This session will address issues in the development and deployment of state-of-the-art soil moisture observation networks, the financing of their long-term operation, data quality assurance, data imputation, and data scaling as well as sensor deployment and assessments of differences between these deployments. We further encourage contributions presenting developments of novel measurement techniques including citizen science initiatives and studies utilizing (primarily) in situ soil moisture to understand and assess hydrological processes, water availability, land-atmosphere feedbacks and soil moisture dependent hazards.
Reliable soil moisture estimation is challenged by sparse in-situ networks, inconsistencies across satellite products, and structural limitations in simplified land-surface models. This study develops a machine learning assisted calibration framework for pyWBM, a Python implementation of the University of New Hampshire Water Balance Model, to generate improved historical reconstructions and ensemble projections of root-zone soil moisture for counties across Illinois. We integrate in-situ observations from nine Illinois State Water Survey stations with satellite and reanalysis soil moisture estimates from Soil Moisture Active Passive Level 4 Carbon Product Version 7 (SMAP L4C Version 7) and North American Land Data Assimilation System Phase 2 (NLDAS-2) model outputs (VIC, NOAH, MOSAIC). Meteorological forcing is obtained from Gridded Surface Meteorological Dataset (GRIDMET) for calibration and Localized Constructed Analogs Version 2 (LOCA2) for future projections. Calibration targets multiple key parameters that control storage dynamics and partitioning processes including available water capacity, wilting point, drying coefficient, runoff shape factor, and Potential Evapotranspiration (PET) scaling coefficients. Using JAX-based automatic differentiation, we evaluate thirteen loss functions and identify three, Root Mean Square Error (RMSE), Outer 50 Percent Root Mean Square Error (Outer50RMSE), and Kiling-Gupta Efficiency (KGE), as the most informative based on performance over the full record, the driest five days per year, and the wettest five days per year. Parameter comparisons reveal robust differences between calibration sources: wilting point is systematically higher when calibrated with in-situ data, even when the ensemble is expanded across alternative loss functions. In contrast, available water capacity does not show a consistent separation between satellite- and in-situ-based estimates. Residuals exhibit slight seasonality, with the Outer50RMSE trained models showing the largest variance. To assess ensemble coverage, we introduce an ensemble coverage metric defined as the ratio between the intersection of ensemble spread and observed soil moisture relative to the observed range. In 6 of 9 counties, satellite-based calibrations produce higher coverage, indicating that multi-source calibration can better represent the overall distribution of soil moisture despite the limited temporal record of in-situ data. Projection ensembles generated using seven-year versus twenty-year calibration windows exhibit consistent drying signals across counties, and longer calibration periods reduce the spread of extreme projections while stabilizing parameter distributions. Overall, the results show that integrating in-situ, satellite, and reanalysis datasets with machine learning–enabled calibration improves model performance, enhances ensemble robustness, and provides more defensible future projections. However, the model still struggles to capture abrupt soil moisture declines and seasonal transitions, highlighting ongoing limitations in simplified water balance models when confronted with extreme hydrologic variability. The framework developed here offers a scalable pathway for generating county-scale soil moisture projections to support drought monitoring, agricultural decision-making, and climate resilience planning.
How to cite: Alam, T., Avila, T., Lafferty, D., Ford, T., and Sriver, R.: Machine Learning Assisted Calibration of pyWBM Using In-Situ, Satellite, and Reanalysis Soil Moisture Data for High Resolution Soil Moisture Ensemble Projections, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-357, https://doi.org/10.5194/egusphere-egu26-357, 2026.
Climate change is intensifying soil moisture variability, atmospheric evaporative demand, and salinity intrusion in agricultural landscapes, creating new challenges for sustainable food production. Understanding how soil hydrology and plant physiological stress interact under these conditions is essential for designing resilient irrigation strategies. This study presents a hydro-physiological assessment of wheat and maize grown under controlled combinations of soil salinity and deficit irrigation, and introduces an Artificial Neural Network (ANN) based Crop Water Stress Index (CWSI) model for real-time decision support in semi-arid farming systems of northern India.
Field experiments (2023–2025) were conducted to measure canopy temperature, air temperature, relative humidity, vapor pressure deficit (VPD), and soil moisture under varying salinity (EC levels) and irrigation regimes. These data were used to develop whole-season and stage-specific ANN models capable of capturing non-linear interactions between soil hydrology, crop physiology, and atmospheric demand. The ANN-based CWSI successfully distinguished mild-to-severe stress transitions and detected early-stage water stress acceleration during periods of high VPD, indicating a propensity toward flash drought development under combined salinity–moisture constraints.
Results show that salinity amplifies crop water stress by reducing effective root-zone moisture availability, leading to higher canopy–air temperature gradients and elevated CWSI values even under moderate irrigation. Stage-specific ANN models achieved strong performance (R² = 0.87–0.94), particularly during flowering and grain filling, where hydrological stress most affects yield. The framework demonstrates how data-driven CWSI modeling can translate complex soil–plant–atmosphere interactions into actionable irrigation insights for farmers.
This work highlights a scalable approach to precision irrigation scheduling, enabling reduced water use without compromising crop health in regions vulnerable to hydrological extremes and sociohydrological pressures. By linking soil hydrology, irrigation management, and physiologically informed stress indicators, the study contributes to sustainable food production strategies in a global climate change context.
How to cite: Dandotia, P. K. and Kotnoor Suryanarayanarao, H. P.: Hydro-Physiological Controls of Crop Water Stress Under Salinity and Deficit Irrigation: An ANN-Based Framework for Sustainable Irrigation Management in a Changing Climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-772, https://doi.org/10.5194/egusphere-egu26-772, 2026.
The MOWAX project investigates monitoring- and modelling concepts as a basis for the assessment of the water budget in Saxony. It operates a dense, multi‑platform soil moisture observation network in collaboration with the German Weather Service (DWD), Sachsenforst, TU Dresden and regional authorities.
The network was designed to represent the dominant landscape properties influencing the water budget in Saxony, including land use, natural areas, soil types, and climatic conditions. It combines up to 10 area‑representative Cosmic Ray Neutron Sensing (CRNS) stations and novel mobile platforms, namely Rail-CRNS (continuous measurements from sensors on trains). We describe our standardized sensor deployment and calibration protocols, automated quality control procedures, and methods for integrating our observations into the modelling framework using the new UFZ timeseries infrastructure. After more than one year of effort, we report on advancements and experiences in pursuing our goals. Based on our strong collaboration with existing observatories and data management infrastructures we are maximizing the utility of ongoing CRNS data for our purposes by establishing a new sensor network.
One of the primary objectives is to enhance and validate the mesoscale Hydrologic Model (mHM) for Saxony by providing continuous, quality‑controlled soil moisture time series. Further, we aim to provide a near-real-time visualization of our observations and model outputs and deliver a valuable data basis that can be used by authorities to support management decisions and urgent actions.
MOWAX is funded by the European Regional Development Fund (EFRE) and by tax revenue on the basis of the budget approved by the Saxon state parliament (funding code 100702604).
How to cite: Thomas, F., Boeing, F., Schlaak, J., Landmark, S., Lange, R., Altdorff, D., Bumberger, J., Marx, A., Dietrich, P., Böttcher, F., Petzold, R., Jäkel, K., and Schrön, M.: Design and deployment of a multi-platform soil moisture monitoring network, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1500, https://doi.org/10.5194/egusphere-egu26-1500, 2026.
The electrical properties of materials, specifically dielectric permittivity (ε) and electrical conductivity (σ), are of interest in a wide variety of applications (e.g. agriculture). For example, in porous media such as soil, ε is strongly correlated with water content, and dielectric sensors are routinely employed to measure soil moisture. Soil moisture sensing technologies have been available in the market for decades, including Time Domain Reflectometry (TDR), Impedance Sensors, Capacitance and Frequency Domain Reflectometers (FDR). These sensors all measure the apparent dielectric permittivity εa, which is a function of both the imaginary dielectric permittivity (εi) and εr. Sensor technology needs to be developed to measure both εr and εi in order to overcome the impact of salts on water content measurements and take the next technological step forward. A new method, the four-voltmeter method (4VM) is a complex dielectric sensor that determines both the εr and εi by measuring voltage amplitudes at multiple circuit nodes. The 4VM improves dielectric permittivity measurements under saline conditions by combining multiple independent admittance estimates to account for conductivity-induced errors, avoid loss of sensitivity, and maintain accuracy across a wide range of salinities. The goal of this project is to assess the performance of 4VM in a sandy soil across a range of salinities up to 50 dS/m and assess its true performance.
How to cite: Rivera, L., Fakhouri, S., and Chambers, C.: Measuring soil moisture and dielectric permittivity in saline environments: Exploring the limits of Complex Dielectric Through Intersections Technology, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2223, https://doi.org/10.5194/egusphere-egu26-2223, 2026.
Notably drier summers and more frequent droughts were reported in Switzerland in the last decades. We analyse these drying trends based on the comprehensive network of in situ soil moisture measurements from the Swiss Soil Moisture Experiment (SwissSMEX), which as of now covers 15 years. We document recent measures that have been taken to secure the SwissSMEX network and to ensure the continuity of its long-term soil moisture timeseries. The analysis focuses on trends in summer and summer half-year anomalies of vertically integrated soil water content and investigates the robustness of the recent drying based on different sets of Swiss Plateau stations. Furthermore, the SwissSMEX-based trends are compared with those from soil moisture of a widely used land reanalysis product (ERA5-Land) and of a merged passive microwave satellite product (European Space Agency Climate Change Initiative ESA CCI).
There is good agreement between the temporal evolution and the drying tendency of SwissSMEX in situ soil moisture based on different sets of Swiss Plateau stations. Comparisons with ERA5-Land and ESA CCI reveal a consistent evolution of soil moisture across the three independent datasets. Summer drying tendencies over the common 2010–2025 period amount to ‑11 mm/decade for ERA5-Land and ESA CCI, and to ‑14 mm/decade for SwissSMEX. While most drying trends are not statistically significant over this short span, ERA5-Land shows significance when extending the analysis period. The findings underscore the need for continued soil moisture monitoring in Switzerland for further investigation of long-term drying trends.
How to cite: Hirschi, M., Michel, D., Schumacher, D. L., Preimesberger, W., and Seneviratne, S. I.: Summer drying of soils in Switzerland: Insights from the SwissSMEX network, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6582, https://doi.org/10.5194/egusphere-egu26-6582, 2026.
Understanding soil water dynamics is crucial for hydrological assessments in Austria’s intensively used landscapes. Reliable soil moisture observations support the understanding of vadose zone processes and can be used to assess infiltration capacity during heavy rainfall events, as well as to evaluate water availability during dry periods. However, sensor-related uncertainties and data quality issues limit the application of soil moisture monitoring networks in hydrological modelling, despite their long-term operation and broad relevance.
The Austrian Hydrological Service operates a nationwide monitoring network measuring soil water content, matric potential, and soil temperature at multiple depths across diverse climatic and land-use conditions. These long-term observations provide an important basis for climate trend analysis and the development of water management strategies. The sustainable use of such datasets depends on robust data management and quality assurance procedures.
This study focuses on establishing a standardized and reliable workflow for transforming raw soil water measurements into publicly accessible indicators. This includes the development of quality control and data processing procedures for Austria’s soil moisture monitoring network. Automated and semi-automated routines are used to identify measurement errors related to sensor problems, signal drift, and implausible temporal behaviour. These routines are complemented by systematic data correction procedures. The resulting quality-controlled time series form the basis for deriving soil water indicators (e.g. the Soil Water Index) and enable near-real-time visualization within the national hydrological portal eHYD.
The presented workflow improves the consistency, reliability, and accessibility of long-term soil moisture observations by providing a framework for quality control and data processing. This approach is transferable to other soil moisture monitoring systems with similar challenges regarding data quality, long-term maintenance, and operational use.
How to cite: Darmann, F., Jagersberger, V., Eybl, J., Breinl, K., Strauss, P., and Weninger, T.: From raw measurements to indicators: workflows for quality-controlled soil moisture monitoring in Austria, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8959, https://doi.org/10.5194/egusphere-egu26-8959, 2026.
Authors: Boquera, Lola ; Janeras, Marc ; Lladós, Agnès ; Portell, Xavier and Vicens, Marc
Institut Cartogràfic i Geològic de Catalunya. Parc de Montjuïc 08038, Barcelona, Spain. https://www.icgc.cat/
XMS-Cat is a soil moisture observation network implemented by the Cartographic and Geological Institute of Catalonia (ICGC) to characterize climatic conditions and soil moisture throughout Catalonia. Each station in the network measures soil temperature and volumetric water content at several depths (typically 5, 20, 50, and 100 cm), as well as atmospheric variables such as rainfall, air temperature, humidity, and solar radiation. The network currently provides high-quality, open-access data for farmers, land managers, and scientists (Soil monitoring network ICGC website: https://visors.icgc.cat/mesurasols/#9.67/42.4378/0.7495).
While volumetric water content measured by XMS-Cat sensors is a quantitative measure of soil moisture, shallow landslides triggered by rainfall are more closely related to the soil water energy state, which can be better assessed using water potential sensors. Consequently, in 2023, an experimental phase was initiated in which new XMS-Cat stations were supplemented with both types of sensors.The purpose of this enhancement in addition to deepening knowledge of soil water status is threefold: (1) strengthening soil-related hazard assessment, such as slope stability,(2) improving characterization of the vegetation water stress; and (3)introducing data redundancy to enhance network resilience.
This contribution provides further details of the network reconfiguration and the initial studies conducted.
Keywords: soil moisture, in situ monitoring, network, volumetric water content, water potential, agriculture, vegetation water stress, slope stability, Landslide hazard.
How to cite: Boquera, L.: Enhancing the Catalan Soil Moisture Observation Network (XMS-Cat): from agricultural and climatic applications to hazard assessment. , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9319, https://doi.org/10.5194/egusphere-egu26-9319, 2026.
Soil moisture is a critical state variable in hydrological systems, acting as both an initial and a boundary condition for physically based hydrological models. Its spatial and temporal variability strongly influences the partitioning of rainfall into infiltration, overland flow and subsurface runoff, which regulates the magnitude, timing and threshold behaviour of extreme events such as flash floods and soil degradation. However, the extensive and multiscale variability of soil moisture has challenged hydrological scientists for over two decades. A common approach to address this issue is to perform distributed point sampling of soil moisture and apply geostatistical methods to analyze spatial relationships and patterns, perform interpolations and provide uncertainty estimates for predictions.
In this study, we aim to quantify the spatial variability of soil moisture at the hillslope scale, as this variability is a key factor controlling hydrological responses and erosion dynamics. The research area is an agricultural hillslope in the Attert River Basin, Luxembourg, where severe erosion occurs year-round on agricultural parcels due steep slopes and extreme rainfall events. A nested cluster sampling design was implemented to cover as much area as possible and to represent a wide range of distance classes to perform geostatistical analysis.
Two soil moisture campaigns were conducted under wet and dry conditions. Soil moisture was measured at 110 cluster points using Time Domain Reflectometry (TDR), which records dielectric permittivity and converts it into volumetric water content using general onboard calibration equations, selected according to soil texture. While these factory calibrations are widely used, they can introduce errors when applied to soils with specific hydraulic properties or textures. Therefore, 15 soil samples (3 per cluster) were collected for gravimetric determination of soil moisture to validate the TDR measurements.
During both campaigns, the TDR measurements revealed a negative bias compared to the gravimetric measurements. Empirical variogram models were fitted for both datasets, with and without the data correction for the bias. The wet case, in comparison to the dry case, exhibited a shorter effective range (~145 m) and a higher nugget-to-sill ratio (~0.4), indicating weaker spatial correlation and a larger relative contribution of small-scale variability. In contrast, the dry case showed a longer effective range (~190 m) and a lower nugget-to-sill ratio (~0.3) reflecting stronger spatial organization and more coherent soil moisture patterns. These differences arise because under wet conditions, increased hydraulic connectivity and redistribution promote local-scale variability and reduce large-scale spatial organization. On the other hand, drier conditions enhance the influence of soil texture, rooting depth and evapotranspiration patterns that operate over larger spatial scales.
How to cite: Yahşi, D., Hoffmeister, S., Mälicke, M., Martínez-Carreras, N., Iffly, J. F., and Zehe, E.: Assessing Spatial Variability of Soil Moisture Across an Erosion-Prone Agricultural Hillslope , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12085, https://doi.org/10.5194/egusphere-egu26-12085, 2026.
The importance of irrigation water management has increased in recent years with the declining summer availability due to climate change, especially for surface waters. The diffusion of pressure irrigation systems has led to higher water efficiency exploiting a demand-based irrigation, overcoming the turn-based limitation of classical flood irrigation. GUARDIANS project (https://guardians-project.eu/), funded by the Horizon Europe program and involving 22 partners from 9 countries, has the goal to transfer this approach shift in the context of small farms, developing and demonstrating IT technologies in several study areas. One of these case studies is the irrigation reservoir of Rivoira (Boves, Piedmont, NW Italy), built in 2017 and having a capacity of 42000 m3. The reservoir is connected to a pressure irrigation network serving about 300 ha of cropfields mainly owned by small farmers.
To improve water management in the study area based on actual soil moisture readings, low-cost sensors were tested for ground-based measurement of volumetric water content (VWC). Their affordability makes them suitable for small farms, while remote data transmission enables continuous monitoring across multiple points within the same field.
These sensors, however, present several challenges. Calibration procedures that balance accuracy and simplicity are essential: for example, the choice is between calibrating each sensor or deriving a calibration formula that applies to all of them, or between calibrating sensors for each soil type or with a formula that works for all types. Furthermore, practical considerations for field installation and reliable long-term data transmission are crucial. Measurement quality must also be carefully evaluated, making sensor redundancy important to compensate for devices that may go offline or produce anomalous readings over time.
This work focuses on operational challenges and solutions adopted during calibration, installation, and data management of low-cost soil moisture sensors in the context of seven small farms. The comparison with meteorological data and recorded irrigation events makes it possible to check the performance of the sensors installed during the previous irrigation season, thereby allowing conclusions to be drawn about the reliability of sensors. In particular, the field monitoring campaign revealed similar dynamic behaviour among sensors, which correctly responded to irrigation and rainfall events; however, significant offsets in their absolute VWC values were observed. These discrepancies may be attributed to spatial heterogeneity in field VWC distribution, as well as to sensor drift over time, and deserve particular consideration.
Overall, low-cost sensors can play an important role in improving irrigation management, but several operational challenges need to be addressed to fully exploit their potential.
This study is carried out within the framework of the GUARDIANS project, funded by the European Union through the Horizon Europe Programme - Farm2Fork (Grant Agreement n. 101084468).
How to cite: Gallia, L., Tavernelli, G., Vallauri, D., Allisiardi, C., Tesio, F., and Casasso, A.: Low-cost soil moisture monitoring: experiences from a technology transfer project for small farms , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13497, https://doi.org/10.5194/egusphere-egu26-13497, 2026.
The ITC Faculty at the University of Twente operates a soil moisture monitoring network consisting of approximately 20 stations that continuously measure volumetric soil water content and soil temperature at up to five depths between 5 and 80 cm. The network was originally established in 2009; over time, several stations have been removed, while others have been added. Its initial purpose was to support the calibration and validation of satellite-based soil moisture products. Recent applications use soil moisture and groundwater monitoring to support adapted water management practices, including adjustable weirs and controlled drainage. For this purpose, supplementary soil moisture stations have been installed in smaller clusters within projects conducted in collaboration with local farmers and the regional water authority Vechtstromen. The quality-checked dataset from 2009-2020 has been published by van der Velde et al. (2023) and also added to the International Soil Moisture Network (ISMN). Furthermore, real-time and historical soil moisture data contribute to the Dutch drought portal. Recently, the soil moisture network has been integrated into the development of a larger multi-sensor infrastructure at the ITC, supported by the NWO-funded Sectorplan in Earth and Environmental Sciences.
The collected data will be analyzed to investigate long-term trends, responses to meteorological extremes, and spatial variability in soil moisture across the Twente region. Furthermore, data from soil moisture, meteorological, groundwater, and additional sensors, together with remote sensing observations, will serve as calibration and validation data for an integrated hydrological model. This framework aims to investigate the effects of local agricultural water management practices on water fluxes and water balance components, such as evapotranspiration, groundwater recharge, and surface runoff, and to scale up field-level adaptation measures and their effects to the regional scale. Insights from these investigations are expected to support the identification of sustainable and resilient water management practices from field to regional scales, helping to better cope with increasing water-related challenges such as droughts and flooding.
References: van der Velde, R., Benninga, H.-J. F., Retsios, B., Vermunt, P. C., and Salama, M. S.: Twelve years of profile soil moisture and temperature measurements in Twente, the Netherlands, Earth Syst. Sci. Data, 15, 1889–1910, https://doi.org/10.5194/essd-15-1889-2023, 2023.
How to cite: Tügel, F., Vermunt, P., Ucer, M., Koop, F., Signora, F., and van der Tol, C.: A long-term soil moisture monitoring network in Twente, the Netherlands: observations, applications, and perspectives, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13748, https://doi.org/10.5194/egusphere-egu26-13748, 2026.
High-resolution soil moisture data is a critical component for understanding the hydrological cycle and establishing climate adaptation strategies, particularly in the complex mountainous terrains of the Far East Asian region. Recognizing the significance of this data within the southern part of the Korean Peninsula, the Korea Institute of Hydrological Survey operates in-situ soil moisture monitoring networks to provide standardized, high-quality hydrological data. Located in mountainous regions with long-term operational history, these networks are co-located with evapotranspiration and streamflow stations, facilitating efficient and integrated water balance studies.
To ensure high data reliability for global research applications, KIHS implements a multi-stage quality control (QC) framework for its SM datasets. We have developed an automated outlier detection system based on the International Soil Moisture Network (ISMN) protocols to identify and filter physical anomalies such as spike, break and constant values. Furthermore, to provide continuous data, KIHS utilizes a hybrid framework of statistical methods and machine learning algorithms for gap-filling. This framework integrates CDF Matching, Kalman Filter, and SARIMAX with non-linear models like Random Forest and KNN, ensuring robust and continuous time-series data even under challenging field conditions.
These high-quality datasets are shared internationally through ISMN and are highly recommended for the calibration and validation of satellite products such as SMAP and Sentinel, particularly during the non-frozen period from April to November. The objective of this presentation is to present KIHS's soil moisture monitoring networks and QC methodologies and to demonstrate the academic significance of soil moisture observation stations in the Korean Peninsula.
keywords : soil moisture, the Korea Peninsula, mountainous terrain, monitoring networks, long-term operation, QC frameworks
How to cite: Lee, Y. J., Kim, K. Y., and Kim, C. Y.: Enhancing Soil Moisture Data Reliability in South Korea: Advanced Quality Control and Ensemble Gap-filling of the KIHS Network, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16168, https://doi.org/10.5194/egusphere-egu26-16168, 2026.
Establishment of the Israeli Soil Moisture Monitoring Network
Dotan Perlstein [a, b], Ehud Strobach [a], Daniel Kurzman [a], Ori Adam [b], Marc Perel [c]
a Soil, Water and Environmental Sciences, Agricultural Research Organization, Rishon Letzion, Israel
b Institute of Earth Sciences, The Hebrew University, Jerusalem, Israel
c Agrometeorological Division, Israel Ministry of Agriculture
Volumetric water content in unsaturated soil is a complex state variable, highly significant to both agriculture and climate science, but until recently available only in low temporal and spatial resolution. However, recent simplified sensor technologies, advances in digital data logging and telemetry, the emergence of data‑driven analysis methods, together with increasing demand for ground‑truth observations, catalyzed the establishment of soil water monitoring networks worldwide.
Recently, one such network has been established in Israel, through collaboration between the Agricultural Research Organization, Volcani Institute and the Agrometeorological Division of the Israeli Ministry of Agriculture, integrated within the existing infrastructure of above-ground, in-situ meteorological stations. Locations for the soil monitoring stations were selected based on geographic considerations, representing all major soil types and heterogeneous climatic conditions in Israel.
At present, there are 28 operational soil monitoring stations, equipped with TDR‑based soil probes installed at four depths: 10, 30, 70, and 150 cm below ground surface, providing 10‑minute measurements of volumetric soil water content and soil temperature. To minimize disturbance‑induced bias, sensors are installed into undisturbed vertical soil faces exposed by mechanical excavation. Procedures for automated quality control, data validation and user‑interface development are currently underway.
Preliminary results are presented from several stations. For instance, the Mevo Horon station, characterized by a soil profile of mixed carbonate bedrock and rendzina soils, has accumulated more than two years of continuous observations. The data indicate that soil water content at 10 cm depth exhibited more than ten wetting–drying cycles during the 2023–2024 winter season, whereas only a single infiltration event was detectable at 30 and 70 cm depths. At 150 cm depth, soil water content showed no discernible response to the annual hydrological cycle. Diurnal soil temperature signal is clearly observed only at 10 cm depth, with the diurnal thermal wave substantially attenuated even at 30 cm depth, throughout the year.
How to cite: Perlstein, D., Strobach, E., and Adam, O.: Establishment of the Israeli Soil Moisture Monitoring Network, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18942, https://doi.org/10.5194/egusphere-egu26-18942, 2026.
Climate change is increasing both the duration of dry periods and the intensity of precipitation events, yet dense, long-term soil moisture records - particularly in rural areas - remain scarce. These records are necessary to understand regional water balances, validate remote sensing data and hydrological models, and provide information for drought-resistant land management. Wassermeisterei is a citizen-led soil moisture monitoring network in the Fläming region around Potsdam and Berlin, Germany. It provides residents with low-cost sensors to continuously measure soil moisture at four depths in the topsoil and subsoil across a growing network of over 70 sites. Participants receive structured education (courses, hands-on-workshops, and online materials) and are supported to install and maintain sensors in their communities (e.g., agricultural land, grassland, gardens, forests). A real-time LoRaWAN network feeds monitored data into a collaboratively developed, interactive public water map, making soil moisture data accessible and actionable for local communities and stakeholders. Through community building, shared data analysis, and practical resources for replication, the bottom-up citizen science project promotes local responsibility, closes observation gaps in a cost-effective manner, and potentially creates a replicable model for other soils and land use contexts. This presentation examines the integration of citizen science data into formal databases and assesses the scientific value of data from the soil moisture network. Furthermore, the possibility of using this information to improve regional climate resilience by providing data on the water balance of different land use types is explored.
How to cite: Sachße, H., Diehl, D., Baumgarten, N., Hertel, E., Büks, F., and Kluge, B.: Own the Data, Understand the Land: Citizens as Key Players in Soil Moisture Monitoring?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20609, https://doi.org/10.5194/egusphere-egu26-20609, 2026.
Long-term in situ observations of soil moisture are essential to understand eco-hydrological processes, in the vadose zone and to provide ground reference for remote sensing, especially in Sahelian Africa where such datasets are poorly available. Since 2019, a dense network of time domain reflectometry sensors (CS655 model, Campbell Scientific) has been continuously monitoring soil moisture at the “Faidherbia Flux” experimental site in Sob, Senegal. The system records high-resolution data across multiple locations and depths, from 10 cm down to 480 cm.
However, these data face particular quality issues representative of sandy soils in semi-arid agroecosystems. The main challenges stem from (1) the limited accuracy of the standard Topp calibration under a narrow range of soil water content dominated by dry soil conditions (2) the influence of strong diurnal thermal fluctuations on dielectric measurement near the soil surface. High accuracy is particularly required when it is expected to process reliable modelling based on retention curves, very steep in the case of sandy soils.
To address these questions, we designed an experimental protocol combining in situ and laboratory calibrations. In situ calibration was performed during three distinct hydrological periods—dry (June), intermediate (January), and wet (October) to cover the full range of soil water natural conditions. The results revealed a strong correlation between CS655 readings and gravimetric moisture values (R² = 0.97), but also a consistent underestimation of actual soil moisture by CS655 sensor.
In the laboratory, undisturbed soil samples were collected from two depths (20 cm and 80 cm), chosen based on contrasting bulk densities likely to influence sensor response and potentially require distinct correction relationships. These samples were subjected to controlled temperature variations (from 25 °C to 45 °C) and progressive moisture levels (from 17% to 0%). At a reference temperature of 25 °C, a relationship between the sensor readings and the actual soil moisture was first established, resulting in a correction coefficient for water content. This relationship confirmed the underestimation of soil water content by CS655 observed in the field. Then, for each moisture level, the slope of the sensor response to temperature was calculated. The average of these slopes defined a temperature correction coefficient.
Based on this two-step approach, we developed a three-variable calibration model, linking measured soil moisture, actual soil moisture, and soil temperature variations. Applying these corrections to field data significantly improved the accuracy and robustness of the CS655 readings. The systematic underestimation bias was corrected, and temperature-driven fluctuations were substantially reduced, allowing a more reliable interpretation of daily and seasonal moisture dynamics.
These findings highlight the importance of sensor calibration protocols for long-term soil moisture monitoring in our ecosystem type. Our work contributes to global efforts aimed at improving in situ networks and supporting satellite validation and hydrological modeling in arid and semi-arid regions.
How to cite: Faye, M. L., Diedhiou, M., and Do, F.: Calibration Of CS655 Soil Moisture Sensors Under Sahelian Conditions: Effects Of Moisture And Temperature, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21379, https://doi.org/10.5194/egusphere-egu26-21379, 2026.
Soil moisture is a key variable influencing land–atmosphere interactions, hydrological extremes, ecosystem processes, and agricultural productivity. The International Soil Moisture Network (ISMN) provides a global, freely-accessible repository of quality-controlled in situ soil moisture observations to support Earth system science, remote sensing validation, and model development through standardized and traceable data. The ISMN compiles soil moisture time series from a wide range of regional, national, and international monitoring networks. Contributing datasets are harmonized in terms of format, metadata, and temporal resolution and subjected to a uniform, rule-based quality control (QC) procedure to ensure research-ready data.
Each observational data point undergoes thirteen plausibility checks, resulting in flagging data as “good” or “dubious”. These checks fall into three categories: (i) a geophysical range verification, identifying thresholds exceedances (e.g., soil moisture < 0% Vol); (ii) geophysical consistency checks, comparing observations with ancillary in situ data or NASA’s GLDAS Noah model data (e.g., flagging of soil moisture when soil temperature is below 0°C); and (iii) spectrum-based approaches, using the first and second derivatives of soil moisture timeseries to detect irregular patterns such as spikes, breaks, or plateaus.
In this work, we propose targeted adaptations to the existing QC flagging strategy to reduce false positives, where valid measurements are incorrectly marked as “dubious”. These refinements increase the proportion of data points flagged as “good” by up to 15% for the entire database. Also, we are proposing the revision of several flags which are originally optimized for the validation of remote sensing products to enhance usability across broader scientific applications, while still maintaining their utility for the remote sensing community. Finally, we will introduce an AI based change detection algorithm designed to identify and potentially homogenize structural breaks and impute missing or “dubious” values in soil moisture timeseries, such as those caused by sensor replacements. This would enable the generation of longer, more consistent time series records suitable for statistically robust trend analyses.
How to cite: Korres, W., Olarinoye, T., Mercier, D., and Zink, M.: The International Soil Moisture Network (ISMN): revised flagging strategy and AI assisted quality control, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23056, https://doi.org/10.5194/egusphere-egu26-23056, 2026.
