# Data Smoothing - Reducing the "Noise" in NDVI Active

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, raw data are processed using techniques that filter out noise and produce a clearer, more representative data set.

Filtering techniques vary. Compositing—merging maximum NDVI values acquired over (typically) 7-, 8-, 10-, 14-, or 16-day intervals—increases data quality. But residual effects of sub-pixel clouds, prolonged cloudiness, and other negative elements require further processing in the form of data smoothing. 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.

At 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. The window is moved one period at a time, resulting in a family of regression lines associated with each data point. This family of lines is then averaged at each point, and interpolated between points, to provide a continuous, relatively smooth NDVI signal over time. 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. A final operation assures that all peak NDVI values in the moving window are retained. The resulting relationship between raw and smoothed data is statistically based.

Below are other science projects associated with this project.

#### Methods for Deriving Metrics

#### Methods for Deriving Metrics

#### Validation

#### Validation

#### Methods for Deriving Metrics

#### Methods for Deriving Metrics

#### Challenges in Deriving Phenological Metrics

#### Challenges in Deriving Phenological Metrics

#### Deriving Phenological Metrics from NDVI

#### Deriving Phenological Metrics from NDVI

- Overview
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, raw data are processed using techniques that filter out noise and produce a clearer, more representative data set.

Filtering techniques vary. Compositing—merging maximum NDVI values acquired over (typically) 7-, 8-, 10-, 14-, or 16-day intervals—increases data quality. But residual effects of sub-pixel clouds, prolonged cloudiness, and other negative elements require further processing in the form of data smoothing. 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.

At 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. The window is moved one period at a time, resulting in a family of regression lines associated with each data point. This family of lines is then averaged at each point, and interpolated between points, to provide a continuous, relatively smooth NDVI signal over time. 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. A final operation assures that all peak NDVI values in the moving window are retained. The resulting relationship between raw and smoothed data is statistically based.

- 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...link#### 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...link#### 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...link#### Methods for Deriving Metrics

#### 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...link#### 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...link#### 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...