Mapping and uncertainty analysis of crop residue cover using sequential gaussian cosimulation with quickbird images

In the United States, about 30% of agricultural lands have been classified as highly erodible, contributing to continuous degradation of soil productivity. Conservation tillage is a well-known best management practice to conserve soil water resources. Crop residue reduces soil losses from water and wind erosion and increases sequestration of carbon in the soil. Crop residue retention is considered a soil conservation practice, and accurately estimating and mapping crop residue percentage cover is critical (Pacheco and McNairn 2010). Traditionally, ground survey-based methods are often used to estimate crop residue percentage cover (Bannari et al. 2006; Pacheco et al. 2006). However, this method is very time-consuming. Thus, developing a satellite-derived mapping algorithm to rapidly quantify crop residue percentage cover at field and landscape scales has become very important.