A profile shape correction to reduce the vertical sensitivity of cosmic-ray neutron sensing of soil moisture

n recent years, cosmic-ray neutron sensing (CRNS) has shown a large potential among proximal sensing techniques to monitor soil moisture noninvasively, with high frequency and a large support volume (radius up to 240 m and sensing depth up to 80 cm). This signal is, however, more sensitive to closer distances and shallower depths. Inherently, CRNS-derived soil moisture is a spatially weighted value, different from an average soil moisture as retrieved by a sensor network. In this study, we systematically test a new profile shape correction on CRNS-derived soil moisture, based on additional soil moisture profile measurements and vertical unweighting, which is especially relevant during pronounced wetting or drying fronts. The analyses are conducted with data collected at four contrasting field sites, each equipped with a CRNS probe and a distributed soil moisture sensor network. After applying the profile shape correction on CRNS-derived soil moisture, it is compared with the sensor network average. Results show that the influence of the vertical sensitivity of CRNS on integral soil moisture values is successfully reduced. One to three properly located profile measurements within the CRNS support volume improve the performance. For the four investigated field sites, the RMSE decreased 11–53% when only one profile location was considered. We therefore recommend to install along with a CRNS at least one soil moisture profile in a radial distanceProfile-shape-corrected, CRNS-derived soil moisture is an unweighted integral soil moisture over the support volume, which is easier to interpret and easier to use for further applications

A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-Alpine headwater catchment in Germany

Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while conventional sensors typically suffer from small spatial support. With a sensor footprint up to several hectares, cosmic-ray neutron sensing (CRNS) is a modern technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national-scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km2: the pre-Alpine Rott headwater catchment in Southern Germany, which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, this network was designed to study root-zone soil moisture dynamics at the catchment scale. The observations of the dense CRNS network were complemented by extensive measurements that allow users to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from unmanned areal systems (UASs), permanent and temporary wireless sensor networks, profile probes, and comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive data set to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatiotemporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land–atmosphere exchange processes. The data set is available through the EUDAT Collaborative Data Infrastructure and is split into two subsets: https://doi.org/10.23728/b2share.282675586fb94f44ab2fd09da0856883 (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.bd89f066c26a4507ad654e994153358b (Fersch et al., 2020b)

COSMOS-Europe : a European network of cosmic-ray neutron soil moisture sensors

We thank TERENO (Terrestrial Environmental Observatories), funded by the Helmholtz-Gemeinschaft for the financing and maintenance of CRNS stations. We acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) of the research unit FOR 2694 Cosmic Sense (grant no. 357874777) and by the German Federal Ministry of Education of the Research BioökonomieREVIER, Digitales Geosystem – Rheinisches Revier project (grant no. 031B0918A). COSMOS-UK has been supported financially by the UK’s Natural Environment Research Council (grant no. NE/R016429/1). The Olocau experimental watershed is partially supported by the Spanish Ministry of Science and Innovation through the research project TETISCHANGE (grant no. RTI2018-093717-BI00). The Calderona experimental site is partially supported by the Spanish Ministry of Science and Innovation through the research projects CEHYRFO-MED (grant no. CGL2017-86839- C3-2-R) and SILVADAPT.NET (grant no. RED2018-102719-T) and the LIFE project RESILIENT FORESTS (grant no. LIFE17 CCA/ES/000063). The University of Bristol’s Sheepdrove sites have been supported by the UK’s Natural Environment Research Council through a number of projects (grant nos. NE/M003086/1, NE/R004897/1, and NE/T005645/1) and by the International Atomic Energy Agency of the United Nations (grant no. CRP D12014). Acknowledgements. We thank Peter Strauss and Gerhab Rab from the Institute for Land and Water Management Research, Federal Agency for Water Management Austria, Petzenkirchen, Austria. We thank Trenton Franz from the School of Natural Resources, University of Nebraska–Lincoln, Lincoln, NE, United States. We also thank Carmen Zengerle, Mandy Kasner, Felix Pohl, and Solveig Landmark, UFZ Leipzig, for supporting field calibration, lab analysis, and data processing. We furthermore thank Daniel Dolfus, Marius Schmidt, Ansgar Weuthen, and Bernd Schilling, Forschungszentrum Jülich, Germany. The COSMOS-UK project team is thanked for making its data available to COSMOS-Europe. Luca Stevanato is thanked for the technical details about the Finapp sensor. The stations at Cunnersdorf, Lindenberg, and Harzgerode have been supported by Falk Böttcher, Frank Beyrich, and Petra Fude, German Weather Service (DWD). The Zerbst site has been supported by Getec Green Energy GmbH and Jörg Kachelmann (Meteologix AG). The CESBIO sites have been supported by the CNES TOSCA program. The ERA5-Land data are provided by ECMWF (Muñoz Sabater, 2021). The Jena dataset was retrieved at the site of The Jena Experiment, operated by DFG research unit FOR 1451.Peer reviewedPublisher PD

Dynamic groundwater recharge simulations based on cosmic‐ray neutron sensing in a tropical wet experimental basin

none8siAlthough cosmic-ray neutron sensing (CRNS) is probably the most promising noninvasive proximal soil moisture measurement technique at the field scale, its application for hydrological simulations remains underexplored in the literature so far. This study assessed the use of CRNS to inversely calibrate soil hydraulic parameters at the intermediate field scale to simulate the groundwater recharge rates at a daily timescale. The study was conducted for two contrasting hydrological years at the Guaraira experimental basin, Brazil, a 5.84-km(2), a tropical wet and rather flat landscape covered by secondary Atlantic forest. As a consequence of the low altitude and proximity to the equator low neutron count rates could be expected, reducing the precision of CRNS while constituting unexplored and challenging conditions for CRNS applications. Inverse calibration for groundwater recharge rates was used based on CRNS or point-scale soil moisture data. The CRNS-derived retention curve and saturated hydraulic conductivity were consistent with the literature and locally performed slug tests. Simulated groundwater recharge rates ranged from 60 to 470 mm yr(-1), corresponding to 5 and 29% of rainfall, and correlated well with estimates based on water table fluctuations. In contrast, the estimated results based on inversive point-scale datasets were not in alignment with measured water table fluctuations. The better performance of CRNS-based estimations of field-scale hydrological variables, especially groundwater recharge, demonstrated its clear advantages over traditional invasive point-scale techniques. Finally, the study proved the ability of CRNS as practicable in low altitude, tropical wet areas, thus encouraging its adoption for water resources monitoring and management.noneBarbosa, Luís Romero; Coelho, Victor Hugo R.; Scheiffele, Lena M.; Baroni, Gabriele; Ramos Filho, Geraldo M.; Montenegro, Suzana M. G. L.; Almeida, Cristiano das N.; Oswald, Sascha E.Barbosa, Luís Romero; Coelho, Victor Hugo R.; Scheiffele, Lena M.; Baroni, Gabriele; Ramos Filho, Geraldo M.; Montenegro, Suzana M. G. L.; Almeida, Cristiano das N.; Oswald, Sascha E

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

In the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling