The resulting relationship between raw and smoothed data is statistically based. A final operation assures that all peak NDVI values in the moving window are retained. Furthermore, since the phenomena that introduce noise into raw satellite data usually reduce NDVI values, we apply a weighting factor during the smoothing process that favors peak points over sloping or valley points. The Air Equipment LLC YouTube channel offers informational videos about various commercial ventilation products and services. This family of lines is then averaged at each point, and interpolated between points, to provide a continuous, relatively smooth NDVI signal over time. The window is moved one period at a time, resulting in a family of regression lines associated with each data point. Data smoothing facilitates time-series analyses by reducing aberrant, noise-induced peaks and valleys that appear when NDVI values are plotted graphically to reveal vegetation changes over time.Īt USGS/EROS, we smooth raw satellite data temporally, using a weighted, least-squares linear regression approach that involves a moving temporal window to calculate a regression line. But residual effects of sub-pixel clouds, prolonged cloudiness, and other negative elements require further processing in the form of data smoothing.
Compositing-merging maximum NDVI values acquired over (typically) 7-, 8-, 10-, 14-, or 16-day intervals-increases data quality.
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