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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #290879
Title: Efficient sampling schemes for assessing soil tillage intensity

Author
item Daughtry, Craig
item Beeson, Peter
item GALLOZA, M - Purdue University
item Stern, Alan
item Sadeghi, Ali
item Hunt Jr, Earle
item CRAWFORD, M - Purdue University

Submitted to: International Geoscience and Remote Sensing Symposium Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 4/15/2013
Publication Date: 7/21/2013
Citation: Daughtry, C.S., Beeson, P.C., Galloza, M.S., Stern, A.J., Sadeghi, A.M., Hunt Jr, E.R., Crawford, M.M. 2013. Efficient sampling schemes for assessing soil tillage intensity. In: International Geoscience and Remote Sensing Symposium Proceedings, July 21-26, 2013, Melbourne, Australia. 2013 CDROM.

Interpretive Summary:

Technical Abstract: Crop residue or plant litter is the portion of a crop left in the field after harvest. Crop residues on the soil surface provide a protective barrier against water and wind erosion and reduce the amounts of soil, nutrients, and pesticides that reach streams and rivers. Management of crop residues is an integral part of most conservation tillage systems. Soil tillage and crop residue harvesting for feed, fiber, or bio-energy are management practices that reduce crop residue mass and cover. 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 standard technique used by USDA Natural Resource Conservation Service (NRCS) to quantify crop residue cover, the line-point transect, is impractical for monitoring crop residue cover and tillage intensity in many fields in a timely manner. Remote sensing approaches require sufficient surface reference (or ground-truth) data for crop residue cover to train and evaluate the algorithms. Generally, this requires measuring residue cover with a line-point transect in at least 100 locations per test site and involves a substantial investment of man-hours. Our objectives were: (1) to estimate crop residue cover using remotely sensed data over agricultural sites in Iowa and Indiana, and (2) to evaluate alternative, less labor-intensive sampling schemes for acquiring crop residue cover surface reference data. Test sites in central Iowa and west central Indiana site were defined by the Hyperion scenes (7.5 km x ~80 km). Crop residue cover was measured in corn and soybean fields shortly after planting. Hyperion images were acquired, atmospherically corrected, geo-registered, and converted to apparent reflectance. The Cellulose Absorption Index (CAI) was calculated using the corrected Hyperion data. Crop residue cover was linearly related to CAI; however, the slope of residue cover vs. CAI increased as scene water content increased. The ratio water index was used to adjust crop residue cover estimated by CAI when moisture conditions varied from scene to scene or within a scene due to topography. Three alternative sampling schemes for acquiring crop residue cover surface reference data were evaluated. The accuracy of soil intensity tillage classification was not significantly degraded by the alternative sampling schemes compared to the classifications using all of surface reference data. For this limited set of Hyperion images, the alternative sampling strategies required substantially fewer surface reference samples and less time in the field than the traditional approach. Crop residue cover and soil tillage intensity may be mapped relative to environmentally sensitive zones over diverse agricultural landscapes. Areas requiring site-specific conservation practices could be highlighted for further examination. The overall result will be less soil erosion and improved soil and water quality.