Cosmic-ray Neutron Sensing Research Articles

Depth extrapolation of field-scale soil moisture time series derived with cosmic-ray neutron sensing (CRNS) using the soil moisture analytical relationship (SMAR) model

Depth extrapolation of field-scale soil moisture time series derived with cosmic-ray neutron sensing (CRNS) using the soil moisture analytical relationship (SMAR) model

Comparison of Soil Water Content from SCATSAR-SWI and Cosmic Ray Neutron Sensing at Four Agricultural Sites in Northern Italy: Insights from Spatial Variability and Representativeness

Comparison of Soil Water Content from SCATSAR-SWI and Cosmic Ray Neutron Sensing at Four Agricultural Sites in Northern Italy: Insights from Spatial Variability and Representativeness

Variability of hydrogen sources within nominally anhydrous soil across a desert-oasis ecoregion, Northwest China

The accuracy of soil moisture measurements by cosmic-ray neutron sensing (CRNS) is significantly influenced by hydrogen sources within nominally anhydrous soil (NAS), particularly in arid regions. However, the variability of hydrogen in NAS, specifically soil lattice water (H2OL) and water equivalent of soil organic matter (H2OSOM), is seldom investigated in arid heterogeneous landscapes. This study assessed H2OL and H2OSOM amounts across four land covers (forest, cropland, gravel desert, and sandy desert) and three soil depths (0–10 cm, 10–20 cm, and 20–30 cm), and employed a support vector machine (SVM) to estimate H2OL amounts. Results indicated that H2OL amounts varied by land cover: 7.48 % in cropland, 6.06 % in forest, 5.78 % in gravel desert, and 3.17 % in sandy desert·H2OSOM amounts were 1.14 % in cropland, 0.71 % in forest, 0.65 % in sandy desert, and 0.27 % in gravel desert. The higher H2OL and H2OSOM in cropland and forest soils can be attributed to greater silt and clay fractions and organic matter content. Both H2OL and H2OSOM amounts decreased with soil depth. The SVM model accurately estimated H2OL amounts (R2 = 0.74) with a low root mean squared error (1.16 %), identifying soil organic matter as the key variable. Understanding the variability of H2OL and H2OSOM is crucial for accurate CRNS-based soil moisture estimation across diverse land covers, thereby enhancing the applications of CRNS in dry conditions.

Effect of Biomass Water Dynamics in Cosmic-Ray Neutron Sensor Observations: A Long-Term Analysis of Maize-Soybean Rotation in Nebraska.

Precise soil water content (SWC) measurement is crucial for effective water resource management. This study utilizes the Cosmic-Ray Neutron Sensor (CRNS) for area-averaged SWC measurements, emphasizing the need to consider all hydrogen sources, including time-variable plant biomass and water content. Near Mead, Nebraska, three field sites (CSP1, CSP2, and CSP3) growing a maize-soybean rotation were monitored for 5 (CSP1 and CSP2) and 13 (CSP3) years. Data collection included destructive biomass water equivalent (BWE) biweekly sampling, epithermal neutron counts, atmospheric meteorological variables, and point-scale SWC from a sparse time domain reflectometry (TDR) network (four locations and five depths). In 2023, dense gravimetric SWC surveys were collected eight (CSP1 and CSP2) and nine (CSP3) times over the growing season (April to October). The N0 parameter exhibited a linear relationship with BWE, suggesting that a straightforward vegetation correction factor may be suitable (fb). Results from the 2023 gravimetric surveys and long-term TDR data indicated a neutron count rate reduction of about 1% for every 1 kg m-2 (or mm of water) increase in BWE. This reduction factor aligns with existing shorter-term row crop studies but nearly doubles the value previously reported for forests. This long-term study contributes insights into the vegetation correction factor for CRNS, helping resolve a long-standing issue within the CRNS community.

Interannual Variability of Water and Heat Fluxes in a Woodland Savanna (Cerrado) in Southeastern Brazil: Effects of Severe Drought and Soil Moisture

The Brazilian Cerrado biome is known for its high biodiversity, and the role of groundwater recharge and climate regulation. Anthropogenic influence has harmed the biome, emphasizing the need for science to understand its response to climate and reconcile economic exploration with preservation. Our work aimed to evaluate the seasonal and interannual variability of the surface energy balance in a woodland savanna (Cerrado) ecosystem in southeastern Brazil over a period of 19 years, from 2001 to 2019. Using field micrometeorological measurements, we examined the variation in soil moisture and studied its impact on the temporal pattern of energy fluxes to distinguish the effects during rainy years compared to a severe drought spell. The soil moisture measures used two independent instruments, cosmic ray neutron sensor CRNS, and FDR at different depths. The measures were taken at the Pé de Gigante (PEG) site, in a region of well-defined seasonality with the dry season in winter and a hot/humid season in summer. We gap-filled the energy flux measurements with a calibrated biophysical model (SiB2). The long-term averages for air temperature and precipitation were 22.5 °C and 1309 mm/year, respectively. The net radiation (Rn) was 142 W/m2, the evapotranspiration (ET) and sensible heat flux (H) were 3.4 mm/d and 52 W/m2, respectively. Soil moisture was marked by a pronounced negative anomaly in the 2014 year, which caused an increase in the Bowen ratio and a decrease in Evaporative fraction, that lasted until the following year 2015 during the dry season, despite the severe meteorological drought of 2013/2014 already ending, which was corroborated by the two independent measurements. The results showed the remarkable influence of precipitation and soil moisture on the interannual variability of the energy balance in this Cerrado ecosystem, aiding in understanding how it responds to strong climate disturbances.

Evaluation of reanalysis soil moisture products using cosmic ray neutron sensor observations across the globe

Abstract. Reanalysis soil moisture products are valuable for diverse applications, but their quality assessment is limited due to scale discrepancies when compared to traditional in situ point-scale measurements. The emergence of cosmic ray neutron sensors (CRNSs) with field-scale soil moisture estimates (∼ 250 m radius, up to 0.7 m deep) is more suitable for the product evaluation owing to their larger footprint. In this study, we perform a comprehensive evaluation of eight widely used reanalysis soil moisture products (ERA5-Land, CFSv2, MERRA2, JRA55, GLDAS-Noah, CRA40, GLEAM and SMAP L4 datasets) against 135 CRNS sites from the COSMOS-UK, COSMOS-Europe, COSMOS USA and CosmOz Australia networks. We evaluate the products using six metrics capturing different aspects of soil moisture dynamics. Results show that all reanalysis products generally exhibit good temporal correlation with the measurements, with the median temporal correlation coefficient (R) values spanning 0.69 to 0.79, though large deviations are found at sites with seasonally varying vegetation cover. Poor performance is observed across products for soil moisture anomalies time series, with R values varying from 0.46 to 0.66. The performance of reanalysis products differs greatly across regions, climate, land covers and topographic conditions. In general, all products tend to overestimate data in arid climates and underestimate data in humid regions as well as grassland. Most reanalysis products perform poorly in steep terrain. Relatively low temporal correlation and high bias are detected in some sites from the west of the UK, which might be associated with relatively low bulk density and high soil organic carbon. Overall, ERA5-Land, CRA40, CFSv2, SMAP L4 and GLEAM exhibit superior performance compared to MERRA2, GLDAS-Noah and JRA55. We recommend that ERA5-Land and CFSv2 could be used in humid climates, whereas SMAP L4 and CRA40 perform better in arid regions. SMAP L4 has good performance for cropland, while GLEAM is more effective in shrubland regions. Our findings also provide insights into directions for improvement of soil moisture products for product developers.

Effects of soil moisture variations on the neutron spectra measured above ground: feasibility of a soil moisture monitor system based on neutron moderating cylinders

Mapping the soil moisture is a key activity in water management and sustainable agriculture, especially in regions characterised by fragile agri-food systems and water scarcity. Cosmic Ray Neutron Sensors (CRNS) is a contactless nuclear technology used for estimating soil moisture (SM) content on a 20–30 m scale at the landscape level. Very interestingly, this corresponds to the so-called intermediate scale gap between the local probes, operating on the meter scale, and the satellite-based technologies, working on the kilometre scale and above. In state-of-art CRNS, the cosmic neutrons degraded by the soil are simply counted by a slightly moderated thermal neutron counter. After a calibration procedure, the SM is inferred by combining this count rate with environmental parameters: the atmospheric pressure, temperature and the air humidity. As the SM affects not only the environmental neutron fluence rate but also its energy distribution, this study was organised in such a way to understand if a CRNS with spectrometric capabilities could provide improved information on the SM distribution. To this aim, an environmental neutron spectrometer was designed by extending the Bonner Spheres to a more sensitive system made of moderating cylinders embedding long BF3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ _3 $$\\end{document} proportional counters, the Moderating Cylinders Spectrometer (MCS). Relying on literature environmental neutron spectra, corresponding to different SM values in a standardised soil, the count rates in the MCS were calculated for different values of SM. To simulate various counting scenarios, these count rates were associated to different levels of “realistic” uncertainties and unfolded by means of the FRUIT code. The resulting neutron spectra are compared to the literature ones, allowing at estimating the resolving power of the spectrometer in terms of SM.

Evapotranspiration prediction for European forest sites does not improve with assimilation of in situ soil water content data

Abstract. Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 % to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters.

Technical Note: Revisiting the general calibration of cosmic-ray neutron sensors to estimate soil water content

Abstract. Cosmic-ray neutron sensing (CRNS) is becoming increasingly popular for monitoring soil water content (SWC). To retrieve SWC from observed neutron intensities, local measurements of SWC are typically required to calibrate a location-specific parameter, N0, in the corresponding transfer function. In this study, we develop a generalized conversion function that explicitly takes into account the different factors that govern local neutron intensity. Thus, the parameter N0 becomes location independent, i.e. generally applicable. We demonstrate the feasibility of such a “general calibration function” by analysing 75 CRNS sites from four recently published datasets. Given the choice between the two calibration strategies – local or general – users will wonder which one is preferable. To answer this question, we estimated the resulting uncertainty in the SWC by means of error propagation. While the uncertainty in the local calibration depends on both the local reference SWC itself and its error, the uncertainty in the general calibration is mainly governed by the errors in vegetation biomass and soil bulk density. Our results suggest that a local calibration – generally considered best practice – might often not be the best option. In order to support the decision which calibration strategy – local or general – is actually preferable in the user-specific application context, we provide an interactive online tool that assesses the uncertainty in both options (https://cosmic-sense.github.io/local-or-global, last access: 23 February 2024).

Temperature-Corrected Calibration of GS3 and TEROS-12 Soil Water Content Sensors

The continuous monitoring of soil water content is commonly carried out using low-frequency capacitance sensors that require a site-specific calibration to relate sensor readings to apparent dielectric bulk permittivity (Kb) and soil water content (θ). In fine-textured soils, the conversion of Kb to θ is still challenging due to temperature effects on the bound water fraction associated with clay mineral surfaces, which is disregarded in factory calibrations. Here, a multi-point calibration approach accounts for temperature effects on two soils with medium to high clay content. A calibration strategy was developed using repacked soil samples in which the Kb-θ relationship was determined for temperature (T) steps from 10 to 40 °C. This approach was tested using the GS3 and TEROS-12 sensors (METER Group, Inc. Pullman, WA, USA; formerly Decagon Devices). Kb is influenced by T in both soils with contrasting T-Kb relationships. The measured data were fitted using a linear function θ = aKb + b with temperature-dependent coefficients a and b. The slope, a(T), and intercept, b(T), of the loam soil were different from the ones of the clay soil. The consideration of a temperature correction resulted in low RMSE values, ranging from 0.007 to 0.033 cm3 cm−3, which were lower than the RMSE values obtained from factory calibration (0.046 to 0.11 cm3 cm−3). However, each experiment was replicated only twice using two different sensors. Sensor-to-sensor variability effects were thus ignored in this study and will be systematically investigated in a future study. Finally, the applicability of the proposed calibration method was tested at two experimental sites. The spatial-average θ from a network of GS3 sensors based on the new calibration fairly agreed with the independent area-wide θ from the Cosmic Ray Neutron Sensor (CRNS). This study provided a temperature-corrected calibration to increase the accuracy of commercial sensors, especially under dry conditions, at two experimental sites.

Testing a novel sensor design to jointly measure cosmic-ray neutrons, muons and gamma rays for non-invasive soil moisture estimation

Abstract. Cosmic-ray neutron sensing (CRNS) has emerged as a reliable method for soil moisture and snow estimation. However, the applicability of this method beyond research has been limited due to, among others, the use of relatively large and expensive sensors. This paper presents the tests conducted on a new scintillator-based sensor especially designed to jointly measure neutron counts, muons and total gamma rays. The neutron signal is first compared against two conventional gas-tube-based CRNS sensors at two locations. The estimated soil moisture is further assessed at four agricultural sites, based on gravimetric soil moisture collected within the sensor footprint. Muon fluxes are compared to the incoming neutron variability measured at a neutron monitoring station and total gammas counts are compared to the signal detected by a gamma ray spectrometer. The results show that the neutron dynamic detected by the new scintillator-based CRNS sensor is well in agreement with conventional CRNS sensors. The derived soil moisture also agreed well with the gravimetric soil moisture measurements. The muons and the total gamma rays simultaneously detected by the sensor show promising features to account for the incoming variability and for discriminating irrigation and precipitation events, respectively. Further experiments and analyses should be conducted, however, to better understand the accuracy and the added value of these additional data for soil moisture estimation. Overall, the new scintillator design shows to be a valid and compact alternative to conventional CRNS sensors for non-invasive soil moisture monitoring and to open the path to a wide range of applications.

Employing NDVI as vegetation correction variable to improve soil moisture measurements of mobile cosmic-ray neutron sensor near the Qilian Mountains

Cosmic-ray neutron sensors (CRNS) are a powerful tool for measuring soil moisture on a hectometer scale, and mobile cosmic-ray neutron technology holds significant importance for upscaling soil moisture. However, vegetation strongly affects with CRNS soil moisture measurements. Variations in vegetation over time change how the CRNS is affected, which is more challenging than a stable vegetation cover, particularly during CRNS roving, where one crosses many vegetation covers. To correct vegetation, we hypothesized that Normalized Difference Vegetation Index (NDVI) can representvegetation hydrogen pools. From 2020 to 2023, at the 74 plots with varying vegetation cover near the Qilian Mountains, the experiments for soil moisture measurements were conducted in shrubs, forests, deserts, farmlands, and grasslands using a Cosmic-ray Neutron Rover. The measured neutron intensity clearly varied across different landscapes, and the calibrated parameter N0 differed among 74 plots. The variations in vegetation cover resulted in low measurement accuracy for CRNS. All three NDVI vegetation correction methods (NDVI-θNDVI method, NDVI-NDVIave method, and NDVI-N0 method) offered by the study could improve accuracy of soil moisture measurements. In the optimal NDVI-N0 method, the relationship between NDVI and N0 was established using a power function y = 373.9 (x + 0.015)−0.136 (R2 = 0.77), which had the capability to decrease the root mean square error between oven-dried and measured soil moisture from 0.093 to 0.032 g g−1 in our study area. Employing NDVI instead of biomass for CRNS vegetation correction can substantially reduce workload and effectively enhance CRNS soil moisture measurements, showing the potential for upscaling soil moisture.

Can a Sparse Network of Cosmic Ray Neutron Sensors Improve Soil Moisture and Evapotranspiration Estimation at the Larger Catchment Scale?

AbstractCosmic‐ray neutron sensors (CRNS) fill the gap between locally measured in‐situ soil moisture (SM) and remotely sensed SM by providing accurate SM estimation at the field scale. This is promising for improving hydrologic model predictions, as CRNS can provide valuable information on SM in the root zone at the typical scale of a model grid cell. In this study, SM measurements from a network of 12 CRNS in the Rur catchment (Germany) were assimilated into the Terrestrial System Modeling Platform (TSMP) to investigate its potential for improving SM, evapotranspiration (ET) and river discharge characterization and estimating soil hydraulic parameters at the larger catchment scale. The data assimilation (DA) experiments (with and without parameter estimation) were conducted in both a wet year (2016) and a dry year (2018) with the ensemble Kalman filter (EnKF), and later verified with an independent year (2017) without DA. The results show that SM characterization was significantly improved at measurement locations (with up to 60% root mean square error (RMSE) reduction), and that joint state‐parameter estimation improved SM simulation more than state estimation alone (more than 15% additional RMSE reduction). Jackknife experiments showed that SM at verification locations had lower and different improvements in the wet and dry years (an RMSE reduction of 40% in 2016 and 16% in 2018). In addition, SM assimilation was found to improve ET characterization to a much lesser extent, with a 15% RMSE reduction of monthly ET in the wet year and 9% in the dry year.

Mesoscale soil moisture survey by mobile cosmic-ray neutron sensor across various landscapes in the Heihe River Basin

The cosmic-ray neutron technology is a novel method for upscaling soil moisture, holding significant importance in drought monitoring, soil water storage estimation, calibration of remote sensing products, and data assimilation of land surface models. However, most studies conducted soil moisture measurement experiments using static Cosmic-Ray Neutron Sensors (CRNS) and are often limited to a single landscape, lacking in-situ measurement for the cosmic-ray neutron rover across various landscapes. This study carried out soil moisture observation experiments using a cosmic-ray neutron rover in nine grassland plots, nine farmland plots, and nine desert plots of the Heihe River Basin, Northwest China. The neutron counts displayed clear variations among different plots, and an equation could represent the relationship between neutron intensity and multiple variables near the ground. The parameter N0 was the smallest for grassland and greatest for desert, and there was a significant negative correlation between Normalized Difference Vegetation Index and N0 (P <0.05). Furthermore, the performance of CRNS notably improved after considering soil lattice water and soil organic matter. The Root Mean Square Error (RMSE) between the measured and oven-dried soil moisture increased with radius. Soil moisture measured by CRNS strongly supported the 100 m diameter footprint assumption (RMSE <0.044 g/g). The study offers valuable insights into the application and promotion of cosmic-ray neutron technology across various landscapes within a basin.

Estimation of root zone soil moisture at point scale based on soil water measurements from cosmic-ray neutron sensing in a karst catchment

The cosmic-ray neutron sensing (CRNS) is an emerging method for continuously monitoring soil water content (SWC) at an intermediate scale. However, when multiple hydrologic units are present within its footprint, the potential application of CRNS in water resources management is restricted. Here we propose a new strategy to predict point-scale SWC in root zone established on CRNS-based soil moisture and improved relative difference method. A total of 768 days of soil moisture data were collected by CRNS at the intermediate scale and EC-TM sensors at the point scale in a karst catchment. The original and improved mean relative difference methods predicted point-scale SWCs within and without the effective measuring depth, respectively. The mean effective measuring depth was 13.16 cm, ranging from 10.13 to 19.23 cm. Both land use type and soil structure played essential roles in regulating point scale SWC in the soil profile. Point-scale SWC in root zone can be predicted accurately (P < 0.001) based on SWC data derived from the CRNS system. The prediction accuracy of point scale SWC can be improved by increasing the averaging time of the soil moisture values. Our results demonstrated that the proposed strategy was reliable for CRNS to predict SWC beyond the effective measurement depth. This study provides a good perspective for effectively managing of water resources in areas with complex hydrological processes.

A change in perspective: downhole cosmic-ray neutron sensing for the estimation of soil moisture

Abstract. Above-ground cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of the field-scale soil moisture content in the upper decimetres of the soil. However, large parts of the deeper vadose zone remain outside of its observational window. Retrieving soil moisture information from these deeper layers requires extrapolation, modelling or other methods, all of which come with methodological challenges. Against this background, we investigate CRNS for downhole soil moisture measurements in deeper layers of the vadose zone. To render calibration with in situ soil moisture measurements unnecessary, we rescaled neutron intensities observed below the terrain surface with intensities measured above a waterbody. An experimental set-up with a CRNS sensor deployed at different depths of up to 10 m below the surface in a groundwater observation well combined with particle transport simulations revealed the response of downhole thermal neutron intensities to changes in the soil moisture content at the depth of the downhole neutron detector as well as in the layers above it. The simulation results suggest that the sensitive measurement radius of several decimetres, which depends on soil moisture and soil bulk density, exceeds that of a standard active neutron probe (which is only about 30 cm). We derived transfer functions to estimate downhole neutron signals from soil moisture information, and we describe approaches for using these transfer functions in an inverse way to derive soil moisture from the observed neutron signals. The in situ neutron and soil moisture observations confirm the applicability of these functions and prove the concept of passive downhole soil moisture estimation, even at larger depths, using cosmic-ray neutron sensing.

Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands

In arid and semi-arid areas, soil moisture (SM) plays a crucial role in land-atmosphere interactions, hydrological processes, and ecosystem sustainability. SM data at large scales are critical for related climatic, hydrological, and ecohydrological research. Data fusion based on satellite products and model simulations is an important way to obtain SM data at large scales; however, little has been reported on the comparison of the data fusion methods in different categories. Here, we compared the performance of two widely used data fusion methods, the Ensemble Kalman Filter (EnKF) and the Back-Propagation Artificial Neural Network (BPANN), in the degraded grassland site (DGS) and the alpine grassland site (AGS). The SM data from the Community Land Model 5.0 (CLM5.0) and the Soil Moisture Active and Passive (SMAP) were fused and validated against the observations of the Cosmic-Ray Neutron Sensor (CRNS) to avoid the impacts of scale-mismatch. Results show that compared with the original data sets at both sites, the RMSE of the fused data by BPANN (FD-BPANN) and EnKF (FD-EnKF) had improved by more than 50% and 31%, respectively. Overall, the FD-BPANN performs better than the FD-EnKF because the BPANN method assigned higher weights to input data with better performance and the EnKF method is affected by the strong variabilities of both the fused CLM5.0 and SMAP data and the CRNS data. However, in terms of the percentile range, the FD-BPANN showed the worst performance, with overestimations in the low SM range of 25th percentile (&lt;Q25), because the BPANN method tends to be trapped in a local minimum. The BPANN method performed better in humid areas, then followed by semi-humid areas, and finally arid and semi-arid areas. Moreover, compared with the previous studies in arid and semi-arid areas, the BPANN method in this study performed better.

Three years of soil moisture observations by a dense cosmic-ray neutron sensing cluster at an agricultural research site in north-east Germany

Abstract. Cosmic-ray neutron sensing (CRNS) allows for the estimation of root-zone soil water content (SWC) at the scale of several hectares. In this paper, we present the data recorded by a dense CRNS network operated from 2019 to 2022 at an agricultural research site in Marquardt, Germany – the first multi-year CRNS cluster. Consisting, at its core, of eight permanently installed CRNS sensors, the cluster was supplemented by a wealth of complementary measurements: data from seven additional temporary CRNS sensors, partly co-located with the permanent ones; 27 SWC profiles (mostly permanent); two groundwater observation wells; meteorological records; and Global Navigation Satellite System reflectometry (GNSS-R). Complementary to these continuous measurements, numerous campaign-based activities provided data by mobile CRNS roving, hyperspectral imagery via UASs, intensive manual sampling of soil properties (SWC, bulk density, organic matter, texture, soil hydraulic properties), and observations of biomass and snow (cover, depth, and density). The unique temporal coverage of 3 years entails a broad spectrum of hydro-meteorological conditions, including exceptional drought periods and extreme rainfall but also episodes of snow coverage, as well as a dedicated irrigation experiment. Apart from serving to advance CRNS-related retrieval methods, this data set is expected to be useful for various disciplines, for example, soil and groundwater hydrology, agriculture, or remote sensing. Hence, we show exemplary features of the data set in order to highlight the potential for such subsequent studies. The data are available at doi.org/10.23728/b2share.551095325d74431881185fba1eb09c95 (Heistermann et al., 2022b).

Combining cosmic-ray neutron sensor and fallout 137Cs to explore the connection of soil water content with soil redistribution in an agroforestry hillslope

To ensure sustainable agricultural management, there is a need not only to quantify soil erosion rates but also to obtain information on the status of soil water content and soil loss under different soil types and land uses. A clear understanding of the temporal dynamics and the soil moisture spatial variability (SMSV) will help to control soil degradation by hydrological processes. This study represents the first attempt connecting cosmic-ray neutron sensors (CRNS) with soil erosion research, a novel approach to explore the complex relationships between soil water content (SWC) and soil redistribution processes using two of the most powerful nuclear techniques, CRNS and fallout 137Cs.Our preliminary results indicate that CRNS captured soil moisture dynamics along the study toposequence and demonstrated the sensitivity of neutron sensors to investigate the effect of parent material on soil water content. The Empirical Orthogonal Function (EOF) analysis of the comprehensive data from seven CRNS surveys revealed that one dominant spatial structure (EOF1) explains 89.2% of SMSV. The soil redistribution rates estimated with 137Cs at the nine locations along the hillslope, together with local factors related to soil properties (SOC, soil depth, hydraulic conductivity) and land use showed significant correlations with EOF. This study provides strong field evidence that soil type significantly affect SMSV, highlighting the key impact on soil erosion and sedimentation rates. Nevertheless, more research is needed to investigate the specific contributions of soil properties to the spatial variability of soil moisture and their subsequent effects on soil redistribution dynamics of interest for soil management.

Investigating the Potential of Cosmic-Ray Neutron Sensing for Estimating Soil Water Content in Farmland and Mountainous Areas

The conventional methods of estimating soil water content (SWC) are mainly based on in situ measurements at sampling points and remote sensing measurements over an entire region. In view of these methods, cosmic-ray neutron sensing (CRNS) has received increasing attention in recent years as a mesoscale, noncontact SWC estimation technology that can provide more accurate and timely estimates of SWC over a larger area. In this study, we estimated SWC using both CRNS and soil-mounted detectors in farmland and mountainous areas, and evaluated the accuracy of the estimations at two experimental sites. Ultra-rapid adaptable neutron-only simulation (URANOS) was used to simulate the detection radius and depth of the two experimental sites and to obtain the spatial weights of the CRNS footprint. The results show that the theoretical range of detection was reduced in farmland compared to mountainous areas during the experimental period, suggesting that farmland retained more SWC even with less precipitation. Spatial weights were simulated to calculate the SWC of sampling points, and the weighted and averaged SWC were then correlated with CRNS. The weighting calculation improves the accuracy of CRNS estimations, with a determination coefficient (R2) of 0.645 and a root mean square error (RMSE) of 0.046 cm3·cm−3 for farmland, and reproduces the daily dynamics of SWC. The R2 and RMSE in mountainous areas are 0.773 and 0.049 cm3·cm−3, respectively, and the estimation accuracy of CRNS cannot be improved by the weighting calculation. The estimation accuracy of CRNS is acceptable in both regions, but the mountainous terrain obstructs neutron transmission, causing a deviation between the actual and theoretical neutron footprints in mountainous areas. Thus, the accuracy of SWC estimation is limited in mountainous terrain. In conclusion, this study demonstrates that CRNS is suitable for use in farmland and mountainous areas and that further attention should be given to the effects of topography and vegetation when it is applied in mountainous environments.