Mapping Tillage Practices Using Spatial Information Techniques

Monitoring tillage practices is important for explaining soil quality and yield trends, and their impact on environmental quality. However, a common problem in sustainable residue management is scarcity of accurate residue maps. Because predictive insights on soil quality dynamics across a spatial domain are vital, this entry explicates on a new remote sensing-based technique for assessing surface residue cover. Here, an empirical model for mapping surface residue cover was created by integrating line-transect % residue cover field measurements with information gleaned from ground spectroradiometers and Advanced Wide-Field Sensor (AWiFS) satellite imagery. This map was validated using non-photosynthetic vegetation (NPV) fractional component extracted by spectral mixture analysis (SMA). SMA extracts fractional components of sensed signals in imagery, which within agricultural fields are NPV, green vegetation, bare soil, and shade. A stepwise linear regression between residue estimates by line transect and map generated using satellite imagery had R² = 87%. Upon map categorization according to surface residue for a single AWiFS imagery encompassing an area of 836,868 ha, but focused on corn (Zea mays) fields within South Dakota, revealed that <4% of these corn fields had >15% surface residue cover left in the field by November 2009. Findings such as these may guide policy on soil quality, which is directly correlated with residue management. In the future, the spatial distribution of surface residues remaining after harvest in field planted with other crops and other seasons will be mapped. Besides, the efficacy of integrating hyperspectral sensor data to enhance accuracy will be investigated.