Plant Traits Help Explain the Tight Relationship between Vegetation Indices and Gross Primary Production

Remotely-sensed Vegetation Indices (VIs) are often tightly correlated with terrestrial ecosystem CO2 uptake (Gross Primary Production or GPP). These correlations have been exploited to infer GPP at local to global scales and over half-hour to decadal periods, though the underlying mechanisms remain incompletely understood. We used satellite remote sensing and eddy covariance observations at 10 sites across a California climate gradient to explore the relationships between GPP, the Enhanced Vegetation Index (EVI), the Normalized Difference Vegetation Index (NDVI), and the Near InfraRed Vegetation (NIRv) index. EVI and NIRv were linearly correlated with GPP across both space and time, whereas the relationship between NDVI and GPP was less general. We explored these interactions using radiative transfer and GPP models forced with in-situ plant trait and soil reflectance observations. GPP ultimately reflects the product of Leaf Area Index (LAI) and leaf level CO2 uptake (Aleaf); a VI that is sensitive mainly to LAI will lack generality across ecosystems that differ in Aleaf. EVI and NIRv showed a strong, multiplicative sensitivity to LAI and Leaf Mass per Area (LMA). LMA was correlated with Aleaf, and EVI and NIRv consequently mimic GPP’s multiplicative sensitivity to LAI and Aleaf, as mediated by LMA. NDVI was most sensitive to LAI, and was relatively insensitive to leaf properties over realistic conditions; NDVI lacked EVI and NIRv’s sensitivity to both LAI and Aleaf. These findings carry implications for understanding the limitations of current VIs for predicting GPP, and also for devising strategies to improve predictions of GPP.