Synoptic Monitoring of Gross Primary Productivity of Maize Using Landsat Data

Authors

Anatoly A. Gitelson, University of Nebraska at LincolnFollow
Andrés Viña, Michigan State University
Jeffrey G. Masek, NASA Goddard Space Flight Center, Greenbelt, MD
Shashi Verma, University of Nebraska - LincolnFollow
Andrew E. Suyker, University of Nebraska - LincolnFollow

Document Type

Article

Date of this Version

4-2008

Comments

Published in IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 5, NO. 2, APRIL 2008, pp. 133-137. Copyright © 2008 IEEE. Used by permission.

Abstract

There is a growing interest in monitoring the gross primary productivity (GPP) of crops due mostly to their carbon sequestration potential. Both within- and between-field variability are important components of crop GPP monitoring, particularly for the estimation of carbon budgets. In this letter, we present a new technique for daytime GPP estimation in maize based on the close and consistent relationship between GPP and crop chlorophyll content, and entirely on remotely sensed data. A recently proposed chlorophyll index (CI), which involves green and near-infrared spectral bands, was used to retrieve daytime GPP from Landsat Enhanced Thematic Mapper Plus (ETM+) data. Because of its high spatial resolution (i.e., 30 × 30 m/pixel), this satellite system is particularly appropriate for detecting not only between- but also within-field GPP variability during the growing season. The CI obtained using atmospherically corrected Landsat ETM+ data was found to be linearly related with daytime maize GPP: root mean squared error of less than 1.58 gC · m⁻² · d⁻¹ in a GPP range of 1.88 to 23.1 gC · m⁻² · d⁻¹; therefore, it constitutes an accurate surrogate measure for GPP estimation. For comparison purposes, other vegetation indices were also tested. These results open new possibilities for analyzing the spatiotemporal variation of the GPP of crops using the extensive archive of Landsat imagery acquired since the early 1980s.