A physically based vegetation index for improved monitoring of plant phenology
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.