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

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Abstract The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible