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LINEAR UNMIXING OF MULTIDATE HYPERSPECTRAL IMAGERY FOR CROP YIELD ESTIMATION

LINEAR UNMIXING OF MULTIDATE HYPERSPECTRAL IMAGERY FOR CROP YIELD ESTIMATION. Bin Luo 1 , Chenghai Yang 2 and Jocelyn Chanussot 3 1 LIESMARS, Wuhan University, Wuhan, China 2 U.S. Department of Agriculture, Weslaco, Texas, USA 3 Grenoble Institute of Technology, Grenoble, France

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LINEAR UNMIXING OF MULTIDATE HYPERSPECTRAL IMAGERY FOR CROP YIELD ESTIMATION

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  1. 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

  2. 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

  3. Plant Soil Mixture 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.

  4. Evaluate unsupervised linear unmixing approaches on hyperspectral images for crop yield estimation Use multi-date hyperspectral data for improving estimation results Objectives and Procedures VCA (Vertex Component Analysis Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

  5. Linear mixture model of hyperspectral images X = MS + n M = unmixing matrix S = 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 Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

  6. 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 Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

  7. 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 Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

  8. Grain Sorghum Yield Data Collection Ag Leader PF3000 Yield Monitor Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

  9. Yield Data Crop yield images of the two fields. Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

  10. 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

  11. 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 Fusion of Multi-date Unmixing Results where Y is the yield data Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

  12. 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

  13. 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

  14. Fusion of Multi-date Unmixing Results Correlation coefficients between the yield data and the (combined) crop abundances of Field 1 Correlation coefficients between the yield data and the (combined) crop abundances of Field 2 Recall that Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

  15. 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. Conclusions Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

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