Cosmic-ray Neutron Probe Research Articles

Multi-scale assessment of a cosmic-ray neutron probe observation of soil moisture for surface layer applications in a mountainous forest environment

Soil moisture is critical to the Earth's water cycle as it influences water exchange between the land, atmosphere, and oceans. This study assessed the potential of a cosmic-ray neutron probe (CRNP) to monitor soil moisture dynamics in a forested mountain region, which is critical for water resource management and ecosystem health. Accurate soil moisture monitoring is essential for effectively managing forested mountainous areas, but various limitations exist in such environments. To overcome the complexities of soil moisture monitoring, an observatory consisting of a CRNP, automatic weather sensors, and dataloggers was established. Fifteen capacitance sensor (CS) stations were placed strategically within the CRNP's footprint to ensure strict evaluation. Although each CS measurement revealed the heterogeneity of the experimental site, temporal stability analysis determined the representative soil moisture. The validation of the CRNP and the average results of the 15 surface CS sensors revealed that they were strongly correlated (R = 0.94). It means that the CRNP can describe the dynamics of soil moisture that fluctuate according to physical properties, underscoring its reliability in estimating intermediate scale soil moisture. This correlation remained robust despite differences in rainfall patterns over three years. These findings indicate that the CRNP may be a valuable tool for monitoring soil moisture in a challenging environment. Additionally, the evaluation of the Advanced Microwave Scanning Radiometer-2 (AMSR2), Advanced Scatterometer (ASCAT), and Global Land Data Assimilation System (GLDAS) demonstrated the usefulness of CRNP in supplying reference data. The AMSR2 showed an inadequate temporal pattern (R = 0.35), and the ASCAT achieved the highest correlation agreement (R = 0.67) with the CRNP. The GLDAS also showed close agreement, although with some discrepancies (R = 0.52). The results of this study support the utilization of CRNP and provide insight into establishing and expanding a soil moisture network.

Application of cosmic‐ray neutron probes for measuring soil moisture in rocky areas of the Taihang Mountains, North China

AbstractThe cosmic‐ray neutron probe (CRNP) is a mesoscale and noninvasive method for measuring soil moisture and has been widely studied and applied. However, studies of its applicability in rocky mountainous areas are still challenging in complex topography and high gravel content. In this study, a field experiment was carried out to assess the applicability of the CRNP for measuring soil moisture in rocky areas of Taihang Mountains of North China. The results showed that the Pearson correlation coefficient and the root mean square error between the soil moisture from CRNP and the drying method are 0.911 and 0.025 m3 m−3, respectively, indicating that the CRNP can estimate the average soil moisture well in the study area. Compared with the capacitive sensor, the CRNP overestimated soil moisture when small rainfall events occurred, which was caused by the interception of canopy and litter. The nonlinear weighting method performed better than the linear weighting method in representing average soil moisture within the CRNP footprint. The high gravel content that contained high lattice water content reduced the penetration depth of CRNP. Biomass reduces the accuracy of the CRNP by affecting the neutron intensity. In summary, CRNP can measure soil moisture accurately in rocky areas of the Taihang Mountains, especially in dry environments with low biomass.

Effects of Biomass and Soil Water Content Distribution on Cosmic Ray Neutron Probe Measurement

Cosmic ray neutron probes (CRNPs) provide continuous monitoring of average near-surface soil water content (SWC) on hectometer scales. However, the performance of CRNPs on surfaces of highly heterogeneous vegetation and SWC remains uncertain. This study evaluated three vegetation calibration methods with the correction of vegetation distribution developed for a CRNP on the Loess Plateau of China. Three plots with different vegetation distributions were selected and equipped with CRNPs and SWC sensors, and their biomass as well as distribution were measured by an unmanned aerial vehicle (UAV) equipped with a RedEdge multispectral camera. We found that the parameter N0, which is neutron flux in dry soil, was best represented by the biomass at average growth conditions of the monitoring period, yielding the lowest RMSE (0.068). The Veg–N0 vegetation calibration method reduced the RMSE the most between the CRNP SWC and the Kriging-weighted SWC, and the correction of the spatial distribution of the vegetation further reduced the RMSE. The cooperation between the CRNP and the UAV could obtain the regional averaged SWC accurately. This study makes up for the lack of vegetation calibration for the CRNP on the Loess Plateau, which should help develop sustainable vegetation management and ecohydrological management strategies on the Loess Plateau, so as to protect water security in the region.

High Spatial and Temporal Soil Moisture Retrieval in Agricultural Areas Using Multi-Orbit and Vegetation Adapted Sentinel-1 SAR Time Series

The retrieval of soil moisture information with spatially and temporally high resolution from Synthetic Aperture Radar (SAR) observations is still a challenge. By using multi-orbit Sentinel-1 C-band time series, we present a novel approach for estimating volumetric soil moisture content for agricultural areas with a temporal resolution of one to two days, based on a short-term change detection method. By applying an incidence angle normalization and a Fourier Series transformation, the effect of varying incidence angles on the backscattering signal could be reduced. As the C-band co-polarized backscattering signal is prone to vegetational changes, it is used in this study for the vegetational correction of its related backscatter ratios. The retrieving algorithm was implemented in a cloud-processing environment, enabling a potential global and scalable application. Validated against eight in-situ cosmic ray neutron probe stations across the Rur catchment (Germany) as well as six capacitance stations at the Apulian Tavoliere (Italy) site for the years 2018 to 2020, the method achieves a correlation coefficient of R of 0.63 with an unbiased Root Mean Square Error of 0.063 m3/m3.

Examining the variation of soil moisture from cosmic-ray neutron probes footprint: experimental results from a COSMOS-UK site

Utilising cosmic-ray neutron probes is a relatively new approach in obtaining larger area soil moisture and various operational monitoring networks have been established worldwide utilising this technology to measure operationally this parameter. One such network located in the United Kingdom (UK) is the Cosmic-ray Soil Moisture Observing System, so-called COSMOS-UK, established in 2013. The present study aims at investigating the true footprint and the variations within the footprint detectable area at the COSMOS-UK sites using as a case study one such site located in Riseholme, UK. At the selected experimental site extensive fieldwork was conducted in July 2017 that allowed examining the agreement among the soil moisture data retrieved by the Time Domain Transmissometer (TDT) sensors and the corresponding estimates from the COSMOS-UK network station probe. The COSMOS-UK site footprint was compared using GPS-aided information from ground instrumentation, assisted by drone imagery acquisition and the implementation of geospatial interpolation methods in a Geographical Information System (GIS) environment. Altogether, this information was used for assessing the soil moisture footprint extent from the COSMOS-UK site. The COSMOS-UK station footprint was representative for an area shorter in size than the alleged footprint of 600 m diameter, as generally proposed in various relevant investigations. The COSMOS network slightly overestimated soil moisture content measured by the TDT sensor probes installed in the area. Our study findings although concern specifically the studied experimental site contribute towards efforts aiming at assessing the COSMOS-UK soil moisture measurement footprint showcasing the added value of geospatial analysis in this direction.

Using Soil Moisture Measured by the Cosmic Ray Neutron Probe in Hydrological Models

Kozmik ışınlar yeryüzüne çarptığında ortaya çıkan nötronlar hidrojen ile temas ettiklerinde enerjilerini kaybetmektedirler. Enerjisi azalmış nötronlar sayılarak bir bölgenin toprak nemi yüksek başarı ile tespit edilebilmektedir. Bu prensip ile çalışan kozmik ışın nötron sayacı (CRNP) ülkemizde ilk olarak Niğde-Çakıt havzasında denenmiş ve elde edilen sonuçlar bu çalışmada sunulmuştur. Yaklaşık 670 metre çapında bir alan için toprak nemi bilgisini sürekli olarak sağlayabilen CRNP, yüksek mekânsal ve zamansal çözünürlükte toprak nemi verisine ihtiyaç duyan hidrolojik çalışmalar için önemli bir alternatif oluşturmaktadır. 11 Kasım 2016 ile 01 Temmuz 2019 tarihleri arasında, CRNP ile saatlik olarak düzenli veri temini yapılmış olup, elde edilen verilerin aynı bölgede bulunan zaman alanı reflektometresi (TDR) cihazıyla da çok uyumlu sonuçlar verdiği tespit edilmiştir. Biriktirmeli kavramsal bir model olan NAM modeli ile hidrolojik değişkenlerin akıma olan etkileri belirlenebilmektedir. Bu çalışmada CRNP ile elde edilen toprak nemi verileri NAM kavramsal modelinin iyileştirilmesinde kullanılmış, modelin kalibrasyonunda toprak nemi verilerinin de dikkate alınmasıyla Çakıt Havzası debi verileri için Kling-Gupta Verimlilik skoru 0,56(Kalibrasyon) ve 0,42(Doğrulama)'dan 0,81(Kalibrasyon) ve 0,64(Doğrulama)'ya yükselmiştir. Hem Çakıt Havzası hem de Darboğaz Alt Havzası için istatistiksel ölçütlerin çoğunda benzer gelişmeler kaydedilmiştir.

Evaluating a new temperature-vegetation-shortwave infrared reflectance dryness index (TVSDI) in the continental United States

Accurate dryness monitoring is important for formulating reasonable response measures to reduce social and economic losses caused by drought. The land surface temperature (LST), shortwave infrared (SWIR) reflectance, and vegetation index (VI) are popular remote sensing (RS) indices that can be individually used to characterize surface dryness. Given the interactions of these factors, limitations are inevitably associated with using a single factor. Integrated dryness indices that combine LST or SWIR reflectance with the VI have thus been successively proposed and applied for dryness monitoring and soil moisture (SM) retrieval work. However, the advantages of these three indicators have not yet been combined to construct a more comprehensive dryness index. In this study, we integrated the LST, enhanced vegetation index (EVI), and SWIR reflectance and developed an integrated satellite-based dryness index with simple calculations, called the temperature vegetation shortwave infrared reflectance dryness index (TVSDI). The proposed TVSDI was thoroughly assessed in the continental United States (CONUS) using the following data: the soil moisture active passive (SMAP) SM; six commonly used dryness indices (i.e., temperature vegetation soil moisture dryness index (TVMDI), temperature vegetation dryness index (TVDI), modified perpendicular dryness index (MPDI), perpendicular dryness index (PDI), standardized precipitation evapotranspiration index (SPEI), and standardized precipitation index (SPI)); in situ SM data collected from 24 Cosmic-ray neutron probe (CRNP) sites covering different climates, soil types, and land cover types; and the United States Drought Monitor (USDM) maps. The results demonstrated that the TVSDI was significantly correlated with SMAP SM (R = − 0.75, p < 0.01) and exhibited better performance than the use of LST, EVI, and SWIR reflectance individually. Moreover, the TVSDI and the other six commonly used dryness indices exhibited good spatiotemporal consistency, all with consistency areas > 60%. The evaluation based on in situ SM from 24 CRNP sites indicated that the TVSDI exhibited more stability and accuracy than other satellite-based agricultural dryness indices (TVMDI, MPDI, PDI, and TVDI). Moreover, the spatial patterns of TVSDI maps were not only well-matched with SMAP SM maps but also provided more detailed spatial information. TVSDI maps could capture more dryness and drought variations in natural ecosystems and areas with less intensive human activities than USDM maps. Furthermore, the application of the TVSDI for dryness monitoring in the CONUS revealed that the dryness distributions differed greatly across different geographic regions at monthly and annual scales. In conclusion, the TVSDI was found to be a reliable and accurate satellite-based dryness index.

Review article: Performance assessment of radiation-based field sensors for monitoring the water equivalent of snow cover (SWE)

Abstract. Continuous and spatially distributed data of snow mass (water equivalent of snow cover, SWE) from automatic ground-based measurements are increasingly required for climate change studies and for hydrological applications (snow hydrological-model improvement and data assimilation). We present and compare four new-generation sensors, now commercialized, that are non-invasive and based on different radiations that interact with snow for SWE monitoring: cosmic-ray neutron probe (CRNP), gamma ray monitoring (GMON) scintillator, frequency-modulated continuous-wave radar (FMCW radar) at 24 GHz and global navigation satellite system (GNSS) receivers (GNSSr). All four techniques have relatively low power requirements, provide continuous and autonomous SWE measurements, and can be easily installed in remote areas. A performance assessment of their advantages, drawbacks and uncertainties is discussed from experimental comparisons and a literature review. Relative uncertainties are estimated to range between 9 % and 15 % when compared to manual in situ snow surveys that are also discussed. Results show the following. (1) CRNP can be operated in two modes of functioning: beneath the snow, it is the only system able to measure very deep snowpacks (&gt; 2000 mm w.e.) with reasonable uncertainty across a wide range of measurements; CRNP placed above the snow allows for SWE measurements over a large footprint (∼ 20 ha) above a shallow snowpack. In both cases, CRNP needs ancillary atmospheric measurements for SWE retrieval. (2) GMON is the most mature instrument for snowpacks that are typically up to 800 mm w.e. Both CRNP (above snow) and GMON are sensitive to surface soil moisture. (3) FMCW radar needs auxiliary snow-depth measurements for SWE retrieval and is not recommended for automatic SWE monitoring (limited to dry snow). FMCW radar is very sensitive to wet snow, making it a very useful sensor for melt detection (e.g., wet avalanche forecasts). (4) GNSSr allows three key snowpack parameters to be estimated simultaneously: SWE (range: 0–1000 mm w.e.), snow depth and liquid water content, according to the retrieval algorithm that is used. Its low cost, compactness and low mass suggest a strong potential for GNSSr application in remote areas.

The Footprint Characteristics of Cosmic Ray Thermal Neutrons

AbstractThe advance of the cosmic ray neutron (CRN) sensing method for estimating field scale soil moisture relied largely on simulations of the footprint properties of epithermal neutrons (∼0.5 eV–100 keV). Commercially available CRN probes are usually additionally equipped with a thermal neutron (&lt;0.5 eV) detector. The potential of these measurements is rarely explored because relevant features of thermal neutrons, such as the footprint and the sensitivity to soil moisture are unknown. Here, we used neutron transport modeling and a river crossing experiment to assess the thermal neutron footprint. We found that the horizontal thermal neutron footprint ranges between 43 and 48 m distance from the probe and that the vertical footprint extends to soil depths between 10 and 65 cm depending on soil moisture. Furthermore, we derived weighting functions that quantify the footprint characteristics of thermal neutrons. These results will enable new applications of thermal neutrons.

Correction efficiency and error characteristics for cosmic-ray soil moisture on mountainous terrain

A Cosmic-Ray Neutron Probe (CRNP) is a promising tool for obtaining reliable intermediate scale soil moisture. Although there are sound ways of correcting neutron counts and calibrating soil moisture, it is difficult to obtain all the data required for corrections and calibration in a complex environment. In this study, three major correcting variables (i.e., absolute humidity, air pressure, incoming neutron flux intensity) were evaluated to determine their impacts on the neutron counts in the Seolmacheon experimental site (SMC) located in mountainous terrain. Efficiency of correction factors for atmospheric water vapor (fwv), air pressure (fp,), and incoming neutron flux intensity (fi) was independently assessed through comparisons between the variables and a inversely calculated N0 (N0,inv). Particular attention was given to the expected biases and errors in estimated soil moisture (SMCRNP) with and without each correction factor, especially at the early stage of the calibration period. Air pressure had the greatest impact on neutron count at the SMC site, followed by absolute humidity. The dependency of neutron count on air pressure (absolute humidity) was found from the inverse relationship with N0,inv that is not corrected for air pressure (absolute humidity), with correlation coefficient −0.88 (−0.78). When fp (fwv) was applied, the dependency of neutron count on the corresponding variable was significantly reduced, with a correlation coefficient 0.06 (−0.07). On the other hand, the effect of applying fi was contrary, slightly degrading the result. When neutron count was not corrected for air pressure (absolute humidity), the performance of SMCRNP decreased, with a correlation coefficient of 0.74 (0.90) and Root Mean Square Error (RMSE) of 0.030 m3/m3 (0.026 m3/m3) at best. The best result was obtained when both of them were corrected (r = 0.95, RMSE = 0.014 m3/m3). As data accumulates, error metrics in terms of bias and variability converged at approximately one periodic cycle, implying at least one seasonal cycle is necessary for recalibration. However, in a case where a variable with large intra-seasonal variability (e.g., air pressure in this study) is missing, it is recommended to adopt a multi-point N0 method.The procedures suggested in this study allow independent evaluations of the factors for which neutron count is corrected, thus providing a basis for judging which factors are appropriate and which are not. In addition, N0 can be more efficiently tuned according to the characteristics of the errors in SMCRNP. Based on these analyses, CRNP stations will be able to selectively apply favorable correction factors under various kinds of local characteristics, making it easier to achieve target accuracy and precision.

A Novel Lithium Foil Cosmic-Ray Neutron Detector for Measuring Field-Scale Soil Moisture

During the past decade, cosmic-ray neutron sensing technology has enabled researchers to reveal soil moisture spatial patterns and to estimate landscape-average soil moisture for hydrological and agricultural applications. However, reliance on rare materials such as helium-3 increases the cost of cosmic-ray neutron probes (CRNPs) and limits the adoption of this unique technology beyond the realm of academic research. In this study, we evaluated a novel lower cost CRNP based on moderated ultra-thin lithium-6 foil (Li foil system) technology against a commercially-available CRNP based on BF3 (boron trifluoride, BF-3 system). The study was conducted in a cropped field located in the Konza Prairie Biological Station near Manhattan, Kansas, USA (325 m a.s.l.) from 10 April 2020 to 18 June 2020. During this period the mean atmospheric pressure was 977 kPa, the mean air relative humidity was 70%, and the average volumetric soil water content was 0.277 m3 m−3. Raw fast neutron counts were corrected for atmospheric pressure, atmospheric water vapor, and incoming neutron flux. Calibration of the CRNPs was conducted using four intensive field surveys (n &amp;gt; 120), in combination with continuous observations from an existing array of in situ soil moisture sensors. The time series of uncorrected neutron counts of the Li foil system was highly correlated (r2 = 0.91) to that of the BF-3 system. The Li foil system had an average of 2,250 corrected neutron counts per hour with an uncertainty of 2.25%, values that are specific to the instrument size, detector configuration, and atmospheric conditions. The estimated volumetric water content from the Li foil system had a mean absolute difference of 0.022 m3 m−3 compared to the value from the array of in situ sensors. The new Li foil detector offers a promising lower cost alternative to existing cosmic-ray neutron detection devices used for hectometer-scale soil moisture monitoring.

Modeling Soil Water Content and Crop-Growth Metrics in a Wheat Field in the North China Plain Using RZWQM2

Soil water content (SWC) is an important factor restricting crop growth and yield in cropland ecosystems. The observation and simulation of soil moisture contribute greatly to improving water-use efficiency and crop yield. This study was conducted at the Shandong Yucheng Agro-ecosystem National Observation and Research Station in the North China Plain. The study period was across the winter wheat (Triticum aestivum L.) growth stages from 2017 to 2019. A cosmic-ray neutron probe was used to monitor the continuous daily SWC. Furthermore, the crop leaf area index (LAI), yield, and aboveground biomass of winter wheat were determined. The root zone quality model 2 (RZWQM2) was used to simulate and validate the SWC, crop LAI, yield, and aboveground biomass. The results showed that the simulation errors of SWC were minute across the wheat growth stages and mature stages in 2017–2019. The root mean square error (RMSE) and relative root mean square error (RRMSE) of the SWC simulation at the jointing stage of winter wheat were 0.0296 and 0.1605 in 2017–2018, and 0.0265 and 0.1480 in 2018–2019, respectively. During the rain-affected days, the RMSE (0.0253) and RRMSE (0.0980) for 2017–2018 were significantly lower than those of 2018–2019 (0.0301 and 0.1458, respectively), indicating that rain events decreased the model accuracy in the dry years compared to the wet years. The simulated LAIs were significantly higher than the measured values. The simulated yield value of winter wheat was 5.61% lower and 3.92% higher than the measured yield in 2017–2018 and in 2018–2019, respectively. The simulated value of aboveground biomass was significantly (45.48%) lower than the measured value in 2017–2018. This study showed that, compared with the dry and cold wheat growth period of 2018–2019, the higher precipitation and temperature in 2017–2018 led to a poorer simulation of SWC and crop-growth components. This study indicated that annual abnormal rainfall and temperature had a significant influence on the simulation of SWC and wheat growth, especially under intensive climate-change stress conditions.

The Indian COSMOS Network (ICON): Validating L-Band Remote Sensing and Modelled Soil Moisture Data Products

Availability of global satellite based Soil Moisture (SM) data has promoted the emergence of many applications in climate studies, agricultural water resource management and hydrology. In this context, validation of the global data set is of substance. Remote sensing measurements which are representative of an area covering 100 m2 to tens of km2 rarely match with in situ SM measurements at point scale due to scale difference. In this paper we present the new Indian Cosmic Ray Network (ICON) and compare it’s data with remotely sensed SM at different depths. ICON is the first network in India of the kind. It is operational since 2016 and consist of seven sites equipped with the COSMOS instrument. This instrument is based on the Cosmic Ray Neutron Probe (CRNP) technique which uses non-invasive neutron counts as a measure of soil moisture. It provides in situ measurements over an area with a radius of 150–250 m. This intermediate scale soil moisture is of interest for the validation of satellite SM. We compare the COSMOS derived soil moisture to surface soil moisture (SSM) and root zone soil moisture (RZSM) derived from SMOS, SMAP and GLDAS_Noah. The comparison with surface soil moisture products yield that the SMAP_L4_SSM showed best performance over all the sites with correlation (R) values ranging from 0.76 to 0.90. RZSM on the other hand from all products showed lesser performances. RZSM for GLDAS and SMAP_L4 products show that the results are better for the top layer R = 0.75 to 0.89 and 0.75 to 0.90 respectively than the deeper layers R = 0.26 to 0.92 and 0.6 to 0.8 respectively in all sites in India. The ICON network will be a useful tool for the calibration and validation activities for future SM missions like the NASA-ISRO Synthetic Aperture Radar (NISAR).

Corrigendum: Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys

Cosmic ray neutron (CRN) sensing allows for non-invasive soil moisture measurements at the field scale and relies on the inverse correlation between aboveground measured epithermal neutron intensity (1eV – 100 keV) and environmental water content. The measurement uncertainty follows Poisson statistics and thus increases with decreasing neutron intensity, which corresponds to increasing soil moisture. In order to reduce measurement uncertainty, the neutron count rate is usually aggregated over 12 or 24 h time windows for stationary CRN probes. To obtain accurate soil moisture estimates with mobile CRN rover applications, the aggregation of neutron measurements is also necessary and should consider soil wetness and driving speed. To date, the optimization of spatial aggregation of mobile CRN observations in order to balance measurement accuracy and spatial resolution of soil moisture patterns has not been investigated in detail. In this work, we present and apply an easy-to-use method based on Gaussian error propagation theory for uncertainty quantification of soil moisture measurements obtained with CRN sensing. We used a 3rd order Taylor expansion for estimating the soil moisture uncertainty from uncertainty in neutron counts and compared the results to a Monte Carlo approach with excellent agreement. Furthermore, we applied our method with selected aggregation times to investigate how CRN rover survey design affects soil moisture estimation uncertainty. We anticipate that the new approach can be used to improve the strategic planning and evaluation of CRN rover surveys based on uncertainty requirements.

Integrating Invasive and Non-invasive Monitoring Sensors to Detect Field-Scale Soil Hydrological Behavior

The Critical Zone Observatory (CZO) in the Alento River Catchment (ARC; southern Italy) has been collecting data for some decades and recently became part of the TERENO (TERrestrial ENvironmental Observatories) long-term ecosystem infrastructure network. In 2016, cosmic-ray neutron probes (CRNP, non-invasive proximal sensors) together with SoilNet wireless sensor networks (invasive ground-based sensors) were installed in two sub-catchments (MFC2 and GOR1), characterized by different weather, topographic, pedological, and land-use conditions. The SoilNet sensors are measuring soil apparent permittivity (converted into soil moisture), electrical conductivity, and temperature at two soil depths of 0.15 m and 0.30 m in 20 locations around the CRNP. The SoilNet sensor network includes also soil matric pressure potential sensors at the same soil depths and locations. A major aim of this study is to compare the hydrological response of the two experimental sites to climate forcings (rainfall minus potential evapotranspiration, hereby defined as rainfall deficit) through the use of the monthly Soil Moisture Index (SMI) computed from daily values of soil moisture measured by the CRNPs. For both sites, strong correlations are detected between monthly rainfall deficit and SMI, even if the cropland site (MFC2) is characterized by more extreme dry and wet conditions. For these two different sites, our investigations also aim at identifying the dominant controls governing the seasonal dynamics of spatial soil moisture patterns. The majority of the spatial variance in the cropland site is explained by terrain attributes, under both wet and dry conditions, whereas the second-order statistical moment of soil moisture in the forested site is mostly explained by topographic factors under wet conditions during the rainy season. For both sites, our data show that soil texture exerts a minimal impact on the spatial variations of soil moisture.

Monitoring of Snowpack Dynamics With Cosmic-Ray Neutron Probes: A Comparison of Four Conversion Methods

Common snow monitoring instruments based on hydrostatic pressure such as snow pillows are often influenced by various disturbing effects, which result in a reduced quality of the snow cover and snow water equivalent estimates. Such disturbing effects include energy transport into the snowpack, wind fields, and variations of snow properties within the snowpack (e.g. ice layers). Recently, it has been shown that Cosmic-Ray Neutron Probes (CRNP) are a promising technique to monitor snow pack development. CRNP can provide larger support and need lower maintenance compared to conventional sensors. These instruments are sensitive to the intensity of epithermal neutrons that are produced in the soil by cosmic radiation and are widely used to determine soil moisture in the upper decimetres of the ground. The application of CRNP for snow monitoring is based on the principle that snow water moderates the epithermal neutron intensity, which can be directly related to the snow water equivalent (SWE) of the snow pack. In this study, long-term CRNP measurements in the Pinios Hydrologic Observatory (PHO), Greece, were used to test different methods for converting neutron count rates to snow pack characteristics: i) linear regression, ii) standard N0-calibration function, iii) a physically-based calibration approach, and iv) thermal to epithermal neutron ratio. For this, a sonic sensor located near the CRNP was used to compare CRNP-derived snow pack dynamics with snow depth measurements. We found that the above-ground CRNP is well suited for measurement of field scale SWE, which is in agreement with findings of other studies. The analysis of the accuracy of the four conversion methods showed that all methods were able to determine the mass of the snow pack during the snow events reasonably well. The N0-calibration function and the physically-based calibration function performed best and the thermal to epithermal neutron ratio performed worst. Furthermore, we found that SWE determination with above-ground CRNP can be affected by other influences (e.g. heavy rainfall). Nevertheless, CRNP-based SWE determination is a potential alternative to established method like snow depth-based SWE methods, as it provides SWE estimate for a much larger scales (12-18 ha).

Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys

Cosmic ray neutron (CRN) sensing allows for non-invasive soil moisture measurements at the field scale and relies on the inverse correlation between aboveground measured epithermal neutron intensity (1 eV−100 keV) and environmental water content. The measurement uncertainty follows Poisson statistics and thus increases with decreasing neutron intensity, which corresponds to increasing soil moisture. In order to reduce measurement uncertainty, the neutron count rate is usually aggregated over 12 or 24 h time windows for stationary CRN probes. To obtain accurate soil moisture estimates with mobile CRN rover applications, the aggregation of neutron measurements is also necessary and should consider soil wetness and driving speed. To date, the optimization of spatial aggregation of mobile CRN observations in order to balance measurement accuracy and spatial resolution of soil moisture patterns has not been investigated in detail. In this work, we present and apply an easy-to-use method based on Gaussian error propagation theory for uncertainty quantification of soil moisture measurements obtained with CRN sensing. We used a 3rd order Taylor expansion for estimating the soil moisture uncertainty from uncertainty in neutron counts and compared the results to a Monte Carlo approach with excellent agreement. Furthermore, we applied our method with selected aggregation times to investigate how CRN rover survey design affects soil moisture estimation uncertainty. We anticipate that the new approach can be used to improve the strategic planning and evaluation of CRN rover surveys based on uncertainty requirements.

Assimilation of Cosmic‐Ray Neutron Counts for the Estimation of Soil Ice Content on the Eastern Tibetan Plateau

AbstractAccurate observations and simulations of soil moisture phasal forms are crucial in cold region hydrological studies. In the seasonally frozen ground of eastern Tibetan Plateau, water vapor, liquid, and ice coexist in the frost‐susceptible silty‐loam soil during winter. Quantification of soil ice content is thus vital in the investigation and understanding of the region's freezing‐thawing processes. This study focuses on the retrieval of soil ice content utilizing the in situ soil moisture (i.e., liquid phase) and cosmic ray neutron measurements (i.e., total water including liquid and ice), with Observing System Simulation Experiments. To derive the total soil water from neutron counts, different weighting methods (revised, conventional, and uniform) for calibrating the cosmic‐ray neutron probe (CRNP) were intercompared. The comparison showed that the conventional nonlinear method performed the best. Furthermore, to assimilate fast neutrons using the particle filter, the STEMMUS‐FT (Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil) model was used as the physically based process model, and the COSMIC model (Cosmic‐ray Soil Moisture Interaction Code) used as the observation operator (i.e., forward neutron simulator). Other than background inputs from disturbed initializations in the STEMMUS‐FT, model uncertainties were predefined to assimilate fast neutrons. We observed that with enough spread of uncertainties, the updated states could mimic the CRNP observation. In all setups, assimilating CRNP measurements could enhance total soil water analyses, which consequently led to the improved detection of soil ice content and therefore the freezing thawing‐process at the field scale.

Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models

Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moisture values, which were inferred from the CRNP station in Turkey, are also validated using a time domain reflectometer (TDR) installed at the same location and soil water content values obtained from a land surface model (Noah LSM) at various depths (0.1 m, 0.3 m, 0.6 m and 1.0 m). The CRNP has a very good correlation with TDR where both measurements show consistent changes in soil moisture due to storm events. Satellite soil moisture products obtained from the Soil Moisture and Ocean Salinity (SMOS), the METOP-A/B Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Climate Change Initiative (CCI) and a global land surface model Global Land Data Assimilation System (GLDAS) are compared with the soil moisture values obtained from CRNP stations. Coefficient of determination ( r 2 ) and unbiased root mean square error (ubRMSE) are used as the statistical measures. Triple Collocation (TC) was also performed by considering soil moisture values obtained from different soil moisture products and the CRNPs. The validation results are mainly influenced by the location of the sensor and the soil moisture retrieval algorithm of satellite products. The SMAP surface product produces the highest correlations and lowest errors especially in semi-arid areas whereas the ASCAT product provides better results in vegetated areas. Both global and local land surface models’ outputs are highly compatible with the CRNP soil moisture values.

Extension of cosmic-ray neutron probe measurement depth for improving field scale root-zone soil moisture estimation by coupling with representative in-situ sensors

Accurate estimation of field-scale root-zone soil moisture (RZSM) is essential for hydro-meteorological and agricultural management. The cosmic-ray neutron probe (CRNP) is an innovative technique for field-scale soil moisture observation. However, it is limited at deeper depths, and requires suitable methods for scaling the CRNP effective depth to represent the root-zone layer (up to 100 cm depth). A merging framework was developed in this study for improving field-scale RZSM with the CRNP via coupling the representative ancillary RZSM information with cosmic-ray soil moisture. By using ancillary RZSM retrieved at the most time stable location, this approach alleviates the problems associated with lack of independent datasets, while maintaining scale representativeness. A linear autoregressive model was adopted to forecast the errors between two input datasets (the cosmic-ray soil moisture and ancillary RZSM) and a reference product, which was computed by spatially weighting the deepest in-situ soil moisture measurements apart from the most time stable location. The variances of estimated errors were then used to compute a suitable weight for each single product by following the original linear combination of forecasts, and subsequently merging the ancillary RZSM and cosmic-ray soil moisture. Performance of the merged RZSM in comparison to input datasets and exponential filter-based RZSM over three different environments was evaluated against the reference RZSM product. The results indicated that differences in vegetation coverages related to total aboveground and belowground biomass accumulation, root water uptake rate and canopy density are the major factors controlling the temporal variation in merged RZSM, and they can be partly interpreted via the framework procedure. Superior performance achieved by the merging framework demonstrated its robustness in improving field-scale RZSM measurement compared to other products. This study also underlines the strong relationship between input data quality and the performance of the selected merging method, with respect to variations of CRNP effective depths. Overall, the merging framework is simple to apply, enables unrestricted-use in different environments, and is flexible to combine further standalone data sources.