Synoptic monitoring of gross primary productivity of maize using landsat data

Anatoly A Gitelson, Andrés Viña, Jeffrey G. Masek, Shashi B. Verma, Andrew E. Suyker

Research output: Contribution to journalArticlepeer-review

84 Scopus citations


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.

Original languageEnglish (US)
Article number4454217
Pages (from-to)133-137
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number2
StatePublished - Apr 2008

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering


Dive into the research topics of 'Synoptic monitoring of gross primary productivity of maize using landsat data'. Together they form a unique fingerprint.

Cite this