Footprint characteristics revised for field‐scale soil moisture monitoring with cosmic‐ray neutrons

Soil moisture is a key variable in atmospheric modelling to resolve the partitioning of net radiation into sensible and latent heat fluxes. Therefore, high resolution spatio-temporal soil moisture estimation is getting growing attention in this decade. The recent developments to observe soil moisture at field scale (170 to 250 m spatial resolution) using Cosmic Ray Neutron Sensing (CRNS) technique has created new opportunities to better resolve land surface atmospheric interactions; however, many challenges remain such as spatial resolution mismatch and estimation uncertainties. Our study couples the Noah-MP land surface model to the Data Assimilation Research Testbed (DART) for assimilating CRN intensities to update model soil moisture. For evaluation, the spatially distributed Noah-MP was set up to simulate the land surface variables at 1 km horizontal resolution for the Rott and Ammer catchments in southern Germany. The study site comprises the TERENO-preAlpine observatory with five CRNS stations and additional CRNS measurements for summer 2019 operated by our Cosmic Sense research group. We adjusted the soil parametrization in Noah-MP to allow the usage of EU soil data along with Mualem-van Genuchten soil hydraulic parameters. We use independent observations from extensive soil moisture sensor network (SoilNet) within the vicinity of CRNS sensors for validation. Our detailed synthetic and real data experiments are evaluated for the analysis of the spatio-temporal changes in updated root zone soil moisture and for implications on the energy balance component of Noah-MP. Furthermore, we present possibilities to estimate root zone soil parameters within the data assimilation framework to enhance standalone model performance.

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Novel Cosmic Ray Neutron Sensor Accurately Captures Field-Scale Soil Moisture Trends under Heterogeneous Soil Textures

Water ◽

10.3390/w13213038 ◽

2021 ◽

Vol 13 (21) ◽

pp. 3038

Author(s):

Kade D. Flynn ◽

Briana M. Wyatt ◽

Kevin J. McInnes

Keyword(s):

Soil Moisture ◽

Water Conservation ◽

Cosmic Ray ◽

Atmospheric Conditions ◽

Rainfall Runoff ◽

Field Scale ◽

Near Surface ◽

Heterogeneous Soil ◽

In Situ Sensors

Soil moisture is a critical variable influencing plant water uptake, rainfall-runoff partitioning, and near-surface atmospheric conditions. Soil moisture measurements are typically made using either in-situ sensors or by collecting samples, both methods which have a small spatial footprint or, in recent years, by remote sensing satellites with large spatial footprints. The cosmic ray neutron sensor (CRNS) is a proximal technology which provides estimates of field-averaged soil moisture within a radius of up to 240 m from the sensor, offering a much larger sensing footprint than point measurements and providing field-scale information that satellite soil moisture observations cannot capture. Here we compare volumetric soil moisture estimates derived from a novel, less expensive lithium (Li) foil-based CRNS to those from a more expensive commercially available 3He-based CRNS, to measurements from in-situ sensors, and to four intensive surveys of soil moisture in a field with highly variable soil texture. Our results indicate that the accuracy of the Li foil CRNS is comparable to that of the commercially available sensors (MAD = 0.020 m3 m−3), as are the detection radius and depth. Additionally, both sensors capture the influence of soil textural variability on field-average soil moisture. Because novel Li foil-based CRNSs are comparable in accuracy to and much less expensive than current commercially available CRNSs, there is strong potential for future adoption by land and water managers and increased adoption by researchers interested in obtaining field-scale estimates of soil moisture to improve water conservation and sustainability.

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Understanding soil moisture dynamics through cosmic rays: a global analysis

10.5194/egusphere-egu21-13250 ◽

2021 ◽

Author(s):

Daniel Power ◽

Rafael Rosolem ◽

Miguel Rico-Ramirez ◽

Darin Desilets ◽

Sharon Desilets

Keyword(s):

Soil Moisture ◽

Cosmic Rays ◽

Cosmic Ray ◽

Fast Neutrons ◽

Field Scale ◽

Hydrogen Atoms ◽

Soil Moisture Dynamics ◽

Large Sample ◽

Wide Range ◽

Moisture Dynamics

Despite its importance in many hydrological and environmental applications, direct estimates of soil moisture at the field-scale is still challenging. The spatial gap between point scale sensors and satellite derived products is becoming increasingly important to consider in the push for hyper-resolution (sub)kilometre-hydrometeorological models. Cosmic-Ray Neutron Sensors (CRNS) can help to bridge this spatial gap. CRNS provide estimates of field-scale (sub-kilometre) root-zone integrated soil moisture typically at hourly intervals. They achieve this by counting fast neutrons which are produced in the atmosphere from incoming cosmic rays. Fast neutrons are mitigated primarily by hydrogen atoms, and it is this relationship that allows us to estimate field averaged soil moisture. National networks of CRNS are available in the USA, Australia, the UK, and Germany, along with individual sites across the globe. As these networks have expanded, so has our knowledge on best practices for calibration and correction of the sensor measurements. However, there continues to be a divergence and lack of harmonization in some processing data methods leading to an additional uncertainty when comparing sensors in different networks. This can undermine efforts to employ large-sample hydrological analysis of CRNS across a wide range of climate and biomes. To provide an easily accessible platform for multi-site comparison worldwide, we developed the Cosmic Ray Sensor Python tool (crspy). Crspy is an open-source Python package which is designed to process CRNS data from global networks in a uniform and harmonized way (https://www.github.com/danpower101/crspy). Additionally, crspy has been developed for multi-site &#8216;big-data&#8217; analysis in hydrology. Our crspy tool produces detailed information in the form of metadata for each site, using both site specific data as well as global data products to give information on soil properties (SoilGridsv2), land cover/aboveground biomass (ESA CCI) and climate data (ERA5-land). Our preliminary analysis and tool development was carried out using data from more than 100 sites globally from the public domain. We will present an analysis of this large sample of data, utilising the harmonized soil moisture readings along with detailed metadata for each site. We aim to increase our understanding of the dominant mechanisms controlling soil moisture dynamics which will undoubtedly be useful in multiple areas of research such as catchment classification, agriculture and irrigation, and hydrological model development.

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Field scale root zone soil moisture estimation by coupling cosmic-ray neutron sensor with soil moisture sensors

10.5194/egusphere-egu2020-4618 ◽

2020 ◽

Author(s):

Hami Said ◽

Georg Weltin ◽

Lee Kheng Heng ◽

Trenton Franz ◽

Emil Fulajtar ◽

...

Keyword(s):

Water Management ◽

Soil Moisture ◽

Root Zone ◽

Cosmic Ray ◽

Agricultural Water ◽

Field Scale ◽

Agricultural Water Management ◽

Soil Moisture Sensors ◽

Major Disadvantage ◽

Filter Approach

Since it has become clear that climate change is having a major impact on water availability for agriculture and crop productivity, an accurate estimation of field-scale root-zone soil moisture (RZSM) is essential for improved agricultural water management. The Cosmic Ray Neutron Sensor (CRNS) has recently been used for field-scale soil moisture (SM) monitoring in large areas and is a credible and robust technique. Like other remote or proximal sensing techniques, the CRNS provides only SM data in the near surface. One of the challenges and needs is to extend the vertical footprint of the CRNS to the root zone of major crops. This can be achieved by coupling the CRNS measurements with conventional methods for soil moisture measurements, which provide information on soil moisture for whole rooting depth.The objective of this poster presentation is to estimate field-scale RZSM by correlating the CRNS information with that from soil moisture sensors that provide soil moisture data for the whole root depth. In this study, the Drill and Drop probes which provide continuous profile soil moisture were selected. The RZSM estimate was calculated using an exponential filter approach.Winter Wheat cropped fields in Rutzendorf, Marchfeld region (Austria) were instrumented with a CRNS and Drill & Drop probes. An exponential filter approach was applied on the CRNS and Drill and drop sensor data to characterize the RZSM. The preliminary results indicate the ability of the merging framework procedure to improve field-scale RZSM in real-time. This study demonstrated how to combine the advantages of CRNS nuclear technique (especially the large footprint and good representativeness of obtained data) with the advantages of conventional methods (providing data for whole soil profile) and overcome the shortcoming of both methods (the lack of information in the deeper part of soil profile being the major disadvantage of CRNS and the spatial limitation and low representativeness of point data being the major disadvantage of conventional capacitance sensors). This approach can be very helpful for improving agricultural water management.

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Review: Repeated electromagnetic induction measurements for mapping soil moisture at the field scale: validation with data from a wireless soil moisture monitoring network

10.5194/hess-2016-93-sc1 ◽

2016 ◽

Author(s):

Anonymous

Keyword(s):

Soil Moisture ◽

Electromagnetic Induction ◽

Scale Validation ◽

Monitoring Network ◽

Field Scale ◽

Soil Moisture Monitoring

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Soil moisture monitoring at the field scale using automatic capacitance probes

European Journal of Soil Science ◽

10.1046/j.1365-2389.1998.4940637.x ◽

1998 ◽

Vol 49 (4) ◽

pp. 637-648 ◽

Cited By ~ 36

Author(s):

A. CHANZY ◽

J. CHADOEUF ◽

J. C. GAUDU ◽

D. MOHRATH ◽

G. RICHARD ◽

...

Keyword(s):

Soil Moisture ◽

Field Scale ◽

Soil Moisture Monitoring

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COSMOS-UK: National soil moisture and hydrometeorology data for empowering UK environmental science

10.5194/essd-2020-287 ◽

2020 ◽

Author(s):

Hollie M. Cooper ◽

Emma Bennett ◽

James Blake ◽

Eleanor Blyth ◽

David Boorman ◽

...

Keyword(s):

Soil Moisture ◽

Real Time ◽

Land Surface ◽

Large Scale ◽

Environmental Science ◽

Cosmic Ray ◽

Fast Neutrons ◽

High Temporal Resolution ◽

Field Scale ◽

Near Surface

Abstract. The COSMOS-UK observation network has been providing field scale soil moisture and hydrometeorological measurements across the UK since 2013. At the time of publication a total of 51 COSMOS-UK sites have been established, each delivering high temporal resolution data in near-real time. Each site utilises a cosmic-ray neutron sensor, which counts fast neutrons at the land surface. These measurements are used to derive field scale near-surface soil water content, which can provide unique insight for science, industry, and agriculture by filling a scale gap between localised point soil moisture and large-scale satellite soil moisture datasets. Additional soil physics and meteorological measurements are made by the COSMOS-UK network including precipitation, air temperature, relative humidity, barometric pressure, soil heat flux, wind speed and direction, and components of incoming and outgoing radiation. These near-real time observational data can be used to improve the performance of hydrological models, validate remote sensing products, improve hydro-meteorological forecasting and underpin applications across a range of other scientific fields. The most recent version of the COSMOS-UK dataset is publically available at https://doi.org/10.5285/37702a54-b7a4-40ff-b62e-d14b161b69ca (Stanley et al., 2020).

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Penetration depth of Cosmic-ray neutron sensing for soil moisture under extreme vertical soil moisture gradients in measurements and modelling

10.5194/egusphere-egu21-15240 ◽

2021 ◽

Author(s):

Lena M. Scheiffele ◽

Jannis Weimar ◽

Daniel Rasche ◽

Benjamin Fersch ◽

Sascha E. Oswald

Keyword(s):

Soil Moisture ◽

Sensor Network ◽

Penetration Depth ◽

Transport Model ◽

Neutron Transport ◽

Soil Column ◽

Weighted Average ◽

Monitoring Network ◽

Cosmic Ray ◽

Soil Moisture Monitoring

Cosmic-Ray Neutron Sensing (CRNS) delivers an integral value of soil moisture over a radial footprint of 150 to 240 m and a penetration depth of 15 to 83 cm. The support volume, especially in the vertical extent, decreases with increasing soil moisture. As the sensor is most sensitive to upper soil layers and the signal contribution decreases with increasing depth, the vertical distribution of moisture influences the signal received by the neutron detector. Additional soil moisture measurements are required to estimate the penetration depth of the CRNS measurement. These may be provided by profile measurements of a soil moisture monitoring network equipped with buried electromagnetic sensors. Different horizontal and vertical weighting schemes exist to compare the integrated CRNS value to an integrated (weighted) average value from a sensor network by adjusting reference measurements to the spatial sensitivity of the sensor. The vertical weighting was developed based on hydrodynamic modelling of a soil column and a neutron transport model (MCNPx). Since then the development of the Ultra Rapid Adaptable Neutron-Only Simulation (URANOS) opened up the possibilities for more complex neutron simulations to understand and interpret the CRNS signal. Simulations confirmed the large influence of soil moisture on the penetration depth of the sensor for homogeneous vertical soil moisture distributions, rarely occurring in natural environments. While in recent years the influence of horizontal heterogeneities on the signal generation was the focus of several studies, the influence of vertical gradients achieved less attention.Against this background, we evaluate data from a field site in southern Germany with clayey soils and influence of shallow groundwater, where a CRNS is operated in parallel to a soil moisture monitoring network. We observe a good match between the time series of CRNS derived soil moisture and the weighted soil moisture from the sensor network during infiltration events. Several times during summer, however, topsoil dries and a strong vertical gradient develops (0.20 m&#179; m-&#179; in 5 cm to 0.50 m&#179; m-&#179; in 20 cm depth). During these periods the weighted sensor network underestimates CRNS derived soil moisture by up to 0.25 m&#179; m-&#179;. We hypothesize, that the estimation of the penetration depth does not hold for these extreme soil moisture gradients and that neutrons penetrate deeper into the soil and probe the wetter layers. The combination of observed neutron intensities as well as dedicated neutron transport simulations using the URANOS and MNCP6 model code will help to understand the site-specific signal behavior, explain differences observed in the data and lastly, gain information on the behavior of neutron intensities under vertically varying soil moisture contents.

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Improving soil moisture predictions from a land surface model by optimisation of pedotransfer functions.

10.5194/ismc2021-81 ◽

2021 ◽

Author(s):

Elizabeth Cooper ◽

Eleanor Blyth ◽

Hollie Cooper ◽

Richard Ellis ◽

Ewan Pinnington ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Cosmic Ray ◽

Pedotransfer Functions ◽

Small Scale ◽

Surface Model ◽

Land Surface Models ◽

Field Scale ◽

Surface Models

Accurate soil moisture predictions from land surface models are important in hydrological, ecological and agricultural applications. Despite increasing availability of wide area soil moisture measurements, few studies have combined soil moisture predictions from models with in-situ observations beyond the point scale. This work uses the LAVENDAR data assimilation framework to markedly improve soil moisture estimates from the JULES land surface model using field scale Cosmic Ray Neutron sensor observations from the UKCEH COSMOS-UK network. Rather than directly updating modelled soil moisture estimates towards measured values, we optimize constants in the underlying pedotransfer functions (PTF) which relate soil texture to soil hydraulics parameters. In this way we generate a single set of newly calibrated PTFs based on field scale observations from a number of UK sites with different soil types. We demonstrate that calibrating PTFs in this way can improve the performance of JULES. Further, we suggest that calibrating PTFs for the soils on which they are to be used and at the scales at which land surface models are applied (rather than on small-scale soil samples) will ultimately improve the performance of land surface models, potentially leading to improvements in flood, drought and climate projections.

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