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Cosmic-ray neutron sensing (CRNS) is a promising proximal soil sensing technique to estimate soil moisture at intermediate scale and high temporal resolution. However, the signal shows complex and non-unique response to all hydrogen pools near the land surface, providing some challenges for soil moisture estimation in practical applications. Aims of the study were 1) to assess the uncertainty of CRNS as a stand-alone approach to estimate volumetric soil moisture in cropped field 2) to identify the causes of this uncertainty 3) and possible improvements. Two experimental sites in Germany were equipped with a CRNS probe and point-scale soil moisture network. Additional monitoring activities were conducted during the crop growing season to characterize the soil-plant systems. This data is used to identify and quantify the different sources of uncertainty (factors). An uncertainty analysis, based on Monte Carlo approach, is applied to propagate these uncertainties to CRNS soil moisture estimations. In addition, a sensitivity analysis based on the Sobol’ method is performed to identify the most important factors explaining this uncertainty. Results show that CRNS soil moisture compares well to the soil moisture network when these point-scale values are weighted to account for the spatial sensitivity of the signal and other sources of hydrogen (lattice water and organic carbon) are added to the water content. However, the performance decreases when CRNS is considered as a stand-alone method to retrieve the actual (non-weighted) volumetric soil moisture. The support volume (penetration depth and radius) shows also a considerable uncertainty, especially in relatively dry soil moisture conditions. Four of the seven factors analyzed (the vertical soil moisture profile, bulk density, incoming neutron correction and the calibrated parameter N0) were found to play an important role. Among the possible improvements identified, a simple correction factor based on vertical point-scale soil moisture profiles shows to be a promising approach to account for the sensitivity of the CRNS signal to the upper soil layers.
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.
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.