What happens when you take satellite products and add soil water potential data? New data sources offer tools for growers to optimize production in the field. But the task of implementing them is often difficult. Research work is underway and offers a guide on how data from soil and space can work together to make the job of irrigation scheduling easier. In this presentation, METER’s Dr. Colin Campbell explains the formula for prescribing irrigation events that will get you the yields you want.

2. PREDICTABLE YIELDS
USING REMOTE & FIELD
MONITORING
Colin S. Campbell1,2, Bryan G. Hopkins3, Neil C. Hansen3, Ryan G.
Smith4, Florian Detsch1, Tim Appelhans1, and Ryan Christiansen5
1METER Group, Inc., 2Washington State University, 3Brigham Young
University, 4Missouri S&T University, 5BKR Farms

3. SOUTHERN IDAHO DRY FARM

4. MANAGING IRRIGATION
WHEN TO TURN WATER ON & OFF
What we need
• Knowledge of if and how much water is available to plants
• Information at the same scale as the ability to control the water (# of valves)
What we have
• Measurements of water in the field
• Drone data
• Satellite products
Next steps
• Combine them to extract key information from each, drive overall knowledge
Challenge
• How are these scales connected and does combining them provide meaningful
results?

5. CASE STUDY–YEAR 1
VARIABLE RATE IRRIGATION
PRESCRIPTION
Create a systematic approach to developing VRI prescription maps that improve water and
energy use in an economical way

6. PROBLEM
More complicated than expected
• Significant variation in soil water, ET, stress,
crop water productivity
Predicted moisture stress did not
correlate with yield (from yield
mapping system)
Yield(Mgha-1)
2016 Average Crop Stress (Ks)

7. RESEARCH SITE
Site
Center pivot variable rate irrigation
2018–Seed potatoes
2019–Irrigated wheat
3-year rotation
Grower
Innovative grower with potato yield monitor and
variable rate irrigation system (VRI)
Telemetry
Data logger 906 = ‘Site 6’ or ‘6’
Data logger 907 = ‘Site 7’ or ‘7’
etc.

8. SATELLITE
Source Physical Analog Sensing Parameter Technical Details
NDVI (Landsat 8) Canopy greenness Reflected red and near infrared
light
Repeat measurements every 12
days. Cloudy scenes removed.
Thermal image (Landsat 8) Canopy
temperature
Emitted surface infrared
radiation
~11 um wavelength. Repeat
measurements every 12 days.
Cloudy scenes removed
Radar backscatter
(Sentinel-1)
Plant and soil
moisture
Radar backscatter 5.6 cm wavelength. Repeat
measurements every 8 days
Normalized Difference
Water Index
(Sentinel-2)
Plant moisture Reflected infrared light 5-day repeats, 10 – 60 m
resolution. Cloudy scenes
removed.

9. QUICK FIELD
DEPLOYMENT
Sensor auto-recognition
Bluetooth smartphone setup
Integrated solar panel
GPS location data
Cloud-enabled via cellular
network

10. INSTRUMENTATION
ZL6TEROS 21 TEROS 12ATMOS 41 IRT

11. SITE INFORMATION
YEAR 1
Field Sites (6 total)
Measurements Depths or Heights Trade Name
Water content, temp, EC 6,18, 30 in. TEROS 12
Matric potential, temp 6, 18 in. TEROS 21
Canopy temp 6 ft. (3 sites) Infrared Thermometer (IRT)
All-in-one weather
station
6 ft. (3 sites) ATMOS 41 (Solar, wind, T,
RH, pressure, precipitation)
Cloud telemetry N/A ZL6

12. SENSOR INSTALLATION
Auger 4 ft. deep x 4 in.wide installation hole
(3 minutes;low effort and site disturbance)
Durable epoxy and steel needles
Tool inserts sensors exactly perpendicular to
soil wall
Installed at 6, 18, and 30 in.

13. MOISTURE SENSOR
INSTALLATION

14. MOISTURE SENSOR
INSTALLATION

15. DATA STORAGE AND
VISUALIZATION

16. 2018 RESULTS:SINGLE FIELD
SOIL WATER CONTENT (18 in.)

17. 2018 RESULTS:SINGLE FIELD
MATRIC POTENTIAL (18 in.)
Matric Potential (kPa)
-5 to -100 Plant
optimal
-100 to -1500 Stress
< -1500 Permanent
wilt

18. SITE STRESS
EFFECTS ON YIELD
1 Mg/ha = 7.96 cwt/ac
Site Days in Stress Yield
(Mg/ha)
9 42 31.47
12 53 32.77
10 44 37.44
11 0 39.67
6 0 40.08
7 16 40.33

19. SATELLITE PREDICTION OF
YIELD FROM WATER POTENTIAL
WEAK BUT SIGNIFICANT CORRELATION
Yield(Mg/ha)
Matric Potential Delta

20. YEAR 2
CAN WE EXPAND EFFORT?
7 fields growing several varieties of
potatoes
• Cemetery, Max’s Pivot, Barbara’s West Mini,
Home Place NW, Home Place SW, Reed’s Middle,
and Anderson Pivot
Grower-purchased systems
• VWC @ 6 and 12 in., matric potential @ 12 in.,
rain gauge, cloud-connected data logger
Limited funds dictated only a single
measurement system per field

21. 2019 SITE SELECTION
In situ measurement sites
selected by satellite
estimation of seasonal
wetness across each field
Locations for:
1. Average moisture across
season
2. Driest moisture location
across season

22. SOIL VOLUMETRIC WATER CONTENT
Vine kill

23. IN SITU SOIL MATRIC POTENTIAL
Vine kill
Matric Potential (kPa)
-5 to -100 Plant optimal
-100 to -1500 Stress
< -1500 Permanent wilt

24. TARGET RANGE
Vine kill
Matric Potential (kPa)
-5 to -100 Plant optimal
-100 to -1500 Stress
< -1500 Permanent wilt

25. CROP STRESS
DAYS BELOW -100 kPa
2018 Potato Field
(Variable Rate Irrigation)
Site Days in Stress Yield
(cwt/ac)
9 42 251
12 53 261
10 44 298
11 0 316
6 0 319
7 16 321
Field Name Days
Below
-100 kPa
Yield at
Sensor
(cwt/ac)
Average
Yield at
Field
(cwt/ac)
Difference
(cwt/ac)
Standard
Deviation of
Yield
(cwt/ac)
Cemetery 18 327 282 45.3 58.1
Barbara's West Mini 14 302 318 -16.7 84.4
Max’s Pivot 17 321 290 33.4 78.8
Home Place NW 0 255 248 6.4 55.7
Home Place SW 0 329 270 59.7 58.9
Anderson Pivot 6
Reed’s Middle 21

26. NDWI CORRELATION WITH
POTATO YIELD

27. OVERALLYIELD CONNECTIONS
TO SATELLITE
Not much correlation between
yield and satellite derived NDWI
Yield data from seven fields
shows no clear patterns

28. GROWER FEEDBACK
Looked at data on ZENTRA Cloud every day!
Much lower water use
• Typical year: 20 to 25 in of water
• This year: 17.5 to 19 in. of water
• Note: cooler year – need verification from ET analysis
Potato ‘average’ sites–right on average
• Dug potatoes at typically dry spot and typically wet spot along with measured
location
• Always close to average of the field, like yield map finding

29. SUMMARY
Picking single measurement site with
satellite data worked well
Grower irrigating to in situ water
potential produced great potato
yields and seemed to use less water
Scaling up using a single season’s
satellite data was not successful.
More data needed.

30. QUESTIONS?