In the world of specialty crops, there is disagreement on how well weather-driven insect, disease, and frost prediction models actually perform. Dr. Dave Brown, former director of Washington State University’s AgWeatherNet spent years comparing different weather data sources and how those data affect the accuracy of common environmental models used by orchard growers. In this 20-minute webinar, he shares the surprising things he learned.
Decrease chances of crop damage with one simple practice
Find out how you can increase the accuracy of your predictive models and decrease frost, insect, and disease incidents by doing just one thing differently—improving the quality of your weather data. Discover:
Microclimates: what are the conditions like inside a crop canopy versus outside?
Virtual data vs. weather station data: Which is better?
How do site-specific weather data vs. regional network data compare?
How much does a small decrease in data quality affect the accuracy of your models?
What’s the value of in-orchard measurements?
What are some best practices for higher data quality?
For 20 years as a faculty member at Montana State University and Washington State University (WSU) Dr. Dave Brown pursued research on soil sensors, spatial data science, and digital agriculture. At both universities, he served in many leadership roles for major research projects, academic programs, and most recently as Director of the WSU AgWeatherNet program. In this capacity, Dr. Brown hired and supervised a team of meteorologists who pursued research and extension activities focused on evaluating and improving the quality of weather data used for agricultural decisions.
Launched in 2006, the URC has hosted competitions since 2007 and boasts contestants from around the globe, including the United States, Canada, India, Bangladesh, Poland, and Egypt. Each year, contestants are given point scores based on how quickly they complete a series of tasks and how closely each task conforms to parameters outlined by the competition guidelines. This year, teams must complete a terrain traverse, a simulated equipment servicing, an astronaut assist, and the retrieval and measurement of a non-contaminated soil sample.
Collaboration and Challenges
Byron Cragg, Science Team Lead for the Titan Rover Team out of California State University, Fullerton, says it’s been an uphill battle. “We’ve had to design the systems we are using to control our rover, retrieve our data, and keep our data organized from the ground up. We’ve also needed to make our rover robust in case a battery or a motor fails during the competition.”
It is no easy feat to build a rover for the Utah desert, let alone send instrumentation to Mars. This is why it has taken a multi-disciplinary team to build the physical components, robotic arm, telecommunications, and scientific cache on Titan Rover. Cragg says his team consists of scientists, computer engineers, electrical engineers, mechanical engineers, geologists, chemists, and biologists all working together.
A prototype image of the Titan Rover.
Titan Rover Features
The CSU rover is outfitted with sophisticated features like Leap Motion infrared sensors that allow Titan Rover’s robotic arm to be controlled by a human counterpart moving their arm in free space. When the user moves their arm and hand position, the arm on Titan Rover is given a signal from the command center to move accordingly.
Cragg is responsible for the 3D printed science cache that uses a 3” auger and a capacitance sensor to measure a soil sample’s volumetric water content, temperature, and bulk electrical conductivity. During the competition, the team will also be required to construct a stratigraphic column from HD images transmitted by the rover, as well as measure soil temperature at a depth of 10cm.
“It comes down to designing the pieces to communicate and work together to perform the tasks correctly,” Cragg says about the challenges ahead. “It’s one thing to build the rover,” he adds, “but it’s another to complete the requirements.”
While ambitions of a colonized Mars are on the horizon and research pushes on, like the Titan Rover project, progress will require collaboration and teamwork. In the meantime, good luck to all the Earthlings who will be competing in the Utah desert this June.
With the recent news coverage of the SMAP (Soil Moisture Active Passive) satellite launch, researchers may wonder: what does remote sensing mean for the future of in situ measurements? We asked two scientists, Drs. Colin Campbell and Chris Lund, for answers to this complex question. Here’s what they had to say:
What is SMAP?
SMAP is an orbiting earth observatory that estimates soil moisture content in the top 5 cm of soil over the entire earth. The mission is three years long with measurements taken every 2-3 days. This will allow seasonal changes around the world to be observed over time, improving our ability to manage water resources and better parameterize land surface models. SMAP determines the amount of water found between the minerals, rocky material, and organic particles found in soil by measuring the ability of radar to penetrate the soil. The wetter the soil is, the less the radar will penetrate. SMAP has two different sensors on the platform: an L band aperture radar with a resolution of about a kilometer when it’s looking straight down (the pixel size is about 1 km by 1 km), combined with a passive radiometer with about 40 km of resolution. This combination creates a synthetic product that takes advantage of the sensitivity of the radiometer.
What does SMAP mean for in situ soil water content measurement?
It’s all about scale: In some ways, comparing in situ to SMAP measurements is like comparing apples to…well…mountain-sized apples. The two forms of measurement use vastly different scales. In situ soil moisture sensors measure water content at the volume of several liters of soil, maximum. Even the sensor with the largest field of sensitivity, the neutron probe, can only integrate a volleyball-sized volume. On the other hand, SMAP measures at a resolution of 1 km2, which is larger than the size of a quarter section, a large field for many farmers. Global soil moisture maps will allow scientists using SMAP to look at big picture applications like weather, climate and hydrological forecasting, drought, and flooding, while more detailed in situ measurements will tell a farmer when it’s time to water, or help researchers discover exactly why plants are growing in one location versus another. The difference in spatial scale makes the two forms of measurement useful for very different research purposes and applications. However, there are applications where the two measurements can be complementary. Most notably, in situ measurements are often temporally rich while being spatially poor. But, SMAP can be used to scale in situ measurements to areas where in situ measurements are absent. In situ measurements can also be used as a source of validation data for SMAP-derived values for any location where both in situ and SMAP measurements overlap. Thus, there is opportunity for synergy when pairing SMAP and in situ measurements.
Satellite image in Winter.
What can SMAP do that in situ measurement can’t?
Scientists say they’ve seen a relationship between the top 5 cm of soil moisture and some factors related to climate change and weather. Because in situ soil sensors sample across a spatial footprint of a few meters, it can be very difficult to use their data to say anything about processes occurring across broad spatial scales; two liters of soil is not going to tell you anything about weather or flooding. SMAP can help us better understand the interaction between the land surface and atmosphere, improving our understanding of the global water cycle as well as regional and global climate. This will help with forecasting crop yield, pest pressure, and disease…that’s big picture research.
The productivity of a forest also may depend on the general soil moisture measured by SMAP. For instance, if we got an idea of the soil moisture and greenness of a forest, we could tie together the approximate water availability and the resulting biomass accumulation with incoming solar radiation. Better biomass accumulation models could lead to better validation of global carbon cycle models.
SMAP will also be able to detect dry areas across the U.S. and challenges they might present. Surface runoff that leads to flooding could also be predicted as scientists will be able to see where soils reach saturated conditions.
In other applications, people working on global water or energy budgets have to parameterize the land surface in terms of how wet or dry it is. That’s the big advantage of SMAP’s relatively new data sets. Any time you’re running a regional climate model you have to parameterize what the soil moisture is in order to partition surface heat flux into sensible and latent heat flux. If there’s a lot of available water, it’s weighted more toward evaporation and less toward sensible heat flux. In areas where there’s little available water and low evaporation, you get high surface temperatures and sensible heat flux. So SMAP will be important for model parameterization as we haven’t had a good global data set for soil moisture until now.
In situ sensors show how much water is lost from the root zone and what is still left.
What can in situ sensors do that SMAP can’t?
In irrigated agriculture, farmers need to know when and how much to irrigate. In situsensors give them this information by showing how much water was lost from the root zone and what is still left. SMAP is unable to tell you what’s down in the root zone; it only reaches to 5 cm. Additionally, 1 km resolution is larger than most irrigation blocks. These factors mean that it will be difficult to make irrigation decisions from SMAP alone.
Scientists using in situ sensors are concerned with the soil moisture available in a local area because their time resolution is excellent and they have the ability to resolve what’s happening in particular conditions related to crops or natural systems. Natural systems are often heterogeneous, meaning there may be adjacent areas with different types of vegetation including trees, shrubs, and grass. Tree roots may grow deep while grass roots are shallow. Being able to look over all these different areas without averaging them together, as SMAP does, is critical in some applications.
What about geotechnical applications? Literature suggests SMAP output can help predict landslides. It is more likely that it can only see when the soil is generally saturated and generate a warning. But in slopes that are at risk of landslides, in situ monitoring with sensors such as tensiometers to measure positive pore water pressure may be more useful for determining when a slide is imminent.
SMAP, like in situ water content measuring systems, is also limited by the fact that it measures the amount, not the availability, of water. If it measures 23% water content in a certain area, that measurement may not tell us what we want to know. A clay soil at 23% VWC will be close to wilting point while a sand would be above the plant optimal range. SMAP doesn’t measure the energy status of water (water potential), so even if SMAP tells us a field has water content, that water might not be readily available. Water availability must be determined through a pedo-transfer function or moisture release curve appropriate for a specific soil type (It is possible to overlay SMAP data on soil type data to estimate energy state, but this might not be fine enough resolution to be useful).
How do SMAP and in situ instruments work together? The key is ground truthing in situ soil moisture measurements with SMAP type satellites and vice versa. Ground-based measurements at specific locations can be matched with satellite information to extrapolate over a field and gain confidence in the small continuous scale alongside the larger infrequent scale. It’s analogous of a video camera recording one plant continuously while a single shot camera snaps whole-field pictures every day. With the SMAP “single-shot” we can say, something changed from time A to time B, but we don’t know what happened in the middle (rain event, etc.). In situ measurements will tell us the details of what happened in between each snapshot. Putting both data sets together and matching trends, we can show correlation and complete the soil moisture picture. Basically, In situ measurements provide temporally rich information about soil moisture from a postage stamp-sized area of earth’s surface (driven by highly localized conditions), whereas SMAP gives us the ability to monitor broad scale spatiotemporal patterns across all of earth’s surface (driven by synoptic conditions).
Dr. Christopher Lund is a research scientist and product manager for METER’s new irrigation management instrumentation group. He has more than a decade of experience working with land surface flux measurements, terrestrial water budgets, and soil-vegetation-atmosphere transfer scheme modeling. Prior to joining METER, he served as a research scientist on the NASA-CSUMB SIMS (Satellite Irrigation Management Support) Project, a multi-year collaboration between the California Department of Water Resources, NASA, and CSU Monterey Bay providing California growers with novel irrigation decision support tools. Dr. Lund’s current research focuses on developing cost-effective irrigation management instrumentation for commercial markets. Dr. Lund will be giving a talk on innovations in agricultural remote sensing at the Third Professional Workshop on Technology For Irrigation Scheduling. He will talk about his work with the SIMS team and what growers can do with remote sensing data to estimate things like evapotranspiration. He’ll also address how to improve those estimates by combining them with field measurements from ground based instrumentation such as soil moisture sensors.
Image: USGS Landsat Project Website
“The advantage of satellite remote sensing is that it allows you to look at many fields at once and also integrate across spatial variability. The down side is it doesn’t give you access to everything you might want for irrigation management, so there are certain things you have to measure on the ground. When it comes to remote sensing data and ground measurements, I don’t think it’s an either/or situation. I think the future is hybrid products utilizing both remote sensing and ground based measurements,” he says.
He will also speak on how satellite derived NDVI data can benefit from new inexpensive ground based-sensors like the SRS. This enables scientists to make sure that their satellite NDVI data accurately reflect what’s happening on the ground.
The seminar will be held at the Third Professional Workshop On Technology For Irrigation Scheduling on February 11, 2015 at the CREA auditorium, Calle Jose Galan Merino Sevilla, Spain.
METER’s founder, Dr. Gaylon S. Campbell was born in Blackfoot, Idaho, and grew up on a dry farm in Juniper, Idaho. He went to school in Logan, Utah, finally attending Utah State University where he received a B. S. in Physics in 1965 and an M. S. in Soil Physics in 1966. He was granted a Ph. D. in Soil Physics from Washington State University in 1968. He became an officer in the U. S. Army in 1969, doing meteorological research at White Sands Missile Range, New Mexico. In 1971 he returned to Washington State University as Assistant Professor of Biophysics and Assistant Soil Scientist. There he taught and did research in Environmental Biophysics and Soil Physics until 1998. Since 1998 he has worked as vice president, engineer, and scientist at Decagon Devices, Inc (now METER). He has written three books, over 100 refereed journal articles and book chapters, and has several patents. Today we are interviewing him about his book, An Introduction to Environmental Biophysics.
Dr. Campbell is the author of An Introduction to Environmental Biophysics
Where did you get the knowledge to write the book?
I was hired to teach Environmental Biophysics at Washington State University in 1971, and when I looked around for a textbook to go with the class, there weren’t any that fit very well. I knew what I wanted to teach in the class, and some of the principles were in books that were available, but a lot weren’t. So I started writing up notes to hand out to the students and then improved them over time.
One of the important sources of knowledge for my book was John Montieth’s book, Principles of Environmental Physics. Its first edition came out in 1973. It’s a wonderful book. I didn’t know about it until one of my students brought it into class and let me borrow it overnight.
I went home and started reading it. I read it all night, and by morning I’d finished it. I have read some novels that could keep me awake all night, but that’s the only science book I ever read that could do it.
I was really excited about his approach because it was perfect for what I wanted to do in the class. However, it was at a different level than I needed, so I went ahead and developed my own notes, but his book certainly was an important source.
I started writing up notes to hand out to the students and then improved them over time.
How difficult was it to understand the theory behind what you were writing about?
When I’d take a class in school, I felt like I never understood what was in that class until I attended the next class. Then when I got a bachelor’s degree, I thought, I hope nobody expects me to know something just because I have this degree, because I don’t feel like I know anything. I hoped when I earned a masters degree that it would be better, but I got there and thought, oh boy, I still don’t know anything. It was probably when I took my prelim exam that I finally felt confident enough that I could be a soil physicist if I had to.
But I was wrong about that. I really didn’t understand physics very well, even then. It was when I had to teach it that the real understanding came. When I understood it well enough to lecture about it was when I felt like I had really mastered the theories and understood them at the level that I wanted to.
I suppose that came one piece at a time. In the beginning, I certainly didn’t understand things as well as I did later on. And that still happens today. I learn things that I hadn’t understood before. So I guess when you ask how hard it was: it was an ongoing process. Even when somebody’s already laid it out for you, it doesn’t mean you’re going to understand it. But when you lecture about it and write about it, those are the processes that help to deepen your knowledge and understanding.
When you lecture about a subject and write about it, those are the processes that help to deepen your knowledge and understanding.
The subject is extremely complicated, but people are always saying how easy it is to understand environmental biophysics from your book. How did you bring it down to the level of the students?
When I was in the Army, the philosophy they had was, “If the student hasn’t learned, the teacher hasn’t taught.” That was not the philosophy that you normally encountered at the university. Many professors complained often about how lousy their students were. I never found it to be that way. I always thought my students were getting better and better.
I think it comes down, to some extent, to the philosophy the teacher has. We often see teachers come in and fill the board with equations and wonder why their students don’t understand them. But it’s likely the teacher hasn’t looked at it from the standpoint of the students. The student is going to gain understanding by the same path the teacher did. Professors work and work to put together a wonderful picture of things, and once they have that wonderful picture, they tend to want to dump the whole thing on the student. But students can’t assimilate the whole picture all at once. They have to go step by step too.
If people wanted to learn from your book, what is the best way to get the principles down?
It’s no accident that there are lots of both worked examples and problems for students to solve. I don’t think you can learn physics without solving problems, and so the best way to do it is to look through the ones that we’ve solved in the book and then look through the problems we give at the end of the chapters and solve them. That, I think, is the best way to get there.