LINEAR UNMIXING OF MULTIDATE HYPERSPECTRAL IMAGERY FOR CROP YIELD ESTIMATION

LINEAR UNMIXING OF LINEAR UNMIXING OF MULTIDATE HYPERSPECTRAL MULTIDATE HYPERSPECTRAL IMAGERY FOR CROP YIELD IMAGERY FOR CROP YIELD ESTIMATIONESTIMATION

Bin Luo1, Chenghai Yang2 and Jocelyn Chanussot3

1 LIESMARS, Wuhan University, Wuhan, China2 U.S. Department of Agriculture, Weslaco, Texas, USA3 Grenoble Institute of Technology, Grenoble, France

IGARSS 2011; 24 – 29 July, 2011; Vancouver, Canada

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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

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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

Objectives and Procedures• Evaluate unsupervised linear unmixing approaches on

hyperspectral images for crop yield estimation• Use multi-date hyperspectral data for improving estimation results

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Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield EstimationLinear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

VCA (Vertex Component Analysis

• Linear mixture model of hyperspectral images

X = MS + nM = unmixing matrixS = abundance matrix

Unmixing of Hyperspectral Images

• VCA (Vertex Component Analysis) to extract endmembers

Red cross: hyperspecral data X

Blue circles: endmembers M

Abundance S: Random between 0 – 1

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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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Geometric Correction, Rectification & Calibration

• Geometric correction Reference line approach

• Rectification Georeference images to UTM

with GPS ground control points

• 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

Raw Corrected


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Grain Sorghum Yield Data Collection

Ag Leader PF3000 Yield Monitor


Yield Data

Crop yield images of the two fields.

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Fusion of Multi-date Unmixing Results

Flow chart of the fusion of the multi-date unmixing results

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• 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

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M18(k) of Field 1 M29(k) of Field 1

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M18(k) of Field 2 M29(k) of Field 2

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M1 M2 M3 M4

C(Mi, Y) 0.739 0.748 0.780 0.764

Correlation coefficients between the yield data and the (combined) crop abundances of Field 1

M1 M2 M3 M4

C(Mi, Y) 0.648 0.721 0.735 0.701

Correlation coefficients between the yield data and the (combined) crop abundances of Field 2

Recall that

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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.

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LINEAR UNMIXING OF MULTIDATE HYPERSPECTRAL IMAGERY FOR CROP YIELD ESTIMATION

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