Crop classification using MODIS NDVI data denoised by wavelet: A case study in Hebei Plain, China

Abstract

Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping. However, the temporal crop signatures generated from these data were always accompanied by noise. In this study, a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented. The detail crop planting patterns in Hebei Plain, China were classified using denoised time-series MODIS NDVI data at 250 m resolution. The denoising approach improved original MODIS NDVI product significantly in several periods, which may affect the accuracy of classification. The MODIS NDVI-derived crop map of the Hebei Plain achieved satisfactory classification accuracies through validation with field observation, statistical data and high resolution image. The field investigation accuracy was 85% at pixel level. At county-level, for winter wheat, there is relatively more significant correlation between the estimated area derived from satellite data with noise reduction and the statistical area (R2 = 0.814, p < 0.01). Moreover, the MODIS-derived crop patterns were highly consistent with the map generated by high resolution Landsat image in the same period. The overall accuracy achieved 91.01%. The results indicate that the method combining T-IDW and DWT can provide a gain in time-series MODIS NDVI data noise reduction and crop classification.