Generation and evaluation of the VIIRS land surface phenology product

Vegetation phenology is widely acknowledged to be a sensitive indicator of the response of ecosystems to climate change, and phenological shifts have been shown to exert substantial impacts on ecosystem function, biodiversity, and carbon budgets at multiple scales. Therefore, long-term records of the phenology of the vegetated land surface are critical in exploring the biological response to environmental change at regional to global scales. Land surface phenology (LSP) from satellite observations has been extensively used to monitor the dynamics of terrestrial ecosystems in the face of a changing climate. Here we introduce and describe the global land surface phenology (GLSP) product derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) data at a gridded spatial resolution of 500 m. This new product will provide continuity for the Moderate Resolution Imaging Spectroradiometer (MODIS) GLSP product that has been produced on an operational basis since 2001. The VIIRS GLSP algorithm uses daily VIIRS Nadir BRDF (Bidirectional Reflectance Distribution Function)-Adjusted Reflectance (NBAR) data as the primary input to calculate the two-band enhanced vegetation index (EVI2) at each 500 m pixel. The temporal EVI2 trajectory is modeled using a hybrid piecewise logistic function to track the seasonal vegetation development, detect phenological transition dates, calculate the magnitude of vegetation greenness development, and characterize the confidence of phenology detections. The VIIRS GLSP algorithm has been implemented across the contiguous United States, and the resulting phenological metrics have been evaluated through comparisons with species-specific field phenological observations, Landsat phenology retrievals, and the MODIS phenology detections. The results demonstrate that the VIIRS GLSP metrics are of high quality and are in a good agreement with the other independent satellite and field observations. The results also indicate that the uncertainty in the VIIRS GLSP retrievals is primarily associated with missing high quality observations in VIIRS EVI2 time series.