A Guide to Maintaining and Calibrating Field-Installed Soil and Plant Moisture Sensors
Soil-and plant-based moisture sensors enhance irrigation management and water conservation (Elsadek, 2018) by offering real-time data on soil and plant water status. However, their effectiveness in commercial field conditions is often restrained by design limitations, battery failures, probe corrosion, mineral buildup, damaged cables, sensor displacement, and wireless connectivity problems. Soil variability further impacts accuracy, as variations in texture, salinity, and temperature can significantly influence readings (Caldwell et al., 2022; Kisekka et al., 2024; Chandel et al., 2025). Sensors drift over time (Hardie, 2022) and frequent recalibration is needed to avoid collecting inconsistent data (Yu et al., 2021). Regular upkeep, including cleaning probes, replacing batteries, recalibrating/readjusting, safeguarding sensors from weather extremes, and verifying readings with manual measurements, is crucial for making reliable irrigation decisions. To ensure their accuracy, calibration of soil moisture sensors can be performed in the field (Cosh et al., 2025) or in the laboratory (FAO, 2023). Field calibration typically involves collecting soil samples for drying and estimating the volumetric water content, using soil bulk density (Elnemr et al., 2019; Elsadek, 2018) or using reference plots and known irrigation levels to fine-tune sensor outputs.
Arizona’s hot, arid, climate and highly variable soils present additional challenges. Soil moisture sensors need site-specific calibration for soil texture, bulk density, and salinity (Singh et al., 2018), as standard calibrations often fail in sandy, clay, and calcareous soils common across the state (Post, 1977). Extreme heat accelerates sensor drift and probe wear, while saline irrigation water influences electrical conductivity measurements (Qi et al., 2024). Uneven wetting from drip or micro-sprinkler irrigation and shallow sensor sampling depths further complicate data interpretation. Deploying multiple sensors at various depths and locations, guided by local soil and irrigation knowledge, helps improve reliability.
Sap-flow or heat dissipation sensors are widely used plant-based tools to monitor water movement within plant and trees (Davis et al., 2012; Alizadeh et al., 2021). Under Arizona conditions, rapid changes in transpiration, uneven sap flow, probe misalignment, plant injuries and fluctuating sapwood properties can cause significant errors. These sensors are best suited for tracking relative changes over time in plant water use or stress rather than determining exact irrigation needs. Calibration of sap-flow sensors can be done directly using stem segments through destructive plant sampling or determined non-destructively using whole plants through a mass balance approach with weighing lysimeters, or indirectly by linking sensor data with plant water potential, stomatal conductance, or canopy temperature (Wang et al., 2017; Dix and Aubrey, 2021; Alizadeh et al., 2021). Correct installation, calibration, maintenance, and data processing/analysis are essential for improving sensor performance and supporting effective irrigation and crop stress management. This publication aims to highlight common issues related to soil moisture sensors and sap-flow probes, focusing on installation, calibration, and maintenance, while providing effective solutions to enhance the performance of these sensors in managing crop stress and water usage.
Common issues and appropriate solutions
Soil moisture sensors
Soil moisture sensors often face challenges related to variability in soil properties and environmental conditions (Elshikha et al., 2024). In soils with varying textures and structures, a single sensor may not reflect the entire root zone, while compacted, gravelly, or high-salinity soils can produce biased readings (Hardie, 2022). Extreme heat, or prolonged wet conditions, can degrade sensor electronics or housing. Debris buildup and trampled probes reduce data accuracy as well as sensor lifespan. Wireless sensors are particularly prone to battery depletion and connectivity issues, which can lead to data loss or erratic measurements. Physical damage to cables by rodents or loose connections can further disrupt performance. Examples of trampled probes, damaged probes, and wires are shown in Figures 1 and 2.
Figure 1a. Impact-related damage to a trampled soil moisture probe.
Irrigation Lab - University of Arizona, Maricopa Agricultural Center.
Figure 1b. Damage to trampled soil moisture, other sections were probably damaged during extraction.
Irrigation Lab - University of Arizona, Maricopa Agricultural Center
Figure 2a. Soil moisture dataloggers and wiring issues disrupting data recording due to damaged datalogger and disconnected wires.
Irrigation Lab - University of Arizona, Maricopa Agricultural Center
Figure 2b. Damaged wires disrupting data recording.
Irrigation Lab - University of Arizona, Maricopa Agricultural Center
Common solutions include installing multiple sensors at representative locations and depths, performing appropriate cleaning to remove debris, protecting sensors from harsh weather, protecting data loggers from wild animals and machinery, and recalibrating when it is necessary. Comparing soil moisture readings from the installed sensors to the measured moisture contents, using a known accurate sensor, or a valid accurate method such as a soil-drying oven (FAO, 2023), on the same soil and under the same environmental conditions, is appropriate for validating moisture readings while identifying drift or offsets. Usually, manufacturers provide guidelines on how to calibrate and implement each specific sensor, which are highly recommended to follow. The simplest way to calibrate soil moisture sensors, as recommended in FAO (2023), is summarized below:
- Use aluminum trays to collect soil samples at the same depths as the installed sensors,
- Weigh each sample in the field, immediately after collecting, to get fresh weight (Wf, g),
- Dry samples in a soil-drying oven (example shown in Figure 3) at 105 °C (221 °F) until a constant dry weight is reached
- Re-weigh each sample to get the dry weight (Wd, g),
- Subtract the weight of aluminum trays from both Wf and Wd,
- Calculate the percentage of volumetric soil moisture (θ) content using soil bulk density (ρβ, g/cm3):
- θ (%) = ρβ * (Wf − Wd)/ (Wd) * 100
- Compare paired volumetric soil moisture (gravimetric vs sensor data) and apply eventual adjustments to calibrate the sensors.
Calculation of volumetric soil moisture using the gravimetric method requires knowledge of soil bulk density (ρβ), which describes how compact the soil is and represents the dry soil mass per unit volume. Bulk density varies with soil texture, compaction, organic matter, and depth, and can differ across a field. The standard way to measure bulk density is by collecting an undisturbed soil core of known volume, drying it at 105 °C until constant weight, and dividing the dry soil mass by the core volume (Blake and Hartge, 1986; Hillel, 2004). While this approach provides accurate values, it requires specialized sampling tools and access to a drying oven, making routine or repeated measurements impractical for most growers; as a result, bulk density is often unknown at the farm scale.
Although laboratory or gravimetric calibration methods provide accurate absolute soil moisture values, their reliance on bulk density measurements, repeated sampling, and laboratory equipment makes them timeconsuming and difficult to maintain throughout the season under commercial field conditions. For practical field use, growers may rely on approximate bulk density values based on soil texture, with sandy soils typically ranging from about 1.4-1.7 g/cm³, loamy soils from about 1.2-1.5 g/cm³, and fine-textured or clay soils from about 1.0-1.3 g/cm³ (Weil and Brady, 2016). Although these estimates introduce uncertainty in absolute soil moisture values, they are generally sufficient for relative comparisons and sensor standardization used in irrigation scheduling.
An alternative and practical approach commonly used for sensors requiring calibration is sensor standardization rather than full laboratory calibration, where sensors are interpreted based on their relative response to irrigation events instead of absolute volumetric water content values (Evett et al., 2012; Cosh et al., 2025). Growers can monitor sensor readings before irrigation, during water application, and after irrigation while visually or physically assessing soil conditions. When soils are visibly saturated following irrigation, the corresponding sensor reading can be interpreted as a near-saturation reference. As soils dry progressively between irrigation events, declining sensor readings reflect relative drying trends, and readings observed immediately prior to irrigation can be associated with field-specific irrigation thresholds (Irmak et al., 2012). While this method does not provide exact volumetric soil moisture values, it allows growers to consistently track wetting and drying patterns, identify over- or under-irrigation, and schedule irrigation based on relative changes that are repeatable within a field. This approach reduces reliance on laboratory calibration and makes sensor data more accessible and actionable for onfarm decision-making (Evett et al., 2012; Cosh et al., 2025).
Wireless sensors require frequent battery checks and replacement when needed, or considering solar-powered sensors, as well as securing cables, maintaining strong wireless signal paths, and performing regular physical inspections. Furthermore, ensuring proper placement to maintain network reliability and avoiding interference from nearby electronic devices can improve sensor performance. An example of a damaged antenna and a dead battery of a soil moisture data logger is shown in Figure 4
Figure 3. Drying soil samples in a laboratory oven for gravimetric moisture measurement.
Irrigation Lab - University of Arizona, Maricopa Agricultural Center
Figure 4a. Soil moisture datalogger with a damaged antenna.
Irrigation Lab - University of Arizona, Maricopa Agricultural Center
Figure 4b. Datalogger failure due to a dead battery.
Irrigation Lab - University of Arizona, Maricopa Agricultural Center
Soil moisture status can also be measured as water tension using tensiometers (Figure 5) or other soil water potential instruments, such as gypsum blocks. The Watermark sensor is an example of a gypsum block sensor [Figure 6] (McCann et al., 1992). Electrodes embedded in the block measure the electrical resistance of the surrounding media, which is then converted into an estimate of soil water tension. Tensiometers can fail due to the formation of air bubbles or the accumulation of sediments, resulting in blockage in the water column or sensor body. In addition, leaks or faulty seals reduce measurement reliability. Recommended maintenance includes removing blockages, refilling water columns, checking seals, and verifying readings against manual measurements. Proper installation depth and orientation are also crucial to maintain accuracy. Gypsum blocks will dissolve over time in the soil and are expected to be replaced.
Figure 5a. Example of soil moisture tensiometer components
Figure 5b. Example of soil moisture tensiometer field installation.
Figure 6a. Example of a Watermark soil water tension sensor.
Figure 6b. Example of a Watermark soil water tension sensor
Sap-flow sensors
Sap-flow sensors measure plant water uptake and movement through main stems or tree trunks as a means of monitoring water stress. Such sensors are sensitive to probe alignment, insertion depth, probe spacing, and crop type. Rapid fluctuations in transpiration, especially under high heat or low humidity, can cause highly variable readings. Plant wounding during probe insertion can alter sapwood behavior, and assumptions about constant thermal properties may introduce errors. Considering such issues and challenges, sap-flow sensors require calibration for high performance and improved data accuracy (Forster, 2017; Dix and Aubrey, 2021).
Sap-flow sensors can be used with trees or small plants, as shown in Figure 7. Compared principles of operation, capabilities, and limitations for common sapflow measurement methods, and their applications are presented in Table 1.
Figure 7a. Sap-flow sensors measuring sap-flow at different tree locations.
Edaphic Scientific https:// edaphic.com.au/products/sap-flow-sensors/
Figure 7b. Sap-flow sensors measuring sap-flow in small stems.
Edaphic Scientific https:// edaphic.com.au/products/sap-flow-sensors/
Based on the different principles and issues of sap-flow sensors (Table 1), it is recommended to take the following considerations for their field deployment:
- Precise installation, then monitor probe placement throughout the season
- Read and interpret daily fluctuations
- Focus data interpretation on relative changes in plant water use rather than absolute values
Recent advances in stem water potential (SWP) sensing offer growers a promising tool for improved irrigation management in woody perennial crops. Traditionally, SWP measurements have relied on the Scholander pressure chamber, which provides accurate indicators of plant water status but is labor-intensive and yields only discrete data points. The study by Kisekka et al. (2024) evaluated two embedded SWP sensor technologies, microtensiometers (MT) and osmotic cells, across nine commercial almond orchards in California’s Central Valley and compared sensor outputs to conventional pressure chamber measurements. The microtensiometer sensors demonstrated generally high agreement with pressure chamber measurements and provided near-continuous, sub-hourly data, capturing diurnal patterns and tree recovery following irrigation events. By contrast, osmotic cell sensors showed more variable performance. The ability of MT sensors to track real-time changes in plant water status offers significant advancement for precision irrigation scheduling, enabling growers to better match water applications to crop demand, reduce water use, and mitigate stress, especially important under increasing climate variability and water scarcity.
Table 1. Compared common sap-flow measurement methods
| Method | Principle | Capability | Limitation | Application (Reference) |
|---|---|---|---|---|
| Thermal dissipation (Granier) | Constant heat applied; temperature difference between heated and reference probes indicates sap flux density | Simple design, low cost, widely used | Requires species-specific calibration, sensitive to temperature gradients, invasiveness | Forestry and long-term monitoring (Granier, 1987) |
| Heat pulse velocity | Short heat pulse injected; sensors track movement through xylem | High accuracy, can measure bidirectional flow | Complex installation, probe spacing critical, destructive | Research studies on stem hydraulics (Forster, 2017) |
| Stem heat balance | Entire stem section heated; energy balance equations used to calculate flow | Non-invasive to sapwood, works on small stems | High power demand, less common, sensitive to environmental noise | Crop studies and small plants (Smith and Allen, 1996) |
| Ribbonized sensors | Integrated flexible platform combining heater and probes | Reduced invasiveness, easy installation, IoT-ready | Still developing, limited field validation | Urban forestry and IoT networks (Jones et al., 2020) |
| NMR-based sensors | Nuclear magnetic resonance detects water movement non-invasively | Non-destructive, accurate | Expensive, requires specialized equipment | Cutting-edge research and high-value crops (Windt and Blümler, 2015) |
Recommended practices
Maintaining and calibrating soil and plant moisture sensors are essential practices for precision agriculture. By following routine maintenance, proper calibration, and best practices, farmers and growers can ensure efficient water use, healthier crops, and sustainable farming operations. Across all sensor types, drift over time and environmental stressors like heat, waterlogging, or intense sunlight can degrade sensor performance. Regular recalibration if required by the sensor manufacturer, protective coverings, careful placement, and routine inspections are essential. Comparing sensor readings with manual moisture measurements, performing crop- or soil- specific calibration, and deploying multiple sensors to account for spatial variability all contribute to reliable data collection and better irrigation decisions. Recommended common practices include:
- Inspect and secure probes, sensors, cables, and connectors to prevent damage and signal loss.
- Calibrate/recalibrate sensors periodically according to manufacturer recommendations to account for sensor drift and environmental changes.
- Always perform site-specific/crop-specific calibration rather than relying on factory defaults.
- If laboratory calibration is not feasible, soil moisture data can be evaluated based on relative wetting and drying trends following irrigation events instead of absolute values.
- Protect sensors from extreme weather conditions using appropriate coverings/housing.
- Mark sensors in the field with tall flags to ensure visibility and prevent damage from vehicles or machinery.
- Check and replace bad batteries regularly to avoid downtime.
- Update sensor firmware/software to maintain optimal performance.
- Avoid placing sensors near sources of electrical interference.
- Confirm adequate cellular signal strength in the area for smooth data transmission.
- Clean and store sensors properly when not in use to prevent damage.
- Avoid creating an easy flow path for water down to the sensor during the installation process.
- You may have to use multiple sensors to capture the extent of the data variability.
Conclusions
Soil moisture and sap-flow sensors can be valuable tools for improving irrigation management and crop water use efficiency, but their effectiveness depends on proper installation, calibration, and ongoing maintenance. Field conditions such as soil variability, extreme temperatures, salinity, physical damage, and power or connectivity issues can significantly affect sensor accuracy and reliability, especially in environments like the arid climate of Arizona. Without routine inspection and calibration, sensor drift and localized measurement errors can lead to misleading data and poor irrigation decisions.
This guide highlights that no single sensor or method can fully capture the complexity of soil-plant-water interactions. Using multiple sensors at representative locations and depths, validating readings with manual or gravimetric measurements, and applying site- and crop-specific calibration are essential steps for obtaining reliable information. Similarly, sap-flow sensors are best suited for tracking relative changes in plant water use and stress rather than absolute irrigation requirements, especially under variable environmental conditions.
Sensors should be viewed as decision-support tools rather than standalone solutions. When combined with grower experience, knowledge of soil and crop characteristics, and good irrigation practices, well-maintained and properly calibrated sensors can support more efficient water use, reduce crop stress, and contribute to long-term agricultural sustainability.
Disclaimer
This publication does not endorse or promote any brand, product, or trademark. Any references to product names, trademarks, or companies are included for informational purposes only.
References
Alizadeh, A., A. Toudeshki, R. Ehsani, K. Migliaccio, and D. Wang. (2021). Detecting tree water stress using a trunk relative water content measurement sensor. Smart Agricultural Technology 1, https://doi.org/10.1016/j.atech.2021.100003.
Blake, G. R., and Hartge, K. H. (1986). Bulk Density. In A. Klute (Ed.), Methods of Soil Analysis, Part 1: Physical and Mineralogical Methods (pp. 363–375). American Society of Agronomy, Madison, WI.
Caldwell, T. G., Cosh, M. H., Evett, S. R., Edwards, N., Hofman, H., Illston, B. G., Meyers, T., Skumanich, M., and Sutcliffe, K., (2022). In Situ Soil Moisture Sensors in Undisturbed Soils. J. Vis. Exp. (189), e64498, https://doi:10.3791/64498
Chandel, A., Swami, D. and Joshi, N. (2025). Calibration complexities: full-scale error impact and simultaneous variation of salinity, temperature, and moisture content on sensor performance in soil. Environ Dev Sustain 27, 26823-26843. https://doi.org/10.1007/s10668-024- 04812-1
Cosh, M., Rivera, L., Ayres, E., Sehgal, V., Becker, E., Caldwell, T., and Gaur, N. (2025). Soil Moisture Data Quality Guidance. Drought.gov Technical Report. https:// www.drought.gov/sites/default/files/2024-12/Soil- Moisture-Data-Quality-Guidance-12-09-2024.pdf
Dix, M.J., and Aubrey, D.P. (2021). Recalibrating Best Practices, Challenges, and Limitations of Estimating Tree Transpiration Via Sap Flow. Curr Forestry Rep 7, 31–37 (2021). https://doi.org/10.1007/s40725-021-00134-x.
Davis, T. W., Kuo, C.-M., Liang, X., and Yu, P.-S. (2012). Sap Flow Sensors: Construction, Quality Control and Comparison. Sensors, 12(1), 954-971. https://doi.org/10.3390/s120100954
Elnemr, M.K., El-Sheikha, A.M., Elsadek, E.A. (2019). Determination of Optimal Location of Soil Moisture Sensing Devices for Trickle Irrigation Systems. Misr J.Agric. Eng. 36, 157–174. https://doi.org/10.21608/mjae.2019.94446
Elsadek, E. 2018. Use of automatic control to improve the performance of field irrigation systems. Damietta University, Egypt. https://doi.org/10.13140/RG.2.2.25176.98567
Elshikha, D. E., Katterman, M., Attalah, S., Waller, P., Thorp, K., Alshraah, S., Sanyal, D., Norton, R., and Orr, E. (2024). Guidance for Soil Moisture Sensor Selection: Market Analysis and Decision-Making Strategies. University of Arizona Cooperative Extension Publication No. AZ2082, 1-7. https://repository.arizona.edu/ handle/10150/672949.
Evett , S. R., Schwartz, R. C., Casanova, J. J., and Heng, L. K. (2012). Soil water sensing for water balance, ET and WUE. Agricultural Water Management, 104, 1–9. https://doi.org/10.1016/j.agwat.2011.12.002
FAO. 2023. Standard operating procedure for soil moisture content by gravimetric method. Rome. https://openknowledge.fao.org/server/api/core/bitstreams/e8811ce2-af62-…
Forster, M.A. (2017). How Reliable Are Heat Pulse Velocity Methods for Estimating Tree Transpiration? Forests 2017, 8, 350; https://doi:10.3390/f8090350
Granier, A. (1987). Evaluation of transpiration in a Douglasfir stand by means of sap flow measurements. Tree Physiology, 3(4): 309–320. https://www.researchgate.net/publication/8687474_Evaluation_of_transpir… in_a_Douglas-fir_stand_by_means_of_sap_flow_ measurements
Hardie, M. (2022). Review of Novel and Emerging Proximal Soil Moisture Sensors for Use in Agriculture. Sensors 2020, 20, 6934. https://doi/10.3390/s20236934
Hillel, D. (2004). Introduction to Environmental Soil Physics. Elsevier Academic Press. Available online at Archive.org: https://archive.org/details/introductiontoen0000hill
Kisekka, I., Peddinti, S. R., Savchik, P., Yang, L., Culumber, M., Bali, K., Milliron, L., Edwards, E., Nocco, M., Reyes, C. A., Mahoney, R. J., Shackel, K., and Fulton, A. (2024). Multisite evaluation of microtensiometer and osmotic cell stem water potential sensors in almond orchards. Computers and Electronics in Agriculture, 227, 109547. https://doi.org/10.1016/j.compag.2024.109547.
Jones, T.S., Winbourne, J.B., Hutyra, and L.R. (2020). Ribbonized sap flow: an emerging technology for the integration of sap flow sensor components onto a single platform. Ecosphere, 11(6): e03135. https://do:10.1002/ecs2.3135.
McCann, I.R., Kincaid, D.C., and Wang, D. (1992). Operational characteristics of the Watermark Model 200 soil water potential sensor for irrigation management. Appl. Eng. Agric., 8, 603-609
Peters, T. P., Desta, K., and Nelson, L. (2013) Practical Use of Soil Moisture Sensors and Their Data for Irrigation Scheduling. Washington State University Extension Fact Sheet FS083E
Post, D. F. (1977). Specific Surface Characteristics of Arizona Soils. Journal of the Arizona Academy of Science, 12(1), 12-14. https://doi.org/10.2307/.
Qi , Q., Yang, H., Zhou, Q., Han, X., Jia, Z., Jiang, Y., Chen, Z., Hou, L., and Mei, S. (2024). Performance of Soil Moisture Sensors at Different Salinity Levels: Comparative Analysis and Calibration. Sensors, 24(19), 6323. https://doi.org/10.3390/s24196323.
Sensoterra (2023). Soil calibration guide. Wireless soil moisture sensors. https://www.sensoterra.com/wpcontent/uploads/2023/06/Sensoterra-Soil-Ca….
Singh, J., Rudnick, T. Lo., Dorr, Burr, C.A., Werle, R., Shaver, T.M., Munoz-Arriola, F. (2018). Performance assessment of factory and field calibrations for electromagnetic sensors in a loam soil. Agricultural Water Management, Volume 196, 2018. ISSN 0378-3774. https://doi.org/10.1016/j.agwat.2017.10.020
Smith, D.M., and Allen, S.J. (1996). Measurement of sap flow in plant stems. Journal of Experimental Botany, Volume 47, Issue 12, December 1996, Pages 1833–1844, https://doi.org/10.1093/jxb/47.12.1833
Yu L M, Gao W L, Shamshiri R R, Tao S, Ren Y Z, Zhang Y J, and Su G L. (2021). Review of research progress on soil moisture sensor technology. Int J Agric and Biol Eng, 2021; 14(4): 32–42. https://DOI/10.25165/j.ijabe.20211404.6404
Wang, Y., X. Zhang, X. Xiao, J. Heitman, R. Horton, T. Ren. (2017). An empirical calibration for heat-balance sapflow sensors in maize. Agronomy Journal, 109 (3), 1122- 1128.
Weil, R.R., and Brady, N.C. (2016). The Nature and Properties of Soils (15th ed.). Pearson Education. ISBN: 978-1-292- 16223-2.
Windt, C.W., and Blümler, P. (2015). A portable NMR sensor to measure dynamic changes in the amount of water in living stems or fruit and its potential to measure sap flow. Tree Physiology, 35(4): 366–375. DOI: 10.1093/treephys/tpu105.