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Projected changes in rainfall for the western United States are uncertain with respect to seasonality and direction. Plant phenological patterns (initiation of growth and the production of flowers and fruit) are discrete plant responses to changing climate and indicators for ecosystem services such as net carbon exchange and pollination. Yet, phenology monitoring is challenging across remote spatially extensive rangelands. To explore the performance of inexpensive technology for capturing phenological patterns in an arid upland grassland landscape in southern New Mexico, we coupled daily estimates of canopy greenness from digital cameras (i.e., phenocams) with weekly field observations of plant phenology to evaluate the role of phenocam metrics as a proxy for estimates of primary productivity. Daily phenocam greenness estimates and weekly field observations of phenology including ocular estimates of percent canopy greenness were made for deciduous C3 shrubs honey mesquite (Prosopis glandulosa) and C4 perennial black grama grasses (Bouteloua eriopoda) for three growing seasons (Feb 2012 – July 2014). We showcase tools for visualizing patterns in near-surface landscape phenology and highlight advantages and disadvantages of phenocams for phenology monitoring. Field estimates of canopy greenness closely corresponded with greenness index values providing confidence in interpretations of the greenness responses derived from phenocam images. Mesquite transitioned from minimum to maximum greenness over 15 days between 20 Apr and 5 May in all years. Black grama green-up quickly follows summer rain events over six days in 2012 and 2013. Daily depictions of greenness demonstrate that canopy development in this water-limited system occurs rapidly and that phenocams can provide data needed to characterize greenness; however field sampling once to twice weekly is required to monitor flower and fruit or seed production. Next steps are to incorporate these results with those from time series UAV imagery and satellite remotely sensed imagery.