Journal Article

FZJ-2019-00615

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png On the Information Content of Cosmic-Ray Neutron Data in the Inverse Estimation of Soil Hydraulic Properties

Brunetti, G. ; Simunek, J. ; Bogena, H.FZJ* ; Baatz, R.FZJ* ; Huisman, J. A.FZJ* ; Dahlke, H. ; Vereecken, H. (Corresponding author)FZJ*

2019
GeoScienceWorld Alexandria, Va.

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Please use a persistent id in citations: http://hdl.handle.net/2128/21602  doi:10.2136/vzj2018.06.0123

Abstract: Observations of soil moisture content from remote sensing platforms can beused in conjunction with hydrological models to inversely estimate soil hydraulicproperties (SHPs). In recent years, cosmic-ray neutron sensing (CRNS) has provento be a reliable method for the estimation of area-average soil moisture at fieldscales. However, its use in the inverse estimation of the effective SHPs is largelyunexplored. Thus, the main objective of this study was to assess the informationcontent of aboveground fast-neutron counts to estimate SHPs using botha synthetic modeling study and actual experimental data from the Rollesbroichcatchment in Germany. For this, the forward neutron operator COSMIC was externallycoupled with the hydrological model HYDRUS-1D. The coupled model wascombined with the Affine Invariant Ensemble Sampler to calculate the posteriordistributions of effective soil hydraulic parameters as well as the model-predictiveuncertainty for different synthetic and experimental scenarios. Measured watercontents at different depths were used to assess estimated SHPs. The analysis ofboth synthetic and actual CRNS data from homogenous and heterogeneous soilprofiles, respectively, led to confident estimations of the shape parameters a andn, while higher uncertainty was observed for the saturated hydraulic conductivity.Furthermore, results demonstrated that neutron data are less influenced bylocal sources of uncertainty compared with near-surface point measurements.The simultaneous use of CRNS and water content data further reduced the overalluncertainty, opening up new perspectives for the combination of CRNS withother remote sensing techniques for the inverse estimation of the effective SHPs.

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Contributing Institute(s):

1. Agrosphäre (IBG-3)

Research Program(s):

1. 255 - Terrestrial Systems: From Observation to Prediction (POF3-255) (POF3-255)

Appears in the scientific report 2019

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Agriculture, Biology and Environmental Sciences ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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