Curve fitting of MODIS NDVI time series in the task of early crops identification by satellite images

Curve fitting of noisy NDVI time series is studied in this research. Six fitting functions with numbers of parameters from 3 to 10 are assessed: piecewise linear, asymmetric Gaussian, double logistic, Fourier series, a polynomial and a cubic spline. Root mean square error, maximum error and a number of incorrect curve fitting procedures are used as the quality criteria. It has been found that the cubic spline is the best fitting function and can be used to generate NDVI time series. Examples of generated NDVI time series by data of the previous years are realized. Generated data can be used for early crops identification by NDVI time series when learning sample of the current year is small.