Estimation of crop residue has increasing relevance for agricultural land management related to erosion, carbon sequestration and water quality. Automated methods utilizing remotely sensed data potentially provide capability to accomplish this over large areas with greater accuracy and at lower cost than traditional "windscreen" surveys. This study investigated application of the cellulose absorption index (CAI) and constrained linear spectral mixing analysis (CLSMA) to Hyperion hyperspectral data to estimate soy and corn crop residue coverage on agricultural lands in Indiana.
