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United States Department of Agriculture

Agricultural Research Service

Title: An Evaluation of Soil Moisture and Vegetation Estimation Using Passive/active Microwave and Optical Remote Sensing

Authors
item Bolten, J - UNIV OF SC
item Lakshmi, V - UNIV OF SC
item Gasiewski, A - NOAA
item Njokue, G - NASA/JPL
item Jackson, Thomas

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: November 1, 2002
Publication Date: December 8, 2002
Citation: Bolten, J., Lakshmi, V., Gasiewski, A.J., Njoku, G., Jackson, T.J. 2002. An Evaluation of Soil Moisture and Vegetation Estimation Using Passive/Active Microwave and Optical Remote Sensing. American Geophysical Union, 2002, EOS Transactions of AGU. 83:F508.

Technical Abstract: Advances in remote sensing technology and their applications to hydrology and land surface modeling have progressed over the last decade. The abundance of available aircraft/satellite-based platforms, in addition to extensive validation studies, has led to robust retrieval algorithms of geophysical parameters (i.e., vegetation and soil moisture) supporting global change monitoring efforts. The newly launched Aqua satellite will allow global coverage of these surface parameter estimates. In order to best simulate the surface properties using satellites, it is necessary to combine several temporal and spatial resolutions. Given that sensors have varying frequencies, spatial/temporal resolutions and sensitivities to vegetation and surface roughness, proper methods for accounting for these differences are required for algorithm development. This work will emphasize an analysis of soil moisture and vegetation parameter retrievals using a)Satellite (Landsat Thematic Mapper) data, b)Aircraft (Passive/Active L/S -Band instrument (PALS) data, and the Polarimetric Scanning Radiometer (PSR) data), c)In-situ data acquired during the 1999 Southern Great Plains experiment (SGP99). The temporal and spatial co-location of these instruments enables an assessment of remote sensing capabilities combining multiple wavelengths, active/passive/optical data, and polarization ratios/frequency indexes for optimum parameter estimation.

Last Modified: 4/20/2014
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