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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: Microwave Vegetation Indexes Derived from Satellite Microwave Radiometers

Authors
item Shi, J - UNIV. CALIF. SANTA BARBAR
item Jackson, Thomas
item Tao, J - VISITING SCIENTIST
item Du, J - VISITING SCIENTIST
item Bindlish, R - SSAI

Submitted to: International Geoscience and Remote Sensing Symposium Proceedings
Publication Type: Proceedings
Publication Acceptance Date: July 1, 2007
Publication Date: November 1, 2007
Citation: Shi, J., Jackson, T., Tao, J., Du, J., Bindlish, R. 2007. Microwave vegetation indexes derived from satelite microwave radiometers. In: Proceedings of the International Geoscience and Remote Sensing Symposium, July 23-27, 2007, Barcelona, Spain. p. 1412-1415.

Technical Abstract: Major uncertainties in deriving vegetation indices from satellite measurements are the effects of atmosphere and background soil conditions. Through numerical simulations by surface emission model – Advanced Integral Equation Model (AIEM), we found that bare surface emissivities at different frequencies can be well characterized by a linear function with parameters that are dependent on the pair of frequencies to be used. This makes it possible to minimize the surface emission signal and maximize the vegetation signal when using multi-frequency radiometer measurements. Using the radiative transfer model ('-' model), a linear relationship between the brightness temperatures observed at two adjacent radiometer frequencies can be derived. It can be shown that the microwave vegetation index derived by the intercept and slope of this linear function depends only on vegetation properties and can be derived from the dual-frequency and dual-polarization measurements. We will demonstrate the theoretical basis of this new microwave vegetation index and show comparisons of the microwave derived vegetation index with the optical sensor derived NDVI measurements.

Last Modified: 12/19/2014
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