remote sensing for deriving crop information ... · nitrogen management yellow flowers optical...
TRANSCRIPT
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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
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• Multispectral
• fewer broad bands
• Hyperspectral
• many narrow bands
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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
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REMOTE SENSING
• Biomass
• Leaf area index
• Canopy cover
• Flowering
• Moisture deficiency/excess
• Nutrient deficiency (N)
• Disease
• Weed infestations
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PHENOTYPING • Expression of an organism’s genetic material as
influenced by the environment
CROP PHENOTYPING • Growth
• Development
• Yield
• Quality
• Tolerance
• Resistance
• Architecture
• Adaptation
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• 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
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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)
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LETHBRIDGE RESEARCH AND DEVELOPMENT CENTRE
UAV imagery
(False Colour
Composite)
August 2014
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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
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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
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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)
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Yum!!
IMAGE CALIBRATION
Ref
lect
ance
val
ue (
%)
Digital number
PSEUDO-INVARIATE TARGETS
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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
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IMAGE CAPTURE AND IMAGE MOSAICS
Manual tie points needed to align images
Adequate overlap for good imagery
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Red
NIR NIR
BEFORE AFTER
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JULY 9
JULY 28
FALSE COLOUR COMPOSITES (NIR=R, R=RG)
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“NDVI”= (NIR-RED)/(NIR+RED)
July 9
July 28
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-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
“
“
“ “
“ “
“ “
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“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)
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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
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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.
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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)
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Reflectance image
July 7, 2015
DN image
July 7, 2015
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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)
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NDVI derived from reflectance image
July 7, 2015
NDVI derived from DN image
July 7, 2015
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Modified Triangular Vegetation Index
derived from reflectance image
July 7, 2015
Modified Chlorophyll Absorption Ratio Index
derived from reflectance image
July 7, 2015
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• 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?
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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.
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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