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Core IdeasHigh‐resolution soil texture maps can improve efficiencies in precision irrigation.Noncontact EMI methods can generate high‐resolution maps quickly and at low cost.Mapping with EMI requires robust correlations between soil ECaand soil texture.Accuracy of high‐resolution maps may be reduced by nontarget soil characteristics.Neural networks can predict soil properties accurately by combining measurements. High‐resolution soil maps are needed to guide precision irrigation, fertilization, and crop management decisions. A mobile, noncontact device can be used to map apparent electromagnetic conductivity (ECa) with detailed spatial resolution (<10 m). Maps of other characteristics can be generated by determining the correlation between the measured ECaand physical samples. Soil mapping services are now offered by commercial vendors and are used to estimate properties that are relevant to soil management, including nitrate and micronutrients, soil pH, texture, and water holding capacity. In this study, ECamaps were compared with 390 colocated physical samples, distributed over three cultivated fields comprising a total area of 140 ha. Principal component analysis and linear regression were used to identify factors that confounded ECameasurement and reduced map fidelity. When correlation was conducted in each field independently, low variability in soil texture reduced the accuracy of predicting texture from ECa. To compensate, a combination of four properties was used to train a neural network model, which yielded an improved prediction. Using such methods to establish regionally specific models for ECareadings could enable robust mapping of soil characteristics via rapid and low‐cost electromagnetic measurements. The results confirm that ECacan be a used to map texture at field scales and that additional quality controls can augment the predictive power of ECa.

Empirical functions are widely used in hydrological, agricultural, and Earth system models to parameterize plant water uptake. We infer soil water potentials at which uptake is downregulated from its well‐watered rate and at which uptake ceases, in biomes with <60% woody vegetation at 36‐km grid resolution. We estimate thresholds through Bayesian inference using a stochastic soil water balance framework to construct theoretical soil moisture probability distributions consistent with empirical distributions derived from satellite soil moisture observations. The global median Nash–Sutcliffe efficiency between empirical soil moisture distributions and theoretical distributions using reference constants, inferred median parameters per biome, and spatially variable inferred parameters are 0.38, 0.59, and 0.8, respectively. Spatially variable thresholds capture location‐specific vegetation and climate characteristics and can be connected to biome‐level water uptake strategies. Results demonstrate that satellite soil moisture probability distributions encode information, valuable to understanding biome‐level ecohydrological adaptation and resistance to climate variability. Vegetation regulates a large fraction of the terrestrial water and carbon cycles as it adapts and responds to changing environmental conditions such as soil moisture availability, yet our ability to characterize diversity in vegetation soil water‐use behavior at large scales is limited. In this study, we analyze satellite observations to estimate thresholds that are commonly used to approximate when vegetation extracts water from the soil. We show that the newly found values are more consistent with global patterns of soil moisture compared to constants found in the literature. Spatially variable plant water uptake thresholds reflect land cover and climate characteristics and can be connected to water‐use strategies in biomes not dominated by trees. We estimate, based on satellite soil moisture, soil water potentials when water uptake is downregulated and when water uptake ceasesInferred thresholds capture vegetation and climate characteristics and reflect water uptake strategies in biomes with <60% woody vegetation

Most parameterizations used in Large Eddy Simulations of the atmospheric boundary layer are based on the assumption that subgrid‐scale fluxes are aligned against spatial gradients of transported quantities (down‐gradient closures). Based on field experiments, we determine the distribution and most probable relative orientations of the subgrid‐scale (SGS) heat flux relative to parameterizations based on the temperature gradient. We show that, under neutral and unstable atmospheric stability, the SGS heat flux most likely lies within the geometric span of the so called mixed tensor eddy diffusivity model.