Midwest Climate and Agriculture - Monitoring Tillage Practices with NASA Remote Sensors

Concerns about climate change have driven efforts to reduce or offset greenhouse gas emissions. Agricultural activity has drawn considerable attention because it accounts for nearly twelve percent of total anthropogenic emissions. Depending on the type of tillage method utilized, farm land can be either a source or a sink of carbon. Conventional tillage disturbs the soil and can release greenhouse gases into the atmosphere. Conservational tillage practices have been advocated for their ability to sequester carbon, reduce soil erosion, maintain soil moisture, and increase long-term productivity. If carbon credit trading systems are implemented, a cost-effective, efficient tillage monitoring system is needed to enforce offset standards. Remote sensing technology can expedite the process and has shown promising results in distinguishing crop residue from soil. Agricultural indices such as the CAI, SINDRI, and LCA illuminate the unique reflectance spectra of crop residue and are thus able to classify fields based on percent crop cover. The CAI requires hyperspectral data, as it relies on narrow bands within the shortwave infrared portion of the electromagnetic spectrum. Although limited in availability, hyperspectral data has been shown to produce the most accurate results for detecting crop residue on the soil. A new approach to using the CAI was the focus of this study. Previously acquired field data was located in a region covered by a Hyperion swath and is thus the primary study area. In previous studies, ground-based data were needed for each satellite swath to correctly calibrate the linear relationship between the index values and the fraction of residue cover. We hypothesized that there should be a standard method which is able to convert index values into residue classifications without ground data analysis. To do this, end index values for a particular data set were assumed to be associated with end values of residue cover percentages. This method may prove to be more practical for end-users such as the USDA to quickly assess residue cover in a given region.