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Agronomy Journal Abstract - Soil Tillage, Conservation & Management

Spectral Estimates of Crop Residue Cover and Density for Standing and Flat Wheat Stubble


This article in AJ

  1. Vol. 104 No. 2, p. 271-279
    Received: June 7, 2011

    * Corresponding author(s): jonathan.aguilar@ars.usda.gov
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  1. Jonathan Aguilar *a,
  2. Robert Evansb,
  3. Merle Vigilc and
  4. Craig S. T. Daughtryd
  1. a USDA-ARS Northern Plains Laboratory, Mandan ND 58554
    b USDA-ARS Northern Plains Agricultural Research Laboratory, Sidney, MT 59270
    c USDA-ARS Central Great Plains Research Station, Akron, CO 80720
    d USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705


Crop residue is important for erosion control, soil water storage, filling gaps in various agroecosystem-based modeling, and sink for atmospheric carbon. The use of remote sensing technology provides a fast, objective, and efficient tool for measuring and managing this resource. The challenge is to distinguish the crop residue from the soil and effectively estimate the residue cover across a variety of landscapes. The objective of this study is to assess a select Landsat Thematic Mapper (TM) and hyperspectral-based indices in estimating crop residue cover and amount for both standing and laid flat, and between two winter wheat (Triticum aestivum L.) harvest managements (i.e., stripper-header and conventional header) and fallow following proso-millet (Panicum miliaceum L.) plots. The primary plots were located in Colorado with additional plots in eastern Montana, Oregon, and Washington states. Data collected include hyperspectral scans, crop residue amount (by weight) and residue cover (by photo-grid). Mean analyses, correlation tests, and spectral signature comparison show that the relative position of the crop residues affected the values of some remote sensing indices more than harvest management. Geographical location did not seem to influence the results. There was not enough evidence to support the use of these indices to accurately estimate the amount of residue. Hyperspectral data may deliver better estimates, but in its absence, the use of two or more of these datasets might improve the estimation of residue cover. This information will be useful in guiding analysis of remotely sensed data and in planning data acquisition programs for crop residue, which are essentially nonexistent at present.

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