Remote-sensing on the Mars and the superior - IEEE-GRSS

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Remote-sensing on the Mars and the superior - IEEE-GRSS
LINEAR UNMIXING OF MULTIDATE
HYPERSPECTRAL IMAGERY FOR
CROP YIELD ESTIMATION
Bin Luo1, Chenghai Yang2 and Jocelyn Chanussot3
1
LIESMARS, Wuhan University, Wuhan, China
2 U.S. Department of Agriculture, Weslaco, Texas, USA
3 Grenoble Institute of Technology, Grenoble, France
IGARSS 2011; 24 – 29 July, 2011; Vancouver, Canada
Mapping Yield Variation for
Precision Agriculture
• Remote sensing imagery has been
commonly used for estimating crop
yield variation
• Vegetation indices (e.g., NDVI)
• With hyperspectral imagery, the
number of VIs is large
• Spectral unmixing can be used to
derive abundance images
2
Spectral Mixing
• A pixel can be considered as a
mixture of plants and soil.
• Spectral unmixing can quantify crop
canopy fraction within each pixel.
• A crop fraction image is a more
direct measure of plant abundance
than NDVI
• Plant abundance is indicative of
crop yield.
Plant
Soil
Mixture
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Objectives and Procedures
• Evaluate unsupervised linear unmixing approaches on hyperspectral
images for crop yield estimation
• Use multi-date hyperspectral data for improving estimation results
VCA (Vertex
Component
Analysis
Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation
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26-July-2011
Unmixing of Hyperspectral Images
• Linear mixture model of hyperspectral images
X = MS + n
M = unmixing matrix
S = abundance matrix
• VCA (Vertex Component
Analysis) to extract endmembers
 Red cross:
hyperspecral data X
 Blue circles:
endmembers M
 Abundance S:
Random between 0 – 1
Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation
Airborne Hyperspectral Images
• Hyperspectral system





Spectral range: 467–932 nm
Swath width: 640 pixels
Bands: 128
Radiometric: 12 bit (0–4095)
Pixel size: ~1 m
• Study site
 Two grain sorghum fields in south Texas
 13.4 ha and 14.0 ha in size
• Image timing
 Shortly before and after crop reached
maximum canopy cover
 18-May-2001 and 29-May-2001
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Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation
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Geometric Correction, Rectification &
Calibration
• Geometric correction
 Reference line approach
• Rectification
 Georeference images to UTM
with GPS ground control points
Raw
Corrected
• Radiometric calibration
 Three tarps with reflectance of 4, 32, and 48% were used to
convert digital counts to reflectance
• 102 bands were used for analysis
Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation
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Grain Sorghum Yield Data Collection
Ag Leader PF3000 Yield Monitor
Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation
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Yield Data
Crop yield images of the two fields.
Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation
26-July-2011
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Fusion of Multi-date Unmixing Results
Flow chart of the fusion of the multi-date unmixing results
Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation
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Fusion of Multi-date Unmixing Results
• M18(k) and M29(k) as the abundances of crop extracted on the date 18 May
2001 and 29 May 2001 at the kth pixel
• Evaluation – Correlation coefficients
where Y is the yield data
Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation
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Fusion of Multi-date Unmixing Results
M18(k) of Field 1
M29(k) of Field 1
Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation
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26-July-2011
Fusion of Multi-date Unmixing Results
M18(k) of Field 2
M29(k) of Field 2
Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation
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Fusion of Multi-date Unmixing Results
Correlation coefficients between the yield data
and the (combined) crop abundances of Field 1
C(Mi, Y)
M1
M2
M3
M4
0.739
0.748
0.780
0.764
Correlation coefficients between the yield data
and the (combined) crop abundances of Field 2
C(Mi, Y)
M1
M2
M3
M4
0.648
0.721
0.735
0.701
Recall that
Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation
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26-July-2011
Conclusions
• Crop abundances obtained by the unsupervised
linear unmixing are strongly correlated to crop
yield data.
• The fusion of crop abundances obtained from
images taken at different dates significantly
improves the correlation with yield.
Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

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