In desert cities, accurate measurements of vegetation area within residential lots are necessary to understand drivers of change in water consumption. Most residential lots are smaller than an individual 30-m pixel from Landsat satellite images and have a mixture of vegetation and other land covers. Quantifying vegetation change in this environment requires estimating subpixel vegetation area. Mixture-tuned match filtering (MTMF) has been successfully used for subpixel target detection. There have been few successful applications of MTMF to subpixel abundance estimation because the relationship observed between MTMF estimates and ground measurements of abundance is noisy. We use a ground truth dataset over 10 times larger than that available for any previous MTMF application to estimate the bias between ground data and MTMF results. We find that MTMF underestimates the fractional area of vegetation by 5% to 10% and show that averaging over multiple pixels is necessary to reduce noise in the dataset. We conclude that MTMF is a viable technique for fractional area estimation when a large dataset is available for calibration. When this method is applied to estimating vegetation area in Las Vegas, Nevada, spatial and temporal trends are consistent with expectations from known population growth and policy changes.