Integrating satellite and tower phenology: a case-study in real-time ecological forecasting

Abstract:

Phenological transitions have large impacts on ecosystem processes, species interactions, and climate. However, phenology is a critical source of uncertainty in projections of climate change on terrestrial ecosystems and the current generation of ecosystem models are highly variable and biased in their phenology predictions.

Most phenological modeling has focused on diagnosing phenological variability and predicting long term responses to climate scenarios. Phenological predictions for the current season, on the other hand, are being made based on long-term means or expert opinion rather than real data.

To our knowledge previous research has not applied operational data assimilation approaches to produce operational, real-time forecasts of phenology. We present a phenology forecast data product that is automatically updated every day using current observations and weather forecasts. Specifically we fuse MODIS NDVI and PhenoCam based GCC with a threshold logistic process model at five sites across eastern forests, from North Carolina to New Hampshire. Prior to application, models were calibrated (2000-2012) using a Bayesian state space model. Forecasts for fall 2013, spring 2014, and fall 2014 were then generated on a daily basis using a particle filter.

The system successfully tracked seasonal phenology but forecasts showed high uncertainty and sensitivity to alternative model structures. Furthermore, we found that current phenological models in the literature are not formulated in a way that allows for dynamic forecasts. Work remains to be done to extend this work to a fully spatial context. In particular there is a need to determine the spatial range of influence of the tower PhenoCam data and to account for both land cover and random effects. More broadly, this work demonstrates the possibilities for the development of real-time ecological forecasting in other areas.