Previous Article in Journal
Instrumentation and Measurement Methods for Industry 4.0 and IoT
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Instruments
Volume 10
Issue 2
10.3390/instruments10020031
instruments-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Beyond Helium-3: Instruments for Cosmic-Ray Neutron Sensing Based on Boron-10 Neutron Detectors

by
Markus Köhli
1,2,* and
Jannis Weimar
1,2
1
Physikalisches Institut, Heidelberg University, Im Neuenheimer Feld 226, 69120 Heidelberg, Germany
2
StyX Neutronica GmbH, Cecil-Taylor-Ring 12-18, 68309 Mannheim, Germany
*
Author to whom correspondence should be addressed.
Instruments 2026, 10(2), 31; https://doi.org/10.3390/instruments10020031
Submission received: 3 April 2026 / Revised: 5 May 2026 / Accepted: 18 May 2026 / Published: 21 May 2026
(This article belongs to the Section Sensing Technologies and Precision Measurement)
Browse Figures
Review Reports Versions Notes

Abstract

Cosmic-Ray Neutron Sensing (CRNS) has become a standard method for non-invasive soil moisture monitoring at the field scale. With most CRNS sensors being derivatives from scientific nuclear equipment, the development of instruments based on alternative neutron detection technologies is a major development goal for CRNS. We present a modular instrument family based on boron-10-lined proportional counters, specifically designed for long-term autonomous field operation. The system is controlled by a data logger supporting various telemetry options and external SDI-12 environmental sensors, while the frontend electronics use pulse-height and pulse-length information to suppress non-neutron background and electronic noise. Our results show high energy efficiency, with the latest generation close to 50 mW, allowing solar-powered operation even in challenging environments. The performance of the instruments is validated within long-term field deployments in different settings, showing that boron-10-based systems provide a scalable, low-power and cost-efficient alternative for the next generation of CRNS monitoring networks.

1. Introduction

Accurate knowledge about soil moisture as a key variable hydrological cycles is most relevant in understanding weather patterns and agricultural productivity [1,2,3]. Cosmic-Ray Neutron Sensing (CRNS) [4,5,6,7] has become in recent years an established methodology for non-invasive measurements of soil moisture [8]. With its sensitive volume of the top few decimeters of several hectares [9] it bridges an important gap between point measurements, and remote sensing products [10,11,12]. The method relies on the inverse relationship between the near-surface cosmogenic neutron flux and the hydrogen content of the surrounding environment, with soil water providing the dominant contribution [13]. Low maintenance requirements and suitability for continuous long-term observations [14] lead to its integration in larger networks [15,16,17]. Instrumentwise the current development focuses on the transition from helium-3-based to helium-3-free instruments, which makes the method more scalable and, for suitable detector designs, lower in acquisition cost. This allows the method to advance into fields of application not only in hydrology, but also in irrigation management, the validation of remote sensing data and climate-resilient monitoring strategies.

1.1. Neutron Detectors for CRNS

CRNS instruments are usually mounted 1–2 m above the ground and can be equipped with two types of sensors: one bare detector for the thermal neutron flux and one counter enclosed by ∼25 mm of polyethylene moderator, making the system most sensitive to epithermal-to-fast neutrons [18,19], which show the strongest scaling for soil moisture. Fast and thermal regimes exhibit a much weaker dependence on environmental hydrogen than the epithermal-to-fast range, but might provide useful information on vegetation and biomass dynamics [20,21,22]. The energy sensitivity (response function) from the usual epithermal detector extends, however, into the thermal and fast domains, which leads to artifacts such as the ’road effect’ [23]. Thermal neutron contamination can constitute up to 20% of the signal [24] and vice versa the bare detector receives a comparable contribution from epithermal/fast neutrons [25]. Technically, systems are well understood with CRNS detectors tested in neutron reference fields are reported to be in good agreement to Monte Carlo simulations [26].
The common neutron converter types used in CRNS are: helium-3 or boron-trifluoride (10BF3) and boron-10-lined or lithium-6 gaseous counters as well as lithium-6-loaded scintillators with optical readout. The scarcity of helium-3 motivates adoption of alternative technologies [27,28,29]. The signal response to soil moisture depends on the moderator design, not on the converter itself. The moderator can be modified to partially suppress neutrons from undesired directions [30,31] or thermal contributions [32,33], though at the cost of counting statistics.

1.2. Conventional Detector Designs

Helium-3 acts simultaneously as converter and counting gas. A converted neutron releases 764 keV, shared between a proton (573 keV) and a triton (191 keV) emitted back to back. Partial energy loss of one product hitting the tube wall leads to the typical plateau features in the spectrum. Most of the first COSMOS instruments [6] and roving systems [34,35,36,37,38] are helium-3-based. 10BF3 also serves as a neutron converter but is also a quencher, requiring an admixture of a counting gas. This limits the absorption efficiency [39,40] and in combination with their toxicity and it impedes a wider deployment.

1.3. Alternative Solutions for Gaseous Detectors

A micrometer-thick coating of 10B or 10B4C on the inner tube wall is used with conventional counting gases such as Ar:CO2 [41]. Because only part of each reaction-product track lies in the gas, the resulting continuous energy spectrum necessitates a low-energy cut-off that increases sensitivity to gain fluctuations or threshold drifts [19], imposing stricter requirements on frontend electronics. The comparably low conversion efficiency per layer can be compensated by multi-tube arrangements [42]. With a very similar design Lithium-6 metal can be used as a converter. An increasing number of CRNS sites now use lithium-based instruments [43,44,45].
CRNS instruments need to be optimized for low count rates. In this respect, it is important to note that alpha-emitter contamination in tube materials can reach 10 − 4 cm−2 s−1 and, with standard alloys, may account for up to one third of all recorded events, since the resulting continuous energy deposition spectrum overlaps with the signal region [46,47]. High-purity materials or inner coatings of such effectively suppress this background.

1.4. Scintillator Technologies

Neutron scintillation detectors require a converter embedded in a scintillator matrix, a lightguide and a photosensitive element [48]. Two strategies are common: a bulk LiF-loaded scintillator of relatively low converter concentration or a thin ZnS(Ag) coating highly loaded with converter [49,50]. Scintillators respond to muons, gammas and electrons in addition to neutrons. Signal identification can be realized based on pulse shape analysis [51]. Unlike gaseous detectors, plastic scintillators contain hydrogen and therefore act as moderators themselves.

1.5. Measurement Accuracy and Representativeness

The measurement accuracy of Cosmic-Ray Neutron Sensing is primarily subject to the statistical nature of the low-intensity neutron flux. The precision follows counting statistics where the uncertainty is proportional to the inverse square root of the total number of detected neutrons, leading to the general rule that doubling the precision requires a fourfold increase in either measurement duration or count rate. The absolute uncertainty is also dependent on the measured soil moisture value; see Figure 1 (right). Intervals exceeding 12 h are generally avoided as otherwise other quantities can be limiting factors including the hydrological dynamics itself. Environmental factors such as elevation significantly influence the count rate, which doubles approximately every 900 m.
The spatial representativeness of the signal is described by a characteristic horizontal and vertical weighting function with exponential shape. This weighting gives the strongest sensitivity to the near-field and topsoil but integrates over a field-scale footprint; see Figure 1 (left). For the vertical weighting, see literature reference works [13].

2. Materials and Methods

Microcontrollers have become a basis for the development of neutron detector electronics, particularly when used as digitizer and slow-control units in combination with dedicated analog front-end boards; for further examples, see also [52,53,54,55].
In our proportional-counter readout, the architecture was adapted to fully make use of the technical capabilities of such microcontrollers, which allow, for example, to measure pulse height and pulse length at very low power consumption at low to medium count rates. Other functional elements like high-voltage supply and the analog signal path require separate components and logic.
The first nCatcher generation was implemented on an ATmega328P-based Arduino Nano platform (Microchip Technology Inc., Chandler, AZ, USA); see also application references in [17,19,47,56,57,58,59,60,61,62]. In the current system generation, the logger relies on the ATSAM3X8E architecture of the Arduino Due (Microchip Technology Inc., Chandler, AZ, USA), whereas newer detector-side controller concepts are implemented on STM32 microcontrollers (STMicroelectronics International N.V., Plan-les-Ouates, Geneva, Switzerland); see, for example, [63,64] and Table 1.

2.1. Frontend Electronics: nCatcher

The nCatcher readout board—see Figure 2 (left) or [47] for the previous version—integrates high-voltage bias generation, up to five channels with analog amplifiers, pulse discrimination, peak detection and slow control. The design is intended for low-count-rate neutron detectors which require a maximum of 2000 V. Due to its adaptability, signal pulses of helium, boron or lithium-based detectors can be recorded with a pulse height resolution of 1024 channels. The low-voltage supply features digital 5 V for the controller, digital-to-analog converters (DACs), comparator logic and communication interface.

2.1.1. High-Voltage Supply and Bias Distribution

The bias voltage is generated on-board by a dedicated high-voltage module. Its adjustable output voltage is highly stable against temperature drifts and the actual monitor values of voltage and current are provided.
The high-voltage supply board ‘nAccelerator’ converts a 5 V low-voltage input into the detector operating voltage. This module was developed due to the challenging demands of CRNS instruments with respect to high temperature stability, low ripple and noise as well as low power consumption.
On the nCatcher board—see Figure 2 (left)—the high-voltage output is then routed through a passive filtering network and is distributed to the detector channels through individual 10 M Ω resistors with a 270 pF decoupling capacitor.

2.1.2. Analog Signal Stage

Each channel has an inverting charge-sensitive preamplifier based on a low-bias-current operational amplifier with clamp diode protection. The signal is integrated by 4.4 M Ω with a 1 pF capacitor in parallel leading to a characteristic pulse time of around 4.4 µs. The signal is subsequently shaped by overlapping high-pass and low-pass filtering with characteristic cutoff frequencies of 12 kHz and 24 kHz, respectively, followed by a non-inverting amplifier with a gain of 22. These frequencies are shaping constants for transient proportional counter pulses and were chosen to suppress slow baseline variations and high-frequency noise. One branch is forwarded to the comparator that triggers an output if the analog signal passes a threshold. The time difference between the falling and rising edge is measured as the pulse length. The other branch is fed into an active peak-detector circuit based on a dual operational amplifier, a Schottky diode network and a hold capacitor. The peak value is in that way buffered and read by the microcontroller through an analog input. After conversion, the peak detector is reset by discharging the hold capacitor through an analog switch.

2.1.3. Controller and Interfaces

An STM32 microcontroller serves as the main control unit and interface of the board. It reads the high-voltage monitor outputs, enables or disables the HV module, counts the signal length and reads out the peak-detector via its ADC. The on-board temperature and humidity are recorded in parallel.
For communication with an external system, the board provides a differential I2C interface, which allows for operation with longer cable lengths than foreseen for single-ended I2C and in addition an RS-485 connection. Alternatively, an SDI-12 interface is available for operation with standardized scientific data loggers.

2.2. Pulse Height and Pulse Length Selection

The discrimination of neutron-induced events from non-neutron background and electronic noise is achieved through a dual-parameter selection of pulse height and pulse length. As boron-10-lined proportional counters have a continuous energy deposition spectrum, one cannot rely on energy thresholds alone as is usually the case for helium-3 detectors. The nCatcher electronics analyzes the specific correlation between the peak signal amplitude and the time-over-threshold [19]. This two-dimensional approach relies on a parametrized ’banana-shaped’ signal region characteristic for neutron events and is shown in Figure 3. All events outside the dashed selection region are rejected.
A secondary thin band visible outside the neutron selection region is attributed to microdischarges, which in this case were identified as moisture effects in combination with residues at the connector. The weak high-pulse-height feature extending to the right is attributed to trace radioactive impurities in the tube material. At the very low count rates relevant for CRNS, even material contaminations emitting ions on the scale of hours or days are visible. Dedicated quantitative gamma-source rejection factors are not shown here. Due to the low gas pressure, electron and muon signatures and gamma-induced interactions are strongly suppressed and available source tests did not produce a statistically significant response in the accepted neutron region; see also [19].

2.3. Data Logger

The integration of autonomous sensors into cloud-based networks follows the broader trend of Internet of Things (IoT) applications in environmental monitoring, specially adressing real-time decision support for Smart Farming [65,66]. The logger—see Figure 2 (right)—serves as the central control, communication and power-management unit of the instrument. Its architecture is not limited to simple data acquisition but is designed as a complete field controller for autonomous CRNS instruments.

2.3.1. Electronics Design

Its digital core is based on an ATSAM3X8E microcontroller, i.e., a 32-bit ARM Cortex-M3 running with 3.3 V logic in combination with a dedicated ATmega16U2 (Microchip Technology Inc., Chandler, AZ, USA) for USB communication. The controller interfaces the detector frontend electronics, manages auxiliary sensors and communication to peripherals as well as subsequently responsible for data acquisition and transfer.
The logger board features the following components. A DS3231(SN) (Analog Devices, Inc., Wilmington, MA, USA) temperature-stabilized real-time clock with a backup battery and a non-volatile SPI memory are provided by an FRAM, and for removable mass storage, SD cards can be used. The board furthermore includes a BME280 (Bosch Sensortec GmbH, Reutlingen, Germany) sensor for local measurements of pressure, temperature and relative humidity. Communication interfaces are implemented for both local sensors and telemetry. The main peripheral interface is realized by a half-duplex RS-485 transceiver. Optionally, an I2C connection is provided, and for external environmental sensors, the logger has an SDI-12 interface. An onboard SIM7600 4G (SIMCom Wireless Solutions Limited, Shanghai, China) modem provides cellular communication and GNSS functionality. Alternatively, a BC95-G (Quectel Wireless Solutions Co., Ltd., Shanghai, China) NB-IoT modem in combination with an M[6-9]N (u-blox AG, Thalwil, Switzerland) GPS module can be configured.
For autonomous field operation, the logger additionally integrates a solar charger for photovoltaic input with battery backup output.

2.3.2. Logger Functionality

The functional architecture and signal/data flow of the logger are summarized in Figure 4. It shows the interaction between detector frontend, environmental sensor interfaces, timing and storage components, telemetry modules and power-management elements.
The logger serves as the central control, acquisition and communication unit of the measurement system. During each acquisition cycle, the logger collects neutron and slow-control data from the nCatcher frontend, supplements the data stream then with local or external sensor feeds, timestamps the data and stores or transmits the resulting records.
In addition to raw data from the neutron detector and environmental sensor information, the logger acquires diagnostic parameters such as high voltage, threshold settings, temperature, humidity and general status information. A number of external SDI-12 sensors can be configured, these may include meteorological, such as ATMOS14/41 (METER Group, Inc., Pullman, WA, USA), or atmospheric like LI-710 (LI-COR Environmental, Inc., Lincoln, NE, USA) and in situ soil sensors such as SMT-100 (TRUEBNER GmbH, Neustadt, Germany) or HygroVUE5, SoilVUE10, TEROS-12 and CS616 (Campbell Scientific, Inc., Logan, UT, USA) as well as PR2 profile probe (Delta-T Devices Ltd., Cambridge, United Kingdom) and Drill & Drop (Sentek Sensor Technologies, Stepney, SA, Australia). For sensor references and calibration; see [67,68,69,70,71,72,73]. Remote communication can be realized by modems with either UDP, FTP, HTTP POST, MQTT, or optionally LoRa, depending on hardware and firmware configuration.

2.3.3. Telemetry and Data Integration

The telemetry framework implemented into the logger is designed for data transmission from the sensor to remote storage systems, either through cellular or local LoRa networks, supporting various protocols such as FTP, MQTT or UDP for maximizing compatibility with different network infrastructures. Once transmitted, the records are stored into a time-series database, such as InfluxDB, where the data are further processed. This allows for seamless integration with data visualization and analysis tools to facilitate accessing the information through dedicated (API) interfaces or dashboards like Grafana for real-time visualization of time-series and statistical interpretation of soil moisture dynamics; see also Figure 5.

2.4. Instrument Pool

The stationary Stx instrument family comprises several modular CRNS systems that address different requirements in terms of detector size and temporal resolution; see also Figure 6.
The S1 is a slim detector configuration intended primarily for long-term agricultural monitoring at a resolution of around 10 h. The SP presents itself as a larger installation with improved counting statistics and thus better suitability for hourly averages and challenging environments [17,19]. The intermediate S2 and S2+ systems provide a compromise between installation size, time resolution and cost. All these systems are based on helium-3-free boron-converter proportional counters with Ar:CO2 counting gas and combine one or several detector tubes, the same nCatcher readout electronics, logger and autonomous PV/battery supply in a modular outdoor platform [19,64]. Depending on the measurement context, different external SDI-12 sensors are adopted. These systems have been installed both in application-oriented agricultural projects such as ADAPTER and in larger scientific monitoring networks [17,64]. In addition, the S2L system with a lithium-foil multi-wire proportional chamber (MWPC) [43] achieves around 3800 cph. All count rates are stated for sea level, 10% soil moisture and average German latitude. In comparison, under the same conditions, the Hydroinnova CRS1000 [6] yields around 900 cph. For a comparison see Table 2.
The 125 cm length boron-lined proportional counters are produced in-house and are optimized for CRNS detectors. With around 1.5 µm thick boron carbide coatings, the single counter thermal neutron efficiency at 25 meV is around 9–10%. Measurements indicate a thermal neutron contribution, understood as integral over the thermal neutron peak, of approximately 15% without shielding and about 1–3% with the gadolinium-based thermal shield. A detailed metrological characterization of the detector response is provided in [19,26]. As a comparison, the market price for such a boron-10-based tube lies around EUR 2000, an in terms of instrument count rate comparable to helium-3 tube around EUR 3000. The cost of the systems depends on the configuration, including logger, solar panel, battery and pole, which typically adds substantial costs beyond the active neutron detection unit. As approximate tentative reference points, commercial systems with comparable application scope to the S1 include the Hydroinnova CRS1000 (Hydroinnova LLC., Albuquerque, NM, USA) at about 25% lower count rate and the Finapp3 (Finapp S.r.l., San Pietro in Cariano, Italy) at about 10% lower count rate [26].

2.5. Field Deployment

The handling of the Stx instruments is designed for straightforward and autonomous field deployment. For the installation, typically a mechanical insertion of a ground screw into the soil is chosen, which for usual soils can be drilled in by hand. Once the base is positioned, the main detector unit is mounted and aligned; see Figure 7. Then, the subsystems are connected to the foldout logger unit: photovoltaic supply, the neutron detector and environmental sensors. Data acquisition parameters and other parameters are managed via a configuration file on the SD card. Upon powering the system, the integrated display allows for directly checking the proper instrument operation, such as current count rates, the high-voltage status and telemetry signal quality.

2.6. Applications and Data Management

The integration of CRNS technology into environmental monitoring networks has significantly advanced hydrological monitoring with the current installations COSMOS [6], COSMOS-UK [16], CosmOz Australia [15], ADAPTER [17] and smaller-scaled networks in India [76], Germany [64] and Ireland [77]. Due to the comparably large footprint, CRNS data can serve as a validation tool of remote sensing products [78,79,80]. Soil moisture data are especially valuable for drought monitoring [81] and early warning systems [82], for the calculation of specialized indices such as the Standardized Precipitation Evapotranspiration Index (SPEI) or the Palmer Drought Severity Index (PDSI) [83]. In agricultural management, accurate information on soil water content allows for the optimization of irrigation practices and water use efficiency [74,84,85]. Furthermore, long-term monitoring [86] supports hydrological research into water flow dynamics, infiltration rates and groundwater recharge, as well as the assessment of climate change impacts on ecosystem resilience [8]. The system integration involves automated data collection by instruments at predefined intervals and subsequent transmission via cellular, LoRa or satellite networks. Data storage, quality control and analysis are performed on cloud platforms.

3. Results

3.1. Thermal Stability of Signal Discrimination

The stability of the nCatcher electronics was evaluated for the S1 system over a period of several years—see Section 3.3—with temperature range from −10 °C to +55 °C.
In a linear multivariate regression analysis, the pulse length exhibited a drift coefficient of −0.02 units/°C. Given the typical signal range of 100 to 350 units, a 30 °C temperature shift results in a variation of less than 0.7 units (<0.3%). Similarly, the pulse height drift was quantified at 0.046 units/°C. These measured drifts are an order of magnitude smaller than the characteristic width of the neutron ’banana’ region in the PL-PH space. Hence, the analysis of the pulse length and pulse height confirms good thermal stability.

3.2. Power Consumption and Energy Efficiency

Energy efficiency of the system is a critical factor for long-term autarcic operation, not only for dimensioning PV panel and battery. It also determines whether an instrument can be deployed in challenging environments like forests. The power consumption varies between operational states and could be reduced within each generation. The newer generation (S1p) runs in combination with the optimized nCatcher 4.0 electronics with the nAccelerator high voltage module, which consumes only 2 mA at 12 V. In contrast, the previous S1 system, with the nCatcher 3.x and a CAEN A7508P (CAEN S.p.A., Viareggio, Italy) high-voltage module, requires approximately 8 mA. The total power consumption—see Table 3—refers to a complete setup without external SDI-12 sensors. An SD card is included in the budget. Current draws can vary slightly between models. The row labeled duty-cycle averaged total represents an empirical long-term average over a typical acquisition setting, 85:15 sleep ratio and telemetry cycle.
For more complex configurations, such as the S2+ (triple analog stage) or the S2L (lithium-foil based multi-wire proportional chamber detector), the power requirements increase accordingly. The S2L has a CAEN A7502P module, which adds approximately 4 mA of quiescent current and requires higher output power for its multi-wire proportional chamber, resulting in a total increase of roughly 6 mA compared to the base configuration. Hence, even the more complex Stx system configurations are well within a power budget of less than 0.5 W and the most recent S1p achieves a significant reduction in consumption with the instrument reaching nearly 50 mW.

3.3. Long-Term Operation

We show an exemplary timeseries of a CRNS instrument deployed within an urban environment. Around 40% of the surface is covered by infrastructure. Located at a former military base, most soils had been excavated and back- or refilled. This anthropogenic sediment therefore provides a base with laterally rather homogeneous conditions and average densities of around 1.48 g/cm3, yet with a very high hydraulic conductivity. The challenge in such an environment for in that case the smallest sensor is to resolve the rather fast hydrological response with a significant signal damping. The result—see Figure 8—under these circumstances is a time series which is consistent with the expected behavior of a stationary CRNS deployed in a heterogeneous urban footprint.
The raw neutron counts were corrected using established CRNS preprocessing steps before conversion to soil moisture [7]. Atmospheric pressure was corrected following [6] with a reference pressure of 1013 mbar and an atmospheric attenuation length of 136 mbar. Air humidity effects were corrected following the implementation in the UTS framework and summarized in [7]. Incoming neutron intensity was corrected using the Jungfraujoch neutron monitor from NMDB with I ref = 150 . Biomass was considered negligible at this site. Periods affected by snow are interpreted qualitatively as snow-water effects. For the neutron intensity to soil moisture conversion, UTS with the ’MCNPdrf’ parameter set was used.
After correction for incoming neutron intensity, atmospheric pressure and air humidity, the neutron signal shows a comparatively smooth temporal evolution, with emphasized links to precipitation-driven wetting and subsequent drying phases. The comparatively low noise level of the corrected neutron data, despite count rates of roughly 700–1000 cph, also indicates that the expected precision for a stationary CRNS, after temporal aggregation, can be achieved. The CRNS soil moisture evaluation reproduces the main seasonal and precipitation-wise soil moisture dynamics seen by the weighted SMT reference—see also [9,87]—but with reduced amplitude. After applying the urban areal correction—see Appendix A—the CRNS-derived soil moisture shows a stronger wetting and drying response and agrees more closely with the weighted in situ reference, which is consistent with the theoretical expectation that sealed surfaces contribute a comparatively invariant neutron background and therefore damp the apparent soil moisture dynamics of the hydrologically active fraction. As the in situ SMT-100 sensors [88] do not cover the full support volume an exact overlap is not to be expected. Particularly distinct during the winter period is the appearance of snow: here, the derived CRNS moisture signal rises strongly as a distinct water layer forms on top of the soil. For the interpretation of snow water equivalent by CRNS, we refer to [89,90,91,92,93]. The timeseries demonstrates that the corrected CRNS-derived soil moisture enables the precise measurement of the field-scale dynamics with good temporal resolution.

4. Outlook

4.1. Roving

By mounting a detector on a vehicle, the measured area can be extended from several hectares to the square-kilometer scale.
With stationary CRNS being optimized for monitoring within a fixed footprint, mobile applications are carried out to resolve spatial heterogeneities or along regional transects. This technique, commonly referred to as roving, uses the same detection principles as stationary probes but with much larger detectors to maximize the count rate and hence minimize acquisition time. Yet, data typically need to be corrected for the ‘road effect’ [23] or changing soil texture along the track. Roving provides a scalable solution for characterizing soil moisture from the field scale up to mesoscale assessments of entire catchments and basins. This approach has been applied in a variety of environmental contexts [35,36,37,38,94,95,96]. An illustrative example of the spatial coverage achieved during a representative roving campaign of 2 h is presented in Figure 9.

4.2. Monolithic Instrument Architecture: The S1p System

The latest evolution of the S1 series, the new S1p, introduces a major overhaul by transitioning from a modular assembly to a fully monolithic instrument architecture; see Figure 10. Conventional CRNS setups typically rely on a distributed configuration, consisting of a separate logger and battery enclosure connected via external cabling to a detector on a pole. The S1p integrates all main components into a single cylindrical enclosure. The main development of this design is the S1p ‘penthouse’ unit. This integrated head houses the new logger, the telemetry modules and the battery as an extension of the detector housing. The solar panel and the environmental sensors (pressure, temperature and humidity) are mounted directly on top of the instrument cap. This design shift removes the need for external cables or mounting solutions. Consequently, the S1p can be deployed as a single unit on a ground screw, which reduces installation time and effort. As a more integrated solution, it also reduces the number of external components and lowers the acquisition cost relative to the modular S1 configuration.

5. Conclusions

We presented the Stx instrument family as a scalable, cost-efficient and helium-3-free solution for Cosmic-Ray Neutron Sensing. The modular architecture relies on boron-10-lined proportional counters with dedicated frontend electronics, data logging and telemetry. The pulse-height-pulse-length analysis provides a methodology to suppress non-neutron and electronic background, which is necessary because of the continuous energy spectrum characteristic of boron-10-lined detectors. Main results are the complete overhaul in electronics and system design. We achieved a significant gain in efficiency with close to 50 mW duty-cycle averaged total system power with the latest S1p generation. This represents a roughly fivefold improvement over the previous S1 generation. With the transition to the monolithic S1p architecture and complementary roving applications, the Stx platform is well positioned to support the continued expansion of CRNS monitoring networks in hydrology, precision agriculture and climate-resilient land management.

Author Contributions

Conceptualization, M.K. and J.W.; methodology, M.K. and J.W.; software, M.K. and J.W.; validation, M.K. and J.W.; formal analysis, M.K. and J.W.; investigation, M.K. and J.W.; resources, M.K. and J.W.; data curation, M.K. and J.W.; writing—original draft preparation, M.K.; writing—review and editing, M.K. and J.W.; visualization, M.K. and J.W.; supervision, M.K.; project administration, M.K. and J.W.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.

Funding

The presented solutions were developed within several projects and partially funded by the DFG (German Research Foundation) research unit FOR 2694 Cosmic Sense via the project 357874777.

Data Availability Statement

Data and designs available on request from the authors. Further information can be obtained from the following project URLs: nCatcher repository: https://gitlab.com/mkoehli/ncatcher (accessed on 4 May 2026), Data logger repository: https://gitlab.com/mkoehli/arduinologger (accessed on 4 May 2026).

Acknowledgments

M.K. and J.W. acknowledge Weingut Politschek and Honeycamp Development GmbH for providing sites used in this publication (Gundelsheim, Mannheim-Vogelstang). M.K. acknowledges Elli Groner and Avshalom Babad.

Conflicts of Interest

The authors Markus Köhli and Jannis Weimar hold CEO positions at StyX Neutronica GmbH.

Appendix A. Areal Correction Procedure

For the urban test campaign in Mannheim (Vogelstang), Germany, the CRNS sensor was installed at the site ‘Honeycamp’ from 04/2021 onwards. Approximately 40% of the surrounding area was classified as sealed surface with roads, paved yards and building-related infrastructure, based on the land-cover delineation shown in Figure A1. This percentage is only an unweighted areal descriptor and is not be interpreted as a direct signal fraction, because the CRNS footprint is strongly distance-weighted. The signal-contribution correction of [97,98] was applied to account for the damping of the neutron signal by hydrologically mostly invariant areas. It combines the radial sensitivity of the detector with the intensity-moisture relation to estimate the relative contribution of different surface classes to the measured count rate.
The correction was performed on a 1000 × 1000 pixel raster representation of the footprint. Static non-soil elements were assigned fixed soil moisture-equivalent hydrogen pools according to the material values in [99] (and updated in [100]). For example, representative values of 10% for concrete and 30% for asphalt were used. These material-specific equivalent water contents were then combined with the footprint weighting function. Only the unsealed fraction was assumed to have dynamic soil moisture variations. Conceptually integrated in [101], the measured neutron counts were rescaled by the signal contribution of the hydrologically active area in order to eliminate the effect of dynamical range reduction in the sealed urban surroundings and to obtain a signal more representative of the topsoil within the footprint. For the present site, the environmental mapping is approximate but sufficient for showing the effect of the correction.
Instruments 10 00031 g0a1
Figure A1. Soil moisture correction procedure for the sensor location in Mannheim, Germany (49°30′52.95″ N, 8°33′6.45″ E). (Left) Google Earth bird’s eye view of the site in 2022 with the inner circle amounting to 63% and the outer circle to 86% of the signal with GIS representation of the same area (middle) and (right) converted sealed area map.
Figure A1. Soil moisture correction procedure for the sensor location in Mannheim, Germany (49°30′52.95″ N, 8°33′6.45″ E). (Left) Google Earth bird’s eye view of the site in 2022 with the inner circle amounting to 63% and the outer circle to 86% of the signal with GIS representation of the same area (middle) and (right) converted sealed area map.
Instruments 10 00031 g0a1

References

  1. Koster, R.D.; Dirmeyer, P.A.; Guo, Z.; Bonan, G.; Chan, E.; Cox, P.; Gordon, C.T.; Kanae, S.; Kowalczyk, E.; Lawrence, D.; et al. Regions of Strong Coupling Between Soil Moisture and Precipitation. Science 2004, 305, 1138–1140. [Google Scholar] [CrossRef]
  2. Vereecken, H.; Kasteel, R.; Vanderborght, J.; Harter, J. Upscaling Hydraulic Properties and Soil Water Flow Processes in Heterogeneous Soils: A Review. Vadose Zone J. 2007, 6, 1–28. [Google Scholar] [CrossRef]
  3. Seneviratne, S.I.; Corti, T.; Davin, E.L.; Hirschi, M.; Jaeger, E.B.; Lehner, I.; Orlowsky, B.; Teuling, A.J. Investigating soil moisture-climate interactions in a changing climate: A review. Earth Sci. Rev. 2010, 99, 125–161. [Google Scholar] [CrossRef]
  4. Zreda, M.; Desilets, D.; Ferré, T.; Scott, R. Measuring soil moisture content non-invasively at intermediate spatial scale using cosmic-ray neutrons. Geophys. Res. Lett. 2008, 35, L21402. [Google Scholar] [CrossRef]
  5. Desilets, D.; Zreda, M.; Ferré, T. Nature’s neutron probe: Land surface hydrology at an elusive scale with cosmic rays. Water Resour. Res. 2010, 46, W11505. [Google Scholar] [CrossRef]
  6. Zreda, M.; Shuttleworth, W.; Zeng, X.; Zweck, C.; Desilets, D.; Franz, T.; Rosolem, R. COSMOS: The COsmic-ray Soil Moisture Observing System. Hydrol. Earth Syst. Sci. 2012, 16, 4079–4099. [Google Scholar] [CrossRef]
  7. Köhli, M. Soil moisture measurements by Cosmic-Ray Neutron Sensing: A critical review. Geoderma 2026, 465, 117626. [Google Scholar] [CrossRef]
  8. Bogena, H.R.; Huisman, J.A.; Güntner, A.; Hübner, C.; Kusche, J.; Jonard, F.; Vey, S.; Vereecken, H. Emerging methods for noninvasive sensing of soil moisture dynamics from field to catchment scale: A review. WIREs Water 2015, 2, 635–647. [Google Scholar] [CrossRef]
  9. Schrön, M.; Köhli, M.; Scheiffele, L.; Iwema, J.; Bogena, H.; Lv, L.; Martini, E.; Baroni, G.; Rosolem, R.; Weimar, J.; et al. Improving Calibration and Validation of Cosmic-Ray Neutron Sensors in the Light of Spatial Sensitivity. Hydrol. Earth Syst. Sci. 2017, 21, 5009–5030. [Google Scholar] [CrossRef]
  10. Loew, A.; Bell, W.; Brocca, L.; Bulgin, C.E.; Burdanowitz, J.; Calbet, X.; Donner, R.V.; Ghent, D.; Gruber, A.; Kaminski, T.; et al. Validation practices for satellite-based Earth observation data across communities. Rev. Geophys. 2017, 55, 779–817. [Google Scholar] [CrossRef]
  11. Peng, J.; Albergel, C.; Balenzano, A.; Brocca, L.; Cartus, O.; Cosh, M.H.; Crow, W.T.; Dabrowska-Zielinska, K.; Dadson, S.; Davidson, M.W.; et al. A roadmap for high-resolution satellite soil moisture applications—Confronting product characteristics with user requirements. Remote Sens. Environ. 2021, 252, 112162. [Google Scholar] [CrossRef]
  12. Li, Z.L.; Leng, P.; Zhou, C.; Chen, K.S.; Zhou, F.C.; Shang, G.F. Soil moisture retrieval from remote sensing measurements: Current knowledge and directions for the future. Earth Sci. Rev. 2021, 218, 103673. [Google Scholar] [CrossRef]
  13. Köhli, M.; Schrön, M.; Zreda, M.; Schmidt, U.; Dietrich, P.; Zacharias, S. Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray neutrons. Water Resour. Res. 2015, 51, 5772–5790. [Google Scholar] [CrossRef]
  14. Bogena, H.; Schrön, M.; Jakobi, J.; Ney, P.; Zacharias, S.; Andreasen, M.; Baatz, R.; Boorman, D.; Duygu, M.; Eguibar-Galán, M.; et al. COSMOS-Europe: A European network of cosmic-ray neutron soil moisture sensors. Earth Syst. Sci. Data 2022, 14, 1125–1151. [Google Scholar] [CrossRef]
  15. Hawdon, A.; McJannet, D.; Wallace, J. Calibration and correction procedures for cosmic-ray neutron soil moisture probes located across Australia. Water Resour. Res. 2014, 50, 5029–5043. [Google Scholar] [CrossRef]
  16. Evans, J.G.; Ward, H.C.; Blake, J.R.; Hewitt, E.J.; Morrison, R.; Fry, M.; Ball, L.A.; Doughty, L.C.; Libre, J.W.; Hitt, O.E.; et al. Soil water content in southern England derived from a cosmic-ray soil moisture observing system—COSMOS-UK. Hydrol. Process. 2016, 30, 4987–4999. [Google Scholar] [CrossRef]
  17. Ney, P.; Köhli, M.; Bogena, H.; Goergen, K. CRNS-based monitoring technologies for a weather and climate-resilient agriculture: Realization by the ADAPTER project. In Proceedings of the 2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor); IEEE: Piscataway, NJ, USA, 2021; pp. 203–208. [Google Scholar] [CrossRef]
  18. Jakobi, J.; Huisman, J.A.; Vereecken, H.; Diekkrüger, B.; Bogena, H.R. Cosmic Ray Neutron Sensing for Simultaneous Soil Water Content and Biomass Quantification in Drought Conditions. Water Resour. Res. 2018, 54, 7383–7402. [Google Scholar] [CrossRef]
  19. Weimar, J.; Köhli, M.; Budach, C.; Schmidt, U. Large-Scale Boron-Lined Neutron Detection Systems as a 3He Alternative for Cosmic Ray Neutron Sensing. Front. Water 2020, 2, 16. [Google Scholar] [CrossRef]
  20. Heidbüchel, I.; Güntner, A.; Blume, T. Use of cosmic-ray neutron sensors for soil moisture monitoring in forests. Hydrol. Earth Syst. Sci. 2016, 20, 1269–1288. [Google Scholar] [CrossRef]
  21. Brogi, C.; Jakobi, J.; Huisman, J.; Schmidt, M.; Montzka, C.; Bates, J.; Akter, S.; Bogena, H. Cosmic-ray neutron sensors provide scale-appropriate soil water content and vegetation observations for eddy covariance stations in agricultural ecosystems. Agric. For. Meteorol. 2025, 373, 110731. [Google Scholar] [CrossRef]
  22. Al-Mashharawi, S.K.; Steele-Dunne, S.C.; El Hajj, M.M.; López Valencia, O.M.; López Camargo, O.A.; Pouget, G.; Doussan, C.; Courault, D.; McCabe, M.F. Exploring the use of thermal neutron counts to track orchard phenological development. Front. Water 2026, 8, 1749654. [Google Scholar] [CrossRef]
  23. Schrön, M.; Rosolem, R.; Köhli, M.; Piussi, L.; Schröter, I.; Iwema, J.; Kögler, S.; Oswald, S.; Wollschläger, U.; Samaniego, L.; et al. Cosmic-Ray Neutron Rover Surveys of Field Soil Moisture and the Influence of Roads. Water Resour. Res. 2018, 54, 6441–6459. [Google Scholar] [CrossRef]
  24. Köhli, M.; Schrön, M.; Schmidt, U. Response functions for detectors in cosmic ray neutron sensing. Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrometers Detect. Assoc. Equip. 2018, 902, 184–189. [Google Scholar] [CrossRef]
  25. Piscitelli, F.; Mauri, G.; Laloni, A.; Hall-Wilton, R. Verification of He-3 proportional counters’ fast neutron sensitivity through a comparison with He-4 detectors: He-3 and He-4 proportional counters’ fast neutron sensitivity and evaluation of the cosmic neutron fluxes at ESS. Eur. Phys. J. Plus 2020, 135, 577. [Google Scholar] [CrossRef]
  26. Millán-Callado, M.Á.; Méndez Villafañe, R.; Adam, B.A.S.; Blahušiak, P.; Di Chicco, A.; Dietz, M.; Köhli, M.; Lutz, B.; Reginatto, M.; Vykydal, Z.; et al. Response characterization of Cosmic-Ray Neutron Sensors in neutron metrology reference fields. EGUsphere 2025. preprint. [Google Scholar] [CrossRef]
  27. Shea, D.; Morgan, D. The Helium-3 Shortage: Supply, Demand, and Options for Congress. In CRS Report for Congress; Congressional Research Service: Washington, DC, USA, 2010; Report R41419. [Google Scholar]
  28. Kouzes, R.; Lintereur, A.; Siciliano, E. Progress in alternative neutron detection to address the helium-3 shortage. Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrometers Detect. Assoc. Equip. 2015, 784, 172–175. [Google Scholar] [CrossRef]
  29. Lintereur, A.T.; Ely, J.H.; Kouzes, R.T.; Siciliano, E.R.; Swinhoe, M.T.; Woodring, M.L. Alternatives to Helium-3 for neutron multiplicity counters. In Proceedings of the 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC); IEEE: Piscataway, NJ, USA, 2012; pp. 547–553. [Google Scholar] [CrossRef]
  30. Francke, T.; Heistermann, M.; Köhli, M.; Budach, C.; Schrön, M.; Oswald, S. Assessing the feasibility of a directional cosmic-ray neutron sensing sensor for estimating soil moisture. Geosci. Instrum. Methods Data Syst. 2022, 11, 75–92. [Google Scholar] [CrossRef]
  31. Badiee, A.; Wallbank, J.; Pulido Fentanes, J.; Trill, E.; Scarlet, P.; Zhu, Y.; Cielniak, G.; Cooper, H.; Blake, J.; Evans, J.; et al. Using Additional Moderator to Control the Footprint of a COSMOS Rover for Soil Moisture Measurement. Water Resour. Res. 2021, 57, e2020WR028478. [Google Scholar] [CrossRef]
  32. D’Mellow, B.; Thomas, D.J.; Joyce, M.J.; Kolkowski, P.; Roberts, N.J.; Monk, S.D. The replacement of cadmium as a thermal neutron filter. Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrometers Detect. Assoc. Equip. 2007, 577, 690–695. [Google Scholar] [CrossRef]
  33. Andreasen, M.; Jensen, K.; Desilets, D.; Franz, T.; Zreda, M.; Bogena, H.; Looms, M. Status and Perspectives on the Cosmic-Ray Neutron Method for Soil Moisture Estimation and Other Environmental Science Applications. Vadose Zone J. 2017, 16, 1–11. [Google Scholar] [CrossRef]
  34. Dong, J.; Ochsner, T.; Zreda, M.; Cosh, M.; Zou, C. Calibration and Validation of the COSMOS Rover for Surface Soil Moisture Measurement. Vadose Zone J. 2014, 13, 1–8. [Google Scholar] [CrossRef]
  35. Vather, T.; Everson, C.; Franz, T.E. Calibration and Validation of the Cosmic Ray Neutron Rover for Soil Water Mapping within Two South African Land Classes. Hydrology 2019, 6, 65. [Google Scholar] [CrossRef]
  36. Jakobi, J.; Huisman, J.A.; Schrön, M.; Fiedler, J.; Brogi, C.; Vereecken, H.; Bogena, H.R. Error Estimation for Soil Moisture Measurements with Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2020, 2, 10, Correction in Front. Water 2020, 2, 604482. https://doi.org/10.3389/frwa.2020.604482. [Google Scholar] [CrossRef]
  37. Wu, S.; Zhang, Y.; Kang, W.; Zhao, W. Mesoscale soil moisture survey by mobile cosmic-ray neutron sensor across various landscapes in the Heihe River Basin. Res. Cold Arid Reg. 2023, 15, 211–218. [Google Scholar] [CrossRef]
  38. Zhang, Y.; Wu, S.; Zhao, W.; Xiao, J. Mesoscale soil moisture measurements along the rover route using the mobile cosmic-ray neutron sensing in the eastern Tibetan Plateau. Geoderma 2024, 450, 117046. [Google Scholar] [CrossRef]
  39. Segrè, E.; Wiegand, C. Boron Trifluoride Neutron Detector for Low Neutron Intensities. Rev. Sci. Instrum. 1947, 18, 86–89. [Google Scholar] [CrossRef]
  40. Yamashita, M.; Stephens, L.; Smith, A.; Patterson, H. Detection Efficiency of Bare and Moderated BF3-Gas-Filled Proportional Counters for Isotropic Neutron Fluxes. J. Nucl. Sci. Technol. 1966, 3, 343–353. [Google Scholar] [CrossRef]
  41. Kowalski, T. Analytical approach and calculation of gas gain in Ar-CO2 mixture. J. Instrum. 2020, 15, P07008. [Google Scholar] [CrossRef]
  42. Mashao, D.C.; Alton, T.L.; Binnersley, C.L.; Bradnam, S.C.; Croft, S.; Joyce, M.J.; Packer, L.W.; Turner, T.; Wild, J.A.; Aspinall, M.D. Boron-coated straw detector efficiency comparison with 3He proportional counters—A prospect for cosmic ray neutron monitoring. Int. J. Mod. Phys. E 2025, 34, 2545004. [Google Scholar] [CrossRef]
  43. Forsyth, A.; Teal, T.; Inglis, A. Silverside neutron detector performance. In Proceedings of the 2017 IEEE International Symposium on Technologies for Homeland Security (HST); IEEE: Piscataway, NJ, USA, 2017; pp. 1–7. [Google Scholar] [CrossRef]
  44. Patrignani, A.; Ochsner, T.; Montag, B.; Bellinger, S. A Novel Lithium Foil Cosmic-Ray Neutron Detector for Measuring Field-Scale Soil Moisture. Front. Water 2021, 3, 673185. [Google Scholar] [CrossRef]
  45. Flynn, K.D.; Wyatt, B.M.; McInnes, K.J. Novel Cosmic Ray Neutron Sensor Accurately Captures Field-Scale Soil Moisture Trends under Heterogeneous Soil Textures. Water 2021, 13, 3038. [Google Scholar] [CrossRef]
  46. Debicki, Z.; Jedrzejczak, K.; Karczmarczyk, J.; Kasztelan, M.; Lewandowski, R.; Orzechowski, J.; Szabelska, B.; Szabelski, J.; Tokarski, P.; Wibig, T. Helium counters for low neutron flux measurements. Astrophys. Space Sci. Trans. 2011, 7, 511–514. [Google Scholar] [CrossRef]
  47. Köhli, M.; Weimar, J.; Schmidt, S.; Schmidt, F.P.; Lambertz, A.; Weber, L.; Kaminski, J.; Schmidt, U. Arduino-Based Readout Electronics for Nuclear and Particle Physics. Sensors 2024, 24, 2935. [Google Scholar] [CrossRef]
  48. Osovizky, A.; Pritchard, K.; Ziegler, J.; Binkley, E.; Yehuda-Zada, Y.; Tsai, P.; Thompson, A.; Cooksey, C.; Siebein, K.; Hadad, N.; et al. 6LiF:ZnS(Ag) Mixture Optimization for a Highly Efficient Ultrathin Cold Neutron Detector. IEEE Trans. Nucl. Sci. 2018, 65, 1025–1032. [Google Scholar] [CrossRef]
  49. Stowell, P.; Fargher, S.; Steer, C.; Thompson, L. Scintillating thermal neutron detectors for cosmic ray soil moisture monitoring. J. Instrum. 2021, 16, P11039. [Google Scholar] [CrossRef]
  50. Gianessi, S.; Polo, M.; Stevanato, L.; Lunardon, M.; Ahmed, H.S.; Weltin, G.; Toloza, A.; Budach, C.; Biro, P.; Francke, T.; et al. Assessment of a new non-invasive soil moisture sensor based on cosmic-ray neutrons. In Proceedings of the 2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor); IEEE: Piscataway, NJ, USA, 2021; pp. 290–294. [Google Scholar] [CrossRef]
  51. Gianessi, S.; Polo, M.; Stevanato, L.; Lunardon, M.; Francke, T.; Oswald, S.E.; Said Ahmed, H.; Toloza, A.; Weltin, G.; Dercon, G.; et al. Testing a novel sensor design to jointly measure cosmic-ray neutrons, muons and gamma rays for non-invasive soil moisture estimation. Geosci. Instrum. Methods Data Syst. 2024, 13, 9–25. [Google Scholar] [CrossRef]
  52. Rahman, N.A.A.; Lombigit, L.; Abdullah, N.A.; Azman, A.; Dolah, T.; Muzakkir, A.; Jaafar, Z.; Mohamad, G.H.P.; Ramli, A.A.M.; Zain, R.M.; et al. Arduino based radiation survey meter. In Proceedings of the AIP Conference Proceedings; AIP Publishing LLC.: Melville, NY, USA, 2016. [Google Scholar] [CrossRef]
  53. Axani, S.; Frankiewicz, K.; Conrad, J. The CosmicWatch Desktop Muon Detector: A self-contained, pocket sized particle detector. J. Instrum. 2018, 13, P03019. [Google Scholar] [CrossRef]
  54. Lee, J.; Lee, M.; Jang, M.; Lim, J.M. Comparison of Arduino Nano and Due processors for time-based data acquisition for low-cost mobile radiation detection system. J. Instrum. 2022, 17, P03015. [Google Scholar] [CrossRef]
  55. Bocci, V.; Ali, B.; Chiodi, G.; Kubler, D.; Iacoangeli, F.; Masi, L.; Recchia, L. Cosmo ArduSiPM: An All-in-One Scintillation-Based Particle Detector for Earth and Space Application. Sensors 2024, 24, 3836. [Google Scholar] [CrossRef]
  56. Fersch, B.; Francke, T.; Heistermann, M.; Schrön, M.; Döpper, V.; Jakobi, J.; Baroni, G.; Blume, T.; Bogena, H.R.; Budach, C.; et al. A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-alpine headwater catchment in Germany. Earth Syst. Sci. Data Discuss. 2020, 12, 2289–2309. [Google Scholar] [CrossRef]
  57. Li, T.; Jeřábek, J.; Noreika, N.; Dostál, T.; Zumr, D. An overview of hydrometeorological datasets from a small agricultural catchment (Nučice) in the Czech Republic. Hydrol. Process. 2021, 35, e14042. [Google Scholar] [CrossRef]
  58. Brogi, C.; Pisinaras, V.; Köhli, M.; Dombrowski, O.; Hendricks-Franssen, H.J.; Babakos, K.; Chatzi, A.; Panagopoulos, A.; Bogena, H.R. Monitoring irrigation in small orchards with cosmic-ray neutron sensors. Sensors 2023, 23, 2378. [Google Scholar] [CrossRef]
  59. Heistermann, M.; Francke, T.; Schrön, M.; Oswald, S.E. Technical Note: Revisiting the general calibration of cosmic-ray neutron sensors to estimate soil water content. Hydrol. Earth Syst. Sci. 2024, 28, 989–1000. [Google Scholar] [CrossRef]
  60. Fäth, J.; Kneisel, C. Multi-method soil moisture monitoring at two temperate forest stands in Germany. Discov. Appl. Sci. 2024, 6, 573. [Google Scholar] [CrossRef]
  61. Grosse, P.M.; Marret, E.; Scheiffele, L.; Dimitrova Petrova, K.; Francke, T.; Altdorff, D.; Heistermann, M.; Schiel, M.; Neumann, C.; Scheffler, D.; et al. The Potsdam Soil Moisture Observatory: High-coverage reference observations at kilometer scale. Earth Syst. Sci. Data 2026, 18, 1703–1727. [Google Scholar] [CrossRef]
  62. Brogi, C.; Nieberding, F.; Scheiffele, L.; Daccache, A.; Schrön, M.; Bogena, H.R. Irrigation Monitoring With Cosmic-Ray Neutron Sensors: Unraveling Field Experiments With Neutron Transport Simulations. Water Resour. Res. 2026, 62, e2025WR043105. [Google Scholar] [CrossRef]
  63. Altdorff, D.; Oswald, S.E.; Zacharias, S.; Zengerle, C.; Dietrich, P.; Mollenhauer, H.; Attinger, S.; Schrön, M. Toward Large-Scale Soil Moisture Monitoring Using Rail-Based Cosmic Ray Neutron Sensing. Water Resour. Res. 2023, 59, e2022WR033514. [Google Scholar] [CrossRef]
  64. Heistermann, M.; Altdorff, D.; Francke, T.; Schrön, M.; Grosse, P.M.; Markert, A.; Bauriegel, A.; Biró, P.; Attinger, S.; Beyrich, F.; et al. Soil moisture monitoring with cosmogenic neutrons: An asset for the development and assessment of soil moisture products in the state of Brandenburg (Germany). Nat. Hazards Earth Syst. Sci. 2026, 26, 465–486. [Google Scholar] [CrossRef]
  65. Jawad, H.; Nordin, R.; Gharghan, S.; Jawad, A.; Ismail, M. Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review. Sensors 2017, 17, 1781. [Google Scholar] [CrossRef] [PubMed]
  66. Rajasekaran, T.; Anandamurugan, S. Challenges and Applications of Wireless Sensor Networks in Smart Farming—A Survey. In Advances in Big Data and Cloud Computing; Springer: Singapore, 2018; pp. 353–361. [Google Scholar] [CrossRef]
  67. Bogena, H.R.; Huisman, J.A.; Schilling, B.; Weuthen, A.; Vereecken, H. Effective calibration of low-cost soil water content sensors. Sensors 2017, 17, 208. [Google Scholar] [CrossRef]
  68. Ramirez-Cuesta, J.M.; Sánchez, J.M.; Piqueras, J.G.; Montoya, F.; Pueyo, I.B.; Intrigliolo, D.S.; Lopez-Urrea, R. Reliability of Turbulent Fluxes Measurements Provided by a Novel Sensor on a Pistachio Orchard. In Proceedings of the 2024 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor); IEEE: Piscataway, NJ, USA, 2024; pp. 107–111. [Google Scholar] [CrossRef]
  69. Mozumder, R.S.; Debnath, R.; Rahman, F.; Ahmed, A.A.; Bin Alam, M.J. Field Validation of a Low-Cost IoT-Based System for Real-Time Monitoring of Hydrologic and Physical Behavior of Slope. In Proceedings of the 2026 International Conference on Computing, Networking and Communications (ICNC); IEEE: Piscataway, NJ, USA, 2026; pp. 309–315. [Google Scholar] [CrossRef]
  70. Cominelli, S.; Rivera, L.D.; Brown, W.G.; Ochsner, T.E.; Patrignani, A. Calibration of TEROS 10 and TEROS 12 electromagnetic soil moisture sensors. Soil Sci. Soc. Am. J. 2024, 88, 2104–2122. [Google Scholar] [CrossRef]
  71. Wilson, T.B.; Kochendorfer, J.; Diamond, H.J.; Meyers, T.P.; Hall, M.; French, B.; Myles, L.; Saylor, R.D. A field evaluation of the SoilVUE10 soil moisture sensor. Vadose Zone J. 2023, 22, e20241. [Google Scholar] [CrossRef]
  72. Francesca, V.; Osvaldo, F.; Stefano, P.; Paola, R.P. Soil Moisture Measurements: Comparison of Instrumentation Performances. J. Irrig. Drain. Eng. 2010, 136, 81–89. [Google Scholar] [CrossRef]
  73. Dhakal, M.; West, C.P.; Deb, S.K.; Kharel, G.; Ritchie, G.L. Field Calibration of PR2 Capacitance Probe in Pullman Clay-Loam Soil of Southern High Plains. Agrosyst. Geosci. Environ. 2019, 2, 1–7. [Google Scholar] [CrossRef]
  74. Brogi, C.; Bogena, H.; Köhli, M.; Huisman, J.; Hendricks-Franssen, H.J.; Dombrowski, O. Feasibility of irrigation monitoring with cosmic-ray neutron sensors. Geosci. Instrum. Methods Data Syst. 2022, 11, 451–469. [Google Scholar] [CrossRef]
  75. Wang, K.; Ma, L.; Yang, C.; Bian, Z.; Zhang, D.; Cui, S.; Wang, M.; Chen, Z.; Li, X. Recent Progress in Gd-Containing Materials for Neutron Shielding Applications: A Review. Materials 2023, 16, 4305. [Google Scholar] [CrossRef] [PubMed]
  76. Montzka, C.; Bogena, H.; Zreda, M.; Monerris, A.; Morrison, R.; Muddu, S.; Vereecken, H. Validation of spaceborne and modelled surface soil moisture products with cosmic-ray neutron probes. Remote Sens. 2017, 9, 103. [Google Scholar] [CrossRef]
  77. Daly, E.; Finkele, K.; Hochstrasser, T.; Jarmain, C.; Richards, K.; Fenton, O.; Murphy, P.N.; Cummins, T.; Saunders, M.; Johnston, P.M.; et al. The Irish Soil Moisture Observation Network—ISMON. In Proceedings of the Irish National Hydrology Conference 2021: Proceedings; Office of Public Works: Trim, Ireland, 2021; pp. 1–11. [Google Scholar]
  78. Schmidt, T.; Schrön, M.; Li, Z.; Francke, T.; Zacharias, S.; Hildebrandt, A.; Peng, J. Comprehensive quality assessment of satellite- and model-based soil moisture products against the COSMOS network in Germany. Remote Sens. Environ. 2024, 301, 113930. [Google Scholar] [CrossRef]
  79. Zheng, Y.; Coxon, G.; Woods, R.; Power, D.; Rico-Ramirez, M.A.; McJannet, D.; Rosolem, R.; Li, J.; Feng, P. Evaluation of reanalysis soil moisture products using cosmic ray neutron sensor observations across the globe. Hydrol. Earth Syst. Sci. 2024, 28, 1999–2022. [Google Scholar] [CrossRef]
  80. Jeong, J.; Lee, S.; Cho, S.; Kim, K.; Choi, M. Multi-scale assessment of a cosmic-ray neutron probe observation of soil moisture for surface layer applications in a mountainous forest environment. Agric. For. Meteorol. 2024, 356, 110155. [Google Scholar] [CrossRef]
  81. Zink, M.; Samaniego, L.; Kumar, R.; Thober, S.; Mai, J.; Schäfer, D.; Marx, A. The German drought monitor. Environ. Res. Lett. 2016, 11, 074002. [Google Scholar] [CrossRef]
  82. Luong, T.T.; Pöschmann, J.; Kronenberg, R.; Bernhofer, C. Rainfall Threshold for Flash Flood Warning Based on Model Output of Soil Moisture: Case Study Wernersbach, Germany. Water 2021, 13, 1061. [Google Scholar] [CrossRef]
  83. Vicente-Serrano, S.M.; Begueria, S.; López-Moreno, J.I. A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index. J. Clim. 2010, 23, 1696–1718. [Google Scholar] [CrossRef]
  84. Franz, T.; Wahbi, A.; Vreugdenhil, M.; Weltin, G.; Heng, L.; Oismueller, M.; Strauss, P.; Dercon, G.; Desilets, D. Using Cosmic-Ray Neutron Probes to Monitor Landscape Scale Soil Water Content in Mixed Land Use Agricultural Systems. Appl. Environ. Soil Sci. 2016, 2016, 4323742. [Google Scholar] [CrossRef]
  85. Li, D.; Schrön, M.; Köhli, M.; Bogena, H.R.; Weimar, J.; Jiménez Bello, M.; Han, X.; Martínez Gimeno, M.; Zacharias, S.; Vereecken, H.; et al. Can Drip Irrigation be Scheduled with Cosmic-Ray Neutron Sensing? Vadose Zone J. 2019, 18, 190053. [Google Scholar] [CrossRef]
  86. Dorigo, W.; Himmelbauer, I.; Aberer, D.; Schremmer, L.; Petrakovic, I.; Zappa, L.; Preimesberger, W.; Xaver, A.; Annor, F.; Ardö, J.; et al. The International Soil Moisture Network: Serving Earth system science for over a decade. Hydrol. Earth Syst. Sci. 2021, 25, 5749–5804. [Google Scholar] [CrossRef]
  87. Wang, X.; Liu, R.; Köhli, M.; Marach, J.; Wang, Z. Monitoring Soil Water Content and Measurement Depth of Cosmic-Ray Neutron Sensing in the Tibetan Plateau. J. Hydrometeorol. 2025, 26, 155–167. [Google Scholar] [CrossRef]
  88. Nieberding, F.; Huisman, J.A.; Huebner, C.; Schilling, B.; Weuthen, A.; Bogena, H.R. Evaluation of Three Soil Moisture Profile Sensors Using Laboratory and Field Experiments. Sensors 2023, 23, 6581. [Google Scholar] [CrossRef]
  89. Zweck, C.; Zreda, M.; Desilets, D. Snow shielding factors for cosmogenic nuclide dating inferred from Monte Carlo neutron transport simulations. Earth Planet. Sci. Lett. 2013, 379, 64–71. [Google Scholar] [CrossRef]
  90. Schattan, P.; Köhli, M.; Schrön, M.; Baroni, G.; Oswald, S. Sensing Area-Average Snow Water Equivalent with Cosmic-Ray Neutrons: The Influence of Fractional Snow Cover. Water Resour. Res. 2019, 55, 10796–10812. [Google Scholar] [CrossRef]
  91. Gugerli, R.; Salzmann, N.; Huss, M.; Desilets, D. Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier. Cryosphere 2019, 13, 3413–3434. [Google Scholar] [CrossRef]
  92. Bogena, H.R.; Herrmann, F.; Jakobi, J.; Brogi, C.; Ilias, A.; Huisman, J.A.; Panagopoulos, A.; Pisinaras, V. Monitoring of Snowpack Dynamics with Cosmic-Ray Neutron Probes: A Comparison of Four Conversion Methods. Front. Water 2020, 2, 19. [Google Scholar] [CrossRef]
  93. Kim, H.; Sproles, E.; Tuttle, S.E. Influence of snow spatial variability on cosmic ray neutron snow water equivalent (SWE): Case study in a northern prairie. Cryosphere 2025, 19, 3177–3191. [Google Scholar] [CrossRef]
  94. Chrisman, B.; Zreda, M. Quantifying mesoscale soil moisture with the cosmic-ray rover. Hydrol. Earth Syst. Sci. 2013, 17, 5097–5108. [Google Scholar] [CrossRef]
  95. Avery, W.A.; Finkenbiner, C.; Franz, T.E.; Wang, T.; Nguy-Robertson, A.L.; Suyker, A.; Arkebauer, T.; Muñoz Arriola, F. Incorporation of globally available datasets into the roving cosmic-ray neutron probe method for estimating field-scale soil water content. Hydrol. Earth Syst. Sci. 2016, 20, 3859–3872. [Google Scholar] [CrossRef]
  96. Gaspar, L.; Franz, T.E.; Navas, A. Cosmic-Ray Neutron Sensor Backpack for Assessing Spatial and Temporal Variations in Soil Water Content in an Agroforestry System in Northern Spain. Land 2025, 14, 744. [Google Scholar] [CrossRef]
  97. Schrön, M.; Zacharias, S.; Womack, G.; Köhli, M.; Desilets, D.; Oswald, S.E.; Bumberger, J.; Mollenhauer, H.; Kögler, S.; Remmler, P.; et al. Intercomparison of cosmic-ray neutron sensors and water balance monitoring in an urban environment. Geosci. Instrum. Methods Data Syst. 2018, 7, 83–99. [Google Scholar] [CrossRef]
  98. Schrön, M.; Köhli, M.; Zacharias, S. Signal contribution of distant areas to cosmic-ray neutron sensors—Implications for footprint and sensitivity. Hydrol. Earth Syst. Sci. 2023, 27, 723–738. [Google Scholar] [CrossRef]
  99. Köhli, M.; Schrön, M.; Zacharias, S.; Schmidt, U. URANOS v1.0—The Ultra Rapid Adaptable Neutron-Only Simulation for Environmental Research. Geosci. Model Dev. 2023, 16, 449–477. [Google Scholar] [CrossRef]
  100. Köhli, M.; Schrön, M.; Zacharias, S.; Schmidt, U. URANOS—A novel voxel engine Neutron Transport Monte-Carlo Simulation. J. Phys. Conf. Ser. 2025, 3130, 012017. [Google Scholar] [CrossRef]
  101. Schrön, M. CORNish PASDy—COsmic-Ray Neutron Flavored PASDy. 2025. Available online: https://git.ufz.de/CRNS/cornish_pasdy (accessed on 15 March 2026).
Instruments 10 00031 g001
Figure 1. (Left) Footprint of the CRNS method independent of instrument type [13]. The variable θ denotes volumetric soil moisture, h air humidity, both fixed in the plot for the corresponding horizontal footprint weighting function W r ( θ , h ) . The radii indicated by dashed lines represent cumulative signal contributions under the given soil moisture conditions. (Right) Soil moisture-dependent accuracy for different systems and acquisition intervals; for corresponding count rates, see Section 2.4.
Figure 1. (Left) Footprint of the CRNS method independent of instrument type [13]. The variable θ denotes volumetric soil moisture, h air humidity, both fixed in the plot for the corresponding horizontal footprint weighting function W r ( θ , h ) . The radii indicated by dashed lines represent cumulative signal contributions under the given soil moisture conditions. (Right) Soil moisture-dependent accuracy for different systems and acquisition intervals; for corresponding count rates, see Section 2.4.
Instruments 10 00031 g001
Instruments 10 00031 g002
Figure 2. Current version of the frontend electronics module nCatcher (left, single channel configuration) and S1p data logger (middle and right). The nCatcher board layout follows from top to bottom the subsections within Section 2.1 with analog and digital parts strictly separated. The high-voltage module is on the backside. The data logger board layout is more compressed with power components in the top segment, infrastructure and microcontroller in the middle and telemetry and interfaces in the bottom part.
Figure 2. Current version of the frontend electronics module nCatcher (left, single channel configuration) and S1p data logger (middle and right). The nCatcher board layout follows from top to bottom the subsections within Section 2.1 with analog and digital parts strictly separated. The high-voltage module is on the backside. The data logger board layout is more compressed with power components in the top segment, infrastructure and microcontroller in the middle and telemetry and interfaces in the bottom part.
Instruments 10 00031 g002
Instruments 10 00031 g003
Figure 3. Pulse length vs. pulse height plot (both arbitrary units) over approximately one year. All events between the dashed lines are considered neutron events. Events outside the selection region are rejected as non-neutron background or electronic artifacts. The secondary thin band outside the neutron region is attributed to microdischarges. The weak high-pulse-height events are due to trace radioactive impurities in the tube material. The inlet shows the pulse-height spectrum alone.
Figure 3. Pulse length vs. pulse height plot (both arbitrary units) over approximately one year. All events between the dashed lines are considered neutron events. Events outside the selection region are rejected as non-neutron background or electronic artifacts. The secondary thin band outside the neutron region is attributed to microdischarges. The weak high-pulse-height events are due to trace radioactive impurities in the tube material. The inlet shows the pulse-height spectrum alone.
Instruments 10 00031 g003
Instruments 10 00031 g004
Figure 4. Schematic of the current data logger components and input/output logic.
Figure 4. Schematic of the current data logger components and input/output logic.
Instruments 10 00031 g004
Instruments 10 00031 g005
Figure 5. Example of a frontend realization based on Grafana of a sensor inside a vineyard near Gundelsheim (49°17′15″ N, 9°10′20″ E), Germany, with temperature and humidity values of air and detector casing, readings of locally installed in situ probes and evaluated CRNS moisture product.
Figure 5. Example of a frontend realization based on Grafana of a sensor inside a vineyard near Gundelsheim (49°17′15″ N, 9°10′20″ E), Germany, with temperature and humidity values of air and detector casing, readings of locally installed in situ probes and evaluated CRNS moisture product.
Instruments 10 00031 g005
Instruments 10 00031 g006
Figure 6. Current instrument pool and historical developments: (Left) System SP ‘Schwarze Puppen’ from 2021 with 1900–4800 cph, (middle) S2 from 2023 with 1800–2400 cph and (right) S1 in single-pole configuration with around 1000 cph. The detectors are equipped with gadolinium-based thermal neutron shields [19,74,75] (green mantle).
Figure 6. Current instrument pool and historical developments: (Left) System SP ‘Schwarze Puppen’ from 2021 with 1900–4800 cph, (middle) S2 from 2023 with 1800–2400 cph and (right) S1 in single-pole configuration with around 1000 cph. The detectors are equipped with gadolinium-based thermal neutron shields [19,74,75] (green mantle).
Instruments 10 00031 g006
Instruments 10 00031 g007
Figure 7. Installation of an S1 system: ground screw base (left), SD card configuration (middle) and fully set up system (right). The logger shows on its display status information (from left to right and top to bottom): environmental parameters and count rate, GPS data, system clock, battery power, cellular connectivity, detector health and histograms with historical data like temperature, pulse height spectrum and neutron count rate with their respective minimum and maximum values.
Figure 7. Installation of an S1 system: ground screw base (left), SD card configuration (middle) and fully set up system (right). The logger shows on its display status information (from left to right and top to bottom): environmental parameters and count rate, GPS data, system clock, battery power, cellular connectivity, detector health and histograms with historical data like temperature, pulse height spectrum and neutron count rate with their respective minimum and maximum values.
Instruments 10 00031 g007
Instruments 10 00031 g008
Figure 8. Example of a CRNS time series from 2023 to 2026 in an urban area (49°30′52.95″ N, 8°33′6.45″ E). The upper panel shows neutron count rates raw and after the standard CRNS corrections (corr) for incoming neutron intensity, atmospheric pressure and air humidity. The middle panel displays the corresponding correction factors. The lower panel compares the resulting CRNS soil moisture product with the area-corrected CRNS estimate for the hydrologically active, unsealed part of the footprint, and with in situ probes at 5, 15, and 25 cm depth. The purple line shows the depth-weighted SMT reference. A 72 h running mean is applied to all (hourly) data. Daily precipitation is shown as blue bars.
Figure 8. Example of a CRNS time series from 2023 to 2026 in an urban area (49°30′52.95″ N, 8°33′6.45″ E). The upper panel shows neutron count rates raw and after the standard CRNS corrections (corr) for incoming neutron intensity, atmospheric pressure and air humidity. The middle panel displays the corresponding correction factors. The lower panel compares the resulting CRNS soil moisture product with the area-corrected CRNS estimate for the hydrologically active, unsealed part of the footprint, and with in situ probes at 5, 15, and 25 cm depth. The purple line shows the depth-weighted SMT reference. A 72 h running mean is applied to all (hourly) data. Daily precipitation is shown as blue bars.
Instruments 10 00031 g008
Instruments 10 00031 g009
Figure 9. Example of a CRNS roving campaign in the Sayeret Shaked Park in Israel (31°16′20.2″ N, 34°39′13.3″ E). (Left) Google Earth bird’s eye view of the site in 2015, (middle) evaluated CRNS soil moisture along the track and (right) converted and extrapolated map. The color code spans a soil moisture range of 2% to 22%.
Figure 9. Example of a CRNS roving campaign in the Sayeret Shaked Park in Israel (31°16′20.2″ N, 34°39′13.3″ E). (Left) Google Earth bird’s eye view of the site in 2015, (middle) evaluated CRNS soil moisture along the track and (right) converted and extrapolated map. The color code spans a soil moisture range of 2% to 22%.
Instruments 10 00031 g009
Instruments 10 00031 g010
Figure 10. The new S1p instrument in monolithtic design deployed in an agricultural context. The environmental sensors for temperature, pressure and relative humidity are mounted in combination with the GSM and GPS antenna on top of the sensor with the upper part housing the data logger and frontend electronics. With its higher efficiency, a smaller PV panel can be used.
Figure 10. The new S1p instrument in monolithtic design deployed in an agricultural context. The environmental sensors for temperature, pressure and relative humidity are mounted in combination with the GSM and GPS antenna on top of the sensor with the upper part housing the data logger and frontend electronics. With its higher efficiency, a smaller PV panel can be used.
Instruments 10 00031 g010
Table 1. Summary of selected microcontrollers and typical development boards or systems in which they are used.
Table 1. Summary of selected microcontrollers and typical development boards or systems in which they are used.
MCUType/CoreClockI/OVoltageUsed Also in
ATmega328P8-bit AVR16 MHz14 digital + 8 analog5 VArduino Nano
ATSAM3X8E32-bit ARM Cortex-M384 MHz54 digital + 12 analog + 2 DAC3.3 VArduino Due
ATmega16U28-bit AVR USB MCU16 MHz22 GPIO2.7–5.5 VArduino USB port
STM32L412KB32-bit ARM Cortex-M4F32 MHz26 digital + 10 analog1.71–3.6 VNucleo-L412KB
STM32U385RG32-bit ARM Cortex-M3332 MHz51 digital + 17 analog + 2 DAC1.71–3.6 VNucleo-U385RG
Table 2. CRNS-relevant system-level specifications of the Stx detector family. Values are representative for the configurations discussed in this work and can vary with deployment geometry and firmware settings. The target signal is the epithermal-to-fast neutron component after moderation and thermal shielding; thermal neutrons are treated as a residual contribution rather than as the primary measurement channel.
Table 2. CRNS-relevant system-level specifications of the Stx detector family. Values are representative for the configurations discussed in this work and can vary with deployment geometry and firmware settings. The target signal is the epithermal-to-fast neutron component after moderation and thermal shielding; thermal neutrons are treated as a residual contribution rather than as the primary measurement channel.
SystemDetector ConceptRate [cph]Solution
S1pSingle boron-lined∼1000Agricultural applications
S1Single boron-lined∼1000Long-term monitoring
S2/S2+2/3 tube boron-lined1800–2400Higher temporal resolution
SPMulti-counter boron-lined1900–4800Hourly monitoring
S2LLithium-foil MWPC∼3800High-statistics CRNS measurements
Table 3. Current consumption of the previous system S1 and the newly developed S1p at 12 V input.
Table 3. Current consumption of the previous system S1 and the newly developed S1p at 12 V input.
SystemOperational StateCurrent [mA]Power [mW]
S1Boot-up/Initialization48576
Active (Standard)36432
Modem transmission80–120960–1440
Low Power Mode (Modem off)10120
Duty-cycle averaged total22264
S1pBoot-up/Initialization45540
Active (Modem off)34408
Active (Modem on/idle)40480
Modem transmission (FTP)70–110840–1320
Low Power Mode (Modem off)2.530
Duty-cycle averaged total4.554
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Köhli, M.; Weimar, J. Beyond Helium-3: Instruments for Cosmic-Ray Neutron Sensing Based on Boron-10 Neutron Detectors. Instruments 2026, 10, 31. https://doi.org/10.3390/instruments10020031

AMA Style

Köhli M, Weimar J. Beyond Helium-3: Instruments for Cosmic-Ray Neutron Sensing Based on Boron-10 Neutron Detectors. Instruments. 2026; 10(2):31. https://doi.org/10.3390/instruments10020031

Chicago/Turabian Style

Köhli, Markus, and Jannis Weimar. 2026. "Beyond Helium-3: Instruments for Cosmic-Ray Neutron Sensing Based on Boron-10 Neutron Detectors" Instruments 10, no. 2: 31. https://doi.org/10.3390/instruments10020031

APA Style

Köhli, M., & Weimar, J. (2026). Beyond Helium-3: Instruments for Cosmic-Ray Neutron Sensing Based on Boron-10 Neutron Detectors. Instruments, 10(2), 31. https://doi.org/10.3390/instruments10020031

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop