AbstractThe seasonal dynamics of plant communities are important indicators for assessment of long‐term vegetation patterns and provide valuable information to predict ecosystem responses to climate change. However, increased frequency of extreme weather events can force ecosystems into unstable states, which leads to greater uncertainty in determining phenological metrics (e.g., growing season length). To better understand these uncertainties, we utilized 9 years of eddy covariance and remote sensing data to parameterize models of seasonal ecosystem respiration (Re) for two subtropical longleaf pine forests (mesic and xeric), with similar vegetation but different water holding capacity. We compared two commonly used algorithms to extract phenology metrics, the growth rate (GR) and third derivative (TD) methods, which are usually used without justification. We determined the impact of algorithm selection on estimating key biological dates related to plant community carbon dynamics (e.g., start, end, and length of physiologically active season, specifically Re), characterized the model's response to extreme weather events, and compared estimates to those derived via remotely sensed greenness from the enhanced vegetation index (EVI). We observed that periods of winter warming increased duration of physiological activity in terms of Re, and summer water limitation caused multi‐peaked, asymmetric behavior, creating significant uncertainties. We found that choice of phenology metric extraction algorithm significantly impacted biological event dates; the GR method estimated longer phenophases than the TD in both sites, as well as earlier starting and later ending dates for phenophases. Because the TD method was unable to give estimates during the buffer period of phenophase transition under certain weather conditions, the GR method may be more suitable for studies in subtropical forests. Dates derived from EVI greenness rarely matched those of plant community seasonal dynamics models, especially in spring and summer. The estimated length of Re from the model was significantly longer than that derived from EVI, indicating that the use of EVI could result in shorter growing season estimates and greater uncertainty. Our results provide direction for optimization of future approaches to extract phenological metrics and better scientific understanding of forest land surface phenology, as weather anomalies become more common with climate change.