TY - GEN A1 - Schrön, Martin A1 - Köhli, Markus A1 - Scheiffele, Lena A1 - Iwema, Joost A1 - Bogena, Heye R. A1 - Lv, Ling A1 - Martini, Edoardo A1 - Baroni, Gabriele A1 - Rosolem, Rafael A1 - Weimar, Jannis A1 - Mai, Juliane A1 - Cuntz, Matthias A1 - Rebmann, Corinna A1 - Oswald, Sascha A1 - Dietrich, Peter A1 - Schmidt, Ulrich A1 - Zacharias, Steffen T1 - Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 636 KW - forested headwater catchment KW - moisture observing system KW - soil-water content KW - parameterization methods KW - scale KW - field KW - dynamics KW - observatories KW - networks Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-419134 IS - 636 SP - 5009 EP - 5030 ER - TY - JOUR A1 - Schroen, Martin A1 - Koehli, Markus A1 - Scheiffele, Lena A1 - Iwema, Joost A1 - Bogena, Heye R. A1 - Lv, Ling A1 - Martini, Edoardo A1 - Baroni, Gabriele A1 - Rosolem, Rafael A1 - Weimar, Jannis A1 - Mai, Juliane A1 - Cuntz, Matthias A1 - Rebmann, Corinna A1 - Oswald, Sascha A1 - Dietrich, Peter A1 - Schmidt, Ulrich A1 - Zacharias, Steffen T1 - Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity JF - Hydrology and earth system sciences : HESS N2 - 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. Y1 - 2017 U6 - https://doi.org/10.5194/hess-21-5009-2017 SN - 1027-5606 SN - 1607-7938 VL - 21 SP - 5009 EP - 5030 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Schrön, Martin A1 - Rosolem, Rafael A1 - Köhli, Markus A1 - Piussi, L. A1 - Schröter, I. A1 - Iwema, J. A1 - Kögler, S. A1 - Oswald, Sascha A1 - Wollschläger, U. A1 - Samaniego, Luis A1 - Dietrich, Peter A1 - Zacharias, Steffen T1 - Cosmic-ray Neutron Rover Surveys of Field Soil Moisture and the Influence of Roads JF - Water resources research N2 - 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. KW - road effect KW - field-scale KW - soil moisture KW - cosmic ray neutrons KW - mobile survey KW - COSMOS rover Y1 - 2018 U6 - https://doi.org/10.1029/2017WR021719 SN - 0043-1397 SN - 1944-7973 VL - 54 IS - 9 SP - 6441 EP - 6459 PB - American Geophysical Union CY - Washington ER -