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Journal of Applied Remote Sensing

Estimation of agricultural soil properties with imaging and laboratory spectroscopy
Author(s): Tingting Zhang; Lin Li; Baojuan Zheng
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Paper Abstract

Two EO-1 Hyperion images covering a Cicero Creek reservoir of central Indiana were analyzed using partial least squares (PLS) regression to estimate soil properties, including soil moisture, soil organic matter (SOM), total carbon (C), total phosphorus (P), total nitrogen (N), and clay content. PLS results for Hyperion image spectra were compared with those for laboratory measured spectra using several statistics, including the coefficient of determination (R 2 ) and RPD (the ratio of standard deviation of sample chemical concentration to root mean square error). PLS was conducted in two phases: phase-1 used all samples for calibration to determine outliers and then models were recalibrated after outlier removal; phase-2 split the resulting samples from phase 1 into two subsets for calibration and validation, respectively. Based on R 2 and RPD values, the results from the phase-1 calibration indicate that PLS can estimate all soil properties from laboratory spectra and some soil properties from Hyperion spectra, and the phase 2 results suggest that PLS can predict SOM, total C, and total N using Hyperion reflectance spectra. It was found that spectral resolution has impacts on the PLS performance in estimating the soil properties considered in this investigation.

Paper Details

Date Published: 11 February 2013
PDF: 25 pages
J. Appl. Rem. Sens. 7(1) 073587 doi: 10.1117/1.JRS.7.073587
Published in: Journal of Applied Remote Sensing Volume 7, Issue 1
Show Author Affiliations
Tingting Zhang, Institute of Remote Sensing Applications (China)
Lin Li, Indiana Univ.-Purdue Univ. Indianapolis (United States)
Baojuan Zheng, Indiana Univ.-Purdue Univ. Indianapolis (United States)

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