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

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

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

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

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 availabilityEstimate 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)

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%