Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while conventional sensors typically suffer from small spatial support. With a sensor footprint up to several hectares, cosmic-ray neutron sensing (CRNS) is a modern technology to address that challenge.
So far, the CRNS method has typically been applied with single sensors or in sparse national-scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km
As a result, we provide a unique and comprehensive data set to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatiotemporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land–atmosphere exchange processes. The data set is available through the EUDAT Collaborative Data Infrastructure and is split into two subsets:
Soil moisture is a key state variable of the Earth's environmental system, controlling various processes at various scales: the exchange of water and energy between the land surface and the atmosphere, runoff generation and groundwater recharge, vegetation development and growth in natural and managed systems, or the release of greenhouse gases from soils.
But while soil moisture is a much desired quantity in research and applications, its observation remains a challenge
Cosmic-ray neutron sensing (CRNS) is a modern measurement technique to close the scale gap in soil moisture observation. According to simulations by
The CRNS sensor is sensitive to the ambient density of neutrons in the near-surface atmosphere. The main source of cosmic-ray-induced neutrons exhibits energies between
To date, a wide range of neutron detectors optimized for soil moisture monitoring exist, and these use various gases or coating materials for neutron detection. An overview of detectors used in our field campaign is provided in Sect.
The intensity of detected neutrons is mainly controlled by the interaction with hydrogen pools in the sensor footprint, of which soil moisture is typically the most important, though not the only one. A standard approach to estimate the gravimetric (
In that form, the transfer function requires the calibration of parameter
The calibration of
Yet, calibration can only account for the effect of static hydrogen pools at a specific point in time, while hydrogen pools such as vegetation are typically dynamic. In addition, the collection and processing of soil samples for measuring thermogravimetric soil moisture are particularly labor intensive.
One of the challenges in using CRNS for soil moisture observation is thus to separate the part of the signal that is related to soil moisture from those parts affected by other hydrogen pools such as vegetation and soil organic carbon
While the horizontal footprint of a single CRNS sensor already exceeds the spatial support of conventional techniques, further attempts have been made to enhance the spatial coverage such as roving CRNS and networks of CRNS sensors.
The term
In contrast, networks of CRNS sensors operate multiple stationary CRNS sensors. Some initiatives have established national CRNS monitoring networks with the aim of supporting environmental monitoring at larger scales. These networks are being implemented under the acronym of COSMOS (the COsmic-ray Soil Moisture Observing System). The first network was established in the USA by the University of Arizona and has already deployed more than 60 CRNS sensors at various locations across the USA
To better cover the spatial and temporal scales, the combination of both stationary and roving approaches has been explored
The Cosmic Sense research unit, funded by the German Research Foundation (DFG), addresses the above challenges in a concerted effort with a consortium of eight German partner institutions.
In this context, one component of the Cosmic Sense project is the targeted joint operation of a large number of CRNS sensors in a dense observational network. The scientific aims behind these joint field campaigns (JFCs) are
to systematically explore, at the landscape level, the effect of heterogeneity of soil and vegetation in the CRNS footprint on the neutron signals; to investigate the consistency of signals obtained from CRNS sensors of different manufacturers and sensitivities; to monitor water storage in the root zone, as the most dynamic storage component in a catchment, and to possibly assimilate these observations in a hydrological model; to establish a network with overlapping horizontal CRNS footprints in order to investigate the potential of constraining soil moisture estimates in space and time and thus to establish space–time representations of soil moisture at a resolution higher than the resolution of a single sensor; to better understand the influence of hydrogen pools other than soil moisture on the CRNS signal and to develop corresponding correction procedures; and to evaluate the relation between the spatial and temporal dynamics of CRNS footprints and thermal infrared remote sensing imagery.
As mentioned in the previous section, networks of a large number of CRNS sensors have been established before; however, as sparse national-scale networks they are not geared towards the scientific subjects elaborated above. Hence, the joint operation of 24 CRNS sensors that took place from early May to late July 2019 is unprecedented in its scope as it features a large number of detectors in an area of just 1 km
In this paper, we present this unique data set as a contribution to the CRNS community but also to those researchers interested in exploring the potential of dense volume-integrated soil moisture observations from a hydrological perspective. The data set was split into two subsets and is freely available from EUDAT at
We start by providing a detailed overview and justification of the choice of the study area (Sect.
In the selection process for the field campaign's study location the requirements of the different involved research projects needed to be reconciled. Important criteria were long-term climate and soil moisture observations, contiguous land cover, good accessibility by foot and car for the mobile applications, a substantial fraction of nonforested area for the remote sensing (UAS) campaigns, shallow and time-variable groundwater levels, and a self-contained hydrological catchment to enable hydrological modeling. With the 1 km
The Rott headwater catchment in Fendt near Peißenberg, Bavaria, Germany: the map shows the most important features of the study area and the JFC. In terms of land use, we only highlight the forested area and a patch of cropland – the remaining parts are mainly meadows or grassland, with some scattered settlements – as well as roads and streams. In terms of instrumentation, we focus on showing the locations of the CRNS sensors with a 150 m radius as a typical footprint size for a medium level of soil moisture
The Fendt site of the TERENO Pre-Alpine Observatory (595 m a.s.l.) is located in the Rott headwater catchment. In the area, several multi- and interdisciplinary campaigns as well as some long-term ecological experiments have been carried out over the past years
The younger morphodynamics of the region were governed by glacial and post-glacial processes of the Quaternary. The shallow, U-shaped valley of the Rott was carved into older sediments (molasse) by the Isar-Loisach glacier about 25 000 years ago, and this was followed by kettle lake sedimentation and fluvial erosion processes. Whereas towards the side slopes of the valley gravels mixed with loamy and silty fractions are predominant, we find mainly silty and loamy sediments at the lower elevations with peaty compositions towards the draining rivulet
In the Rott headwater catchment, between the deeper Tertiary (molasse) and the Quaternary sedimentation layers, shallow aquifers have formed with hydraulic heads that range between 4 and 0.2 m below ground from the margins to the center of the headwater catchment.
The prevalent soil class is Cambic Stagnosol, which originated from the glacial parent material. Typical clay, silt, and sand fractions are 32 %, 41 %, and 27 %
The boundaries of the Rott headwater catchment were delineated according to the surface topography (DEM1 1 m
This section describes the measurements conducted during the campaign. Other complementary data are described in Sect.
The core innovation and overall motivation of this data set is the use of a dense network of 24 CRNS sensors in a study area of roughly 1 km
Around this core data set we carried out various measurements that are required to utilize, study, and evaluate observed neutron counts from the CRNS network for the purpose of soil moisture retrieval: meteorological observations (Sect.
The data presented in this study consist, for large parts, of time series data recorded at well-defined, sparse, and static points in space (e.g., neutron counts, meteorological variables, soil moisture, or permittivity). For such data, we decided to implement a simple, transparent, and easy-to-use data model that is based on standard text tables (character (tabulator) separated values, csv): each sensor (or sensor unit) is characterized in attribute tables, including a unique identifier (ID), location (latitude and longitude in WGS 84 reference system), measurement depth or height (in meters above or below the surface), and a set of sensor-specific attributes that are documented in the attribute table's header. The time series of observations are provided in additional text tables: each sensor is represented by one file that is named according to the unique sensor ID. The first column in any such file holds the date and time (UTC, ISO 8601). Any other columns represents the measured variables which are documented in the file header, including the physical units.
Time series data with a fixed spatiotemporal resolution (e.g., data from SoilNet) are provided in netCDF files, which include an explicit documentation of all dimensions and observational variables but which are also accompanied by an overview text table that contains key attributes of the measurement locations.
Any exceptions from these data models (e.g., for the CRNS roving, the land use and soil data, the digital elevation model, or the remote sensing data) are explicitly elaborated in the corresponding subsections and by using metadata files. For polygon data, we use the ESRI shapefile and the GeoJSON format, and for gridded data we use the GeoTIFF format.
Further details of the data repository are given in the code and data availability section (Sect. 6).
The permanent meteorological instrumentation at the Rott headwater catchment (also known as the TERENO site Fendt) consists of an eddy-covariance flux tower and several precipitation sensors. Besides the high-frequency measurements of the eddy-flux system (the data of which are available through the Integrated Carbon Observation System, ICOS, and TERENO), standard climate variables are recorded every minute. The meteorological observations and their respective devices selected for the presented data set are listed in Table
Meteorological instrumentation in the Rott headwater catchment (instrumentation is part of the TERENO Pre-Alpine Observatory at the Fendt site).
During the period from mid-May to mid-July 2019, a total of 24 stationary CRNS sensors were operated, though not all sensors were measuring at all times. CRNS sensor no. 8 in the northeast had already been installed as part of TERENO infrastructure, and it continued operation after the JFC. The temporal data availability is illustrated in Fig.
Overview of CRNS sensors installed during the JFC, including manufacturer, model, and converter technology; the availability of standard moderated detector tubes for epithermal neutrons (mod) and additional bare tubes for thermal neutron detection (bare); the dominant land cover in the footprint of the sensor; the maximum measurement depth of colocated profile probes near the CRNS sensor; and the sensitivity factor (ratio between raw neutron counts of the individual sensor and the calibrator unit no. 20).
Table
Dimension of CRNS sensors employed in the campaign. The blocks illustrate the size of the detectors; the actual units also comprise other components, combined in a slightly larger housing.
Raw (not standardized, uncorrected) neutron count rates (in counts per hour, cph) recorded at an integration interval of 20 min by six exemplary CRNS sensors representing different manufacturers/models (sensor ID in parentheses, see Table
For all CRNS sensors, the detection chamber is surrounded by a so-called moderator material in order to thermalize (i.e., slow down) epithermal neutrons for detection
The CRNS sensor units were equipped with varying meteorological sensors for air temperature, relative humidity, and air pressure, all of which are required to correct for atmospheric effects on epithermal neutron count rates. For some units, such sensors were only available internally in the logger box (making the observations less representative of the sensor footprint), while some units also featured external meteorological sensors. A more detailed specification of the placement of meteorological sensors is included in the metadata of the individual CRNS sensor units. All CRNS sensors were set up to record neutron counts and additional variables at a temporal interval of 20 min.
At 19 of the 24 CRNS sensor locations a profile probe (40 or 100 cm maximum measurement depth; see Sect.
The locations of the CRNS sensors are shown in Fig. to achieve a maximum coverage of the catchment area in order to capture soil moisture storage dynamics in the root zone as an important dynamic part of the catchment's water balance and a key control of groundwater recharge; antagonistic to the former, to achieve a maximum overlap of CRNS footprints in order to better constrain the estimation of soil moisture patterns in space and time from multiple CRNS signals; to achieve a balance of coverage between the different land cover and soil types, most notably with regard to meadow versus forest cover and loamy/silty versus peaty soils; to represent different positions along hill slopes inclined towards the northward draining rivulet; to avoid properties for which owners did not grant permission for trespassing nor installing equipment (mostly in the west–southwest); to use locations with sufficient insolation for solar panels; to efficiently incorporate existing observational infrastructure (such as the SoilNet in the northwest); to keep a minimum distance of 15 meters to roads in order to minimize the effect of roads on CRNS measurements to ensure proximity to tracks for the installation of the heavy StX-140-5-15 units and to enable comparisons with the cosmic rover measurements.
The trade-off between the aim of maximum coverage versus maximum overlap was resolved by increasing the density of CRNS sensors in the northwest, where the permanent SoilNet allows for optimal validation.
Neutron count rates at different observation locations were obtained with different CRNS sensor types. Even within the same sensor type, the effective sensitivity varies. Between all sensors used in this study, the sensitivity between the least and the most sensitive sensor (CRS-1000 and Lab-C, respectively) is expected to vary by an order of magnitude. In order to compare neutron count rates observed at different locations, these count rates need to be normalized to a standard level. Given that the effective sensitivity of the instrument is unknown, we need to introduce a reference standard. For that purpose, we placed a mobile CRNS sensor (calibrator, Hydroinnova, no. 20 in Table
During the campaign, portable cosmic-ray neutron sensors were used
to study the spatial variability of neutron intensity and soil moisture in the whole catchment and particularly in between the stationary sensors, to validate the spatial representativeness of the stationary sensors and its dependency on different land use types, and to investigate differences in the sensitivity of the stationary sensors by using the mobile sensor as a reference standard.
We used three types of mobile devices:
Intercalibration of the stationary sensor (middle) using the mobile rover on a hand wagon (left) and the mobile calibrator unit (right), with minimum record periods of 20 min and 24 h, respectively.
All detector systems were set to integrate neutron counts over 10 s, and the driving speed ranged from 10 to 100 m min
The neutron intensity at ground level strongly depends on the incoming cosmic-ray neutron flux. In order to account for variations of that incoming flux, researchers typically use neutron monitor (NM) recordings from the Neutron Monitor Database ( Fendt site, Irkutsk NM, Jungfraujoch NM,
Nevertheless, open questions about the suitability of neutron monitor data for local CRNS applications still exist, as pointed out by
Combinations of multiple Bonner spheres can be used to estimate the spectral flux distribution (i.e., the energy spectrum) of secondary cosmic-ray neutrons
Figure
Neutron count rate
Roving tracks and relative neutron intensity. Each panel represents a roving campaign day and a rover type (UFZ rover, FZJ rover, hand wagon). The color code qualitatively indicates the variability of observed corrected neutron intensity within a single roving campaign, where a higher neutron intensity corresponds to drier conditions. Forested and built-up areas as well as roads are shown in gray, and the watershed is represented by the broken black line; stationary CRNS sensor locations are printed with black circles. Basemap data are from OSM
As pointed out in Sect.
In this study, we combined several measurement techniques. The Rott headwater catchment study site at Fendt is partly equipped with permanent soil moisture measurement devices (SoilNet, see Sect.
In addition to these continuous observations, manual sampling was carried out in an intensive campaign from 25 to 26 June 2019 (see Sect.
The permanent soil moisture monitoring data at Fendt are available since June 2015. In total, 55 vertical profiles were distributed in the northwest part of the Rott headwater catchment, covering an area of about 9 ha (Fig.
We provide the SoilNet data in a compressed binary netCDF-4 format. The 15 min interval time series cover the period May 1–31 July. We stored each variable in a separate field with the dimensions profile ID, depth, and time. The geographical locations of the profiles are also contained in the file, thus enabling geostatistical analyses. We did not fill data gaps that resulted from sensor malfunction or transmission errors.
In addition to the permanent SoilNet we installed flexible variants of such a sensor system, the so-called “wireless soil moisture sensor networks”
Across the catchment, nine locations were equipped with vertical measurement profiles: eight locations with two profiles each and one location with four profiles (i.e., 18 profiles in total). The locations were chosen to cover different land use types and to closely accompany CRNS sensor nos. 4, 9, 11, 13, 14 (four profiles), 19, 22, and 24 (see Fig.
Soil moisture profile probes were installed throughout the study area in order to monitor the vertical distribution of soil moisture over time. The vertical distribution pattern is not only affecting the vertical CRNS footprint
We employed FDR-based profile probes PR2 of three variants (PR2/6 analogue, PR2/4 SDI, PR2/6 SDI, Delta-T Devices LLC, Cambridge, England, UK). These were installed in the direct vicinity of the 19 stationary CRNS sensors outside the SoilNet, at a maximum distance of 1.5 m. PR2/4 measures at 10, 20, 30, and 40 cm depth, PR2/6 additionally yields values for 60 and 100 cm depth. Table
The voltage readings
A large number of manual soil moisture measurements were carried out from 25 to 26 June 2019, and these were useful for increasing the spatial coverage of the continuously recorded soil moisture data, enabling the calibration of the moisture sensors and obtaining basic soil properties (bulk density, residual water content, organic matter content, texture).
The standard method for measuring soil moisture and other soil properties is collecting soil cores from various depths and analyzing them with thermogravimetry (referred to as
Altogether, the following sampling design was applied during the intensive campaign: within 2 m around each CRNS sensor, a thermogravimetric profile was collected (details below). In the direct vicinity, an FDR profile was collected as an additional reference (details below). Four FDR profiles, surrounding the CRNS sensor in all cardinal directions within 3–6 m distance complemented the survey in close proximity to the CRNS sensors. Finally, randomly selected locations (under the constraints of access permission and of accounting for different land cover types in the footprint) served for closing remaining gaps in the design. The resulting collection consisted of 23 thermogravimetric and 139 FDR profiles. All measurement locations were surveyed with the Differential Global Positioning System (DGPS).
For collecting a thermogravimetric profile, a pit was excavated. Soil cores were horizontally extracted with cylinders (4 cm height, 5.6 cm diameter), at depths of 0 to 25 cm with an increment of 5 cm, where the measurement depth signified the distance between the soil surface and the upper edge of the sampling ring. Two replicate cores were extracted for each depth in each pit. Water content was determined by drying the samples at a temperature of 105
The FDR profile resulted from handheld ML2 ThetaProbes (Delta-T Devices LLC, Cambridge, UK) performed in vertically augered holes of incrementally increasing depths. The depth increments of 5 cm correspond to those of the thermogravimetric measurements. Here, the depth refers to the upper end of the electrodes after the probe had been fully inserted. At each depth, the probe was vertically inserted, read, and extracted three times, with a slight rotation after each time, in order to capture microscale variability. Sensor voltage
We tested various published equations for converting
Cosmic-ray neutron sensors are affected by all hydrogen pools within the footprint. Therefore, water stored in plants and hydrogen as a component of the plant tissue had to be quantified. The applied methods differ for grassland (more dynamic due to mowing operations) and forest (higher total biomass).
Aboveground biomass on grassland and cropland sites was sampled three times (14–16 May, 6 June and 17 July) at the same 45 locations (see Fig.
The layout of this figure corresponds to Fig.
The vegetation water content can be estimated from weight loss after drying. For the amount of hydrogen and oxygen stored in cellulose,
Most grassland patches experienced multiple farming operations during the campaign, namely mowing, drying of the cut grass, baling, and removal of hay. These operations generally took place on patches defined by their respective ownership. The status of these patches (see Fig.
Woodland covers a considerable fraction of the study area. While this forest is largely dominated by spruce (
Forest mapping consisted of a plot-based and a tree-based survey (for locations, see Fig.
Additionally, the forest undergrowth and litter mass was determined at six locations. For this purpose, all litter and plant material within a 30 cm
Thermal imagery was acquired covering a 60 m radius of 14 different CRNS sensors (see Fig.
Summary of UAS flights. The areas correspond to the CRNS sensor ID (see Fig.
For the data acquisition the UAS MK Okto XL 6S12 (HiSystems GmbH, Moormerland, Germany) equipped with a radiometric calibrated FLIR Tau 2 336 (FLIR Systems, Inc., Wilsonville, OR, USA) was used. This thermal camera uses a VOx microbolometer focal plane array which is sensitive to wavelengths from 7.5 to 13.5
Flights were performed at an altitude of 100 m and a speed of approximately 5 m s
Three steps of data preprocessing were performed before the creation of orthomosaics: for each individual scene capture, the camera creates multiple frames. The frame with the highest image quality according to the Agisoft Image Quality tool was selected for further processing. The extracted frames were temperature corrected as proposed by
Discharge observations, which are, for example, needed for setting up hydrological or land surface models of the study area, were derived from water level measurements (Datalogger Type 575-II, HT-Hydrotechnik, Obergünzburg, Germany), taken at the outlet of the Rott headwater catchment in the north (see Fig.
For hydrological modeling and water balance assessments, information about the groundwater dynamics are also of interest.
The saturated zone at the Fendt valley bottom can be differentiated into a shallow aquifer situated on top of and between the Quaternary sediment layers and a thicker, deeper confined aquifer. For each groundwater layer, a hydraulic head measurement is available during the campaign period. The observation well for the shallow aquifer is located in the vicinity of the climate station, and the one for the deeper aquifer is situated below the country road, south of the SoilNet at the center between CRNS sensor nos. 12, 18, and 24 (see Fig.
This section introduces several additional and useful data sets that are not part of this data publication and are provided by institutions or research collaborators without direct involvement in the Cosmic Sense project.
The Cosmic Sense joint field campaign 2019 was carried out at the same time as the ScaleX 2019 campaign of KIT Campus Alpin, which involved additionally the MOSES (Modular Observation Solutions for Earth Systems) test campaign for the heat wave event chain. From these activities, complementary measurements were performed, including sensible and latent heat fluxes, net ecosystem exchange, turbulence statistics, planetary boundary layer depth, surface temperature, surface emissivity, surface thermal infrared images, vertical profiles of wind speed and direction, and water vapor and air temperature lidar profiles at Hohenpeißenberg. As of now, access to these data needs to be requested from the individual project leaders of ScaleX 2019. Further description and contact information is available at
Long-term hydrometeorological and ecological data for the TERENO Pre-Alpine Observatory and the Rott headwater catchment (Fendt) are available via the TERENO Data Discovery Portal (
A high-resolution digital elevation (DEM) or terrain model (DTM) can be helpful, particularly for hydrological applications, e.g., for identifying flow paths or estimating the depth of the groundwater table, or for forest biomass estimation (see Sect.
Soil maps (Bodenübersichtskarte 1 : 25 000) for Bavaria are available in vector (shapefile) format published under CC BY-3.0 license by the Bavarian Environmental Agency (LfU,
During fieldwork and for visualization, we used OpenStreetMap data layers
The German Weather Service (DWD) provides open climate data via
The prime motivation of this paper is to present a comprehensive data set to the scientific community. That data set provides all the information required to estimate, analyze, and put into context spatiotemporal soil moisture patterns from different sensors and at different scales.
The heart of the data set is the dense cluster of CRNS sensors. Although an in-depth data analysis is, by definition, beyond the scope of this data publication, we would still like to give an impression of the potential of this methodology as well as of the temporal and spatial variability of soil moisture in the context of this study. In order to convey such an impression, we applied a standard processing workflow to estimate soil moisture from neutron count rates. As we only consider this an illustration, not a study result, we only briefly outline the corresponding steps in the following:
Standardize neutron count rates to a common sensitivity level, which is the sensitivity of the calibrator sensor (see Table Correct neutron count rates for the effects of incoming cosmic neutron flux, barometric pressure, and atmospheric water vapor. For that purpose, we applied the standard procedure summarized by Calibrate the Average (or smooth) neutron count rates in time in order to increase the signal-to-noise ratio. In order to illustrate the behavior over the entire study period, we applied a moving average with a window size of 24 h; convert the smoothed neutron intensity to volumetric soil moisture using the calibrated Interpolate the soil moisture estimates
It should be noted that the above processing workflow uses only parts of the entire data set in order to roughly characterize soil moisture patterns in space and time. The scientific potential of the data set, however, is in combining the various observations at various scales. Figure
Dynamics of meteorological variables and soil moisture as estimated from neutron count rates.
The mobile roving campaign on 27 June (Figs.
Roving with a hand wagon, passing every stationary sensor except nos. 1, 21, and 22. Basemap © OpenStreetMap contributors 2020. Distributed under a Creative Commons BY-SA License.
Overview of data categories and their path in the repository.
For data sharing and archiving we used the EUDAT Collaborative Data Infrastructure (
The data described in this paper are available from EUDAT
We developed the R package FDR2soilmoisture, version
0.116 (
With this study, we present and provide a unique and comprehensive data set to several research communities: to those who investigate methods to retrieve soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatiotemporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land–atmosphere exchange processes.
The data set is unique in that it involves, for the first time, a dense network of 24 CRNS sensors in a catchment area of 1 km
The data set is comprehensive in that it not only includes all the data that are required to interpret the neutron count rates from stationary sensors (meteorological time series, local soil moisture observations, soil properties such as bulk density and organic carbon content, and vegetation biomass), but also various rich data sets that allow users to put CRNS-based soil moisture estimates into various spatiotemporal contexts: CRNS roving campaigns, thermal imaging from multiple UAS overflights, multiple WSN clusters, and time series of discharge observations at the catchment outlet and groundwater levels.
In this way, the presented data set will be a valuable resource to those seeking a better understanding of cosmic-ray neutron signals and for advancing scientific tools for CRNS-based soil moisture retrieval, as well as to those who aim to use these data and instruments for hydrological and hydrogeological applications.
The lead authors BF, TF, MH, MS, VD, and JJ were in charge of designing and conducting the JFC, processed the data, and drafted this paper. MF, SO, HB, AG, HK, and SZ are PIs of Cosmic Sense and participated in the fieldwork. GB, TB, HJHF, BK, and US are PIs of the group who contributed to the planning of the campaign. MKa, AP, DR, LS, and JW designed and conducted fieldwork as members of Cosmic Sense, and MKö supported the data analysis. TG conducted fieldwork and contributed to data processing; CB conducted a large share of the fieldwork; SSS coordinated the campaign and participated in the fieldwork. MZ inspected the field site and served as an advisor. All authors contributed to the writing of the manuscript. BH provided the mini-NM and contributed to the analysis of its data. RK contributed to the conducting and analysis of the gravimetric soil sampling at Fendt. VM installed the Bonner spheres and processed the data. HM developed and installed the temporary wireless sensor networks and carried out the corresponding data processing. IV maintained the permanent soil moisture network at Fendt and processed the sensor data for publication together with BF.
Jannis Weimar and Markus Köhli hold CEO positions at StyX Neutronica GmbH, Heidelberg, Germany. Marek Zreda has been a scientific adviser of Lab C, LLC, Sheridan, USA.
We thank the Scientific Team of the ScaleX Campaign 2019 for their contribution. The joint field campaign was furthermore supported by the MOSES project (Modular Observation Solutions for Earth Systems) of the Helmholtz Association, which also funded the Jülich CRNS rover. We express out gratitude to Konstantin Herbst (University of Kiel) for calculating the cutoff rigidity of the Fendt site, as well as Thomas Brall (HMGU) and Florian Wagner (HMGU) for their support during the installation of the Bonner spheres. We would like to thank the Paterzell Airfield staff for their cooperation and permission to use their airspace for UAS-based data acquisition.
We are indebted to all landowners granting permission to access their property and enduring additional heavy traffic during the already painful restrictions by the road construction works. The tremendous efforts in the field were only possible thanks to the help of the technical staff and involved students.
We gratefully acknowledge the services provided by EUDAT (namely B2DROP, B2SHARE, B2HANDLE), which greatly facilitated the workflows within the project and the publication of these data.
Base map data are copyrighted by OpenStreetMap contributors and are available from
This research has been supported by the Deutsche Forschungsgemeinschaft (grant no. FOR 2694, Large-Scale and High-Resolution Mapping of Soil Moisture on Field and Catchment Scales – Boosted by Cosmic-Ray Neutrons) and the Helmholtz Association (MOSES and TERENO).
This paper was edited by Alexander Gelfan and reviewed by two anonymous referees.