Monitoring crop gross primary productivity using Landsat data
Anatoly
A
Gitelson, University of Nebraska, agitelson2@unl.edu
(Presenting)
Yi
Peng, University of Nebraska, ypeng2@unlnotes.unl.edu
Jeffrey
G
Masek, NASA Goddard Space Center, jeffrey.g.masek@nasa.gov
Galina
P
Keydan, University of Nebraska, gkeydan2@unl.edu
Donald
C
Rundquist, University of Nebraska, drundqui@unlnotes.unl.edu
Shashi
B
Verma, University of Nebraska, sverma@unlnotes.unl.edu
Andrew
E
Suyker, University of Nebraska, asuyker@unlnotes.unl.edu
We present a new technique for GPP estimation in crops is founded on the close and consistent relationship between GPP and crop chlorophyll content, and based entirely on remotely sensed data. A recently proposed Green Chlorophyll Index (Green CI), which employs the green and the NIR spectral bands, was used to retrieve daytime GPP from Landsat ETM+ data. Due to its high spatial resolution (i.e., 30x30m/pixel), this satellite system is particularly appropriate for detecting not only between but also within field GPP variability during the growing season. Both within and between field variability are important components of the crop GPP monitoring, particularly for the estimation of carbon budgets. The Green CI retrieved from atmospherically corrected Landsat ETM+ data taken in 2001 through 2008 was found to be linearly related with GPP of irrigated and rainfed maize and soybeans explaining about 90% of GPP variation. Green CI constitutes an accurate surrogate measure for GPP estimation. These results open new possibilities for analyzing the spatio-temporal variation of the GPP of crops using the extensive archive of Landsat imagery acquired since the early 1980s.
Presentation Type: Poster
Poster Session: Carbon Cycle Science
NASA TE Funded Awards Represented:
Gitelson, Anatoly
A Satellite-Based Quantification of Carbon Exchange of the Dominant Ecosystems (Maize-Soybean) in the NACP Mid-Continent Intensive (MCI) Region