NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types

Time series of vegetation indices (e.g. normalized difference vegetation index [NDVI]) and color indices (e.g. green chromatic coordinate [G_CC]) based on radiometric measurements are now available at different spatial and temporal scales ranging from weekly satellite observations to sub-hourly in situ measurements by means of near-surface remote sensing (e.g. spectral sensors or digital cameras). In situ measurements are essential for providing validation data for satellite-derived vegetation indices. In this study we used a recently developed method to calculate NDVI from near-infrared (NIR) enabled digital cameras (NDVI_C) at 17 sites (for a total of 74 year-sites) encompassing six plant functional types (PFT) from the PhenoCam network. The seasonality of NDVI_C was comparable to both NDVI measured by ground spectral sensors and by the moderate resolution imaging spectroradiometer (MODIS). We calculated site- and PFT-specific scaling factors to correct NDVI_C values and recommend the use of site-specific NDVI from MODIS in order to scale NDVI_C. We also compared G_CC extracted from red-green-blue images to NDVI_C and found PFT-dependent systematic differences in their seasonalities. During senescence, NDVI_C lags behind G_CC in deciduous broad-leaf forests and grasslands, suggesting that G_CC is more sensitive to changes in leaf color and NDVI_C is more sensitive to changes in leaf area. In evergreen forests, NDVI_C peaks later than G_CC in spring, probably tracking the processes of shoot elongation and new needle formation. Both G_CC and NDVI_C can be used as validation tools for the MODIS Land Cover Dynamics Product (MCD12Q2) for deciduous broad-leaf spring phenology, whereas NDVI_C is more comparable than G_CC with autumn phenology derived from MODIS. For evergreen forests, we found a poor relationship between MCD12Q2 and camera-derived phenology, highlighting the need for more work to better characterize the seasonality of both canopy structure and leaf biochemistry in those ecosystems. Our results demonstrate that NDVI_C is in excellent agreement with NDVI obtained from spectral measurements, and that NDVI_C and G_CC can complement each other in describing ecosystem phenology. Additionally, NDVI_C allows the detection of structural changes in the canopy that cannot be detected by visible-wavelength imagery.