Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat

Forests play important roles in the Earth's climate system and global carbon cycle. Therefore, a critical need exists to improve our understanding of how the growing seasons of forests are changing, and by extension, how the composition and function of forests will respond to future climate change. Coarse spatial resolution satellite remote sensing has been widely used to monitor and map the phenology of terrestrial ecosystems at regional to global scales, and despite widespread agreement that the growing season of Northern Hemisphere forests is changing, the spatial resolution of these data sources imposes significant limitations on the character and quality of inferences that can be drawn from them. In particular, the spatial resolution afforded by instruments such as MODIS does not resolve ecologically important landscape-scale patterns in phenology. With this issue in mind, here we evaluate the ability of a newly developed Landsat phenology algorithm (LPA) to reconstruct a 32-year time series for the start and end of the growing season in North American temperate and boreal forests. We focus on 13 “sidelap” regions located between overlapping Landsat scenes that span a large geographic range of temperate and boreal forests, and evaluate the quality and character of LPA-derived start and end of growing season (SOS and EOS) dates using several independent data sources. On average, SOS and EOS dates were detected for about two-thirds of the 32years included in our analysis, with the remaining one-third missing due to cloud cover. Moreover, there was generally better agreement between ground observations and LPA-derived estimates of SOS dates than for EOS across the 13 sites included in our study. Our results demonstrate that, despite the presence of time series gaps, LPA provides a robust basis for retrospective analysis of long-term changes in spring and autumn deciduous forest phenology over the last three decades. Finally, our results support the potential for monitoring land surface phenology at 30m spatial resolution in near real-time by combining time series from multiple sensors such as the Landsat Operational Land Imager and the Sentinel 2 MultiSpectral Instrument.