Vegetation phenology plays an important role in regulating processes of terrestrial ecosystems. Dynamic ecosystem models (DEMs) require representation of phenology to simulate the exchange of matter and energy between the land and atmosphere. Location-specific parameterization with phenological observations can potentially improve the performance of phenological models embedded in DEMs. As ground-based phenological observations are limited, phenology derived from remote sensing can be used as an alternative to parameterize phenological models. It is important to evaluate to what extent remotely sensed phenological metrics are capturing the phenology observed on the ground. We evaluated six methods based on two vegetation indices (VIs) (i.e., Normalized Difference Vegetation Index and Enhanced Vegetation Index) for retrieving the phenology of temperate forest in the Agro-IBIS model. First, we compared the remotely sensed phenological metrics with observations at Harvard Forest and found that most of the methods have large biases regardless of the VI used. Only two methods for the leaf onset and one method for the leaf offset showed a moderate performance. When remotely sensed phenological metrics were used to parameterize phenological models, the bias is maintained, and errors propagate to predictions of gross primary productivity and net ecosystem production. Our results show that Agro-IBIS has different sensitivities to leaf onset and offset in terms of carbon assimilation, suggesting it might be better to examine the respective impact of leaf onset and offset rather than the overall impact of the growing season length.

In recent decades, satellite-derived start of vegetation growing season (SOS) has advanced in many northern temperate and boreal regions. Both the magnitude of temperature increase and the sensitivity of the greenness phenology to temperature-the phenological change per unit temperature-can contribute the advancement. To determine the temperature-sensitivity, we examined the satellite-derived SOS and the potentially effective pre-season temperature (T eff) from 1982 to 2008 for vegetated land between 30°N and 80°N. Earlier season vegetation types, i.e., the vegetation types with earlier SOSmean (mean SOS for 1982-2008), showed greater advancement of SOS during 1982-2008. The advancing rate of SOS against year was also greater in the vegetation with earlier SOSmean even the T eff increase was the same. These results suggest that the spring phenology of vegetation may have high temperature sensitivity in a warmer area. Therefore it is important to consider temperature-sensitivity in assessing broad-scale phenological responses to climatic warming. Further studies are needed to explore the mechanisms and ecological consequences of the temperature-sensitivity of start of growing season in a warming climate.

Changes of vegetation phenology in response to climate change in the temperate forests have been well documented recently and have important implications on the regional and global carbon and water cycles. Predicting the impact of changing phenology on terrestrial ecosystems requires an accurate phenology model. Although species-level phenology models have been tested using a small number of vegetation species, they are rarely examined at the regional level. In this study, we used remotely sensed phenology and meteorological data to parameterize the species-level phenology models. We used a remotely sensed vegetation index (Two-band Enhanced Vegetation Index, EVI2) derived from the Moderate Resolution Spectroradiometer (MODIS) 8-day reflectance product from 2000 to 2010 of New England, United States to calculate remotely sensed vegetation phenology (start/end of season, or SOS/EOS). The SOS/EOS and the daily mean air temperature data from weather stations were used to parameterize three budburst models and one senescence model. We compared the relative strengths of the models to predict vegetation phenology and selected the best model to reconstruct the "landscape phenology" in New England from year 1960 to 2010. Of the three budburst models tested, the spring warming model showed the best performance with an averaged Root Mean Square Deviation (RMSD) of 4.59 days. The Akaike Information Criterion supported the spring warming model in all the weather stations. For senescence modeling, the Delpierre model was better than a null model (the averaged phenology of each weather station, averaged model efficiency = 0.33) and has a RMSD of 8.05 days. A retrospective analysis using the spring warming model suggests a statistically significant advance of SOS in New England from 1960 to 2010 averaged as 0.143 days per year (p = 0.015). EOS calculated using the Delpierre model and growing season length showed no statistically significant advance or delay between 1960 and 2010 in this region. These results suggest the applicability of species-level phenology models at the regional level (and potentially terrestrial biosphere models) and the feasibility of using these models in reconstructing and predicting vegetation phenology.