1 Exploiting Multisensor Spectral Data to Improve Crop Residue Cover Estimates for Management of Agricultural Water Quality Magda S. Galloza 1, Melba M. Crawford 2 School of Civil Engineering, Purdue University and Laboratory for Applications of Remote Sensing {mgalloza 1, mcrawford 2 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 Satellite Track Landsat-7 ETM+ 185 Km EO-1 Hyperion 7.5 Km EO-1 ALI 37 Km

5 Research Motivation Transect Method vs. Remote Sensing based Method 66 ft 100 beads

6 Research Motivation Land Cover Characteristics −Agricultural Cover Discrimination and Assessment

7 Research Motivation Evaluate performance of multispectral and hyperspectral data for estimating residue cover over local and extended areas Evaluate performance of next generation sensors Landsat 8 Operational Land Imager (OLI) Investigate sensor fusion scenarios Potential contribution of hyperspectral data for improving (calibrating) residue cover estimates derived from wide coverage multispectral data Contributions of multisensor fusion

8 Band based indicesBand based indices −Based on the absorption characteristics (reflectance) of RC −Linear relationship between RC and indices exploited via regression models Multispectral NDTIMultispectral NDTI (Normalized Difference Tillage Index) −Empirical models developed and validated locally −Applicable to multiple sensors: ASTER, Landsat, ALI (EO-1) −Sensitive to soil characteristics Approach - NDTI Band EO-I (ALI) Spectral Range (µm) Bandwidth (nm) GSD (m) Pan B1p B B B B B4p B5p B B Band L7 ETM+ Wavelength (nm) Bandwidth (nm) GSD (m) Pan B B B B B B B Where: - TM7: Landsat TM band 7 or equivalent - TM5: Landsat TM band 5 or equivalent NDTI = (TM5 - TM7)/(TM5 + TM7)

9 Proposed Approaches - CAI Hyperspectral CAI -Hyperspectral CAI - (Cellulose Absorption Index) −Related to the depth of the absorption feature (2100 nm) −Demonstrated to accurately detect estimate RC [Daughtry, 2008] −Robust to crop and soil types characteristics −Limited coverage and availability Where: - R2.0, R2.1, R2.2: average response of 3 bands centered at 2000 nm, 2100 nm and 2200 nm respectively CAI = 0.5 * (R2.0 + R2.2) – R2.1 Estimate of the depth of the cellulose absorption feature

10 Study Location / Field Data

11 Remote Sensing Data ( ) Data Available Spring 2008 Fall 2008 Spring 2009 Fall 2009 Spring 2010 Fall 2010 EO-1 ALI/HyperionMay 6Nov 1No DataOct 11May 23Nov 2 Landsat TMJune 11Nov 2May 29Oct 20April 22Nov 8 SpecTIR AirborneNo Data Nov 3June 5No DataMay 24No Data Satellite Track Landsat-7 ETM+ 185 Km EO-1 Hyperion 7.5 Km EO-1 ALI 37 Km

12 Linear Models Substitute in Model

13 Model 1 - CAI Index Field Residue Cover (%) Total Area SpecTIR 4m (hec) SpecTIR 30m (hec) Hyperion (hec) 0% - 25% % - 50% % - 75% % - 100% Watershed Scale Evaluation SpecTIR (4m) EO-1 Hyperion (30m) SpecTIR (30m) Field Residue Cover (%) Residue Coverage SpecTIR 4m (hec) SpecTIR 30m (hec) Hyperion 30m (hec) 0% - 25% % - 50% % - 75% % - 100% % - 25% 26% - 50% 51% - 75% 76% - 100% Resample

14 Model 2 – NDTI Index Residue Cover (%) Total Area Landsat (hec) ALI (hec) SpecTIR 4m (hec) 0% - 25% % - 50% % - 75% % - 100% Model 2 - ALI Watershed Scale Evaluation Model 2 – Landsat TM Model 1 – SpecTIR (4m) Residue Cover (%) Residue Coverage Landsat (hec) ALI (hec) SpecTIR 4m (hec) 0% - 25% % - 50% % - 75% % - 100% % - 25% 26% - 50% 51% - 75% 76% - 100%

15 Residue Cover (%) Landsat Difference (hec) ALI Difference (hec) -85% - -80% % - -60% % - -40% % - -20% % - 0% % - 20% % - 40% % - 60% CAI (SpecTIR) vs. NDTI (Landsat/ALI) SpecTIR vs. Landsat TM SpecTIR vs. ALI -85% - -80% -70% - -60% -59% - -40% -39% - -20% -19% - 0% 1% - 20% 21% - 40% 41% - 60%

16 Little Pine Creek Model Applied to Darlington Region Residue Cover (%) ALI (hec) ACRE Model (hec) 0% - 25% % - 50% % - 75% % - 100% Watershed Scale Evaluation Little Pine Creek Data Darlington Data (ALI) Model 2 0% - 25% 26% - 50% 51% - 75% 76% - 100%

17 Little Pine Creek Model Applied to Darlington Region Residue Cover (%) SpecTIR (hec) ACRE Model (hec) 0% - 25% % - 50% % - 75% % - 100% Watershed Scale Evaluation Little Pine Creek Data (Model 1) Darlington Data (SpecTIR) Model 1 0% - 25% 26% - 50% 51% - 75% 76% - 100%

18 Model 3 – Substitution in Model 1 Residue Cover (%) Model 3 (hec) SpecTIR (hec) 0% - 25% % - 50% % - 75% % - 100% Model 3 - (Substitution Model) Watershed Scale Evaluation Model 2 – SpecTIR (30m) Substitute in Model 1 0% - 25% 26% - 50% 51% - 75% 76% - 100%

19 Multispectral – not sensitive enough to the low coverage Potential improvement from next Landsat generation ALI multispectral sensor provides better residue cover estimates in comparison with Landsat TM Future Directions Weighted least squares method for multisensor fusion Effect of soil moisture Assimilate RC information into a hydrologic model -Pushbroom vs. whiskbroom -Radiometrically ALI – 12-bit (vs. 8 bit) -ALI SNR between four and ten times larger than SNR for TM - Operational Land Imager (OLI) on the Landsat Follow- on Mission - will be similar to the ALI sensor Conclusions and Future Work - The OLI design features a multispectral imager with pushbroom architecture of ALI heritage

20 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.

21 SpecTIR 30m vs. Hyperion 30m Residue Cover (%) Difference (hec) -60% - -40% % - -20% % - 0% % - 20% % - 40% % - 60% % - 70% % - -40% -39% - -20% -19% - 0% 1% - 20% 21% - 40% 41% - 60% 61% - 70%