|HAN, EUNJIN - Purdue University|
|MERWADE, VENKATESH - Purdue University|
Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/23/2012
Publication Date: 6/1/2012
Citation: Han, E., Heathman, G.C., Merwade, V., Cosh, M.H. 2012. Application of observation operators for field scale soil moisture averages and variances in agricultural landscapes. Journal of Hydrology. 444-445:34-50.
Interpretive Summary: Scale difference between ground-based and remotely sensed soil moisture observations has been an issue for validation of remote sensing data, soil moisture data assimilation and calibration of hydrologic models. This study aims to link two different scales of soil moisture estimates by upscaling single point measurements to field averages for representing field-scale agricultural areas (~ 2 ha) located within the Upper Cedar Creek Watershed in northeastern Indiana. Several statistical methods, mainly focusing on cumulative distribution function (CDF) matching, are tested to upscale point measurements to spatially representative soil moisture. These transforming equations are termed observation operators. The main objectives of this study are: 1) to find appropriate way(s) for upscaling point soil moisture measurements to field averages by comparing different observation operators; 2) to explore whether the observation operator from CDF matching is transferable in time and in space (between different fields and between different soil layers); 3) to find the most influential factors affecting the temporal or spatial transferability of the observation operators, and 4) to estimate spatial variability (standard deviation) of soil moisture within the study areas using the same point measurements and CDF matching method. The CDF matching is found to be the optimum upscaling method. Results indicate that the observation operators are transferable in space, but not in time. Rainfall characteristics and crop types are most likely major factors affecting the transferability of observation operators. In addition, the CDF matching approach is found to be an effective method to deduce soil moisture variability (standard deviation) from single point measurements.
Technical Abstract: Soil moisture is a key variable in understanding hydrologic processes and energy fluxes at the land surface. In spite of developing technologies for in-situ soil moisture measurements and increased availability of remotely sensed soil moisture data, scaling issues between soil moisture observations and also model grid sizes remain obstacles for full utilization of the available data types. In addition, proper linkage of soil moisture estimates across different scales of observations and model predictions is essential for the calibration and validation of current and upcoming space-borne surface soil moisture retrievals, as well as the successful application of data assimilation techniques. This study aims to link two different scales of soil moisture estimates by upscaling point soil moisture measurements to field average in representing field-scale agricultural watersheds (~ 2 ha) located within the Upper Cedar Creek Watershed in northeastern Indiana. Several statistical methods, mainly focusing on cumulative distribution function (CDF) matching, are tested to upscale point measurements to the field average soil moisture. These transforming equations are termed observation operators. This study also tests the temporal and spatial (horizontal and vertical) transferability of the observation operators. Results indicate that the observation operators were not transferable in time, but were spatially transferable. In addition, the CDF matching approach is also shown to be an effective method to deduce soil moisture variability (standard deviation) from single point measurements.