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Proceedings Paper

Remote sensing of near-surface soil properties with the airborne terrestrial applications sensor
Author(s): Dana G. Sullivan; Joey N. Shaw; Paul L. Mask; Doug Rickman; Jeff Luvall; J. M. Wersinger
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Paper Abstract

Evaluation of near-surface soil properties via remote sensing (RS) could facilitate soil survey mapping, erosion prediction, fertilization regimes, and allocation of agrochemicals. The objective of this study was to evaluate the relationship between soil spectral signature and near surface soil properties in conventionally managed row crop systems. High-resolution RS data were acquired over bare fields in the Coastal Plain, Appalachian Plateau, and Ridge and Valley provinces of Alabama using the Airborne Terrestrial Applications Sensor (ATLAS) multispectral scanner. Soils ranged from sandy Kandiudults to fine textured Rhodudults. Surface soil samples (0-1 cm) were collected from 163 sampling points for soil water content, soil organic carbon (SOC), particle size distribution (PSD), and citrate dithionite extractable iron (Fed) content. Results showed that covariance among soil properties combined with mixed signatures limited our ability to identify discrete spectral response patterns for near-surface soil attributes. Dry, sandy epipedons at the Coastal Plain site provided ideal conditions and allowed for better discrimination among soil properties. Using ATLAS thermal infrared (TIR) bands, this study provides evidence that thermal spectra are more sensitive to small changes in near -surface mineral, organic and water content.

Paper Details

Date Published: 9 November 2004
PDF: 10 pages
Proc. SPIE 5544, Remote Sensing and Modeling of Ecosystems for Sustainability, (9 November 2004);
Show Author Affiliations
Dana G. Sullivan, USDA Agricultural Research Service (United States)
Joey N. Shaw, Auburn Univ. (United States)
Paul L. Mask, Auburn Univ. (United States)
Doug Rickman, NASA Marshall Space Flight Ctr. (United States)
Jeff Luvall, NASA Marshall Space Flight Ctr. (United States)
J. M. Wersinger, Auburn Univ. (United States)

Published in SPIE Proceedings Vol. 5544:
Remote Sensing and Modeling of Ecosystems for Sustainability
Wei Gao; David R. Shaw, Editor(s)

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