Monitoring the Hydrological Cycle

These two SIR-C/X SAR radar images show the change in soil moisture in the Chickasha, Oklahoma, agricultural region between April 12 (shortly after ~2 centimeters of rain) and April 15, 1994, following three days of drying. These images illustrate an average change in soil moisture over this area of about 10% (approximately 20% to 10%), which matches the changes measured on the ground. Although still undergoing validation, the results from this technique of observing and calculating soil moisture are very promising. The redistribution of solar energy over the Earth is central to global climate studies. Water plays a fundamental role in this redistribution through the energy associated with evapotranspiration (the transfer of water from the Earth's surface to the atmosphere by evaporation from lakes, streams, etc., soil surfaces, and transpiration from plants), the transport of atmospheric water vapor, and precipitation. In addition to its major role in global climate conditions, spatial distribution of soil moisture also affects local weather patterns. Monitoring soil moisture is essential in studies of vegetation and ecology, and soil moisture maps help in predicting and monitoring floods. LightSAR's Additional Contribution: LightSAR has the unique capability of collecting multiple polarization-mode data covering a meaningful swath of land on a continuous and frequent-enough basis to contribute significantly to the mapping of soil moisture. The sensitivity to soil moisture illustrated here should have many hydrologic applications. For example, this technique could be used to estimate the surface soil moisture for the land surface boundary conditions used in mesoscale meteorological models. Model studies from the 1993 midwestern flooding have demonstrated that high levels of soil moisture are essentially re-cycled back to the atmosphere and can result in severe rain storms. One possible practical application of these measurements could be used to improve short-term severe weather forecasting by using actual measured moisture conditions at the land surface rather than constant or estimated values. Technical Details and Theory Discipline-related Links References

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