measurements of vegetation area are necessary to understand drivers of changes in household water consumption. Measuring vegetation change in a heterogeneous urban environment requires sub-pixel estimation of vegetation area. Mixture Tuned Match Filtering has been successfully applied to target detection for materials that only cover small portions of a satellite image pixel. There have been few successful applications of MTMF to fractional area estimation, despite theory that suggests feasibility. We use a ground truth dataset over ten times larger than that available for any previous MTMF application to estimate the bias between ground truth data and matched filter results. We find that the MTMF algorithm underestimates the fractional area of vegetation by 5-10%, and calculate that averaging over 20 to 30 pixels is necessary to correct this bias. We conclude that with a large ground truth dataset, using MTMF for fractional area estimation is possible when results can be estimated at a lower spatial resolution than the base image. When this method is applied to estimating vegetation area in Las Vegas, NV spatial and temporal trends are consistent with expectations from known population growth and policy goals.