Research Article
Online Measurement of Soil Organic Carbon
as Correlated with Wheat Normalised Difference Vegetation
Index in a Vertisol Field
Yücel Tekin,
1
Yahya Ulusoy,
1
Zeynal TümsavaG,
2
and Abdul M. Mouazen
3
1
Vocational School of Technical Science, Uludag University, 16059 Bursa, Turkey
2
Agricultural Faculty, Uludag University, 16059 Bursa, Turkey
3
Environmental Science and Technology Department, Cranfield University, Bedfordshire MK43 0AL, UK
Correspondence should be addressed to Y¨ ucel Tekin; ytekin@uludag.edu.tr
Received 14 April 2014; Revised 10 June 2014; Accepted 12 June 2014; Published 3 July 2014
Academic Editor: Hayati Filik
Copyright © 2014 Y¨ ucel Tekin et al. Tis is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Tis study explores the potential of visible and near infrared (vis-NIR) spectroscopy for online measurement of soil organic carbon
(SOC). It also attempts to explore correlations and similarities between the spatial distribution of SOC and normalized diferential
vegetation index (NDVI) of a wheat crop. Te online measurement was carried out in a clay vertisol feld covering 10 ha of area
in Karacabey, Bursa, Turkey. Kappa statistics were carried out between diferent SOC and NDVI data to investigate potential
similarities. Calibration model of SOC in full cross-validationresulted in a good accuracy (
2
= 0.75, root mean squares error
of prediction (RMSEP) = 0.17%, and ratio of prediction deviation (RPD) = 1.81). Te validation of the calibration model using
laboratory spectra provided comparatively better prediction accuracy (
2
= 0.70, RMSEP = 0.15%, and RPD = 1.78), as compared
to the online measured spectra (
2
= 0.60, RMSEP = 0.20%, and RPD = 1.41). Although visual similarity was clear, low similarity
indicated by a low Kappa value of 0.259 was observed between the online vis-NIR predicted full-point (based on all points measured
in the feld, e.g., 6486 points) map of SOC and NDVI map.
1. Introduction
Soil organic carbon (SOC), the major component of soil
organic matter, is extremely important for land use and
management. Agricultural management of land plays an
important role in global warming mitigation due to its efects
on SOC dynamics [1]. Many management practices that
are efective in increasing SOC are also advantageous in
increasing aggregate stability, enhancing soil fertility, and
improving crop yield. It is achieved by adding organic
materials, composts, manure, and other recycled organic
materials to the soil. A method to map the spatial variability
of SOC would be a very useful tool to optimize the spatial
distribution of artifcially added SOC.
Proximal and remote sensors are being increasingly used
in agriculture to control and manage farming inputs. For
example, they are extensively used in precision agriculture
(PA) in order to identify proper targets and needs of crops
for variable rate applications [2]. However, the main require-
ment, for these sensors, is their robustness and more impor-
tantly they must provide accurate and meaningful data. One
of the most rapid and promising measurement techniques
for PA applications is the visible and near infrared (vis-NIR)
spectroscopy. It is a simple and nondestructive analytical
method that can be used to enhance, complement, or replace
conventional methods of soil analyses. It is particularly
useful to overcome some of the limitations of conventional
laboratory methods and may be utilized to predict several
soil properties simultaneously [3]. Vis-NIR spectroscopy has
become the most attractive technique for end-users of PA, as
some recent studies by Mouazen et al. [4], Viscarra-Rossel
and Chen [5], Tekin et al. [6], and Kodaira and Shibusawa [7]
Hindawi Publishing Corporation
e Scientific World Journal
Volume 2014, Article ID 569057, 12 pages
http://dx.doi.org/10.1155/2014/569057