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Biblioteca Surface soil water content spatial organization within irrigated and non-irrigated agricultural fields

Surface soil water content spatial organization within irrigated and non-irrigated agricultural fields

Surface soil water content spatial organization within irrigated and non-irrigated agricultural fields

Resource information

Date of publication
Diciembre 2012
Resource Language
ISBN / Resource ID
AGRIS:US201400189829
Pages
55-61

Understanding soil water content, θ, variability is important for monitoring and modeling of land surface processes as well as land and water management practices. With regards to in situ θ probes, it is sometimes assumed that a single local measurement can represent the larger domain, mostly for practical reasons. But there is a substantial amount of variability in θ at the field scale. As part of the Bushland Evapotranspiration and Agricultural Remote Sensing Experiment 2008 (BEAREX08), a high-density sensor network and intensive observational periods were developed to fully describe the θ conditions at the surface on the field scale, in support of the hydro-meteorological measurements being collected. A total of 20 θ stations were distributed over an irrigated and a non-irrigated field (~10 ha each) and high-density (~every 5 cm) transects were measured for a high-detailed record. The network was able to provide large scale estimates of θ with an accuracy (root mean square error, RMSE) of 0.035 m3/m3. The network was temporally stable with the exception being immediately during and after irrigation events. Irrigation caused significant increases in coefficients of variation due to the length of time (8–12 h) necessary to irrigate the entire field. The spatial distribution of surface θ was significantly affected by the row structure of the cotton plants, which was North–South in the field where transect measurements were made with a row spacing of 76 cm. At scales

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Authors and Publishers

Author(s), editor(s), contributor(s)

Cosh, Michael H.
Evett, Steven R.
McKee, Lynn

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