|HAN, EUNJIN - Purdue University|
Submitted to: Interagency Conference on Research in the Watersheds
Publication Type: Proceedings
Publication Acceptance Date: 6/29/2011
Publication Date: 9/26/2011
Citation: Heathman, G.C., Cosh, M.H., Han, E., Jackson, T.J. 2011. Spatio-temporal analysis of surface and subsurface soil moisture for remote sensing applications within the Upper Cedar Creek Watershed [abstract]. In: Proceedings of the Interagency Conference on Research in the Watersheds, September 26-30, 2011, Fairbanks, Alaska. 2011 CD ROM.
Technical Abstract: Soil moisture is an intrinsic state variable that varies considerably in space and time. From a hydrologic viewpoint, soil moisture controls runoff, infiltration, storage and drainage. Soil moisture determines the partitioning of the incoming radiation between latent and sensible heat fluxes. Although soil moisture is highly variable, if measurements of soil moisture at the field or watershed scale are repeatedly observed, certain locations can often be identified as being temporally stable and representative of an area average. Temporal stability analysis (TSA) is a statistical approach for describing the persistence of spatial patterns and characteristic behavior of soil moisture. Using TSA, this study was aimed at determining the adequacy of long term point-scale surface and subsurface soil moisture measurements in representing field and watershed scale averages that will serve as in situ ground truth locations for the NASA Soil Moisture Active Passive calibration and validation program. Soil moisture data were obtained by frequency-domain reflectometry (FDR) sensors permanently installed at depths of 5, 20, 45, and 60 cm located within the USDA, Upper Cedar Creek Watershed (UCCW) monitoring network in northeastern Indiana. In two agricultural fields (2.23 and 2.71 ha), twenty additional FDR sensors, spaced 70 m apart, were installed at depths of 5 and 20 cm in each field with automated data collection being transmitted every 30 min from June 29 through September 21, 2010. Additionally, meteorological data (i.e., rainfall, air temperature) were obtained from existing weather stations in the network. Spatio-temporal analysis revealed persistent patterns in surface soil moisture at the watershed scale and within each field and identified sites that were temporally stable. However, results showed that soil water patterns differed between preferred states (wet/dry) and were dominated by lateral and vertical fluxes, respectively. At the field scale, locations that were optimal for estimating field-average water contents over time were different from permanent sensor locations. However, minimum offset values could be applied to the permanent sensor data to obtain representative field average values of surface soil moisture. TSA of 20 cm soil moisture showed little correlation with surface 'v TSA results in terms of comparable temporally stable sites at both scales. The results are of relevance for interpreting and downscaling coarser resolution soil moisture data such as that retrieved from active and passive microwave platforms, as well as, calibration and validation of remotely sensed soil moisture.