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United States Department of Agriculture

Agricultural Research Service


item Sano, Edson
item Qi, J.
item Huete, Alfredo
item Moran, Mary

Submitted to: Proceedings of the Latino American Seminar on Radar Remote Sensing
Publication Type: Proceedings
Publication Acceptance Date: 11/27/1997
Publication Date: N/A
Citation: Sano, E.E., Qi, J., Huete, A.R., Moran, M.S. 1997. The use of sar/tm synergy for estimating soilmoisture content over a semi-arid rangeland. In:Proceedings 2nd Latino-American Seminar on Radar Remote Sensing, Sept. 11-12, 1997, Santos, Sao Paulo, Brazil. p. 175-183.

Interpretive Summary: Regional maps of soil moisture are important for many applications, including urban and rural planning, global climate analysis and water resource monitoring. Unfortunately, there is no accepted method for mapping soil moisture over large areas. There is evidence that images acquired by satellite-based sensors which measure the backscatter from radar beams can be used to estimate soil moisture. In this study, radar images were combined with other easily obtained data to map the surface soil moisture over a semi-arid rangeland. This appears to be a promising approach for mapping regional soil moisture with reasonable expense and minimal labor.

Technical Abstract: The C-band ERS-1 SAR data were combined with the Landsat TM data to improve the soil moisture estimates in a semiarid region. The SAR data were compared with the soil moisture measurement at three conditions: a) without any correction for soil roughness and vegetation effects; b) corrected for soil roughness and effects; and c) corrected for both soil roughness and vegetation effects. The soil roughness effects were taken into account by using a dry season SAR image. The vegetation influence was considered by using an empirical relationship between SAR and leaf area index data, the latter being derived from TM images. Results indicated that the contribution of soil roughness and vegetation in the radar backscatter were significant and they must be taken into account to obtain accurate soil moisture estimations.

Last Modified: 10/16/2017
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