Agricultural crop residues have a significant role in nutrient cycling, carbon sequestration, and soil erosion. In the Midwestern United States, residue cover is strongly related to tillage practices. Estimates of residue cover have been obtained from various multispectral and hyperspectral indices. Methods based on hyperspectral data yield superior results and are more robust to variation in soils and vegetation cover, but are limited by availability of data. Approaches that utilize multispectral data require local field data to calibrate models and have limited generalization over extended areas. The goal of this study is to investigate approaches to exploit the superior discrimination capability of hyperspectral data to improve results derived from multispectral data over extended areas and reduce dependence on local field based measurements. A classification based approach which utilizes the CAI hyperspectral index to identify labeled data associated with residue cover classes corresponding to intensity of tillage, and a regression modeling approach which relates CAI and NDTI, are evaluated to estimate residue coverage in soybean and corn fields in typical agricultural lands in central Indiana, U.S.A.
