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Shifts in wildflower phenology in response to climate change are well documented in the scientific literature. The majority of studies have revealed phenological shifts using in-situ observations, some aided by citizen science efforts (e.g., National Phenology Network). Such investigations have been instrumental in quantifying phenological shifts but are challenged by the fact that limited resources often make it difficult to gather observations over large spatial scales and long-time frames. However, recent advances in finer scale satellite imagery may provide new opportunities to detect changes in phenology. These approaches have documented plot level changes in vegetation characteristics and leafing phenology, but it remains unclear whether they can also detect flowering in natural environments. Here, we test whether fine-resolution imagery (&lt;10 m) can detect flowering and whether combining multiple sources of imagery improves the detection process. Examining alpine wildflowers at Mt. Rainier National Park (MORA), we found that high-resolution Random Forest (RF) classification from 3-m resolution PlanetScope (from Planet Labs) imagery was able to delineate the flowering season captured by ground-based phenological surveys with an accuracy of 70% (Cohen’s kappa = 0.25). We then combined PlanetScope data with coarser resolution but higher quality imagery from Sentinel and Landsat satellites (10-m Sentinel and 30-m Landsat), resulting in higher accuracy predictions (accuracy = 77%, Cohen’s kappa = 0.39). The model was also able to identify the timing of peak flowering in a particularly warm year (2015), despite being calibrated on normal climate years. Our results suggest PlanetScope imagery holds utility in global change ecology where temporal frequency is important. Additionally, we suggest that combining imagery may provide a new approach to cross-calibrate sensors to account for radiometric irregularity inherent in fine resolution PlanetScope imagery. The development of this approach for wildflower phenology predictions provides new possibilities to monitor climate change effects on flowering communities at broader spatiotemporal scales. In protected and tourist areas where the flowering season draws large numbers of visitors, such as Mt. Rainier National Park, the modeling framework presented here could be a useful tool to manage and prioritize park resources.

Detecting Montane Flowering Phenology with CubeSat Imagery