You’ve buried soil water content and water potential (soil suction) sensors in the ground, installed an ATMOS 41 in the field, and set up your ZL6 data logger. Your network of instruments has been collecting data for weeks or even all season.
Now what? Knowing how to extrapolate meaningful inferences from your data, forming big picture conclusions about what is happening, and identifying and troubleshooting issues can sometimes be challenging.
Learn everything you need to know about analyzing soil moisture data.
In this article, we will step through multiple data sets to understand how soil water content, soil temperature, soil water potential, and atmospheric measurements can be used to discover the meaning behind the traces. Within this article you will learn how to identify the following events in your data:
Behavior of soil moisture sensors in different soil types
In this 20-minute webinar, METER scientist Leo Rivera compares available methods and teaches how to combine the latest technology to generate full, accurate curves with hundreds of points in only a couple of days—instead of a couple of months.
Combine technology for more accurate soil moisture release curves
If you’re still spending months generating a handful of points to produce only a partial soil characteristic curve—old-school methods are holding you back. What if you could create a soil moisture release curve in just 48 hours? And not just a curve with a few points, but a detailed absorption and desorption curve composed of hundreds of points that show exactly what happens as your soil absorbs and desorbs water throughout the entire range of water potentials?
Change the way you understand your soil
Partial curves made with older methods don’t give you enough data for a complete picture of what’s happening in your soil. Hundreds of studies show that faster, high-precision modern methods are more accurate—so you can reach better conclusions that stand up to rigorous scientific scrutiny. In this 20-minute webinar, METER scientist Leo Rivera compares available methods and teaches how to combine the latest technology to generate full, accurate curves with hundreds of points in only a couple of days—instead of a couple of months. Learn:
The science behind current available methods
The pros and cons of each method
Advances in soil moisture release curve technology
Leo Rivera operates as a research scientist and Hydrology Product Manager at METER Group, the world leader in soil moisture measurement. He earned his undergraduate degree in Agriculture Systems Management at Texas A&M University, where he also got his Master’s degree in Soil Science. There he helped develop an infiltration system for measuring hydraulic conductivity used by the NRCS in Texas. Currently, Leo is the force behind application development in METER’s hydrology instrumentation including HYPROP and WP4C. He also works in R&D to explore new instrumentation for water and nutrient movement in soil.
In this latest chalk talk video, METER soil scientist and application expert, Leo Rivera, discusses the use of pedotransfer functions (PTFs) for predicting soil properties such as hydraulic conductivity and field capacity.
He explains that while direct measurements are ideal, PTFs can provide rapid, cost-effective alternatives. PTFs use soil texture, particle size distribution, and bulk density as inputs, with accuracy depending on the quality of the database and the input data. There are limitations, such as PTFs not accounting for soil structure and organic matter, which can significantly impact hydraulic conductivity, thus, Rivera recommends using PTFs judiciously. PTFs can be especially helpful for large-scale assessments, and he suggests seeking tools that incorporate more parameters for improved accuracy.
0:00 Hi. My name is Leo Rivera, and this is a METER Chalk Talk.
0:11 Today I want to talk about a topic that I get asked a lot about, and it has to do with how to predict some of these soil properties that we typically measure. So we often have used tools to measure things like soil hydraulic conductivity, retention curves, predicting field capacity and permanent wilting point. And ideally, that’s the best way we can do it, is by making those measurements. But that’s not always an option. So there are times where we need to be able to more rapidly assess soil properties, where we don’t have the budget to assess some of these more expensive soil properties to measure. And there are tools available to make these predictions rather than making the measurements. And I want to talk about those today to make sure that you understand the capabilities of these tools, that they’re out there, but also understand the limitations. And so the primary tool I’m going to talk about is a pedotransfer function, or a PTF.
So typically, with a pedotransfer function, we’re using something like soil texture. So for example, I have here an image of the soil triangle, and I can say I have a clay loam soil. So I’ve got my clay loam right here. And my goal is to predict field capacity. My goal is to predict field capacity for minus 33 kPa, and permanent wilting point, which is minus 1500 kPa, typically to make those measurements, it’s going to take several days to several months, depending on how you choose to make that measurement. But that’s not always an option. So we can use a petal transfer function to take that property, like soil texture, and predict those values. And pedotransfer functions also can be used to predict things like hydraulic conductivity. So you can see an example of a hydraulic conductivity graph here as well. So a pedotransfer function can be a really powerful tool to predict some of these properties that are typically take more time or more expensive to measure, and maybe you don’t have the time to make those measurements. So it’s really important to understand how a pedotransfer function works before utilizing this tool.
So pedotransfer functions utilize databases, whether it’s soil survey or other generated databases, where you have a lot of soil data, and you have all of these data, like texture, density, where they’ve already measured, hydraulic conductivity and some of these other soil properties. It then takes your input and utilizes that database to best predict what those values are that you’re trying to assess. So if I’m trying to predict, for example, field capacity, you can input parameters like soil texture, particle size distribution and bulk density. And you can do this in various orders. You can use soil texture on its own. You can use particle size distribution on its own, or you can combine particle size distribution and soil bulk density together to make these predictions. And it’s going to go into that database and try and make its best prediction based on the data available on that database.
Your pedotransfer function is only going to be as strong as the data that’s in the database, but it’s also only going to be as powerful as how good of an input you give it to predict these these values. So if we’re using soil texture on its own, as you can imagine, if I was predicting a clay as a soil texture, we’ll just use that as our example. If we look at our clay soil on the on the soil texture triangle, that is a huge range of combinations of sand, silt and clay fraction. So as you can imagine, that’s a pretty broad area that you’re trying to predict from, and there’s a higher potential for error in that prediction. Now, if we were to refine that and use the particle size distribution, so if we knew our exact sand, silt and clay fraction that we were trying to predict, we could then refine our predictions. We’re going to get rid of that circle, and we’re going to refine our prediction, saying, our soil has exactly this amount of sand, silt and clay. And that’s going to refine how the pedotransfer function is pulling those data from in the database to predict those values.
But as we know, soil texture on its own and particle size on its own only tells part of the story. So we can further refine that by adding our bulk density into that prediction, which is going to help improve the prediction of either field capacity or hydraulic conductivity. And in some areas, that should be fine. And so as long as you’re happy with that level of error, that’s fine. But, especially when we’re looking at things like hydraulic conductivity, we know there are other factors that play a big role in hydraulic conductivity, such as soil structure. So ideally, we would be including structure in our prediction, and organic matter in our prediction, because we all we know that these play a significant role in how soil transmits water, but most pedotransfer function, tools like Rosetta and Soil View don’t really take these into account.
So when you’re looking at these values, especially trying to assess measurements like hydraulic conductivity, you need to understand these limitations when using these tools. Now, there are other databases and pedotransfer function tools out there that are doing a better job of taking some of these into account. And if you’re going to use those tools, you want to try to make sure, if you’re if you’re really concerned with the accuracy of your values that you’re using pedotransfer function models that take more of these parameters into account. So the more inputs you can have into your prediction, the more accurate you’re likely going to come out with your predictions of these factors.
I just wanted to cover some of the basics on pedotransfer functions, and if you’re going to use them. They’re really powerful tools if we need to use them, especially when we’re trying to characterize large areas. It’s not always feasible to make measurements across these large areas, and they can help give us a little more data to work off of, rather than just the measurements on their own to try and characterize what’s happening across the large watershed, for example. But we need to understand how our inputs can affect the accuracy of those predictions. If you want to learn more about this or other topics, please visit us on our website www.metergroup.com or on our YouTube channel under meter talk talks and thank you for watching.
In 2017, Hurricane Maria ravaged the island of Puerto Rico, with category 5 winds topping out at 174 mph (282 km/h).
In this mountainous nation with the 9th highest road density in the world, thousands of landslides wreaked havoc on the large number of rural communities that became cut off from supplies and travel. Dr. Stephen Hughes, a professor in the Department of Geology at the University of Puerto Rico Mayagüez, has turned this catastrophe into a lesson by harnessing before and after data to develop a landslide susceptibility map with resolution down to every 5 m. Join us as we discuss with him the process of developing landslide prediction across the entire island nation.
Stephen is a professor in the department of geology at the University of Puerto Rico-Mayagüez. He obtained his bachelors in geology and earth science from the University of North Carolina at Chapel Hill and his PhD in geology from North Carolina State University. He teaches classes in structural geology, geomorphology, and field geology, and his research projects have focused mostly on tropical landslides and landscape evolution, with the funding of such organizations as the NSF, USGS, USDA-NRCS, and NOAA.
The views and opinions expressed in the podcast and on this posting are those of the individual speakers or authors and do not necessarily reflect or represent the views and opinions held by METER.
You’ve buried soil water content and water potential sensors in the ground, installed an ATMOS 41 in the field, and set up your ZL6 data logger. Your network of instruments has been collecting data for days, weeks, or even all season. Now what? Performing soil moisture data analysis for your research location is one thing. Knowing how to extrapolate meaningful inferences and conclusions to understand what is happening and troubleshoot issues is completely different.
In this article, we will step through multiple data sets to understand how soil water content, soil temperature, soil water potential, and atmospheric measurements can be used to discover the meaning behind the traces. Within this article you will learn how to identify the following events in your data:
Behavior of soil moisture sensors in different soil types
Infiltration
Flooding
Soil cracking
Freezing
Spatial variability
Temperature effects
Diurnal patterns due to hydraulic redistribution
Broken sensors
Installation problems
Each example will be represented by a graph. It is not necessary to understand every aspect of information within these graphs. Each one is used as an illustration of common soil moisture data patterns you might run into and how to extrapolate the most useful information possible from the patterns seen. Each graph will have a box in the upper right-hand side corner with the soil type and crop type so you have a better understanding of the variables at play.
All of the data provided was collected by data loggers, such as our ZL6 series, and uploaded to ZENTRA Cloud for remote viewing at the convenience of the user. All data sets are either from METER’s own instrumentation or are supplied by the data owner and are included with their permission.
Figure 1. ZL6 Basic data logger with data collected and stored within the ZENTRA Cloud platform
Effects of soil types
Figure 2. Water content and water potential measurements for a turf grass in loamy sand in wet conditions
In Figure 2 we see the data from an engineered loamy sand with a cover crop of turf grass. Our goal when executing our experiments in this example was to improve irrigation in turf grass. This grass had a fairly shallow root zone, the middle of which was about six cm deep and the bottom at about 10 cm. Over time, this example showed first relatively wet conditions to start through June and July, a fixed drying period condition in July and August, and drying until the cessation of water uptake in August and September.
This graph shows two soil moisture data types: volumetric water content on the left y-axis and matric potential, or water potential, on the right y-axis. Time is on the x-axis ranging from early summer to the start of fall. To understand what these data clusters can tell us, we must look at each data set individually.
There’s a lot to consider when collecting soil moisture measurements.
Get your soil moisture questions answered in our Office Hours series.
Join Environment Support Manager, Chris Chambers, and Director of Science Outreach, Leo Rivera, as they discuss submitted questions all about getting the best soil moisture measurements.
In the full episode, they discuss:
How difficult is the calibration of dielectric sensors?
How does soilless media affect the operation of dielectric sensors?
How much can organic soil amendments influence soil moisture?
Is it possible to determine the soil hydraulic properties from soil water content?
Why volumetric water content instead of gravimetric water content?
What is the best way to correct for the temperature sensitivity of sensors?
Drs. Kim Novick and Jessica Guo team up to discuss the vital role water potential measurement plays in both plant and soil sciences and the work they are doing to establish the first-of-its-kind nationwide water potential network. Join their discussion to understand how a communal knowledge of these measurements could impact what we know about climate change and ecology as a whole.
A water potential measurement network could increase our understanding of climate change and ecology.
Dr. Kim Novick is a professor, Paul H. O’Neill Chair, Fischer Faculty Fellow, and director of the Ph.D. Program in Environmental Sciences at Indiana University. She earned her bachelor’s and Ph.D. in environmental science at Duke University’s Nicholas School of the Environment. Her research areas span ecology and conservation, hydrology and water resources, and sustainability and sustainable development, with specific interests in land-atmosphere interactions, terrestrial carbon cycling, plant ecophysiology, and nature-based climate solutions.
Dr. Jessica Guo is a plant ecophysiologist and data scientist who studies plant-environment interactions under extreme climate conditions. She earned her bachelor’s in environmental biology from Columbia University and her Ph.D. in biological sciences from Northern Arizona University. She is currently at the University of Arizona, where she blends her passion for reproducible workflows, interactive visualizations, and hierarchical Bayesian models with her expertise in plant water relations.
The views and opinions expressed in the podcast and on this posting are those of the individual speakers or authors and do not necessarily reflect or represent the views and opinions held by METER.
As world water demand increases and supplies decrease, how can we turn more of the water we use for agriculture into biomass? In this webinar, Dr. Campbell dives deep into the measurement and implications of making the most of every drop of water.
Learn how to measure the amount of water a crop will need.
Crops turn sunlight, water, carbon dioxide, and nutrients into food
The availability of those resources puts limitations on the amount of food a crop can produce. A previous webinar considered the limitations of sunlight. In this 30-minute webinar, world-renown environmental biophysicist, Dr. Gaylon S. Campbell, discusses how to measure the amount of water a crop will need and how to use that value to predict the amount of biomass it will produce.
Achieve maximum biomass from every drop
Join Dr. Campbell as he discusses the measurements and calculations needed to know how much biomass a given environment can produce. Dr. Campbell will discuss:
How resource capture models work
How biomass production and water use are linked
Examples of effective uses of water resource capture models
Instrumentation needed to determine water and radiation limitations on yield
How to use soil and atmospheric measurements to quantify crop water capture
Water budgets and how they are used to get transpiration and biomass production
Dr. Campbell has been a research scientist and engineer at METER for 19 years following nearly 30 years on faculty at Washington State University. Dr. Campbell’s first experience with environmental measurement came in the lab of Sterling Taylor at Utah State University making water potential measurements to understand plant water status. Dr. Campbell is one of the world’s foremost authorities on physical measurements in the soil-plant-atmosphere continuum. His book written with Dr. John Norman on Environmental Biophysics provides a critical foundation for anyone interested in understanding the physics of the natural world. Dr. Campbell has written three books, over 100 refereed journal articles and book chapters, and has several patents.
Abiotic stress in plants: How to assess it the right way
As a plant researcher, you need to effectively assess crop performance, whether you’re selecting the best variety, trying to understand abiotic stress tolerance, studying disease resistance, or determining climate resilience. But if you’re only measuring weather data, you might be missing key performance indicators. Water potential is underutilized by plant researchers in abiotic stress studies even though it is the only way to assess true drought conditions when determining drought tolerance in plants. Learn what water potential is and how it can improve the quality of your plant study.
Soil directly impacts plant growth via nutrient availability, disease pressure, root growth, and water availability.
Quantitative genetics in plant breeding: why you need better data
If you’ve studied plant populations, you’re probably familiar with the simplified equation in Figure 1 that represents how we think about the impact of genetics and the environment on observable phenotypes.
Figure 1. Phenotype = Genotype + Environment
This equation breaks down the observed phenotype (plant height, yield, kernel color, etc.) into the effects from the genotype (the plants underlying genetics) and the effects of the environment (rainfall, average daily temperature, etc.). You can see from this equation that the quality of your study directly depends on the kind of environmental data you collect. Thus, if you’re not measuring the right type of data, the accuracy of your entire study can be compromised.
Water potential: the secret to understanding water stress in plants
Drought studies are notoriously difficult to replicate, quantify, or even design. That’s because there is nothing predictable about drought timing, intensity, or duration, and it’s difficult to make comparisons across sites with different soil types. We also know that looking at precipitation alone, or even volumetric water content, doesn’t adequately describe the drought conditions that are occurring in the soil.
Figure 2. The TEROS 21 is a field sensor used to measure soil water potential
Soil water potential is an essential tool for quantifying drought stress in plant research because it allows you to make quantitative assessments about drought and provides an easy way to compare those results across field sites and over time. Let’s take a closer look to see why.
In our latest podcast, Dr. Bruce Bugbee, Professor of Crop Physiology and Director of the Crop Physiology Lab at Utah State University, discusses his space farming research and what we earthlings can learn from space farming techniques.
International space station
Find out what happens to plants in a zero-gravity environment and how scientists overcome the particular challenges of deploying measurement sensors in space. He also shares his research on the efficacy of LED lights for indoor growing.
Dr. Bruce Bugbee is a Professor of Crop Physiology, Director of the Crop Physiology Laboratory at Utah State University, and the President of Apogee Instruments.
His work includes collaborating with NASA to develop closed life-support systems for long-term space missions. He’s been involved with the development of crop-growing systems for future life on the Moon, in addition to in-orbit or in-space shuttles. He’s worked on projects for Mars farming, including the use of fiber optics for indoor lighting, And as a part of this research, he was involved in the creation of the NASA Space Technology Research Institute’s Center for the Utilization of Biological Engineering in Space (or CUBES).
Dr. Bugbee also has long been a critic of the use of indoor farming as a means of solving food shortages, due to the large amount of electricity needed to provide light for photosynthesis. His recent work in this area has included studies into the efficacy of LED lights for indoor growing. (Credit: Wikipedia)
The views and opinions expressed in the podcast and on this posting are those of the individual speakers or authors and do not necessarily reflect or represent the views and opinions held by METER.
