TY - JOUR A1 - Weimar, Jannis A1 - Köhli, Markus A1 - Budach, Christian A1 - Schmidt, Ulrich T1 - Large-scale boron-lined neutron detection systems as a 3He alternative for Cosmic Ray Neutron Sensing JF - Frontiers in water N2 - Cosmic-Ray neutron sensors are widely used to determine soil moisture on the hectare scale. Precise measurements, especially in the case of mobile application, demand for neutron detectors with high counting rates and high signal-to-noise ratios. For a long time Cosmic Ray Neutron Sensing (CRNS) instruments have relied on He-3 as an efficient neutron converter. Its ongoing scarcity demands for technological solutions using alternative converters, which are Li-6 and B-10. Recent developments lead to a modular neutron detector consisting of several B-10-lined proportional counter tubes, which feature high counting rates via its large surface area. The modularity allows for individual shieldings of different segments within the detector featuring the capability of gaining spectral information about the detected neutrons. This opens the possibility for active signal correction, especially useful when applied to mobile measurements, where the influence of constantly changing near-field to the overall signal should be corrected. Furthermore, the signal-to-noise ratio could be increased by combining pulse height and pulse length spectra to discriminate between neutrons and other environmental radiation. This novel detector therefore combines high-selective counting electronics with large-scale instrumentation technology. KW - CRNS KW - neutron KW - detector KW - soil moisture KW - readout electronics KW - boron-10 KW - helium-3 alternative Y1 - 2020 U6 - https://doi.org/10.3389/frwa.2020.00016 SN - 2624-9375 VL - 2 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Döpper, Veronika A1 - Jagdhuber, Thomas A1 - Holtgrave, Ann-Kathrin A1 - Heistermann, Maik A1 - Francke, Till A1 - Kleinschmit, Birgit A1 - Förster, Michael T1 - Following the cosmic-ray-neutron-sensing-based soil moisture under grassland and forest BT - exploring the potential of optical and SAR remote sensing JF - Science of remote Sensing N2 - Deriving soil moisture content (SMC) at the regional scale with different spatial and temporal land cover changes is still a challenge for active and passive remote sensing systems, often coped with machine learning methods. So far, the reference measurements of the data-driven approaches are usually based on point data, which entails a scale gap to the resolution of the remote sensing data. Cosmic Ray Neutron Sensing (CRNS) indirectly provides SMC estimates of a soil volume covering more than 1 ha and vertical depth up to 80 cm and is thus able to narrow this scale gap. So far, the CRNS-based SMC has only been used as validation source of remote sensing based SMC products. Its beneficial large sensing volume, especially in depth, has not been exploited yet. However, the sensing volume of the CRNS, which is changing with hydrological conditions, bears challenges for the comparison with remote sensing observations. This study, for the fist time, aims to understand the direct linkage of optical (Sentinel 2) and SAR (Sentinel 1) data with CRNS-based SMC. Thereby, the CRNS-based SMC is obtained by an experimental CRNS cluster that covers the high temporal and spatial SMC variability of an entire pre-alpine subcatchment. Using different Random Forest regressions, we analyze the potentials and limitations of both remote sensing sensors to follow the CRNS-based SMC signal. Our results show that it is possible to link the CRNS-based SMC signal with SAR and optical remote sensing observations via Random Forest modelling. We found that Sentinel 2 data is able to separate wet from dry periods with a R2 of 0.68. It is less affected by the changing soil volume that contributes to the CRNS-based SMC signal and it is able to assign a land cover specific SMC distribution. However, Sentinel 2 regression models are not accurate (R2 < 0.21) in mapping the CRNSbased SMC for the frequently mowed grassland areas of the study site. It requires soil type and topographical information to accurately follow the CRNS-based SMC signal with Random Forest regression. Sentinel 1 data instead is affected by the changing soil volume that contributes to the CRNS-based SMC signal. It has reasonable model performance (R2 = 0.34) when the CRNS data correspond to surface SMC. Also for Sentinel 1 the retrieval is impacted by the mowing activities at the test site. When separating the CRNS data set into dry and wet periods, soil properties and topography are the main drivers of SMC estimation. Sentinel 1 or Sentinel 2 data add the existing temporal variability to the regression models. The analysis underlines the need of combining optical and SAR observations (Sentinel 1, Sentinel 2) as well as soil property and topographical information to understand and follow the CRNS-based SMC signal for different hydrological conditions and land cover types. KW - Sentinel 1 KW - Sentinel 2 KW - soil texture KW - topography KW - sensing volume KW - Random Forest regression KW - CRNS Y1 - 2022 U6 - https://doi.org/10.1016/j.srs.2022.100056 SN - 2666-0172 VL - 5 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Stevanato, Luca A1 - Baroni, Gabriele A1 - Oswald, Sascha A1 - Lunardon, Marcello A1 - Mareš, Vratislav A1 - Marinello, Francesco A1 - Moretto, Sandra A1 - Polo, Matteo A1 - Sartori, Paolo A1 - Schattan, Paul A1 - Rühm, Werner T1 - An alternative incoming correction for cosmic-ray neutron sensing observations using local muon measurement JF - Geophysical research letters N2 - Measuring the variability of incoming neutrons locally would be usefull for the cosmic-ray neutron sensing (CRNS) method. As the measurement of high energy neutrons is not so easy, alternative particles can be considered for such purpose. Among them, muons are particles created from the same cascade of primary cosmic-ray fluxes that generate neutrons at the ground. In addition, they can be easily detected by small and relatively inexpensive detectors. For these reasons they could provide a suitable local alternative to incoming corrections based on remote neutron monitor data. The reported measurements demonstrated that muon detection system can detect incoming cosmic-ray variations locally. Furthermore the precision of this measurement technique is considered adequate for many CRNS applications. KW - CRNS KW - soil-moisture KW - neutrons KW - muons KW - cosmic-rays Y1 - 2022 U6 - https://doi.org/10.1029/2021GL095383 SN - 0094-8276 SN - 1944-8007 VL - 49 IS - 6 PB - American Geophysical Union CY - Washington ER -