Soil moisture is an essential climate variable of the Earth system. Understanding its spatiotemporal dynamics is essential for predicting weather patterns and climate variability, monitoring and mitigating the effects and occurrence of droughts and floods, improving irrigation in agricultural areas, and sustainably managing water resources. Here we review in depth how soils can remember information on soil moisture anomalies over time, as embedded in the concept of soil moisture memory (SMM). We explain the mechanisms underlying SMM and explore its external and internal drivers; we also discuss the impacts of SMM on different land surface processes, focusing on soil‐plant‐atmosphere coupling. We explore the spatiotemporal variability, seasonality, locality, and depth‐dependence of SMM and provide insights into both improving its characterization in land surface models and using satellite observations to quantify it. Finally, we offer guidance for further research on SMM.

Cosmic ray neutron (CRN) sensing allows for non-invasive soil moisture measurements at the field scale and relies on the inverse correlation between aboveground measured epithermal neutron intensity (1 eV−100 keV) and environmental water content. The measurement uncertainty follows Poisson statistics and thus increases with decreasing neutron intensity, which corresponds to increasing soil moisture. In order to reduce measurement uncertainty, the neutron count rate is usually aggregated over 12 or 24 h time windows for stationary CRN probes. To obtain accurate soil moisture estimates with mobile CRN rover applications, the aggregation of neutron measurements is also necessary and should consider soil wetness and driving speed. To date, the optimization of spatial aggregation of mobile CRN observations in order to balance measurement accuracy and spatial resolution of soil moisture patterns has not been investigated in detail. In this work, we present and apply an easy-to-use method based on Gaussian error propagation theory for uncertainty quantification of soil moisture measurements obtained with CRN sensing. We used a 3^rd order Taylor expansion for estimating the soil moisture uncertainty from uncertainty in neutron counts and compared the results to a Monte Carlo approach with excellent agreement. Furthermore, we applied our method with selected aggregation times to investigate how CRN rover survey design affects soil moisture estimation uncertainty. We anticipate that the new approach can be used to improve the strategic planning and evaluation of CRN rover surveys based on uncertainty requirements.

Agroecosystem models that simulate crop growth as a function of weather conditions and soil characteristics are among the most promising tools for improving crop yield and achieving more sustainable agricultural production systems. This study aims at using spatially distributed crop growth simulations to investigate how field‐scale patterns in soil properties obtained using geophysical mapping affect the spatial variability of soil water content dynamics and growth of crops at the square kilometer scale. For this, a geophysics‐based soil map was intersected with land use information. Soil hydraulic parameters were calculated using pedotransfer functions. Simulations of soil water content dynamics performed with the agroecosystem model AgroC were compared with soil water content measured at two locations, resulting in RMSE of 0.032 and of 0.056 cm ³ cm ⁻³ , respectively. The AgroC model was then used to simulate the growth of sugar beet ( Beta vulgari s L.), silage maize ( Zea mays L.), potato ( Solanum tuberosum L.), winter wheat ( Triticum aestivum L.), winter barley ( Hordeum vulgare L.), and winter rapeseed ( Brassica napus L.) in the 1‐ by 1‐km study area. It was found that the simulated leaf area index (LAI) was affected by the magnitude of simulated water stress, which was a function of both the crop type and soil characteristics. Simulated LAI was generally consistent with the observed LAI calculated from normalized difference vegetation index (LAI _NDVI ) obtained from RapidEye satellite data. Finally, maps of simulated agricultural yield were produced for four crops, and it was found that simulated yield matched well with actual harvest data and literature values. Therefore, it was concluded that the information obtained from geophysics‐based soil mapping was valuable for practical agricultural applications.

Accurate soil moisture (SM) monitoring is key in irrigation as it can greatly improve water use efficiency. Recently, cosmic-ray neutron sensors (CRNSs) have been recognized as a promising tool in SM monitoring due to their large footprint of several hectares. CRNSs also have great potential for irrigation applications, but few studies have investigated whether irrigation monitoring with CRNSs is feasible, especially for irrigated fields with a size smaller than the CRNS footprint. Therefore, the aim of this study is to use Monte Carlo simulations to investigate the feasibility of monitoring irrigation with CRNSs. This was achieved by simulating irrigation scenarios with different field dimensions (from 0.5 to 8 ha) and SM variations between 0.05 and 0.50 cm³ cm⁻³. Moreover, the energy-dependent response functions of eight moderators with different high-density polyethylene (HDPE) thickness or additional gadolinium thermal shielding were investigated. It was found that a considerable part of the neutrons that contribute to the CRNS footprint can originate outside an irrigated field, which is a challenge for irrigation monitoring with CRNSs. The use of thin HDPE moderators (e.g. 5 mm) generally resulted in a smaller footprint and thus stronger contributions from the irrigated area. However, a thicker 25 mm HDPE moderator with gadolinium shielding improved SM monitoring in irrigated fields due to a higher sensitivity of neutron counts with changing SM. This moderator and shielding set-up provided the highest chance of detecting irrigation events, especially when the initial SM was relatively low. However, variations in SM outside a 0.5 or 1 ha irrigated field (e.g. due to irrigation of neighbouring fields) can affect the count rate more than SM variations due to irrigation. This suggests the importance of retrieving SM data from the surrounding of a target field to obtain more meaningful information for supporting irrigation management, especially for small irrigated fields.

For the first time, we combine depth‐specific soil information obtained from the quantitative inversion of ground‐based multicoil electromagnetic induction data with the airborne hyperspectral vegetation mapping of 1 × 1‐m pixels including Sun‐induced fluorescence (F) to understand how subsurface structures drive above‐surface plant performance. Hyperspectral data were processed to quantitative F and selected biophysical canopy maps, which are proxies for actual photosynthetic rates. These maps showed within‐field spatial patterns, which were attributed to paleo‐river channels buried at around 1‐m depth. The soil structures at specific depths were identified by quantitative electromagnetic induction data inversions and confirmed by soil samples. Whereas the upper plowing layer showed minor correlation to the plant data, the deeper subsoil carrying vital plant resources correlated substantially. Linking depth‐specific soil information with plant performance data may greatly improve our understanding and the modeling of soil‐vegetation‐atmosphere exchange processes.