Predicting spring phenology in deciduous broadleaf forests: NEON phenology forecasting community challenge

Yiluan Song, Uttam Bhat, Stephan B. Munch, Kai Zhu as a team in the forecasting challenge
Agricultural and Forest Meteorology 2024 (Paper led by Dr. Kathryn I. Wheeler)
Paper Code

Highlights

  • We participated in a community forecast challenge to predict daily plant greenness with our model GPEDM out of 18 models.
  • Forecasts across teams showed that historical means of greenness on each day of year were difficult to outperform.
  • GPEDM had the best performance among similar models that are data-driven, dynamic (includes previous state), and use covariates.
  • For the study sites, several static models or models without covariates had better performances than GPEDM in predicting greenness values.
  • GPEDM showed its advantage in accurately predicting the ecologically important greenup transition dates.

The forecasting challenge

Our model

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Gaussian Process time-delay embedding model with a spatial covaraince function layer and a temporal covariance function layer (Sugihara et al., 2012).

Exmaple of forecasts

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An example of forecasted greenness values (GCC) submitted by teams on 11 May 2021 for Harvard Forest, with PhenoCan images on dates of 15% and 85% greenup.

Model evaluation

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Mean predictive skill by model relative to the day of year (DOY) Mean null model.


"for the 15 %, 50 %, and 85 % greenup transition dates, PEG, GPEDM, and greenbears_gams beat the DOY Mean model furthest out"
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Forecast horizon, or number of days before the transition dates that each forecast model did better at forecasting greenness (GCC) than day of year mean model across the range of all sites.

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