Modelling vegetation phenology at six field stations within the U.S. Great Plains: constructing a 38-year timeseries of GCC, VCI, NDVI, and EVI2 using PhenoCam imagery and DAYMET meteorological records

Abstract

AbstractVegetation phenology models still rely on temperature as the primary limiting factor to growth. They generally do not recognize the importance of photoperiod and water availability, which can cause them to under-perform. Moreover, few models have used machine learning algorithms to find relationships in the data. In this paper, four Vegetation Indexes (VIs), namely the green chromatic coordinate (GCC), the vegetation contrast index (VCI), the normalized difference vegetation index (NDVI) and the two-band enhanced vegetation index (EVI2), are predicted for the North American Great Plains. This is possible by using six PhenoCams, Daily Surface Weather and Climatological Summaries (DAYMET), processing them with the machine learning algorithm XGBoost (XGB) and comparing them with seven phenophase stages throughout a growth cycle. Examining the results, GCC was the best fitting model with an R2 of 0.946, while EVI2 was the poorest with an R2 of 0.895. Also, the results indicate that changing temperature and precipitation patterns are driving a significant change in phenology of the grasslands. We developed a model capable of explaining 90 to 93% of the variability in four VIs across six grassland PhenoCam sites over the growing season using the XGB regression. Our model demonstrates the importance of including photoperiod, temperature, and precipitation information when modeling vegetation phenology. Finally, we were able to construct a 38-year phenology record at each PhenoCam location.