Abstract. 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.

Recent advances in wireless sensor technology allow monitoring of soil moisture dynamics with high temporal resolution at varying spatial scales. The objectives of this study were to: (i) develop an efficient strategy for monitoring soil moisture dynamics at the hillslope scale using a wireless sensor network; and (ii) characterize spatial patterns of soil moisture and infer hydrological processes controlling the dynamics of such patterns, using a method of analysis that allows the identification of the relevant hydrological dynamics within large data sets. We combined soil hydrological and pedological expertise with geophysical measurements and methods from digital soil mapping for designing the monitoring setup for a grassland hillslope in the Schäfertal catchment, central Germany. Hypothesizing a wet and a dry soil moisture state to be characteristic of the spatial pattern of soil moisture, we described the spatial and temporal evolution of such patterns using a method of analysis based on the Spearman rank correlation coefficient. We described the persistence and switching mechanisms of the two characteristic states, inferring the local properties that control the observed spatial patterns and the hydrological processes driving the transitions. The spatial organization of soil moisture appears to be controlled by different processes in different soil horizons, and the topsoil's moisture does not mirror processes that take place within the soil profile. The results will help to improve conceptual understanding for hydrological model studies at similar or smaller scales and to transfer observation concepts and process understanding to larger or less instrumented areas.

Abstract. Electromagnetic induction (EMI) measurements are widely used for soil mapping, as they allow fast and relatively low-cost surveys of soil apparent electrical conductivity (ECa). Although the use of non-invasive EMI for imaging spatial soil properties is very attractive, the dependence of ECa on several factors challenges any interpretation with respect to individual soil properties or states such as soil moisture (θ ). The major aim of this study was to further investigate the potential of repeated EMI measurements to map θ , with particular focus on the temporal variability of the spatial patterns of ECa and θ . To this end, we compared repeated EMI measurements with high-resolution θ data from a wireless soil moisture and soil temperature monitoring network for an extensively managed hillslope area for which soil properties and θ dynamics are known. For the investigated site, (i) ECa showed small temporal variations whereas θ varied from very dry to almost saturation, (ii) temporal changes of the spatial pattern of ECa differed from those of the spatial pattern of θ , and (iii) the ECa-θ relationship varied with time. Results suggest that (i) depending upon site characteristics, stable soil properties can be the major control of ECa measured with EMI, and (ii) for soils with low clay content, the influence of θ on ECa may be confounded by changes of the electrical conductivity of the soil solution. Further, this study discusses the complex interplay between factors controlling ECa and θ , and the use of EMI-based ECa data with respect to hydrological applications.

Abstract. In the last years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among soil hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters 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 neutrons and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed 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 approach is extensively tested with two calibration and four time series datasets from a variety of sites and conditions. In all cases, the revised averaging method robustly improved the performance of the CRNS product and even helped to reveal otherwise hidden hydrological processes. The presented approach increases the overall accuracy of CRNS products and will have impact on all their applications in agriculture, hydrology, and modeling.

Successful adoption of precision viticulture at the farm level depends on the appreciation of vineyard spatial variability. Knowing the spatial variability of soil properties is a challenge, often very expensive and labor intensive. An alternative approach could be the combined utilization of proximal and remote sensors. This study combined proximal (Geonics EM38‐MK2) and remote (normalized difference vegetation index, NDVI) sensing aimed at mapping homogeneous zones (HZs) of two 3.5‐ha vineyards in the Chianti wine district (Italy). Two HZs in each vineyard were obtained by a k‐means clustering of the first two factors of the principal component analysis performed on four maps: (i) apparent electrical conductivity, obtained by EM38‐MK2 at 0 to 75 cm (ECa1) and (ii) 0 to150 cm (ECa2); (iii) topographic wetness index (TWI), calculated from a digital elevation model; and (iv) NDVI extrapolated by multispectral airborne images. Only ECa1 and ECa2 were correlated with some physical (silt and gravel content) and hydrologic (available water capacity) features of the soils. These two variables could also better discriminate the two HZs with respect to NDVI and TWI. The grapes (Vitis vinifera L.) of the selected HZs were separately harvested and vinified to test the differences in the wine quality. Significant differences emerged between the wines produced from the two HZs, especially in terms of color intensity, dry extract, and anthocyanin content. A wine tasting after 6‐mo aging of the wines confirmed the differences between the wines produced in the two zones, especially in terms of color, structure, and total score.