@article{SchroenZachariasWomacketal.2018,
  author    = {Schr{\"o}n, Martin and Zacharias, Steffen and Womack, Gary and K{\"o}hli, Markus and Desilets, Darin and Oswald, Sascha and Bumberger, Jan and Mollenhauer, Hannes and K{\"o}gler, Simon and Remmler, Paul and Kasner, Mandy and Denk, Astrid and Dietrich, Peter},
  title     = {Intercomparison of cosmic-ray neutron sensors and water balance monitoring in an urban environment},
  series = {Geoscientific instrumentation, methods and data systems},
  volume    = {7},
  journal   = {Geoscientific instrumentation, methods and data systems},
  number    = {1},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {2193-0856},
  doi       = {10.5194/gi-7-83-2018},
  pages     = {83 -- 99},
  year      = {2018},
  abstract  = {Sensor-to-sensor variability is a source of error common to all geoscientific instruments that needs to be assessed before comparative and applied research can be performed with multiple sensors. Consistency among sensor systems is especially critical when subtle features of the surrounding terrain are to be identified. Cosmic-ray neutron sensors (CRNSs) are a recent technology used to monitor hectometre-scale environmental water storages, for which a rigorous comparison study of numerous co-located sensors has not yet been performed. In this work, nine stationary CRNS probes of type "CRS1000" were installed in relative proximity on a grass patch surrounded by trees, buildings, and sealed areas. While the dynamics of the neutron count rates were found to be similar, offsets of a few percent from the absolute average neutron count rates were found. Technical adjustments of the individual detection parameters brought all instruments into good agreement. Furthermore, we found a critical integration time of 6 h above which all sensors showed consistent dynamics in the data and their RMSE fell below 1\% of gravimetric water content. The residual differences between the nine signals indicated local effects of the complex urban terrain on the scale of several metres. Mobile CRNS measurements and spatial simulations with the URANOS neutron transport code in the surrounding area (25 ha) have revealed substantial sub-footprint heterogeneity to which CRNS detectors are sensitive despite their large averaging volume. The sealed and constantly dry structures in the footprint furthermore damped the dynamics of the CRNS-derived soil moisture. We developed strategies to correct for the sealed-area effect based on theoretical insights about the spatial sensitivity of the sensor. This procedure not only led to reliable soil moisture estimation during dry-out periods, it further revealed a strong signal of intercepted water that emerged over the sealed surfaces during rain events. The presented arrangement offered a unique opportunity to demonstrate the CRNS performance in complex terrain, and the results indicated great potential for further applications in urban climate research.},
  language  = {en}
}
@article{SchroenRosolemKoehlietal.2018,
  author    = {Schr{\"o}n, Martin and Rosolem, Rafael and K{\"o}hli, Markus and Piussi, L. and Schr{\"o}ter, I. and Iwema, J. and K{\"o}gler, S. and Oswald, Sascha and Wollschl{\"a}ger, U. and Samaniego, Luis and Dietrich, Peter and Zacharias, Steffen},
  title     = {Cosmic-ray Neutron Rover Surveys of Field Soil Moisture and the Influence of Roads},
  series = {Water resources research},
  volume    = {54},
  journal   = {Water resources research},
  number    = {9},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1029/2017WR021719},
  pages     = {6441 -- 6459},
  year      = {2018},
  abstract  = {Measurements of root-zone soil moisture across spatial scales of tens to thousands of meters have been a challenge for many decades. The mobile application of Cosmic Ray Neutron Sensing (CRNS) is a promising approach to measure field soil moisture noninvasively by surveying large regions with a ground-based vehicle. Recently, concerns have been raised about a potentially biasing influence of local structures and roads. We employed neutron transport simulations and dedicated experiments to quantify the influence of different road types on the CRNS measurement. We found that roads introduce a substantial bias in the CRNS estimation of field soil moisture compared to off-road scenarios. However, this effect becomes insignificant at distances beyond a few meters from the road. Neutron measurements on the road could overestimate the field value by up to 40 \% depending on road material, width, and the surrounding field water content. The bias could be largely removed with an analytical correction function that accounts for these parameters. Additionally, an empirical approach is proposed that can be used without prior knowledge of field soil moisture. Tests at different study sites demonstrated good agreement between road-effect corrected measurements and field soil moisture observations. However, if knowledge about the road characteristics is missing, measurements on the road could substantially reduce the accuracy of this method. Our results constitute a practical advancement of the mobile CRNS methodology, which is important for providing unbiased estimates of field-scale soil moisture to support applications in hydrology, remote sensing, and agriculture. Plain Language Summary Measurements of root-zone soil moisture across spatial scales of tens to thousands of meters have been a challenge for many decades. The mobile application of Cosmic Ray Neutron Sensing (CRNS) is a promising approach to measure field soil moisture noninvasively by surveying large regions with a ground-based vehicle. Recently, concerns have been raised about a potentially biasing influence of roads. We employed physics simulations and dedicated experiments to quantify the influence of different road types on the CRNS measurement. We found that the presence of roads biased the CRNS estimation of field soil moisture compared to nonroad scenarios. Neutron measurements could overestimate the field value by up to 40 \% depending on road material, width, surrounding field water content, and distance from the road. We proposed a correction function that successfully removed this bias and works even without prior knowledge of field soil moisture. Tests at different study sites demonstrated good agreement between corrected measurements and other field soil moisture observations. Our results constitute a practical advancement of the mobile CRNS methodology, which is important for providing unbiased estimates of field-scale soil moisture to support applications in hydrology, remote sensing, and agriculture.},
  language  = {en}
}