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Title:   Author  item
                                            Serbin, Guyitem
                                            Daughtry, Craigitem
                                            Hunt Jr, Earleitem
                                            McCarty, Gregoryitem
                                            Doraiswamy, Paul
  Submitted to: American Society of Agronomy Meetings
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 4/9/2008
                                    Publication  Date: 10/6/2009
                                    Citation: Serbin, G., Daughtry, C.S., Hunt, E.R., McCarty, G.W., Doraiswamy, P.C. 2008. Improvement of remote sensing of crop residue cover by accounting for green vegetation and soil spectral properties [abstract]. American Society of Agronomy Meetings. 2008 CDROM.Interpretive Summary:
                                Technical Abstract: Conservation tillage methods are beneficial as they disturb soil less and leaves increased crop residue cover (CRC) after planting on the soil surface.  CRC helps reduce soil erosion, evaporation, and the need for tillage operations in fields.  Greenhouse gas emissions are reduced to due to less fossil fuel usage by farm machinery.  Also, the increased humification of crop residues help sequester carbon to the soil.  As farmers can sell carbon credits and receive government incentives for instituting conservation tillage methods, an efficient verification method is needed.  Optical remote sensing from satellite and aircraft allows for estimation of CRC in an efficient manner compared with traditional surface-based methods.  CRC estimation in the U.S. Corn Belt was found to be most effective when utilizing multi- and hyperspectral sensors that include bands between 2000-2500 nm.  The Cellulose Absorption Index (CAI) was the most effective index under all circumstances, as it specifically targets an absorption at 2101 nm unique to dry cellulose and other sugars but not soil minerals or green vegetation (GV).  The Landsat TM based index Normalized Difference Tillage Index (NDTI) was not as effective, but could be used under limited circumstances.  The presence of green vegetation significantly decreased the accuracy of NDTI, but the use of the Normalized Difference Vegetation Index (NDVI) in conjunction provides ability to exclude high GV pixels and to help improve sub-pixel classifications for low GV pixels.  Accuracies were also improved when accounting for soil spectral properties when calculation calibration lines for NDVI and NDTI.


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