Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO₂ flux tower data

Authors

Joshua L. Kalfas, University of OklahomaFollow
Xiangming Xiao, University of OklahomaFollow
Diana X. Vanegas, University of OklahomaFollow
Shashi B. Verma, University of Nebraska-LincolnFollow
Andrew E. Suyker, University of Nebraska-LincolnFollow

Document Type

Article

Date of this Version

2011

Citation

Published in Agricultural and Forest Meteorology 151 (2011), pp 1514– 1528. doi 10.1016/j.agrformet.2011.06.007

Comments

Copyright © 2011 Elsevier B.V. Used by permission.

Abstract

Abstract

Information on gross primary production (GPP) of maize croplands is needed for assessing and monitoring maize crop conditions and the carbon cycle. A number of studies have used the eddy covariance technique to measure net ecosystem exchange (NEE) of CO₂ between maize cropland fields and the atmosphere and partitioned NEE data to estimate seasonal dynamics and interannual variation of GPP in maize fields having various crop rotation systems and different water management practices. How to scale up in situ observations from flux tower sites to regional and global scales is a challenging task. In this study, the Vegetation Photosynthesis Model (VPM) and satellite images from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to estimate seasonal dynamics and interannual variation of GPP during 2001–2005 at five maize cropland sites located in Nebraska and Minnesota of the U.S.A. These sites have different crop rotation systems (continuously maize vs. maize and soybean rotated annually) and different water management practices (irrigation vs. rain-fed). The VPM is based on the concept of light absorption by chlorophyll and is driven by the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI), photosynthetically active radiation (PAR), and air temperature. The seasonal dynamics of GPP predicted by the VPM agreed well with GPP estimates from eddy covariance flux tower data over the period of 2001–2005. These simulation results clearly demonstrate the potential of the VPM to scale-up GPP estimation of maize cropland, which is relevant to food, biofuel, and feedstock production, as well as food and energy security.