In recent years, cosmic‐ray neutron sensing (CRNS) has shown a large potential among proximal sensing techniques to monitor soil moisture noninvasively, with high frequency and a large support volume ...(radius up to 240 m and sensing depth up to 80 cm). This signal is, however, more sensitive to closer distances and shallower depths. Inherently, CRNS‐derived soil moisture is a spatially weighted value, different from an average soil moisture as retrieved by a sensor network. In this study, we systematically test a new profile shape correction on CRNS‐derived soil moisture, based on additional soil moisture profile measurements and vertical unweighting, which is especially relevant during pronounced wetting or drying fronts. The analyses are conducted with data collected at four contrasting field sites, each equipped with a CRNS probe and a distributed soil moisture sensor network. After applying the profile shape correction on CRNS‐derived soil moisture, it is compared with the sensor network average. Results show that the influence of the vertical sensitivity of CRNS on integral soil moisture values is successfully reduced. One to three properly located profile measurements within the CRNS support volume improve the performance. For the four investigated field sites, the RMSE decreased 11–53% when only one profile location was considered. We therefore recommend to install along with a CRNS at least one soil moisture profile in a radial distance <100 m and a measurement depth down to 50 cm. Profile‐shape‐corrected, CRNS‐derived soil moisture is an unweighted integral soil moisture over the support volume, which is easier to interpret and easier to use for further applications.
Although cosmic‐ray neutron sensing (CRNS) is probably the most promising noninvasive proximal soil moisture measurement technique at the field scale, its application for hydrological simulations ...remains underexplored in the literature so far. This study assessed the use of CRNS to inversely calibrate soil hydraulic parameters at the intermediate field scale to simulate the groundwater recharge rates at a daily timescale. The study was conducted for two contrasting hydrological years at the Guaraíra experimental basin, Brazil, a 5.84‐km², a tropical wet and rather flat landscape covered by secondary Atlantic forest. As a consequence of the low altitude and proximity to the equator low neutron count rates could be expected, reducing the precision of CRNS while constituting unexplored and challenging conditions for CRNS applications. Inverse calibration for groundwater recharge rates was used based on CRNS or point‐scale soil moisture data. The CRNS‐derived retention curve and saturated hydraulic conductivity were consistent with the literature and locally performed slug tests. Simulated groundwater recharge rates ranged from 60 to 470 mm yr–1, corresponding to 5 and 29% of rainfall, and correlated well with estimates based on water table fluctuations. In contrast, the estimated results based on inversive point‐scale datasets were not in alignment with measured water table fluctuations. The better performance of CRNS‐based estimations of field‐scale hydrological variables, especially groundwater recharge, demonstrated its clear advantages over traditional invasive point‐scale techniques. Finally, the study proved the ability of CRNS as practicable in low altitude, tropical wet areas, thus encouraging its adoption for water resources monitoring and management.
Core Ideas
Cosmic‐ray neutron sensing (CRNS) can detect field‐scale soil moisture also at low latitudes.
Soil hydraulic parameters were calibrated using CRNS and inverse simulations.
Daily groundwater recharge rates were simulated using CRNS‐based soil hydraulic parameters.
CRNS‐based recharge was matching independent area average recharge estimates.
In the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture ...data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling.
Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while conventional sensors typically suffer from small spatial support. With a sensor ...footprint up to several hectares, cosmic-ray neutron sensing (CRNS) is a modern technology to address that challenge.
Climate change increases the occurrence and severity of
droughts due to increasing temperatures, altered circulation patterns, and
reduced snow occurrence. While Europe has suffered from drought ...events in
the last decade unlike ever seen since the beginning of weather recordings,
harmonized long-term datasets across the continent are needed to monitor
change and support predictions. Here we present soil moisture data from 66
cosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short)
covering recent drought events. The CRNS sites are distributed across Europe
and cover all major land use types and climate zones in Europe. The raw
neutron count data from the CRNS stations were provided by 24 research
institutions and processed using state-of-the-art methods. The harmonized
processing included correction of the raw neutron counts and a harmonized
methodology for the conversion into soil moisture based on available in situ
information. In addition, the uncertainty estimate is provided with the
dataset, information that is particularly useful for remote sensing and
modeling applications. This paper presents the current spatiotemporal
coverage of CRNS stations in Europe and describes the protocols for data
processing from raw measurements to consistent soil moisture products. The
data of the presented COSMOS-Europe network open up a manifold of potential
applications for environmental research, such as remote sensing data
validation, trend analysis, or model assimilation. The dataset could be of
particular importance for the analysis of extreme climatic events at the
continental scale. Due its timely relevance in the scope of climate change
in the recent years, we demonstrate this potential application with a brief
analysis on the spatiotemporal soil moisture variability. The dataset,
entitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray
Neutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich:
https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021).
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).
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