CN111595806A - Method for monitoring soil carbon component by using mid-infrared diffuse reflection spectrum - Google Patents
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Abstract
The invention discloses a method for monitoring soil carbon components by utilizing a mid-infrared diffuse reflection spectrum technology, belonging to the technical field of soil detection. In particular to a method for rapidly detecting the content change of total carbon, organic carbon and inorganic carbon in farmland soil by using a mid-infrared diffuse reflection spectrometer. The method adopts a diffuse reflectance spectroscopy method, takes the measured value of the soil carbon by a chemical method as a basis, and combines a Partial Least Squares Regression (PLSR) algorithm to establish a mid-infrared spectrum (MIR) model of three soil carbon components of total soil carbon, organic carbon and inorganic carbon in Quzhou county, thereby establishing a method for quickly, accurately and efficiently detecting the content of the total soil carbon, the organic carbon and the inorganic carbon. The method can rapidly and accurately realize the determination of the carbon components of the three types of soil, has analysis efficiency greatly superior to that of the traditional method, provides a feasible method for carrying out real-time monitoring on large-area soil carbon content change in China, and also provides a new technical means for the work of precision agriculture, soil digital mapping, agricultural ecological environment protection, soil health evaluation and the like.
Description
Technical Field
The invention belongs to the technical field of soil detection, and particularly relates to a method for monitoring a soil carbon component by using a mid-infrared diffuse reflection spectrum. In particular to a method for rapidly detecting the content change of total carbon, organic carbon and inorganic carbon in soil of county farmland by utilizing a Fourier transform mid-infrared diffuse reflection spectrometer.
Background
Soil carbon, one of the key attributes of soil, plays an important role in soil structure formation, soil fertility preservation, global climate change regulation and land ecosystem stability maintenance, is the core of soil quality, and is the important basis of sustainable agriculture. Soil carbon, especially organic carbon, directly participates in various physical, chemical and biological processes of soil, and analysis of different soil carbon component contents has important significance for determining soil carbon sources, grasping soil quality change in time and guiding agricultural production, and is always important research content in the subject fields of agriculture, environment and the like. At present, the conventional chemical analysis method for soil carbon, such as potassium dichromate oxidation method, for determining the content of total carbon and organic carbon in soil has the defects of low determination efficiency, poor real-time performance, pollution and the like, so the conventional chemical analysis method cannot meet the requirement of rapidly and efficiently monitoring the content change of soil carbon, and the development of large-area soil carbon research and monitoring work is seriously hindered.
The mid-infrared spectroscopy based on the theory that the fundamental frequency is absorbed by the vibration in the molecule has the characteristics of strong spectral characteristics, easier information extraction, good reproducibility, simple and convenient operation, high testing speed, less sample consumption, environmental protection and suitability for batch sample determination, and the application of the mid-infrared spectroscopy in soil carbon analysis is increasing day by day, thereby gradually becoming the key point of research of scholars at home and abroad. The invention adopts a mid-infrared diffuse reflection spectrum method, and the basic principle is as follows: when light irradiates the sample and enters the sample through refraction, the light reacts with sample molecules to generate reflection, refraction, diffraction and absorption phenomena, finally, the light scattered to all directions of the space from the surface of the sample is called diffuse reflection light, and the diffuse reflection light enters the sample once and reacts with the sample molecules, so the diffuse reflection light carries the structure and composition information of the sample, and the basis of the technical work of the diffuse reflection spectrometer is provided.
At present, domestic research for quantifying soil carbon based on a mid-infrared diffuse reflection spectroscopy method is less. Therefore, the soil in Quzhou county is taken as a research object, the diffuse reflection spectroscopy is adopted, the soil carbon measurement value in the traditional method is taken as a basis, the PLSR algorithm is combined, and the MIR prediction model of total soil carbon, organic carbon and inorganic carbon in the Quzhou county is established, so that the rapid measurement of various soil carbon component indexes is realized, and the method has important significance and practical value for further developing the research of the mid-infrared spectroscopy technology on the aspect of soil carbon measurement in China, developing green agriculture, digital agriculture and the like.
Disclosure of Invention
The invention aims to provide a method for monitoring soil carbon components by using mid-infrared diffuse reflectance spectrum, which is characterized in that a mid-diffuse reflectance spectrum method is adopted, a chemical method soil carbon measurement value is taken as a basis, a PLSR algorithm is combined, and MIR prediction models of soil total carbon, organic carbon and inorganic carbon are established, so that a method for rapidly, accurately and efficiently detecting the content of the soil total carbon, the organic carbon and the inorganic carbon is established, and the method comprises the following steps:
(1) collecting and preparing farmland soil samples: the method comprises the following steps of collecting county soil samples by adopting a 1km x 1km grid survey method, coding according to a sampling sequence and recording sample information in detail, wherein the method specifically comprises the following steps: sample number, collection date, sampling depth, longitude and latitude, altitude, land utilization mode and sampler; air-drying all collected soil samples, removing sundries such as plant residues, gravels and the like, grinding, passing all the soil samples through a nylon sieve with the aperture of 2mm, and storing for later use;
(2) collecting diffuse reflection spectrograms of infrared bands in all soil samples to obtain an original spectrogram: a Tensor II Fourier transform mid-infrared spectrometer developed and produced by German Bruker company is adopted to measure the spectrum of a soil sample, and the instrument is provided with an MCT (cadmium mercury telluride) detector cooled by liquid nitrogen and used for collecting 7500-600 cm-1Resolution of 4cm-1Scanning 32 times; measuring two spectral data of each soil sample, and taking the data after arithmetic mean as the original spectral data of the soil sample; the data storage uses the OPUS format. However, for spectral analysis and modeling, only 4000-600 cm of the mid-infrared band spectrum is used-1；
(3) Representative sample screening: uniformly selecting samples in the spectral feature space by using a KS method to screen out soil samples with strong representativeness for determination of a soil carbon chemical method and MIR modeling;
(4) selecting a spectrum pretreatment method: in order to reduce the influence of the state of the spectrometer, the measurement environment and the sample grinding and sieving, 3 types of spectrum preprocessing of baseline correction, scattering correction and scale scaling are carried out on all spectrum data, and each type of spectrum preprocessing comprises a plurality of preprocessing methods. The baseline correction comprises a first derivative and a second derivative, and mainly deducts the influence of the background or drift of the instrument on the spectral signal; the scattering correction comprises Multivariate Scattering Correction (MSC) and standard normal variable transformation (SNV), and mainly eliminates the influence of scattering on the spectrum caused by uneven particle distribution and different particle sizes; the scale scaling comprises maximum and minimum normalization, constant offset elimination and straight line subtraction, and mainly eliminates adverse effects caused by overlarge scale difference. Comparing the model established after the spectrum pretreatment with the model established without the pretreatment, and selecting the optimal spectrum pretreatment method of each soil carbon component from the models;
(5) constructing a spectrum prediction model: dividing the representative samples screened in the step (3) into a correction set and a verification set according to a KS method, establishing a regression relation between the spectral signals and the component content by adopting a PLSR algorithm and taking the infrared spectrum data independent variable of the soil samples of the correction set and the soil carbon content as dependent variables, and verifying the accuracy of the model by using the independent verification set. In modeling PLSR, the factor number is selected as the most important factor. Setting the maximum factor number as 20, and determining by a leave-one-cross verification method, wherein the optimal factor number is the factor number corresponding to the minimum RMSECV value; the specific process of leave-one-out cross validation includes: selecting a sample test model from the correction set each time to predict the quality, using the rest samples to train data, repeating the process until each sample is set as test data, and calculating RMSECV;
(6) and (3) evaluating a model: r with verification setp 2And performing model evaluation on the three parameters of RMSEP and RPD. Wherein R is2Evaluation of the fitness of the model, R2The higher the value, the better the fitting effect of the model, and the stronger the capability of the model to explain the dependent variable; the stability of the RMSE evaluation model and the predictability of the RPD evaluation model are realized, and the higher the RPD value is, the lower the RMSE is, and the better the model is; in addition, further makeAnd (3) carrying out quality class classification on the model by using an RPD value: when the RPD is more than or equal to 3.0, the calibration effect is good, and the established calibration model can be used for actual detection; when 2.5<RPD<3.0, quantitative analysis is feasible, but the prediction precision is to be improved; when RPD is less than or equal to 2.5, it is difficult to use for quantitative analysis.
The invention has the beneficial effects that:
1. the method can realize rapid and accurate prediction of the content of the total carbon, organic carbon and inorganic carbon in the soil.
2. Makes up the defects of the conventional laboratory chemical analysis method.
3. The soil samples with strong representativeness are selected from a large number of samples to be used for the determination of soil carbon by a chemical method and MIR modeling, so that the model building speed is greatly increased, the storage space of a model base is reduced, and the model is convenient to update and maintain.
4. The sampling range covers the whole Quzhou county, and the county-area-range soil carbon component content change real-time monitoring can be realized.
5. The method is expected to be popularized to soil carbon analysis in northern China and even national farmlands and other ecological systems such as grasslands, forests and the like.
Drawings
FIG. 1 is a schematic diagram showing the correlation between the predicted value and the actual value of the soil total carbon spectrum model.
FIG. 2 is a schematic diagram showing the correlation between the predicted value and the actual value of the soil organic carbon spectrum model.
FIG. 3 is a schematic diagram showing the correlation between the predicted value and the true value of the soil inorganic carbon spectrum model.
Detailed Description
The invention provides a method for monitoring soil carbon components by utilizing a mid-infrared diffuse reflection spectrum technology, and the invention is further explained by combining with an embodiment.
The method selects a typical agricultural county Quzhou county in Huang-Huai-Hai plain of the food main producing area in China as a research area. Study area overview: in the middle of North China plain in Quzhou county, 5 towns and 342 administrative villages in the district, the total area of the land is 667km2Cultivated area 47782hm2(ii) a The parent soil is the flood and alluvial deposit of river, and the soil is selected from the group consisting of moisture soil,Saline soil and brown soil are the main, and the soil texture comprises sandy soil, sandy loam, light loam, medium loam and clay; the regional crops mainly comprise wheat, corn, cotton and vegetables, and have high multiple cropping index, large land investment and higher agricultural intensification degree.
(1) Soil sample collection and preparation
The sampling method comprises collecting farmland soil samples by 1km × 1km grid survey method, wherein the total area of the whole county of Quzhou is 667km2The method comprises the steps of setting 730 grids, not sampling county-area edge grids, leaving 580 grids after removal, collecting soil samples in a selected farmland by an S-shaped sampling method, collecting 5 soil samples in each farmland soil, and preparing 1 part of soil sample by a quartering method. Coding is carried out according to the sampling sequence and the sample information is recorded in detail, which specifically comprises the following steps: sample information such as sample number, acquisition time, sampling depth, longitude and latitude, altitude, land utilization mode, sampler and the like.
Preparing a soil sample: spreading the collected soil sample indoors for natural air drying, removing sundries such as plant residues, gravels and the like, grinding, passing through a nylon sieve with the aperture of 2mm, and storing for later use.
(2) Mid infrared spectral collection
Because the farmland soil sample is solid particles with irregular shapes, and the size, the shape and the uniformity of the particles can greatly influence the diffuse reflection spectrum data, the influence of the particle scattering effect on the spectrum acquisition can be reduced as much as possible by grinding and sieving. In addition, the soil water has strong absorption on the mid-infrared spectrum and is overlapped with the absorption waveband of the organic carbon, so that the prediction accuracy of the mid-infrared spectrum model of the soil carbon is obviously influenced. Therefore, further processing of the soil sample is required prior to collection of the mid-infrared spectrum. Specifically, the method comprises grinding, sieving with 0.25mm nylon sieve, drying in an oven at 105 deg.C for 2 hr, and storing in a desiccator until MIR collection is completed.
The spectrometer used was a Tensor II Fourier transform mid-infrared spectrometer developed by Bruker, Germany. The following preparations are required before spectrum acquisition: reducing the temperature and humidity of the laboratory environment, wherein the temperature is less than or equal to 25 ℃, and the relative humidity is less than or equal to 60%; the preheating of the instrument is more than or equal to 1h after starting up. Then the instrument is operatedThe stability of the measuring software OPUS 7.5, and the position of the interference peak is determined by using a check signal in an 'advanced measurement' menu; the basic state of the instrument is detected by using the running OQ test under the menu of the instrument state, and whether the instrument is normal is checked by using the running PQ test. Setting scanning parameters: spectral range of 7500-600 cm-1Resolution of 4cm-1The number of scans was 32. Soil samples were placed in instrument-fitted 24-well sample trays to ensure that the sample surface in each well was flat, with the wells 5mm in diameter and 3mm deep. The diffuse reflection standard sample is placed at the upper left corner of the sample disc, the background is subtracted from each scanning to correct the noise in the atmosphere and the instrument, 2 pieces of spectral data are collected from each soil sample, the data after arithmetic averaging is used as the original spectral data of the soil sample, and the data storage format is OPUS.
(3) Representative sample screening and soil carbon reference value determination
Representative sample selection within the study area: and screening 80 soil samples by using a K-S method for measuring reference values of a soil carbon chemical method and establishing an MIR model. The MIR spectrum is high in collection speed and small in sample amount, so that a representative sample in a research area is screened out based on the sample spectrum, the test time and the analysis cost of a large-batch sample chemical method can be reduced, more importantly, when a sample outside a model is encountered, the application range of the model can be expanded through fewer samples, and the model can be updated and maintained conveniently.
The chemical determination method of the soil carbon comprises the following steps: the contents of total carbon and organic carbon in the soil are measured by adopting an Elementar vacuum MACROcube element analyzer in Germany, and the content of inorganic carbon is obtained by adopting the subtraction method of the contents of total carbon and organic carbon.
(4) Spectrum prediction model construction
For spectral analysis and modeling, only 4000-600 cm of mid-infrared band spectrum is used-1. The use of different spectral preprocessing methods has different improvements and influences on the soil carbon prediction model by eliminating 10 kinds of light such as constant offset, subtracting a straight line, SNV, maximum and minimum normalization, MSC, first derivative, second derivative, first derivative + subtracting a straight line, first derivative + SNV, first derivative + MSCThe spectrum preprocessing method performs spectrum preprocessing on the raw spectrum data of the 80 soil samples. And comparing the model established after the spectrum pretreatment with the model established without the pretreatment, and selecting the optimal spectrum pretreatment method of each soil carbon component from the models. Sample set division is needed before modeling, and the area representative samples screened in the step (3) are divided into a correction set and a verification set, wherein the correction set is 70% and 56 samples, and the verification set is 30% and 24 samples. For the selection of calibration and validation set samples, the division was performed according to the range of reference values determined by chemical methods in combination with the K-S method. The modeling method adopts a classical method PLSR in spectral analysis, takes mid-infrared spectrum data independent variable of a soil sample of a correction set and soil carbon content as a dependent variable, establishes a regression relationship between a spectral signal and component content, and utilizes an independent verification set to test the accuracy of a model. In the modeling of PLSR, the most important factor (LV) is the selection. Selecting PLSR factor number: the maximum factor number is set to be 20, and the optimal factor number is the factor number corresponding to the minimum RMSECV value determined by a leave-one-out cross-validation method. The optimal factor numbers for each carbon component model are detailed in table 1.
(5) Model evaluation and application
R with verification setp 2And performing model evaluation on the three parameters of RMSEP and RPD. Wherein R is2Evaluation of the fitness of the model, R2The higher the value, the better the fitting effect of the model, and the stronger the capability of the model to explain the dependent variable; stability of the RMSE evaluation model and predictability of the RPD evaluation model, and the higher the RPD value, the lower the RMSE and the better the model. In addition, the RPD values are further used for quality class classification of the model: when the RPD is more than or equal to 3.0, the calibration effect is good, and the established calibration model can be used for actual detection; when 2.5<RPD<3.0, quantitative analysis is feasible, but the prediction precision is to be improved; when RPD is less than or equal to 2.5, it is difficult to use for quantitative analysis. The results of the model evaluations are detailed in Table 1.
After the model is established, the mid-infrared diffuse reflection spectrum information of soil samples with unknown carbon content and similar properties in Quzhou county can be brought into the model, and the content of the carbon component in the soil can be predicted.
TABLE 1 accuracy evaluation results of PLSR model for soil carbon component content in Quzhou county
The final quantitative model is established by combining the optimal PLSR factor number and a spectrum preprocessing method, and the result shows that: the RPD values of the model of the content of carbon components in the soil around the curve are all higher than 3.0, R2The values are all larger than 0.90, which indicates that the quantitative analysis result of the established model is more accurate. For the spectroscopic model, the stability of the model can be further analyzed by comparing the predicted and actual values of the model. A schematic diagram of the correlation between the chemical measured values of the total carbon, organic carbon and inorganic carbon of the soil and the PLSR predicted value is drawn (as shown in fig. 1, fig. 2 and fig. 3), and it can be seen from the diagram that the predicted values and measured values of the three soil carbon components have good consistency in the whole data set range, and the data points are uniformly distributed in 1: both sides of line 1 indicate higher stability of the model.
In conclusion, the model can be used for rapidly and accurately measuring the carbon components of the three types of soil, the analysis efficiency is greatly superior to that of the traditional method, a feasible method is provided for carrying out real-time monitoring on large-area soil carbon content change in China, and a new technical means is provided for the work of precision agriculture, soil digital mapping, agricultural ecological environment protection, soil health evaluation and the like.
Claims (5)
1. A method for monitoring soil carbon components by utilizing a mid-infrared diffuse reflection spectroscopy technology is characterized in that a diffuse reflection spectroscopy method is adopted, soil carbon measurement values in a traditional method are taken as the basis, a PLSR algorithm is combined, and an MIR prediction model of soil carbon is established, so that a method for rapidly, accurately and efficiently detecting the content of three soil carbon components including total soil carbon, organic carbon and inorganic carbon is established; the method comprises the following steps:
(1) collecting and preparing a soil sample: collecting soil samples in county by adopting a 1km multiplied by 1km grid survey method, coding according to a sampling sequence and recording sample information in detail; air-drying all collected soil samples, removing sundries such as plant residues and gravels, grinding, passing through a nylon sieve with the aperture of 2mm, and storing for later use;
(2) collecting diffuse reflection spectrograms of infrared bands in all soil samples to obtain an original spectrogram: the wavelength range is set to be 7500-600 cm-1Resolution of 4cm-1Scanning 32 times; however, for spectral analysis and modeling, only 4000-600 cm of the mid-infrared band spectrum is used-1；
(3) Representative sample selection: uniformly selecting samples in a spectral feature space by using a KS (Kennard-Stone) method based on Euclidean distances among variables to screen out soil samples with strong representativeness for determination of a soil carbon chemical method and MIR modeling;
(4) selecting a spectrum pretreatment method: in order to reduce the influence of the state of a spectrometer, the measurement environment and the sample grinding and sieving, 3 types of spectrum preprocessing of baseline correction, scattering correction and scale scaling are carried out on all spectrum data, and the optimal spectrum preprocessing method of each soil carbon component is selected according to the modeling effect;
(5) constructing a spectrum prediction model: the modeling method adopts a classical method PLSR in spectrum analysis, all samples are divided into a correction set and a verification set in the modeling process, the correction set is 70 percent and 56 samples, and the verification set is 30 percent and 24 samples; selecting a correction set and a verification set sample, and dividing according to a chemical reference value range by combining a KS method;
(6) and (3) evaluating a model: using the coefficient of determination (R)2) The Root Mean Square Error (RMSE) and the relative analysis error (RPD) are used as precision evaluation indexes to comprehensively judge the quality of the model; wherein R is2Evaluation of the fitness of the model, R2The higher the value, the better the fitting effect of the model, and the stronger the capability of the model to explain the dependent variable; stability of the RMSE evaluation model and predictability of the RPD evaluation model, and the higher the RPD value, the lower the RMSE and the better the model.
2. The method as claimed in claim 1, wherein the spectrometer used in step (2) is a Tensor II Fourier transform mid-infrared spectrometer developed and produced by Bruker, Germany; measuring two spectral data of each soil sample, and taking the data after arithmetic mean as the original spectral data of the soil sample; the data storage uses the OPUS format.
3. The method according to claim 1, wherein the class 3 spectrum preprocessing method in the step (4) specifically comprises: the baseline correction comprises a first derivative and a second derivative, and mainly deducts the influence of the background or drift of the instrument on the spectral signal; the scattering correction comprises Multivariate Scattering Correction (MSC) and standard normal variable transformation (SNV), and mainly eliminates the influence of scattering on the spectrum caused by uneven particle distribution and different particle sizes; the scale scaling comprises maximum and minimum normalization and constant offset elimination, and mainly eliminates adverse effects caused by overlarge scale difference; and comparing the model after the spectrum pretreatment with the model without the pretreatment, and selecting the optimal spectrum pretreatment method of each soil carbon component from the model.
4. The method according to claim 1, wherein the modeling method in step (5) employs a PLSR method based on a factor analysis theory; the method has the relationship between a spectrum information point matrix and a property concentration matrix to be measured, obtains the optimal principal component factor or latent variable by performing factor analysis on the spectrum matrix and a target component matrix, and selects the optimal number of the latent variables to establish an optimal regression model according to the accumulated contribution rate of the latent variables to the two matrixes.
5. The method of claim 1, wherein the RPD in step (6) evaluates the predictability of the model, uses the RPD values for quality classification of the model: when the RPD is more than or equal to 3.0, the calibration effect is good, and the established calibration model can be used for actual detection; when the RPD is more than 2.5 and less than 3.0, quantitative analysis is feasible, but the prediction precision is to be improved; when RPD is less than or equal to 2.5, it is difficult to use for quantitative analysis.
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