Hongxiao Jin

Using a spectral vegetation index (VI) is an efficient approach for monitoring plant phenology from remotely-sensed data. However, the quantitative biophysical meaning of most VIs is still unclear, and, particularly at high northern latitudes characterized by low green biomass renewal rate and snow-affected VI signals, it is difficult to use them for tracking seasonal vegetation growth and retrieving phenology. In this study we propose a physically-based new vegetation index for characterizing terrestrial vegetation canopy green leaf area dynamics: the plant phenology index (PPI). PPI is derived from the solution to a radiative transfer equation, is computed from red and near-infrared (NIR) reflectance, and has a nearly linear relationship with canopy green leaf area index (LAI), enabling it to depict canopy foliage density well. This capability is verified with stacked-leaf measurements, canopy reflectance model simulations, and field LAI measurements from international sites. Snow influence on PPI is shown by modeling and satellite observations to be less severe than on the Normalized Difference Vegetation Index (NDVI) or the Enhanced Vegetation Index (EVI), while soil brightness variations in general have moderate influence on PPI. Comparison of satellite-derived PPI to ground observations of plant phenology and gross primary productivity (GPP) shows strong similarity of temporal patterns over several Nordic boreal forest sites. The proposed PPI can thus serve as an efficient tool for estimating plant canopy growth, and will enable improved vegetation monitoring, particularly of evergreen needle-leaf forest phenology at high northern latitudes.