Remote estimation of crop gross primary production with Landsat data

Authors

Anatoly Gitelson, University of Nebraska-LincolnFollow
Yi Peng, University of Nebraska-LincolnFollow
Jeffrey G. Masek, NASA Goddard Space Flight Center, Greenbelt, MDFollow
Donald Rundquist, University of Nebraska - LincolnFollow
Shashi Verma, University of Nebraska - LincolnFollow
Andrew E. Suyker, University of Nebraska - LincolnFollow
John M. Baker, National Laboratory for Agriculture and the Environment, USDA, Ames, IAFollow
Tilden Meyers, NOAA, Oak Ridge, TNFollow

ORCID IDs

Anatoly Gitelson

Date of this Version

5-2012

Citation

Remote Sensing of Environment 121 (2012) 404–414; doi:10.1016/j.rse.2012.02.017

Comments

A US government work.

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 PARpotential, 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.