Soil Moisture Monitoring Research Articles

Quality control and improvement of GNSS-IR soil moisture robust inversion model

Global navigation satellite system (GNSS) interferometric reflectometry (GNSS-IR) technology can realize continuous and dynamic monitoring of soil moisture at areas. The GNSS signal is highly susceptible to the influence of external factors, causing anomalous terms in the process of characteristic parameters extraction. In this study, an integrated outlier detection method is proposed. This method first detects outlier based on Inter-Quartile Range, Grubbs test and Hampel filter outlier detection methods for the characteristic parameters data, and derives the outlier location by complementing the detection results with each other and corrected by rainfall data. Then, four kinds of outlier repair methods, namely, moving average, locally weighted primary linear regression, locally weighted quadratic linear regression, and robust locally weighted quadratic linear regression, were used to repair and filter out the optimal data according to the location of the outlier. Finally, three machine learning methods, namely, Bagging Tree, Support Vector Machine (SVM), and Gaussian Process Regression (GPR), were combined to build a soil water content inversion model and evaluate the accuracy. The results show that the proposed comprehensive outlier detection method in this study can accurately detect the location of outlier. The correlation between the characteristic parameters and in-situ soil moisture can be effectively improved after remediation treatment. The outlier repair helped to improve the inversion accuracy of the soil moisture inversion model, and the R2 of the soil moisture inversion model increased by 13.21 % to 27.08 % (mean 18.25 %), the RMSE decreased by 12.97 % to 23.61 % (mean 18.16 %). The comprehensive outlier detection and repair method proposed in this study can provide reference for the quality control of input data before the establishment of GNSS-IR model, and effectively improve the inversion accuracy of GNSS-IR soil moisture inversion model.

Heated fibre optics to monitor soil moisture under successive saturation–drying cycles: An experimental approach

AbstractIn recent decades, distributed temperature sensing (DTS) has emerged as a robust technology for environmental applications, enabling high‐resolution temperature measurements along fibre optic cables (FOCs). The actively heated fibre optic (AHFO) method is employed to monitor soil moisture (, m3 m−3), wherein the soil temperature subsequent to the application of a heat pulse is measured by a DTS (AHFO‐DTS approach). Despite significant improvements in the application of AHFO‐DTS under controlled and natural conditions, the thermal behaviour of soil during multiple saturation–natural drying cycles has been insufficiently evaluated. This study aimed to address this gap by constructing an experimental horizontal soil profile in the laboratory for the application of the AHFO‐DTS method during two successive saturation–drainage–evaporation (SDE) cycles. Three heating strategies were applied to a metallic alloy in contact with a FOC, and calibration models were used to correlate with the thermal conductivity (), cumulative temperature increase (), and maximum temperature increase (). The results indicated that during the second SDE cycle, the highest errors in estimates were observed with the low power‐short heat pulse, whereas the application of the low power‐long duration and high power‐short duration pulses improved the accuracy of calculations. Additionally, errors in estimates escalated under wetter conditions, attributed to a shift in soil heat transfer capacity from the first to the second SDE cycle for > 0.10 m3 m−3. This behaviour was ascribed to thermal hysteresis, arising from the contact resistance of the FOC and the alloy with the surrounding soil. Furthermore, the method exhibited the least sensitivity to this effect and yielded reliable estimates, thus its adoption is recommended. Moreover, the use of the low power‐long duration heating strategy is suggested as it promotes a trade‐off between energy saving and accurate estimates. We concluded that assessing soil thermal response under multiple SDE cycles enhances the comprehension of the AHFO‐DTS method. Overall, our findings provide insights into enhancing the applicability of this approach under field conditions, particularly following irrigation schedules and natural rainfall events.

Effects of Seasonal Freezing and Thawing on Soil Moisture and Salinity in the Farmland Shelterbelt System in the Hetao Irrigation District

Water resources are scarce, and secondary soil salinization is severe in the Hetao Irrigation District. Farmland shelterbelt systems (FSS) play a critical role in regulating soil water and salt dynamics within the irrigation district. However, the understanding of soil water and salt migration within FSS during the freeze–thaw period remains unclear due to the complex and multifaceted interactions between water and salt. This study focused on a typical FSS and conducted comprehensive monitoring of soil moisture, salinity, temperature, and meteorological parameters during the freeze–thaw period. The results revealed consistent trends in air temperature and soil temperature overall. Soil freezing durations exceeded thawing durations, and both decreased with an increasing soil depth. At the three critical freeze–thaw nodes, the soil moisture content at a 0–20 cm depth was significantly lower than at a 40–100 cm depth (p < 0.05). The soil water content increased with time and depth at varying distances from the shelterbelt, with an average increase of 7.63% after freezing and thawing. The surface water content at the forest edge (0.3H, 4H) was lower than inside the farmland (1H, 2H, 3H). Soil salt accumulation occurred during both freezing stable periods and melting–thawing periods in the 0–100 cm soil layer near the forest edge (0.3H, 4H), with the highest soil salinity reaching 0.62 g·kg−1. After the freeze–thaw period, the soil salt content in each layer increased by 11.41–47.26% compared to before the freeze–thaw period. Salt accumulation in farmland soil near the shelterbelt was stronger than in the far shelterbelt. The multivariate statistical model demonstrated goodness of fit for soil water and salt as 0.94 and 0.72, respectively, while the BP neural network model showed goodness of fit for soil water and salt as 0.82 and 0.85, respectively. Our results provide an efficient theoretical basis for FSS construction and agricultural water management practices.

Limited sensitivity of permafrost soils to heavy rainfall across Svalbard ecosystems

Together with warming air temperatures, Arctic ecosystems are expected to experience increases in heavy rainfall events. Recent studies report accelerated degradation of permafrost under heavy rainfall, which could put significant amounts of soil carbon and infrastructure at risk. However, controlled experimental evidence of rainfall effects on permafrost thaw is scarce. We experimentally tested the impact and legacy effect of heavy rainfall events in early and late summer for five sites varying in topography and soil type on the High Arctic archipelago of Svalbard. We found that effects of heavy rainfall on soil thermal regimes are small and limited to one season. Thaw rates increased under heavy rainfall in a loess terrace site, but not in polygonal tundra soils with higher organic matter content and water tables. End-of-season active layer thickness was not affected. Rainfall application did not affect soil temperature trends, which appeared driven by timing of snowmelt and organic layer thickness, particularly during early summer. Late summer rainfall was associated with slower freeze-up and colder soil temperatures the following winter. This implies that rainfall impacts on Svalbard permafrost are limited, locally variable and of short duration. Our findings diverge from earlier reports of sustained increases in permafrost thaw following extreme rainfall, but are consistent with observations that maritime permafrost regions such as Svalbard show lower rainfall sensitivity than continental regions. Based on our experiment, no substantial in-situ effects of heavy rainfall are anticipated for thawing of permafrost on Svalbard under future warming. However, further work is needed to quantify permafrost response to local redistribution of active layer flow under natural rainfall extremes. In addition, replication of experiments across variable Arctic regions as well as long-term monitoring of active layers, soil moisture and local climate will be essential to develop a panarctic perspective on rainfall sensitivity of permafrost.

Development of IoT based intelligent irrigation system using particle swarm optimization and XGBoost techniques

A crop needs regular watering throughout its life to grow well. Irrigation improves food growth. Machines irrigate plants. The dry Sahel, which gets a lot of rain during the summer season but is dry in winter, needs irrigation. When it doesn't rain enough, crops need watering. By constantly monitoring soil moisture, humidity, temperature, and pH, precision agriculture reduces water use and increases crop output. Precision gardening uses less water. In many wealthy nations, efficient farming requires the internet of things (IoT). Particle swarm optimization (PSO) and XGBoost are used in this IoT-based intelligent watering system. Humidity and moisture sensors gather soil data at grass roots. Sensors constantly gather this data. These data are useless for smart watering. PSOselects smart watering data. This reduces central cloud info storage. Then, machine learning methods are trained using soil humidity, moisture, crop, and weather data. These programs can calculate a crop's water requirements. IoT devices control irrigation system water flow and results in saving fresh water. XGBoost algorithm is saving water from 23% to 27% for different crops.

Calculating a minimum overlap period for successful intercalibration of soil moisture sensors

AbstractLong‐term in situ soil moisture monitoring inevitably requires sensors to be replaced. Ensuing discontinuities in the data record can be mitigated by intercalibration, however it is unclear how long the existing sensor needs to remain alongside the newly installed before there is enough overlapping data to generate a robust intercalibration. We used 154 pairs of established and newly installed sensors within the Marena, Oklahoma, In Situ Sensor Testbed to determine if there is a minimum overlap time that should be considered when planning upcoming replacements. Hourly observations of the existing sensor were linearly calibrated to those of the newly installed sensor with coefficients determined from overlap periods incremented by 30 days until a reference period of 2 years was reached. The resulting bias, root‐mean‐square error, and correlation coefficient for sensor pairs indicate that a minimum of 6 to 9 months of overlapping data are required to generate a successful intercalibration. Extending that to a full year before decommissioning the old sensor results in a stable intercalibration with higher confidence.

A Sensor to Monitor Soil Moisture, Salinity, and Temperature Profiles for Wireless Networks

This article presents a wireless in situ sensor designed to continuously monitor profiles of parameters in porous media, such as soil moisture, salinity, and temperature. A review of existing in situ soil sensors reveals that it is the only device capable of measuring the complex permittivity of the medium, allowing for conversions into moisture and salinity that are independent of the instrument. Flow perturbation and invasiveness have also been minimized to maintain good representativeness. Plans include autonomous networks of such sensors, facilitated by the use of the recent radio mode LoRaWAN and cost optimizations for series production. Costs were reduced through electronic simplification and integration, and the use of low-cost modular sensing parts in soil, while still maintaining high measurement quality. A complete set of sensor data recorded during a three-month trial is also presented and interpreted.

IoT Based Crop Monitoring System

The IoT-Based crop monitoring System is a cutting-edge solution designed to enhance agricultural productivity and efficiency by utilizing sensor technology and automation. The core objective is to automate the irrigation process based on real-time soil moisture data. The systememploysanESP8266 module to facilitate the seamless exchange of data between various sensors and Firebase, a real-time cloud database. This interconnected system comprises two primary fields, each equipped with soil and soil moisture sensors, temperature sensors, smoke detectors, UV light, buzzer, solenoid valves, and mini water pump. The ESP8266 module serves as the central hub for collecting data from the sensors and transmitting it to the Firebase database. The data includes crucial information such as soil moisture levels, temperature, smoke detection, and UV light intensity. Furthermore, the system integrates alarm functionality through the buzzer and precise irrigation control via the solenoid valve and mini water pump. The data stored in Firebase is accessible through a web application built using React.js and Node.js. This web interface provides users with a graphical representation of the gathered data, allowing them to monitor the agricultural conditions in real-time. The main key features are Data Visualization in which users can remotely monitor soil moisture levels and receive real-time updates on crop conditions through a web application. Resource Efficiency by automating irrigation, this model optimizes water usage, reducing waste and improving water conservation. This paper not only showcases the integration of IoT technology in agriculture but also serves as a practical and environmentally responsible solution for modern farming practices.

AI ENABLED WATER CONSERVATION FOR IRRIGATION USING IOT

This project introduces a smart irrigation system that utilizes Internet of Things (IoT) technology and machine learning algorithms to enhance water management in agriculture. The system employs a series of IoT sensors distributed across the field to consistently monitor key environmental factors such as soil moisture levels, temperature, humidity, and others. The collected data is analysed using machine learning Cat Boosting algorithm. This algorithm analyze the data to determine the optimal irrigation schedule based on crop water requirements, soil conditions, weather forecasts, and historical data. The system controls irrigation equipment such as pumps, valves, and sprinklers to deliver precise amounts of water to crops at the right time. Continuous feedback from the system allows for refinement of irrigation schedules, leading to improved water conservation, increased crop yield, and cost savings for farmers. This project discusses the benefits of such a system, including water conservation, increased crop yield, cost savings, environmental sustainability, and remote monitoring and control capabilities. Overall, the integration of IoT and machine learning technologies offers a powerful solution for sustainable agriculture, enabling data-driven decision-making processes to optimize water usage and maximize crop productivity. Keywords— Smart irrigation system, Internet of Things(IOT)technology, Water conservation, Environmental parameters, Soil moisture monitoring, Increased crop yield, Machine learning algorithms

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages

Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat.

The impact of natural vegetation restoration on surface soil moisture of secondary forests and shrubs in the karst region of southwest China

AbstractSoil moisture is crucial to vegetation restoration in karst areas, and climate factors and vegetation restoration are key factors affecting changes in soil moisture. However, there is still much controversy over the long‐term changes in soil moisture during vegetation restoration. In order to reveal the changes in soil moisture during vegetation restoration, we conducted long‐term positioning monitoring of soil moisture at 0–10 and 10–20 cm on secondary forests sample plot (SF, tree land) and shrubs sample plot (SH, shrub land) in karst areas from 2013 to 2020. The results showed that the aboveground biomass of SF and SH increased by 50% and 240%, respectively, and the soil moisture of the SF and SH showed an increasing trend. When shrubs are restored to trees in karst areas, the soil moisture becomes more stable. However, the correlation coefficients (R2) between the annual rainfall and the annual average soil moisture of SF and SH are 0.84 and 0.55, respectively, indicating that soil moistures in tree land are more affected by rainfall. The soil moisture of shrubs and trees are relatively low during the months of alternating rainy and dry seasons. Rainfall has a very significant impact on the soil moisture of tree land, while air temperature and wind speed have a significant impact on the soil moisture of tree land, but the soil moistures of shrub land are very significantly affected by rainfall and relative humidity. Therefore, during the process of vegetation restoration from shrubs to trees, the main meteorological factors that affect soil moisture changes will change. The results are important for understanding the hydrological processes in the ecological restoration process of different vegetation types in karst areas.

Automatic Water Pumping System for Smart Greenhouse Using Renewable Energy Resources

This paper proposes an automatic water pumping system for a smart greenhouse, optimizing plant irrigation using renewable energy. It features sensors monitoring soil moisture, a pump powered by solar or wind, and a control unit adjusting pump operation based on sensor readings. The system, linked to the Internet, enables remote monitoring and control. Expected benefits include reduced manual watering, decreased reliance on non-renewable energy, and enhanced greenhouse efficiency. Smart greenhouses aim to enhance plant cultivation sustainability, with a crucial role played by the automated irrigation system, comprising a control unit, pump, and moisture sensors. Internet connectivity facilitates remote monitoring and adjustment for informed greenhouse operation.

Comparative analysis of SAOCOM and Sentinel-1 data for surface soil moisture retrieval using a change detection method in a semiarid region (Douro River’s basin, Spain)

The growing interest in low-frequency SAR for soil parameter retrieval has led to the development of new active L-band satellites, that will provide novel surface soil moisture products and retrieval possibilities; however, due to data unavailability so far, limited applications have investigated the use of change detection models using L-band satellite SAR data. Since July 2020, high revisit time, high-resolution acquisitions by the Satélite Argentino de Observación COn Microondas (SAOCOM) Argentinian-Italian constellation have become accessible over Europe. Therefore, this research presents an investigation of the potential of multi-temporal L-band SAOCOM-1 for monitoring soil moisture variations underneath low and sparse agricultural vegetation. Moreover, it proposes a procedure for the mitigation of roughness contribution, by exploiting the entropy parameter derived from the dual-polarimetric decomposition. L-band sensitivity to soil moisture has been jointly evaluated in respect of Sentinel-1 C-band data by (1) comparing the temporal profiles of the backscattering coefficient, γ0, at VV and VH polarization, with the support of decomposition parameters (entropy and ᾱ), NDVI and precipitation data; (2) regression analysis with in situ soil moisture measurements, obtained by the REMEDHUS network in the Douro River basin (Spain); 3) evaluating the soil moisture retrievals obtained at C- and L- band using a change detection method. Finally, the effectiveness of the roughness normalization procedure for SAOCOM data has been validated using in situ data. L-band co-polarized γ0 has proved to be the best configuration for soil moisture inversion, being relatively insensitive to vegetation, as demonstrated by decomposition results and trend interpretation. Overall, regressions detected an R2 22% higher at L-band than C-band, with values up to 0.74 for VV (R̄2=0.32) and up to 0.47 for the VH band (R̄2=0.14). Co-polarized data obtained R2 on average 62.1% and 74.7% higher for SAOCOM and Sentinel-1. The retrieval models show an ubRMSD of 7.1% for SAOCOM data and 8.3% for Sentinel-1. The application of the proposed roughness normalization procedure to SAOCOM led to an ubRMSD of 6.7% improving the retrieved soil moisture trend by 7.9%. This exploratory analysis demonstrated SAOCOM data potential for soil moisture mapping and would serve as a foundation for more advanced retrieval procedures.

РАДІОВИМІРЮВАЛЬНИЙ ПЕРЕТВОРЮВАЧ ВОЛОГОСТІ ҐРУНТУ НА БАЗІ ПЕРВИННОГО СЕНСОРА YL-69

Soil moisture monitoring in agriculture is becoming an integral part of ensuring optimal conditions for plant growth and achieving high yields, as well as an important environmental aspect for preserving natural resources and ecosystems. The increasing popularity of wireless sensor networks for measuring soil moisture underscores the need for the development of modern technologies in this field. This enables a continuous flow of real-time data on soil conditions, which is crucial for effective agricultural management and ecological balance. The paper presents the development of a radio-based soil moisture transducer with a moisture-sensitive resistive element based on a transistor structure with negative resistance. The YL-69 resistive sensor was utilized as the primary soil moisture sensor. An analytical expression for the conversion function of the radio-based soil moisture transducer is determined. Computer simulation of the transducer's operation was conducted in the OrCAD environment, resulting in a family of current-voltage characteristics for the investigated radio-based transducer. A region with negative differential resistance is observed on the current-voltage characteristics. An experimental conversion function of the radio-based soil moisture transducer based on the YL-69 primary sensor was obtained. From the conversion function, it is evident that as soil moisture increases in the range from 0% to 50%, the frequency of the output signal of the radio-based soil moisture transducer decreases from 680 kHz to 452 kHz. The highest sensitivity value of the radio-based soil moisture transducer is achieved in the soil moisture measurement range from 20% to 45% and is 8.7 kHz/%. The operation of radio-based soil moisture transducers is based on the functional dependence of semiconductor device impedance on soil moisture, which opens up wide prospects for improving agricultural technologies and preserving the environment. This transducer can be used for measuring soil moisture in agriculture, which can be utilized to create automatic systems for monitoring soil moisture and watering plants.

Multi-Model Comprehensive Inversion of Surface Soil Moisture from Landsat Images Based on Machine Learning Algorithms

Soil moisture plays an important role in maintaining ecosystem stability and sustainable development, especially for the upper reaches of the Yellow River region. Therefore, accurately and conveniently monitoring soil moisture has become the focus of scholars. This study combines three machine learning algorithms: random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN)—with the traditional monitoring of soil moisture using remote sensing indices to construct a more accurate soil moisture inversion model. To enhance the accuracy of the soil moisture inversion model, 27 environmental variables were screened and grouped, including vegetation index, salinity index, and surface temperature, to determine the optimal combination of variables. The results show that screening the optimal independent variables in the Xijitan landslide distribution area lowered the root mean square error (RMSE) of the RF model by 16.95%. Of the constructed models, the combined model shows the best applicability, with the highest R2 of 0.916 and the lowest RMSE of 0.877% with the test dataset; the further research shows that the BPNN model achieved higher overall accuracy than the other two individual models, with the test set R2 being 0.809 and the RMSE 0.875%. The results of this study can provide a theoretical reference for the effective use of Landsat satellite data to monitor the spatial and temporal distribution of and change in soil water content on the two sides of the upper Yellow River basin under vegetation cover.

WSN-Driven Advances in Soil Moisture Estimation: A Machine Learning Approach

Soil moisture estimation is crucial for agricultural productivity and environmental management. This study explores the integration of Wireless Sensor Networks (WSNs) with machine learning (ML) and deep learning (DL) techniques to optimize soil moisture estimation. By combining data from WSN nodes with satellite and climate data, this research aims to enhance the accuracy and resolution of soil moisture estimation, enabling more effective agricultural planning, irrigation management, and environmental monitoring. Five ML models, including linear regression, support vector machines, decision trees, random forests, and long short-term memory networks (LSTM), are evaluated and compared using real-world data from multiple geographical regions, which includes a dataset from NASA’s SMAP project, supplemented by climate data, which employs both active and passive sensors for data collection. The outcomes demonstrate that the LSTM model consistently outperforms other ML algorithms across various evaluation metrics, highlighting the effectiveness of WSN-driven approaches to soil moisture estimation. The study contributes to the advancement of soil moisture monitoring technologies, offering insights into the potential of WSNs combined with ML and DL for sustainable agriculture and environmental management practices.

IoT Based Smart Irrigation System

In recent years, the integration of Internet of Things (IoT) technology into agriculture has shown great potential for enhancing irrigation practices, thereby improving crop yield and water efficiency. This paper presents the design, implementation, and performance evaluation of an IoT-based smart irrigation system tailored for agricultural applications. The proposed system employs various sensors to monitor soil moisture levels, weather conditions, and crop water requirements in real time. Data collected from these sensors are transmitted wirelessly to a central control unit, which utilizes an intelligent algorithm to optimize irrigation scheduling and water distribution. The system also incorporates remote monitoring and control capabilities, allowing farmers to access and manage irrigation operations from anywhere via a web or mobile interface. Experimental results demonstrate the effectiveness of the IoT-based smart irrigation system in conserving water, reducing energy consumption, and enhancing crop productivity. Moreover, the scalability, reliability, and cost-effectiveness of the proposed solution make it suitable for deployment in both small-scale and large- scale agricultural settings. Key Words: IoT, smart irrigation, agriculture, soil moisture sensor, wireless communication, optimization, remote monitoring

IOT BASED SMART PLANT MONITORING SYSTEM

In agriculture, the efficient use of water resources is crucial for maximizing crop yield. This project introduces an open-source, IoT-based smart system designed to predict irrigation requirements by monitoring soil moisture, temperature, humidity, and external factors like obstacles or intruders using infrared sensors. The system employs an algorithm that integrates sensed data to make irrigation predictions. Implemented on a pilot scale, the system wirelessly collects sensor data via cloud-based web services. Both web and mobile interfaces provide real-time insights and decision support based on sensor analysis. Results indicate significant differences between monitored and unmonitored environments, impacting plant health in the short and long term. Graphical representations aid in visualizing environmental variations over time intervals. Future enhancements may include integrating other technologies to predict rainfall, manage pH levels, and secure data stored in the cloud. This would enhance the system's long-term viability and reduce dependency on manual operation. Key Words: Agriculture, IoT (Internet of Things), Water Resources, Soil Moisture, Temperature, Humidity, Irrigation Management, Open-Source Technology ,Sensor Nodes etc.

Intelligent decision‐making for fertigation treatment of tomatoes cultivated in greenhouse: An experimental study

AbstractTo verify the effectiveness of the intelligent decision method for fertigation, an automatic control system for fertigation in greenhouses was designed, and three intelligent decision methods based on evapotranspiration (T1), soil moisture (T2) and accumulated temperature (T3) were tested. Intelligent decisions included monitoring meteorological information, automatically monitoring soil moisture, utilizing fertigation application systems and using automated control modules. The system was stable and accurately controlled according to the decision scheme. The results showed that the average errors of the automated control system for decision‐making and irrigation were 1.1 and 0.8%, respectively. The study findings serve as a reference for the integration of intelligent irrigation decision‐making and control systems and for further improving the efficiency of water and fertilizer utilized. Compared with those of the control, the three intelligent decision‐making methods increased the tomato yield by 8, 12 and 7%, respectively. In addition, the irrigation water and fertilizer levels decreased significantly compared with those in the control treatment. Although the accuracy of the soil water content (SWC) estimated based on ET and temperature in irrigation decision‐making is low, the general trend is consistent with practice. In addition, the irrigation water use efficiency (IWUE) and partial factor productivity of fertilizer (PFP) were significantly improved. Similarly, the IWUE in T1 was the highest (60 kg m⁻3), and the PFP in T3 was the highest (669 kg kg⁻¹).

Combined 2D- and 3D ERT monitoring as a geophysical tool for investigating spatial and temporal soil moisture fluctuations in a pine-beech forest

As climate change continues, forests are increasingly suffering from drought stress, which is leading to widespread forest dieback, but also to increased mortality of individual trees. In this regard, the impact of small-scale differences in water availability on individual trees has not yet been sufficiently studied to determine possible responses of different tree species to future droughts. Since conventional soil moisture monitoring and sampling methods only consider single points or small volumes, Electrical Resistivity Tomography (ERT) is becoming increasingly important to cover a larger survey area and to detect small-scale heterogeneities with regard to soil properties and soil moisture.The current study describes the application of a combined two- and three-dimensional geoelectrical monitoring approach with daily measurements over two years (May 2021 – April 2023) in a forest ecosystem in Lower Franconia (northwestern Bavaria, Germany), which is strongly affected by climate change. Soil water content, soil matric potential, throughfall, and stem flow are also measured at the forest site as well as precipitation at an adjacent forest clearing.The seasonally (long-term) and precipitation-driven (short-term) temporal change of soil resistivity is correlated strongly with the measured soil water content and matric potential. The applied 3D-ERT approach also allowed a first three-dimensional monitoring of the subsurface below a European beech located in the middle of the measuring grid with a daily resolution. The corresponding results also provide first indications that besides soil moisture changes also chemical processes in the subsurface may influence temporal resistivity changes in the soil of forest sites.The results of this study show that daily 3D-ERT is very suitable for investigating small-scale as well as short- and long-term variations in soil moisture, which is becoming increasingly important for understanding the causal relationships considering tree mortality and the subsurface.