全球变化知识资源中心

Using solely vegetation indices (VIs) from remote sensing is not always sufficient to accurately detect spring leaf phenology, i.e., the leaf unfolding date (LUD). Several current phenology products failed to provide reliable LUD estimates for specific regions and plant functional types, e.g., evergreen species at mid-low latitudes. Therefore, increasing efforts have been made to improve LUD modeling by combining VIs and meteorological variables. Temperature before the growing season ('preseason' henceforth) plays an important role in regulating spring phenology. With ground observations of LUD (LUDOBS) across different plant functional types (PFTs) in China during 2001-2014, we analyzed the response of LUDOBS to preseason temperature temporally and spatially, and proposed an improved LUD modeling algorithm by developing a temperature-based scale factor to adjust the traditional VI-based (i.e., two band enhanced vegetation index (EVI2)) LUD estimates. We found that the new algorithm can better characterize the spatial and temporal patterns of LUD variability for different PFTs, especially for evergreen species where MODIS phenology product failed to provide reliable LUD estimates. Furthermore, we investigated the spatio-temporal patterns of LUD over China with respect to both different vegetation types and climate systems. We showed that for similar to 70% pixels, our new model predicted an overall later LUDs than MODIS phenology product, possibly suggesting an overestimated greening potential of China's terrestrial ecosystems. Our study suggests that preseason temperature plays a previously neglected role in modeling spring LUD and instead of using VIs or temperature alone, a combination of temperature and VIs can improve the prediction of spring phenology.