Land surface phenology (LSP) enables ecosystem scale tracking of the drivers and consequences of a changing climate, but it’s useability is limited in some areas. In dryland ecosystems low vegetation cover is the primary limitation in LSP detection. Low vegetation cover can cause the growing season vegetation index (VI) to be indistinguishable from the dormant season VI, making phenology extraction impossible. Here, using both simulated data and multi-temporal drone imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, and sensor observation error. We found that plants with distinct VI signals, such as deciduous shrubs with a high leaf area index, require at least 30-40% fractional cover on the landscape to consistently detect pixel level phenology. Evergreen plants, which have lower VI amplitude between dormant and growing seasons, require considerably higher cover and can sometimes have undetectable phenology even with 100% fractional cover. We also found that even with adequate cover, biases in phenological metrics can still be up to 20 days, and can never be 100% accurate due to observation error from shadows, viewing angle, and atmospheric interference. Our work also highlights some understudied limitations in drylands LSP. For example some areas may have the occasional season which meets a minimum VI threshold for detecting phenology, potentially indicating a higher than normal productive year. Our results show that these years may happen solely due to chance as opposed to increased productivity. 
The ability to detect phenology in drylands with current satellite sensors is severely limited in many areas with low vegetation cover, such as the Sonoran and Mojave desert regions, as well as areas with large amounts of evergreen vegetation, such as sagebrush ecosystems in the Wyoming and Columbia basins. Along with recent studies our work suggests that the timing and amplitude of peak productivity is the best way to study dryland LSP with current satellite based sensors. Future sensor technologies with sub-meter resolution will allow for identification of individual plants and are the best path forward for studying large scale phenological trends in drylands.