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Posts tagged ‘Measurements’

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.

Image of the earth from far away

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.

Two workers working hard in a field

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.

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Best Research Instrument Hacks

We wanted to highlight innovative ways people have modified their instrumentation to fit their research needs.  Here, Georg von Unold, founder and president of UMS (now METER) illustrates ingenuity in a story that inspired the invention of the first UMS tensiometer and what could be one of the greatest scientific instrument hacks of all time.

Image of the Bavarian Alps with snow on top

The Bavarian Alps

An Early Penchant for Ingenuity

In 1986, graduating German students were required to join the military or perform civil service.  Von Unold chose to do a civil service project investigating tree mortality in the alpine region of the Bavarian Mountains.  He explains, “We were trying to understand pine tree water stress in a forest decline study related to storms in certain altitudes where trees were inexplicably falling over. The hypothesis was that changing precipitation patterns had induced water stress.”  

To investigate the problem, von Unold’s research team needed to find tensiometers that could measure the water stress of plants in the soil, which was not easy. The tensiometers von Unold found were not able to reach the required water potential without cavitating, so he decided to design a new type of tensiometer.  He says, “I showed my former boss the critical points. It must be glued perfectly, the ceramic needed defined porosity, a reliable air reference access, and water protection of the pressure transducer. I explained it with a transparent acrylic glass prototype to make it easier to understand. At a certain point, my boss said, “Okay, please stop. I don’t understand much about these things, but you can make those on your own.”

Two snorkels protecting a data logger from relative humidity

Two snorkels protected a data logger predecessor from relative humidity.

Snorkels Solve a Research Crisis

The research team used those tensiometers (along with other chemical and microbial monitoring) to investigate why trees only in the precise altitude of 800 to 1100 meters were dying. One challenge facing the team was that they didn’t have access to anything we might call a data logger today.  Von Unold says, “We did have a big process machine from Schlumberger that could record the sensors, but it wasn’t designed to be placed in alpine regions where maximum winter temperatures reached -30℃ or below. We had to figure out how to protect this extremely expensive machine, which back then cost more than my annual salary.“

Von Unold’s advisor let him use the machine, cautioning him that the humidity it was exposed to could not exceed 80%, and the temperature must not fall below 0℃.  As von Unold pondered how to do this, he had an idea. Since the forest floor often accumulated more than a meter of snow, he designed an aluminum box with two snorkels that would reach above the snow.  The snorkels were guided to a height of two meters.  Using these air vents, he sucked a small amount of cold, dry air into the box. Then, he took his mother’s hot iron, bought a terminal switch to replace the existing one (so it turned on in the range of 0-30℃), and mounted a large aluminum plate on the iron’s metal plate to better distribute the heat.

Von Unold says, “Pulling in the outside air and heating it worked well. The simple technique reduced the relative humidity and controlled the temperature inside the box. Looking back, we were fortunate there wasn’t condensing water and that we’d selected a proper fan and hot iron. We didn’t succeed entirely, as on hot summer days it was a bit moist inside the box, but luckily, the circuit boards took no damage.”

Fog in trees in a pine forest

Tree mortality factors were only found at the precise altitude where fog accumulated.

Finding Answers

Interestingly, the research team discovered there was more to the forest decline story than they thought. Fog interception in this range was extremely high, and when it condensed on the needles, the trees absorbed more than moisture.  Von Unold explains, “In those days people of the Czech Republic and former East Germany burned a lot of brown coal for heat. The high load of sulfur dioxide from the coal reduced frost resistivity and damaged the strength of the trees, producing water stress.  These combined factors were only found at the precise altitude where the fog accumulated, and the weakened trees were no match for the intense storms that are sometimes found in the Alps.”  Von Unold says once the East German countries became more industrialized, the problem resolved itself because the people stopped burning brown coal.

Share Your Hacks with Us

Do you have an instrument hack that might benefit other scientists?  Send your idea to [email protected].

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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.

NDVI Sensor

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.

NDVI Sensor

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.

NDVI Sensor

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.  

NDVI Sensor

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.

NDVI Sensor

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.

NDVI Sensor

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.

NDVI Sensor

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.

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

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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.

Green crops in a field

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.

Diagram depicting NDVI Sensor data

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.

Diagram of Maricopa Aircraft Data

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)”—>

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Get More From Your NDVI Sensor

Modern technology has made it possible to sample Normalized Difference Vegetation Index (NDVI) 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.

Flat map of the earth depicting NDVI amounts covering the contents

Figure 1: NDVI is sensitive to the amount of vegetation cover that is present across the earth’s surface.

NDVI – Global

The broadest way to think of NDVI is data obtained from an earth orbiting satellite. In the figure above, you can see highly vegetated areas that have high NDVI values represented by dark green colors across the globe.  Conversely, areas of low vegetation have low NDVI values, which look brown.  NDVI is sensitive to the amount of vegetation cover that is present across the earth’s surface.

NDVI – Local

How might NDVI be useful at the plot level? Figure 2 below shows a successional gradient where time zero is a bare patch of soil, or a few forbs or annual grasses. If we leave that patch of ground for enough time, the vegetation will change: shrubs may take over from grasses and eventually we might see a forest. Across a large area, we may also move from grasslands to forest. In an agricultural system, there is yearly turnover of vegetation—from bare field to plant emergence, maturity, and senescence. This cycle repeats itself every year.  Within these growth cycles NDVI helps to quantify the canopy growth that occurs over time as well as the spatial dynamics that occur across landscapes.

Diagram depicting seasonal growth plotted against spatiotemporal variation

Figure 2: Seasonal growth plotted against spatiotemporal variation

Spectral Reflectance Data

So where does NDVI come from? In Figure 3, the x-axis plots wavelength of light within the electromagnetic spectrum; 450 to 950 nm covers both the visible region and a portion of the near infrared. On the y-axis is percent reflectance.  This is a typical reflectance spectrum from green vegetation.

Chart reflecting data and electromagnetic radiation

Figure 3: Spectral Reflectance Data. (Figure and Images: landsat.gsfc.nasa.gov)

The green hyperspectral line is what we would expect to get from a spectral radiometer.  Reflectance is typically low in the blue region, higher in the green region, and lower in the red region. It shifts dramatically as we cross from the visible to the near infrared. The two vertical bars labeled NDVI give you an idea of where a typical NDVI sensor measures within the spectrum.  One band is in the red region and the other is in the near-infrared region.  

NDVI capitalizes on the large difference between the visible region and the near infrared portion of the spectrum. Healthy, growing plants reflect near-infrared strongly.  The two images on the right of the figure above are of the same area.  The top image is displayed in true color, or three bands–blue, green and red. The image below is a false color infrared image.  The three bands displayed are blue, green, and in place of red, we used the near infrared. The bright red color indicates a lot of near infrared reflectance which is typical of green or healthy vegetation.

The reason NDVI is formulated with red and near infrared is because red keys in on chlorophyll absorption, and near infrared is sensitive to canopy structure and the internal cellular structure of leaves.  As we add leaves to a canopy, there’s more chlorophyll and structural complexities, thus we can expect decreasing amounts of red reflectance and higher amounts of near-infrared reflectance.

How Do We Calculate the NDVI?

Calculation equation of NDVI

The Normalized Difference Vegetation Index takes into account the amount of near-infrared (NIR) reflected by plants. It is calculated by dividing the difference between the reflectances (Rho) in the near-infrared and red by the sum of the two.  NDVI values typically range between negative one (surface water) and one (full, vibrant canopy). Low values (0.1 – 0.4) indicate sparse canopies, while higher values (0.7 – 0.9) suggest full, active canopies.  

The way we calculate the percent reflectance is to quantify both the upwelling radiation (the radiation that’s striking the canopy and then reflected back toward our sensor) as well as the total amount of radiation that’s downwelling (from the sky) on a canopy.  The ratio of those two give us percent reflectance in each of the bands.

Next Week: Learn about NDVI applications, limitations, and how to correct for those limitations.

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

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Piñon Pine: Studying the Effects of Climate Change on Drought Tolerance

In the name of science, Henry Adams has killed a lot of trees. Adams, a PhD student at the University of Arizona, is studying the effect of climate change and drought on Piñon Pines. The Piñon Pine, a conifer with an extensive root system, grows at high elevations in the Southwest. Its root system makes the Piñon Pine remarkably drought tolerant, but in 2002- 03, an extended drought in combination with a bark beetle outbreak killed 12,000 hectares of the trees. It was a 100 year drought, the driest period on record, and interestingly it coincided with temperatures 2 to 3˚C above recorded averages.

Biosphere 2 glass dome where researchers study the effects of climate change

Biosphere 2. Image: wickipedia.org.

Research in Biosphere 2

Adams and his advisors wondered if increasing temperatures due to climate change might exacerbate the effects of drought and accelerate tree die-off. The University of Arizona has an unusual opportunity to test drought conditions and temperature change in its Biosphere 2 lab. Biosphere 2, a unique 3-acre enclosed “living laboratory” in the high Arizona desert, once hosted 8 people for two years of self-contained survival living. Now it hosts research projects, and Adams was able to use space inside to induce drought in two separate treatments of transplanted Piñon pines, one at ambient temperatures and one at temperatures 4˚C above ambient.

Sobering Outlook for the Piñon Pine

“Obviously, the warmer trees should die first,” says Adams. “But we want to test whether temperature change, independent of other factors, accelerates mortality.” If that acceleration in fact occurs, a shorter drought, the kind the Piñon Pine has historically been able to wait out, might cause a significant die-off.

Image of a close up on a Piñon Pine branch

Piñon Pine. Image: Naturesongs.com

Measuring Drought Response

Naturally, Adams and his colleagues did more than just watch how fast trees would die without water. They also studied the trees physiological response to drought, measuring gas exchange, water potential, and stomatal conductance. To measure stomatal conductance, they used a leaf porometer, making almost 9,000 separate measurements in sessions that lasted from sunup to sundown on one very long day once each week.

Stomatal Conductance in Conifers

There isn’t much guidance in the porometer manual for people who want to use it on conifers, so Adams “played around with it a little bit” on non-drought stressed trees before he started his study. He found that the best way to get good readings was to cover the aperture with a single layer of needles. “Needles are this three-dimensional thing,” he explains. “They have stomata on several sides, depending on the species. If you imagine that the fingers on your hand are needles sticking up from a branch, we just took those and pushed them together to make sure that there was just a one needle thick covering over the aperture. If you spread your fingers, that’s what it would be like if you didn’t totally cover the aperture-then you underestimate the conductance. We also found that if we stuck several layers in there, we could drive the conductance number up.

Next week: Find out how the researchers made comparisons at leaf level, transplanted the trees, and future implications for the Piñon Pine.

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

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German Researchers Directly Measure Climate Change Effects Using Lysimeter Network (part 2)

In Germany, scientists are measuring the effects of tomorrow’s climate change with a vast network of 144 large lysimeters (see part 1).  This week, read about the intense precision required to move the soil-filled lysimeters, how problems are prevented, and how the data is used by scientists worldwide.

Image of truck moving the Lysimeters

Moving the lysimeters

Moving the Lysimeters is not Easy

As noted previously, one TERENO lysimeter weighs between 2.5 and 3.5 tons depending on the soil and the water saturation, so the problem of transporting it without compacting the soil or causing cracks in the soil column caused Georg many sleepless nights.   He explains, “We found a truck with an air venting system, which could prevent vibrations in a wide range. We made a wooden support structure, bought 100 car springs, and loaded the lysimeter on this frame.  After some careful preparation and design adjustments, I told the truck driver, ‘take care, I’m recording the entire drive with my acceleration sensor and data logger so I can see if you are driving faster than I allow.”  Each lysimeter soil surface level was marked to check if the lysimeter was rendered useless due to transport, and the truck was not allowed to go over a railway or a bump in the road faster than 2 km per hour to avoid the consequences of compaction and cracking.

Image of a Tensiometer sticking out of the ground

Tensiometers and soil moisture sensors monitor the hydraulic conditions inside the lysimeters.

Preventing Problems

Understanding the water potential inside the intact lysimeter core is not trivial. Georg and his team use maintenance-free tensiometers, which overcome the typical problem of cavitation in dry conditions as they don’t need to be refilled. Still, this parameter is so critical they installed 3 of them and took the median, which can be weighed in case one of the sensors is not working. Georg says, “There is a robust algorithm behind measuring the true field situation with tensiometers.”

What Happens With the Data?

Georg hopes that many researchers will take advantage of the TERENO lysimeter network data (about 4,000 parameters stored near-continuously on a web server). He says, “Researchers have free access to the data and can publish it. It’s wonderful because it’s not only the biggest project of its kind, each site is well-maintained, and all measurements are made with the same equipment, so you can compare all the data.”  (Contact Dr. Thomas Puetz for access). Right now, over 400 researchers are working with those data, which has been used in over 200 papers.

Picture depicting a Lysimeter plant in a garden with a CO2 fumigation facility located in Austria

Lysimeter plant with CO2 fumigation facility in Austria.

What’s the Future?

Georg thinks 40,000 data points arriving every minute will give scientists plenty of information to work on for years to come. Each year, more TERENO standard lysimeters are installed to enlarge the database. The ones in TERENO have a 1 m2 surface area, which is fine for smaller plants like wheat or grass, but is not a good dimension for big plants like trees and shrubs. Georg points out that you have to take into account effort versus good data. Larger lysimeters present exponentially larger challenges. He admits that, “With the TERENO project, they had to make a compromise. All the lysimeters are cut at a depth of 1.5 m. If there is a mistake, it is the same with all the lysimeters, so we can compare on climate change effects.”  He adds, “After six years, we now have a standard TERENO lysimeter design installed over 200 times around the world, where data can be compared through a database, enhancing our understanding of water in an era of climate change.”

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Read about 12 large Ecotron weighing lysimeters measuring climate change at the University of Hasselt.

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

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

Top Five Blog Posts in 2016

In case you missed them the first time around, here are the most popular Environmental Biophysics.org blog posts in 2016.

Lysimeters Determine if Human Waste Composting can be More Efficient

Waste in the water canals

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

Image of a researchers hand holding soil

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?

Road winding through a mountain pass

“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

Data Logger in an orange bury-able box sitting on next to installation site

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 of 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?

Image of green wheat and a bright blue sky

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

Close up of a leaf on a tree

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?

Close up image of tree bark

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”—>

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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.

Image of a researcher using a WP4C Dew Point Hygrometer to test a sample

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.

Image of an example of a moisture release curve in the form of a graph

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.

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.

Image of a METER Tensiometer in the ground

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

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 water potential”—>

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

Get more information on applied environmental research in our

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 measurementThe 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

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 water potential”—>

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

Get more information on applied environmental research in our