Soil moisture data analysis is often straightforward, but it can leave you scratching your head with more questions than answers. There’s no substitute for a little experience when looking at surprising soil moisture behavior.
Join Dr. Colin Campbell April 21st, 9am PDT as he looks at problematic and surprising soil moisture data.
Understand what’s happening at your site
METER soil scientist, Dr. Colin Campbell has spent nearly 20 years looking at problematic and surprising soil moisture data. In this 30-minute webinar, he discusses what to expect in different soil, environmental, and site situations and how to interpret that data effectively. Learn about:
Telltale sensor behavior in different soil types (coarse vs. fine, clay vs. sand)
Possible causes of smaller than expected changes in water content
Factors that may cause unexpected jumps and drops in the data
What happens to dielectric sensors when soil freezes and other odd phenomena
Surprising situations and how to interpret them
Undiagnosed problems that affect plant-available water or water movement
Why sensors in the same field or same profile don’t agree
My name is Colin Campbell, I’m a research scientist here at METER group. Today we’re going to spend time doing a data deep dive. We’ll be looking at some data coming from my research site on the Wasatch Plateau at 10,000 feet (3000 meters) in the middle of the state of Utah.
Right now, I’m interested in looking at the weather up on the plateau. And as you see from these graphs, I’m looking at the wind speeds out in the middle of three different meadows that are a part of our experiment. At 10,000 feet right now, things are not that great. This is a picture I collected today. If you look very closely, there’s an ATMOS 41 all-in-one weather station. It includes a rain gauge. And down here is our ZENTRA ZL6 logger. It’s obviously been snowing and blowing pretty hard because we’ve got rime ice on this post going out several centimeters, probably 30 to 40 cm. This is a stick that tells us how deep the snow is up on top.
One of the things we run into when we analyze data is the credibility of the data and one day someone was really excited as they talked to me and said, “At my research site, the wind speed is over 30 meters per second.” Now, 30 meters per second is an extremely strong wind speed. If it were really blowing that hard there would be issues. For those of you who like English units, that’s over 60 miles an hour. So when you look at this data, you might get confused and think: Wow, the wind speed is really high up there. And from this picture, you also see the wind speed is very high.
But the instrument that’s making those measurements is the ATMOS 41. It’s a three-season weather station, so you can’t use it in snow. It’s essentially producing an error here at 30 meters per second. So I’ll have to chop out data like this anemometer data at the summit where the weather station is often encrusted with snow and ice. This is because when snow builds up on the sonic anemometer reflection device, sometimes it simply estimates the wrong wind speed. And that’s what you’re seeing here.
This is why it’s nice to have ZENTRA cloud. It consistently helps me see if there’s a problem with one of my sensors. In this case, it’s an issue with my wind speed sensors. One of the other things I love about ZENTRA Cloud is an update about what’s going on at my site. Clearly, battery use is important because if the batteries run low, I may need to make a site visit to replace them. However, one of the coolest things about the ZL6 data logger is that if the batteries run out, it’s not a problem because even though it stops sending data over the cellular network, it will keep saving data with the batteries it has left. It can keep going for several months.
I have a mix of data loggers up here, some old EM60G data loggers which have a different voltage range than these four ZL6 data loggers. Three of these ZL6s are located in tree islands. In all of the tree islands, we’ve collected enough snow so the systems are buried and we’re not getting much solar charging. The one at the summit collects the most snow, and since late December, there’s been a slow decline in battery use. It’s down. This is the actual voltage on the batteries. The battery percentage is around 75%. The data loggers in the two other islands are also losing battery but not as much. The snow is just about to the solar charger. There’s some charging during the day and then a decrease at night.
So I have the data right at my fingertips to figure out if I need to make a site visit. Are these data important enough to make sure the data loggers call in every day? If so, then I can decide whether to send someone in to change batteries or dig the weather stations out of the snow.
I also have the option to set up target ranges on this graph to alert me whether the battery voltage is below an acceptable level. If I turn these on, it will send me an email if there’s a problem. So these are a couple of things I love about ZENTRA cloud that help me experiment better. I thought I’d share them with you today. If you have questions you want to get in contact me with me, my email is [email protected]. Happy ZENTRA clouding.
Every researcher’s goal is to obtain usable field data for the entire duration of a study. A good data set is one a scientist can use to draw conclusions or learn something about the behavior of environmental factors in a particular application. However, as many researchers have painfully discovered, getting good data is not as simple as installing sensors, leaving them in the field, and returning to find an accurate record. Those who don’t plan ahead, check the data often, and troubleshoot regularly often come back to find unpleasant surprises such as unplugged data logger cables, soil moisture sensor cables damaged by rodents, or worse: that they don’t have enough data to interpret their results. Fortunately, most data collection mishaps are avoidable with quality equipment, some careful forethought, and a small amount of preparation.
Before selecting a site, scientists should clearly define their goals for gathering data.
Make no mistake, it will cost you
Below are some common mistakes people make when designing a study that cost them time and money and may prevent their data from being usable.
Site characterization: Not enough is known about the site, its variability, or other influential environmental factors that guide data interpretation
Sensor location: Sensors are installed in a location that doesn’t address the goals of the study (i.e., in soils, both the geographic location of the sensors and the location in the soil profile must be applicable to the research question)
Sensor installation: Sensors are not installed correctly, causing inaccurate readings
Data collection: Sensors and logger are not protected, and data are not checked regularly to maintain a continuous and accurate data record
Data dissemination: Data cannot be understood or replicated by other scientists
When designing a study, use the following best practices to simplify data collection and avoid oversights that keep data from being usable and ultimately, publishable.
Dr. Yossi Osroosh, Precision Ag Engineer in the Department of Biological Systems Engineering at Washington State University, discusses where and why IoT fits into irrigation water management. In addition, he explores possible price, range, power, and infrastructure road blocks.
Wireless sensor networks collect detailed data on plants in areas of the field that behave differently.
Studies show there is a potential for water savings of over 50% with sensor-based irrigation scheduling methods. Informed irrigation decisions require real-time data from networks of soil and weather sensors at desired resolution and a reasonable cost. Wireless sensor networks can collect data on plants in a lot of detail in areas of the field that behave differently. The need for wireless sensors and actuators has led to the development of IoT (Internet of Things) solutions referred to as Low-Power Wide-Area Networking or LPWAN. IoT simply means wireless communication and connecting to some data management system for further analysis. LPWAN technologies are intended to connect low-cost, low-power sensors to cloud-based services. Today, there are a wide range of wireless and IoT connectivity solutions available raising the question of which LPWAN technology best suits the application?
IoT Irrigation Management Scenarios
The following are scenarios for implementing IoT:
buying a sensor that is going to connect to a wireless network that you own (i.e., customer supplied like Wi-Fi, Bluetooth),
buying the infrastructure or at least pieces of it to install onsite (i.e., vendor managed LPWAN such as LoRaWAN, Symphony Link), and
relying on the infrastructure from a network operator LPWAN (e.g., LTE Cat-M1, NB-IOT, Sigfox, Ingenu, LoRWAN).
This is how cellular network operators or cellular IoT works. LPWAN technology fits well into agricultural settings where sensors need to send small data over a wide area while relying on batteries for many years. This distinguishes LPWAN from Bluetooth, ZigBee, or traditional cellular networks with limited range and higher power requirements. However, like any emerging technology, certain limitations still exist with LPWAN.
Individual weather and soil moisture sensor subscription fees in cellular IoT may add up and make it very expensive where many sensors are needed.
IoT Strengths and Limitations
The average data rate in cellular IoT can be 20 times faster than LoRa or Symphony Link, making it ideal for applications that require higher data rates. LTE Cat-M1 (aka LTE-M), for example, is like a Ferrari in terms of speed compared to other IoT technologies. At the same time, sensor data usage is the most important driver of the cost in using cellular IoT. Individual sensor subscription fee in cellular IoT may add up and make it very expensive where many sensors are needed. This means using existing wireless technologies like traditional cellular or ZigBee to complement LPWAN. One-to-many architecture is a common approach with respect to wireless communication and can help save the most money. Existing wireless technologies like Bluetooth LE, WiFi or ZigBee can be exploited to collect in-field data. In this case, data could be transmitted in-and-out of the field through existing communication infrastructure like a traditional cellular network (e.g., 3G, 4G) or LAN. Alternatively, private or public LPWAN solutions such as LoRaWAN gateways or cellular IoT can be used to push data to the cloud. Combination of Bluetooth, radio or WiFi with cellular IoT means you will have fewer bills to pay. It is anticipated that, with more integrations, the IoT market will mature, and costs will drop further.
Many of LPWAN technologies currently have a very limited network coverage in the U.S. LTE Cat-M1 by far has the largest coverage. Ingenu, which is a legacy technology, Sigfox and NB-IOT have very limited U.S. coverage. Some private companies are currently using subscription-free, crowd-funded LoRaWAN networks to provide service to U.S. growers: however, with a very limited network footprint. Currently, cellular IoT does not perform well in rural areas without strong cellular data coverage.
In two weeks: Dr. Osroosh continues to discuss IoT strengths and limitations in part 2.
What can you do when you need data from all over the world in a short amount of time? Many scientists, including ones at JPL/NASA, are crowdsourcing their data collection.
Projects range from ground truthing NASA satellite data, to spotting migration patterns, to collecting microbes.
Darlene Cavalier, Professor of Practice at Arizona State University is the founder of SciStarter, a website where scientists make data collection requests to a community of volunteers who are interested in collecting and analyzing data for scientific research.
Who Collects the Data?
SciStarter was an outgrowth of Cavalier’s University of Pennsylvania graduate school project where she sought to connect people who didn’t have formal science degrees with scientists who needed their help. She says, “We know from various National Science Foundation reports that many people without science degrees are interested in participating in and learning about science. The challenge was that there was no easy way to find those opportunities.”
One project invites UK citizens to find and take pictures of orchids.
Cavalier started SciStarter, in part, to create a “one-stop shop” resource where people could easily search and find projects best suited to their locations and interests. She says, “We have over 1,600 projects and events. Projects range from ground truthingNASA satellite data, to spotting migration patterns, to collecting microbes.” One project,sponsored by the National History Museum in London, invites UK citizens to find and take pictures of orchids with their smartphones, so scientists can study the effect of climate change on UK flowering times.
How Are Volunteers Recruited?
Volunteers are recruited through SciStarter’s partnerships with the National Science Teachers Association, Discover Magazine, the United Nations, PBS and more. One of the most visible ways that volunteers are enlisted is through an organization Cavalier started called Science Cheerleader. The organization consists of 300 current and former NFL and NBA cheerleaders who are scientists and engineers. These role models visit youth sports groups, go to science festivals, and talk in schools. During their appearances they engage people of all ages in actual citizen science projects. Darlene says, “This is our way of casting a wide net and making new audiences aware of these opportunities.”
Science cheerleader consists of 300 current and former NFL and NBA cheerleaders who are now scientists and engineers.
What’s the Ultimate Goal?
Cavalier is determined to create pathways between citizen science and citizen science policy. She says, “The hope is after people engage in citizen science projects, they will want to participate in deliberations around related science policy. Or perhaps policy decision makers will want to be part of the discovery process by contributing or analyzing scientific data.” Darlene has partnered with Arizona State University and other organizers to form a very active network called Expert and Citizen Assessment of Science and Technology (ECAST). This group seeks to unite citizens, scientific experts, and government decision makers in discussions evaluating science policy. Cavaliers says, “The process allows us to discover ethical and societal issues that may not come up if there were only scientists and policy makers in a room. It’s a network which allows us to take these conversations out of Washington D.C. The conversations may originate and ultimately circle back there, but the actual public deliberations are held across the country, so we get a cross-section of input from different Americans.” ECAST has been contracted by NASA, NOAA, the Department of Energy, and others to explore specific policy questions that would benefit from the public’s input.
ECAST is a network which allows us to take science policy conversations out of Washington D.C.
Overcoming Obstacles
Cavalier says the SciStarter team constantly works to remove challenges and impediments to public participation. She explains, “We’ve found it can be difficult to articulate the geographic bounds of a project because when a researcher says, “this project can be done in a watershed,” it doesn’t mean anything to most people. So SciStarter spent time developing a system of “Open Streetmap and USGS databases that show land-type coverage.”
Another obstacle to some types of research is access to instrumentation. Darlene comments, “The NASA Soil Moisture Active Passive (SMAP) project really opened our eyes to how many obstacles can exist between the spectrum of recruiting, training, equipping, and fully engaging a participant.” This year, SciStarter is building a database of citizen science tools and instruments and will begin to create the digital infrastructure to map tools to people and projects through a “Build, Borrow, Buy” function on project pages.
“The NASA Soil Moisture Active Passive (SMAP) project really opened our eyes to how many obstacles can exist to full engagement.”
What’s Next?
Darlene says that sometimes scientists who want accurate data without knowing about or identifying a particular sensor for participants to use often create room for data errors. To address this problem, SciStarter and Arizona State University will be hosting a summit this fall where scientists, citizen scientists, and commercial developers of instrumentation will meet to determine if it’s possible to fill gaps to develop and scale access to inexpensive, modular instruments that could be used in different types of research. You can learn more about crowdsourcing your data collection with SciStarter here.
