Validation Active
Documenting the effectiveness of the phenology metrics is difficult given the current scarcity of ground verification data at the appropriate scale (i.e., vegetation-canopy phenology). However, current efforts by the National Phenology Network to build and organize ground observation databases will go far toward addressing these shortcomings. In the meantime, two examples of coincident ground observations and phenology metrics can serve as a validation demonstration.
In the first example (see graph to the right), ground observations of grass height are compared to AVHRR NDVI and derived phenology metrics. Partners from The Nature Conservancy (TNC) made observations of grass height in the Tallgrass Prairie Preserve, Oklahoma, on a weekly basis for two years (1995 and 1996; green bars). We extracted AVHRR NDVI data over the same sites and same time period (black line), smoothed the data (red line), and extracted phenology metrics (SOS as a yellow diamond and EOS as a blue diamond). Grass height and NDVI data closely mirror each other and the phenology metrics correspond well to periods of increasing (SOS) and decreasing (EOS) growth. Interestingly, TNC personnel noted a period of drought (red triangle) during the summer of 1996 that corresponds to a period of decreased NDVI and subsequent reduced Time-Integrated NDVI.
In a second example (see graph right), AVHRR NDVI was plotted against gross primary production (GPP) over an Agriflux site in Mandan, North Dakota, for the year 2000. Rising NDVI (blue line) corresponded well to carbon uptake (magenta line) and the SOST phenology metric (blue circle) corresponded well to increasing carbon uptake at the beginning of the growing season.
Below are other science projects associated with this project.
Methods for Deriving Metrics
Methods for Deriving Metrics
Challenges in Deriving Phenological Metrics
Deriving Phenological Metrics from NDVI
Data Smoothing - Reducing the "Noise" in NDVI
- Overview
Documenting the effectiveness of the phenology metrics is difficult given the current scarcity of ground verification data at the appropriate scale (i.e., vegetation-canopy phenology). However, current efforts by the National Phenology Network to build and organize ground observation databases will go far toward addressing these shortcomings. In the meantime, two examples of coincident ground observations and phenology metrics can serve as a validation demonstration.
In the first example (see graph to the right), ground observations of grass height are compared to AVHRR NDVI and derived phenology metrics. Partners from The Nature Conservancy (TNC) made observations of grass height in the Tallgrass Prairie Preserve, Oklahoma, on a weekly basis for two years (1995 and 1996; green bars). We extracted AVHRR NDVI data over the same sites and same time period (black line), smoothed the data (red line), and extracted phenology metrics (SOS as a yellow diamond and EOS as a blue diamond). Grass height and NDVI data closely mirror each other and the phenology metrics correspond well to periods of increasing (SOS) and decreasing (EOS) growth. Interestingly, TNC personnel noted a period of drought (red triangle) during the summer of 1996 that corresponds to a period of decreased NDVI and subsequent reduced Time-Integrated NDVI.
In a second example (see graph right), AVHRR NDVI was plotted against gross primary production (GPP) over an Agriflux site in Mandan, North Dakota, for the year 2000. Rising NDVI (blue line) corresponded well to carbon uptake (magenta line) and the SOST phenology metric (blue circle) corresponded well to increasing carbon uptake at the beginning of the growing season.
- Science
Below are other science projects associated with this project.
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...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...Challenges in Deriving Phenological Metrics
Regardless of the method used, it is difficult to create algorithms sufficiently robust to derive phenological metrics from certain types of real time-series NDVI curves (as opposed to modeled or simulated data). For example, in desert shrublands (see below), time series NDVI shows little seasonal amplitude. Ideally, algorithms should be able to identify these regions and assign them a value such...Deriving Phenological Metrics from NDVI
Plotting time-series NDVI data produces a temporal curve that summarizes the various stages that green vegetation undergoes during a complete growing season. Such curves can be analyzed to extract key phenological variables, or metrics, about a particular season, such as the start of the growing season (SOS), peak of the season (POS), and end of the season (EOS). These characteristics may not...Data Smoothing - Reducing the "Noise" in NDVI
The reflected light waves that satellite sensors detect coming from vegetation on the Earth's surface can be altered or blocked by a variety of phenomena, including aerosols and clouds in the atmosphere as well as changing illumination patterns and the angle at which the satellite views the ground at any given time. These phenomena introduce "noise" into raw satellite data. To address this problem...