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Why Soil Temperature Fluctuations Result in Inaccurate Soil Moisture Measurements?

Updated: Apr 26


They say "necessity is the mother of invention". As a young researcher, I needed a reliable and affordable moisture sensor that I could use in my research at university and recommend to growers. Over the years, I had tried too many soil moisture sensors that were horrible. So this time, I decided to develop my own soil moisture sensor. I took the advantage of my academic experience and expertise in agriculture, soil physics and electronics, and designed a sensor to meet the needs of my research group (IDRG) and the agricultural community. This, however, was not as easy journey, and my team and I faced many challenges. 

This article is focused on one of these challenges, i.e. temperature sensitivity of soil moisture sensors, and how I broke down the issue and tackled it. 

Temperature Sensitivity of Soil Sensors

Substrate or soil temperature (Ts) is a useful parameter in crop management. Unfortunately, it is also a key factor contributing to the inaccuracy of water content measurements by almost all commercial soil moisture sensors. This is because Ts changes soil moisture sensor readings (Wraith and Or, 1999; Seyfried and Grant, 2007). This can cause moisture readings to Go Up when the substrate/soil is actually drying out and vice versa.

Rockwool moisture sensor
Figure 1. Soil moisture measurements recorded by a commercial sensor from a major U.S. manufacturer. Soil moisture readings are clearly correlated with soil temperature. Soil moisture measurement errors are significant.

Processes Affected by Temperature Fluctuations

Temperature fluctuations can affect the final accuracy of soil moisture measurements by impacting three main processes:

  1. Manufacturing (e.g. factory calibration)

  2. Soil-specific calibration

  3. In-field soil moisture measurements

Factory Calibration

If temperature fluctuations are significant in the lab in which the sensor is being assembled and calibrated, then the calibration coefficients that are determined and programmed into the sensor (digital models) are not reliable. This error can be minimized if a temperature chamber or a temperature-controlled room is used. As far as I am aware, no soil moisture sensor manufacturer in the U.S. or elsewhere uses a temperature chamber to calibrate individual sensors. The main reason being the high cost of this additional step, which can be up to several hundred dollars per sensor. 

Soil-Specific Calibration

I used to read in the literature that all soil moisture sensors need soil-specific calibration, otherwise they will provide inaccurate readings. Some companies provide soil-specific sensor calibration as a service and charge their customers thousands of dollars for. Soil-specific sensor calibration can be conducted in the lab or in the field. What is not discussed in the literature and most users are not aware of, however, is that temperature fluctuations can undermine the process of soil-specific calibration. 

In-Field Measurements

If sensors are installed directly in the soil, the change in soil moisture readings is driven by both temperature and water loss (due to evaporation and/or transpiration). This will result in in-field measurements showing a trend that is more correlated with Ts than it is with plant water use. This can potentially render any measurements at a resolution of less than 24 hour useless. Also, because the calibration process was affected by Ts (soil moisture readings being a function of temperature), it may have not improved the accuracy of soil moisture measurements.

Temperature Effect on Soil Moisture Readings

The temperature affects soil moisture measurements in two key ways:

  1. by affecting the sensor electronic circuitry

  2. by changing the electrical properties of the soil

Temperature Effect on Sensor Electronic Circuitry

Electronic components used in soil moisture sensors have properties that change as a function of temperature. The general perception shared among both sensor users and manufacturers is that, depending on the sensor design, these changes can nullify each other or add up. There is also this belief that a linear relationship exists between temperature and sensor output. If the temperature constant is known or determined, it can be applied to the output moisture readings. In digital sensors, it can be saved on the sensor memory and automatically applied to the readings. However, things are more complicated than that! 

In an electric circuitry, we are dealing with impedance, a combination of resistance and reactance, rather than plain resistance. In this case, an overall non-linear response to change in temperature is almost guaranteed, because each impedance component has a different response curve or response to temperature. 

In order to observe the non-linear behavior I just explained, two conditions need to be met:

  1. wide (enough) temperature range

  2. large (enough) sensor sample size

The temperature response might be linear in a given range, and non-linear in another. The temperature response is also highly sensor dependent. This is because the electronic components used to make sensors come with a bit of tolerance, therefore each sensor having a different response curve. In my experience, this source of non-uniformity cannot be avoided in low-cost sensors. 

Temperature Effect on Soil Electrical Properties

In some ways, the soil/substrate is an extension of the sensor electronics. Most affordable [commercial] soil moisture sensors measure volumetric water content (VWC) by indirectly measuring the dielectric permittivity of the bulk soil. The soil is what sits between the sensor prongs/rods (electrodes) to create a so-called capacitor. As a reminder, a capacitor is basically made up of two electrodes (often metal conductors) separated by a dielectric material as insulator.

The soil is what sits between the sensor prongs/rods (electrodes) to create a so-called capacitor. As a reminder, a capacitor is basically made up of two electrodes (often metal conductors) separated by a dielectric material as insulator.

The dielectric is correlated with temperature, which means soil moisture measurements using any dielectric-based sensor are correlated with temperature. 

This problem is worse in sensors with prongs made of steel rods and those that measure both electrical conductivity and moisture by separating the real and imaginary components of the dielectric. This is because the two components (converted to moisture and EC) have shown to have unpredictable behavior and often opposing correlation with temperature. Despite the benefits of using steel rods, I have been avoiding them in my sensor design to stay away from this known behavior. 

The problem with temperature effect on soil properties is worse in sensors with prongs made of steel rods and those that measure both electrical conductivity and moisture by separating the real and imaginary components of the dielectric.

Rockwool moisture sensor
Figure 2. Soil moisture readings are automatically compensated in real-time for temperature effect and sent to the computer for further processing.

It is worth noting that the actual temperature sensitivity of commercially available soil moisture sensors of any kind is usually higher than indicated by the manufacturers. The answer most [advanced] users of soil moisture sensors are used to hearing is that the problem of temperature dependency has a complex nature and it is impossible to resolve. 


Some potential solutions such as multiple regression analysis or averaging have been suggested by researchers (Saito et al., 2013; Kapilaratne and Lu, 2017) to remove the effect of soil temperature on soil moisture sensor readings. These strategies are suggested and often required for sensors that are installed outdoors, near the soil surface (because of higher temperature fluctuations) or used to measure the moisture content of soilless media (e.g. rockwool, coco). However, these solutions are not verified by others or generalized. More importantly, no real-time, automatic solution has been adopted or implemented by sensor users or manufacturers.

I my sensor design, I included an accurate temperature sensor in the design of the soil moisture sensor, avoided using steel electrode arrays, and employed an adequately high measurement frequency. I also tried to minimize the effect of temperature on the electronics by optimizing the circuit design. 

Finally, I carried out a series of experiments with a large number of sensors in a wide temperature range. I analyzed collected data to derive the temperature response curve for the sensors. Based on the response function and using multiple regression analysis, I developed a temperature-compensation algorithm. The algorithm takes sensor-specific parameters, substrate temperature and substrate moisture readings as inputs and compensates sensor output signal (water content measurements) for temperature effects. Sensor measurements are normalized to 25 °C, which means the readings are compensated for fluctuations that are caused by temperature difference from a baseline temperature rather than absolute temperature. 

The sensor is semi-digital and only stores the sensor-specific parameters in its memory. The algorithm needs to reside on a dedicated reader or data logger. The reader can then process sensor data in real-time and yield moisture readings that are independent of soil temperature consultations. 



Kapilaratne, R.G.C.J., Lu, M., 2017. Automated general temperature correction method for dielectric soil moisture sensors. Journal of Hydrology, 551:203-216.

Saito, T., Fujimaki, H., Yasuda, H., Inosako, K., Inoue, M., 2012. Calibration of temperature effect on dielectric probes using time series field data. Vadose Zone Journal, 12(8)

Seyfried, M.S., Grant, L.E., 2007. Temperature effects on soil dielectric properties measured at 50 MHz. Vadose Zone J., 6: 759–765.

Wraith, J.M., Or, D., 1999. Temperature effects on soil bulk dielectric permittivity measured by time domain reflectometry: Experimental evidence and hypothesis development. Water Resources Research, 35: 361–369.



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