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Posts tagged ‘soil moisture’

Jun 10

How to analyze soil moisture data

CONTRIBUTORS

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.

A photograph of a ZL6 next to a tablet showing ZENTRA Cloud data
Figure 1. ZL6 Basic data logger with data collected and stored within the ZENTRA Cloud platform
Effects of soil types
A graph showing water content and water potential measurements for a turf grass in loamy sand in wet conditions
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.

Read the full article→

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May 7

How to analyze soil moisture data

CONTRIBUTORS

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.

Farm field
Learn how to identify and understand behavior of soil moisture sensors in different soil types

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.

A photograph of a ZL6 next to a tablet showing ZENTRA Cloud data
Figure 1. ZL6 Basic data logger with data collected and stored within the ZENTRA Cloud platform
Effects of soil types
A graph showing water content and water potential measurements for a turf grass in loamy sand in wet conditions
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.

Read the full article here: https://metergroup.com/measurement-insights/how-to-analyze-soil-moisture-data/

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Apr 10

Office Hours Episode 11: Soil Moisture

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: 

  1. How difficult is the calibration of dielectric sensors? 
  2. How does soilless media affect the operation of dielectric sensors? 
  3. How much can organic soil amendments influence soil moisture? 
  4. Is it possible to determine the soil hydraulic properties from soil water content? 
  5. Why volumetric water content instead of gravimetric water content? 
  6. What is the best way to correct for the temperature sensitivity of sensors? 
  7. And more. 

Watch the full episode now: https://metergroup.com/office-hours-qa/office-hours-11-soil-moisture-measurements/

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Mar 17

Combining in situ soil moisture with satellite data for improved irrigation recommendations

Improving irrigation requires smart data gathering to help growers make better choices in the field. Measuring in situ creates high-resolution, temporal data enabling us to see clearly what’s happening over time—but only at a single point. Satellites show data across a large spatial scale but are hampered by revisit frequencies, clouds, and resolution limits.

Often we see information in a silo, looking at one type of data or another. The challenge to researchers is how to connect across these scales and combine the information to make better irrigation decisions. In this webinar, Dr. Colin Campbell explores the future of irrigation and research he’s been doing with collaborators at Brigham Young University. Learn:

  • How researchers are combining in situ, drone, and satellite measurements to extract key information
  • How these data can be connected across scales 

Watch it now

 

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May 13

Soil Moisture 101: Need-to-Know Basics

Harness the power of soil moisture

Researchers measure evapotranspiration and precipitation to understand the fate of water—how much moisture is deposited, used, and leaving the system. But if you only measure withdrawals and deposits, you’re missing out on water that is (or is not) available in the soil moisture savings account. Soil moisture is a powerful tool you can use to predict how much water is available to plants, if water will move, and where it’s going to go.

Trees fallen in a forest and being supported by other trees

Soil moisture 101 explores soil water content vs. soil water potential

What you need to know

Soil moisture is more than just knowing the amount of water in soil. Learn basic principles you need to know before deciding how to measure it. In this 20-minute webinar, discover:

  • Why soil moisture is more than just an amount
  • Water content: what it is, how it’s measured, and why you need it
  • Water potential: what it is, how it’s different from water content, and why you need it
  • Whether you should use soil moisture sensors, water potential sensors, or both
  • Which sensors measure each type of parameter

Watch the webinar

Learn more

Download the “Complete guide to irrigation management”—>

Soil Moisture 201: Moisture Release Curves—Revealed

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

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

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Feb 19

Why does my soil moisture sensor read negative?

How is a negative number possible?

METER soil moisture sensors measure the dielectric constant of the substrate in which they are installed. They are designed to measure soil, which has a dielectric constant of around 5.

Researcher holding a TEROS 12 soil sensor in front of a wheat field

METERTEROS 12 soil moisture sensor

Water has a dielectric of approximately 80, so if we assume that a dry soil has a dielectric of 5 (VWC = 0.00 m3/m3), then changes to the bulk dielectric read by the soil moisture sensor will be attributable to changes in water content. If you read a METER sensor in air, which has a dielectric constant of 1, you will quite naturally get a negative number.   

Improving accuracy of dielectric soil moisture sensors

There are two common causes for negative readings on a METER soil moisture sensor:  

1) Poor contact with the soil resulting from improper installation or disturbance

Air gaps next to a sensor will contribute the lower dielectric of air to the measurement resulting in an underestimation of VWC. Air gaps can arise if enough care is not taken to pack soil around the sensor body to approximate native bulk density. Sensors that have been disturbed, such as having a cable tripped over, can also develop air gaps that can result in negative results in dry soils. (To reduce the possibility of air gaps when installing METER sensors, use the new TEROS borehole installation tool) 

2) A calibration that is inappropriate for the soil in which the sensor is installed

If the standard mineral calibration is used, an error of ~ 3-4% can be expected in METER sensor readings. Negative numbers can be observed in oven-dry soils (by definition a VWC of 0.0 m3/m3) down to ~ – 0.02 m3/m3 with no malfunction of the sensor. The dielectric constant of the soil is assumed to be 5 and this is a valid assumption in the majority of soils of primarily mineral composition. If your soil has a different dielectric constant, such as can occur in soils with high organic matter content, then the uncertainty in your measurements will increase. This is not a large problem because METER sensors can be calibrated to match a given soil with very little investment in resources.

Want more details?  

Watch our webinar titled Why Does My Sensor Read Negative below. This webinar is designed for those who use electromagnetic sensors (capacitance/TDR/FDR) to measure soil water content. Learn about the theory behind the measurements. Dr. Doug Cobos discusses:

  • What is volumetric water content?
  • Dielectric measurement theory basics
  • Dielectric mixing models
  • Why might a sensor read a negative VWC?
  • Can a sensor really have 2% VWC accuracy for all soils?
  • Sources of error in dielectric measurement methods
  • Improving accuracy of dielectric measurements

 

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”—>

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Jan 11

Water holding and temperature patterns of canopy soil in an old-growth forest

The deadline is fast approaching to apply for the 2019 Grant A. Harris Fellowship. The fellowship awards $10,000 in METER research instrumentation to six U.S. or Canadian graduate students studying any aspect of agricultural, environmental, or geotechnical science.

Camila Tejo Haristoy using METER soil moisture and temperature sensors

(Image source: https://vimeo.com/69136931)

Camila Tejo Haristoy, former University of Washington grad student, was a Grant A. Harris Fellowship winner. She used METER soil moisture and temperature sensors to study the water holding and temperature patterns of canopy soil in an old-growth Sitka Spruce forest in Washington state. Sitka Spruce tree crowns contain large accumulations of organic matter known as “canopy soil”.  These accumulations provide substrate and habitat for a broad community of plants, insects, and other arboreal species. Using tree-climbing techniques, Camila installed soil moisture sensors in the canopy soils of spruce trees from an old-growth stand in the Olympic Peninsula, Washington.

This study characterized for the first time environmental conditions associated with soil mats within the crown of spruce trees, providing a framework for understanding the distribution and activity of epiphytic plants, nutrient dynamics, and associated canopy organisms.

Watch the documentary

Watch a fascinating 7-minute documentary of Camila’s interesting and exciting research. The documentary description: “Camila spends long rainy days climbing into treetops, taking temperature and moisture measurements, and collecting soil and plant samples. In the process, she interacts with a seldom seen, barely understood, and lushly beautiful environment.” (source https://vimeo.com/69136931)

Watch the video 

Recharge your research

Apply for the Grant A. Harris Fellowship today.

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

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

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Aug 7

5 ways site disturbance impacts your data—and what to do about it

Lies we tell ourselves about site disturbance

When it comes to measuring soil moisture, site disturbance is inevitable. We may placate ourselves with the idea that soil sensors will tell us something about soil water even if a large amount of soil at the site has been disturbed. Or we might think it doesn’t matter if soil properties are changed around the sensor because the needles are inserted into undisturbed soil.

Rolling farm fields

The key to reducing the impact of site disturbance on soil moisture data is to control the scale of the disturbance.

The fact is that site disturbance does matter, and there are ways to reduce its impact on soil moisture data. Below is an exploration of site disturbance and how researchers can adjust their installation techniques to fight uncertainty in their data.

Non-disturbance methods don’t measure up—yet

During a soil moisture sensor installation, it’s important to generate the least amount of soil disturbance possible in order to obtain a representative measurement. Non-disturbance methods do exist, such as satellite, ground-penetrating radar, and COSMOS. However, these methods face challenges that make them impractical as a single approach to water content. The satellite has a large footprint, but generally measures the top 5-10 cm of the soil, and the resolution and measurement frequency is low. Ground-penetrating radar has great resolution, but it’s expensive, and data interpretation is difficult when a lower boundary depth is unknown. COSMOS is a ground-based, non-invasive neutron method that measures continuously and reaches deeper than a satellite over an area up to 800 meters in diameter. But it is cost-prohibitive in many applications and sensitive to both vegetation and soil, so researchers have to separate the two signals. These methods aren’t yet ready to displace soil moisture sensors, but they work well when used in tandem with the ground truth data that soil moisture sensors can provide.

Read more…

Get more info on applied environmental research in our

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

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

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Feb 12

3 Insider Strategies for a More Accurate Soil Moisture Picture (part 2)

Different readings in soil moisture sensors are caused by spatial variation in water content (see part 1). These readings provide researchers with valuable information about soil texture, watering patterns, and water use. This week, learn two more strategies to keep in mind when trying to understand the varying patterns of soil moisture at your research or irrigation site.

Tree standing in a green field

In some crop studies, it may be important to account for horizontal variation.

Strategy #2: Crop Studies—Representing Variation in a Homogeneous Environment

In some research projects, it will be important to account for horizontal variation. How variable is the water content across a field? We did an experiment in which we set out a transect across a field of bare, tilled soil. Using a METER EC-5 soil moisture sensor connected to Procheck meter, we sampled water content at one-meter intervals over a 58-meter distance. The individual readings are shown in Figure 1.

Soil volumetric water content and measurement number chart

Figure 1. You can determine how many samples are necessary to characterize a homogeneous area in about an hour using an EC-5 soil moisture sensor and a ProCheck.

In this data set, the samples are not spatially correlated. The variation is apparent. The mean water content of the data set is 0.198 m3m-3. The standard deviation is 0.023 m3m-3 . The coefficient of variation is 12%. Using some simple geostatistics, we determined that three carefully placed sites would adequately represent the variation present in this very homogeneous environment. Of course, in some environments, samples will not be independent. If a semivariogram indicates that some underlying spatial factor influences soil moisture variability, you will have to consider that in your experimental design.

Forest of trees

By taking into account the major relevant sources of soil moisture variation, you can plan enough sampling locations to draw conclusions from your data.

Strategy #3: Ecology Studies—Heterogeneous Environments

On a forested hillside, horizontal variation in soil moisture will obviously be significant. Determining how many sensors to use and where to place them is not at all trivial. Stratified sampling—systematically sampling from more uniform subgroups of a heterogeneous population—may be a better way to deal with this kind of variety. The researcher classifies the site into strata (eg., forested canopy, brush, hillside, valley) and evaluates the number of samples needed to statistically represent the variation present within each stratum.

Many people allow for the variation in soil moisture values that come from the slope, orientation, vegetation, and canopy cover. Some fail to consider the important soil-level variations that come from soil type and density. By taking into account the major relevant sources of soil moisture variation, you can plan enough sampling locations to draw reasonable conclusions from your data. Choose too few locations, and you run the risk of missing the patterns that will lead to higher-level understanding. Choose too many, and not only will you be unable to afford your experiment, but you may also miss the patterns altogether as your experiment overflows with random abundance.

Image is an example of a heterogeneous research area with different slopes and vegetation

Sometimes researchers want to compare dissimilar sites.

Comparing Data from Different Sites or Strata

Comparing absolute water content numbers can give confusing results. Both measurements are volumetric water content, but 35% here vs. 15% there actually tells us very little. Was the site in sand or clay, or something in between? If conditions at the two sites are virtually identical, the comparison may make some sense. But often, researchers want to compare dissimilar sites.

Volumetric water content and depth in a chart

Figure 2. Changes in VWC with depth (convention: negative values indicate depths below soil surface) for the same time period at Site 1.

Water potential measurements determined by converting absolute volumetric water content to soil water potential using a moisture characteristic curve specific to each soil type can be used to compare results across sites. Comparing relative values—quantities of water used in centimeters for example—can also be both useful and valid.

Figure 3 below illustrates an experiment we performed in a dryland field where water content measurements were made over a growing season at 30, 60, 90, 120, and 150 cm below a wheat crop.  The graph of soil moisture data shows how water is taken up from successively deeper layers. By subtracting one profile from another and summing over the layers where change occurs (for instance, in Figure 2 above, subtract the far left line from the far-right line to see how much water was used from May 10th to August 21st), you can determine the amount of water used by the plants over a particular period.  If similar data were taken at different sites or in different strata, these relative values, in terms of quantified water use, could form the basis of solid comparison studies.

Soil water content in winter wheat

Figure 3. Soil water content in winter wheat measured at 30 cm increments

Read more about accurate soil moisture:  Can you sample the profile without a profile probe?  Find out.

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

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

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Jan 29

3 Insider Strategies for a More Accurate Soil Moisture Picture (Part 1)

How Do you Know You’re Getting Accurate Soil Moisture?

Researchers and irrigators may wonder if their soil moisture sensors are accurate because probes at different locations in the same field have different water content readings. Different readings in soil moisture sensors are caused by spatial variation in water content. These readings provide researchers valuable information about soil texture, watering patterns, and water use. Here are some ideas and strategies to keep in mind when trying to understand the varying patterns of soil moisture at your research or irrigation site. Click the links for more in-depth information about accurate soil moisture.

Grapes on the vines down isles

One irrigator noticed a few sensors indicating low water content after a heavy rain that had uniformly wetted his vineyard.

Horizontal vs. Vertical Variation

It’s helpful to distinguish variation in the vertical from variation in the horizontal. Most people expect strong vertical variation due to wetting and drying patterns, soil horizonation, and compaction. Water content can vary drastically over distances of only a few centimeters, especially near the soil surface. Horizontal variation is typically less pronounced in a bare or uniformly planted field, and at a given depth, it might be quite small. But surprisingly large variations can exist, indicating isolated patches of sand or clay or differences in topography. One irrigator noticed a few sensors indicating low water content after a heavy rain that had uniformly wetted his vineyard. Knowing that sand has a low field capacity water content, he surmised (correctly) that he had found the sandy areas in the vineyard.

Researcher holding an ECHO EC-5 in front of soil

Soil moisture sensors sometimes measure unexpected things.

Unexpected Readings

Because properly installed dielectric soil moisture sensors lie in undisturbed (and therefore unanalyzed) soil, they sometimes measure unexpected things. One researcher buried a probe in what appeared to be a very dry location and was startled to measure 25 to 30% volumetric water content. Those readings made the soil appear saturated, but obviously, it wasn’t. She dug down to the sensor and found a pocket of clay. As she discovered, it is impossible to get much information from an absolute water content measurement without knowing what type of soil the sensor is in.

Since we expect variation, how do we account for it? How many probes are needed to adequately characterize the water content in an application or experiment? There is no simple answer to this question. The answer will be affected by your site, your goals, and how you plan to analyze your data. Here are some things you might consider as you plan.

Sun rising behind a wheat field

If a field will be irrigated as a unit, it should be monitored as a unit at one representative spot.

Strategy #1: Irrigation—Use Soil Moisture as an Indicator

What information do you have when you know a field’s volumetric water content? That number independently tells an irrigator very little. Soil moisture can be used like a gauge to show when a field is full and when it needs to be refilled, but the “full” and “empty” are only meaningful in context.

The goals of irrigation are to keep root zone water within prescribed limits and to minimize deep drainage. Understanding and monitoring the vertical variation lets you correlate a real-time graph of water use data with above-ground field conditions and plant water needs. It makes sense to place probes both within and below the root zone.

By contrast, measuring horizontal variation—placing sensors at different spots in the field—is not very helpful. If a field will be irrigated as a unit, it should be monitored as a unit at one representative spot. Because there’s no way to adjust water application in specific spots, there’s no benefit to quantifying spatial variation in the horizontal. Like a float in a gas tank, a set of soil moisture sensors in the right spot will adequately represent the changing soil moisture condition of the whole field.

We recommend a single probe location in each irrigation zone with a minimum of one probe in the root zone and one probe below it. Additional probes at that site, within and below the root zone, will increase the reliability of the information for the irrigation manager, at minimal additional cost.

In two weeks: Learn two more techniques researchers use in crop studies and ecology studies to account for variability in order to obtain an accurate soil moisture picture.

Get more info on applied environmental research in our

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

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