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Various studies have demonstrated that spectral indices derived from remotely sensed data can be used to quantify crop residue cover, if adequately calibrated using in situ data. This study evaluates the capability of the Normalized Difference Tillage Index (NDTI) derived from Advance Land Imager (ALI) relative to that of Landsat Thematic Mapper (TM) and the performance of the Cellulose Absorption Index (CAI) derived from Hyperion and airborne hyperspectral data acquired over central Indiana watersheds. A framework based on Cumulative Distribution Function (CDF) matching is also proposed to leverage the superior predictive capability of hyperspectral based indices to improve predictions of multispectral based indices over extended regions. ALI data consistently yielded crop residue models with lower root mean square error (RMSE) values than those developed using Landsat TM data. Hyperspectral based indices were generally superior in predictive capability to the NDTI based predictions. Observation operators derived from the CDF matching method were successful in scaling multiple data sets to achieve models with lower RMSE and improved predictive capability over the entire range of index values.