1. Spatio-temporal dynamics of perennial
energy crops in the U.S. Midwest
agricultural lands
Cuizhen (Susan) Wang
Associate Professor, Dept. of Geography, University of Missouri
E-mail: wangcu@missouri.edu; Tel: 1-573-884-0895
with co-authors
Gary Stacey, Center for Sustainable Energy, MU
Felix B. Fritschi, Division of Plant Sciences, MU
Wyatt Thompson, FAPRI, and Dept. of Agricultural/Applied Economics, MU
Timothy C. Matisziw, Dept. of Geography, Dept. of Civil/Environmental
Engineering, MU
Zhengwei Yang, USDA/NASS
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2. Introduction
 Biomass exceeds 3% of energy supplies and is the largest
source of renewable energy in the United States;
 Upon an optimistic estimate, biomass feedstocks could
replace 30% of domestic petroleum consumption by 2030
(Perlack et al. 2005);
 Corn ethanol currently constitutes 99% of US biofuel
(Farrel et al. 2006).
 The US biofuel refiners budgeted 4.2 billion bushels of corn
(1/3 of US corn production) in the 2009-2010 marketing
year (Economic Research Service 2010).
Environmental
Ecological
socio-economic concerns
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3.  Native prairie grasses are identified by DOE as a model
cellulosic crop, an alternative of bioenergy feedstock.
 Warm-season native grasses currently grow in mixed
conditions with cool-season forage grasses, and have not
been mapped in any published agricultural databases.
Current spatial
distributions and
temporal dynamics?
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(Source: Oak Ridge National Lab)18

4. Study area and data sets
 The Midwest agricultural region
Validation sites:
Flint Hills, KS
The largest unplowed
tallgrass prairie remn.
(>80% native grasses).
Cherokee Plain, MO
Sandhills upland
prairie, NE
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5.  Satellite imagery and published maps
• 500-m, 8-day MODIS surface reflectance products (MOD09A1);
- 4 scenes;
-NDVI time series (46 scenes/year)
- 10-year period (00-09);
• Cropland Data Layers (CDL)
- USDA NASS
- 12 states
- 2007
• Major crops in the Great Plains
- Grass (tall/short/cool-season);
- Corn+Soybean;
- Winter wheat
- Spring wheat
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6. Approach
 Time series analysis
• median filter  spikes removal
• Savitzky-Golay filter  curve smoothing
• Asymmetric Gaussian simulation
• extracting phenology matrices Source: Jonsson and Eklundh 2004.
TIMESAT
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7. Example time series: (Source: Wang et al. 2011)
Corn Soybean Winter wheat
WSG grass CGS grass
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8.  Phenology metrics
TIMESAT extracted (3 out of 11):
• End of season: when NDVI decrease to 20% of amplitude;
• Growing length: number of dates in start-end of seasons;
• Cumulative growth (∑NDVI):NDVI integral in start-end of seasons;
Self-identified:
• peak date: dates of peak NDVI;
- Early: peak date falls in DOY 1-161 (Jan – Mid June)
- Middle: peak date falls in DOY 145-193 (May - Mid July)
- Late: peak date falls in DOY 161-313 (Mid June – Mid Nov)
• Summer dry-down (∆NDVI): decrease of NDVI in spring-summer if
peak NDVI falls in early stage (especially useful for winter wheat);
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9. Longitudal shifts
Peak Date:
(2days/degree)
Source: Wang et al. 2011 9 / 18

10. Peak NDVI Peak NDVI
0.4
0.6
0.7
0.9
0.3
0.5
0.8
100
200
150
250
2000 2000
2001 2001
2002 2002
2003 2003
2004 2004
Peak_date
Peak_NDVI
2005 2005
2006 2006
2007 2007
2008 2008
Climate-induced shifts
2009 2009
Seaon Length End Date
80
120
200
240
320
200
240
320
160
280
280
360
2000 2000
2001 2001
2002 2002
2003 2003
2004 2004
End_date
2005 2005
Season Length
2006 2006
2007 2007
2008 2008
2009 2009
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11. Phenology metrics inventory (CART results)
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
PDatww 145 145 153 145 145 145 145 137 153 153
Lencorn_ 184 200 184 184 184 184 184 184 176 176
sw
Endsw 261 261 269 261 261 261 261 261 269 269
Lentallgra 252 252 236 244 260 244 236 260 236 236
ss
PVALww 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.6 0.5 0.5
_sw
PVALtall 0.6 0.6 0.6 0.6 0.6 0.6 0.5 0.6 0.6 0.6
grass
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12.  Phenology-based decision tree (concept framework)
W. wheat; Y W. wheat
Peak in CSG Summer dry-
early spr. down
Short grow Corn/Soy; Y
Time S. wheat;
season Early end S. wheat
series Short grs
Late peak date Y Corn/Soy
Long grow WSG; Y
Low peak value Short grs
season CSG
Shorter season Y WSG
CSG

13. (thresholds
flowchart)
For more details, please refer to Wang et
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al., Annuals of AAG, 101(4), 2011.

14. Results
 The Midwest crop maps, 2000-2009
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15. Flint Hill, KS (2000-2009)
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16. Cherokee Plain, MO (with past studies)
DOY Date Sensor
52 2/21/2007 ASTER Taberville
73 3/14/2006 TM Pr.
92 4/2/2007 TM
A 2-year MDC project
106
111
4/16/2007
4/21/2007
AWIFS (A)
AWIFS (A)
134 5/14/2007 AWIFS (B)
140 5/20/2007 AWIFS (A)
WKT Pr.
153 06/02/2006 TM
172 06/21/2007 TM
Osage Pr..
188 07/07/2007 AWIFS (A)
192 7/11/2007 AWIFS (B)
202 7/21/2007 ASTER
220 8/8/2007 TM
228 8/16/2007 ASTER
240 8/28/2007 AWIFS (B)
271 9/27/2008 TM
292 10/19/2007 ASTER Pr. State Park
303 10/29/2008 TM
313 11/9/2006 TM
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17. Summary and future research
 Native warm-season grasses in the Midwest hold unique
phenology metrics (time series analysis);
 Phenology metrics vary with inter-annual climate dynamics
(phenology metrics inventory);
 The 20+ million ha of native grasses (upon validation) in the
Midwest indicates its high bioenergy potential;
 The spatially explicit energy crop map is a quantitative
supplement to county-level biomass supplies.
Next……?
 Future investigation:
• region-wide validation!
• biomass quant. of energy crops;
• Bioenergy policy and LULC.
ORNL Switchgrass production. 18
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18. Thanks!
Acknowledgement: This research is supported by the Mizzou Advantage
Project. We would like to thank Le T. Ngan, Wei Zhang, Qing Chang in
Dept. of Geography and D.J. Donahue at FAPRI in data process. Also our
thanks to USDA/NASS for providing the CDL data that serve as excellent
reference in this research.
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