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Proceedings Paper

Microwave vegetation indices derived from satellite microwave radiometers
Author(s): T. J. Jackson; J. C. Shi; J. Tao
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Paper Abstract

Vegetation indices are valuable in many fields of geosciences. Conventional, visible-near infrared, indices are often limited by the effects of atmosphere, background soil conditions, and saturation at high levels of vegetation. In this study, the theoretical basis for a new type of passive microwave vegetation indices (MVIs) based on data from the Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua satellite is developed. Numerical simulation results were used to establish relationships of bare soil surface emissivities at different frequencies. Using a radiative transfer model, a linear relationship between the brightness temperatures observed at two adjacent radiometer frequencies can be derived. The intercept and slope of this linear function depend only on the vegetation properties and can be used as vegetation indices. These can be derived from the dual-frequency and dual-polarization satellite measurements under the assumption that there is no significant impact of the polarization dependence on the vegetation signals. To demonstrate the potential of the new microwave vegetation indices, we compared them with the Normalized Difference of Vegetation Index (NDVI) derived using MODIS at continental and global scales. The results indicate that the MVIs provide a complementary dataset for monitoring global short vegetation and seasonal phenology from space.

Paper Details

Date Published: 10 September 2008
PDF: 9 pages
Proc. SPIE 7083, Remote Sensing and Modeling of Ecosystems for Sustainability V, 708302 (10 September 2008); doi: 10.1117/12.790529
Show Author Affiliations
T. J. Jackson, U.S. Dept. of Agriculture (United States)
J. C. Shi, Univ. of California, Santa Barbara (United States)
J. Tao, Beijing Normal Univ. (China)


Published in SPIE Proceedings Vol. 7083:
Remote Sensing and Modeling of Ecosystems for Sustainability V
Wei Gao; Hao Wang, Editor(s)

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