Tillage practices are known to have a large impact on the quantity of runoff and sediment delivered to a water body along with soil organic carbon storage/evolution rates, hence spatial knowledge of tillage practices is of great value for accurate watershed modeling and soil carbon modeling.  Collection of this data using optical remote sensing of tillage data has been proven in a number of studies to estimate average residue cover with acceptable accuracy when field boundaries can be determined.  The most important times for monitoring residue cover in the Northern US are after fall tillage/before spring tillage and just after planting, however, optical satellite imagery is only available at regularly recurring intervals when cloud cover is not present, which means data is often not collected under optimum conditions.  Therefore, to move remote sensing of residue cover from the category of research novelty to proven resource survey technique the impact of increasing amounts of living plant material on these indices must be quantified.  Using tillage indices such as the Normalized Difference Tillage Index and multiple spring Landsat images of the area near Ames, IA, the rates of decay of tillage index reflection with time and changes in index values with increasing leaf index are analyzed and discussed and a method for automating determination of these indexes is proposed.