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ARS Home » Research » Publications at this Location » Publication #94948
Title: SOIL MOISTURE EVALUATION USING A CALIBRATED SENSOR NETWORK AND A SOIL- VEGETATION-ATMOSPHERE-TRANSFER MODEL

Author
item HYMER, D - USDA-SWRC TUCSON AZ
item Moran, Mary
item KEEFER, T - USDA-SWRC TUCSON AZ

Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/15/1999
Publication Date: N/A
Citation: N/A

Interpretive Summary: Soil moisture plays a critical role in the distribution and flow of water within and between many ecosystems. Unfortunately, the spatial and temporal distribution of soil moisture has proven difficult to measure because many traditional methods are often time consuming, expensive, or inaccurate. Using a combination of recent technologies, however, it may be possible to integrate traditional methods with satellite and computer model measurements to estimate soil moisture over large areas and time. The results of this work will provide a new methodology for measuring soil moisture and will, ultimately, yield a better understanding of hydrologic processes in semi-arid regions.

Technical Abstract: Recent studies have proposed that images from Synthetic Aperture Radar (SAR) sensors can be used to map spatially distributed soil moisture patterns within 5 cm of the surface. Unfortunately, many hydrologic applications require vadose zone soil moisture measurements rather than surface soil moisture measured by the SAR sensor. By combining SAR- derived surface soil moisture maps with a Soil-Vegetation-Atmosphere- Transfer (SVAT) model, it may be possible to obtain spatially distributed, temporally continuous information on vadose zone soil moisture. The first step in developing such a combined approach is to investigate the accuracy and precision of a SVAT model to estimate surface and vadose zone soil moisture over time. In this experiment, we evaluated the Simultaneous Heat and Water (SHAW) model by comparing its soil moisture estimates to a calibrated, one year, hourly soil moisture data set at three different depths under bare soil and shrub cover surfaces. Analysis indicated that the SHAW model overestimated soil moisture at each depth under bare soil and underestimated soil moisture at each depth under shrub cover. Based on this research, future studies should focus on calibration of the SHAW model and the assimilation of remotely sensed data as a primary model input.