Cosmic Ray Neutron Soil Moisture Estimation Using Physically Based Site‐Specific Conversion Functions

Andreasen, Mie; Jensen, Karsten H.; Bogena, Heye; Desilets, Darin; Zréda, Marek; Looms, Majken C.

doi:10.1029/2019wr026588

Cited by 24 publications

(29 citation statements)

References 39 publications

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“…Here, we consider neutrons ≤0.5 eV and define these as thermal neutrons. This cutoff energy allows for a comparison with earlier modeling results with the Monte Carlo N-Particle Extended (MCNPX) model (e.g., Andreasen et al, 2016Andreasen et al, , 2020McJannet et al, 2014).…”

Section: Evaluation Of Model Resultsmentioning

confidence: 99%

The Footprint Characteristics of Cosmic Ray Thermal Neutrons

Jakobi

Huisman

Köhli

et al. 2021

Geophysical Research Letters

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Cosmic ray neutron (CRN) sensing is a non-invasive method for intermediate scale soil moisture measurements (Zreda et al., 2008). This method relies on the inverse dependence of aboveground epithermal neutrons (energy range from ∼0.5 eV to 100 keV) on the environmental hydrogen content in a footprint of 130-240 m radius and soil depths ranging from 15 to 83 cm (Köhli et al., 2015;Schrön et al., 2017). In terrestrial environments, most hydrogen is stored in water in soils. Therefore, it is possible to infer soil moisture content from the amount of aboveground epithermal neutrons. Secondary hydrogen pools, such as biomass, have a large impact on the measurement accuracy, especially when they are not constant in time. For reliable soil moisture estimation, these secondary pools thus need to be considered (Baatz et al., 2014;Bogena et al., 2013;. Recently, it was found that by additionally considering the thermal neutron intensity below ∼0.5 eV, aboveground biomass can be inferred using the ratio of thermal to epithermal neutrons (Jakobi et al., 2018;Tian et al., 2016).CRN sensors are currently installed in approximately 200 locations worldwide (Andreasen et al., 2017). Many of these locations are also instrumented with thermal neutron detectors. However, these extensive data sets are rarely explored because key properties of the thermal neutron signal, such as the footprint of thermal neutrons, are not well defined. Preliminary investigations suggest that the thermal neutron footprint is smaller than the epithermal neutron footprint and in the order of tens of meters (Bogena et al., 2020).

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Section: Evaluation Of Model Resultsmentioning

confidence: 99%

The Footprint Characteristics of Cosmic Ray Thermal Neutrons

Jakobi

Huisman

Köhli

et al. 2021

Geophysical Research Letters

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“…During the study period, the field was cultured with rotations of spring and winter barley, with grass-species as cover crop during the 5 autumn and winter season. The ploughed root zone of 30 cm contains approximately 4.5% organic matter (Andreasen et al, 2020), while there is little organic matter content below 30 cm. The soil is sandy with only very little clay content (Vasquez, 2013).…”

Section: Study Sitementioning

confidence: 99%

Multi-objective calibration of the Community Land Model Version 5.0 using in-situ observations of water and energy fluxes and variables

Denager

Sonnenborg

Looms

et al. 2022

Preprint

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Abstract. This study evaluate water and energy fluxes and variables in combination with parameter optimization of the state-of-the-art land surface model Community Land Model version 5 (CLM5), using six years of hourly observations of latent heat flux, sensible heat flux, groundwater recharge and soil moisture. The results show that multi-objective calibration in combination with truncated singular value decomposition and Tikhonov regularization is a powerful method to improve the current practice of using look-up tables to define parameter values in land surface models. Furthermore, reliability of the optimized model parameters can be estimated by statistical measures such as identifiability and relative error variance reduction. As in most other eddy covariance studies, closure of the land surface energy balance is not achieved on observation data. However, using direct measurement of turbulent fluxes as target variable, the parameter optimization is capable of matching simulations and observations of latent heat, especially during the summer period, while simulated sensible heat is clearly biased. The fact that CLM5 is not capable of matching sensible heat, not even with advanced parameter optimization of model parameter values, suggests that the lack of energy closure is due to biases in the sensible heat flux. The results from this study contribute to improvements in model characterization of water and energy fluxes. It is underlined that parameter calibration using available observations of hydrologic and energy fluxes and variables is necessary to obtain the optimal parameter set of a land surface model.

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“…In 2007, a long-term hydrological observatory, HOBE, was set up in the Skjern catchment with the aim to enhance 150 the understanding of hydrological processes at catchment scale and the impacts of anthropogenic and natural changes such as LUC and climate change (Jensen and Illangasekare, 2011). In the course of these multidisciplinary investigations, soil moisture monitoring has been implemented with a network of capacitance probes (Bircher et al, 2012) as well as three CRNS systems for stationary measurement of temporal dynamics of soil moisture at the dominant LUC types, agriculture, heathland and forest (Andreasen et al, 2020) (Fig. 1).…”

Section: Study Areamentioning

confidence: 99%

“…A recent study investigated the representativeness of these capacitance probes and concluded that dynamics in soil moisture exhibit low deviation while absolute values can be difficult to obtain precisely (Denager et al, 2020). Within the framework of HOBE, three stationary CRN stations were established in 2013 and 2014 (Andreasen et al, 2020) (Fig. 1).…”

Section: Ground Observationvalidation Datamentioning

confidence: 99%

“…The measured CRN intensity is sensitive to soil moisture in the upper decimeters of the ground within an area of hectometers (Andreasen et al, 2017). The standard N0-calibration function (Desilets et al, 2010) was used to convert CRN intensity measurements to volumetric soil moisture (Andreasen et al, 2020). Daily moving averages were calculated to obtain acceptable statistics.…”

Section: Ground Observationvalidation Datamentioning

confidence: 99%

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Exploring the combined use of SMAP and Sentinel-1 data for downscaling soil moisture beyond the 1 km scale

Meyer

Zhang

Kragh

et al. 2021

Preprint

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Abstract. Soil moisture estimates at high spatial and temporal resolution are of great value for optimizing water and agricultural management. To fill the gap between local ground observations and coarse spatial resolution remote sensing products we use SMAP and Sentinel-1 data together with a unique dataset of ground based soil moisture estimates by cosmic ray neutron sensors (CRNS) and capacitance probes to test the possibility of downscaling soil moisture to the sub-kilometre resolution. For a study area with diverse land use in Denmark we first show that SMAP soil moisture and Sentinel-1 backscatter time series correlate well with in situ observations of CRNS. Sentinel-1 backscatter in VV and VH polarization both show a strong correlation with CRNS soil moisture at higher spatial resolutions (20 m–400 m) and exhibit distinct and meaningful signals at different land cover types. At a first glance satisfying statistical correlations with CRNS soil moisture time series and capacitance probes are obtained using the SMAP Sentinel-1 downscaling algorithm. However, the spatial distribution of soil moisture pattern is inconclusive. Accounting for different land use in the downscaling algorithm improved the spatial distribution slightly. However, the investigated downscaling algorithm cannot fully account for the vegetation dependency at sub-kilometre resolution. The study implies that further research is needed in the modification of the downscaling algorithm to produce representative soil moisture pattern at fine scale, however backscatter signals appear informative.

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Cosmic Ray Neutron Soil Moisture Estimation Using Physically Based Site‐Specific Conversion Functions

Cited by 24 publications

References 39 publications

The Footprint Characteristics of Cosmic Ray Thermal Neutrons

The Footprint Characteristics of Cosmic Ray Thermal Neutrons

Multi-objective calibration of the Community Land Model Version 5.0 using in-situ observations of water and energy fluxes and variables

Exploring the combined use of SMAP and Sentinel-1 data for downscaling soil moisture beyond the 1 km scale

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