1. NUE Workshop: Improving NUE using Crop Sensing, Waseca, MN
Comparing Different Remote Sensing Approaches for Early Season Nitrogen Deficiency Detection in Corn
Yuxin Miao1, David J. Mulla1, Gyles W. Randall2, Jeff A. Vetsch2, and Roxana Vintila3
1. Precision Agriculture Center, University of Minnesota.
2. Southern Research and Outreach Center, University of Minnesota.
3. Research Institute of Soil Science and Agrochemistry (ICPA), Romania.

2. Different Sensing Approaches for Precision N Management
Chlorophyll Meter:
SPAD 502
GreenSeeker:
CropScan Multispectral Radiometer:

3. Different Sensing Approaches for Precision N Management
Aerial or Satellite -based Remote Sensing:
High spatial resolution remote sensing images are potentially cheaper, more efficient and more spatially detailed than chlorophyll meter or other ground-based hand-held sensors.
Hyperspectral Remote Sensing
(Image from

4. Objectives
Identify hyperspectral bands (wavelengths), band ratios and vegetation indices that are sensitive to early season corn plant N status;
To compare the effectiveness of different sensing approaches to monitor early season corn plant N status and detect N deficiency:
SPAD Meter;
GreenSeeker;
CropScan multispectral radiometer;
Aerial hyperspectral remote sensing; and,
Aerial multispectral remote sensing (simulated).

5. Materials and Methods – Study Sites
Field 1: Corn-Soybean Rotation
Field 2: Corn-Corn Rotation

6. Materials and Methods – N Treatments
3 x 15.2 m
Field 1, Corn-Soybean Rotation

7. Materials and Methods – N Treatments
Field 2, Corn-Corn Rotation

8. Materials and Methods – Data Collection
N Concentration:
V9: Whole plant sampling, 10 plants: N concentration, biomass;
R1: Ear leaf, 10 leaves;
Harvest: grain and stover.
SPAD Meter:
F1: V9, V11, R1, and R3;
F2: V7, V9-10, V12, R1, R3
Collected 30 readings from each plot.
GreenSeeker:
F1: V9 and V11;
F2: V8, V9-10, V11-V12 and V12.
CropScan Multispectral Radiometer:
V6 and V9;
About 50 cm above the canopy, three samples each plot.

9. Materials and Methods – Data Collection
Aerial Hyperspectral Remote Sensing:
AISA-Eagle (AE) Hyperspectral Imager
61 bands from 392 – 982 nm, at 8.76 – 9.63nm;
At 0.75 m spatial resolution;
V9, R1, R2 and R4;
Pixels of the central two rows in each plot were averaged;
Simulated Multispectral Remote Sensing:
Landsat ETM+ sensor’s four broad bands:
Blue: nm;
Green: nm;
Red: nm;
NIR: nm.

10. Materials and Methods – Band Combinations
Simple Ratio (SR)
Ratio
Definition
Reference
Green Index
Zarco-Tejada & Miller (ZTM)
PSSRa
PSSRb
PSSRc
SPRI
SR1
SR2
SR3
SR4
SR5
SR6
SR7
R554/R677
R750/R710
R800/R680
R800/R635
R800/R470
R430/R680
NIR/Red = R801/R670
NIR/Green=R800/R550
R700/R670
R740/R720
R675/(R700 x R650)
R672/(R550 x R708)
R860/(R550 x R708)
Smith et al., 1995
Zarco-Tejada et al., 2001
Blackburn, 1998
Penuelas et al., 1994
Daughtry et al., 2000
Buschman and Nagel, 1993
McMurtrey et al., 1994
Vogelman et al., 1993
Chappelle et al., 1992
Datt, 1998

11. Materials and Methods – Band Combinations
Difference Index (DI) and Normalized Difference Index (NDI)
Index
Definition
Reference
DI1
DVI
NDVI
Green NDVI
PSNDb
PSNDc
NPCI
NPQI
SIPI
mND705
mSR705
NDI1
NDI2
NDI3
R800-R550
R800-R680
(R800-R680)/(R800+R680)
(R801-R550)/(R800+R550)
(R800-R635)/(R800+R635)
(R800-R470)/(R800+R470)
(R680-R430)/(R680+R430)
(R415-R435)/(R415+R435)
(R800-R445)/(R800-R680)
(R750-R705)/(R750+R705-2 x R445)
(R750-R445)/(R705-R445)
(R780-R710)/(R780-R680)
(R850-R710)/(R850-R680)
(R734-R747)/(R715+R726)
Buschman and Nagel, 1993
Jordan, 1969
Lichtenthaler et al., 1996
Daughtry et al., 2000
Blackburn, 1998
Penuelas et al., 1994
Barnes et al., 1992
Penuelas et al., 1995
Sims and Gamon, 2002
Datt, 1999
Vogelman et al., 1993

12. Materials and Methods – Band Combinations
Integrated Index (II)
Index
Definition
Reference
MCARI
TCARI
OSAVI
TCAVI/OSAVI
TVI
MCARI/OSAVI
RDVI
MSR
MSAVI
MTVI
[(R700-R670)-0.2x(R700-R550)](R700/R670)
3x[(R700-R670)-0.2x(R700-R550)(R700/R670)]
(1+0.16)(R800-R670)/(R800+R )
0.5x[120x(R750-R550)-200x(R670-R550)]
(R800-R670)/SQRT(R800+R670)
(R800/R670-1)/SQRT(R800/R670+1)
0.5x[2xR800+1-SQRT((2xR800+1)2-8x(R800-R670))]
1.2x[1.2x(R800-R550)-2.5x(R670-R550)]
Daughtry et al., 2000
Haboudane et al., 2002
Rondeaux et al., 1996
Broge and Leblanc, 2000
Zarco-Tejada et al., 2004
Rougean and Breon, 1995
Chen, 1996
Qi et al., 1994
Haboudane et al., 2004
Broad Band Combinations
NIR/Green;
NIR/Red;
Blue NDVI;
Green NDVI;
Red NDVI.

13. Materials and Methods – Analysis
Correlation analysis;
Multiple linear regression;
Nitrogen Sufficiency Index (NSI)
Yield, Plant N, SPAD, or index
NSI =
x 100%
Reference value
F1: the average of the highest two preplant N rates: 168 and 202 kg ha-1.
F2: 224 kg ha-1.
NSI of Plant N Concentration as standard

14. Results and Discussion: Plant N Variability
36.1 34
31.9
28.2
24.9
CV = 9.33%
CV = 14.06%
18.7
202 kg ha-1
224 kg ha-1

15. Results and Discussion: Impact of N Rate on Reflectance
CropScan MSR
Hyperspectral RS

16. Results and Discussion: Sensitive Wavelengths
Correlation between plant N concentration and CropScan reflectance at V9 nm nm

17. Results and Discussion: Sensitive Wavelengths
Correlation between plant N concentration and hyperspectral reflectance at V9 nm
554 and 563nm
695nm

18. Results and Discussion: Sensitive Indices
Correlation with Plant N Concentrations at V9
Field 1
Field 2
CropScan:
GNDVI:
0.51
0.72
NIR/Green: R800/R550
0.50
0.71
NDI2
0.39
0.71
Hyperspectral:
NDI1
0.47
0.78
NDI2
0.41
0.79
SPAD Meter:
0.58
0.85
GreenSeeker NDVI:
0.26
0.49
Simulated Landsat ETM+:
GNDVI:
0.31
0.68

19. Results and Discussion: Multiple Regression
Correlation Coefficient
Field 1
Field 2
SPAD Meter:
0.58
0.85
CropScan:
6 (F1)/5(F2) bands
0.77
0.79
5 (F1)/4(F2) Indices
0.67
0.79
Hyperspectral:
4 bands
0.73
0.89
4 (F1)/3(F2)indices
0.66
0.88
Simulated Landsat ETM+:
3 bands
0.56
0.89
2 indices
0.57
0.88

20. Results and Discussion: N Sufficiency Index

21. Results and Discussion: N Deficiency Detection
Treatment Level, Field 1, Corn-Soybean Rotation
CropScan
Hyper
Landsat ID
Yield
Plant N
SPAD GS G
TVI
NIR/G 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

22. Results and Discussion: N Deficiency Detection
Treatment Level, Field 2
CropScan
Hyper
Landsat ID
Yield
Plant N
SPAD GS
SR7
DI1
NIR 1 2 3 4 5 6 7 8 9 10 11 12 13 14

23. Results and Discussion: N Deficiency Detection
Plot Level, Field 1, Corn-Soybean Rotation
Deficient Plots
Sufficient Plots
DS
SD
Overall Accuracy (%)
Plant N Content 34
(44) 26
(16)
100
SPAD Meter 3 26 48
(35)
GreenSeeker 15 17 19 9 53
(47)
CropScan: MCARI 22 14 12 12 60
(60)
Hyperspectral: MCARI 25 11 9 15 60
(55)
Landsat ETM+: Green 17 15 17 11 53
(62)

24. Results and Discussion: N Deficiency Detection
Plot Level, Field 2, Corn-Corn Rotation
Deficient Plots
Sufficient Plots
DS
SD
Total Accuracy (%)
Plant N Content 29
(46) 27
(10)
100
SPAD Meter 21 25 8 2 82
(71)
GreenSeeker 10 26 19 1 64
(43)
CropScan: TCARI/OSAVI 18 22 11 5 71
(59)
Hyperspectral: SR7 21 22 8 5 77
(61)
Landsat ETM+: NIR/Green 17 20 12 7 66
(57)

25. Results and Discussion: Promising Indices
Normalized Difference Index 2 (NDI2)
R850-R710
NDI2 =
R850-R680

26. Results and Discussion: Promising Indices
Simple Ratio 7
R860
SR7 =
R550 x R708

27. Conclusions than in corn-soybean rotation field at V9;
The sensors performed better in corn-corn rotation field
than in corn-soybean rotation field at V9;
The NIR region was most sensitive to N deficiency at V9;
Reflectance at around nm, 696 nm, and NIR region
was highly correlated with corn plant N concentration at V9;
SPAD meter readings and GreenSeeker NDVI data had the
highest and lowest correlation coefficients with corn plant N
concentration, respectively;
Hyperspectral aerial remote sensing has a good potential to
monitor spatial corn N variation, and identify N deficiency
at V9, especially in corn-corn rotation fields;
SR7 and NDI2 were promising indices for N deficiency
identification and deserve further testing.

28. Thank you for your attention!
Any Questions?