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Remote Sensing for Deriving Crop
Information: Opportunities and Challenges
Anne M. Smith Agriculture and Agri-Food Canada
Lethbridge Research and Development Centre
Canola Innovation Day
December 3rd 2015
Saskatoon, SK
• Multispectral
• fewer broad bands
• Hyperspectral
• many narrow bands
0
1
2
3
4
5
6
0 1 2 3 4 5 6 Measured GLAI
RMSE=0.47
R^2=0.91
Es
tim
ate
d G
LA
I
LAI
y = 0.14Ln(x) - 0.66 R2= 0.72
0.5
0.6
0.7
0.8
0.9
1.0
0 2 4 6 8 10 12 14
Fresh weight (kg/ha x 1000)
ND
VI
BIOMASS NITROGEN MANAGEMENT
YELLOW FLOWERS
OPTICAL REMOTE SENSING
FOR MEASURING
BIOPHYSICAL PARAMETERS
EXAMPLES
REMOTE SENSING
• Biomass
• Leaf area index
• Canopy cover
• Flowering
• Moisture deficiency/excess
• Nutrient deficiency (N)
• Disease
• Weed infestations
PHENOTYPING • Expression of an organism’s genetic material as
influenced by the environment
CROP PHENOTYPING • Growth
• Development
• Yield
• Quality
• Tolerance
• Resistance
• Architecture
• Adaptation
• Biomass
• Leaf area index
• Canopy cover
• Flowering
• Moisture deficiency/excess
• Nutrient deficiency (N)
• Disease
• Growth
• Development
• Yield
• Quality
• Tolerance
• Resistance
• Architecture
• Adaptation
REMOTE SENSING PHENOTYPING
REMOTE SENSING DATA AVAILABILITY
Sensor
Swath
width
(km)
Spatial
resolution
(m)
Spectral
bands
Temporal
resolution
(Days)
Cost
AVHRR 2399 1100 4 1 $0.00 /km2
MODIS
2330
250
500
1000
2
5
29
1 $0.00 /km2
Landsat-5
Landsat 7
ETM+
185 30
60
6
1 16 $0.00/km2
SPOT-5 60
5
10-20
1
4 26 $4.00#/km2
RapidEye 77 5 5 5.5 $1.40#/km2
Quickbird/
Worldview 16.5
0.5/0.6
2.0/2.4
1
4 3.5 $22.00#/km2
Airborne/UAS Variable Variable Variable As required $4.00-$7.00 /ac
# minimum area requirement (differs based on archived or tasked acquisitions)
LETHBRIDGE RESEARCH AND DEVELOPMENT CENTRE
UAV imagery
(False Colour
Composite)
August 2014
Improving Grower Profitability and Competitiveness
through Mitigation of Limitations to Potato Yield
• Collaboration industry and AAFC
• To develop a new system for identifying and
overcoming limitations to potato yield in New Brunswick.
• To develop approaches to using remote sensing
data to identify zones within potato fields in which
yield is limited
• To identify the soil physical, chemical or biological
limitations to yield in zones of suboptimal yield in grower
fields
REMOTE SENSING IMAGE ACQUISITION
Target
radiance/
reflectance
Processed
images
• UAVs are versatile
compared to satellites
• 15 fields
• in-season biophysical
data collection in 4-5
fields (assumption of
image calibration)
• yield
Optical
LiDAR
Thermal
Radar
WHAT INFLUENCES IMAGE ACQUISITION?
• Environmental factors
– Sun’s geometry • Time of day, time of year
– Atmosphere
– Flight altitude
• Camera parameters
– Camera settings (f-stop, exposure, ISO
settings)
– Vignetting and radial displacement
– Colour processing (demosaicking)
Yum!!
IMAGE CALIBRATION
Ref
lect
ance
val
ue (
%)
Digital number
PSEUDO-INVARIATE TARGETS
CAMERA CALIBRATION
Camera calibration
facility University of
Lethbridge
0
50
100
150
200
250
358 455 554 654 754
Dig
ital
nu
mb
er
(DN
)
Wavelength (nm)
RGB Camera Blue
Green
Red
0
50
100
150
200
250
400 500 600 700 800 900 1000
Cam
era
dig
ital
nu
mb
er (D
N)
Wavelength (nm)
NIR Camera GreenRedNIR
y = 6E-10x3 - 2E-05x2 + 0.1711x - 319.53, R² = 1.00
0
50
100
150
200
250
2000 4500 7000 9500 12000
Cam
era
dig
ital
nu
mb
er (D
N)
Light intensity
625 nm
IMAGE CAPTURE AND IMAGE MOSAICS
Manual tie points needed to align images
Adequate overlap for good imagery
Red
NIR NIR
BEFORE AFTER
JULY 9
JULY 28
FALSE COLOUR COMPOSITES (NIR=R, R=RG)
“NDVI”= (NIR-RED)/(NIR+RED)
July 9
July 28
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Jul-09 Jul-28 Aug-11 Sept-04 Sept-18
ND
VI
DatePT 0 PT 1 PT 2 PT 3 PT4 PT 5 PT 6 PT 7
PT 8 PT 9 PT 10 PT 11 PT 12 PT 13 PT 14
“NDVI” AND YIELD
y = 149.12x + 10.49, R² = 0.880
10
20
30
40
-0.05 0.00 0.05 0.10 0.15
Mar
keta
ble
Yie
ld (t
/ha)
Normalized difference index value
Jul-09
y = 120.44x - 1.86, R² = 0.51
0
10
20
30
40
0.00 0.10 0.20 0.30
Mar
keta
ble
Yie
ld (t
/ha)
Normalized difference index value
Jul-28
y = 125.76x - 3.44, R² = 0.28
0
10
20
30
40
0.00 0.10 0.20 0.30
Mar
keta
ble
Yie
ld (t
/ha)
Normalized difference index value
Aug-11
“
“
“ “
“ “
“ “
“NDVI”
July 9
July 9
24%
51%
25%
Low
Medium
High
Poor 84 cwt/ac (33% of good)Good 257 cwt/ac
1 2 3 4 5 6 7 8 9 10 11 12 13 140
0
100
200
300
400
500
Sampling location
Ma
rket
ab
le t
ub
er
yie
ld (
cwt/
ac)
CANOPY COVER
y = 0.41x + 9.02, R² = 0.78
0
5
10
15
20
25
30
35
40
0 20 40 60 80
Ma
rke
tab
le Y
ield
(t/
ha
)Canopy Cover (%)
July 9
IMPLICATIONS FOR PHENOTYPING
• Qualitative information
– Within dates can assess relative differences in growth within
plots/fields.
– Not suitable for estimating LAI or biomass over time.
• Quantitative information
– Canopy cover can be quantified over time providing
information on canopy development.
– Onset and duration of flowering.
IMAGE ACQUISITION 2015
Target
radiance/
reflectance
Calibrated
reflectance
images
Pre-flight calibration
Image
processing
Calibration
target
Incident light
sensor
Four cameras
(data collected in
discrete bands of
green, red, red-
edge, NIR)
Reflectance image
July 7, 2015
DN image
July 7, 2015
Reflectance image
July 7, 2015
DN image
July 7, 2015
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Green Red Red-Edge NIRR
efle
ctan
ce (
0-1)
Band
Soil (Sun) Vegetation (Sun) Soil (Cloud) Vegetation (Cloud)
0
50
100
150
200
250
300
Green Red Red-Edge NIR
Dig
ital
Nu
mb
er (D
N)
Band
Soil (Sun) Soil (Cloud) Vegetation (Sun) Vegetation (Cloud)
NDVI derived from reflectance image
July 7, 2015
NDVI derived from DN image
July 7, 2015
Modified Triangular Vegetation Index
derived from reflectance image
July 7, 2015
Modified Chlorophyll Absorption Ratio Index
derived from reflectance image
July 7, 2015
• 2015
– time series of images (0, 44, 55 and 65 DAP) for 19 fields
– image mosaic
– variety of vegetation indices
– relationships to biophysical data
• 2013 and 2014
– image mosaic
– revisit image calibration
– comparison amongst fields?
WHERE TO FROM HERE?
TAKE HOME MESSAGE FOR PHENOTYPING
• Opportunities
– UAV best option
• Flexible in time
• High spatial resolution
– Sensor selection
• No calibration provides qualitative information but limited quantitative
information.
• With calibration opportunity exists to provide quantitative information
over time and amongst plots/fields.
• Challenge
– Define the information required and put together the optimal
system.
THANK YOU!
• Funding
– Potatoes New Brunswick, McCain Foods Canada, AAFC
Agri-Innovation Program, and the Enabling Agricultural
Research and Innovation program of the NB Department of
Agriculture, Aquaculture and Fisheries.
• McCain Foods Canada
– UAV, sensors and image collection.
• Participating growers.
• Dr. Bernie Zebarth, Ginette Decker, Ingrid Oseen.
• Dr. Craig Coburn, University of Lethbridge
ACKNOWLEDGEMENTS