Location: Hydrology and Remote Sensing LaboratoryTitle: Remote sensing of soil tillage intenstiy in a CEAP watershed in central Iowa Author
Submitted to: Interagency Conference on Research in the Watersheds
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/12/2011
Publication Date: 9/26/2011
Citation: Daughtry, C.S., Beeson, P.C., Milak, S., Hunt, E.R., Sadeghi, A.M. 2011. Remote sensing of soil tillage intenstiy in a CEAP watershed in central Iowa. Interagency Conference on Research in the Watersheds: Observing, studying, and managing for change. U.S. Geological Survey Scientific Investigation Report 2011-5169. 168-173. Interpretive Summary: Crop residues on the soil surface decrease soil erosion and runoff, increase soil organic matter, improve soil quality, increase water infiltration and reduce the amounts of nutrients and pesticides that reach stream and rivers. Three useful categories of tillage based on crop residue cover after planting have been defined: intensive tillage has <15% residue cover; reduced tillage has 15-30% residue cover; conservation tillage has >30% residue cover. Thus, quantification of crop residue cover is required to evaluate the effectiveness and extent of conservation tillage practices as well as the extent of biofuel harvesting. For agricultural fields, the standard technique for measuring crop residue cover is the line-point transect method which is time-consuming and prone to errors. Advanced remote sensing approaches for assessing crop residue cover are robust and require minimal surface reference data for mapping soil tillage intensity across agricultural landscapes. The South Fork watershed is a Conservation Effects Assessment Project (CEAP) watershed in central Iowa. Farmer surveys, surface reference data, and remotely sensed data provided spatially-explicit input data for the hydrologic and soil carbon models. Crop and soil management scenarios were evaluated using watershed- and field-scale models. The environmental effects of crop residue removal can be minimal for some geographic areas but much greater in others. No single model can address the wide range of agronomic, environmental, and economic questions likely to be posed by farmers, stakeholders, and policymakers related to harvesting crop residues for biofuels. An interconnected suite of models is proposed.
Technical Abstract: Crop residues on the soil surface decrease soil erosion, increase soil organic matter, improve soil quality, and reduce the amount of nutrients and pesticides that reaches streams and rivers. Crop residue cover is often used to classify soil tillage intensity. Quantification of crop residue cover is required to evaluate the effectiveness and extent of conservation tillage practices as well as the extent of bio-fuel harvesting. The NRCS standard technique for measuring crop residue cover is the line-point transect method which is time-consuming, prone to errors, and not suitable for watershed scale studies. Our overall goal is to develop decision support tools that will assist farmers and land managers evaluate the impact of bio-fuel harvesting on soil and water quality. The study site is the South Fork watershed, a USDA Conservation Effects Assessment Project (CEAP) watershed. Cropland accounts for 88% of the 788 km 2 watershed and corn and soybeans are grown on 99% of the cropland. Farmer surveys, surface reference data, and remotely sensed data provide the spatially-explicit input data for the hydrologic and soil carbon models. Our hypothesis is that remote sensing can provide the timely and accurate assessments of soil tillage intensity required by theses models. Traditional remote sensing approaches for assessing soil tillage intensity have used the relatively broad band multispectral sensors, such as Landsat TM, SPOT, or AWiFTs. However, crop residues can be brighter or darker than soils depending on soil type, crop type, moisture content, and residue age. Acceptable classification accuracies for 2-3 tillage classes are often possible, but require timely, scene-specific surface reference data for training. With hyperspectral data, physically-based spectral indices that detect absorption features associated with cellulose and lignin are linearly related to crop residue cover. These indices are robust and require minimal surface reference data for mapping soil tillage intensity across agricultural landscapes. Unfortunately, current satellite hyperspectral systems are not capable of imaging the entire watershed in a timely manner. Stratified sampling protocols were developed that used the limited hyperspectral images to provide reliable data to train classifiers of multispectral images. Watershed and regional surveys of soil management practices that affect soil and water quality are possible with this technique.