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United States Department of Agriculture

Agricultural Research Service


item Njoku, E
item Wilson, W
item Yeah, S
item Jackson, Thomas
item Lakshmi, V
item Bolten, J

Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/1/2002
Publication Date: N/A
Citation: N/A

Interpretive Summary: In the development of passive microwave remote sensing as a technique for soil moisture estimation, most studies have emphasized single channel retrievals because they are considered to be the most efficient. Vegetation, surface roughness, surface temperature, and soil texture can significantly affect the relationship between the remotely sensed data and soil moisture. Alternative methods must be studied to determine whether information on surface characteristics can be obtained using simultaneous multifrequency and multipolarization and/or multisensor measurements. A new aircraft instrument prototype was developed for obtaining the data described above. It was flown during a large scale field experiment in 1999. The results complement previous research and show the need for more comprehensive studies with diverse vegetation. Further experimentation and analysis will build on this study and provide guidance for future operational system designs.

Technical Abstract: The Passive and Active L- and S-band airborne radiometer sensor (PALS) was flown for the first time during the 1999 Southern Great Plains (SGP99) experiment. Six days of flight data were acquired near Chickasha and El Reno, Oklahoma. In situ sampling of surface soil moisture and vegetation water content was done at several sites within regions overflown by the aircraft. The PALS data consist of radiometer and radar measurements at L and S bands, with V and H (radiometer) and VV, HH, and VH (radar) polarizations. An overview of the data acquired during SGP99 is presented. The data provide verification of the relative sensitivities of L- and S-band passive and active measurements to surface soil moisture and vegetation cover, and indicate a basis for development of combined passive and active multichannel algorithms for soil moisture and vegetation sensing.

Last Modified: 10/17/2017
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