Crop identification using harmonic analysis of time-series AVHRR NDVI data
Harmonic analysis of a time series of National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer normalized difference vegetation index (NDVI) data was used to develop an innovative technique for crop type identification based on temporal changes in NDVI values. Different crops (corn, soybeans, alfalfa) exhibit distinctive seasonal patterns of NDVI variation that have strong periodic characteristics. Harmonic analysis, or Fourier analysis, decomposes a time-dependent periodic phenomenon into a series of constituent sinusoidal functions, or terms, each defined by a unique amplitude and phase value. Amplitude and phase angle images were produced by analysis of the time-series NDVI data and used within a discriminant analysis to develop a methodology for crop type identification. For crops that have a single distinct growing season and period of peak greenness, such as corn, the majority of the variance was captured by the first and additive terms, while winter wheat exhibited a bimodal NDVI periodicity with the majority of the variance accounted for by the second harmonic term.
harmonic analysis advanced very high resolution radiometer normalized difference vegetation index