Posts from the ‘Irrigation’ Category

Apr 17

Soil Moisture Sensors: Why TDR vs. Capacitance May Be Missing the Point

Time Domain Reflectometry (TDR) vs. capacitance is a common question for scientists who want to measure volumetric water content (VWC) of soil, but is it the right question? Dr. Colin S. Campbell, soil scientist, explains some of the history and technology behind TDR vs. capacitance and the most important questions scientists need to ask before investing in a sensor system.

Image of a telephone poll standing in front of the ocean

TDR began as a technology the power industry used to determine the distance to a break in broken power lines.

Clarke Topp

In the late 1970s, Clarke Topp and two colleagues began working with a technology the power industry used to determine the distance to a break in broken power lines. Time Domain Reflectometers (TDR) generated a voltage pulse which traveled down a cable, reflected from the end, and returned to the transmitter. The time required for the pulse to travel to the end of the cable directed repair crews to the correct trouble spot. The travel time depended on the distance to the break where the voltage was reflected, but also on the dielectric constant of the cable environment. Topp realized that water has a high dielectric constant (80) compared to soil minerals (4) and air (1). If bare conductors were buried in soil and the travel time measured with the TDR, he could determine the dielectric constant of the soil, and from that, its water content. He was thus able to correlate the time it took for an electromagnetic pulse to travel the length of steel sensor rods inserted into the soil to volumetric water content. Despite his colleagues’ skepticism, he proved that the measurement was consistent for several soil types.

TDR sensors consume a lot of power. They may require solar panels and larger batteries for permanent installations.

TDR Technology is Accurate, but Costly

In the years since Topp et al.’s (1980) seminal paper, TDR probes have proven to be accurate for measuring water content in many soils. So why doesn’t everyone use them? The main reason is that these systems are expensive, limiting the number of measurements that can be made across a field. In addition, TDR systems can be complex, and setting them up and maintaining them can be difficult. Finally, TDR sensors consume a lot of power. They may require solar panels and larger batteries for permanent installations. Still, TDR has great qualities that make these types of sensors a good choice. For one thing, the reading is almost independent of electrical conductivity (EC) until the soil becomes salty enough to absorb the reflection. For another, the probes themselves contain no electronics and are therefore good for long-term monitoring installations since the electronics are not buried and can be accessed for servicing, as needed. Probes can be multiplexed, so several relatively inexpensive probes can be read by one set of expensive electronics, reducing cost for installations requiring multiple probes.

Many modern capacitance sensors use high frequencies to minimize effects of soil salinity on readings.

Advances in Electronics Enable Capacitance Technology

Dielectric constant of soil can also be measured by making the soil the dielectric in a capacitor. One could use parallel plates, as in a conventional capacitor, but the measurement can also be made in the fringe field around steel sensor rods, similar to those used for TDR. The fact that capacitance of soil varies with water content was known well before Topp and colleagues did their experiments with TDR. So, why did the first attempt at capacitance technology fail, while TDR technology succeeded? It all comes down to the frequency at which the measurements are made. The voltage pulse used for TDR has a very fast rise time. It contains a range of frequencies, but the main ones are around 500 MHz to 1 GHz. At this high frequency, the salinity of the soil does not affect the measurement in soils capable of growing most plants.

Like TDR, capacitance sensors use a voltage source to produce an electromagnetic field between metal electrodes (usually stainless steel), but instead of a pulse traveling down the rods, positive and negative charges are briefly applied to them. The charge stored is measured and related to volumetric water content. Scientists soon realized that how quickly the electromagnetic field was charged and discharged was critical to success. Low frequencies led to large soil salinity effects on the readings. This new understanding, combined with advances in the speed of electronics, meant the original capacitance approach could be resurrected. Many modern capacitance sensors use high frequencies to minimize effects of soil salinity on readings.

NASA used capacitance technology to measure water content on Mars.

Capacitance Today is Highly Accurate

With this frequency increase, most capacitance sensors available on the market show good accuracy. In addition, the circuitry in them can be designed to resolve extremely small changes in volumetric water content, so much so, that NASA used capacitance technology to measure water content on Mars. Capacitance sensors are lower cost because they don’t require a lot of circuitry, allowing more measurements per dollar. Like TDR, capacitance sensors are reasonably easy to install. The measurement prongs tend to be shorter than TDR probes so they can be less difficult to insert into a hole. Capacitance sensors also tend to have lower energy requirements and may last for years in the field powered by a small battery pack in a data logger.

In two weeks: Learn about challenges facing both types of technology and why the question of TDR vs. Capacitance may not be the right question.

Watch the webinar

In this webinar, Dr. Colin Campbell discusses the details regarding different ways to measure soil moisture and the theory behind the measurements. In addition, he provides examples of field research and what technology might apply in each situation. The measurement methods covered are gravimetric sampling, dielectric methods including TDR and FDR/capacitance, neutron probe, and dual needle heat pulse.

Get more information on applied environmental research in our

Take our Soil Moisture Master Class

Six short videos teach you everything you need to know about soil water content and soil water potential—and why you should measure them together. Plus, master the basics of soil hydraulic conductivity.

Watch it now—>

Download the “Researcher’s complete guide to soil moisture”—>

LinkedIn Facebook Messenger X Reddit Threads Email Copy

Mar 30

New Weather Station Technology in Africa-3

The Trans African Hydro and Meteorological Observatory (TAHMO) project expects to put 20,000 ATMOS 41 weather stations over Africa in order to understand the weather patterns which affect that continent, its water, and its agriculture. In the conclusion of our 3-part series, we interview Dr. John Selker about his thoughts on the project.

The economics of weather data value may be going up because we’re reaching a cusp in terms of humanity’s consumption of food.

In your TEDx talk you estimate that US weather stations directly bring U.S. consumers 31 billion dollars in value per year. Can Africa see that same kind of return?

Even more. The economics of weather data value may be going up because we’re reaching a cusp in terms of humanity’s consumption of food. Africa, one could argue, is the breadbasket for this coming century. Thus, the value of information about where we could grow what food could be astronomical. It’s very difficult to estimate. One application of weather data is crop insurance. Right now, crop insurance is taking off across Africa. The company we’re working with has 180,000 clients just in Kenya. When we talked about 31 billion dollars in the U.S., that is the value citizens report, but you need to add to that protection against floods, increased food production, water supply management, crop insurance and a myriad of other basic uses for weather data. In Africa, the value of this type of protection alone pays for over 1,000 times the cost of the weather stations.

Another application for weather data is that in Africa, the valuation of land itself is uncertain. So if, because of weather station data, we find that a particular microclimate is highly valuable, suddenly land goes from having essentially no value to becoming worth thousands of dollars per acre. It’s really difficult to estimate the impact the data will have, but it could very well end up being worth trillions of dollars. We have seen this pattern take place in central Chile, where land went from about $200/hectare in 1998 to over $3,000/ha now due to the understanding that it was exceptionally suited to growing pine trees, which represented a change in land value exceeding $3 billion.

Does the effect of these weather stations go beyond Africa?

There’s limited water falling on the earth, and if you can’t use weather data to invest in the right seeds, the right fertilizer, and plant at the right time in the right place, you’re not getting the benefit you should from having tilled the soil. So for Africa the opportunity to improve yields with these new data is phenomenal.

In terms of the world, the global market for calories is now here, so if we can generate more food production in Africa, that’s going to affect the price and availability of food around the world. The world is one food community at this point, so an entire continent having inefficient production and ineffective structures costs us all.

Students stand in front of an installation site in Africa

If we can generate more food production in Africa, that’s going to affect the price and availability of food around the world.

You’re collecting data from Africa. Is it time to celebrate yet?

I think this is going to be one of those projects where we are always chilling the champagne and never quite drinking it. It is such a huge scope trying to work across a continent. So I would say we’ve got some stations all over Africa, we’re learning a lot, and we’ve got collaborators who are excited. We have reason to feel optimistic. It will be another five years before I’ll believe that we have a datastream that is monumental. Right now we’re still getting the groundwork taken care of. By September of this year we expect to have five hundred of stations in place, and then two years from now, over two thousand. This will be a level of observation that will transform the understanding of African weather and climate.

This is a project of hundreds of people across the world putting their hands and hearts in to make this possible.

How do you deal with the long wait for results?

In science, there is that sense you get when you want to know something, and you can see how to get there. You have a theory, and you want to prove it. It kind of captures your imagination. It’s a combination of curiosity and the potential to actually see something happen in the world: to go from a place where you didn’t know what was going on to a place where you do know what’s going on. I think about Linus Pauling, who made the early discoveries about the double helix. He had in his pocket the X-ray crystallography data to show that the protein of life was in helical form, and he said, “In my pocket, I have what’s going to change the world.” When we realized the feasibility of TAHMO, we felt much the same way.”

Sometimes in your mind, you can see that path: how you might change the world. It may never be as dramatic as what Pauling did, but even a small contribution has that same excitement of wanting to be someone who added to the conversation, who added to our ability to live more gracefully in the world. It’s that feeling that carries you along, because in most of these projects you have an idea, and then ten years later you say, “why was it that hard?”

Things are usually much harder than your original conception, and that energy and curiosity really helps you through some of the low points in your projects. So, curiosity has a huge influence on scientific progress. Changing the world is always difficult, but the excitement, curiosity, and working with people, it all fits together to help us draw through the tough slogs. In TAHMO, I cannot count the number of people who have urged us to keep the effort moving forward and given a lift just when we needed it most. This is a project of hundreds of people across the world putting their hands and hearts in to make this possible. Having these TAHMO supporters is an awesome responsibility and concrete proof of the generosity and optimism of the human spirit.

Learn how you can help TAHMO.

See weather sensor performance data for the ATMOS 41 weather station.

Explore which weather station is right for you.

Download the “Researcher’s complete guide to soil moisture”—>

LinkedIn Facebook Messenger X Reddit Threads Email Copy

Feb 20

How to Get More From Your NDVI Sensor (Part 3)

In the conclusion of our three-part series on improving NDVI sensor data (see part 2), we discuss how to correct for limitations which occur in high leaf area index (LAI) conditions.

Where there’s a large amount of vegetation, NDVI tends to saturate.

NDVI Limitations – High LAI

NDVI is useful in the midrange of LAI’s as long as you don’t have strong soil effects, but as you approach an LAI above 4, you lose sensitivity. In figure 6, loss of sensitivity is primarily due to a saturation in the red band. Measurements were taken in a wheat canopy and a maize canopy. The near-infrared reflectance is sensitive across the entire spectrum of the wheat and maize canopies, but the red saturates relatively quickly. Where the red starts to saturate is where the NDVI starts to saturate.

Figure 6: Gitelson (2004) J. Plant Phys

Note: NDVI saturates at high LAI’s, however, if your purpose is to get at the fractional interception of light, NDVI tends not to have the saturation issue. In Figure 7, Fpar or the fractional interception of light of photosynthetically radiation is nearly complete far before NDVI saturates. This is because canopies are efficient at intercepting light, and once we get to an LAI of about 4, most of the light has been intercepted or absorbed by the canopy. Thus, incremental increases in LAI don’t significantly affect the FPar variable.

Figure 7: Fractional interception of light is near complete at an LAI around 4. (Gamon et al. (1995) Eco. Apps)

Solution 3- WDRVI

One solution for the NDVI saturation issue is called the Wide Dynamic Range Vegetation Index (WDRVI). Its formulation is similar to NDVI, except for a weighting coefficient that can be used to reduce the disparity between the contribution of the near infrared and red reflectance.

In the WDRVI, a is multiplied by the near-infrared reflectance to reduce its value and bring it closer to the red reflectance value. In doing so, it balances out the red and the near-infrared contribution to the vegetation index.

Figure 8: (Gitelson (2004) J. Plant Phys)

a can range anywhere from 0 to 1. Figure 8 shows that as we use a smaller value of a, we get an increasing linear response of the wide dynamic vegetation index to LAI.

The only drawback of the WDRVI is that the selection of a is subjective. It’s something that you experiment on your own until you find a value for a that is optimal for your solution. People tend to err on the side of a very low value simply because they’ll get closer and closer to a linear response to LAI as a decreases.

Solution 4 – Enhanced Vegetation Index

The enhanced vegetation index (EVI) was designed to enhance sensitivity in high biomass ecosystems, but it also attempts to reduce atmospheric influences. This was a vegetation index created for the purposes of a satellite-based platform. There’s a lot of atmosphere to look through from a satellite to the ground, and sometimes the aerosols in the atmosphere affect the reflectances in the red and the near infrared regions causing spurious observations. The EVI also tries to reduce sensitivity of the index to soil. Thus the EVI is a kind of solution to both extremes.

In the EVI equation, the two major inputs are near infrared and red reflectances. C1 , C2, and L are all parameters that can be estimated, but the blue band is something that has to be measured. Most NDVI sensors are two band sensors, so you don’t have that information in the blue. Plus, with satellites, the blue band is relatively noisy and doesn’t always have the best quality data, thus EVI has limited value.

Solution 6: EVI2 (Enhanced Vegetation Index 2)

Those problems led a scientist named Jiang to come up with a solution. Jiang observed quite a bit of autocorrelation between the red band and the blue band, so he decided to try and formulate EVI without the blue band in what he called the EVI2 (Enhanced Vegetation Index 2). if you’re interested in the mathematics, we encourage you to read his paper, but here we give you the equation in case you’re interested in using it.

Figure 9

When Jiang calculated his EVI2 and compared it to the traditional EVI (Figure 9), it was nearly a one to one relationship. For all intents and purposes EVI2 was equivalent to EVI. Since this avoids blue band, it offers some exciting possibilities as it reduces to just using the two inputs of NIR and red bands to calculate NDVI.

NDVI Sensor Summary

NDVI measurements have considerable value, and though there are extremes where NDVI performs poorly, even in these cases there are several solutions. These solutions all use the near infrared and the red bands, so you can take an NDVI sensor, obtain the raw values of NIR and red reflectances and reformulate them in one of these indices (there are several other indices available that we haven’t covered). So if you’re in a system with extremely high or low LAI, try to determine how near infrared and red bands can be used in some type of vegetation index to allow you to research your specific application.

Download the “Researcher’s complete guide to soil moisture”—>

Download the “Researcher’s complete guide to leaf area index (LAI)”—>

Get more information on applied environmental research in our

LinkedIn Facebook Messenger X Reddit Threads Email Copy

Feb 13

Get More From Your NDVI Sensor (Part 2)

Last week we discussed Normalized Difference Vegetation Index (NDVI) sampling across a range of scales both in space and in time, from satellites sampling the entire earth’s surface to handheld small sensors that measure individual plants or even leaves (see part 1). This week, learn about NDVI applications, limitations, and how to correct for those limitations.

Field with crop seedlings starting to sprout

Limitations of the Normalized Difference Vegetation Index tend to occur at the extremes of the spectrum.

NDVI Applications

People use NDVI to infer things like leaf area index (LAI) or fractional light interception (FPAR) of a canopy. Some scientists also associate NDVI with biomass or yield of a crop. People also use NDVI to get a sense of phenology (general temporal patterns of greenness), as well as where vegetation occurs or how much vegetation is in a particular location.

In Figure 4, you can see how the reflectance spectrum at a given canopy LAI changes with leaf area index, decreasing in the visible range while increasing in the near infrared.

Figure 4

At very low LAI’s, the reflectance spectrum is relatively undifferentiated between red and NIR (black line), but when LAI is high, there’s a strong absorption of red light by chlorophyll with a strong reflectance in the NIR. In fact, as LAI increases, there’s an ever-increasing reflectance in the near infrared around 800 nm.

NDVI Limitations

Limitations of the Normalized Difference Vegetation Index tend to occur at the extremes of the spectrum. Any time there’s very low vegetation cover (majority of the scene is soil), NDVI will be sensitive to that soil. This can confound measurements. On the other extreme, where there’s a large amount of vegetation, NDVI tends to saturate. Notice the negligible difference between spectra at a leaf area index (LAI) of 3 (purple) versus 6 (green). Indeed, in a tropical forest, NDVI will not be sensitive to small changes in the LAI because LAI is already very high. However, several solutions exist.

Solution 1-Soil Adjusted Vegetation Index

Figure 5 shows the results of a study taking spectral measurements of different vegetation indices across a transect of bare soil. Moving from dry clay loam to wet clay loam, we see a very strong response of NDVI due to the wetness of the soil; undesirable if we’re measuring vegetation. We’re not interested in an index that’s sensitive to changes in soil or soil moisture. However, there are a few other indices plotted in figure 5 with much lower sensitivities to variations in the soil across the transect.

Figure 5: Qi et al. (1994) Rem. Sens. Env.

The first one of those indices is the Soil Adjusted Vegetation Index (SAVI). The equation for SAVI is similar to NDVI. It incorporates the same two bands as the NDVI—the near infrared and the red.

Image depicts two equations one is NDVI and the other is SAVI

Soil Adjusted Vegetation Index (Huete (1988) Rem. Sens. Env.)

The only thing that’s different, is the L parameter. L is a soil adjustment factor with values that range anywhere from 0 to 1. When vegetation cover is 100%, L is 0 because there’s no need for a soil background adjustment. However, when vegetation cover is very low, that L parameter will approach one. Because it is difficult to measure exactly how much vegetation cover you have without using NDVI, we can modify the NDVI so it’s not sensitive to soil by guessing beforehand what L should be. It’s common practice to set L to an intermediate value of 0.5. You can see in Figure 5 the Soil Adjusted Vegetation Index or SAVI has a much lower sensitivity to the soil background.

Solution 2- Modified SAVI

The next vegetation index is the modified SAVI (MSAVI). The SAVI equation contains an L parameter that we have to estimate—not an accurate way of handling things. So a scientist named Key developed a universal optimum for L. We won’t get into the math, but he was able to simplify the SAVI equation to where there’s no longer a need for the L parameter, and the only inputs required are the reflectances in the near infrared and the red.

Image depicts two equations SAVI is the top equation while the bottom equation is modified SAVI or MSAVI

Modified SAVI (Qi et al. (1994) Rem. Sens. Env.)

This was a pretty significant advance as it circumvented the need to estimate or independently measure L. When Key compared SAVI to MSAVI, there was virtually no difference between the two indices in terms of their sensitivity to the amount of vegetation and their response to the soil background.

Depicts a compairson of MSAVI and SAVI in terms of dynamic range and noise level

MSAVI compares well with SAVI in terms of dynamic range and noise level (Qi et al. (1994) Rem. Sens. Env.)

Next week: Learn about solutions for high LAI.

Download the “Researcher’s complete guide to soil moisture”—>

Download the “Researcher’s complete guide to leaf area index (LAI)”—>

Get more information on applied environmental research in our

LinkedIn Facebook Messenger X Reddit Threads Email Copy

Jan 2

Lysimeters Determine if Human Waste Composting can be More Efficient

In Haiti, untreated human waste contaminating urban areas and water sources has led to widespread waterborne illness. Sustainable Organic Integrated Livelihoods (SOIL) has been working to turn human waste into a resource for nutrient management by turning solid waste into compost. Read more…

Estimating Relative Humidity in Soil: How to Stop Doing it Wrong

Estimating the relative humidity in soil? Most people do it wrong…every time. Dr. Gaylon S. Campbell shares a lesson on how to correctly estimate soil relative humidity from his new book, Soil Physics with Python, which he recently co-authored with Dr. Marco Bittelli. Read more.…

How Many Soil Moisture Sensors Do You Need?

“How many soil moisture sensors do I need?” is a question that we get from time to time. Fortunately, this is a topic that has received substantial attention by the research community over the past several years. So, we decided to consult the recent literature for insights. Here is what we learned.

Data loggers: To Bury, or Not To Bury

Globally, the number one reason for data loggers to fail is flooding. Yet, scientists continue to try to find ways to bury their data loggers to avoid constantly removing them for cultivation, spraying, and harvest. Chris Chambers, head of Sales and Support at Decagon Devices always advises against it. Read more…

Founders of Environmental Biophysics: Champ Tanner

Image: http://soils.wisc.edu/people/history/champ-tanner/

We interviewed Gaylon Campbell, Ph.D. about his association with one of the founders of environmental biophysics, Champ Tanner. Read more…

And our three most popular blogs of all time:

Do the Standards for Field Capacity and Permanent Wilting Point Need to Be Reexamined?

We asked scientist, Dr. Gaylon S. Campbell, which scientific idea he thinks impedes progress. Here’s what he had to say about the standards for field capacity and permanent wilting point. Read more…

Environmental Biophysics Lectures

During a recent semester at Washington State University, a film crew recorded all of the lectures given in the Environmental Biophysics course. The videos from each Environmental Biophysics lecture are posted here for your viewing and educational pleasure. Read more…

Soil Moisture Sensors In a Tree?

Soil moisture sensors belong in the soil. Unless, of course, you are feeling creative, curious, or bored. Then maybe the crazy idea strikes you that if soil moisture sensors measure water content in the soil, why couldn’t they be used to measure water content in a tree? Read more…

Download the “Researcher’s complete guide to soil moisture”—>

Get more information on applied environmental research in our

LinkedIn Facebook Messenger X Reddit Threads Email Copy

Dec 26

1 Comment

How to Measure Water Potential

In the conclusion of our 3-part water potential series (see part 1), we discuss how to measure water potential—different methods, their strengths, and their limitations.

Image of a mountain with a little snow on the top

Vapor pressure methods work in the dry range.

How to measure water potential

Essentially, there are only two primary measurement methods for water potential—tensiometers and vapor pressure methods. Tensiometers work in the wet range—special tensiometers that retard the boiling point of water (UMS) have a range from 0 to about -0.2 MPa. Vapor pressure methods work in the dry range—from about -0.1 MPa to -300 MPa (0.1 MPa is 99.93% RH; -300 MPa is 11%).

Historically, these ranges did not overlap, but recent advances in tensiometer and temperature sensing technology have changed that. Now, a skilled user with excellent methods and the best equipment can measure the full water potential range in the lab.

There are reasons to look at secondary measurement methods, though. Vapor pressure methods are not useful in situ, and the accuracy of the tensiometer must be paid for with constant, careful maintenance (although a self-filling version of the tensiometer is available).

Here, we briefly cover the strengths and limitations of each method.

Vapor Pressure Methods:

The WP4C Dew Point Hygrometer is one of the few commercially available instruments that currently uses this technique. Like traditional thermocouple psychrometers, the dew point hygrometer equilibrates a sample in a sealed chamber.

WP4C Dew Point Hygrometer

A small mirror in the chamber is chilled until dew just starts to form on it. At the dew point, the WP4C measures both mirror and sample temperatures with 0.001◦C accuracy to determine the relative humidity of the vapor above the sample.

Advantages

The most current version of this dew point hygrometer has an accuracy of ±1% from -5 to -300 MPa and is also relatively easy to use. Many sample types can be analyzed in five to ten minutes, although wet samples take longer.

Limitations

At high water potentials, the temperature differences between saturated vapor pressure and the vapor pressure inside the sample chamber become vanishingly small.

Limitations to the resolution of the temperature measurement mean that vapor pressure methods will probably never supplant tensiometers.

The dew point hygrometer has a range of -0.1 to -300 MPa, though readings can be made beyond -0.1 MPa using special techniques. Tensiometers remain the best option for readings in the 0 to-0.1 MPa range.

Secondary Methods

Water content tends to be easier to measure than water potential, and since the two values are related, it’s possible to use a water content measurement to find water potential.

A graph showing how water potential changes as water is adsorbed into and desorbed from a specific soil matrix is called a moisture characteristic or a moisture release curve.

Example of a moisture release curve.

Every matrix that can hold water has a unique moisture characteristic, as unique and distinctive as a fingerprint. In soils, even small differences in composition and texture have a significant effect on the moisture characteristic.

Some researchers develop a moisture characteristic for a specific soil type and use that characteristic to determine water potential from water content readings. Matric potential sensors take a simpler approach by taking advantage of the second law of thermodynamics.

Matric Potential Sensors

Matric potential sensors use a porous material with known moisture characteristic. Because all energy systems tend toward equilibrium, the porous material will come to water potential equilibrium with the soil around it.

Matric Potential Sensor

Using the moisture characteristic for the porous material, you can then measure the water content of the porous material and determine the water potential of both the porous material and the surrounding soil. Matric potential sensors use a variety of porous materials and several different methods for determining water content.

Accuracy Depends on Custom Calibration

At its best, matric potential sensors have good but not excellent accuracy. At its worst, the method can only tell you whether the soil is getting wetter or drier. A sensor’s accuracy depends on the quality of the moisture characteristic developed for the porous material and the uniformity of the material used. For good accuracy, the specific material used should be calibrated using a primary measurement method. The sensitivity of this method depends on how fast water content changes as water potential changes. Precision is determined by the quality of the moisture content measurement.

Accuracy can also be affected by temperature sensitivity. This method relies on isothermal conditions, which can be difficult to achieve. Differences in temperature between the sensor and the soil can cause significant errors.

Limited Range

All matric potential sensors are limited by hydraulic conductivity: as the soil gets drier, the porous material takes longer to equilibrate. The change in water content also becomes small and difficult to measure. On the wet end, the sensor’s range is limited by the air entry potential of the porous material being used.

METER Tensiometer

Tensiometers and Traditional Methods

Read about the strengths and limitations of tensiometers and other traditional methods such as gypsum blocks, pressure plates, and filter paper here.

Choose the right water potential sensor

Dr. Colin Campbell’s webinar “Water Potential 201: Choosing the Right Instrument” covers water potential instrument theory, including the challenges of measuring water potential and how to choose and use various water potential instruments.

Take our Soil Moisture Master Class

Watch it now—>

Download the “Researcher’s complete guide to water potential”—>

Download the “Researcher’s complete guide to soil moisture”—>

Get more information on applied environmental research in our

LinkedIn Facebook Messenger X Reddit Threads Email Copy

Dec 19

1 Comment

Water Potential: The Science Behind the Measurement (Part 2)

In the second part of this month’s water potential series (see part 1), we discuss the separate components of a water potential measurement. The total water potential is the sum of four components: matric potential, osmotic potential, gravitational potential, and pressure potential. This article gives a description of each component. Read the article here…

Visualize Matric Potential

Next Week: Learn the different methods for measuring water potential and their strengths and limitations.

Take our Soil Moisture Master Class

Watch it now—>

Download the “Researcher’s complete guide to water potential”—>

Download the “Researcher’s complete guide to soil moisture”—>

Get more information on applied environmental research in our

LinkedIn Facebook Messenger X Reddit Threads Email Copy

Dec 11

2 Comments

Secrets of Water Potential: Learn the Science Behind the Measurement

This month in a 3 part series, we will explore water potential —the science behind it and how to measure it effectively.

Pouring water into a glass with ice around the glass

To understand water potential, compare the water in a soil sample to water in a drinking glass.

Water Potential: a Definition

Read the article here…

Next week learn about the four components of water potential—osmotic potential, gravitational potential, matric potential, and pressure potential.

Take our Soil Moisture Master Class

Watch it now—>

Download the “Researcher’s complete guide to water potential”—>

Download the “Researcher’s complete guide to soil moisture”—>

Get more information on applied environmental research in our

LinkedIn Facebook Messenger X Reddit Threads Email Copy

Dec 5

Data Logger Dilemma: To Bury, or Not to Bury—An Update

Recently, we wrote about scientists who were burying their data loggers (read it here). Radu Carcoana, research specialist and Dr. Aaron Daigh, assistant professor at North Dakota State University, used paint cans to completely seal their data loggers before burying them in the fall of 2015.

Data logger in a paint can with sensor cords, being prepared by researchers to be buried

Paint can setup for buried data logger.

They drilled ports for the sensor cables, sealed them up, and when they needed to collect data, they dug up the cans, retrieved the instruments, and downloaded the data in a minute or less.

Here Radu gives an update of what happened when he dug up his buried instruments in the spring.

Results of the Paint Can Experiment

In May of this year, we dug up eighteen units (one data logger and four soil moisture sensors per unit) left in the field since November 2015—over six months.

Did moisture get into the paint cans? —We found only three cans with water in them, purely due to installation techniques used for that specific unit. The other fifteen units were bone dry, although total precipitation for the month of April only amounted to 3.63 inches, plus the snow melt.

How was data recording and recovery? —For six months, every 30 minutes the soil moisture sensors took readings, the data logger recorded, and we retrieved all of the data, complete and unaltered.

Image of a METER Data Logger in a can with water in it, which was a result of a faulty burying installation

Only three cans with water in them, due to installation techniques.

What about power consumption? The batteries were good —over 90% did not need replacement. The power budget provided by five AA batteries was more than enough for reading four soil moisture sensors at 30-minute intervals.

What Happens Now?

In the spring of this year, we installed 18 more units in the third farm field, right after planting soya. We now have 36 individual units (~$1,000 value each unit) buried in the ground in the middle of a field planted with corn or soybean, since the beginning of May.

On October 13-14 (after 5 months), we accessed the first twelve units (Farm A). All 30 minutes of data was read, recorded, and downloaded (since May). The batteries and the other accessories were replaced, and then we sealed and reburied the cans. Only one unit out of twelve had an issue and was replaced: the battery exploded in the can (editor’s note: battery explosion is usually caused by a manufacturing defect and the risk can be lessened by purchasing higher quality batteries, although all types are susceptible to some degree). Since battery leakage will often corrode everything the acid touches, the data logger had to be sent back for repair and there may be partial data loss. The other 24 units (Farm B and C) will be accessed next week, weather permitting.

METER Data Logger open on top of an experimental burying site

Over 90% of batteries did not need replacement.

Is the Paint Can Method Worth it?

We will continue to monitor and retrieve the data from the buried data loggers (We don’t use data loggers suited for wireless communication, because several factors guided us not to). The paint can system works very well if the installation is done correctly, with great attention to detail, and it costs only $2.00/can. However, there are improvements that could be made in order to have this method become a standard in soil research. For instance, though we are still using paint cans and other common materials, advancements in the design of waterproof containers and sturdiness would be a huge step forward. This is just a well thought out concept – a prototype. It proves that burying electronics for a longer period of time can be done if properly executed.

Note: METER’s (formerly Decagon) official position is that you should never bury your data logger. But we couldn’t resist sharing a few stories of scientists who have figured out some innovative methods which may or may not be successful, if tried at other sites.

Download the “Researcher’s complete guide to soil moisture”—>

Download the “Researcher’s complete guide to SDI-12″—>

Get more information on applied environmental research in our

LinkedIn Facebook Messenger X Reddit Threads Email Copy

Nov 14

Water Potential Instruments used to Determine Where Alkali Bee Larvae Get their Water

Alkali bee beds are maintained by farmers near Touchet, Washington to pollinate fields of alfalfa, grown there for seed. The beds are typically a few acres in size and provide a nesting place for the bees, which can increase seed production by as much as 70 percent. Alkali bees are better than honeybees for pollinating alfalfa, as they don’t mind the explosive pollen release of the alfalfa flower.

Alkali Bee

USDA-ARS entomologist, Dr. Jim Cane, is trying to understand optimal bee-soil-water relations to ensure the bees will happily reproduce next year’s pollinators. Dr. Gaylon S. Campbell recently worked with Dr. Cane to measure water relations in bee nesting beds. Here’s what they found out:

Why Water Relations Matter

Alkali bees nest underground. They prefer salty soil surfaces which retard evaporation and discourage plant growth. The soil has to be the right texture, density, and have the correct moisture levels for successful nesting. In addition, the water potential of the larval food provision mass has to be low so it does not mold. Growers apply high levels of sodium chloride to the bee bed surface, and the soil is sub-irrigated to keep the salt near the surface and the subsurface soil moist.

Bottom right: a white larvae on a gold colored provision mass inside one of the tunnels dug by the female.

The female digs a tunnel down to a favorable depth, typically 15-20 cm or more, hollows out a spheroidal shaped cell around 1 cm diameter, and carefully coats the inside of the cell with a special secretion that appears to form a hydraulic and vapor barrier between the soil and the nest contents. She then builds a provision mass from pollen and nectar, shaped like an oblate spheroid with major axis around 6 mm and minor axis 3-4 mm. One egg is laid on the provision mass (which provides food for the larva), and the mother bee then seals up the entrance to the cell and moves on to the next one.

The female coats the inside of the cell with a special secretion that appears to form a hydraulic and vapor barrier between the soil and the nest contents.

Specialized Instruments for Each Measurement

In order to understand moisture relations between the soil, the larva, and the food provision mass, Dr. Cane carefully excavated three soil blocks from one of the bee beds, dissected them to find nests, and Dr. Campbell helped measure water potentials of the eggs, larvae, and provision masses. They also measured matric and total water potentials of bee bed soils.

A researcher with a instrument called a sample chamber psychometer sitting in front of him

A sample chamber psychrometer

A Sample Chamber Psychrometer is the only water potential device with a small enough sample chamber to be able to measure individual eggs and early-stage larvae, which it did. The provision masses were too dry to measure with the psychrometer, so several provisions were combined (to provide sufficient sample size) and measured in a Dew Point Potentiameter, along with the soil samples. Dr. Campbell measured matric potential of the highly saline soils using a tensiometer.

Water Potential Seems Important to the Bees

Dr. Campbell thinks matric potential is important in determining physical condition of the soil (how easy it is for the bees to dig and paint the inside of the nest), but probably has little to do with bee or larva water relations. The water potentials of the eggs and larvae were low (dry), but within the range one sees in living organisms. There was a consistent pattern of larva water potential decreasing with larval growth.

Image of an Alkali Bee seeking shelter in a rain storm in a little tunnel in the dirt

This alkali bee seeks shelter during the rain in a previously dug tunnel.

The exciting part of this experiment was the provision mass water potentials, which were so low that it is more convenient to talk about them in terms of water activity (another measure of the energy state of water in a system, widely used by food scientists). The intact provision masses were drier than any of the soil water potentials and not in equilibrium with the soil. Dr. Campbell says, “It’s interesting that all the provision masses were at water activities that would make them immune to degradation by almost all microbes, both bacteria and fungi.”

Another Interesting Observation

Dr. Cane found one provision mass covered with mold. Soil and plants are full of inoculum, so it is unlikely that the other provision masses lacked spores, but this one was wet enough to be compromised, and the others apparently weren’t. Dr. Campbell says, “There are two possibilities. Either it was put up too wet, or it got wet in the nest. The really interesting question is why all of them don’t get that wet. I think the hydrophobic coating of the nest eliminates all hydraulic contact from the soil to the provision mass, thus eliminating any liquid water flow, which would almost immediately wet the pollen balls. I think it also drastically reduces the vapor conductance from the soil to the ball, making water uptake through the vapor phase slow enough that the provision mass can usually be consumed before its water activity gets high enough for mold to grow.”

Image of a large green tool used to punch holes in the soil for Alkali Bees to nest in laying on top of the soil

Tool the grower uses to punch holes in the nesting beds for the bees to tunnel into.

How Do Larvae Stay Hydrated?

The water activity of the larvae were around 0.99, much higher than either the soil or the provision mass, inspiring the scientists to wonder how they stay hydrated. Dr. Campbell speculates, “They have a water source from their metabolism, since water is a byproduct of respiration (Campbell and Norman, p. 205). It is also possible for biological systems to take up water against a potential gradient by expending energy. There are reports of a beetle which can take up water from a drop of saturated NaCl (water activity 0.75), so it is possible that the larva gets water from the environment that way. There appears to be no shortage of energy available. On the other hand, it would seem like the larval cuticle would need to be pretty impermeable to maintain water balance since the salty soil, and especially the provision mass, are so much drier than the larva.” Dr. Cane notes that, ”For a few exemplar bee species, mature larvae weigh 30-40% more than the provision they ate, with the possibility that the provision undergoes a controlled hydration by the soil atmosphere through the uncoated soil cap of the nest cell.”

In the future, Dr. Campbell is hoping to see more experiments that will answer some of the questions raised, such as measuring individual provision masses to determine why there is some variation in water potential. Dr. Cane will be undertaking experiments to measure moisture weight gain of new provisions exposed to the soil atmosphere of the Touchet nest bed soil.

Download the “Researcher’s complete guide to water potential”—>

Download the “Researcher’s complete guide to soil moisture”—>

Get more information on applied environmental research in our

References

Campbell, G. S. 1985. Soil Physics with BASIC: Transport Models for Soil-Plant Systems. Elsevier, New York.

Campbell, G. S. and J. M. Norman. 1998. An Introduction to Environmental Biophysics. Springer Verlag, N. Y.

Rawlins, S. L. and G. S. Campbell. 1986. Water potential: thermocouple psychrometry. In Methods of Soil Analysis, Part 1. Physical and Mineralogical Methods – Agronomy Monograph 9, 2nd edition.

LinkedIn Facebook Messenger X Reddit Threads Email Copy

Measuring and Modeling the Environment

Posts from the ‘Irrigation’ Category

Clarke Topp

TDR Technology is Accurate, but Costly

Advances in Electronics Enable Capacitance Technology

Capacitance Today is Highly Accurate

Watch the webinar

Take our Soil Moisture Master Class

NDVI Limitations – High LAI

Solution 3- WDRVI

Solution 4 – Enhanced Vegetation Index

Solution 6: EVI2 (Enhanced Vegetation Index 2)

NDVI Sensor Summary

NDVI Applications

NDVI Limitations

Solution 1-Soil Adjusted Vegetation Index

Solution 2- Modified SAVI

And our three most popular blogs of all time:

How to measure water potential

Vapor Pressure Methods:

Secondary Methods

Matric Potential Sensors

Tensiometers and Traditional Methods

Choose the right water potential sensor

Take our Soil Moisture Master Class

Visualize Matric Potential

Take our Soil Moisture Master Class

Water Potential: a Definition

Take our Soil Moisture Master Class

Results of the Paint Can Experiment

What Happens Now?

Is the Paint Can Method Worth it?

Why Water Relations Matter

Specialized Instruments for Each Measurement

Water Potential Seems Important to the Bees

Another Interesting Observation

How Do Larvae Stay Hydrated?