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 necessarily correspond directly to conventional, ground-based phenological events, but do provide indications of ecosystem dynamics.
A complete list of the phenological metrics we extract from smoothed, time-series NDVI data is shown in Table 2. Calculated for each year or growing season, these metrics are the basis for diverse research and monitoring applications, including climate change studies.
To download metrics, click on Data & Tools. For information regarding how metrics are derived, see below.
Table 2. Remote sensing phenology data sets for the conterminous U.S. (Click to View)
- Start of Season - Time
- Acronym: SOST
- Phenological Interpretation: Beginning of measurable photosynthesis in the vegetation canopy
- Description: Day of year identified as having a consistent upward trend in time series NDVI
- Start of Season - NDVI
- Acronym: SOSN
- Phenological Interpretation: Level of photosynthetic activity at the beginning of measurable photosynthesis
- Description: NDVI value (or baseline) identified at the day of year identified as a consistent upward trend in time series NDVI
- End of Season - Time
- Acronym: EOST
- Phenological Interpretation: End of measurable photosynthesis in the vegetation canopy
- Description: Day of year identified at the end of a consistent downward trend in time series NDVI
- End of Season - NDVI
- Acronym: EOSN
- Phenological Interpretation: Level of photosynthetic activity at the end of measurable photosynthesis
- Description: NDVI value corresponding with the day of year identified at the end of a consistent downward trend in time series NDVI
- Time of Maximum
- Acronym: MAXT
- Phenological Interpretation: Time of maximum photosynthesis in the canopy
- Description: Day of year corresponding to the maximum NDVI in an annual time series
- Maximum NDVI
- Acronym: MAXN
- Phenological Interpretation: Maximum level of photosynthetic activity in the canopy
- Description: Maximum NDVI in an annual time series
- Duration
- Acronym: DUR
- Phenological Interpretation: Length of photosynthetic activity (the growing season)
- Description: Number of days from the SOST and EOST
- Amplitude
- Acronym: AMP
- Phenological Interpretation: Maximum increase in canopy photosynthetic activity above the baseline
- Description: Difference between MAXN and SOSN
- Time Integrated NDVI
- Acronym: TIN
- Phenological Interpretation: Canopy photosynthetic activity across the entire growing season
- Description: Daily (interpolated) integration of NDVI above the baseline for the entire duration of the growing season
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...
Validation
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...
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...
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...
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...
Validation
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...
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...
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...