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Use of airborne hyperspectral imagery to map soil parameters in tilled agricultural fields

January 22, 2012

Soil hyperspectral reflectance imagery was obtained for six tilled (soil) agricultural fields using an airborne imaging spectrometer (400–2450 nm, ~10 nm resolution, 2.5 m spatial resolution). Surface soil samples (n = 315) were analyzed for carbon content, particle size distribution, and 15 agronomically important elements (Mehlich-III extraction). When partial least squares (PLS) regression of imagery-derived 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). Comparison of 15 spectral math preprocessing treatments showed that a simple first derivative worked well for nearly all analytes. 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 3 × 3 low-pass filter prior to spectral data extraction improved prediction accuracy. The resulting raster maps showed variation associated with topographic factors, indicating the effect of soil redistribution and moisture regime on in-field spatial variability. High-resolution maps of soil analyte concentrations can be used to improve precision environmental management of farmlands.

Citation Information

Publication Year 2011
Title Use of airborne hyperspectral imagery to map soil parameters in tilled agricultural fields
DOI 10.1155/2011/358193
Authors W. Dean Hively, Gregory W. McCarty, James B. Reeves, Megan W. Lang, Robert A. Oesterling, Stephen R. Delwiche
Publication Type Article
Publication Subtype Journal Article
Series Title Applied and Environmental Soil Science
Series Number
Index ID 70005838
Record Source USGS Publications Warehouse
USGS Organization Eastern Geographic Science Center