remote sensing for deriving crop information ... · nitrogen management yellow flowers optical...

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