Spatial-temporal analysis for noise reduction in NDVI time series

Abstract

MODerate resolution Imaging Spectroradiometer (MODIS) data are largely used in multitemporal analysis of various Earth-related phenomena, such as mapping patterns of vegetation phenology and detecting land use/land cover change. NDVI time series are composite mosaics of the best quality pixels over a period of sixteen days. However, it is common to find low quality pixels in the composition that affect the time series analysis due to errors in the atmosphere conditions and in data acquisition. We present a filtering methodology that considers the pixel position (location in space) and time (position in the temporal data series) to define a new value for the low quality pixel. This methodology estimates the value of the point of interest, based first on a linear regression excluding pixels with low coefficient of determination R2 and second on excluding outliers according to a boxplot analysis. Thus, from the remaining group of pixels, a Smooth Spline is generated in order to reconstruct the time series. The accuracies of estimated NDVI values using Spline were higher than the Savitzky–Golay method.