Remote estimation of crop gross primary production with Landsat data

Anatoly A. Gitelson, Yi Peng, Jeffery G. Masek, Donald C. Rundquist, Shashi Verma, Andrew Suyker, John M. Baker, Jerry L. Hatfield, Tilden Meyers

Research output: Contribution to journalArticle

107 Scopus citations

Abstract

An accurate and synoptic quantification of gross primary production (GPP) in crops is essential for studies of carbon budgets at regional and global scales. In this study, we tested a model, relating crop GPP to a product of total canopy chlorophyll (Chl) content and potential incident photosynthetically active radiation (PAR potential). The approach is based on remotely sensed data; specifically, vegetation indices (VI) that are proxies for total Chl content and PAR potential, which is incident PAR under a condition of minimal atmospheric aerosol loading. Using VI retrieved from surface reflectance Landsat data, we found that the model is capable of accurately estimating GPP in maize, with coefficient of variation (CV) below 23%, and in soybean with CV below 30%. The algorithms established and calibrated over three Mead, Nebraska AmeriFlux sites were able to estimate maize and soybean GPP at tower flux sites in Minnesota, Iowa and Illinois with acceptable accuracy.

Original languageEnglish (US)
Pages (from-to)404-414
Number of pages11
JournalRemote Sensing of Environment
Volume121
DOIs
StatePublished - Jun 1 2012

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Keywords

  • Chlorophyll content
  • Gross primary production
  • Landsat
  • Potential incident photosynthetically active radiation
  • Vegetation index

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

Cite this

Gitelson, A. A., Peng, Y., Masek, J. G., Rundquist, D. C., Verma, S., Suyker, A., Baker, J. M., Hatfield, J. L., & Meyers, T. (2012). Remote estimation of crop gross primary production with Landsat data. Remote Sensing of Environment, 121, 404-414. https://doi.org/10.1016/j.rse.2012.02.017