Fraction of Photosynthetically Active Radiation

by Megan Kanaga Creutzburg

Other Names:

Fraction of Absorbed Photosynthetically Active Radiation (fAPAR)


Photosynthetically active radiation (PAR) is the spectral range from 400-700nm that is used by plants in photosynthesis. The fraction of PAR (fPAR) is a parameter used in remote sensing and in ecosystem modeling that signifies the portion of PAR used by plants. fPAR is commonly used in ecosystem models because it has an important influence on exchanges of energy, water vapor and carbon dioxide between the surface of the earth and the atmosphere. Precipitation and temperature are two of the major factors that determine the proportion of PAR absorbed by plants. It is an important parameter in measuring biomass production because vegetation development is related to the rate at which radiant energy is absorbed by vegetation. fPAR can be measured on the ground with handheld instruments or inferred from satellite imagery over large spatial scales.

The major approaches to generating fPAR estimates from remotely sensed images are:
* Linear Modeling – linear modeling approaches attempt to relate reflectance data recorded by a sensor to field measurements of fPAR using linear regression techniques. Such approaches may correlate field-measured fractional cover with sensor reflectance bands, or to vegetation indices like NDVI (eg. Law & Waring 1994)
* Physical Models – Physical models use principles of how light energy is absorbed or reflected from different surfaces to estimate physical characteristics of vegetation such as fractional cover, LAI and fPAR. Biophysical models incorporate parameters related to how light interacts with processes like photosynthesis, evapotranspiration, stress, and decay of plant material.
* Artificial Neural Networks (ANN) – ANN are networks of simple processes, decisions, or algorithms applied to data that are good at analyzing data from non-linear and non-parametric systems (see Trombetti et al. 2008)
These methods require correction for atmospheric variation and sometimes require bidirectional reflectance normalization. The images are composited over multiple days (i.e., the value for any given pixel in the final image is taken from the highest-quality readings for that pixel across multiple images) to minimize the impact of atmosphere and screening by clouds or snow. The relationship between satellite measures of reflectance and estimates of fPAR will vary depending on the type of vegetation being considered, and thus major land cover type is an important input to calculating fPAR. Satellite measurements of reflected radiation are often used to estimate the fPAR values that are used as an intermediate variable in models of NPP.

Similar Methods


The output from remotely sensed fPAR methods is a map with each pixel assigned a fPAR value between 0 and 1. Numbers close to 0 suggest that little of the PAR is absorbed by plants, and values close to 1 indicate that most of the PAR is absorbed by plants. fPAR and LAI data are commonly packaged together (e.g., in the MODIS products).

Successful Rangeland Uses

fPAR is used as an input in many ecosystem models to characterize:

  • Primary productivity
  • Land cover change
  • Vegetation health
  • Carbon sequestration

Rangeland applications include:

  • Fang et al. (2005) used fPAR, along with EVI, NDVI, LAI, and other measurements, to assess vegetation recovery from anthropogenic disturbance
  • Hunt et al. (2004) used a measure of photosynthetically active radiation to assess carbon sequestration

Application References

  • Fang, H., S. Liang, M.P. McClaran, W.J. D. van Leeuwen, S. Drake, S.E. Marsh, A.M. Thomson, R.C. Izaurralde, and N.J. Rosenberg. 2005. Biophysical Characterization and Management Effects on Semiarid Rangeland Observed From Landsat ETM+ Data. IEEE Transactions on Geoscience and Remote Sensing 43: 125-134
  • Hunt, Jr., E.R., R.D. Kelly, W.K. Smith, J.T. Fahnestock, J.M. Welker, and W. A. Reiners. 2004. Estimation of Carbon Sequestration by Combining Remote Sensing and Net Ecosystem Exchange Data for Northern Mixed-Grass Prairie and Sagebrush–Steppe Ecosystems. USDA Agricultural Research Service, Lincoln, NE. DOI: 10.1007/s00267-003-9151-0

Technical References

  • Asner, G.P., C.A. Wessman, and D.S. Schimel. 1998. Heterogeneity of savanna canopy structure and function from imaging spectrometry and inverse modeling. Ecological Applications 8: 1022-1036.
  • Gower, S.T., Kucharki, C.J., and Norman, J.M. 1999. Direct and Indirect Estimation of Leaf Area Index, fAPAR, and Net Primary Production of Terrestrial Ecosystems. Remote Sensing of Environment 70: 29-51.
  • Fensholt, R., I. Sandholt, and M.S Rasmussen. 2004. Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sensing of Environment 91: 490-507.
  • Fensholt, R., I. Sandholt, M.S. Rasmussen, S. Stisen, and A. Diouf. 2006. Evaluation of satellite based primary production modeling in the semi-arid Sahel. Remote Sensing of Environment 105: 173-188.
  • Hu, J., Y. Su, B. Tan, D. Huang, W. Yang, M. Schull, M.A. Bull, J.V. Martonchik, D.J. Diner, Y. Knyazikhin, and R.B. Myeni. 2007. Analysis of the MISR LAI/FPAR product for spatial and temporal coverage, accuracy, and consistency. Remote Sensing of Environment 107: 334-347.
  • Knyazikhin, Y., J.V., Martonchik, R.B. Myneni, D.J. Diner, and S.W. Running. 1998. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. Journal of Geophysical Research 103: 32257-32275.
  • Knyazikhin, Y., J.V. Martonchik, D.J.Diner, R.B. Myneni, M.M. Verstraete, B.Pinty, and N. Gobron. 1998. Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from atmosphere- corrected MISR data. Journal of Geophysical Research 103: 32239-32256.
  • Myneni, R.B., R.R. Nemani, and S.W. Running. 1997. Estimation of global leaf area index and absorbed PAR using radiative transfer model, IEEE Trans. Geosci. Remote Sens. 35: 1380-1393.
  • Myeni, R.B. and D.L. Williams. 1994. On the relationship between FAPAR and NDVI. Remote Sensing of Environment 49: 200-211.
  • North, P. R. J. 2002. Estimation of fAPAR, LAI, and vegetation fractional cover from ATSR-2 imagery. Remote Sensing of Environment 80: 114-121.
  • Trombetti, M., D. Riaño, M.A. Rubio, Y.B. Cheng, and S.L. Ustin. 2008. Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental US. Remote Sensing of Environment 112: 203-215.


Remotely sensed fPAR estimates are only approximations of true fPAR values. The mathematical models used to calculate fPAR vary widely, and each model contains assumptions and requires specific inputs. It is important to understand the model assumptions and assess the suitability of the model based on the available data, how well the model characterizes the vegetation compared to field measurements, and the desired output. Most models work optimally at a particular scale and in a particular ecosystem type, and the application of an existing model to a new location may require changes to the model. fPAR is often derived from spectral vegetation indices, such as NDVI, but there is no single equation with a set of coefficients that can be applied to images of different surface types. Estimation of fPAR by satellite imaging requires corrections for atmospheric effects, topography and diurnal variations, and values change rapidly throughout the season with changing phenology. fPAR estimates from visible/near-infrared images require a cloudless, clear image, and thus fPAR values are typically chosen from the best quality images over a multiple day period (often an 8 or 10-day window). For areas that are continually cloudy, the use of radar or lidarmay be necessary to assess vegetation characteristics.

Data Inputs

Remote sensing of fPAR requires an image with red and near-infrared bands.

Software/Hardware Requirements

Calculating fPAR requires image processing and statistical/mathematical modeling software.

Sample Graphic

Global fPAR from Modis imagery (source: Running, S.W. and NTSG. 2002 powerpoint presentation).

fPAR values for the Sahel derived from MODIS satellite images (source: Fensholt et al. 2006).

Images of the Ouachita Mountains in the southwestern US, showing how different indices can provide different types of information. The top three panels are images from April and the bottom three panels are from May of 2004. The leftmost panels (top and bottom) show a near natural color image, the middle panels show LAI (leaf area index) calculated from the images, and the right panels show fPAR . (image source: Short, N. 2009. The Remote Sensing Tutorial, Section 3. Online tutorial)

Strong correlation between NDVI and field measured fPAR across multiple sites (source: Fensholt et al 2004).

Additional Information

Existing datasets

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