|MILAK, S - Science Systems, Inc|
|AKHMEDOV, B - Science Systems, Inc|
|Hunt Jr, Earle|
Submitted to: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: 5/15/2011
Publication Date: 9/24/2011
Citation: Daughtry, C.S., Beeson, P.C., Milak, S., Akhmedov, B., Sadeghi, A.M., Hunt, E.R., Tomer, M.D. 2011. Assessing the extent of conservation tillage across agricultural landscapes. In: Proceedings of SPIE 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV, 853116; September 24-27, 2012, Edinburgh, United Kingdom. DOI:10.1117/12.974611.
Technical Abstract: Crop residue (or plant litter) on the soil surface can decrease soil erosion and runoff and improve soil quality. Quantification of crop residue cover is required to evaluate the effectiveness of conservation tillage practices as well as the extent of biofuel harvesting. With Landsat Thematic Mapper bands, crop residues can be brighter or darker than soils depending on soil type, crop type, moisture content, and residue age. With hyperspectral reflectance data, relatively narrow absorption features, centered near 2100 and 2300 nm, can be detected that are associated with cellulose and lignin concentrations. These features are evident in reflectance spectra of crop residues, but not in reflectance spectra of soils. Our objectives are to assess crop residue cover and soil tillage intensity using remotely sensed data, and examine effects of removing crop residues for biofuel on soil and water quality in fields and watersheds. Reflectance spectra of corn, soybean, and wheat residues and a wide range of soils were acquired with a spectroradiometer in the laboratory and in five test sites in Maryland, Indiana, and Iowa. Airborne and satellite hyperspectral images were also acquired for each test site. The cellulose absorption index (CAI), which measured the relative intensity of the absorption feature near 2100 nm, was calculated using three 10-nm bands centered at 2030, 2100, and 2210 nm. For each data set, crop residue cover was linearly related to CAI. Classification accuracy was 66-82% for all scenes. Management scenarios, including crop residue removal, were evaluated using field and watershed-scale models. The levels of crop residue removal that are sustainable vary with soil type, tillage management practices, and climatic conditions. Robust decision support systems will require suites of models to adequately address all agronomic, environmental, and economic issues.