TY - JOUR
T1 - Synoptic monitoring of gross primary productivity of maize using landsat data
AU - Gitelson, Anatoly A
AU - Viña, Andrés
AU - Masek, Jeffrey G.
AU - Verma, Shashi B.
AU - Suyker, Andrew E.
N1 - Funding Information:
Manuscript received July 20, 2007; revised August 29, 2007. This work was supported by in part by the NASA Land Cover and Land Use Change Program under Grant NNG06GG17G, in part by the U.S. Department of Energy (DoE) EPSCoR program under Grant DE-FG-02-00ER45827 and the DoE Office of Science (BER) under Grant DE-FG02-03ER63639, and in part by the Hatch Act.
Funding Information:
The authors would like to thank the support and the use of facilities and equipment provided by the Center for Advanced Land Management Information Technologies and the Carbon Sequestration program, University of Nebraska–Lincoln. The authors would also like to thank Dr. F. Jacob and five anonymous reviewers for their very valuable and constructive criticism, comments, and suggestions to the manuscript. This is a contribution of the University of Nebraska Agricultural Research Division, Lincoln, NE.
PY - 2008/4
Y1 - 2008/4
N2 - 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 in a GPP range of 1.88 to 23.1 ; 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.
AB - 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 in a GPP range of 1.88 to 23.1 ; 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.
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U2 - 10.1109/LGRS.2008.915598
DO - 10.1109/LGRS.2008.915598
M3 - Article
AN - SCOPUS:53149096013
VL - 5
SP - 133
EP - 137
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
SN - 1545-598X
IS - 2
M1 - 4454217
ER -