Remote Estimation of Gross Primary Production in Maize

Authors

Yi Peng, University of Nebraska - Lincoln
Anatoly A. Gitelson, University of Nebraska - LincolnFollow
Galina Keydan, University of Nebraska - Lincoln
Donald C. Rundquist, University of Nebraska - LincolnFollow
Bryan Leavitt, University of Nebraska-LincolnFollow
Shashi B. Verma, University of Nebraska - LincolnFollow
Andrew E. Suyker, University of Nebraska - LincolnFollow

Date of this Version

7-2010

Comments

Published in the Proceedings of the 10th International Conference on Precision Agriculture (July 18-20, 2010, Denver, Colorado, USA). Copyright 2010, Colorado State University. Used by permission.

Abstract

There is a growing interest in the estimation of gross primary productivity (GPP) in crops due to its importance in regional and global studies of carbon balance. We have found that crop GPP was closely related to its total chlorophyll content, and thus chlorophyll can be used as a proxy of GPP in crops. In this study, we tested the performance of various vegetation indices for estimating GPP. The indices were derived from spectral data collected remotely but at close-range over a period of eight years, from 2001 through 2008. The results show that chlorophyll indices, based on near infrared and either the green or red-edge regions of the spectrum, are capable of accurately predicting widely variable GPP in maize under both rainfed and irrigated conditions.