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.
  • IGARSS_2011_GALLOZA.pptx

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

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