MULTI-PLATFORM SOIL MOISTURE SCALING OVER THE SOUTHERN GREAT PLAINS USING IN SITU, SATELLITE RETRIEVAL AND DATA ASSIMILATION
Hydrology and Remote Sensing Laboratory
Project Number: 1245-13610-028-07
Start Date: Sep 01, 2009
End Date: Sep 30, 2013
Soil moisture has been observed using different methodologies; remote sensing using airborne/satellite platforms, intensive ground-based measurements during various field campaigns, and soil moisture monitoring networks. Soil moisture data from remote sensing are usually available at very low spatial resolution and only for a shallow soil depth. In contrast, ground-based and in situ point measurements can extend beyond the rooting zone but are difficult to extrapolate spatially. Reconciling these remote sensing and ground-based, especially in situ networks, each with a different extent, support, and spacing is of paramount significance in the development of robust methodology to predict shallow subsurface soil moisture, surface runoff, and ground water recharge at different spatial scales. This research would establish site specific and generic scaling relationships. This new knowledge would lead to higher spatial resolution information for hydrologic and agricultural decision making.
Building on our past/ongoing field campaigns, experience with in situ networks, multi-platform remote sensing campaigns, hydrologic process-based modeling, and data assimilation research, a comprehensive study of soil moisture scaling behavior will be conducted that focuses on the key issues of measurement support size and the precision of in situ and remote sensing platforms. Research would be conducted in the Southern Great Plains (SGP). Networks in the region would be integrated with data from current and future remote sensing platforms that monitor soil moisture at different resolutions. Characterizing the points (accuracy and reliability) and developing techniques for scaling to these diverse resolutions will be accomplished using a number of statistical and modeling approaches and additional intensive observational studies designed to resolve issues with existing infrastructure.