Remote Sensing for Robust Estimates of Crop Residue Cover and Soil Tillage Intensity

Craig Daughtry, USDA-ARS, craig.daughtry@ars.usda.gov (Presenter)
Peter Beeson, Roger Tory Peterson Institute of Natural History, dehydroindigo@gmail.com
Sushil Milak, SSAI at USDA-ARS Hydrology & Remote Sensing Lab, sushil.milak@ars.usda.gov
Earle Raymond Hunt, USDA ARS, raymond.hunt@ars.usda.gov
Ali Sadeghi, USDA-ARS, ali.sadeghi@ars.usda.gov
Mark Tomer, USDA-ARS, mark.tomer @ars.usda.gov
Doug Karlen, USDA-ARS, doug.karlen@ars.usda.gov

Crop residues on the soil surface form the first line of defense against soil erosion, increase water infiltration, increase soil organic matter, and improve soil and water quality. Thus, management of crop residues is an integral part of most conservation tillage systems. Soil tillage intensity is defined by crop residue cover. Agro-ecosystem models require spatially explicit estimates of soil tillage intensity to accurately evaluate soil and water quality and carbon dynamics in croplands. However, appropriate soil tillage information is non-existent for large regions and severely limits the applicability of these agro-ecosystem models.

Our objectives were to (1) evaluate current remote sensing approaches for assessing crop residue cover and soil tillage intensity, (2) propose alternative remote sensing approaches, and (3) assess impacts of crop management practices on soil and water quality in a watershed in central IA.

The spectral properties of crop residues and soils in crop production fields in MD, IN, and IA were measured with ground-based spectroradiometers, airborne and satellite imaging spectrometers, and satellite multispectral scanners. With broadband multispectral sensors (e.g., Landsat) crop residues and soils were spectrally similar which made discrimination of crop residue cover challenging. Physically-based spectral indices that used either hyperspectral or advanced multispectral (e.g., ASTER) data detected absorption features associated with cellulose and lignin in the crop residues. These indices were robust and required minimal surface reference data to reliably map crop residue cover and soil tillage intensity across agricultural landscapes. Unfortunately, these advanced sensors cannot provide wall-to-wall coverage of large regions. Stratified sampling protocols were proposed that used a limited number of images from the advanced sensors to provide reliable surface reference data for calibrating and verifying the accuracy of soil tillage classes determined using widely available broadband multispectral images.

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 hydrologic and soil carbon models. Crop and soil management scenarios were evaluated using watershed- and field-scale models. An interconnected suite of models was required to address the wide range of agronomic, environmental, and economic questions likely to be posed by farmers, stakeholders, and policymakers related to crop management practices including harvesting crop residues for biofuels.

Presentation Type:  Poster

Session:  Theme 3: Future research direction and priorities: perspectives relevant to the next decadal survey   (Mon 4:30 PM)

Associated Project(s): 

• Daughtry, Craig: Decision Support Systems for Carbon Management Across the US Corn Belt Using NASA Remote Sensing Data Products ...details

Poster Location ID: 196