This document discusses an empirical Earth Observation (EO) based approach to wheat yield forecasting and its adaptation within the GEOGLAM framework. GEOGLAM is an international initiative that aims to inform agricultural decisions through coordinated Earth observations. The approach uses multi-temporal satellite data like NDVI to develop quantitative wheat yield forecasts at regional/national scales. A key challenge is obtaining annual crop type maps for spatially explicit time series. Aggregating high-resolution wheat masks to coarser resolutions can mitigate effects of crop rotations over time.
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Empirical EO based approach to wheat yield forecasting and its adaptation within the GEOGLAM Framework
1. Empirical
EO
based
approach
to
wheat
yield
forecas5ng
and
its
adapta5on
within
the
GEOGLAM
Framework
Inbal
Becker-‐Reshef1,
Eric
Vermote2,
Mark
Lindeman3
,
Jan
Dempewolf1,
Joao
Soares4,
Chris
Jus5ce1
1University
of
Maryland,
2NASA
GSFC,
3USDA
FAS,
4GEO
Secretariat
2. Who
We
Are
Interna5onal
recogni5on
of
up
of
nterna5onal
and
na5onal
agencies
Open
Community
made
cri5cal
ineed
for
improved
real
5me,
reliable,
open
informa5on
on
g monitoring
including
ministries
o
concerned
with
agricultural
lobal
agricultural
produc5on
prospects
f
Ag,
space
agencies,
universi5es,
and
industry
Cri5cal
for
agricultural
policies,
stabilizing
markets,
aver5ng
food
crises
Need
to
increase
food
produc5on
by
50%-‐70%
by
2050
to
meet
demands
3. Context
Monthly Wheat Prices 1960-2011($/Metric Ton)
Source: World Bank
2008
Price
hikes
Droughts:
Australia
&
Ukraine
2010/11
Price
hikes
Drought:
Russia
‘grain
robbery’
1971/2’s
price
hike
Landsat
1
Launched
(1972)
Nominal
wheat
price
in
US
$/metric
Ton
4. G-‐20
GEOGLAM:
Interna5onal
Framework
&
Scope
•
GEOGLAM- Group on Earth Observations (GEO) Global
Agricultural Monitoring Initiative
•
Policy Mandate from G-20
2 related initiatives adopted as part of Action plan on
Food Price Volatility and Agriculture:
1. AMIS (Agricultural Market Information System)
2. GEOGLAM
•
Vision: inform decisions and actions in agriculture
through the use of coordinated and sustained Earth
observations
Ø building on existing agricultural monitoring systems
5. The
GEOGLAM
Components
1. GLOBAL/ REGIONAL
SYSTEM OF SYSTEMS
2. NATIONAL CAPACITY
DEVELOPMENT
3. MONITORING COUNTRIES
AT RISK
Main producer countries, main
crops
for agricultural monitoring
using Earth Observation
Food security assessment
4.
EO
DATA
COORDINATION
5.
METHOD
IMPROVEMENT
through
R&D
coordinaBon
(JECAM)
6.
Data,
products
and
INFORMATION
DISSEMINATION
8. GEOGLAM
Crop
Monitor
Partners
Developing
Monthly
Crop
Condi5on
Assessments
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
(>25
partners
&
growing)
USDA
FAS,
NASS
-‐ Australia
(ABARES,
CSIRO)
NASA
-‐ South
Africa
(NRC)
UMD
-‐ JAXA/Asia
Rice
EC
JRC
-‐ AFSIS
-‐ Indonesia
(LAPAN)
Canada
(Agriculture
-‐ Thailand
(GISTDA)
Canada)
-‐ Vietnam
(VAST,VIMHE)
FAO
-‐ IRRI
China
CropWatch
-‐ Argen5na
(INTA)
Russia
(IKI)
-‐ Brazil
(CONAB,
INPE)
Ukraine
(Hydromet,
-‐ India
(ISRO)
NASU-‐NSAU)
-‐ Mexico
(SIAP)
Kazakhstan
(ISR)
-‐ GEO
SEC
9. Examples
of
Input
Data
Na5onal
–
Global:
EO
indices,
weather,
model
outputs
etc
Synthesize
ay
Anomaly
Growing
Degree
Dand
dis5l
a
range
of
data
&
informa5on
from
mul5ple
sources
while
preserving
the
wealth
of
underlying
data
within
suppor5ng
materials
document
10. Crop
Assessment
Interface
Data
include:
NDVI,
Precip
and
Temperature
Anomalies
from
NASA/UMD
and
JRC
Enables
comparison
between
relevant
datasets
(global,
na5onal
and
regional),
by
crop
type
and
accoun5ng
for
crop
calendars
and
enables
crop
condi5on
labeling
and
commen5ng
to
reflect
na5onal
expert
assessments
14. From
Qualita5ve
to
Quan5ta5ve:
Winter
Wheat
Yield
Forecas5ng
Overall
ObjecWve:
develop
a
prac5cal
and
robust
approach
to
forecast
wheat
yields
at
regional/
na5onal
scales
using
mul5-‐temporal
and
spa5al
resolu5on
earth
observa5ons
16. Strong
Correla5on
Between
NDVI
Peak
and
Wheat
Yield
Example
of
Daily
Normalized
Difference
Vegeta5on
Index
(NDVI
from
MODIS)
2000-‐2008,
Versus
Crop
Yields
(Blue
numbers
are
Yield
(MT/Ha)
)
in
Harper
County
Kansas
Winter
Wheat
emergence
NDVI
peak
Winter
Wheat
seasonal
NDVI
peak
2.35
2.54
2.21
3.36
2.49
2.69
1.61
Year
1.48
2.49
17. Challenge:
wheat
specific
EO
5me
series
• Need
spa5ally
explicit
informa5on
on
crop
type
for
yield
forecas5ng
(wheat
mask)
– Wheat
field
loca5ons
vary
between
years
due
to
crop
rota5ons
• Ideally,
annual
informa5on
on
crop
type
distribu5on
at
the
start
of
the
growing
season
– At
present,
this
type
of
data
is
generally
not
readily
available
18. Spa5al
Resolu5on:
Approach
to
mi5gate
effects
of
crop
rota5ons
Hypothesis: if a year specific wheat map to coarser
resolution is aggregated as a percent wheat mask the per
grid cell percent wheat will become stable at a coarser
resolution
19. Wheat
Distribu5on
In
Kansas
2007
High
Rate
of
Crop
Rota5on
Low
Rate
of
Crop
Rota5on
20. High
Rate
of
Crop
RotaWon
Low
Rate
of
Crop
RotaWon
(wheat
monoculture)
21. High
Rate
of
Crop
RotaWon
Low
Rate
of
Crop
RotaWon
(wheat
monoculture)
22. High
Rate
of
Crop
RotaWon
Low
Rate
of
Crop
RotaWon
(wheat
monoculture)
23. High
Rate
of
Crop
RotaWon
Low
Rate
of
Crop
RotaWon
(wheat
monoculture)
24. At
What
Spa5al
Aggrega5on
Level
does
Per
Grid
Cell
%
Wheat
Stabilize?
Kansas
per
Grid
Cell
Ranges
of
Percent
Wheat
Values
over
5
years
(2006-‐2010)
25. Maximum
NDVI
extracted
for
2006
through
2011
using
6
seasonal
wheat
masks
at
increasing
spa5al
resolu5on
Line
colors
are
presented
according
to
the
year
of
the
wheat
mask
Harper
County:
Wheat
mono-‐culture
26. Maximum
NDVI
extracted
for
2006
through
2011
using
6
seasonal
wheat
masks
at
increasing
spa5al
resolu5on
Line
colors
are
presented
according
to
the
year
of
the
wheat
mask
Decatur
County:
High
rate
of
crop
rotaWon
27. Wheat
Yield
Model
Development
Regression-‐based
model
developed
as
a
func5on
of:
•
a
seasonal
maximum
NDVI
(adjusted
for
background
noise)
•
Per
grid
cell
percent
wheat
%
wheat
per
grid
cell
is
posi5vely
Peak
Seasonal
Vegeta5on
Index
is
posi5vely
&
linearly
correlated
with
yield
and
linearly
correlated
with
peak
seasonal
Vegeta5on
Index
28. Model
Approach:
Generaliza5on
of
VI
to
Yield
Rela5onship
Adjusted Max NDVI vs. Yield Regression
Slopes Stratified by Percent Wheat in 0.05
degree pixels
Yield
(MT/Ha)
Percent
Wheat:
Slope:
Percent
Wheat:
Slope:
Percent
Wheat:
Slope:
Generalized
relaWonship
of
Yield-‐Max
VI
as
a
funcWon
of
%
Wheat
Percent
Wheat:
Slope:
Adjusted
Max
NDVI
Lower
Percent
wheat
à
Higher
regression
slope
Y=9.61+(-‐0.05*X)
Percent
Wheat
29. Kansas
Results:
Kansas
Model
Es5mates
vs.
USDA
NASS
Crop
Sta5s5cs
Model
EsWmates
are
within
7%,
6
weeks
prior
to
harvest
Becker-‐Reshef
I,
Vermote
E,
Lindeman
M,
Jus5ce
C.
2010.
In
Remote
Sensing
of
Environment,
114,
1312–
1323.
30. %
Error
of
Yield
Es5mates
by
Resolu5on
for
2
Scenarios
of
Data
Availability
33. Wheat
Classifica5on
(Decision
Tree)
Three
Landsat
scenes
chosen
for
training:
before
peak,
peak,
and
aser
peak
Early
season
Peak
senescence
34. Model
Results
in
Ukraine:
Model
es5mated
produc5on
vs.
Ukrainian
State
Sta5s5cal
Commitee
Crop
Sta5s5cs
RMSE=
9%
R2=
0.88
2012
2011
The
model
forecasts
are
within
8%
of
final
reported
produc5on
6
weeks
prior
to
beginning
of
harvest
36. Field
Size
Distribu5on:
Guiding
Spa5al
Resolu5on
Requirements
Source:
Fritz
et
al.,
(IIASA)
Based
on
interpola5on
of
50,000
GEOWIKI
valida5on
points
37. JECAM:
R&D
Component
of
GEOGLAM
• a
network
of
study
sites
representa5ve
of
the
world’s
cropping
systems
• Support
monitoring
enhancements
within
opera5onal
agricultural
monitoring
systems
• JECAM
Program
Office
is
coordinated
by
AAFC,
Canada
and
UCL
Sites
in
development
38. Summary
&
Next
Steps
• Cri5cal
need
for
improved
5mely,
reliable
forecasts
• Fluctua5ons
in
produc5on-‐
primarily
driven
by
weather
events-‐
significant
impact
on
market
fluctua5ons
• Developed
a
process
for
qualita5ve
opera5onal
assessments
of
crop
condi5ons
• Promising
results
for
implemen5ng
a
simple
empirical,
generalized
model
for
primary
wheat
producing
countries
• Explore
feasibility
of
adapta5on
of
approach
to
more
complex
systems
– Higher
spa5al
&
temporal
resolu5on
39. Challenges
&
Lessons
Learned
• Understand
user
needs
• Developing
awareness
&
demand
for
RS
based
informa5on
• Opera5onal
user
community
guiding
the
research
agenda
• Cross-‐fer5liza5on-‐
interna5onal
partnerships
are
cri5cal
• Improve
base
layers:
crop
type
maps
and
calendars
• Promise
-‐
RS
landscape
is
advancing
rapidly
– Resolu5on,
temporal
repeat,
quality,
processing
capabili5es,
distribu5on,
data
policy