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(SG )filtering and Continuum removal
(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.
1. Materials and Methods
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°16’~ 34°39’ N , 113°30’~
113°54’ 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 km ;
Northeast from zhongmu county 45.6 kilometers , 120kilometers downtown Kaifeng; East to Yushi county 42.6
kilometers; South to Changge city 20.4 km , Xuchang city 40 km;
Southwest to Yuzhou city 36.5 km , Pingdingshan city 84 km ;
34.5 kilometers west to Xinmi urban area. It is 42kilometers long from north to south and 36 kilometers wide
from east to west. It covers an area of 873 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
884.5915km2 , and the cultivated land is
521.7641 km2 , accounting for 58.59% of the
total land area. The total annual grain output is 273148 t .
According to Integrated Land-use Planning of Xinzheng city (2010-2020),
the protection index of prime farmland in Xinzheng City is 427.73km2 . 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℃, the average annual
precipitation is 676.1mm , the average annual evaporation is
1476.2mm , the average annual sunshine duration is
2,114.2h , the average annual frost day is 67 days, the average
annual total water resources is 147.73 millionm3 , and the per capita water resources are 236m3 . 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.
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 2km ×2km 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
0-30cm 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 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.
1. 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 350-2500nm , with sampling interval of
1.4nm for 350-1000nm , sampling interval of 2nm for
1000-2500nm , and resampling interval of 1nm . 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 50Wis used as light source, probe the view Angle of 25º, light incidence
Angle of 45º, the distance of light source is 15cm , and the
distance of probe is 15cm . 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
(350nm and 2500nm ) are unstable regions of the spectral
data, removed the data of 350-399nm and 2401-2500nm ,which
are greatly affected by external noise.
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, n represents the number of samples,
each sample has an m dimensional vector representation, that is,
each sample has 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,rij is the similarity coefficient between
different samples, namely
(3) Maximum tree generation
With a certain point xi in a relatively
concentrated set of classified objects, as its vertex andrij in the fuzzy similarity matrix 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 λ , cut off the branches of the
weight, get an unconnected graph, each connected branches constitute the
classification of horizontal λ , 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.
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 T and the small number
of cross section N , it was determined as Fixed influence model,
and Ordinary Least Squares Estimation (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 i and samplet , namely soil heavy metal element content
– Constant term or intercept term, representing the cross section ofi (influence of the individual of i )
– Model parameter of the j explanatory variable on thei cross section
– The value of the j explanatory variable on cross sectioni and sample t , namely, the reflectance of hyperspectral
characteristic band of soil heavy metals
– Random error term on cross section i and sample t
k – Number of explanatory variables
1.4.3. Accuracy test method of inversion model
The calibration set determination coefficient 2 and Root Mean Square Error (RMSEC ) are used to verify the
modeling accuracy. Validation set test is based on validation set
determination coefficient v2 ,
Root Mean Square Error (RMSEP ) and Relative Percent Deviation
(RPD ), the Relative Percent Deviation is the ratio about between
the standard deviation and RMSEP of validation set. WhenRPD >2.5, model has excellent predictive ability.
When 2.0<RPD ≤2.5, the model has good quantitative
prediction ability. When 1.8< RPD ≤2.0, the model has
quantitative prediction ability. When 1.40<RPD ≤1.80,
the model has general quantitative prediction ability. When
1.00<RPD ≤1.40, the model has the ability to distinguish
the high value from the low value. When RPD ≤1.00, the model has
no predictive ability (Rossel RAV, et al., 2007). For the modeling set,
the larger 2 is, the smaller RMSEC is,
the higher the modeling accuracy is, and the more stable the model is.
For the verification set, the largerv2 and RPD are, the
smaller RMSEP is, the higher the prediction accuracy is.
2.
Results and discussion
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 (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 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, 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 nm were amplified (Figure.1b). By comparing the details
before and after SG smoothing, it can be seen that SGsmoothing can effectively remove noise, and better preserve the overall
characteristics of spectral curves.
2.2.
Continuum removal
In order to find the sensitive relationship between soil heavy metal
content and spectral reflectance, Continuum removal (CR ) spectral
transformation after 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°(TONG Qingxi, et al., 2006), the actual spectrum
reflectance and envelope line of the corresponding band reflectance
ratio, By normalizing the spectral value to 0~1(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 CR not only enhances the spectral
characteristics of the original spectral curve at 1400nm ,
1900nm and 2200nm , but also highlights the weak absorption
characteristics at 410nm , 500nm and 700nm . 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.
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 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 SG-CR spectral transformation is:
Using the maximum tree classification method, λ = 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 SG-CR spectral transformation is
selected
405nm 、418nm 、781nm 、784nm 、794nm 、805nm 、807nm 、830nm 、831nm 、1079nm 、1085nm 、1251nm 、1267nm 、1308nm 、1309nm 、1410nm 、1836nm 、1860nm 、1897nm 、1898nm 、2080nm 、2137nm 、2149nm 、2156nm 、2184nm 、2382nm 、2395nm. At the same time, according to the significant bands of soil
attributes and SG-CR spectrum, the significant bands of 11 soil
attributes passing the significance level test of P = 0.01 were
selected, i.e406~419nm 、421~423nm 、427~431nm 、1044nm 、1062nm 、1087nm 、1887~1890nm 、2118nm 、2119nm 、2185~2187nm 、2198~2201nm 、2324nm 、2325nm .
The significant band and the inflection point of the correlation
coefficient curve were combined to determine the band
405~419nm 、421~423nm 、427~431nm 、781nm 、784nm 、794nm 、805nm 、807nm 、830nm 、831nm 、1044nm 、1062nm 、1079nm 、1085nm、1087nm 、1251nm 、1267nm、1308nm 、1309nm 、1410nm 、1836nm 、1860nm 、1887~1890nm 、1897nm 、1898nm 、2080nm 、2118nm、2119nm 、2137nm 、2149nm 、2156nm 、2184~2187nm 、2198~2201nm 、2324nm 、2325nm 、2382nm 、2395nmas a common spectral characteristic band of soil attribute spectral
inversion in well-facilitied capital farmland construction area of
Xinzheng City.
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 SG-CRspectral transformation as independent variables of soil attribute
inversion model, and constructed the panel data based on ordinary least
squares estimation (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 adjustmentR̅v 2 is 0.9991, indicating that
the goodness of fit of the model is high. A large F statistic
indicates that the regression coefficient is significant and the
regression model is significant as a whole.