Remote Sensing Phenology


Methods for Deriving Metrics
Methods for Deriving Metrics

Phenological metrics can be derived from satellite data in several ways. Some researchers use complex mathematical models. Others employ threshold-based approaches that use either relative or pre-defined (global) reference values at which vegetative activity is assumed to begin. For example, seasonal midpoint NDVI (SMN) is a threshold-based approach that uses relative reference values to derive metrics.
This approach delineates SOS, for instance, using the midpoint between minimum and maximum NDVI values, thus tying the threshold to the seasonal amplitude of individual pixels and, in turn, the dynamic characteristics of each pixel. By contrast, in a pre-defined threshold approach, if the threshold value in a study using NDVI data is set at 0.099, SOS is the point at which NDVI values reach this point at the beginning of the growing season. This method can be effective for deriving the start of season in localized areas with relatively uniform land cover. But difficulties arise when using it to determine SOS over large areas with varying soil background characteristics or land cover types.
At USGS/EROS, we calculate phenological metrics from time-series NDVI data using a curve derivative method, which employs a backward-looking or delayed moving average (DMA). In essence, DMA values are predicted values based on previous observations along a time-series NDVI curve. In this approach, smoothed NDVI data values are compared to a moving average of the previous n observations to identify departures from an established trend. The trend change is defined as the point where the smoothed NDVI values become larger than those predicted by the DMA. This departure point is labeled as the start of the growing season (SOS). The end of the growing season is calculated in a similar manner, with the moving average run in reverse. Once these two parameters are defined, additional metrics are readily derived.

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