GitHub - lewistrotter PhenoloPy: Python-based extractor of . . . Phenolopy can generate more than a dozen different metrics describing various aspects of vegetation phenology These are explained below The codes presented on the figure above translate to: Highest vegetation value and time of season Maximum value in a timeseries Mean vegetation value and time of values in top 80% of season
Deriving Phenology Metrics from NDVI | U. S. Geological Survey Phenological metrics can be derived from satellite data in several ways Some researchers use complex mathematical models Others employ threshold-based approaches that use either relative or pre-defined (global) reference values at which vegetative activity is assumed to begin
CropPhenology: An R package for extracting crop phenology . . . We present the CropPhenology package, which is designed to extract crop phenology metrics from a time series of vegetation index data which build upon those available from previous software and include new metrics suggested by the agricultural remote sensing literature
R: Calculate phenology metrics on time series in gridded. . . Phenology metrics are estimated from the gap filled, smoothed and interpolated time series This can be done by treshold methods (PhenoTrs) or by using the derivative of the time series (PhenoDeriv)