Monitoring Of Soil Water Content Research Articles

Use of Soil Moisture as an Indicator of Climate Change in the SUPer System

Soil moisture can be an important indicator of climate change in humid and semi-arid areas. This indicator can more efficiently propose different public policies related to climate change than just using precipitation and temperature data. Given the above, the objective of this study is to evaluate changes in soil moisture in the state of Pernambuco during the period 1961–2021, using the System of Hydrological Response Units for Pernambuco. In this study, two river basins in the state of Pernambuco that represent the different climatic conditions of the state were chosen. The results show that in the coastal region there is a tendency towards more saturated soils, and in the semi-arid region there is a tendency towards drier soils. With these results, it is possible to conclude that public policy decisions for the economy, environment, and society must consider this vital water balance variable. Leveraging soil moisture and precipitation data makes it possible to differentiate between flood risks and landslide vulnerabilities, particularly in regions characterized by higher levels of rainfall. Monitoring soil water content in humid and semi-arid areas can significantly enhance early warning systems, thereby preventing loss of life and minimizing the socio-economic impacts of such natural events. As such, this study provides a holistic understanding of the relationship between climatic patterns, soil moisture dynamics, and the occurrence of droughts and floods, ultimately contributing to more effective disaster preparedness and response measures in Pernambuco and similar regions.

ERT-based experimental integrated approach for soil hydrological characterization in rainfall-induced shallow landslides prone areas

Shallow landslides triggered by heavy rainfalls are slope instabilities, developed in the most superficial eluvial layers, involving the first 2 m from the ground level. A crucial predisposing factor in shallow landslides occurrence is the soil water content, generally measured trough sensors installed in the first soil layers. However, despite being a very precise approach, this monitoring technique provides for a site-specific dataset. An integrated method to extend the hydrological characterization from site-specific to slope scale is presented, combining geotechnical analyses, field data monitoring, and geophysical investigations, in two experimental test sites located on Italian Apennines. Ten Electrical Resistivity Tomographies (ERT) of the first soil horizons were performed through different array geometries (2D-3D-Time-Lapse), calibrated and interpreted basing on stratigraphic logs, trenches, and monitored soil water content field data. The test sites colluvial covers composition was analyzed and compared to resistivity values to build conceptual hydrogeological models of the deep-water circulation. In addition, two time-lapse (4D) ERT surveys were performed in both test sites simulating very intense precipitations, to determine the resistivity variations at different soil drainage conditions, thus estimating the average bulk permeability. Bulk permeability can be also a useful input parameter for slope stability models, widely employed in engineering practices. This integrated method proved to be very useful for the hydrogeological characterization of the subsoil at slope scale, where it is susceptible to slope instability, improving the knowledge of water circulation, as well as the bulk permeability heterogeneities, which are shallow landslides triggering parameters.

Spatiotemporal variation and controlling factors of dried soil layers in a semi-humid catchment and relevant land use management implications

The formation of dried soil layers (DSLs) is a broad consequence of climate change (i.e., through temperature increases and/or precipitation decreases) and poor land management practices, where these layers exist has severe negative effects (i.e., retardation of vegetation, negative water cycle balance) on the sustainability of restored ecosystems and health of agriculture. Because of the effort and time required, only sporadic in situ measurements on spatial and temporal DSL variation have been reported, particularly at a catchment scale. However, this information is essential for understanding mechanisms associated with evolutionary DSL processes as well as developing effective land management techniques. Accordingly, this study monitored soil water content to a 5 m depth over a 5-year period in a semi-humid catchment on the Loess Plateau while also investigating temporal DSL dynamics and dominant controls. In the semi-humid catchment, DSLs occurred at 90.7 % of all sampling sites. Spatially and temporally averaged DSL formation depth (DSLFD), DSL thickness (DSLT), and DSL soil water content (DSL-SWC) within the 0–5 m profile were 170 cm, 262 cm, and 9.52 %, respectively. Forest and shrubland DSL regimes were drier than those of grassland. These three DSL indices exhibited significant correlation with temperature variables, while only DSLFD variables significantly correlated to precipitation. Land use type was the predominant control of DSL variation at a catchment scale. A spatial DSLT variation comparison under different land use treatment positions determined the feasibility of planting grass at an upslope position and mixed forest/shrubland + grass at mid-slope and downslope positions in the semi-humid catchment, which provides a good case study for afforestation practices at a catchment scale. Findings from this study are intended to aid in catchment soil and water conservation, soil drought mitigation, and sustainable vegetation management practices on the Loess Plateau as well as other water-limited regions worldwide.

Precipitation and plant community-weighted mean traits determine total transpirable soil water in a desert grassland

The desert grassland has low precipitation, high evaporation and a limited soil water supply. Thus the tension between water shortage and sustainable vegetation restoration is increasingly evident. Change in total transpirable soil water (TTSW) reflects the water utilization and extraction capacity of plant communities, which are the basis for maintaining productivity and stability in communities, as well as the key ecological processes alleviating the above tension. Change in TTSW is influenced by precipitation, vegetation and soil. Quantifying the relative contribution of these factors to change in TTSW is key to the sustainable restoration of desert grassland. However, the current understanding of such effects remains limited. This study examined four typical plant communities in the Ningxia desert grassland, monitoring soil water content and evaluating plant community characteristics and diversity over three consecutive years. Using redundancy analysis and hierarchical partitioning methods, the influence of annual precipitation, vegetation (functional and species diversity, community characteristics) and soil physical properties on TTSW variation were investigated. Findings revealed: (1) The factors contributing to TTSW variation were annual precipitation (41 %-68 %,), vegetation (29 %-45 %), and soil physical properties (0 %-6.7 %). Specifically, community-weighted mean traits (CWM) and functional diversity (FD) explained TTSW variation by 24–37 % and 0.25–11 % respectively. In Gramineae communities, the influence of annual precipitation and vegetation on TTSW variation was consistent across soil depths. However, in degraded Sophora alopecuroides + Artemisia scoparia (SA) communities, the influence of vegetation (CWM, FD, community composition and community function) on TTSW variation was more pronounced in shallow soils (45 %) than in deeper soils (33 %), while annual precipitation effects were more substantial in the deep soil layers (66 %) than in the shallow soil layers (42 %). (2) Annual precipitation and CWM were the main biological and abiotic factors affecting TTSW. Interestingly, increased annual precipitation was negatively correlated with TTSW. Similarly, the primary controlling factor, CWM, also showed a significant negative relationship with TTSW. This relationship varied across community types and could be linear or quadratic, highlighting the need to understand the selection effects mediated by CWM in ensuring sustainable vegetation restoration. This research offers insights into the relationship between plant functional diversity and soil water retention function.

Spatial variability and forecast of soil water in the ultra‐deep loess profile across a south–north transect of the Chinese Loess Plateau

AbstractKnowledge of the spatial variability and forecast of soil water in the ultra‐deep (>21 m) loess profile are important for understanding the chemical, physical, and biological processes in the CZ. In this study, we regularly monitor soil water content (SWC) in deep soil profile along regional transect on the Loess Plateau. Descriptive statistical analysis found that the coefficient of variation (CV) of mean SWC in Ansai and Shenmu were 16.036% and 13.606%, respectively, indicating moderate variability. The CV of mean SWC in Yangling, Changwu, and Fuxian were 4.111%, 7.951%, and 6.117%, respectively, showing low variability. Geo‐statistical analysis indicated that mean SWC showed strong spatial dependence. Wavelet analysis showed that the approximative trend of mean SWC in five sampling sites showed an increased trend along depth series. In addition, a good fit line equation (R2 = 0.329) was established by using observed values and ANN‐forecast of three sites (Yangling, Fuxian, and Shenmu). And the RMSE values of five sample sites, respectively, were 1.302, 4.546, 2.662, 6.231, and 4.293. This study fills the gap in research' ultra‐deep (>21 m) soil water changes and forecast, evidence from soil borehole data. At the same time, our research provides valuable information for the vegetation restoration and modelling of deep soil water.

Technical Note: Revisiting the general calibration of cosmic-ray neutron sensors to estimate soil water content

Abstract. Cosmic-ray neutron sensing (CRNS) is becoming increasingly popular for monitoring soil water content (SWC). To retrieve SWC from observed neutron intensities, local measurements of SWC are typically required to calibrate a location-specific parameter, N0, in the corresponding transfer function. In this study, we develop a generalized conversion function that explicitly takes into account the different factors that govern local neutron intensity. Thus, the parameter N0 becomes location independent, i.e. generally applicable. We demonstrate the feasibility of such a “general calibration function” by analysing 75 CRNS sites from four recently published datasets. Given the choice between the two calibration strategies – local or general – users will wonder which one is preferable. To answer this question, we estimated the resulting uncertainty in the SWC by means of error propagation. While the uncertainty in the local calibration depends on both the local reference SWC itself and its error, the uncertainty in the general calibration is mainly governed by the errors in vegetation biomass and soil bulk density. Our results suggest that a local calibration – generally considered best practice – might often not be the best option. In order to support the decision which calibration strategy – local or general – is actually preferable in the user-specific application context, we provide an interactive online tool that assesses the uncertainty in both options (https://cosmic-sense.github.io/local-or-global, last access: 23 February 2024).

Temperature-Corrected Calibration of GS3 and TEROS-12 Soil Water Content Sensors

The continuous monitoring of soil water content is commonly carried out using low-frequency capacitance sensors that require a site-specific calibration to relate sensor readings to apparent dielectric bulk permittivity (Kb) and soil water content (θ). In fine-textured soils, the conversion of Kb to θ is still challenging due to temperature effects on the bound water fraction associated with clay mineral surfaces, which is disregarded in factory calibrations. Here, a multi-point calibration approach accounts for temperature effects on two soils with medium to high clay content. A calibration strategy was developed using repacked soil samples in which the Kb-θ relationship was determined for temperature (T) steps from 10 to 40 °C. This approach was tested using the GS3 and TEROS-12 sensors (METER Group, Inc. Pullman, WA, USA; formerly Decagon Devices). Kb is influenced by T in both soils with contrasting T-Kb relationships. The measured data were fitted using a linear function θ = aKb + b with temperature-dependent coefficients a and b. The slope, a(T), and intercept, b(T), of the loam soil were different from the ones of the clay soil. The consideration of a temperature correction resulted in low RMSE values, ranging from 0.007 to 0.033 cm3 cm−3, which were lower than the RMSE values obtained from factory calibration (0.046 to 0.11 cm3 cm−3). However, each experiment was replicated only twice using two different sensors. Sensor-to-sensor variability effects were thus ignored in this study and will be systematically investigated in a future study. Finally, the applicability of the proposed calibration method was tested at two experimental sites. The spatial-average θ from a network of GS3 sensors based on the new calibration fairly agreed with the independent area-wide θ from the Cosmic Ray Neutron Sensor (CRNS). This study provided a temperature-corrected calibration to increase the accuracy of commercial sensors, especially under dry conditions, at two experimental sites.

Regional soil water content monitoring based on time-frequency spectrogram of low-frequency swept acoustic signal

Acoustic waves offer a non-destructive, safe, and cost-effective means of monitoring the environment, with a potential application in soil water content monitoring. However, extracting soil water information from acoustic signals is still challenging. To tackle this issue, we have developed a low-frequency swept acoustic signal detection device and system. We conducted soil penetration testing using low-frequency swept acoustic signals. The swept-frequency acoustic signals passing through the soil were transformed into time–frequency spectrogram. Using the Swin-Transformer model, we established a regression model between the time–frequency spectrogram of the swept frequencies and the soil water content. Predictions were made both on a laboratory test dataset and through field trials using the calibrated model. The results indicate that the RMSE, MAE, and R2 values between the observed and the model's outputs of water content (%) for the test laboratory dataset are 0.191, 0.081, and 0.999, respectively, using the Swin-Transformer model. In the case of the field trials, the RMSE, MAE, and R2 values between the predicted and observed values are 6.715 %, 1.829 %, and 0.711, respectively. These studies demonstrate that this method is highly effective in predicting soil water content, with the best results achieved at a resolution of 20 PPI (Pixels Per Inch) and within the frequency range of 260–360 Hz. It provides an efficient approach for acoustic soil water content detection, effectively resolves the difficulty in building models caused by the single-parameter limitation in traditional acoustic model.

Responsive characteristics of soil water regimes to rainfall events in a boreal larch forest in China: Dynamic processes and decoupling effects

Investigating the soil water dynamics in relation to rainfall events is of great importance for enhancing our understanding of eco-hydrological processes and improving soil hydrological models. However, the lack of long-term, in-situ observational data and the neglect of soil moisture decoupling have hindered the study of dynamic responses in soil water regimes to rainfall events in China’s boreal forests with permafrost. This study utilized soil water content monitoring and rainfall observational data from the Xing’an larch (Larix gmenilii) forest in Northeast China during the growing seasons of 2015–2021 to evaluate the dynamic response of soil water regimes to rainfall events. The results show a significant increase in the decoupling strength between soil water and rainfall with increasing soil depth. The thresholds of rainfall amount (RA), duration (RD), and intensity (RI) for triggering soil wetting events also increased. Soil water dynamic processes exhibited inconsistent and complex patterns in response to the hierarchical effects of RA, RD, and RI along the vertical soil profile. However, the dominant controls on soil water dynamic responses to RA, RD, and RI varied depending on specific response metrics, such as the accumulated soil water content increments (ASMI), duration time (DT), and the mean slope of the soil wetting curve (Sm). Interestingly, the dominant factor for affecting response time (RT) changed from RI to RD as soil depths increased. Importantly, the decoupling of soil moisture led to a substantial reduction in the responsiveness of these metrics of subsurface soil water to rainfall, and the role of the decoupling effect diminished with increasing soil depths. These findings highlight the complexity of the dynamic response of soil water to rainfall events and provide new insights into the relationship between soil water dynamics and rainfall features through the introduction of the decoupling of soil moisture in boreal forests.

Understanding hydrologic controls of sloping soil response to precipitation through machine learning analysis applied to synthetic data

Abstract. Soil and underground conditions prior to the initiation of rainfall events control the hydrological processes that occur in slopes, affecting the water exchange through their boundaries. The present study aims at identifying suitable variables to be monitored to predict the response of sloping soil to precipitation. The case of a pyroclastic coarse-grained soil mantle overlaying a karstic bedrock in the southern Apennines (Italy) is described. Field monitoring of stream level recordings, meteorological variables, and soil water content and suction has been carried out for a few years. To enrich the field dataset, a synthetic series of 1000 years has been generated with a physically based model coupled to a stochastic rainfall model. Machine learning techniques have been used to unwrap the non-linear cause–effect relationships linking the variables. The k-means clustering technique has been used for the identification of seasonally recurrent slope conditions in terms of soil moisture and groundwater level, and the random forest technique has been used to assess how the conditions at the onset of rainfall controlled the attitude of the soil mantle to retain much of the infiltrating rainwater. The results show that the response in terms of the fraction of rainwater remaining stored in the soil mantle at the end of rainfall events is controlled by soil moisture and groundwater level prior to the rainfall initiation, giving evidence of the activation of effective drainage processes.

Agronomic Responses of Grapevines to an Irrigation Scheduling Approach Based on Continuous Monitoring of Soil Water Content

The efficient management of irrigation water can affect crop profitability quite significantly. The application of precision irrigation based on soil monitoring can help manage water resources. In viticulture, the irrigation technique is thought to strongly influence grape ripening and the final grape composition. In this study, an irrigation decision support system was compared to a surface drip irrigation system in a commercial vineyard located in Andrea (Southern Italy) planted with Vitis vinifera cv. Montepulciano. We aimed to investigate the ability of the DSS to save water while maintaining an acceptable yield and quality of the grapes. To allow for the comparison, eco-physiological as well as yield parameters were measured during the irrigation periods in both irrigation systems over two years (2019 and 2020). The results indicate that the vines grown using the DSS treatment were less stressed compared to the plants grown using farm irrigation in both years. The yield attributes showed slight or no significant differences between the treatments. The quality results showed no significant differences between the treatments in both years. Our results indicate that with savings of 10% and 17% of the irrigation water in the first and second year, respectively, the DSS was able to maintain good yield and quality levels as compared to the farm irrigation system. These two-year results provide a promising implementation of its use in precision irrigation.

Effects of vegetation on the spatiotemporal distribution of soil water content in re-vegetated slopes using temporal stability analysis

The temporal stability of soil water is a critical state variable affecting hydrological process and structural stability of slopes. In this study, the effect of vegetation on the spatiotemporal characteristics of soil water content (SWC) was assessed via temporal stability analysis. Three types of re-vegetated slopes (grass, shrub, and grass-shrub) were constructed to explore the spatiotemporal distribution of the SWC at depths of 0–100 cm. Then, the effects of different vegetation covers on the SWC were explored. The coefficient of variation, the Spearman rank correlation coefficient, the mean relative difference, and the index of temporal stability were utilized to demonstrate the temporal stability of the SWC. Finally, a method of estimating the soil water status of re-vegetated slopes based on representative locations was developed. The results revealed that soil water dynamics were considerably governed by vegetation and rainfall. The vegetation cover considerably reduced the similarity of the spatial distribution pattern of the soil water, which tended to be more stable under grass cover. The weight-based prediction method developed in this study can more accurately estimate the overall soil water state of re-vegetated slopes. It provides a theoretical basis for the reasonable layout of SWC monitoring points and guidance for the ecological restoration of engineering slopes.

Development of a sandy soil water content monitoring system for greenhouses using Internet of Things

Precision water management is crucial for greenhouse agriculture to maximize crop yields in sandy soil. Due to the low water holding capacity, it is necessary to monitor the water movement in different depths of sandy soil to ensure effective irrigation. Therefore, this study aimed to develop a data acquisition (DAQ) system for sandy soil water content monitoring in an experimental soil bin inside a greenhouse, utilizing the capabilities of the Internet of Things (IoT). A drip irrigation system was implemented, arranged in four pipelines, spaced 60 cm apart, with drippers placed at 30 cm intervals along the pipeline. The overall system was installed in a sandy soil testing bin. A DAQ system was comprised of three basic units: sensor interfacing and circuit board, programming and sensor data acquisition, and data storage and monitoring. A microprocessor was used by interfacing a set of soil water content sensors, ambient temperature, and humidity sensors. The water content sensors were placed in the soil at different depths of 10, 20, 30, 40, and 50 cm, respectively. A microcontroller was used to collect and send the sensor data to monitor and store in memory. During the test, the maximum and minimum average of soil water content, ambient temperature, and humidity values were observed at 33.91±2.5 to 26.95±1.3%, 21.39±2.1 to 42.84±1.7°C, and 48.73±2.3 to 99.90±0.3%, respectively. The water content percentages were varied at different depths of sandy soil due to low water holding capacity. The developed automatic DAQ system would help with remote monitoring and control of greenhouse irrigation, considering the different crop characteristics and environmental conditions.

Estimation of root zone soil moisture at point scale based on soil water measurements from cosmic-ray neutron sensing in a karst catchment

The cosmic-ray neutron sensing (CRNS) is an emerging method for continuously monitoring soil water content (SWC) at an intermediate scale. However, when multiple hydrologic units are present within its footprint, the potential application of CRNS in water resources management is restricted. Here we propose a new strategy to predict point-scale SWC in root zone established on CRNS-based soil moisture and improved relative difference method. A total of 768 days of soil moisture data were collected by CRNS at the intermediate scale and EC-TM sensors at the point scale in a karst catchment. The original and improved mean relative difference methods predicted point-scale SWCs within and without the effective measuring depth, respectively. The mean effective measuring depth was 13.16 cm, ranging from 10.13 to 19.23 cm. Both land use type and soil structure played essential roles in regulating point scale SWC in the soil profile. Point-scale SWC in root zone can be predicted accurately (P < 0.001) based on SWC data derived from the CRNS system. The prediction accuracy of point scale SWC can be improved by increasing the averaging time of the soil moisture values. Our results demonstrated that the proposed strategy was reliable for CRNS to predict SWC beyond the effective measurement depth. This study provides a good perspective for effectively managing of water resources in areas with complex hydrological processes.

Effects of Biomass and Soil Water Content Distribution on Cosmic Ray Neutron Probe Measurement

Cosmic ray neutron probes (CRNPs) provide continuous monitoring of average near-surface soil water content (SWC) on hectometer scales. However, the performance of CRNPs on surfaces of highly heterogeneous vegetation and SWC remains uncertain. This study evaluated three vegetation calibration methods with the correction of vegetation distribution developed for a CRNP on the Loess Plateau of China. Three plots with different vegetation distributions were selected and equipped with CRNPs and SWC sensors, and their biomass as well as distribution were measured by an unmanned aerial vehicle (UAV) equipped with a RedEdge multispectral camera. We found that the parameter N0, which is neutron flux in dry soil, was best represented by the biomass at average growth conditions of the monitoring period, yielding the lowest RMSE (0.068). The Veg–N0 vegetation calibration method reduced the RMSE the most between the CRNP SWC and the Kriging-weighted SWC, and the correction of the spatial distribution of the vegetation further reduced the RMSE. The cooperation between the CRNP and the UAV could obtain the regional averaged SWC accurately. This study makes up for the lack of vegetation calibration for the CRNP on the Loess Plateau, which should help develop sustainable vegetation management and ecohydrological management strategies on the Loess Plateau, so as to protect water security in the region.

Monitoring of soil water content using spherical smart aggregates based on electromechanical impedance (EMI) technique

Real-time monitoring of soil water content is of great significance to prevent many engineering accidents, such as landslide, water seepage of foundation dam, and reduction of foundation bearing capacity. Electromechanical impedance (EMI) technique based smart aggregates (SAs) have showed excellent monitoring capability in various engineering applications. However, there are limited reports on the application of EMI based SAs in the monitoring of the soil water content. In this paper, the EMI based spherical SAs (SSAs) were investigated in the monitoring of soil water content. Firstly, the SSAs were designed and fabricated using the monolithic concrete encapsulation method, and their stable performance in air were evaluated, and confirmed by testing and analyzing the impedance spectrum. Then, the fabricated SSAs were immersed in the water environment for 28 d to ensure the stability under the working conditions, such as the soil with high water content and the hydration process of early-age concrete. Secondly, the monitoring experiments of soil water content were carried out based on the SSAs and the traditional SAs using the EMI technique. The measured impedance signatures under different water contents in soil were quantified by three types of statistical indexes, including root mean square deviation, mean absolute percentage deviation, and correlation coefficient deviation. The experimental results show that compared with the traditional SAs, the SSAs are more sensitive and stable to monitor the soil water content. Finally, the effect of temperature on the performance of SSA based on the EMI technique were conducted experimentally. The results demonstrate that temperature has influence on the monitoring results of the SSAs based on the EMI technique.

Monitoring of Thick Vadose Zone Water Dynamics Under Irrigation Using a 48 m Deep Caisson at the Luancheng Critical Zone Observatory

AbstractUnderstanding soil water dynamics in the deep vadose zone (DVZ, below the root zone) of irrigated farmland is critical for estimating groundwater recharge and assessing the impacts of agricultural activities on groundwater quality. However, the state and movement of soil water in the DVZ remain poorly understood owing to the lack of in‐situ observations. Here, based on a 48‐m deep caisson (Critical Zone Observatory, CZO) established in a typical irrigated farmland in the piedmont region of the North China Plain, we extended the monitoring of soil water content and matric potential across the entire vadose zone profile. We found that a high soil water content and matric potential (close to or slightly higher than the field capacity) was maintained in the DVZ of typical irrigated cropland. The response of soil water in the DVZ to the water inputs at the ground surface was predominantly sequentially delayed with an increase in depth in the DVZ. As a result, the wetting front propagated into the soil layer to a depth of 38 m within 228 days. Moreover, high soil moisture contents and matric potentials in the DVZ resulted in a dramatic but explicable difference between the estimated average pore water velocity (0.73 m year−1) and the wetting front propagation rate (0.16 m day−1). This study demonstrates the unreported rapid response of soil water in the DVZ to water input at the ground surface under intensively irrigated cropland and could promote a better understanding of groundwater recharge processes with a thick unsaturated zone.

A pipeline robot system for monitoring soil water content distribution

Monitoring soil water content (SWC) distribution is crucial for better understanding soil water dynamics and accurate hydrological modeling. In this study, a pipeline robot system for monitoring SWC distribution is designed to improve the current technical problems of insufficient measurement distance and low positioning accuracy of the existing SWC monitoring platform. The system consists of a pipeline robot, a fixed base station and a PVC pipe. The robot could fit into a 55-mm-diameter PVC pipe to measure SWC distribution horizontally, vertically, or at any angle to the horizontal direction. The base station serves as a data collector and also supplies power and distance information for recharging and positioning the robot, respectively. A series of tests for evaluating the performance of the pipeline robot system were conducted. Field experiments were also conducted to monitor soil water infiltration and horizontal distribution of SWC of crop root zone at three plots. The results showed that the minimum radius of volume of sensitivity of the dielectric sensor is about 2.25 cm. The robot for mobile measurement performed stable both in horizontal and vertical directions and the response of the dielectric sensor output to different dielectric materials is significant. The accumulated error of the encoder is effectively decreased from 4.3% to 1.2% by correcting the position error using the laser ranging sensor. The measured SWCs during the calibration experiment were highly correlated with those obtained by the drying method (R2 = 0.990 and RMSE = 0.0181 cm3 cm−3). The results of the field experiments demonstrated that the system is feasible for monitoring soil water infiltration in the vertical direction and SWC distributions of crop root zone horizontally at the three plots, indicating that the potential application of the developed system for long-term monitoring of SWC distribution under field conditions in the near future.

Model-based simulation-optimization of irrigation scheduling – A field evaluation with processing tomatoes

Due to climate change, increased regulation of water resources and competition from other beneficial uses, the agricultural sector is under pressure to use water more efficiently. This paper reports the field evaluation of two model-based simulation-optimization approaches for irrigation scheduling: deterministic optimization and stochastic optimization. The field experiments were conducted at the University of California Davis research farm with a processing tomato crop. The crop growth simulation model used in the study was DSSAT-CROPGRO processing tomato. In order to mitigate the impact of weather forecasts inaccuracies, irrigation schedules were updated every 5 to 10 days, depending on operational constraints. These updates were performed via custom graphical user interfaces that enabled the user to visualize the expected outcomes of various decisions or scenarios before choosing which irrigation schedule to implement. An irrigation treatment based on manual monitoring of soil water content with a field-calibrated neutron probe served as benchmark. The model-based treatments achieved yields and water use efficiencies that were not significantly different from those obtained in the neutron probe-based treatment. These results demonstrate the high potential of model-based simulation-optimization approaches for in-season adaptive irrigation scheduling, especially since neutron probes, which are considered one of the most accurate indirect methods of measuring soil water content, are not commonly used by growers.

Toward Large‐Scale Soil Moisture Monitoring Using Rail‐Based Cosmic Ray Neutron Sensing

AbstractCosmic ray neutron sensing (CRNS) has become a promising method for soil water content (SWC) monitoring. Stationary CRNS offers hectare‐scale average SWC measurements at fixed locations maintenance‐free and continuous in time, while car‐borne CRNS roving can reveal spatial SWC patterns at medium scales, but only on certain survey days. The novel concept of a permanent mobile CRNS system on rails promises to combine the advantages of both methods, while its technical implementation, data processing and interpretation raised a new level of complexity. This study introduced a fully automatic CRNS rail‐borne system as the first of its kind, installed within the locomotive of a cargo train. Data recorded from September 2021 to July 2022 along an ∼9 km railway segment were analyzed, as repeatedly used by the train, supported by local SWC measurements (soil samples and dielectric methods), car‐borne and stationary CRNS. The results revealed consistent spatial SWC patterns and temporary variation along the track at a daily resolution. The observed variability was mostly related to surface features, seasonal dynamics and different responses of the railway segments to wetting and drying periods, while some variations were related to measurement uncertainties. The achieved medium scale of SWC mapping could support large scale hydrological modeling and detection of environmental risks, such as droughts and wildfires. Hence, rail‐borne CRNS has the chance to become a central tool of continuous SWC monitoring for larger scales (≤10‐km), with the additional benefit of providing root‐zone soil moisture, potentially even in sub‐daily resolution.