Climate parameters such as precipitation, air temperature, and wind speed can change considerably across short distances in the natural environment. However, most weather observations either sacrifice spatial resolution for scientific accuracy or research-grade accuracy for spatial resolution.
ATMOS 41 all-in-one weather station
The ATMOS 41 represents an optimization of both. It was carefully engineered to maximize accuracy at a price point that allows for spatially distributed observations. Additionally, because many researchers need to avoid frequent maintenance and long setup times, the ATMOS 41 was designed to reduce complexity and withstand long-term deployment in harsh environments. To eliminate breakage, it contains no moving parts, and it only requires recalibration every two years. Since all 14 measurements are combined in a single unit, it can be deployed quickly and with almost no effort. Its only requirement is to be mounted and leveled on top of a pole with an unobstructed view of the sky.
Comparison testing and sensor-to-sensor variability data
METER released the ATMOS 41 in January 2017 after extensive development and testing with partnerships across the world, in Africa, Europe, and the US. We performed comparison testing with high-quality, research-grade non-METER sensors and conducted time-series testing for sensor-to-sensor variability.
Water has a dielectric of approximately 80, so if we assume that a dry soil has a dielectric of 5 (VWC = 0.00 m3/m3), then changes to the bulk dielectric read by the sensor will be attributable to changes in water content. If you read a METER sensor in air, which has a dielectric constant of 1, you will quite naturally get a negative number.
Improving accuracy of dielectric soil moisture sensors
There are two common causes for negative readings on a METER soil moisture sensor:
1) Poor contact with the soil resulting from improper installation or disturbance
Air gaps next to a sensor will contribute the lower dielectric of air to the measurement resulting in an underestimation of VWC. Air gaps can arise if enough care is not taken to pack soil around the sensor body to approximate native bulk density. Sensors that have been disturbed, such as having a cable tripped over, can also develop air gaps that can result in negative results in dry soils. (To reduce the possibility of air gaps when installing METER sensors, use the new TEROS borehole installation tool)
2) A calibration that is inappropriate for the soil in which the sensor is installed
If the standard mineral calibration is used, an error of ~ 3-4% can be expected in METER sensor readings. Negative numbers can be observed in oven-dry soils (by definition a VWC of 0.0 m3/m3) down to ~ – 0.02 m3/m3 with no malfunction of the sensor. The dielectric constant of the soil is assumed to be 5 and this is a valid assumption in the majority of soils of primarily mineral composition. If your soil has a different dielectric constant, such as can occur in soils with high organic matter content, then the uncertainty in your measurements will increase. This is not a large problem because METER sensors can be calibrated to match a given soil with very little investment in resources.
Want more details?
Watch our webinar titled Why Does My Sensor Read Negative below. This webinar is designed for those who use electromagnetic sensors (capacitance/TDR/FDR) to measure soil water content. Learn about the theory behind the measurements. Dr. Doug Cobos discusses:
What is volumetric water content?
Dielectric measurement theory basics
Dielectric mixing models
Why might a sensor read a negative VWC?
Can a sensor really have 2% VWC accuracy for all soils?
Sources of error in dielectric measurement methods
The deadline is fast approaching to apply for the 2019 Grant A. Harris Fellowship. The fellowship awards $10,000 in METER research instrumentation to six U.S. or Canadian graduate students studying any aspect of agricultural, environmental, or geotechnical science.
(Image source: https://vimeo.com/69136931)
Camila Tejo Haristoy, former University of Washington grad student, was a Grant A. Harris Fellowship winner. She used METER soil moisture and temperature sensors to study the water holding and temperature patterns of canopy soil in an old-growth Sitka Spruce forest in Washington state. Sitka Spruce tree crowns contain large accumulations of organic matter known as “canopy soil”. These accumulations provide substrate and habitat for a broad community of plants, insects, and other arboreal species. Using tree-climbing techniques, Camila installed soil moisture and temperature sensors in the canopy soils of spruce trees from an old-growth stand in the Olympic Peninsula, Washington.
This study characterized for the first time environmental conditions associated with soil mats within the crown of spruce trees, providing a framework for understanding the distribution and activity of epiphytic plants, nutrient dynamics, and associated canopy organisms.
Watch the documentary
Watch a fascinating 7-minute documentary of Camila’s interesting and exciting research. The documentary description: “Camila spends long rainy days climbing into treetops, taking temperature and moisture measurements, and collecting soil and plant samples. In the process, she interacts with a seldom seen, barely understood, and lushly beautiful environment.” (source https://vimeo.com/69136931)
Shortly after the Fukushima disaster, we donated environmental sensors to Dr. Masaru Mizoguchi, a scientist colleague at the University of Tokyo, to help him contrive a more environmentally friendly method to rid rice fields in the villages near Fukushima of the radioactive isotope cesium 137.
Scientists continue to search for ways to prevent the recontamination of the rice paddies.
Since then, his efforts, along with the efforts of a team of scientists and citizens, have made the rice grown in the paddies near the disaster site safe for human consumption. But questions and challenges remain. For instance, what will happen to the contaminated soil surrounding the decontaminated area? Will it settle in nearby stream beds, eventually contaminating the rice paddies? And what kind of erosion will come from the nearby tree-covered and clearcut hillslopes?
Recently, our scientists and videographers visited the villages near Fukushima to film some of the progress being made. Watch the video, and read the full story here.
Every researcher’s goal is to obtain usable field data for the entire duration of a study. A good data set is one a scientist can use to draw conclusions or learn something about the behavior of environmental factors in a particular application. However, as many researchers have painfully discovered, getting good data is not as simple as installing sensors, leaving them in the field, and returning to find an accurate record. Those who don’t plan ahead, check the data often, and troubleshoot regularly often come back to find unpleasant surprises such as unplugged data logger cables, sensor cables damaged by rodents, or worse: that they don’t have enough data to interpret their results. Fortunately, most data collection mishaps are avoidable with quality equipment, some careful forethought, and a small amount of preparation.
Before selecting a site, scientists should clearly define their goals for gathering data.
Make no mistake, it will cost you
Below are some common mistakes people make when designing a study that cost them time and money and may prevent their data from being usable.
Site characterization: Not enough is known about the site, its variability, or other influential environmental factors that guide data interpretation
Sensor location: Sensors are installed in a location that doesn’t address the goals of the study (i.e., in soils, both the geographic location of the sensors and the location in the soil profile must be applicable to the research question)
Sensor installation: Sensors are not installed correctly, causing inaccurate readings
Data collection: Sensors and logger are not protected, and data are not checked regularly to maintain a continuous and accurate data record
Data dissemination: Data cannot be understood or replicated by other scientists
When designing a study, use the following best practices to simplify data collection and avoid oversights that keep data from being usable and ultimately, publishable.
When it comes to measuring soil moisture, site disturbance is inevitable. We may placate ourselves with the idea that soil sensors will tell us something about soil water even if a large amount of soil at the site has been disturbed. Or we might think it doesn’t matter if soil properties are changed around the sensor because the needles are inserted into undisturbed soil.
The key to reducing the impact of site disturbance on soil moisture data is to control the scale of the disturbance.
The fact is that site disturbance does matter, and there are ways to reduce its impact on soil moisture data. Below is an exploration of site disturbance and how researchers can adjust their installation techniques to fight uncertainty in their data.
Non-disturbance methods don’t measure up—yet
During a soil moisture sensor installation, it’s important to generate the least amount of soil disturbance possible in order to obtain a representative measurement. Non-disturbance methods do exist, such as satellite, ground-penetrating radar, and COSMOS. However, these methods face challenges that make them impractical as a single approach to water content. Satellite has a large footprint, but generally measures the top 5-10 cm of the soil, and the resolution and measurement frequency is low. Ground-penetrating radar has great resolution, but it’s expensive, and data interpretation is difficult when a lower boundary depth is unknown. COSMOS is a ground-based, non-invasive neutron method which measures continuously and reaches deeper than a satellite over an area up to 800 meters in diameter. But it is cost prohibitive in many applications and sensitive to both vegetation and soil, so researchers have to separate the two signals. These methods aren’t yet ready to displace soil moisture sensors, but they work well when used in tandem with the ground truth data that soil moisture sensors can provide.
The SATURO and the double-ring infiltrometer are both ring infiltrometers that infiltrate water from the surface into soils. Overall, they compare fairly well (see comparison). The main difference is how they deal with three-dimensional flow in the Kfs calculation. The SATURO uses the multiple-ponded head analysis approach to get a more direct estimation of alpha, which is used to determine how the soil pulls the water laterally. The double-ring infiltrometer uses a larger outer ring to act as a buffer from three-dimensional flow. This requires more water, and literature suggests that it doesn’t perform well. Also, with a double-ring infiltrometer, there is still a need to estimate alpha in the equations. This is typically done from a look-up table based on soil type and often results in error.
The SATURO is an automated infiltrometer which uses the multiple-ponded head analysis approach.
How do SATURO readings compare to double-ring infiltrometer readings?
We compared the SATURO with a 6-inch (15.24 cm) inner ring diameter against a double-ring infiltrometer with a 6-inch (15.24 cm) inner ring diameter and an outer ring with a 12-inch (30.48 cm) diameter.
Hydraulic conductivity is the ability of a porous medium (soil for instance) to transmit water in saturated or nearly saturated conditions. It’s dependent on several factors: size distribution, roughness, tortuosity, shape, and degree of interconnection of water-conducting pores. A hydraulic conductivity curve tells you, at a given water potential, the ability of the soil to conduct water.
One factor that affects hydraulic conductivity is how strong the structure is in the soil you’re measuring.
For example, as the soil dries, what is the ability of water to go from the top of a sample [or soil layer in the field] to the bottom. These curves are used in modeling to illustrate or predict what will happen to water moving in a soil system during fluctuating moisture conditions. Researchers can combine hydraulic conductivity data from two laboratory instruments, the KSAT and the HYPROP, to produce a full hydraulic conductivity curve (Figure 1).
Figure 1. Example of hydraulic conductivity curves for three different soil types. The curves go from field saturation on the right to unsaturated hydraulic conductivity on the left. They illustrate the difference between a well-structured clayey soil to a poorly structured clayey soil and the importance of structure to hydraulic conductivity especially at, or near, saturation.
In Hydrology 301, Leo Rivera, Research Scientist at METER, discusses hydraulic conductivity and the advantages and disadvantages of methods used to measure it.
Watch the webinar below.
Get more info on applied environmental research in our
The HYPROP and WP4C provide the ability to make fast, accurate soil moisture release curves (soil water characteristic curves-SWCCs), but lab measurements have some limitations: sample throughput limits the number of curves that can be produced, and curves generated in a laboratory do not represent their in situ behavior. Lab-produced soil water retention curves can be paired with information from in situ moisture release curves for deeper insight into real-world variability.
Soil water characteristic curves help determine soil type, soil hydraulic properties, and mechanical performance and stability
Moisture release curves in the field? Yes, it’s possible.
Colocating matric potential sensors and water content sensors in situ add many more moisture release curves to a researcher’s knowledge base. And, since it is primarily the in-place performance of unsaturated soils that is the chief concern to geotechnical engineers and irrigation scientists, adding in situ measurements to lab-produced curves would be ideal.
In this brief 20-minute webinar, Dr. Colin Campbell, METER research scientist, summarizes a recent paper given at the Pan American Conference of Unsaturated Soils. The paper, “Comparing in situ soil water characteristic curves to those generated in the lab” by Campbell et al. (2018), illustrates how well in situ generated SWCCs using the TEROS 21 calibrated matric potential sensor and METER’s GS3 water content sensor compare to those created in the lab.