Remotely sensed phenology reveals environmental and management controls on coastal wetland plant communities

Plant phenology is often used as an indicator of ecological processes and responses to changing environmental conditions. Remote sensing enables phenological monitoring across space and time, yet separating vegetation composition or environmental drivers remains challenging in heterogeneous tidal marshes. We analyzed Sentinel-2 EVI time series to derive phenological metrics, grouped pixels into phenological types via clustering, and linked these to vegetation composition and environmental variation in Suisun Marsh, California. Using phenology metrics alone, a PLS-DA classifier achieved an overall accuracy of 0.69 (per-class balanced accuracy of 0.50–0.81), demonstrating that phenology captures meaningful community patterns. However, transition zones exhibited a complex interplay among vegetation, phenology, elevation, and hydrology: mean mixing rates ranged from 1 to 45%, with class-specific error structures (sensitivity = 0–0.80), indicating limited separability where inundation and salinity covary with phenology. The variation in the timing and magnitude of greenness, alongside the differing proportions of vegetation types across phenological types, suggests that these interacting drivers jointly shape seasonal vegetation cycles. Core phenology metrics (start, peak, end of season) effectively distinguished wetland communities with similar aboveground function and aided delineation of wetland–upland transitions. Yet, despite ecological differences, several vegetation types expressed similar phenological behavior, likely due to shared hydrologic and microclimatic regimes and, potentially, spectral mixing at moderate spatial resolution. We provide a comprehensive work that combines management and vegetation classes to disentangle the complex interplay between wetland communities and remotely sensed phenology predictions.