Application of remote sensing technologies for

mapping of cropping pattern and area

estimations for major summer and winter crops

across spatial and temporal scales: Lessons

learnt in Australia

A Potgieter

Climate variability

11 year running mean high variability at

temporal & spatial

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Area (MHa) Production (Mtons)

Impact (Economic, Natural, Social)

Operating within a Variable Climate

• Australian producers are excellent risk

managers. Successful businesses within the

world’s most variable climate and without

subsidies

• Climate risk and change is already built into

the system

• Integration of climate forecasts with targeted

decision making tools – improved decision

making

• Developed world-leading crop production

systems frameworks & decision-support tools

Utility

Utility of crop predictions is a function of timing and accuracy

Early-Low

Late

-Hig

h

1 Jun 19?? to 31 Dec 20??

1 October 2003 to 31 May 2004

1 Jun 19?? to 31 Dec 19??

Start of

fallowForecast

distribution

based on

climate

forecast

Generating Yield Forecast Data Plumes –

Climate Forecast Set (e.g. at 1 June 2004)

Run model using weather data

to date for current season

Run model projections on subset of

historical analogues from climate

forecast to complete season

Regional crop prediction framework

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April May June July Aug Sept Oct Nov

Month

Yie

ld (

t/h

a)

Forecast Worst10% Forecast-median Forecast Best10%

Forecast Climatol Lt-median

Hindcast Qld 1994SOI phases

Best 10%

Median

Worst 10%

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Yie

ld (

t/h

a)

Forecast Worst10% Forecast-median Forecast Best10%

Forecast Climatol Lt-median

Hindcast Qld 1994SOI phases

Best 10%

Median

Worst 10%

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Month

Yie

ld (

t/h

a)

Forecast Worst10% Forecast-median Forecast Best10%

Forecast Climatol Lt-median

Hindcast Qld 1994SOI phases

Best 10%

Median

Worst 10%

Potgieter et. al. 2003, 2005, 2006

Spatial & temporal forecast

© QAAFI 2010 Created 9/08/2010 [Slide 7]

Predicted percentile

median yield

relative to all years

1st July 2015

Temporal scale

(a)

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DOY

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SWQ Wheat

SWQ Barley

SWQ Chickpea

CQ Wheat

CQ Chickpea

SEQ Wheat

SEQ Barley

(b)

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2005

2006

- Traditional approach:

Single date approach e.g.

DOY 225 does not capture

all possibilities of peak crop

canopy across regions

- Proposed approach:

Multi-temporal capturing all

available crop growth

information

- Ability to accurately

discriminate between

crops

Discriminating Crops: “MISSING LINK”

Curve Fitting Procedures

Time

Gre

en

ne

ss v

alu

e (i.e

. EV

I)

peak

Reconstructing of crop growth profile

HANTS (Verhoef et al 1996)

(a)

(b)

(c)

Amplitude

Phase

0 353

Day of Year (DOY)

(a)

(b)

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Amplitude

Phase

(a)

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(c)

(a)

(b)

(c)

Amplitude

Phase

0 353

Day of Year (DOY)

(Verhoef et al 1996; Potgieter et al.

2007, 2010, 2011 )

2005

2006

Discriminating between crops

(a )

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Actual

Reconstructed

(b )

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Actual

Reconstructed

Harmonic analysis of time series

Barley from satellite

Wheat from satellite

Validation & Training – ground truth data http://www.paddockwatch.com.au

Paddock Watch

Example of Winter cropping: Waggamba & Moree 2015

Ground truth & Accuracies: Waggamba 2015

EVI multi-temporal canopy profiles Accuracy of area estimates during

25 May 26 Jun 28 Jul 29 Aug 30 Sep 17 Nov

Jan May Nov

AVG: 87%

Wheat-78%; Chickpea-

98%

Barley-96%

Crop Area Estimates

Waggamba Moree

Other outputs: Land Use: Aug 2013 to Jul 2014

Cropping trajectories over time

AVG: 602k Ha

QLD: Increasing trend in Summer cropping of 36k Ha per year

Regional scale commodity forecasting framework

Potgieter et. al. 2003, 2005, 2006

Summer

2014/15 Winter

2014

Specific & Total Crop Area & Production

Land use patterns Bangladesh (source: Perry Poulton CSIRO)

Southern Bangladesh 16th Jan 2007 Lat:22.89ºN Long:91.40ºE(7173 km2)

Original image

Classified image

Land use class Area (%) Area (ha)

1. Fallow 13.7 217.8

2. Trees 18.3 291.8

3. Ponds 3.2 51.3

4. Cropping (*other) 64.8 1031.2

Total 100 1592.1

What lessons have we learnt?

• Utility of any prediction is based on accuracy and

timing

• Framework high efficacy in predicting crop area

estimates: at crop type level

• Successfully integrated to determine crop

production estimates at regional scale

• What spatial scale is needed? - horses for courses;

depending on what the issue is.

• Temporal resolution of outputs are sometimes more

useful than accuracy: TRENDS

• Ground truthing fields are critical in crop

discrimination

(source: NASA)

Which Satellite platform? Vegetation type:

• 75 current and planned

sensors from 2015 to 203

• Temporal & spatial

resolution

•Accessibility of data:

• 1 m to 1 km resolution

• once every 10 minutes

to ~once every month

The right answer

to the wrong

question

therefore…

Asking the right questions before designing any framework

KEEP IT SIMPLE !!!

Thankyou