Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity

Schrön, Martin; Köhli, Markus; Scheiffele, Lena; Iwema, Joost; Bogena, Heye; Lv, Ling; Martini, Edoardo; Baroni, Gabriele; Rosolem, Rafael; Weimar, Jannis; Mai, Juliane; Cuntz, Matthias; Rebmann, Corinna; Oswald, Sascha E.; Dietrich, Peter; Schmidt, Ulrich; Zacharias, Steffen

doi:10.5194/hess-21-5009-2017

Cited by 110 publications

(242 citation statements)

References 43 publications

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“…This radius varies between 130 and 240 m depending on the site conditions, which is significantly larger than the typical spatial correlation length of soil moisture patterns (Western et al, 2004). The penetration depth of CRNP measurements varies between 15 cm for wet soils to 55 cm for dry soils (Schrön et al, 2017). Due to this large sample volume, the information content of CRNP data could potentially improve the estimation of effective SHPs for LSMs because the scale mismatch between sample volume and model resolution is much smaller.…”

mentioning

confidence: 99%

On the Information Content of Cosmic‐Ray Neutron Data in the Inverse Estimation of Soil Hydraulic Properties

Brunetti

Šimůnek

Bogena

et al. 2019

Vadose Zone Journal

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Core Ideas Cosmic‐ray neutron data are used to inversely estimate soil hydraulic properties. The forward neutron operator COSMIC is coupled with the vadose zone model HYDRUS‐1D. Bayesian analyses confirm the information content of cosmic‐ray neutron data. Observations of soil moisture content from remote sensing platforms can be used in conjunction with hydrological models to inversely estimate soil hydraulic properties (SHPs). In recent years, cosmic‐ray neutron sensing (CRNS) has proven to be a reliable method for the estimation of area‐average soil moisture at field scales. However, its use in the inverse estimation of the effective SHPs is largely unexplored. Thus, the main objective of this study was to assess the information content of aboveground fast‐neutron counts to estimate SHPs using both a synthetic modeling study and actual experimental data from the Rollesbroich catchment in Germany. For this, the forward neutron operator COSMIC was externally coupled with the hydrological model HYDRUS‐1D. The coupled model was combined with the Affine Invariant Ensemble Sampler to calculate the posterior distributions of effective soil hydraulic parameters as well as the model‐predictive uncertainty for different synthetic and experimental scenarios. Measured water contents at different depths were used to assess estimated SHPs. The analysis of both synthetic and actual CRNS data from homogenous and heterogeneous soil profiles, respectively, led to confident estimations of the shape parameters α and n, while higher uncertainty was observed for the saturated hydraulic conductivity. Furthermore, results demonstrated that neutron data are less influenced by local sources of uncertainty compared with near‐surface point measurements. The simultaneous use of CRNS and water content data further reduced the overall uncertainty, opening up new perspectives for the combination of CRNS with other remote sensing techniques for the inverse estimation of the effective SHPs.

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confidence: 99%

On the Information Content of Cosmic‐Ray Neutron Data in the Inverse Estimation of Soil Hydraulic Properties

Brunetti

Šimůnek

Bogena

et al. 2019

Vadose Zone Journal

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“…To this end, different weighting approaches have been proposed for CRNP calibration, taking into account the probe's effective measuring footprint. The methods include the conventional (Franz et al, ), conventional nonlinear (Bogena et al, ), and revised (Schrön et al, ) approaches. Since points in the CRNP's footprint contribute differently to the measured neutron counts, different weights need to be assigned as functions of soil moisture, distance, and depth (Schrön et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…The methods include the conventional (Franz et al, ), conventional nonlinear (Bogena et al, ), and revised (Schrön et al, ) approaches. Since points in the CRNP's footprint contribute differently to the measured neutron counts, different weights need to be assigned as functions of soil moisture, distance, and depth (Schrön et al, ). The equally weighting method has thus been replaced with the conventional and revised methods in current CRNP calibration/validation campaigns as they are based on the cosmic neutron creation and transport theory, that is, weighting is based on the contribution of fast neutron flux from different layers to the total flux over the profile (Bogena et al, ; Franz et al, ; Köhli et al, ; Schrön et al, ; Zreda et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…The conventional (both linear and nonlinear vertical) and revised weighting methods were compared with the equally weighted approach. The weighting methods were carried out following iteration steps summarized in Schrön et al (). The sampled gravimetric soil water data set was averaged using the different weighting methods and used to derive the site‐specific calibration parameter ( N 0 ).…”

Section: Methodsmentioning

confidence: 99%

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Assimilation of Cosmic‐Ray Neutron Counts for the Estimation of Soil Ice Content on the Eastern Tibetan Plateau

et al. 2020

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Accurate observations and simulations of soil moisture phasal forms are crucial in cold region hydrological studies. In the seasonally frozen ground of eastern Tibetan Plateau, water vapor, liquid, and ice coexist in the frost‐susceptible silty‐loam soil during winter. Quantification of soil ice content is thus vital in the investigation and understanding of the region's freezing‐thawing processes. This study focuses on the retrieval of soil ice content utilizing the in situ soil moisture (i.e., liquid phase) and cosmic ray neutron measurements (i.e., total water including liquid and ice), with Observing System Simulation Experiments. To derive the total soil water from neutron counts, different weighting methods (revised, conventional, and uniform) for calibrating the cosmic‐ray neutron probe (CRNP) were intercompared. The comparison showed that the conventional nonlinear method performed the best. Furthermore, to assimilate fast neutrons using the particle filter, the STEMMUS‐FT (Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil) model was used as the physically based process model, and the COSMIC model (Cosmic‐ray Soil Moisture Interaction Code) used as the observation operator (i.e., forward neutron simulator). Other than background inputs from disturbed initializations in the STEMMUS‐FT, model uncertainties were predefined to assimilate fast neutrons. We observed that with enough spread of uncertainties, the updated states could mimic the CRNP observation. In all setups, assimilating CRNP measurements could enhance total soil water analyses, which consequently led to the improved detection of soil ice content and therefore the freezing thawing‐process at the field scale.

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“…Bogena et al 2013;Franz et al 2015Franz et al , 2016Iwema et al 2017, among others) have shown the CRNP to have area-average measurement accuracies of less than 0.03 cm 3 cm −3 against a variety of industry standard SWC point scale probes. The calculated SWC within the measurement volume in a non-linearly weighed average with increased sensitivity near the CRNP (Schrön et al 2017). In order to provide a SWC map, first a spatial map of neutron intensity was estimated, then a calibration function was applied following details in Franz et al (2015) for agricultural fields.…”

Section: Hydrogeophysical Datasetsmentioning

confidence: 99%

Integration of hydrogeophysical datasets and empirical orthogonal functions for improved irrigation water management

et al. 2018

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Precision agriculture offers the technologies to manage for infield variability and incorporate variability into irrigation management decisions. The major limitation of this technology often lies in the reconciliation of disparate data sources and the generation of irrigation prescription maps. Here the authors explore the utility of the cosmic-ray neutron probe (CRNP) which measures volumetric soil water content (SWC) in the top ~ 30 cm of the soil profile. The key advantages of CRNP is that the sensor is passive, non-invasive, mobile and soil temperature-invariant, making data collection more compatible with existing farm operations and extending the mapping period. The objectives of this study were to: (1) improve the delineation of irrigation management zones within a field and (2) estimate spatial soil hydraulic properties to make effective irrigation prescriptions. Ten CRNP SWC surveys were collected in a 53-ha field in Nebraska. The SWC surveys were analyzed using Empirical Orthogonal Functions (EOFs) to isolate the underlying spatial structure. A statistical bootstrapping analysis confirmed the CRNP + EOF provided superior soil hydraulic property estimates, compared to other hydrogeophysical datasets, when linearly correlated to laboratory measured soil hydraulic properties (field capacitydigitalcommons.unl.edu F i n k e n b i n e r e t a l . i n P r e c i s i o n A g r i c u lt u r e , 2 0 1 8 2estimates reduced 20-25% in root mean square error). The authors propose a soil sampling strategy for better quantifying soil hydraulic properties using CRNP + EOF methods. Here, five CRNP surveys and 6-8 sample locations for laboratory analysis were sufficient to describe the spatial distribution of soil hydraulic properties within this field. While the proposed strategy may increase overall effort, rising scrutiny for agricultural water-use could make this technology cost-effective.

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Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity

Cited by 110 publications

References 43 publications

On the Information Content of Cosmic‐Ray Neutron Data in the Inverse Estimation of Soil Hydraulic Properties

On the Information Content of Cosmic‐Ray Neutron Data in the Inverse Estimation of Soil Hydraulic Properties

Assimilation of Cosmic‐Ray Neutron Counts for the Estimation of Soil Ice Content on the Eastern Tibetan Plateau

Integration of hydrogeophysical datasets and empirical orthogonal functions for improved irrigation water management

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