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Mapping soil fertility parameters with airborne hyperspectral imagery

Greg McCarty, USDA - ARS, greg.mccarty@ars.usda.gov (Presenter)
Dean Hively, USGS, whively@usgs.gov
Megan Weiner Lang, USDA FS, megan.lang@gmail.com

Hyperspectral imagery was obtained for tilled (bare soil) agricultural fields using an airborne imaging spectroradiometer (400-2450 nm, ~10 nm spatial resolution, 2.5 m spatial resolution). Surface soil samples (n=315) were analyzed for carbon content, particle size, and 15 agronomically important nutrients and other chemical parameters. When partial least squares (PLS) regression of reflectance spectra was used to predict analyte concentrations, 13 of the 19 analytes were predicted with R2 >0.50, including carbon (0.65), aluminum (0.76), iron (0.75), and silt content (0.79). A simple first derivative worked well for developing nearly all calibrations. The resulting PLS factors were exported as a vector of coefficients and used to calculate predicted maps of soil properties for each field. Image smoothing with a 3x3 low pass filter prior to spectral data extraction improved accuracy. The resulting raster maps showed variation associated with topographic factors, indicating effect of soil redistribution and moisture regime. High-resolution maps of soil fertility parameters can be used to improve precision environmental management of farmlands.

Presentation Type:  Poster

Session:  Global Change Impact & Vulnerability   (Tue 11:30 AM)

Associated Project(s): 

  • Related Activity

Poster Location ID: 230

 


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