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Title:   Author  item
                                            SERBIN, GUY - Foreign Agricultural Service (FAS, USDA)item
                                            Hunt Jr, Earleitem
                                            Daughtry, Craigitem
                                            BROWN, D - Washington State Universityitem
                                            McCarty, Gregoryitem
                                            Doraiswamy, Paul
  Submitted to: International Geoscience and Remote Sensing Symposium Proceedings
                                    Publication Type: Proceedings
                                    Publication Acceptance Date: 7/25/2010
                                    Publication  Date: 7/25/2010
                                    Citation: Serbin, G., Hunt, E.R., Daughtry, C.S., Brown, D.J., McCarty, G.W., Doraiswamy, P.C. 2010. Assessment of spectral indices for crop residue cover estimation. In: Proceedings of the International Geoscience and Remote Sensing Symposium Proceedings, July 25-30, 2010, Honolulu, Hawaii. p. 1827-1830.Interpretive Summary:
                                Technical Abstract: The quantification of surficial crop residue (non-photosynthetic vegetation) cover is important for assessing agricultural tillage practices, rangeland health, and brush fire hazards. The Cellulose Absorption Index (CAI) and the Shortwave Infrared Normalized Difference Residue Index (SINDRI) are two spectral indices that have shown promise for remote estimation of crop residue cover.  CAI and SINDRI utilize three and two spectral bands, respectively, rendering the latter less expensive to implement in future satellite sensors.  This study shows that while CAI always contrasts well among soils, crop residues, and live vegetation, this is not always the case for SINDRI.  A small number of surficial soil samples had positive SINDRI values that have reduced contrasts among crop residues.  Some of these soils were biased by SINDRI-positive component minerals.  As such, SINDRI is less applicable for remote crop residue cover estimation, even with reduced implementation costs.


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