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IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
IGARSS_2011_GALLOZA.pptx
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  • The added earth logo is from the website: http://rst.gsfc.nasa.gov/Sect19/Sect19_2a.html
  • This research is supported by the U.S. Department of Agriculture, the Agricultural Research Service, the Department of Agronomy and its Laboratory and the Laboratory for Applications of Remote Sensing (LARS) at Purdue University.
  • Transcript

    • 1. Exploiting Multisensor Spectral Data to Improve Crop Residue Cover Estimates for Management of Agricultural
      Water Quality
      Magda S. Galloza1, Melba M. Crawford2
      School of Civil Engineering, Purdue University
      and
      Laboratory for Applications of Remote Sensing
      Email: {mgalloza1, mcrawford2}@purdue.edu
      July 28, 2011
      IEEE International Geoscience and Remote Sensing Symposium
    • 2. Outline
      Introduction
      Estimation of crop residue
      Research Motivation
      Evaluation of Hyperspectral/ Multispectral Sensor data for estimating residue cover
      Investigation of approaches for large scale applications
      Methodology
      Experimental Results
      Summary and Future Directions
    • 3.
      • Ecosystem-based management approaches (monitoring and damage assessments)
      Introduction
      Residue Cover (RC): Plant material remaining in field after grain harvest and possible tillage
      - Nutrients
      - Organic material (soil)
      - Agricultural ecosystem stability
      - water evaporation
      - water infiltration
      - moderate soil temperature
      - Critical in sustaining soil quality
      - erosion
      - runoff rates
    • 4. Introduction
      Manual methods of analysis
      Statistical sampling of fields via windshield surveys
      Costly, requires trained personnel
      Line transect method
      Time and labor intensive
      Remote sensing based approaches
      Capability for 100% sampling
      Detect within field variability
      GREATER coverage area
      Potentially reduce subjective errors
      Landsat-7
      ETM+
      185 Km
      EO-1
      ALI
      37 Km
      EO-1
      Hyperion
      7.5 Km
      Satellite Track
    • 5. Research Motivation
      Transect Method vs. Remote Sensing based Method
      66 ft
      100 beads
    • 6. Research Motivation
      • Land Cover Characteristics
      • 7. Agricultural Cover Discrimination and Assessment
    • Research Motivation
      • Evaluate performance of multispectral and hyperspectraldata for estimating residue cover over local and extended areas
      • 8. Evaluate performance of next generation sensors
      • 9. Landsat 8 Operational Land Imager (OLI)
      • 10. Investigate sensor fusion scenarios
      • 11. Potential contribution of hyperspectraldata for improving (calibrating) residue cover estimates derived from wide coverage multispectral data
      • 12. Contributions of multisensor fusion
      • Band based indices
      • 13. Based on the absorption characteristics (reflectance) of RC
      • 14. Linear relationship between RC and indices exploited via regression models
      • 15. Multispectral NDTI (Normalized Difference Tillage Index)
      • 16. Empirical models developed and validated locally
      • 17. Applicable to multiple sensors: ASTER, Landsat, ALI (EO-1)
      • 18. Sensitive to soil characteristics
      Approach - NDTI
      NDTI = (TM5 - TM7)/(TM5 + TM7)
      Where:
      - TM7: Landsat TM band 7 or equivalent
      - TM5: Landsat TM band 5 or equivalent
    • 19. Proposed Approaches - CAI
      • Hyperspectral CAI - (Cellulose Absorption Index)
      • 20. Related to the depth of the absorption feature (2100 nm)
      • 21. Demonstrated to accurately detect estimate RC [Daughtry, 2008]
      • 22. Robust to crop and soil types characteristics
      • 23. Limited coverage and availability
      Estimate of the depth of the cellulose absorption feature
      2000
      2100
      CAI = 0.5 * (R2.0 + R2.2) – R2.1
      2200
      Where:
      - R2.0, R2.1, R2.2: average response of 3 bands centered at 2000 nm, 2100 nm and 2200 nm respectively
    • 24. Study Location / Field Data
    • 25. Remote Sensing Data (2008-2010)
      Landsat-7
      ETM+
      185 Km
      EO-1
      ALI
      37 Km
      EO-1
      Hyperion
      7.5 Km
      Satellite Track
    • 26. Linear Models
      1-
      1-
      2-
      3-
      Substitute in Model 1
      2-
      3-
    • 27. Model 1 - CAI Index
      Watershed Scale Evaluation
      0% - 25%
      26% - 50%
      51% - 75%
      76% - 100%
      EO-1 Hyperion (30m)
      Resample
      SpecTIR (30m)
      SpecTIR (4m)
    • 28. Model 2 – NDTI Index
      Watershed Scale Evaluation
      0% - 25%
      26% - 50%
      51% - 75%
      76% - 100%
      Model 1 – SpecTIR (4m)
      Model 2 - ALI
      Model 2 – Landsat TM
    • 29. CAI (SpecTIR) vs. NDTI (Landsat/ALI)
      -85% - -80%
      -70% - -60%
      -59% - -40%
      -39% - -20%
      -19% - 0%
      1% - 20%
      21% - 40%
      41% - 60%
      SpecTIR vs. ALI
      SpecTIR vs. Landsat TM
    • 30. Little Pine Creek Model Applied to
      Darlington Region
      Model 2
      0% - 25%
      26% - 50%
      51% - 75%
      76% - 100%
      Little Pine Creek
      Data
      Watershed Scale Evaluation
      Darlington
      Data (ALI)
    • 31. Little Pine Creek Model Applied to
      Darlington Region
      Model 1
      0% - 25%
      26% - 50%
      51% - 75%
      76% - 100%
      Little Pine Creek
      Data (Model 1)
      Watershed Scale Evaluation
      Darlington
      Data (SpecTIR)
    • 32. Model 3 – Substitution in Model 1
      Substitute in Model 1
      0% - 25%
      26% - 50%
      51% - 75%
      76% - 100%
      Model 3 - (Substitution Model)
      Watershed Scale Evaluation
      Model 2 – SpecTIR (30m)
    • 33. Multispectral – not sensitive enough to the low coverage
      Conclusions and Future Work
      ALI multispectral sensor provides better residue cover estimates in comparison with Landsat TM
      • Pushbroom vs. whiskbroom
      • 34. Radiometrically ALI – 12-bit (vs. 8 bit)
      • 35. ALI SNR between four and ten times larger than SNR for TM
      Potential improvement from next Landsat generation
      - Operational Land Imager (OLI) on the LandsatFollow- on Mission - will be similar to the ALI sensor
      Future Directions
      Weighted least squares method for multisensor fusion
      Effect of soil moisture
      Assimilate RC information into a hydrologic model
      - The OLI design features a multispectral imager with pushbroom architecture of ALI heritage
    • 36. Thank You
      This research is supported by the U.S. Department of Agriculture, the Agricultural Research Service, the Department of Agronomy and its Laboratory and the Laboratory for Applications of Remote Sensing (LARS) at Purdue University.
    • 37. SpecTIR 30m vs. Hyperion 30m
      -60% - -40%
      -39% - -20%
      -19% - 0%
      1% - 20%
      21% - 40%
      41% - 60%
      61% - 70%

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