Synoptic Monitoring of Gross Primary Productivity of Maize Using Landsat Data

• Published in: IEEE geoscience and remote sensing letters Vol. 5; no. 2; pp. 133 - 137
• Main Authors: Gitelson, A.A., Vina, A., Masek, J.G., Verma, S.B., Suyker, A.E.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-04-2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Atmospheric measurements; Carbon; Chlorophyll; Chlorophylls; Crops; Daytime; Error correction; Information retrieval; Landsat satellites; Maize; Monitoring; Optical imaging; Productivity; Remote monitoring; Remote sensing; Satellites; Spatial resolution; Vegetation mapping; Optical imaging; remote sensing; vegetation mapping
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2008.915598

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.