A comparative study of 17 phenological models to predict the start of the growing season

Yunhua Mo¹, Jing Zhang¹, Hong Jiang² and Yongshuo Fu^1*

• ¹Beijing Normal University, China
• ²Nanjing University, China

Vegetation phenological models play a major role in terrestrial ecosystem modeling, however large uncertainties still occur in phenology models due to unclear mechanisms underlying spring phenological events. Taking into account the asymmetric effects of daytime and nighttime temperature on spring phenology, we analyzed the performance of 17 spring phenological models by combining effects of photoperiod and precipitation. The Global Inventory Modeling and Mapping Study (GIMMS) third-generation normalized difference vegetation index (NDVI3g) data (1982-2014) was used to extract the start of the growing season (SOS) in the North-South Transect of Northeast Asia (NSTNEA). The satellite-derived SOS of deciduous needleleaf forest (DNF), mixed forest (MF), open shrublands (OSL) and woody savannas (WS) has a high correlation coefficient (r) with the model predicted SOS, most of which exceed 0.7. For all vegetation types studied, the models that considering the effect of photoperiod and precipitation did not significantly improve the model performance. As for temperature-based models, compared to the models using the sigmoid temperature response, the model using the growing-degree-day temperature response obtained a lower root mean square error (RMSE). Importantly, we found that daily maximum temperature (Tmax) was most suitable for the spring phenology prediction of DNF, OSL, and WS; daily mean temperature (Tmean) for MF; and daily minimum temperature (Tmin) for grasslands (GL). These findings indicate that future spring phenological models should consider the asymmetric effect between daytime and nighttime temperature across different vegetation types.