QUANTIFYING AND MONITORING NUTRIENT CYCLING, CARBON DYNAMICS AND SOIL PRODUCTIVITY AT FIELD, WATERSHED AND REGIONAL SCALES
Location: Hydrology and Remote Sensing Laboratory
Title: Use of airborne hyperspectral imagery to map soil parameters in tilled agricultural fields
Submitted to: Applied and Environmental Soil Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: May 3, 2011
Publication Date: June 15, 2011
Citation: Hively, W.D., McCarty, G.W., Reeves III, J.B., Lang, M.W., Osterling, R.A., Delwiche, S.R. 2011. Use of airborne hyperspectral imagery to map soil parameters in tilled agricultural fields. Applied and Environmental Soil Science. DOI: 10.1155/2011/358193.
Interpretive Summary: Spatial assessment of soil properties is important for understanding dynamics of carbon and nutrients within agricultural production systems. Accurate mapping of soil properties is made difficult due to high spatial variability observed within agricultural fields, and errors in spatial assessment of soil properties can result from inadequate or biased sampling of the landscape. Measurement technologies based on characteristics of reflected light offers a nondestructive means for measurement soil properties of illuminated soil. We demonstrate that aircraft based spectral cameras can be used to collect data for these measurements. We also demonstrate the utility of new statistical methods for analyzing remote sensing data for accurate measurement of soil properties within production fields. The spatial information gained by this approach may be used for improved implementation of cite specific management and precision agriculture technologies.
Soil hyperspectral reflectance imagery was obtained from an airborne imaging spectrometer (400 to 2450 nm with ~10 nm resolution, 2.5 m spatial resolution) flown over six tilled (bare soil) agricultural fields on the Eastern Shore of the Chesapeake Bay (Queen Anne’s county, MD). Surface soil samples from the fields (n=315) were analyzed for carbon content, particle size distribution, and fifteen agronomically important elements (Mehlich-III extraction). When partial least squares (PLS) regression of imagery-derived reflectance spectra was used to predict analyte concentrations for each sampling location, 13 of the 19 analytes were predicted with R2 >0.50, including carbon (0.65), aluminum (0.76), iron (0.75), and percent silt content (0.79). Comparison of 15 spectral math pre-processing treatments showed that a simple first derivative worked well for nearly all analytes. The resulting PLS factors were exported as a vector of coefficients that was used to calculate predicted maps of soil properties for each field based on imagery spectra. Image smoothing with a 3x3 low pass filter prior to spectral data extraction improved prediction accuracy, and also improved the range and distribution of values in resulting field maps. The resulting maps showed variation associated with topographic factors, indicating the effect of soil redistribution and moisture regime on in-field spatial variability. Results can be used to improve precision environmental management of farmlands.