Methods

Smoothing

The input data to extract phenological metrics is time-series advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) data. NDVI data may be affected by a number of phenomena that contaminate the signal, including clouds, atmospheric perturbations, and variable illumination and viewing geometry. Each of these phenomena reduce the NDVI.

To reduce the contamination of the NDVI signal, we develop a weighted least-squares linear regression approach to temporally smooth the data (Swets, 1999). This approach uses a moving temporal window to calculate a regression line. The window is moved one period at a time, resulting in a family of regression lines associated with each point; this family of lines is then averaged at each point and interpolated between points to provide a continuous temporal NDVI signal.

Also, since the factors that cause contamination usually reduce the NDVI values, we apply a weighting factor that favors peak points over sloping or valley points. A final operation assures that all peak NDVI values are retained. The resulting relationship between the smoothed curve and the original data is statistically based. The smoothed data may be used to improve applications involving the analysis of time-series NDVI data, such as land cover classification, seasonal vegetation characterization, and vegetation monitoring.

Swets, D.L., B.C. Reed, J.R. Rowland, S.E. Marko, 1999. A weighted least-squares approach to temporal smoothing of NDVI. In 1999 ASPRS Annual Conference, From Image to Information, Portland, Oregon, May 17-21, 1999, Proceedings: Bethesda, Maryland, American Society for Photogrammetry and Remote Sensing, CD-ROM, 1 disc.

Phenological Metrics

Deriving metrics that describe the phenology (seasonality) of vegetation growth is key to understanding when changes in the land surface/atmosphere boundary layer take place. These changes include humidity, surface roughness, albedo, and others. Satellite observations provide a unique vantage point from which to observe these changes. The USGS EROS Data Center has archived advanced very high-resolution radiometer (AVHRR) satellite sensor 1-km resolution data over the conterminous US since 1989. The normalized difference vegetation index (NDVI) can easily be derived from the AVHRR and used to analyze greenness characteristics of each 1-km pixel.

Time-series NDVI data track the greenup and senescence cycle of vegetation well (Figure). We have developed an algorithm to extract key phenological phenomena from this time-series curve. Our approach is to utilize a delayed moving average (DMA) as a comparison to the smoothed NDVI time-series. NDVI data values are compared to the average of the previous (user-defined) n NDVI observations to identify departures from an established trend (Reed and others, 1994). The DMA value serves as a predicted value to which the real NDVI values are compared. A trend change is detected where the NDVI value departs from (becomes greater than) the value of the moving average, such as when low NDVI values are predicted by the moving average, but the actual NDVI values are higher. This departure is labeled as the start of the growing season (SOS).

The end of the growing season (EOS) is calculated in a similar manner, but with the moving average running in the opposite direction. Duration of growing season is the difference between the time of EOS and SOS. The peak of the growing season is simply the time of the maximum NDVI. Several other metrics can then be derived, including the rate of greenup (slope from SOS to peak), rate of senescence (slope from peak to EOS), and total integrated NDVI (area under the curve). The figure below summarizes graphically these metrics.

The full set of metrics and their phenological interpretation are shown below. Note that the phenological interpretation is not an absolute value of photosynthesis, but the phenological metrics are surrogates for such values.

• * These data are available through this link.
• The other data sets will be available soon.