CONSERVATION EFFECTS ASSESSMENT FOR THE ST. JOSEPH RIVER WATERSHED
Location: National Soil Erosion Research Lab
Title: Field scale spatiotemporal analysis of surface soil moisture for evaluating point-scale in situ networks
Submitted to: Geoderma
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 10, 2011
Publication Date: January 15, 2012
Citation: Heathman, G.C., Cosh, M.H., Han, E., Jackson, T.J., McKee, L.G., McAfee, S.J. 2012. Field scale spatiotemporal analysis of surface soil moisture for evaluating point-scale in situ networks. Geoderma. 170:195-205.
Interpretive Summary: In measuring soil moisture from agricultural fields, it is always a challenge to find out how the measurement made at a certain location can be used to represent the average moisture content for the field. The technique to identify the suitable sampling point to represent the field average is called stability analysis. In this research, we conducted extensive field scale stability analysis of 5 cm soil moisture measurements in two small agricultural watersheds within the Upper Cedar Creek Watershed in northeastern Indiana. In addition to permanent soil moisture sensors positioned at 5, 20, 40 and 60 cm in each field, 20 additional sensors were installed in both fields at the 5 cm depth and on a 35 m grid for the experimental period from July 15 to September 20, 2009. The two agricultural field sites (AS1 and AS2) in this study are part of a larger environmental monitoring network that serve as areas to compare the effects of no-till and rotational tillage practices on soil hydrology, and sediment and chemical losses. Both fields are in a corn/soybean rotation with the 2.21 ha AS1 field being no-till (NT) and the 2.71 ha AS2 field having rotational tillage (RT) each spring in years when corn is planted. The purpose of this study was to determine to what extent permanently installed point-scale measurements of 5cm surface soil moisture represent local field, time-averaged soil moisture conditions in each field. Such knowledge would provide insight for field scale hydrologic modeling in terms of measured soil moisture data as input for surface soil moisture data assimilation, as well as for calibration and validation of soil moisture model output. In addition, it would be useful to determine how well the point-scale in situ measurements represent local field scale soil moisture dynamics that could potentially serve as ground truth data for large scale remotely sensed surface soil moisture studies. The results showed that time-stable sites, having a non-zero mean relative difference in each field could be used to represent the field average soil moisture content. However, application offset values for the permanent sensor location in AS1 gave poor estimates of field average conditions, whereas the AS2 permanent sensor offset estimates were marginal. This study further emphasizes the need to carefully evaluate in situ soil monitoring networks used in hydrologic studies, be it from a modeling perspective or in the case of advancing our remote sensing capabilities for observing soil moisture from air or space-borne platforms.
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 may be highly variable in space and time, if measurements of soil moisture at the field or small watershed scale are repeatedly observed, certain locations can often be identified as being temporally stable and representative of the an area average. This study is aimed at determining the adequacy of long term point-scale surface soil moisture measurements in representing local field scale averages which may ultimately serve as in situ locations for the calibration and validation of remotely sensed soil moisture. Experimental data were obtained by frequency-domain reflectometry (FDR) sensors permanently installed in two agricultural fields (2.23 and 2.71 ha) at depths of 5, 20, 45, and 60 cm. Twenty additional FDR sensors, spaced 35 m apart, were installed horizontally at a depth of 5cm in each field with automated data collection being transmitted every 30 min from July 15 through September 20, 2009. Additionally, meteorological data were obtained from existing weather stations in each field. The FDR sensors revealed persistent patterns in surface soil moisture within each field and identified sites that were temporally stable. The locations that were optimal for estimating the area-average field water contents were different from the permanent sensor locations in both fields. Permanent sensor data showed approximately 4 and 10% mean relative differences for fields AS1 and AS2, respectively, with relatively large standard deviations. Thus, minimum offset values could be applied to the temporally stable field sites to obtain representative field average values of surface soil moisture. However, use of permanent sensor data for offset estimates gave poor results. These findings are of relevance for applications of geospatial surface soil moisture data assimilation in hydrologic modeling when only point-scale observations are available, as well as, remotely sensed surface soil moisture calibration and validation studies.