A comparison of PhenoCam and satellite indices with in-situ observations for black spruce in the boreal forest of Quebec, Canada

Vegetation phenology plays a key role in regulating ecosystem processes, serving as a sensitive indicator of climate change impacts on ecosystems. Monitoring bud and leaf development is crucial for understanding ecosystem responses to environmental changes. This study compares PhenoCam with phenological observations in evergreen forests. We focused on black spruce [ Picea mariana (Mill.) B.S.P] stands at the Simoncouche Research Station in Laurentides Wildlife Reserve, Quebec, Canada, for 2017–2020. By analyzing bud phenology from time series color indices (GCC, RCC, VCI, and ExG) and comparing them with ground observations, we aim to elucidate the effectiveness of these indices in tracking the growing season. Our results show that GCC is the most effective index for SOS with a mean difference of 13.9 days and both RCC and GCC for tracking the EOS with 10.5 and 11.1 days respectively. ExG also showed a good correlation with field observations, while VCI performed lower in comparison. The integration of a white reflectance panel in the PhenoCam setup proved crucial for normalizing images under varying illumination conditions, enhancing the accuracy of phenological assessments. Further GCC estimates improved to 0.9 day for SOS and 4.2 days for EOS with the inclusion of a reflectance panel. Field observations demonstrated closer alignment with GCC estimates than EVI, emphasizing the potential of combining ground-based and remote sensing technologies for precise phenological monitoring. The research aims to contribute to the broader understanding of how specific PhenoCam indices and calibration of data influence the reliability of phenological studies in evergreen forest ecosystems. • PhenoCam indices are compared with satellite and field data for black spruce phenology. • GCC performed best for identifying SOS, while RCC was more effective for EOS. • Satellite-based EVI outperformed NDVI as a better indicator for evergreen phenology. • The inclusion of a white reflectance panel showed a higher correlation with field data. • The calibration reduced SOS and EOS errors by up to 12.9 and 6.9 days, respectively.