Page Banner

United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: Utilization of point soil moisture measurements for field scale soil moisture averages and variances in agricultural landscapes

Authors
item Han, E -
item Heathman, Gary
item Merwade, V -
item CROW, WADE

Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: April 1, 2012
Publication Date: April 19, 2012
Citation: Han, E., Heathman, G.C., Merwade, V., Crow, W.T. 2012. Utilization of point soil moisture measurements for field scale soil moisture averages and variances in agricultural landscapes[abstract]. BARC Poster Day. No. 28.

Technical Abstract: Soil moisture is a key variable in understanding the hydrologic processes and energy fluxes at the land surface. In spite of new technologies for in-situ soil moisture measurements and increased availability of remotely sensed soil moisture data, scaling issues between soil moisture observations and model grid sizes pose a problem for full utilization of the available data. For example, the scale of point measurements (support area less than 1m^2) typically does not match the size of model grids (support area greater than 10m^2). Therefore, proper linkage of soil moisture estimates across different scales of observations and model predictions is essential for the validation of current and upcoming space-borne surface soil moisture retrievals, as well as improvement of hydrologic model predictions. 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. Temporal and spatial (horizontal and vertical) transferability of different observation operators are also evaluated for practical applications of the upscaling approaches. The CDF matching is found to be the most successful upscaling method followed by the mean relative difference method using temporally stable point measurements. Results indicate that the two observation operators from the CDF matching approach and the mean relative difference method using a temporally stable location are transferable in space, but not in time. Rainfall characteristic is most likely the dominant factor affecting the success of observation operator transferability. Besides field averages, standard deviations of soil moisture within the fields are also estimated using single point measurements. Dynamic variations of standard deviations are successfully predicted through the similar CDF matching method. This approach, which estimates time-series soil moisture variance using point measurements, has potential for various practical applications such as soil moisture data assimilation, land surface modeling, assessing the accuracy of remote sensed soil moisture and establishing optimal in-situ soil moisture networks.

Last Modified: 9/10/2014
Footer Content Back to Top of Page