Noise reduction in MODIS NDVI time series data based on spatial-temporal analysis

Normalized Difference Vegetation Index is a vegetation index widely applied in research. However, noise induced by cloud contamination and atmospheric variability affect the data quality. We propose the reconstruction of time series of MODIS NDVI data based on a quality assessment of the science data sets and on a spatial-temporal analysis of the low quality pixels. The MOD13Q1 product was analyzed over a period of one year. The first task was to identify the pixels with the lowest guarantee of quality. The next step was to recalculate the NDVI values based on spatial and temporal correlations. The results indicate that the spatial-temporal information, combined with pixel quality assessment, is a promising method for reconstructing high-quality MODIS NDVI time series.