AU2021100533A4 - Soil salinity at Yellow River Delta Inversion Method based on Landsat 8 - Google Patents
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Abstract
The present invention provides a soil salinity at Yellow River Delta inversion
method based on Landsat 8, with the following steps: S: collecting and acquiring soil
salinity data; S2: getting and processing of remote sensing image data; S3: extracting
and calculating reflectivity data of bands of Landsat 8 images; S4: conducting
corresponding lg(R), 1/R transformation to each band and analyzing remote sensing
reflectivity and remote sensing reflectivity after transformation and soil salt content to
get a preliminary list of sensitive bands; S5: carrying out a multivariate linear
regression analysis to reflectance of each band after lg(R) and 1g transformation and
soil sat content acquired in step S1, and establishing a model; S6: applying an optimal
prediction model established in the above-mentioned method to Landsat 8 remote
sensing images of a research region Yellow River Delta to get a Landsat 8 soil salinity
remote sensing inversion image. When inversing soil salinity with the inversion model,
conducting a descriptive statistical analysis and comparing with actual soil salinity, it
is found that soil salinity values are basically the same, which establishes that
accuracy of the inversion model is high and stability thereof is good.
-1/1
Ad
jib 2
3
4
5
Figure 1
Description
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jib 2 3 4 5
Figure 1
Soil salinity at Yellow River Delta Inversion Method based on Landsat8
Technical field
The present invention provides an inversion method, specifically soil salinity at Yellow River Delta inversion method based on Landsat 8.
Background
Salinization is a phenomenon that happens in an arid or semi-arid region, when soil water evaporation amount exceeds supply amount from
underground water as the underground water-table rises. Soil salinization
phenomenon in China is severe, with area of salinized soil 1.0*108ha, among
which, proportion of residue salinized soil is as high as 45%, that of modern salinized soil takes the second place and that of potential salinized soil is the
lowest. Coastal salinized soil is located primarily in coastal areas in eastern and southern China, with the coastline 18,000 kilometers and spanning over
24 degrees of latitude.
The Yellow River Delta is a typical coastal soil salinity area, salt content in the soil is a chief factor affecting crop productivity and quality, and
consequently, acquiring accurate salt content in the soil is significant in
improving regional soil quality, increase regional food crop production, increase farmers' income, and realize sustainable development of regional agriculture.
Conventional soil salinity information acquiring is done by survey on the
spot, and with the development and application of remote sensing technology,
retrieving soil salinity information with remote sensing technology has
experienced an upgrade from characterization of soil salinity based on satellite images to estimation of soil salinity based on hyperspectral and near-ground multi-spectral images. Zhang, Tongrui etal conducted corresponding analysis and regression analysis based on actual hyperspectral data and OLI imagery data, and selected a best model which is a linear regression model with SAVI as a dependent variant, which can get soil salinity information at Yellow River Delta winter wheat growing area timely and accurately; Mamat-Sawut etal retrieved quantitatively soil salt content using hyperspectral data in the Keriya
River Basin with a partial least squares regression model and a BO neural
network method by a combination of WorldView-2 images and actual hyperspectral data, wherein value of coefficient of determination R 2 is 0.851,
which can be used to estimate and predict soil salt content in this region more
accurately; Zhao, Gengxing etal established a remote sensing inversion model respectively with a multivariate linear regression method, a partial least
squares regression method, BP neural network, support vector machine and
random forest algorithms, to conduct soil salinity in Yellow River Delta
inversion by fusing multispectral OLI images and hyperspectral HSI images. After research and comparison, it is found that, a model built with BP neural
network algorithms is of the highest accuracy, value of R 2 as high as 0.966,
and with such a model, it is possible to retrieve regional soil salinity timely and
accurately, however, as the computation is done invisibly, it is difficult for a researcher to refer to such a model.
Based on the foregoing analysis, a remote sensing inversion model that covers all types of land exploitation and crop growing and with operational
ease is in need.
Summary
To address the above mentioned problem, the present invention provides
a large scale soil salinity at Yellow River Delta inversion method taken use of data such as OLI images from Landsat 8 and soil salt content data from a laboratory, establishes an estimation model for soil salinity at Yellow River
Delta estimation with mathematical statistical analysis method, and explores
sensitive bands of soil salinity and a best inversion model, to provide technical
support for forthcoming soil salt content information accurate and timely retrieval and construct basis for soil salinization in Yellow River Delta
management, prevention and sustainable development.
The present invention is realized by the following technical solution, a soil
salinity at Yellow River Delta inversion method based on Landsat 8, with the
following steps:
S1: collecting and acquiring soil salinity data; S2: getting and processing
of remote sensing image data;
S3: extracting and calculating reflectance rate data of bands of Landsat 8 images with an ArcGIS10.0 software; S4: conducting corresponding lg(R), 1/R
transformation to each band and analyzing remote sensing reflectance rate
and remote sensing reflectance rate after transformation and soil salt content with an SPSS software to get a preliminary list of sensitive bands; S5: carrying
out a multivariate linear regression analysis to reflectance of each band after
lg(R) and Ig transformation and soil sat content acquired in step S1 with the
SPSS software, and establishing a model to check whether it is possible to establish a model with variation of reflectance rate of each bank and soil salt
content;
S6: establishing a remote sensing inversion model respectively for remote
sensing imagery data after logarithmic transformation and reciprocal
transformation, to compare the remote sensing imagery data model established after a single transformation and the remote sensing imagery data model established after both transformations are done to analyze influence to
model accuracy due to different data transformation;
S7: applying an optimal prediction model established in the above-mentioned method to Landsat 8 remote sensing images of a research region to get a Landsat 8 soil salinity remote sensing inversion image.
Especially, in step S1, to reflect actual conditions in the research region
with sampling points, it is necessary to take into consideration factors such as
types of soil, vegetation and land utilizing manner, which can be realized in the following way: S11: carrying out a grid point distribution, and adjusting
according to the types of land utilizing manner and road communication
conditions, totally 86 sampling points are set, collecting soil in 3-6 spots near
every sampling point with a depth of 0-20cm and mixing evenly; putting lkg-2kg to a plastic bag, labeling properly and recording information such as
coordinates of the sampling point, sampling time, weather conditions, the type
of land utilization manner and the type of vegetation.
S12: drying collected soil samples indoors, picking out grass roots, screening the soil samples with a 1mm screen, and preserving in a wide mouth container for use in subsequent experiments;
S13: test conductivity of the soil samples obtained in step S12, with the
following method: taking some soil samples and distilled water with proportion
thereof 1:5, putting into a glass test tube, stirring for five minutes, filtering and
getting the supernatant, test conductivity with a conductivity gauge, calculating soil salt content data by applying a relationship formula between conductivity
and soil salt content, the formula is as following:
S=3.047EC 1:s-0.493 (r=0.981**, p<0.001), wherein, S stands for soil salt
content, measured by g/kg; EC1 :s stands for conductivity calculation of soil
leaching liquid with soil and water proportion 1:5, measured by ms/c.
Specifically, step S2 is done in the following way: obtaining satellite
Landsat 8 OLI data from the NASA website, and conducting a geometrical rectification with a topographic map of Kenli County, rectification is done with a quadratic polynomial rectification model and a neighboring pixel resampling method, tolerance is controlled within one pixel, and the rectification work is done with Envi5.1 software; getting vector boundary of the research area from a natural resource management authority and cutting remote sensing images rectified in ArcGIS 10.0 platform software and getting remote sensing images of Kenli research area.
Specifically, step S3 is realized by the following method: key of the
present step is to extract spectral value of remote sensing images of a soil sampling point, in ArcGIS10.0, by a "extract value to point" function, extracting
raster value of each image band corresponding to a sampling point according to a coordinate position of the soil sampling point to know remote sensing
reflectivity of the soil sampling point.
Specially, a primary purpose of step S4 is to analyze and determine which
remote sensing bands or transformed band spectral values are closely related to soil salt content, by a relevance study, it is found that spectral value of each band is scarcely related to soil salt content, which shows that, sensitivity of
remote sensing spectral value of each band without transformation to soil salt
content is not high, therefore, it is necessary to conduct mathematic
transformation to the remote sensing spectral values, which includes reciprocal transformation (1/R), logarithm transformation (IgR), and conduct
relevance study between transformed band spectral values and soil salt content, it is observed that relevance degree is improved, and there is a high relativity between remote sensing data and soil data content, specifically it is done in the following way:
S41: conducting mathematic transformation to remote sensing spectral
values of extracted sampling points which includes reciprocal transformation
(1/R), logarithm transformation (IgR);
S42: conducting a relevance study between soil salt content and spectral values of each image band and transformed spectral values by SPSS software, and reaching a relevance relationship between soil salt content of the sampling points and spectral values of each image band and their transformations; a correlation coefficient is used to evaluate how closely two or more articles or sets of relevant variants are correlated, Pearson Simple Correlation Coefficient is used as a correlation coefficient, wherein when absolute value of the correlation coefficient is bigger, the correlation is higher, when the correlation coefficient is close to 1 or -1, the correlation relationship is stronger, while the correlation relationship is weaker when the correlation coefficient is close to 0.
Specially, step S5 is done by the following method:
S51: carrying out multivariate linear regression analysis on remote
sensing variants acquired in step 4, 1/R and Ig(R) transformation forms of reflectivity of each band by SPSS software and soil salt content e, and setting up a regression model;
S52: testing the model, first giving a homogeneity of variance test (F test),
when sig is less than 0.01, the test is passed and the regression analysis is efficient; and giving an accuracy inspection, to test on accuracy and stability of
the model to select a model with better accuracy and stability. Examining
accuracy of formula with coefficient of determination (R 2 ) and root mean
square deviation (RMSE), wherein when R 2 is bigger, and RMSE is smaller, accuracy of the model is higher and the stability better.
Specially, step S6 is done in the following manner:
S61: conducting a multivariate linear regression analysis with sensitive remote sensing variants determined in step S4, logarithm Ig(R) transformation
data of reflectivity of each band obtained from remote sensing data with SPSS software, and soil salt content got in step S1 and getting a coefficient table;
S62: testing the established model, it turned out that F is 8.452, sig is
0.005, which is less than 0.01, therefore the model is eligible and effective; coefficient of determination is 0.304, and RMSE is 6.23;
S63: conducting a multivariate linear regression analysis with sensitive
remote sensing variants determined in step S4, reciprocal 1/R transformation
data of reflectivity of each band obtained from remote sensing data with SPSS software, and soil salt content got in step S1 and getting a coefficient table;
S64: testing the established model, it turned out that F is 7.89, sig is 0.006, which is less than 0.01, therefore the model is eligible and effective; coefficient of determination is 0.295, and RMSE is 6.25;
S65: upon comparison of accuracy of different models, it is found that
accuracy of a hybrid remote sensing model combining reciprocal 1/R and
logarithm Ig(R) transformation of reflectivity of each band is the best, the best
model is Y1=59.94-43.71*Log(B5)+7105.37*1/(B11), which can be used in soil salinity at Yellow River Delta inversion monitoring.
In the soil salinity at Yellow River Delta inversion method based on
Landsat 8 in the present invention, soil salinity at Yellow River Delta is
retrieved by Landsat 8 OLI images, three spectral indicators, namely, spectral reflectivity, reciprocal form of spectral reflectivity and logarithm form of spectral
reflectivity are selected to conduct a relevance study with the soil salinity data,
and choose a spectral indicator of the highest correlation, conduct multivariate
linear regression analysis with bands of eligible correlation significance, and set up a regression model. It turned out that, B5, and B11 OLI images are
significantly correlated to soil salinity, while the reciprocal form thereof exhibits
the highest correlation level; it is sufficient for prediction by establishing a
regression model with reciprocal dataset in the fifth band and soil salinity (R 2 is 0.081 and RMSE is 6.229). When inversing soil salinity with the inversion
model, conducting a descriptive statistical analysis and comparing with actual
soil salinity, it is found that soil salinity values are basically the same, which establishes that accuracy of the inversion model is high and stability thereof is good.
Figure 1 is a soil salt content remote sensing inversion diagram with the soil salinity at Yellow River Delta inversion method based on Landsat 8
according to the present invention, wherein, 1, 2, 3, 4 and 5 stand for different
salinization severity, respectively a non-salinized area, a slightly salinized area,
a medium salinized area, a severely salinized area and salinized soil.
Description of the invention
The present invention provides a large scale soil salinity at Yellow River
Delta inversion method taken use of data such as OLI images from Landsat 8 and soil salt content data from a laboratory, establishes an estimation model for soil salinity at Yellow River Delta estimation with mathematical and
statistical analysis method, and explores sensitive bands of soil salinity and a
best inversion model, to provide technical support for forthcoming soil salt content information accurate and timely retrieval and construct basis for soil
salinization in Yellow River Delta management, prevention and sustainable
development.
Details of the soil salinity at Yellow River Delta inversion method are as following:
S1: collecting and acquiring soil salinity data; to reflect actual conditions in the research region with sampling points, it is necessary to take into
consideration factors such as types of soil, vegetation and land utilizing
manner, which can be realized in the following way: S11: carrying out a grid
point distribution, and adjusting according to the types of land utilizing manner and road communication conditions, totally 86 sampling points are set, collecting soil in 3-6 spots near every sampling point with a depth of 0-20cm and mixing evenly; putting 1kg-2kg to a plastic bag, labeling properly and recording information such as coordinates of the sampling point, sampling time, weather conditions, the type of land utilization manner and the type of vegetation;
S12: drying collected soil samples indoors, picking out grass roots, screening the soil samples with a 1mm screen, and preserving in a wide mouth
container for use in subsequent experiments;
S13: test conductivity of the soil samples obtained in step S12, with the
following method: taking some soil samples and distilled water with proportion
thereof 1:5, putting into a glass test tube, stirring for five minutes, filtering and getting the supernatant, test conductivity with a conductivity gauge, calculating
soil salt content data by applying a relationship formula between conductivity
and soil salt content, the formula is as following:
S=3.047EC 1 :s-0.493 (r=0.981**, p<0.001), wherein, S stands for soil salt content, measured by g/kg; EC 1:s stands for conductivity calculation of soil
leaching liquid with soil and water proportion 1:5, measured by ms/c; in S13,
check whether distribution of exported data is normal by conducting a
Kolmogorov-Smirnov test, and pretreat those that are not normal distributed to conform with normal distribution to enhance accuracy of the model.
S2: getting and processing of remote sensing image data, which is done in the following way: obtaining satellite Landsat 8 OLI data from the NASA
website, and conducting a geometrical rectification with a topographic map of
Kenli County, rectification is done with a quadratic polynomial rectification model and a neighboring pixel resampling method, tolerance is controlled
within one pixel, and the rectification work is done with Envi5.1 software;
getting vector boundary of the research area from a natural resource management authority and cutting remote sensing images rectified in ArcGIS 10.0 platform software and getting remote sensing images of Kenli research area.
S3: extracting and calculating reflectance rate data of bands of Landsat 8 images with an ArcGIS10.0 software, which is realized by the following method:
key of the present step is to extract spectral value of remote sensing images of a soil sampling point, in ArcGIS10.0, by a "extract value to point" function,
extracting raster value of each image band corresponding to a sampling point
according to a coordinate position of the soil sampling point to know lg(R) of
remote sensing reflectivity of each band.
S4: conducting corresponding lg(R), 1/R transformation to each band and
analyzing remote sensing reflectance rate and remote sensing reflectance rate after transformation and soil salt content with an SPSS software to get a
preliminary list of sensitive bands; a primary purpose of the present step is to
analyze and determine which remote sensing bands or transformed band spectral values are closely related to soil salt content, by a relevance study, it is found that spectral value of each band is scarcely related to soil salt content, which shows that, sensitivity of remote sensing spectral value of each band
without transformation to soil salt content is not high, therefore, it is necessary to conduct mathematic transformation to the remote sensing spectral values,
which includes reciprocal transformation (1/R), logarithm transformation (IgR),
and conduct relevance study between transformed band spectral values and soil salt content, it is observed that relevance degree is improved, and there is a high relativity between remote sensing data and soil data content,
specifically it is done in the following way:
S41: conducting mathematic transformation to remote sensing spectral
values of extracted sampling points which includes reciprocal transformation (1/R), logarithm transformation (IgR);
S42: conducting a relevance study between soil salt content and spectral values of each image band and transformed spectral values by SPSS software, and reaching a relevance relationship between soil salt content of the sampling
points and spectral values of each image band and their transformations; a
correlation coefficient is used to evaluate how closely two or more articles or sets of relevant variants are correlated, Pearson Simple Correlation Coefficient
is used as a correlation coefficient, wherein when absolute value of the
correlation coefficient is bigger, the correlation is higher, when the correlation
coefficient is close to 1 or -1, the correlation relationship is stronger, while the correlation relationship is weaker when the correlation coefficient is close to 0.
Table 1
Band b1 b2 b3 b4 b5 b6 b7 b8 b9 b1O
Correlation
coefficient 0.06 0.08 0.11 -0.14 -0.08 0.00 0.07 0.06 0.02 0.01
As can be seen in table 1, a biggest absolute value of correlation coefficients is 0.14, the smallest 0, therefore, there is only a weak correlation,
which means that spectral of each band is not sensitive to salt content, and the reflectivity is low, therefore, to establish a remote sensing inversion model, it is
necessary to conduct several mathematic transformation to the spectral
reflectivity, which comprises reciprocal transformation (1/R), and logarithm
transformation (log(R)), and by conducting a lg(R) transformation and a 1/R
Transformation Correlation coefficient
(TF) B1 B2 B3 B4 B5 B6 B7 B8 B9 B1 B11
0
Logarithm -0.04 -0.1 -0.04 0.00 -0. -0. -0.003 0.091 0.149 -0.304** 6 0.043 0 188 238** TF 7 56
Reciprocal 0.1 0.04 0.00 0. 0 0.048 0.003 -0.090 -0.150 0.295** -0.045 .239* 48 35 0 87 TF
transformation, and conducting a correlation analysis between
transformed spectral reflectivity and soil salt content with SPSS software, to
get a correlation coefficient table 2
Table 2
Remark: ** means significant correlation at significance level 0.01, and*
means significant correlated at significance level 0.05.
As can be seen in table 2, which is correlation relationship between 1/R
and lg(R) transformation of OLI image bands and soil salt content, correlation coefficient between transformed soil spectral reflectivity and soil salt content is
improved as a whole and exhibits closer correlation; in the reciprocal
transformation, absolute value of a largest Pearson Correlation Coefficient (R)
is 0.304, and a smallest one 0; in the logarithm transformation, absolute value of a largest Pearson Correlation Coefficient (R) is 0.295 and a smallest one 0; at P<0.01 level, after 1/R and lg(R) transformation, 1/R and lg(R) data of OLI image bands are significantly correlated to soil salt content in bands B5 and B11, after reciprocal transformation, correlation significance coefficients are respectively -0.304 and -0.238, and after logarithm transformation, correlation
significance coefficients are respectively 0.295 and 0.239.
During research, it is found that, by making some mathematical transformation to hyperspectral data of soil salt content, it is possible to magnify sensitive positions of a curve, which serves to establish a correlation relationship between soil salt content and spectral reflectivity, and enhance quantitative inversion of soil salt content by the images, therefore, in the present invention, reciprocal and logarithm transformation has been done to spectral reflectivity of OLI image bands to improve spectral reflectivity of bands and significant correlation can be seen. By values of correlation coefficients and judging whether significant correlation exists, values after reciprocal and logarithm transformation of the fifth band (B5) and the eleventh band (B11) are selected as sensitive variants of remote sensing bands in a remote sensing inversion model.
S5: carrying out a multivariate linear regression analysis to reflectance of
each band after lg(R) and Ig transformation and soil sat content acquired in step S1 with the SPSS software, and establishing a model to check whether it
is possible to establish a model with variation of reflectance rate of each bank and soil salt content; and step S5 is done in the following manner:
S51: carrying out multivariate linear regression analysis on remote
sensing variants acquired in step 4, 1/R and g(R) transformation forms of
reflectivity of each band by SPSS software and soil salt content, and getting a correlation coefficient table 3;
Table 3
Coeffi S t cient ig
constan 1. 0 59.94 t 75 .08
-43.7 -3. 0 Log(B5) 1 06 .03
7105. 2. 0 1/(B11) 37 03 .04
In the table, statistical magnitude t is used to reflect significance of correlation coefficient; sig is a probability value corresponding to t, therefore, t
and sig are equivalent and it is sufficient to take consideration only of value sig.
It is desired that sig value is smaller than a given significance level, such as 0.05 or 0.01, the closer sig value to zero the better; it can be seen in table 3 that values of sig are close to 0, so the result is acceptable;
A formula reached in table 3 is as following (1):
Y1=59.94-43.71*Log(B5)+7105.37*1(B11)
(1)
Wherein Log(B5) is a logarithm transformed value of reflectivity of band
B5; 1/(B11) stands for a reciprocal transformed value of reflectivity of band
B11.
S52: testing the model, first giving a homogeneity of variance test (F test), as is shown in table 4, when sig is less than 0.01, the test is passes and the regression analysis is efficient; and giving an accuracy inspection, to test on
accuracy and stability of the model to select a model with better accuracy and stability. Examining accuracy of formula with coefficient of determination (R 2 )
and root mean square deviation (RMSE), wherein when R 2 is bigger, and
RMSE is smaller, accuracy of the model is higher and the stability better.
Table 4
R2 RMSE sig F
0.322 6.227 0.011
4.757
As can be seen in table 4, F is 4.757, sig 0.011, sig is less than 0.05, so the model is eligible according to F test, and upon regression analysis it is proved effective; coefficient of determination of the model is 0.322, RMSE
6.227.
S6: establishing a remote sensing inversion model respectively for remote
sensing imagery data after logarithmic transformation and reciprocal transformation, to compare the remote sensing imagery data model
established after a single transformation and the remote sensing imagery data model established after both transformations are done to analyze influence to
model accuracy due to different data transformation;
S61: conducting a multivariate linear regression analysis with sensitive
remote sensing variants determined in step S4, logarithmlg(R) transformation data of reflectivity of each band obtained from remote sensing data with SPSS software, and soil salt content got in step S1 and getting a coefficient table 5 of
the model;
Table 5
Coeffi t Sig cient
Constan 112 3. 0.00 t .19 01 3
-54. -2. 0.00 Log(B5) 08 91 5
Both sig values of the constant and the variant are less than
predetermined 0.01, and are close to 0; consequently, the coefficients of the model are acceptable; and a formula expression (2) is concluded according to table 5 as following:
Y2=112.19-54.08*Log(B5)
(2)
Wherein Log(B5) is a logarithm transformation value of reflectivity of the fifth band.
S62: testing the established model, it turned out that F is 8.452, sig is 0.005, which is less than 0.01, therefore the model is eligible and effective;
coefficient of determination is 0.304, and RMSE is 6.23;
S63: conducting a multivariate linear regression analysis with sensitive
remote sensing variants determined in step S4, reciprocal 1/R transformation
data of reflectivity of each band obtained from remote sensing data with SPSS software, and soil salt content got in step S1 and getting a coefficient table 6;
Table 6
Coeffi t Sig cient
Constan -1 -2. 0.024 t 7.99 30
219 -2. 1/(B11) 0.006 3.07 81
Both sig values of the constant and the variant are less than
predetermined 0.05; consequently, the coefficients of the model are acceptable; and a formula expression (3) is concluded according to table 6 as following:
Y3=-17.99+2193.07*1/(B11)
(3)
Wherein 1/(B11) is a reciprocal transformation value of reflectivity of the
eleventh band.
S64: testing the established model, it turned out that F is 7.89, sig is 0.006, which is less than 0.01, therefore the model is eligible and effective; coefficient of determination is 0.295, and RMSE is 6.25;
S65: upon comparison of accuracy of different models, it is found that
accuracy of a hybrid remote sensing model combining reciprocal 1/R and
logarithm lg(R) transformation of reflectivity of each band is the best, the best
model is Y1=59.94-43.71*Log(B5)+7105.37*1/(B11), which can be used in soil salinity at Yellow River Delta inversion monitoring.
S7: applying an optimal prediction model established in the above-mentioned method to Landsat 8 remote sensing images of a research region to get a Landsat 8 soil salinity remote sensing inversion image, as is shown in figure 1; conducting a descriptive statistical analysis to soil salt
content, which is shown in table 7.
As is shown in figure 1, soil salinity in surface of the research area exhibits obvious spatial distribution difference, and is to a great extent fractionate. Area
of soil with salinity less than lg/kg is 37.5 square kilometers, taking a
proportion of 1.6%, which is classified as unsalinized land, area of soil with salinity 1-2g/kg is 156.14 square kilometers, taking a proportion of 6.7%, and is
classified as slightly salinized land, area of soil with salinity 2-4g/kg 672.87
square kilometers, and is classified as medium salinized land, area of soil with
salinity 4-6 g/kg is 580.3 square kilometers, and is classified as severely salinized land, and area of soil with salinity over 6g/kg is 884.131 square
kilometers, and is classified as salinized still land. A lighter color in the
inversion drawing means higher soil salinity. As also can be seen in figure 1,
soil salinity decreases gradually as distance from Bohai increases. Soil salinity at Northeast China and coastal areas is higher, which belong to salinized still
land, which occupies 884.131 square kilometers, takes a proportion of 37.93%;
while being closer to inland is severely salinized land, which occupies an area of 580.36 square kilometers, taking a proportion of 24.9%; and the medium
salinized land is closest to inland, which occupies 672.87 square kilometers and takes a proportion of 6.7%.
Table 7
Soil salinity / (g/kg) Measured value Inversed value
Maximum 6.8 7.8
Minimum 0.2 0.6
Average 1.41 1.96
Standard deviation 1.04 1.33
Asymmetry coefficient 8.02 9.00
Coefficient of kurtosis 8.02 8.72
Median 1.1 2.1
Coefficient of differentiation 73.76% 74.56%
As can be seen from table 7, difference between measured values and inversed values are not substantial, thus the inversion model provided by the
present invention can be used in soil salinity measurement, which saves
manpower and time significantly.
In the present invention, a correlation analysis is done between spectral reflectivity in Landsat 8 images and soil salinity, bands of significant correlation
are chosen as sensitive bands, making regression analysis between sensitive bands and measured soil salinity data, and establishing a multivariate linear
regression model, to realize inversion of spatial distribution of soil salinity in Kenli County. As a result, it is possible to inverse spatial distribution of soil
salinity in the research area with OLI images, and in the research area, soil
salinity distribution varies greatly with space, and consequently, soil salinity decreases from east to west; the closer to the Yellow River, the lower is soil salinity; the farther to the sea, the less is soil salinity; influenced by seawater, salinity in underground water in Eastern coastal regions in China is relatively high, with a lot of salt deposit, and soil salinity is high; and in natural reserve regions in Northeast China, vegetation covering rate is low and there is seldom human interference, soil salinity is high. Soil salinity varies a lot from one area to another, which can be explained by natural factors such as topography, climate, soil parent materials, and physiognomy etc.
Sensitive spectral bands of OLI images of soil salinity of the research area are B5(845-885nm) and B11(11500-12510nm), among which band B5 is an
optimal band. Rendering a reciprocal transformation and logarithm transformation to the bands and conducting correlation analysis, improved
correlation between the dataset and soil salinity prominently, among which, the
most obvious change happens in reciprocal transformed data. Set up a multivariate regression model with a statistical method, and choose the best
model based on a principle of the higher coefficient of determination the better,
the lower RMSE the better, finally model
Y1=59.94-43.71*Log(B5)+7105.37*1/(B11) is chosen; first of all, conduct a t-test to the model coefficients, wherein P=0.001, which reaches a statistically
significant level at P<0.01 level; and conduct an accuracy test to the model. Therefore, the model is effective in soil salinity prediction, wherein R 2 is 0.322,
and RMSE 6.229, which is of good accuracy and stability.
In step S2 of the present invention, Landsat 8 is the most recently
launched Landsat satellite, carries the Operational Land Imager (OLI) including nine spectral bands and the Thermal Infrared Sensor (TIRS)
instruments including 2 additional thermal infrared bands. An important
advantage of OLI images is adjustment of Band 5, which discriminates
moisture absorption feature at 0.825pm, so that influence from atmospheric absorption to data can be eliminated to a large extent. Furthermore, there are
two new bands, first a blue band for bathymetric mapping, another is a short-wave infrared band for cloud monitoring.
Claims (7)
1. A soil salinity at Yellow River Delta inversion method based on Landsat 8, characterized in that, comprising the following steps:
S1: collecting and acquiring soil salinity data; S2: getting and processing of remote sensing image data;
S3: extracting and calculating reflectance rate data of bands of Landsat 8 images with an ArcGIS10.0 software; S4: conducting corresponding g(R), 1/R transformation to each band and analyzing remote sensing reflectance rate and remote sensing reflectance rate after transformation and soil salt content with an SPSS software to get a preliminary list of sensitive bands; S5: carrying out a multivariate linear regression analysis to reflectivity of each band after lg(R) and 1g transformation and soil sat content acquired in step S1 with the SPSS software, and establishing a model to check whether it is possible to establish a model with transformed values of reflectance rate of each band and soil salt content; S6: establishing a remote sensing inversion model respectively for remote sensing imagery data after logarithmic transformation and reciprocal transformation, to compare the remote sensing imagery data model established after a single transformation and the remote sensing imagery data model established after both transformations are done to analyze influence to model accuracy due to different data transformation;
S7: applying an optimal prediction model established in the above-mentioned method to Landsat 8 remote sensing images of a research region to get a Landsat 8 soil salinity remote sensing inversion image.
2. The soil salinity at Yellow River Delta inversion method based on
Landsat 8 according to claim 1, characterized in that, step S1 is done in a following way:
S11: carrying out a grid point distribution, and adjusting according to types of land utilizing manner and road communication conditions, totally
86 sampling points are set, collecting soil in 3-6 spots near every sampling point with a depth of 0-20cm and mixing evenly; putting 1kg-2kg to a plastic
bag, labeling properly and recording information such as coordinates of the
sampling point, sampling time, weather conditions, the type of land
utilization manner and the type of vegetation.
S12: drying collected soil samples indoors, picking out grass roots, screening the soil samples with a 1mm screen, and preserving in a wide mouth container for use in subsequent experiments;
S13: test conductivity of the soil samples obtained in step S12, with the
following method: taking some soil samples and distilled water with
proportion thereof 1:5, putting into a glass test tube, stirring for five minutes, filtering and getting the supernatant, test conductivity with a conductivity
gauge, calculating soil salt content data by applying a relationship formula
between conductivity and soil salt content, the formula is as following:
S=3.047EC1 :s-0.493(r=0.981**, p<0.001), wherein, S stands for soil
salt content, measured by g/kg; EC:s stands for conductivity calculation of soil leaching liquid with soil and water proportion 1:5, measured by ms/c.
3. The soil salinity at Yellow River Delta inversion method based on Landsat 8 according to claim 1, characterized in that, step S2 is done in the
following way: obtaining satellite Landsat 8 OLI data from NASA website,
and conducting a geometrical rectification with a topographic map of Kenli
County, rectification is done with a quadratic polynomial rectification model and a neighboring pixel resampling method, tolerance is controlled within one pixel, and the rectification work is done with Envi5.1 software; getting vector boundary of the research area from a natural resource management authority and cutting remote sensing images rectified in ArcGIS 10.0 platform software and getting remote sensing images of Kenli research area.
4. The soil salinity at Yellow River Delta inversion method based on Landsat 8 according to claim 3, characterized in that, step S3 is realized by the following method: key of the present step is to extract spectral value of
remote sensing images of a soil sampling point, in ArcGIS10.0, by a "extract value to point" function, extracting raster value of each image band
corresponding to a sampling point according to a coordinate position of the
soil sampling point to know remote sensing reflectivity of the soil sampling
point.
5. The soil salinity at Yellow River Delta inversion method based on
Landsat 8 according to claim 4, characterized in that, step S4 is done in the following way:
S41: conducting mathematic transformation to remote sensing spectral
values of extracted sampling points which includes reciprocal
transformation (1/R), logarithm transformation (IgR);
S42: conducting a correlation study between soil salt content and
spectral values of each image band and transformed spectral values by SPSS software, and reaching a correlation relationship between soil salt
content of the sampling points and spectral values of each image band and their transformations; a correlation coefficient is used to evaluate how
closely two or more articles or sets of relevant variants are correlated, Pearson Simple Correlation Coefficient is used as a correlation coefficient,
wherein when absolute value of the correlation coefficient is bigger, the
correlation is higher, when the correlation coefficient is close to 1 or -1, the correlation relationship is stronger, while the correlation relationship is weaker when the correlation coefficient is close to 0.
6. The soil salinity at Yellow River Delta inversion method based on
Landsat 8 according to claim 5, characterized in that, step S5 is done by the following method:
S51: carrying out multivariate linear regression analysis on remote
sensing variants acquired in step 4, 1/R and Ig(R) transformation forms of
reflectivity of each band by SPSS software and soil salt content, and setting up a regression model;
S52: testing the model, first giving a homogeneity of variance test (F
test), when sig is less than 0.01, the test is passed and the regression analysis is efficient; and giving an accuracy inspection, to test on accuracy
and stability of the model to select a model with better accuracy and stability; examining accuracy of formula with coefficient of determination
(R 2 ) and root mean square deviation (RMSE), wherein when R 2 is bigger,
and RMSE is smaller, accuracy of the model is higher and stability thereof better.
7. The soil salinity at Yellow River Delta inversion method based on
Landsat 8 according to claim 6, characterized in that, step S6 is done in the following manner:
S61: conducting a multivariate linear regression analysis with sensitive
remote sensing variants determined in step S4, logarithm Ig(R)
transformation data of reflectivity of each band obtained from remote sensing data with SPSS software, and soil salt content got in step S1 and getting a coefficient table;
S62: testing the established model, it turned out that F is 8.452, sig is
0.005, which is less than 0.01, therefore the model is eligible and effective; coefficient of determination is 0.304, and RMSE is 6.23;
S63: conducting a multivariate linear regression analysis with sensitive remote sensing variants determined in step S4, reciprocal 1/R
transformation data of reflectivity of each band obtained from remote
sensing data with SPSS software, and soil salt content got in step S1 and getting a coefficient table;
S64: testing the established model, it turned out that F is 7.89, sig is
0.006, which is less than 0.01, therefore the model is eligible and effective; coefficient of determination is 0.295, and RMSE is 6.25;
S65: upon comparison of accuracy of different models, it is found that
accuracy of a hybrid remote sensing model combining reciprocal 1/R and
logarithm lg(R) transformation of reflectivity of each band is the best, the
best model is Y1=59.94-43.71*Log(B5)+7105.37*1/(B11), which can be used in soil salinity at Yellow River Delta inversion monitoring.
-1/1-
Figure 1
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