Comparison of Multitemporal MODIS-EVI Smoothing Algorithms and its Contribution to Crop Monitoring

Time series of MODIS vegetation indices are widely used to map vegetation. However, some noise can affect the temporal profiles. Thus, many techniques have been developed to smooth them. Four algorithms are applied on crop pixels in the Brazilian Amazonian State of Mato Grosso. Comparisons led to the selection of the Weighted Least Squares (WLS) algorithm and the Savitzky-Golay (SG) filter. Those techniques were computed on MODIS data in order to detect six crop classes. Tests of separability show that the smoothed data improved the potential of separability at each MODIS sub-period. Moreover, supervised classifications were then realized. The WLS data refined efficiently the classification result when using C4.5 decision tree. When using the Maximum Likelihood and Spectral Angle Mapper classifiers, the smoothed data did not improve the classification results as compared with those obtained through original MODIS data. However, it required fewer input MODIS images to reach good results. The SG filter led to better results than the WLS algorithm when using those classifiers.