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\begin{document}

\title{The effectiveness of different approaches for in situ measurements of
organic carbon using VNIR in Poyang basin}



\author[1]{Meihua Yang}%
\author[2]{Hongyi Li}%
\author[3]{Shi Zhou}%
\affil[1]{Yuzhang Normal University}%
\affil[2]{JiangXi University of Finance and Economics}%
\affil[3]{Zhejiang University}%


\vspace{-1em}



  
  \date{May 05, 2024}


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\begin{abstract}
In situ visible near infrared diffuse reflectance spectroscopy (VNIR) is
a rapid and in-situ sensing approach and can provide analytical dense
soil data reflecting multiple physical and chemical properties of soil.
A total of 246 in situ soil samples were collected and scanned in
2016-2018. The dataset from 2016-2017 was used as the calibration
dataset to develop the dry ground model and to develop to the in situ
correction matrix using the dry and in situ spectra. The dataset from
2018 was used as the validation dataset using the in situ spectra. Four
in situ correction methods, external parameter orthogonalization (EPO),
direct standardization (DS), piecewise direct standardization (PDS), and
generalized least squares weighting (GLSW) were used to remove the in
situ effect on the spectra. In addition, two models, partial least
squares regression (PLSR) and support vector machine (SVM), were used to
detect the effectiveness of the prediction. The results showed that the
four in situ corrections could remove the error introduced by in situ
measurement to some extent. The four in situ corrections, when combined
with SVM, could better reduce the errors caused by in situ measurements
than the same corrections combined with PLSR. EPO correction
outperformed the other three methods, and EPO-SVM obtained the best
prediction with the lowest RMSE (1.91 g kg-1) and highest Lin's
concordance correlation coefficient (LCCC) (0.84). We conclude that the
EPO-SVM methods using in situ spectra can detect soil organic carbon in
the Poyang Lake area in a rapid and minimally invasive manner.%
\end{abstract}%



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