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Title: Integrating Remote Sensing in Precision Agriculture:


1
Integrating Remote Sensing in Precision
Agriculture Lessons Learned and New Directions
Jim Schepers USDA-ARS Agronomy and Horticulture
Department University of Nebraska
2
Satellites
Clouds Timeliness Turn Around Resolution
Clouds Timeliness
Platforms
3
Spectral Opportunities
70 60 50 40 30 20 10 0


Photosynthesis
Reflectance ()
400 500 600 700 800 900 1000
Wavelength (nm)
4
Spectral Opportunities
70 60 50 40 30 20 10 0


Bare Soil
Reflectance ()
400 500 600 700 800 900 1000
Wavelength (nm)
5
Computer Generated Management Zones
6
Sensor-based mapping
7
Data Collection
Reality
8
Regrouping
9
Soil-based N Management
After Planting
Bare Soil Image
10
Computer Generated Management Zones
Incorporating. . .
Bare Soil Image Elevation Slope EM-38
11
Spectral Options
70 60 50 40 30 20 10 0


Color Film
Reflectance ()
400 500 600 700 800 900 1000
Wavelength (nm)
12
Color IR
Soybean
Hail damage
Sprayer problems
Cultivation in progress
13
UniformityConcerns (May Turn Into Huge Yield
Concerns)
End-gun OFF
14
IrrigationDesign Flaws
Nozzle in canopy
15
Irrigation Design Flaws
Corner unit ON
16
September 1, 2003
1-foot resolution
17
September 1, 2003
1-foot resolution
18
Leaf Anatomy Spectral Interaction
Chlorophyll
Palisade Cells
Spongy Mesophyll
Lower Epidermis
Stoma
Air Space
19
70 60 50 40 30 20 10 0
Young Plant
Reflectance ()
Bare Soil
400 500 600 700 800 900 1000
Wavelength (nm)
20
70 60 50 40 30 20 10 0
Healthy Plant
Reflectance ()
Bare Soil
400 500 600 700 800 900 1000
Wavelength (nm)
21
September 1, 2003
1-foot resolution
22
Normalized Difference Vegetation Index
70 60 50 40 30 20 10 0


NDVI (NIR Red) / (NIR Red)

Near Infra-Red
Reflectance ()
GNDVI (NIR Green) / (NIR Green)
400 500 600 700 800 900 1000
Wavelength (nm)
23
Sensitivity of Vegetation Indices To Various
Chlorophyll Levels
Adapted From Gitelson et al. (1996)
LAI gt2.0
24
(No Transcript)
25
Mosaic Approach to Variable Rate N
Maize
Imagery during grain fill (yield map
proxy) Bare soil image (estimate
OM) Residual N (field average)
Spatial yield goal
Spatial mineralization ??
26
Correlation Coefficient
12-bit aircraft data 24 x 50 plots 1 spatial
resolution
27
Green NDVI vs N Rate over Time
Irrigated Corn
kg N/ha
28
(No Transcript)
29
Field Calibration
Direction of travel
30
Sufficiency Index (SI)
Unknown Reference
Response Index (RI)
Reference Unknown
31
(No Transcript)
32
Making In-Season nitrogen fertilizer
recommendations
33
?
?
What information is needed ?
Are all approaches equally viable ?
34
Building Blocks of N Recommendations
Field-scale yield goal provided by
producer Variable yield goal based on maps (MZ,
etc.) Predicted yield based on crop vigor
Dynamics of crop growth
35
Dry Matter (Mg/ha)
?
GGD
36
Irrigated Corn
What we know Weather in July and August has a
strong influence on yield. How can yield
estimates compensate for weather?
N Uptake
Dry Matter
Total N Uptake (kg/ha)
Dry Matter (Mg/ha)
37
Relative Yield
Dry Matter (Mg/ha)
Mosaic Approach
GGD
38
What we know about corn ? ? ?
39
50 Sun-Screen
40
V6 V8 Avoid all stresses
41
(No Transcript)
42
Influence of Seedling Emergence And Spacing on
Corn Yield
Planter Speed
Tillage
43
(No Transcript)
44
Effect of Plant Spacing On . . . . .
Leaf N Concentration SPAD Readings Yield
45
24,000 plants/acre
46
Conclusion Root exploration is extensive
47
Conclusion For a given N rate, photosynthesis is
proportionate to light interception
48
Conclusion For a given N rate, grain is
proportionate to light interception, which is
proportionate to distance between plants
49
Extrapolated Yield Based on By-Plant Grain
Yield (bu/acre)
Conclusion Roots and leaves of closely spaced
plants explore considerably more area than is
proportionate to the allocated average distance
between adjacent plants
Average Distance (inches)
50
Extrapolated Yield Based on By-Plant Grain
Yield (bu/acre)
Average Distance (inches)
51
By-Plant Yield Variability
weighed grain from individual plants from 20
long sections of 10 rows from different
management zones and N treatments
Nobody told these plants that they couldnt share
above-ground spaces. They also share below-ground
living quarters.
Where do the nutrients come from that contribute
to yield? How about the assimilation of
carbohydrates?
52
What are the causes of different ear
sizes? Emergence date Plant spacing Nutrients
53
What is the appropriate spatial resolution for N
management ?
Field Strip Management Zone Few Plants
What is possible ? What is practical ?
54
Managing N According To - - - -
Macro-heterogeneity
Landscape features
Management zones
Micro-heterogeneity
Individual or Group of Plants
55
Yield
Relative SPAD
kg N/ha
GDD
56
Yield
Relative SPAD
kg N/ha
GDD
57
Yield
V6
V12
Relative SPAD
kg N/ha
GDD
58
Irrigated Corn
What will work for producers? Risk Equipment Econo
mics Labor
N Uptake
Dry Matter
Total N Uptake (kg/ha)
Dry Matter (Mg/ha)
59
Decision Aid Development
In-Season N Management
Calibration
Algorithm
60
Congratulations . . .
You Survived !!!!
61
Thank You
Jim Schepers 402-472-1513 jschepers1_at_unl.edu
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