Net Primary Productivity

by Megan Kanaga Creutzburg

Other Names:

Net Primary Production


Primary production is the rate of organic biomass growth or accumulation by plants. Primary production is commonly split into two components, gross primary productivity (GPP) and net primary productivity (NPP). Gross primary productivity is the overall rate of biomass production by producers, whereas net primary productivity is the remaining fraction of biomass produced after accounting for energy lost due to cellular respiration and maintenance of plant tissue. Thus,
NPP = GPP – respiration.
NPP is an important component of the global carbon budget and is used as an indicator of ecosystem function. NPP can be directly assessed by measuring plant traits or harvesting plant material on the ground, but across large areas remotely sensed images can be used to estimate NPP. NPP is often calculated as a product of fPAR (fraction of photosynthetically active radiation) and light use efficiency (also called radiation use efficiency). Common inputs to NPP models include land cover, phenology, surface meteorology, and leaf area index (LAI).

NPP models for remote sensing vary widely depending on the ecosystem type and desired output, but there are two main ways to estimate vegetation productivity using remotely-sensed images:

  • Linear Modeling – linear modeling approaches attempt to relate reflectance data recorded by a sensor to field measurements of NPP using linear regression techniques. Such approaches may correlate field-measured fractional cover with sensor reflectance bands, or to vegetation indices like NDVI. This method is useful for estimating live biomass.
  • Physical Models – Physical models use principles of how light energy is absorbed or reflected from different surfaces to estimate physical characteristics of vegetation. Biophysical models incorporate parameters related to how light interacts with processes like photosynthesis, evapotranspiration, stress, and decay of plant material. This method uses theoretical models of radiative transfer theory to estimate the absorbed photosynthetically active radiation (see fPAR), and has been successful in predicting biomass across wide scales and different climatic regimes.

See Lu et al. (2006) for a review of various approaches to biomass estimation using remotely sensed data.

These methods require correction for atmospheric variation and sometimes require bidirectional reflectance normalization. The input 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 NPP will vary depending on the type of vegetation being considered, and thus major land cover type is an important input to calculating NPP.

Similar Methods


NPP models produce a map of vegetation biomass for a particular spatial and temporal resolution determined by the input data. The nature of the information depicted on the map (e.g., whether the map shows only live biomass or also includes senescent biomass, the units used, etc.) depends on the NPP model chosen.

Successful Rangeland Uses

Net primary productivity estimates have been used for many purposes:

  • Assessing ecosystem function
  • Estimating crop yields or stocking rates of livestock
  • Monitoring changes in productivity over time
  • Monitoring vegetation health
  • Assessing carbon budget and effects of climate change

Specific rangeland applications include:

  • Hunt and Miyake (2006) compared remotely-sensed estimates of NPP with GIS-based estimates from soil surveys to determine if either approach would be suitable for estimating stocking rates of livestock at a state-wide scale in Wyoming.
  • Reeves et al. (2001) described the applicability of productivity estimates from MODIS data for monitoring rangeland health
  • Wessels et al. (2003) used remotely sensed measures of NPP to evaluate the extent of land degradation in southern Africa

Application References

  • Hunt, Jr, E.R. and B.A. Miyake. 2006. Comparison of stocking rates from remote sensing and geospatial data. Rangeland Ecology and Management 59: 11-18.
  • Reeves, M.C., J.C. Winslow, and S.W. Running. 2001. Mapping weekly rangeland vegetation productivity using MODIS algorithms. Journal of Rangeland Management 54: A90-A105.
  • Wessels, K.J., S.D. Prince, and J. Small. 2003. Monitoring land degradation in southern Africa based on net primary productivity. Geoscience and Remote Sensing Symposium, 2003. IGARSS ’03. Proceedings. 2003 IEEE International 5: 3305- 3307.

Technical References

  • Cheng, W., D.A. Sims, Y. Luo, J.S. Coleman, and D.W. Johnson. 2000. Photosynthesis, respiration, and net primary production of sunflower stands in ambient and elevated atmospheric CO2 concentrations: an invariant NPP:GPP ratio? Global Change Biology 6: 931-941.
  • Choudhury, B.J. 1987. Relationships between vegetation indices, radiation absorption, and net photosynthesis evaluated by a sensitivity analysis. Remote Sensing of Environment 22: 209-233.
  • Cramer, W., D.W. Kicklighter, A. Bondeau, B. Moore III, G. Churkina, B. Nemry, A. Ruimy, A.L. Schloss, et al. 1999. Comparing global models of terrestrial net primary productivity (NPP): overview and key results. Global Change Biology 5 (Suppl. 1): 1-15.
  • 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.
  • 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.
  • Jobbagy, E.G., O.E. Sala, and J.M. Paruelo. 2002. Patterns and controls of primary production in the Patagonian steppe: a remote sensing approach. Ecology 83: 307-319.
  • Lu, D. 2006. The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing 27: 1297-1328.
  • Paruelo, J.M., M. Oesterheld, C.M. Di Bella, A. Martin, J. Lafontaine, M. Cahuepe, and C.M. Rebella. 2000. Estimation of primary production of subhumid rangelands from remote sensing data. Applied Vegetation Science 3: 189-195.
  • Piñeiro, G., M. Oesterheld, and J.M. Paruelo. 2006. Seasonal variation in aboveground production and radiation-use efficiency of temperate rangelands estimated through remote sensing. Ecosystems p: 537-373.
  • Potter, C., S. Klooster, A. Huete, and V. Genovese. 2007. Terrestrial carbon sinks for the United States predicted from MODIS satellite data and ecosystem modeling. Earth Interactions 11: 1-21.
  • Running, S. W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., & Hashimoto, H. 2004. A continuous satellite-derived measure of global terrestrial production. BioScience 54: 547−560.
  • Turner, D.P., W.D. Ritts, W.B. Cohen, S.T. Gower, S.W. Running, M. Zhao, M.H. Costa, A.A. Kirschbaum, J.M. Ham, S.R. Saleska, and D.E. Ahl. 2006. Evaluation of MODIS NPP and GPP products across multiple biomes. Remote Sensing of Environment 102: 282-292.


Remotely sensed NPP estimates are only approximations of true biomass values. The mathematical models used to calculate NPP 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. NPP 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 NPP by satellite imaging requires corrections for atmospheric effects, topography and diurnal variations, and values change rapidly throughout the season with changing phenology. NPP estimates from visible/near-infrared images require a cloudless, clear image, and thus NPP 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 lidar may be necessary to assess vegetation characteristics.

Data Inputs

The remote sensing data inputs for NPP will vary depending on the method that is used to estimate it. Generally, an image with visible and near infrared bands is required. If an empirical approach is being used, ground measurements will be needed in order to derive estimates of NPP.

Software/Hardware Requirements

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

Sample Graphics

Map of global net primary productivity (g C m-2yr-1) from the International Global Biosphere Programme

High correlation (R2=0.91) between the predicted NPP from NASA-CASA models based on EVI (enhanced vegetation index) and field measurements of NPP (source: Potter et al. 2007)

Additional Information

Existing datasets

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