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\begin{document}

\title{Inversion of Soil Properties with Hyperspectral Reflectance in
Well-facilitied Capital Farmland Construction Areas}



\author[1]{qiuxia zhang}%
\author[1]{Wenkai Liu}%
\author[2]{Hebing Zhang}%
\author[2]{Shou-Chen Ma}%
\author[3]{Xinsheng Wang}%
\affil[1]{North China University of Water Resources and Electric Power}%
\affil[2]{Henan Polytechnic University}%
\affil[3]{Henan Institute of Science and Technology}%


\vspace{-1em}



  
  \date{May 27, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Well-facilitied capital farmland construction is an important measure to
enhance the ability to ensure food security and accelerate new-style the
modernization agriculture. Hyperspectral remote sensing can be provide
data basis and technical support for realization the construction of
well-facilitied capital farmland,to provide a reference for exploring
the optimization of well-facilitied capital farmland construction area.
Taking Xinzheng City of main grain producing areas in Henan province as
the research object, using field sampling and indoor hyperspectral
spectroscopy (350\textasciitilde{}2500 \emph{nm}) combined, the spectral
transformations such as Continuum removal( \emph{CR}) are carried out
after the Savitzky-Golay( \emph{SG}) convolution smoothing, the best
hyperspectral bands as the common index of the soil properties were
selected by the correlation analysis and Fuzzy clustering maximum
tree,focused on
405\textasciitilde{}431nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}781nm\textasciitilde{}831nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1044\textasciitilde{}1087nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1251\textasciitilde{}1410nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1836\textasciitilde{}1898nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2080nm\textasciitilde{}2201nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2324\textasciitilde{}2395nm.
The hyperspectral inversion model had been built by Panel date model of
fixed effect variable coefficient based on the ordinary least squares
estimation method ( \emph{OLS}), that is about the panel data of
PH\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}SOM\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}AN\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}AP\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}AK\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}Fe\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}Cr\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}Cd\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}Zn\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}Cu\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}Pb of 116 samples in Xinzheng
City. The results showed that: The Panel date model significantly
overall, the goodness of fit is higher (in the model,
\textsuperscript{2} =0.9991, \emph{F} = 2195.67). The result of
precision test indicates that models performed well in modeling and
predicting with a good ability of quantificational prediction, with RPD
values were greater than 2.5.%
\end{abstract}%



\sloppy


Inversion of Soil Properties with Hyperspectral Reflectance in
Well-facilitied Capital Farmland Construction Areas

Qiuxia Zhang \textsuperscript{1}, Wenkai Liu \textsuperscript{1}, Hebing
Zhang \textsuperscript{2}, Shouchen Ma \textsuperscript{2}, Xinsheng
Wang \textsuperscript{3}

1. School of Public Administration,North China University of Water
Resources and Electric Power, Zhengzhou, China 450046

2. School of Surveying and Landing Information Engineering, Henan
Polytechnic University, Jiaozuo, China 450003

3. School of Life Science \& Technology, Henan Institute of Science and
Technology,Xinxiang,China 453003

Author: Qiuxia Zhang, Phd, Lecturer, the research direction is mining
land reclamation and ecological reconstruction,
E-mail:\emph{zhangqiuxia@ncwu.edu.cn} .

\textbf{Abstract:} Well-facilitied capital farmland construction is an
important measure to enhance the ability to ensure food security and
accelerate new-style the modernization agriculture. Hyperspectral remote
sensing can be provide data basis and technical support for realization
the construction of well-facilitied capital farmland,to provide a
reference for exploring the optimization of well-facilitied capital
farmland construction area. Taking Xinzheng City of main grain producing
areas in Henan province as the research object, using field sampling and
indoor hyperspectral spectroscopy (350\textasciitilde{}2500\emph{nm} )
combined, the spectral transformations such as Continuum
removal(\emph{CR} ) are carried out after the
Savitzky-Golay(\emph{SG} ) convolution smoothing, the best
hyperspectral bands as the common index of the soil properties were
selected by the correlation analysis and Fuzzy clustering maximum
tree,focused on
405\textasciitilde{}431nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}781nm\textasciitilde{}831nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1044\textasciitilde{}1087nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1251\textasciitilde{}1410nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1836\textasciitilde{}1898nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2080nm\textasciitilde{}2201nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2324\textasciitilde{}2395nm.
The hyperspectral inversion model had been built by Panel date model of
fixed effect variable coefficient based on the ordinary least squares
estimation method (\emph{OLS} ), that is about the panel data of
PH\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}SOM\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}AN\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}AP\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}AK\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}Fe\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}Cr\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}Cd\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}Zn\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}Cu\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}Pb of 116 samples in Xinzheng
City. The results showed that: The Panel date model significantly
overall, the goodness of fit is higher (in the model,\textsuperscript{2}
=0.9991,\emph{F} = 2195.67). The result of precision test indicates
that models performed well in modeling and predicting with a good
ability of quantificational prediction, with RPD values were greater
than 2.5.

\textbf{Keywords:} Well-facilitied capital farmland; Hyperspectral;
Panel data model; Maximum tree clustering method;Xinzheng City

\textbf{Introduction}

At present, the core area of grain production has become a national
strategy. Henan Province, as the most important part of it, that is in
the critical period of promoting the ''coordination of the four
modernizations and scientific development''. Agriculture is the basis
for realizing modernization, so must to speed up the transformation of
agricultural development mode ,and improve agricultural quality,
efficiency and competitiveness. Path to agricultural modernization
featuring, of high output efficiency, product safety, resource
conservation and environmental friendliness. However, the important
bottleneck that restricts agriculture sustainable development of Henan,
what is the large number of people and small amount of land, the serious
shortage of reserve resources of cultivated land, and the overall low
quality of cultivated land. Therefore, strengthening Well-facilitied
capital farmland construction has enormous symbolic significance, for
implementing the national strategy of China and promoting agricultural
production.

Well-facilitied capital farmland refers to the basic farmland with
centralized contiguity, supporting facilities, high and stable yield,
good ecology and strong disaster resistance formed ,through rural land
consolidation and construction in a certain period of time, which is
compatible with modern agricultural production and operation
mode(TD/T1033-2012). At present, most of the researches on
Well-facilitied capital farmland construction include Well-facilitied
capital farmland construction demarcation( LI Yilong, et al., 2019; DONG
Fei, et al., 2020; ZHANG Hebing, et al., 2018) potential evaluation(CAI
Xiangwen. et al., 2019; LI C M, et al., 2018), suitability
evaluation(TANG Feng, et al., 2019; CHEN Lin, et al., 2019; ZHANG Jing,
et al., 2020), construction sequence and mode zoning(LI Long, et al.,
2020; WANC Ke, et al., 2021; ZENG Ya, et al., 2020), project
implementation and effect evaluation (MA Xueying, et al., 2018; XIONG
Yufei, et al., 2019; WANG Xiaoqing, et al., 2018), etc. In the Standard
of Well-facilitied Capital Farmland Construction(TD/T1033-2012), it is
clearly proposed to ''the quality of cultivated land after completion
reaches the higher level of the county'', etc .Therefore, the
construction of Well-facilitied capital farmland requires, that the soil
quality should reach the higher level of the region, while the
construction of field projects. '' it should be determined the amount of
fertilizer , according to soil nutrient status, and the soil nitrogen,
phosphorus, potassium, medium and trace elements, organic matter
content, soil acidification, salinity, and should be regularly monitored
other conditions, and should be constantly adjusted the fertilization
formula, according to the actual situation''(NY/T 2148-2012). However,
in the process of Well-facilitied capital farmland construction, in
addition to the more placed on land leveling, improvement of supporting
facilities of roads, ditches and other projects, while strengthen the
rapid acquisition, and real-time monitoring of soil basic information.
However, most research methods are quantitative inversion research for
single soil properties(BIAN Zijin, et al., 2021; WEI Lifei, et al.,
2020; LI zhiyuan, et al., 2021; YU Huan, et al., 2021; Schreiner Simon,
et al., 2021; SHI Yuanyuan, et al., 2021; XU Xitong, et al., 2020).
Inversion models of soil properties and spectral reflectance need to be
built one by one, and the calculation process is complicated and
time-consuming. And Panel data model can be build the three-dimensional
data model at the same time, contain various soil properties of multiple
points, and high spectral characteristics of the band values, By
inversion modeling get multiple soil properties at once, modeling
calculation process more simple, and can according to the relationship
between each model analysis of soil properties, and the influence of
high spectral band characteristic values on each soil properties.
Therefore, It is necessary to study the rapid and nondestructive testing
of soil attribute information in Well-facilitied capital farmland
construction area, so as to provide technical support for the rapid
acquisition, and real-time monitoring about soil attribute, and to
provide support for the optimization of well-facilitied capital farmland
construction area.

This reseach taking Well-facilitied capital farmland construction area
of Xinzheng City as the research object, obtained soil hyperspectral
data by using ASD Field Spec3 ground object spectrometer in laboratory
experiments, and combined with soil properties such as soil PH, organic
matter, nitrogen, phosphorus, potassium, Fe, Cr, Cd, Cu, Zn, Pb, etc. It
performed Savitzky-golay(\emph{SG} )filtering and Continuum removal
(\emph{CR} ) spectral transformation on the original spectral
reflectance. And used Correlation analysis and Fuzzy clustering maximum
tree method to select the common significance band of different soil
attributes as the best hyperspectral characteristic band. This paper
attempts to establish a comprehensive hyperspectral inversion model of
cultivated land soil attributes by using Panel date model, estimate the
influence of hyperspectral characteristic band values on each soil
attribute, and predict the content of each soil attribute, aiming to
provide theoretical and technical support for the rapid acquisition and
real-time monitoring about soil attribute of Well-facilitied capital
farmland construction area.

\textbf{1. Materials and Methods}

\textbf{1.1. The overview of the researched area}

Xinzheng city is located in the central part of Henan Province in China,
is transition zone from the north China plain, western Henan mountain to
eastern Henan plain, is the core of the Central Plains economic zone,
under zhengzhou city, located in 34\selectlanguage{ngerman}°16'\textasciitilde{} 34°39' \emph{N}
, 113°30'\textasciitilde{} 113°54' \emph{E} , north of the provincial
capital Zhengzhou, east of Zhongmu County, Weishi County, south of
Changge City, Yuzhou city. It borders Xinmi city on the west, North of
Zhengzhou city 38 \emph{km} ; Northeast from zhongmu county 45.6
\emph{kilometers} , 120\emph{kilometers} downtown Kaifeng; East to Yushi
county 42.6 kilometers; South to Changge city 20.4 \emph{km} , Xuchang
city 40 km; Southwest to Yuzhou city 36.5 \emph{km} , Pingdingshan city
84 \emph{km} ; 34.5 \emph{kilometers} west to Xinmi urban area. It is
42\emph{kilometers} long from north to south and 36 \emph{kilometers}
wide from east to west. It covers an area of 873 \emph{square
kilometers} and has a total population of 653,000. In 2019, It has
jurisdiction over towns of 9, townships of 1, streets of 3 and
administrative villages of 253, natural villages of 921 and residential
areas of 24.

According to the survey of land use status in 2013, the total land area
of Xinzheng city is 884.5915\emph{km\textsuperscript{2}} , and the
cultivated land is 521.7641 \emph{km\textsuperscript{2}} , accounting
for 58.59\% of the total land area. The total annual grain output is
273148 \emph{t} . According to Integrated Land-use Planning of Xinzheng
city (2010-2020), the protection index of prime farmland in Xinzheng
City is 427.73\emph{km\textsuperscript{2}} . Xinzheng city is warm
temperate continental monsoon climate, moderate temperature, four
distinct seasons; The main disastrous weather is drought, flood, wind,
hail, etc. The average annual temperature is 14.2\selectlanguage{english}, the average annual
precipitation is 676.1\emph{mm} , the average annual evaporation is
1476.2\emph{mm} , the average annual sunshine duration is
2,114.2\emph{h} , the average annual frost day is 67 days, the average
annual total water resources is 147.73
million\emph{m\textsuperscript{3}} , and the per capita water resources
are 236\emph{m\textsuperscript{3}} . There are various soil types,
mainly cinnamon soil, tidal soil and aeolian sand soil. The terrain is
high in the west and low in the east, with shallow hills in the west,
plains in the east and hills in the northwest.

\textbf{1.2. Field collection of soil samples}

According to the soil type, topographic characteristics and spatial
variation characteristics of the study area, and taking into account the
integrity of administrative units (towns or villages as units), sampling
points were laid out using the 2\emph{km} x2\emph{km} regular grid
method. Every point in a spatial database included the basic
information, as its serial number, latitude and longitude coordinates,
township and neighboring villages, etc. According to the map about
sample point and the table of point attribute, and used GPS to
accurately locate the field sampling, and the sampling depth was
0-30\emph{cm} on the surface of the soil, and recorded the coordinates
of the actual sampling points and detailed characteristic information of
the sample site. And collected a total of 154 soil samples in this
sampling, and removed the invasive body such as plant roots and stem
residues and brick and tile fragments. After natural air drying,
grinding and passing through a 1 \emph{mm} sieve, and divided the
samples into four parts by quartering method in duplicate, one was used
for determination of physical and chemical properties in laboratory, the
other was used for determination of soil spectrum. The main soil
properties measured in this study were soil PH, SOM, AN, AP AK, Fe, Cr,
Cd, Zn, Cu and Pb. And carried out the determination method according to
Regional Geochemical Sample Analysis Method(DZ/T 0279-2016). In order to
ensure the quality of analysis, national geochemical standard samples
were used for quality control.

\textbf{1.} \textbf{3. Laboratory test of samples spectra}

This research measured soil spectral reflectance by ASD spectrometer on
treated soil samples under indoor conditions. The spectrum measurement
instrument is an ASD Field Spec 3 spectrometer produced by ASD, USA. The
spectral range is 350-2500\emph{nm} , with sampling interval of
1.4\emph{nm} for 350-1000\emph{nm} , sampling interval of 2\emph{nm} for
1000-2500\emph{nm} , and resampling interval of 1\emph{nm} . Before
spectral measurement, the surface of the soil should be scraped in the
same direction, along the edge of the soil sample vessel with a ruler,
and then filled with soil sample dish are placed black rubber MATS of
reflectivity approximately 0, halogenated lamp with power of
50\emph{W}is used as light source, probe the view Angle of 25o, light
incidence Angle of 45o, the distance of light source is 15\emph{cm} ,
and the distance of probe is 15\emph{cm} . To reduce the influence of
anisotropy soil sample spectra, when measuring turn the sample plate 3
times , each time the rotation angle of about 90o, and obtained the soil
sample spectra about four directions, reference plate calibration is
performed before and after each target spectrum acquisition, repeated
measurement 5 times, a total of 20 times, and used View Spec Pro
software to obtain the average value of spectral reflectance as the
original reflectance spectral value . Because near the two ends of the
band test range (350\emph{nm} and 2500\emph{nm} ) are unstable regions
of the spectral data, removed the data of 350-399\emph{nm} and
2401-2500\emph{nm} ,which are greatly affected by external noise.

\textbf{1.4. Model establishment and accuracy test}

1.4.1. Fuzzy clustering maximum tree method

Fuzzy theory is developed on the mathematical basis of Fuzzy set theory
established by American cybernetics expert Professor L.A.Zadeh in 1965,
which has been widely used in mathematics and many other fields( LIU Qi,
et al., 2004). Fuzzy Clustering Number is a multi-technology, which
classifies objective things by using fuzzy mathematics method,
establishing similarity relation according to characteristics,
similarity and affinity degree of objective(LI Hongxing, et al., 1994;
WANG Peizhuang,1983). Because the classification of reality is often
accompanied by fuzziness, fuzzy clustering theory is more consistent
with objective reality.

The basic steps of Fuzzy clustering analysis using the maximum tree
method are as follows:

(1) Establish sample set matrix

Suppose that the sample set, \emph{n} represents the number of samples,
each sample has an \emph{m} dimensional vector representation, that is,
each sample has \emph{m} indicators, that is

(2) Establish fuzzy similarity matrix

According to the given sample characteristic data, the correlation
coefficient method is used to establish the fuzzy similarity
matrix\selectlanguage{english},\emph{r\textsubscript{ij}} is the similarity coefficient between
different samples, namely

(3) Maximum tree generation

With a certain point \emph{x\textsubscript{i}} in a relatively
concentrated set of classified objects, as its vertex
and\emph{r\textsubscript{ij}} in the fuzzy similarity matrix \emph{R} as
its weight, it is arranged in descending order, requiring no loop (i.e.
circle), until all vertices are connected, forming a special graph,
namely the largest tree (the largest tree may not be unique).

(4) Clustering

Select the appropriate threshold \selectlanguage{greek}\emph{λ} , \selectlanguage{english}cut off the branches of the
weight, get an unconnected graph, each connected branches constitute the
classification of horizontal \selectlanguage{greek}\emph{λ} , \selectlanguage{english}there are several branches
indicating the classification of several categories.

In this paper calculate the fuzzy similarity coefficient between the
correlation coefficient curves of soil attributes and spectral indexes
by systematic clustering method, and constructed a fuzzy similarity
matrix to determine the similarity of the correlation coefficient, that
between different soil attributes and spectral indexes. On this basis,
determined the common hyperspectral inversion bands of different soil
attributes by the maximum tree classification method.

\textbf{1.4.2. Panel data model}

Panel date is also called parallel data, or time series and cross
section date or pool data. It refers to taking multiple cross sections
on time series, sample data formed by sample observation values are
simultaneously selected on these cross sections(SUN jingshui,2010). From
the cross section, it is a cross section observation value formed by
several individuals at a certain moment, and from the longitudinal
section, it is a time series. According to the characteristics of panel
data, the hyperspectral characteristic band values of soil properties of
multiple samples can be regarded as the hyperspectral characteristic
band values of soil properties at a sample point on the cross section,
and a sequence of sample points on the vertical section. Through the
construction of Panel data model, a comprehensive inversion model of
soil properties can be established at the same time, without the need
for individual inversion of each index, which reduces the tedious
process of multi-index inversion(ZHANG Qiuxia, et al., 2017).

Due to the large number of sample points \emph{T} and the small number
of cross section \emph{N} , it was determined as Fixed influence model,
and Ordinary Least Squares Estimation (\emph{OLS} ) was selected to
build the Panel data model. Then, panel data model types are determined
by Analysis of Covariance, namely invariant coefficient model, variable
intercept model and variable coefficient model. In order to reduce the
impact of heteroscedasticity, the natural logarithm of variables was
calculated on both sides of the panel data model equation, and the panel
data model was obtained as:

Where:

-- values of explained variables on cross section \emph{i} and
sample\emph{t} , namely soil heavy metal element content

-- Constant term or intercept term, representing the cross section
of\emph{i} (influence of the individual of \emph{i} )

-- Model parameter of the \emph{j} explanatory variable on the\emph{i}
cross section

-- The value of the \emph{j} explanatory variable on cross
section\emph{i} and sample \emph{t} , namely, the reflectance of
hyperspectral characteristic band of soil heavy metals

-- Random error term on cross section \emph{i} and sample \emph{t}

\emph{k} -- Number of explanatory variables

1.4.3. Accuracy test method of inversion model

The calibration set determination coefficient \textsuperscript{\emph{2}}
and Root Mean Square Error (\emph{RMSEC} ) are used to verify the
modeling accuracy. Validation set test is based on validation set
determination coefficient \emph{\textsubscript{v}\textsuperscript{2}} ,
Root Mean Square Error (\emph{RMSEP} ) and Relative Percent Deviation
(\emph{RPD} ), the Relative Percent Deviation is the ratio about between
the standard deviation and \emph{RMSEP} of validation set.
When\emph{RPD} \textgreater{}2.5, model has excellent predictive
ability. When 2.0\textless{}\emph{RPD} [?]2.5, the model has good
quantitative prediction ability. When 1.8\textless{} \emph{RPD} [?]2.0,
the model has quantitative prediction ability. When
1.40\textless{}\emph{RPD} [?]1.80, the model has general quantitative
prediction ability. When 1.00\textless{}\emph{RPD} [?]1.40, the model has
the ability to distinguish the high value from the low value. When
\emph{RPD} [?]1.00, the model has no predictive ability (Rossel RAV, et
al., 2007). For the modeling set, the larger \emph{\textsuperscript{2}}
is, the smaller \emph{RMSEC} is, the higher the modeling accuracy is,
and the more stable the model is. For the verification set, the
larger\emph{\textsubscript{v}\textsuperscript{2}} and \emph{RPD} are,
the smaller \emph{RMSEP} is, the higher the prediction accuracy is.

\textbf{2. Results and discussion}

\textbf{2.1. Spectral pretreatment}

In the process of ASD spectrometer acquisition, acquisition and
transmission of spectral signals, in addition to the spectral
information of soil itself, spectrometer breeding and interference of
external factors, there may be many ''burr'' noises in spectral curves,
and the signal-to-noise ratio is reduced. In order to obtain the stable
spectrum and improve the signal-to-noise ratio, it is necessary to
smooth the spectral data. Savitzky-golay (\emph{SG} ) convolution
smoothing method was proposed by Savitky and Golay (Savitzhy A, et al.,
1964)in 1964. It is a weighted average method, that obtains smooth point
data by least square fitting of the data, that to be measured in the
moving window interval using polynomial method. It is a widely used
smoothing method at present. In the process of \emph{SG} filtering, need
to be selected appropriate smoothing points and polynomial fitting
times. The more smoothing points are taken, the smoother the spectral
curve will be, but some information will be lost at the same time.
Therefore, \emph{SG} filtering smoothing based on 9-point quadratic
polynomial is adopted. The transform tool used for smoothing and
denoising by Unscrambler 9.7 , as shown in Figure 1.

In order to better highlight the smoothing effect, the band curves of
2000-2400 \emph{nm} were amplified (Figure.1b). By comparing the details
before and after \emph{SG} smoothing, it can be seen that
\emph{SG}smoothing can effectively remove noise, and better preserve the
overall characteristics of spectral curves.

\textbf{2.2. Continuum removal}

In order to find the sensitive relationship between soil heavy metal
content and spectral reflectance, Continuum removal (\emph{CR} )
spectral transformation after \emph{SG} smoothing is required. To
envelope as a spectral analysis method is put forward first by Clark and
Rous in 1984(Clark R N , et al., 1984), is defined as a point in a
straight line connected with the wavelength change reflect or absorb
protruding point of ''peak value'', and make the line in the ''peak
value'' on the outside, is greater than 180\selectlanguage{ngerman}°(TONG Qingxi, et al., 2006),
the actual spectrum reflectance and envelope line of the corresponding
band reflectance ratio, By normalizing the spectral value to
0\textasciitilde{}1(LI Shumin, et al., 2011), the absorption and
reflection characteristics of the spectral curve can be effectively
highlighted and the characteristic bands can be extracted. Through
proper spectral transformation, the influence of various noises can be
reduced or even eliminated, the spectral sensitivity can be improved,
and the prediction ability and stability of the calibration model can be
improved. in the study obtained the de-envelope by constructing pop
database in Envi4.8, as shown in Figure 2.

The reflectance curve of \emph{CR} not only enhances the spectral
characteristics of the original spectral curve at 1400\emph{nm} ,
1900\emph{nm} and 2200\emph{nm} , but also highlights the weak
absorption characteristics at 410\emph{nm} , 500\emph{nm} and
700\emph{nm} . It shows that the weak absorption characteristic
information of the original spectral curve is enhanced, and the
signal-to-noise ratio is improved by de-enveloping spectral
transformation, which is helpful for the extraction of effective
characteristic bands.

\textbf{2.3. Selection of common spectral characteristic bands for soil
properties}

On the basis of soil properties significant band selection about
Xinzheng well-facilitied capital farmland construction area, considering
the needs of different soil property spectrum inversion, combined with
the correlation coefficient curve similarity and inflection point, using
the method of Fuzzy clustering tree, determine the share best band of
hyperspectral inversion of soil properties about Xinzheng
well-facilitied capital farmland construction area.

Through comparative analysis of the correlation coefficient curves of 11
soil attributes and \emph{SG-CR} transformations of Xinzheng City
well-facilitied capital farmland area (Figure 3), it can be seen that
the correlation coefficient curves of the same spectral transformation
have similar inflection points, showing good similarity.

The soil properties corresponding to the row numbers of the fuzzy
similarity matrix were PH, SOM, AN, AK, AP, Fe, Cr, Cd, Zn, Cu and Pb.
The fuzzy similarity matrix of correlation coefficient curves of 11 soil
attributes and \emph{SG-CR} spectral transformation is:

Using the maximum tree classification method, \selectlanguage{greek}\emph{λ} \selectlanguage{english}= 0.76, PH, AP,
Cr, Cd, Pb as a class, SOM, AN as a class, AK, Fe, Cu as a class, Zn as
a class. According to the similarity and inflection point of correlation
coefficient curves, the band of \emph{SG-CR} spectral transformation is
selected 405\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}418\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}781\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}784\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}794\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}805\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}807\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}830\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}831\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1079\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1085\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1251\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1267\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1308\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1309\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1410\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1836\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1860\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1897\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1898\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2080\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2137\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2149\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2156\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2184\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2382\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2395\emph{nm}. At the same time,
according to the significant bands of soil attributes and \emph{SG-CR}
spectrum, the significant bands of 11 soil attributes passing the
significance level test of \emph{P} = 0.01 were selected,
\emph{i.e}406\textasciitilde{}419\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}421\textasciitilde{}423\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}427\textasciitilde{}431\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1044\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1062\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1087\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1887\textasciitilde{}1890\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2118\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2119\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2185\textasciitilde{}2187\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2198\textasciitilde{}2201\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2324\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2325\emph{nm} .

The significant band and the inflection point of the correlation
coefficient curve were combined to determine the band
405\textasciitilde{}419\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}421\textasciitilde{}423\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}427\textasciitilde{}431\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}781\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}784\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}794\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}805\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}807\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}830\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}831\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1044\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1062\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1079\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1085nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1087\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1251\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1267nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1308\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1309\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1410\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1836\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1860\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1887\textasciitilde{}1890\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1897\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1898\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2080\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2118nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2119\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2137\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2149\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2156\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2184\textasciitilde{}2187\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2198\textasciitilde{}2201\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2324\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2325\emph{nm}
\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2382\emph{nm} \begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2395\emph{nm}as a common spectral characteristic band
of soil attribute spectral inversion in well-facilitied capital farmland
construction area of Xinzheng City.

\textbf{2.4. Construction of panel data model}

On the basis of soil types, to divide the sample set into calibration
set and verification set through used Rank-KS(LIU Wei, et al., 2014)
(content gradient method-Kennard-Stone) method. divided into two groups:
calibration set and validation set, about the 154 samples in the study
area. The calibration set included 116 samples for the construction of
soil attribute inversion model, and the validation set included 38
samples for testing the prediction accuracy of the model.

Using the common spectral feature bands selected from
\emph{SG-CR}spectral transformation as independent variables of soil
attribute inversion model, and constructed the panel data based on
ordinary least squares estimation (\emph{OLS} ), about soil attribute
content of 116 soil samples in Xinzheng city, as shown in Table 1.

The results show that the regression coefficient is significantly not 0,
and the sample determination coefficient after
adjustment\emph{R\textsubscript{v}}\selectlanguage{english} \textsuperscript{2} is 0.9991,
indicating that the goodness of fit of the model is high. A large
\emph{F} statistic indicates that the regression coefficient is
significant and the regression model is significant as a whole.

\textbf{2.5. Model accuracy check}

This paper test the accuracy of soil PH, SOM, AN, AP, AK, Fe, Cr, Cd,
Zn, Cu and Pb by the constructed panel data model. The results were
shown in Table 2.

As can be seen from Table 2, each soil attribute in the correction set
has a high coefficient \emph{R} \selectlanguage{english}\textsuperscript{2}, greater than 0.95.
The highest is alkaline hydrolyzed nitrogen (0.998), and the
corresponding root mean square error is 0.67. The lowest value was PH
(0.95), and the corresponding root mean square error was 0.05, both of
which were relatively low, indicating that the inversion model
constructed by the panel data model could simultaneously realize the
inversion of 11 soil properties, have good modeling accuracy.

According to the prediction results of the validation
set,\emph{R\textsubscript{v}}\selectlanguage{english} \textsuperscript{2} of PH, organic matter,
alkali-hydrolyzed nitrogen, Cd and Cu were lower than the modeling set,
and the rest were increased. In addition to Pb, the root mean square
error was lower than that in the modeling set,\emph{R\textsubscript{v}}\selectlanguage{english}
\textsuperscript{2} of other soil attributes were increased. The
relative errors are all greater than 2.5, indicating that the model has
good quantitative prediction ability of PH, SOM, AN, AP, AK, Fe, Cr, Cd,
Zn, Cu and Pb.

In order to more clearly display the modeling accuracy of Panel data of
Fixed influence variable coefficient model, the soil attribute content
diagram (Figure 4) and scatter diagram (Figure 5) of the measured value
and the inversion value.

The comparison between Figure 4 and Figure 5 shows that, except for a
few samples differences, the measured values and predicted values of
most samples are concentrated near \emph{y} = \emph{x} , \emph{i.e.} 1:1
line. The correlation coefficients \emph{r} between measured values and
predicted values all pass the significance test at \emph{P} = 0.01
level. This indicates that the panel data model with
\emph{SG-CR}spectral transformation as independent variable has good
predictive ability, and can be used to invert multiple soil properties
simultaneously.

\textbf{3. Conclusion}

In construction of Well-facilitied capital farmland, in addition to
emphasize on land leveling, roads, canals and other engineering
facilities, and soil quality after completion reaches the higher level
of the county, and the soil PH, SOM, soil nutrient, and soil pollution
characteristic is indispensable basic information of cultivated land
quality evaluation, it is also an important guarantee for the growing
environment of crops. Therefore, the fast acquisition and real-time
monitoring of soil basic information is the premise and foundation for
the construction of Well-facilitied capital farmland.

Based on this, this research took the Well-facilitied capital farmland
construction area in Xinzheng City as the research object, combined
field sampling and indoor hyperspectral determination, conducted
correlation analysis and Fuzzy clustering analysis of soil properties
and \emph{SG-CR} transform spectrum, and selected the best common
hyperspectral characteristic band. And constructed the panel data model
of hyperspectral comprehensive inversion about 116 samples of 11 soil
attribute, to achieve comprehensive and rapid acquisition of soil basic
information. The results show that:

(1) \emph{SG} convolution smoothing of spectral reflectance can
effectively remove noise, while preserving the overall characteristics
of spectral curves, and improving signal-to-noise ratio. The method of
spectral transformation is very important, for extracting characteristic
bands and improving the prediction ability and stability of the model.
By removing the envelope \emph{CR} spectral transformation, the
absorption and reflection characteristics of the spectral curve are
effectively highlighted, the sensitivity of the spectrum is improved,
and the useful information of the spectrum is enhanced.

(2)After the correlation analysis about soil attributes and
\emph{SG-CR}spectral transformation, and using Fuzzy clustering maximum
tree method, combined with the similar inflection point of the
correlation coefficient curve of soil attributes and spectral
reflectance, select the common significance band of different soil
attributes as the best hyperspectral characteristic band and focus on
405\textasciitilde{}431nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}781nm\textasciitilde{}831nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1044\textasciitilde{}1087nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1251\textasciitilde{}1410nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}1836\textasciitilde{}1898nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2080nm\textasciitilde{}2201nm\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2324\textasciitilde{}2395nm.

(3) Based on the panel data model, constructed the comprehensive
inversion model of soil properties in the well-facilitied capital
farmland construction area of Xinzheng city, with the common spectral
characteristic band of \emph{SG-CR} spectral transformation as the
independent variable. The model was significant as a whole, and the
goodness of fit was high(\textsuperscript{2} = 0.9991,\emph{DW} =
2.1899,\emph{F} = 2195.67). The coefficient\emph{R}
\selectlanguage{english}\textsuperscript{2} of determination of 11 soil attributes were all
greater than 0.95, and the root mean square root was small. From the
perspective of relative analysis errors, the relative analysis errors of
soil attributes of \emph{SG-CR} spectral transformation model are all
greater than 2.5, and the measured and predicted values of most samples
are concentrated near the 1:1 line, and the correlation
coefficients\emph{r} all pass the significance test at \emph{P =} 0.01.
It indicates that the panel data models with \emph{SG-CR} spectral
transformation as independent variable have the ability of comprehensive
inversion of soil properties, and have high precision prediction.

\textbf{CRediT authorship contribution statement}

All authors contributed to the study conception and design. Material
preparation, data 395 collection and analysis were performed by
{[}qiuxia Zhang{]}, and {[}xinsheng Wang{]}. The first draft 396 of the
manuscript was written by {[}qiuxia Zhang{]} and all authors commented
on previous 397 versions of the manuscript. All authors read and
approved the final manuscript. 398

\textbf{Declaration of Competing Interest}

The authors declare that they have no known competing financial
interests or personal 400 relationships that could have appeared to
influence the work reported in this paper.

\textbf{Data availability}

The datasets analyzed during the current study are available from the
corresponding author on reasonable request.

\textbf{Funding information}

This study was funded by Joint Funds of the National Natural Science
Foundation of China(U21A20109), Scientific research projects in public
welfare industries of Ministry of Land and Resources of China
(201411022-2),PhD start-up Fund project of North China University of
Water Resources and Electric Power(40934).

\textbf{References}

BIAN Zijin, SUN Lina, TIAN Kang, LIU Benle, et al. (2021). Estimation of
Heavy Metals in Tailings and Soils Using Hyperspectral Technology: A
Case Study in a Tin-Polymetallic Mining Area. \emph{Bulletin of
environmental contamination and toxicology} , 107(6): 1-10. DOI:
10.1007/S00128-021-03311-7

CAI Xiangwen,TANG Mi,SONG Yuening. (2019). On the Construction
Potential of High Standard Basic Farmland in Gongcheng Yao Autonomous
County. \emph{Scientific and Technological Management of Land and
Resources} , 36(05): 56-65. DOI: 10. 3969/j.issn.1009-4210 2019 05 006

CHEN Lin,WU Kening,FENG Zhe,YU Bing, SONG Hengfei. (2019). Suitability
Evaluation of High Standard Farmland Construction from Perspective of
Ecological Civilization Construction. \emph{Soils} , 51(04): 803-812.
DOI:10.13758/j.cnki.tr.2019.04.023.

Clark R N, Roush T L. (1984). Reflectance spectroscopy: Quantitative
analysis technidues for remote sensing applications. \emph{Journal of
Geophysical Research:Solid Earth} (1978-012), 89(B7):6329-6340.

DONG Fei, ZHAO Wei. (2020). Delimitation of Construction Area of
High-Standard Primary Farmland-Taking Nan'an District of Chongqing City
as an Example. \emph{Research of Soil and Water conservation} , 27(02):
344-349+356. DOI: 10.13869/j.cnki.rswc.2020.02.048

DZ/T 0279-2016 Regional Geochemical Sample Analysis Method. (2016).

LI Shumin, LI Hong, SUN Danfeng, et al. (2011). Characteristic and
Diagnostic Bands of Heavy Metals in Beijing Agricultural Soils Based on
Spectroscopy.\emph{Chinese Journal of Soil Science} ,42(3):730-735.
DOI: 10.1155/2011/531540

LI C M, SHAO J A, GUO Y, CAO F, TAN S J. (2018). Construction potential
of high-standard farmland based on landform factors. \emph{Chinese
Journal of Eco-Agriculture} , 26(7): 1067-1079. DOI:
10.13930/j.cnki.cjea.171122

LI Hongxing, WANG Peizhuang. (1994). Fuzzy Mathematics. \emph{Beijing:
National Defense Industry Pres.}

LI Long, WANG Zhaolin,WU Dafang,LIU Yanyan. (2020). Study on Timing
Sequence and Regulation Mode of High Standard Basic Farmland
Construction in Village Based on TOPSIS Model. \emph{Research of Soil
and Water Conservation} , 27(03): 286-293.
DOI:10.13869/j.cnki.rswc.2020.03.041.

LI Yilong, LIAO Heping, ZHANG Yafei, ZHANG Qianqian. (2019).
Construction Conditions and Development Pattern of Well-Facilitated
Farmland in Town Area Based on Rural Vitalization. \emph{Journal of
Southwest University(Natural Science Edition)} ,41(02):90-99.
DOI:10.13718/j.cnki.xdzk.2019.02.013.

LI zhiyuan , DENG fan, HE jun,WEI wei. (2021). Hyperspectral Estimation
Model of Heavy METAL Arsenic in Soil. \emph{Spectroscopy and Spectral
Analysis} , 41(09):2872-2878.
DOI:10.3964/j.issn.1000-0539(2021)09-2872-07

LIU Wei, ZHAO Zong, YUAN Hongfu, et al. (2014). An optimal selection
method of samples of calibration set and validation set for spectral
multivariate analysis. \emph{Spectroscopy and Spectral Analysis} ,
34(4): 947-951. DOI: 10. 3964/j. issn. 1000-0593 (2014) 04-0947-05

LIU Qi, LIN Huaizhong, CHEN Chun.( 2004).Application of the Maximum Tree
Algorithm for Fuzzy Clustering in the Clustering of Web
Page.\emph{Application Research of Computers} , 21(11): 286-287.DOI:
10.3390/rs12203312

MA Xueying,SHAO Jing'an, CAO Fei. (2018). Comprehensive Performance
Evaluation of High Standard Farmland Construction in Mountainous
Counties - A Case Study in Dianjiang Chongqing. \emph{Journal of Natural
Resources} , 33(12): 2183-2199. DOI:10.31497/zrzyxb.20171185

NY/T 2148-2012.Criterion of High Standard Farmland.(2012).

Rossel RAV, Taylor HJ, Mcbratney AB. (2007). Multivariate calibration of
hyperspectral \selectlanguage{greek}γ-\selectlanguage{english}ray energy spectra for proximal soil
sensing.\emph{European Journal of Soil Science} , 58:343-353.
DOI:10.1111/j.1365-2389.2006.00859.x

Savitzhy A, Golay M J E.(1964). Smoothing and differentiation of data
by simplifed least squares procedures. \emph{Analytical Chemistry} ,
36(8):1627-1639.

Schreiner Simon,Culibrk Dubravko, Bandecchi Michele, Gross Wolfgang,
Middelmann Wolfgang. (2021). Soil monitoring for precision farming using
hyperspectral remote sensing and soil
sensors.\emph{Automatisierungstechnik} , 69(4): 116166. DOI:
10.1515/auto-2020-0042

SHI Yuanyuan, ZHAO Junyu, SONG Xianchong, et al. (2021). Hyperspectral
band selection and modeling of soil organic matter content in a forest
using the Ranger algorithm. \emph{PloS one} , 16(6): 0253385.
DOI:10.1371/journal.pone.0253385

SUN jingshui. (2010). Econometrics learning Guide and EViews Application
Guide. \emph{Beijing: Tsinghua University Press} .

TANG Feng, Xu Lei, ZHANG Pengtao, ZHANG Guijun, et al. (2019).
Suitability evaluation and priority area delineation of high standard
basic farmland construction at county level. \emph{Transactions of the
Chinese Society of Agricultural Engineering} , 35(21):242-251.
DOI:10.11975/j.issn.1002-6819.2019.21.030

TD/T1033-2012 Standard for well-facilitied capital farmland
construction. (2012).

TONG Qingxi, ZHANG Bing, Zheng Lanfen, et al. (2006). Hyperspectral
remote sensing: Principle, Technology and Application. \emph{Beijing:
Higher Education Press} .

WANG Xiaoqing, SHI Wenjiao, SUN Xiaofang, WANG Meng. (2018).
Comprehensive benefit evaluation and regional differences of
construction projects of well-facilitated farmland in Huang Huai Hai
region. \emph{Transactions of the Chinese Society of Agricultural
Engineering} , 34(16): 238-248.
DOI:10.11975/j.issn.1002-6819.2018.16.031

WANC Ke, LI Ling, LI Peng. (2021). Study on High Standard Farmland
Construction Based on Ecological Security and Food
Security.\emph{Journal of Ecology and Rural Environment} , 37
(6):706-713. DOI:10.19741/j.issn.1673-4831.2020.0781.

WANG Peizhuang. (1983). Fuzzy Set Theory and Its
Application.\emph{Shanghai: Shanghai Science and Technology Publishing
House} .

WEI Lifei, PU Haochen, WANG Zhengxiang, YUAN Ziran, et al. (2020).
Estimation of Soil Arsenic Content with Hyperspectral Remote
Sensing.\emph{Sensors} , 20(14): 4056. DOI: 10.3390/s20144056

XU Xitong, CHEN Shengbo,Xu Zhengyuan,Yu Yan, et al. (2020). Exploring
Appropriate Preprocessing Techniques for Hyperspectral Soil Organic
Matter Content Estimation in Black Soil Area. \emph{Remote Sensing} ,
12(22): 3765-3765. DOI:10.3390/rs12223765

XIONG Yufei, HE Jing. (2019). Preliminary Study on Quality Control of
High Standard Farmland Construction Projects. \emph{Academic Journal of
Business \& Management} , 1(1): 7-16. DOI: 10.25236/AJBM.190102

YU Huan,LIU Jian,LIU Yaqiu,GU Ziping,WANG Ailing. (2021). Estimation of
Soil Organic Matter Content of Cultivated Land in Hilly Regions with
Hyperspectral. \emph{Journal of Shandong Agricultural University(Natural
Science Edition)} , 52(04):648-653.

ZENG Ya,ZHAO Wei,YANG Liujiao, et al. (2020). Research on Time
Sequence of High-Standard Basic Farmland Construction in Peri -urban
Areas: A Case Study of Nan'an District in Chongqing. \emph{Resources and
Environment in the Yangtze Basin} , 29(02):434-441.

ZHANG Hebing, ZHAO Suxia, CHEN Ningli, ZHANG Qiuxia. (2018). Project
Optimization for Well-facilitied Farmland Construction Based on Coupling
Coordination Degree. \emph{Transactions of the Chinese Society for
Agricultural Machinery} , 49(08):161-168. DOI:10.6041 / j.
issn.1000-1298. 2018.08. 019

ZHANG Qiuxia, ZHANG Hebing, ZHANG Huijuan, et al. (2017). Hybrid
Inversion Model of Heavy Metals with Hyperspectral Reflectance in
Cultivated Soils of Main Grain Producing Areas. \emph{Transactions of
the Chinese Society for Agricultural Machinery} , 48(3): 148-155.
DOI:10.6041/j.issn.1000-1298.2017.03.019

ZHANG Jing, SUN Lu. (2020). High Standard Farmland Construction and Soil
Conservation Evaluation. \emph{IOP Conference Series: Earth and
Environmental Science} , 514(2): 022074. DOI:
10.1088/1755-1315/514/2/022074

\textbf{Appendix}

Figure1 a.The spectral reflectance of before and after SG b. Zoom in
contrast the spectral reflectance of 2000\textasciitilde{}2400nm

Figure 2 The reflectance spectra after Continuum removal

\textbf{Figure 3 Comparision of of correlation coefficient between soil
properties and the Continuum removal reflectance spectra}

\textbf{Figure 4 Inversion of soil properties elements on validation
set}

\textbf{Figure 5 Scatter plot of measured values versus estimated values
by Panel date model}

\textbf{Table 1 Panel date models for soil properties elements}\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllll@{}}
\toprule
Coefficien & PH & SOM & AN & AP & AK & Fe & Cr & Cd & Zn & Cu &
Pb\tabularnewline
\midrule
\endhead
C & C & C & C & C & C & 6.84 & 6.84 & 6.84 & 6.84 & 6.84 &
6.84\tabularnewline
Fixed Influence Coefficient & -5.53 & -6.09 & 0.85 & 19.54 & 9.62 &
-3.67 & -3.53 & -15.13 & 4.28 & 5.17 & -5.52\tabularnewline
405nm & 33.02 & 153.49 & -107.79 & -950.01 & -182.82 & 49.43 & -104.85 &
345.04 & -303.37 & -669.61 & 452.59\tabularnewline
406nm & -52.06 & -294.39 & 174.71 & 1365.99 & 82.18 & -192.23 & 240.56 &
-510.46 & 512.44 & 1006.10 & -925.44\tabularnewline
407nm & -9.73 & 470.04 & 122.84 & -186.55 & 521.91 & 344.81 & -194.45 &
301.41 & -116.87 & -296.99 & 1182.11\tabularnewline
408nm & 147.11 & -1014.64 & -839.27 & -1961.48 & -1395.50 & -470.80 &
162.11 & -166.39 & -680.27 & -465.11 & -2102.65\tabularnewline
409nm & -297.04 & 1622.78 & 1660.49 & 4376.54 & 2453.90 & 643.57 &
-362.05 & 61.41 & 1419.79 & 935.93 & 3447.06\tabularnewline
410nm & 304.53 & -1591.74 & -1727.53 & -4547.27 & -2468.43 & -604.22 &
402.19 & -184.22 & -1453.63 & -851.17 & -3515.82\tabularnewline
411nm & -221.69 & 342.55 & 1069.93 & 5300.93 & 2082.38 & 107.82 & 114.42
& 987.10 & 1749.87 & 864.23 & 1199.43\tabularnewline
412nm & 115.51 & 1406.37 & -52.31 & -6417.12 & -1610.87 & 573.50 &
-862.15 & -2937.10 & -2233.96 & -1054.23 & 1164.39\tabularnewline
413nm & -91.28 & -2311.87 & -349.44 & 7068.12 & 1789.96 & -887.37 &
1170.32 & 4031.25 & 2503.41 & 1830.10 & -2076.97\tabularnewline
414nm & 109.94 & 1871.51 & 110.26 & -5971.28 & -1425.11 & 875.18 &
-1112.39 & -3435.36 & -2237.72 & -2069.80 & 1802.89\tabularnewline
415nm & -141.18 & -933.01 & 318.63 & 4420.07 & 672.29 & -809.73 & 883.40
& 2048.06 & 1799.34 & 1234.85 & -751.94\tabularnewline
416nm & 236.48 & 732.51 & -824.70 & -6222.80 & -1407.90 & 733.01 &
-670.12 & -489.75 & -2199.77 & -1352.49 & 447.82\tabularnewline
417nm & -230.27 & -1260.44 & 696.88 & 7490.20 & 1915.12 & -742.82 &
653.92 & -214.95 & 2373.49 & 1867.73 & -1689.05\tabularnewline
418nm & 57.23 & 1634.95 & 128.70 & -4784.94 & -834.83 & 788.29 & -614.26
& -380.14 & -1438.85 & -1173.10 & 2693.22\tabularnewline
419nm & 36.04 & -1205.25 & -417.34 & 1910.23 & 130.04 & -584.12 & 273.62
& 980.86 & 490.75 & 180.83 & -1697.15\tabularnewline
421nm & 50.78 & 1293.33 & 22.74 & -3842.12 & -1625.98 & 345.70 & 8.75 &
-919.85 & -980.68 & -1036.70 & 1081.70\tabularnewline
422nm & -81.00 & -1396.19 & 86.20 & 4329.70 & 2088.97 & -403.48 & 58.49
& 1206.03 & 1262.37 & 1460.58 & -1233.87\tabularnewline
423nm & 35.27 & 601.64 & -19.98 & -1462.84 & -836.49 & 261.34 & -122.29
& -732.65 & -534.58 & -605.55 & 674.91\tabularnewline
427nm & 0.65 & -332.37 & 61.11 & 476.10 & 34.15 & -304.21 & 242.64 &
320.86 & 219.96 & 104.07 & -990.95\tabularnewline
428nm & -66.93 & 602.87 & 174.01 & 483.18 & 645.61 & 661.20 & -557.22 &
-1309.50 & -28.98 & 706.19 & 1673.66\tabularnewline
429nm & 56.07 & -1103.54 & -259.09 & 849.27 & 339.82 & -603.49 & 339.96
& 1548.48 & 292.55 & -278.98 & -1643.83\tabularnewline
430nm & 22.07 & 986.44 & -136.86 & -2324.94 & -1916.10 & 151.24 & 188.65
& -819.10 & -525.43 & -763.22 & 793.46\tabularnewline
431nm & -15.32 & -257.07 & 136.25 & 600.43 & 967.98 & 60.83 & -154.98 &
310.56 & 105.02 & 363.16 & 17.32\tabularnewline
830nm & -215.36 & 3698.08 & 1662.45 & -11953.80 & -6589.09 & 1690.79 &
-1747.89 & -5665.86 & -1234.73 & -8960.59 & 1525.16\tabularnewline
831nm & 409.91 & -2478.08 & -2288.66 & 5675.05 & 3207.34 & -1406.43 &
1432.47 & 6472.64 & -387.16 & 6078.14 & -664.42\tabularnewline
1044nm & -14.74 & -79.23 & 74.45 & 1174.69 & 343.61 & 25.45 & 83.35 &
125.30 & 311.06 & 284.02 & -225.24\tabularnewline
1062nm & -34.18 & 145.10 & 205.03 & 373.67 & 172.78 & 38.56 & -213.00 &
-128.73 & 133.03 & -418.89 & 35.02\tabularnewline
1079nm & -22.12 & -380.81 & -80.90 & 1557.09 & 642.26 & 80.65 & -7.39 &
-714.37 & 385.31 & 879.35 & -246.60\tabularnewline
1085nm & 79.73 & 224.68 & -262.73 & -2641.23 & -742.39 & 73.06 & -33.63
& -97.15 & -842.88 & -584.07 & 747.15\tabularnewline
1087nm & -5.93 & 101.54 & 138.28 & -87.20 & -246.17 & -181.76 & 156.95 &
397.00 & 130.79 & -64.83 & -204.14\tabularnewline
1251nm & 122.05 & 313.16 & -540.28 & -4296.39 & -1959.71 & -74.03 &
-37.67 & 1042.33 & -1077.92 & -1212.65 & 693.75\tabularnewline
1267nm & -225.14 & -650.78 & 768.55 & 7998.22 & 3819.77 & 460.39 &
-94.29 & -1106.79 & 1669.03 & 3112.68 & -1224.99\tabularnewline
1308nm & -173.69 & 235.55 & 1385.11 & 7567.09 & 3774.58 & 258.33 &
-1080.48 & 1069.39 & 1326.98 & 707.28 & -1063.33\tabularnewline
1309nm & 124.66 & -118.70 & -1060.30 & -5078.62 & -2990.52 & -157.90 &
933.49 & -510.43 & -834.20 & -512.58 & 1168.26\tabularnewline
1410nm & -22.16 & -95.93 & 79.85 & 639.98 & 319.59 & -16.05 & 30.57 &
-287.85 & 198.69 & 276.94 & -136.82\tabularnewline
1836nm & -39.79 & -80.04 & 171.37 & 1196.47 & 548.78 & 28.57 & -20.32 &
-353.94 & 290.06 & 542.24 & -172.54\tabularnewline
1860nm & 9.30 & 0.65 & -76.29 & -458.77 & -174.21 & 12.23 & 50.19 &
162.89 & -120.31 & -129.44 & 56.13\tabularnewline
1887nm & 114.12 & 55.32 & -855.26 & -2247.20 & -2034.64 & -261.07 &
681.20 & 1329.56 & -615.62 & -1215.62 & -28.71\tabularnewline
1888nm & -184.61 & -262.40 & 1600.62 & 4328.27 & 4116.28 & 330.05 &
-948.84 & -2210.04 & 1178.59 & 2627.76 & 110.01\tabularnewline
1889nm & 94.94 & 206.79 & -1009.55 & -2185.98 & -2202.53 & 154.55 &
299.08 & 620.78 & -575.97 & -1583.18 & -300.57\tabularnewline
1890nm & -6.97 & -26.46 & 169.99 & -186.05 & -91.81 & -252.05 & 32.34 &
266.71 & -33.89 & 132.59 & 152.84\tabularnewline
1897nm & -1.32 & 68.64 & 18.89 & -843.97 & 13.40 & 1.29 & -21.73 &
412.53 & -138.16 & -142.30 & 91.73\tabularnewline
1898nm & -5.83 & -32.26 & 21.90 & 932.63 & 125.55 & 41.45 & -38.17 &
-381.07 & 142.85 & 155.57 & -15.16\tabularnewline
2080nm & 14.93 & 15.30 & -46.47 & -256.17 & -133.43 & -17.21 & -5.31 &
185.96 & -74.60 & -208.16 & 59.42\tabularnewline
2118nm & 51.72 & 12.39 & -316.73 & -1354.11 & -754.53 & -163.41 & 294.91
& 691.10 & -242.01 & -740.97 & 8.75\tabularnewline
2119nm & -24.67 & -9.05 & 141.04 & 399.56 & 341.59 & 128.50 & -178.56 &
-695.11 & 64.32 & 619.03 & 45.91\tabularnewline
2137nm & 3.02 & -27.69 & -40.57 & 151.77 & -4.72 & -13.48 & -8.34 &
119.63 & 47.12 & -13.47 & -59.03\tabularnewline
2149nm & -1.08 & -1.61 & -7.37 & -19.71 & -29.55 & 1.05 & -43.72 &
-91.70 & -32.19 & -17.52 & 37.44\tabularnewline
2156nm & -6.56 & -0.70 & 51.03 & 144.33 & 161.66 & 19.77 & -19.76 &
-174.00 & 45.95 & 77.73 & -44.64\tabularnewline
2184nm & 14.69 & -27.60 & -85.33 & -880.23 & -497.16 & -295.07 & -143.05
& 690.23 & -120.59 & -959.91 & -114.53\tabularnewline
2185nm & -16.47 & -5.49 & 131.12 & 1622.98 & 781.90 & 473.70 & 505.54 &
-1060.03 & 146.02 & 1953.79 & 160.62\tabularnewline
2186nm & 25.80 & -12.20 & -100.15 & -1302.62 & -269.90 & -250.03 &
-589.73 & 66.32 & -55.94 & -1260.11 & -299.90\tabularnewline
2187nm & -22.49 & 74.27 & 88.74 & 551.82 & -85.84 & 49.78 & 230.26 &
416.52 & 12.56 & 146.43 & 273.56\tabularnewline
2198nm & -54.11 & -15.18 & 415.74 & 1056.95 & 509.31 & 107.89 & 183.04 &
-858.43 & 352.04 & 652.05 & -188.52\tabularnewline
2199nm & 117.41 & -66.14 & -983.16 & -2821.29 & -1116.33 & -372.61 &
-412.84 & 1829.88 & -840.67 & -1364.11 & 491.79\tabularnewline
2200nm & -65.44 & 246.79 & 758.23 & 1787.28 & 384.85 & 391.63 & 404.03 &
-1268.42 & 527.61 & 607.52 & -241.32\tabularnewline
2201nm & -7.19 & -197.18 & -159.72 & 210.76 & 358.89 & -126.55 & -163.44
& 235.19 & 42.77 & 244.80 & -89.79\tabularnewline
2324nm & 12.60 & 2.50 & -38.68 & 40.85 & -92.19 & -21.19 & 3.58 & 22.74
& 4.99 & -9.95 & 22.85\tabularnewline
2325nm & -7.81 & 0.72 & 7.17 & -200.31 & 36.49 & 17.74 & -6.82 & -13.04
& -38.34 & -23.70 & -6.51\tabularnewline
2382nm & -0.30 & -2.27 & -7.06 & 19.15 & -23.56 & -0.05 & 20.66 & 38.74
& 2.14 & -10.89 & -12.00\tabularnewline
\emph{R}\selectlanguage{english}\textsuperscript{2}= 0.9996
\emph{R\textsubscript{v}}\selectlanguage{english}\textsuperscript{2}= 0.9991 \emph{SSR} =1.51
\emph{DW} = 2.1899 \emph{F}=2195.67 \emph{Prob} = 0 &
\emph{R}\selectlanguage{english}\textsuperscript{2}= 0.9996
\emph{R\textsubscript{v}}\selectlanguage{english}\textsuperscript{2}= 0.9991 \emph{SSR} =1.51
\emph{DW} = 2.1899 \emph{F}=2195.67 \emph{Prob} = 0 &
\emph{R}\selectlanguage{english}\textsuperscript{2}= 0.9996
\emph{R\textsubscript{v}}\selectlanguage{english}\textsuperscript{2}= 0.9991 \emph{SSR} =1.51
\emph{DW} = 2.1899 \emph{F}=2195.67 \emph{Prob} = 0 &
\emph{R}\selectlanguage{english}\textsuperscript{2}= 0.9996
\emph{R\textsubscript{v}}\selectlanguage{english}\textsuperscript{2}= 0.9991 \emph{SSR} =1.51
\emph{DW} = 2.1899 \emph{F}=2195.67 \emph{Prob} = 0 &
\emph{R}\selectlanguage{english}\textsuperscript{2}= 0.9996
\emph{R\textsubscript{v}}\selectlanguage{english}\textsuperscript{2}= 0.9991 \emph{SSR} =1.51
\emph{DW} = 2.1899 \emph{F}=2195.67 \emph{Prob} = 0 &
\emph{R}\selectlanguage{english}\textsuperscript{2}= 0.9996
\emph{R\textsubscript{v}}\selectlanguage{english}\textsuperscript{2}= 0.9991 \emph{SSR} =1.51
\emph{DW} = 2.1899 \emph{F}=2195.67 \emph{Prob} = 0 &
\emph{R}\selectlanguage{english}\textsuperscript{2}= 0.9996
\emph{R\textsubscript{v}}\selectlanguage{english}\textsuperscript{2}= 0.9991 \emph{SSR} =1.51
\emph{DW} = 2.1899 \emph{F}=2195.67 \emph{Prob} = 0 &
\emph{R}\selectlanguage{english}\textsuperscript{2}= 0.9996
\emph{R\textsubscript{v}}\selectlanguage{english}\textsuperscript{2}= 0.9991 \emph{SSR} =1.51
\emph{DW} = 2.1899 \emph{F}=2195.67 \emph{Prob} = 0 &
\emph{R}\selectlanguage{english}\textsuperscript{2}= 0.9996
\emph{R\textsubscript{v}}\selectlanguage{english}\textsuperscript{2}= 0.9991 \emph{SSR} =1.51
\emph{DW} = 2.1899 \emph{F}=2195.67 \emph{Prob} = 0 &
\emph{R}\selectlanguage{english}\textsuperscript{2}= 0.9996
\emph{R\textsubscript{v}}\selectlanguage{english}\textsuperscript{2}= 0.9991 \emph{SSR} =1.51
\emph{DW} = 2.1899 \emph{F}=2195.67 \emph{Prob} = 0 &
\emph{R}\selectlanguage{english}\textsuperscript{2}= 0.9996
\emph{R\textsubscript{v}}\selectlanguage{english}\textsuperscript{2}= 0.9991 \emph{SSR} =1.51
\emph{DW} = 2.1899 \emph{F}=2195.67 \emph{Prob} = 0 &
\emph{R}\selectlanguage{english}\textsuperscript{2}= 0.9996
\emph{R\textsubscript{v}}\selectlanguage{english}\textsuperscript{2}= 0.9991 \emph{SSR} =1.51
\emph{DW} = 2.1899 \emph{F}=2195.67 \emph{Prob} = 0\tabularnewline
\bottomrule
\end{longtable}

Table 2 Calibration and validation for soil properties elements using
Panel date model\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& Calibration set & Calibration set & Validation set & Validation set &
Validation set\tabularnewline
\midrule
\endhead
& \emph{R}\selectlanguage{english}\textsuperscript{2} & \emph{RMSEC} &
\emph{R\textsubscript{v}}\selectlanguage{english}\textsuperscript{2} & \emph{RMSEP} &
\emph{RPD}\tabularnewline
PH & 0.95 & 0.05 & 0.92 & 0.06 & 3.56\tabularnewline
SOM & 0.98 & 0.31 & 0.97 & 0.47 & 5.68\tabularnewline
AN & 0.998 & 0.67 & 0.997 & 0.85 & 20.74\tabularnewline
AP & 0.989 & 0.96 & 0.998 & 0.31 & 24.64\tabularnewline
AK & 0.983 & 3.80 & 0.987 & 1.79 & 8.73\tabularnewline
Fe & 0.96 & 0.54 & 0.988 & 0.29 & 9.05\tabularnewline
Cr & 0.99 & 1.03 & 0.992 & 1.05 & 11.20\tabularnewline
Cd & 0.96 & 0.012 & 0.89 & 0.02 & 2.98\tabularnewline
Zn & 0.988 & 0.52 & 0.991 & 0.82 & 10.36\tabularnewline
Cu & 0.992 & 0.68 & 0.989 & 0.52 & 9.23\tabularnewline
Pb & 0.987 & 0.77 & 0.989 & 0.70 & 9.38\tabularnewline
\bottomrule
\end{longtable}

\selectlanguage{english}
\FloatBarrier
\end{document}

