CN104897592A - Monitoring method of salt ion content in saline soil based on hyperspectral technology - Google Patents
Monitoring method of salt ion content in saline soil based on hyperspectral technology Download PDFInfo
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Abstract
The invention discloses a monitoring method of salt ion content in saline soil based on a hyperspectral technology. The method comprises the following main steps: (1) collecting a soil sample in the wild and pre-treating the sample; (2) drying and sifting the soil sample; (3) measuring the total salt and eight salt ions in the soil sample; (4) carrying out hyperspectral measurement on the soil sample to obtain the original spectral reflectance characteristic curve of the soil sample; (5) processing the original spectral value, and carrying out correlation analysis between the spectral value and the measured salt ion content; (6) introducing the construction method of a support vector machine model, and performing model building and precision evaluation on the screened spectral transformation form. The monitoring method is relatively high in the prediction accuracy for each salt ion content in soil, provides a novel approach for the indoor measurement of the salt ion content in saline soil, and provides a novel way for achieving quick and lossless monitoring of the harm types of regional soil salinization.
Description
Technical field
The present invention relates to soil salt fast monitored field, particularly, relate to a kind of salinization soil salt ion content monitoring method based on hyperspectral technique.
Background technology
Xinjiang region is by its parent soil material, topography and geomorphology, weather conditions and the factor acting in conjunction such as artificial, seriously, this kind of fertility is low, ecology fragility for the soil salinization and secondary salinization harm, bad environments is the bottleneck of Regional Economic Development and ecological construction always.
Along with the development of remote sensing technology and image processing techniques, remote sensing is utilized to become the Main Means of soil salinization monitoring.Conventional soil salination monitoring adopts field soil fixed point investigation method, not only time-consuming, effort, and measuring point is few, representative poor, cannot realize large area Real-Time Monitoring.High-spectrum remote-sensing sensor can obtain nano level atural object continuous spectrum information, meticulous spectral resolution reflects the fine feature of object spectrum, to make according to diagnostic spectral absorption characteristics identifying atural object, carries out Remote Sensing Quantitative Analysis, the chemical composition etc. of research atural object becomes possibility.
The soluble-salt of saliferous clay mainly comprises sulfate, chloride and the carbonate of sodium, potassium, calcium, magnesium etc., grasping soil salt ion composition in time, is accurately administer alkaline land and prevent its prerequisite worsened further, to the saliferous dynamic monitoring of quickening and comprehensive regulation important in inhibiting.Existing mainly concentrating on about the research work of salinization soil harmfulness is sought in salt ion total amount, and much research all shows that the harm of single salt pair crop in soil is more direct, its extent of injury is higher than total salt content, and visible soil salt ion composition and concentration have indicative to the harm evaluating salinization soil more.Support vector machine (support vector machines, SVM) be that the VC being based upon Statistical Learning Theory ties up on theoretical and Structural risk minization basis, the practical problemss such as small sample, non-linear, high dimensional data and local minimum can be solved preferably, have flexible, directly perceived, clear, operation efficiency is high, easily with the feature such as multi-source information is combined.
Mostly be in existing similar technique to adopt hyperspectral technique monitoring soil total salt content, organic matter and moisture, also rare record in the EO-1 hyperion monitoring method of salinity eight-ions.
There is following defect in existing method:
(1) hyperspectral technique is utilized effectively to extract in the process of soil attribute information, soil moisture content situation is not also well contrasted, often when measuring soil spectrum information by virtue of experience or existing part result of study adopt aeration drying, the EO-1 hyperion quantitative inversion of soil salt ion can adopt fresh soil or drying and processing not clear.
(2) along with the reduction of soil particle diameter, the reflectivity of soil surface improves, the degree of depth of absorption peak is caused to reduce, relation between the spectral signature of different-waveband and soil attribute can change, what usual mensuration soil spectrum all adopted crosses 2mm sieve, and when measuring soil salt ion, whether other particle diameter process can improve fitting precision and await further research.
(3) indoor hyperspectral analysis technology because of its influence factor more easy to control compared with field, and be widely used in soil resource environmental evaluation, the EO-1 hyperion determination influences of incident angle to soil property of lab simulation light source is obvious, high-spectral data between Different Light incident angle may be caused without sharing, therefore, the light source incidence angle measured for soil salt ion EO-1 hyperion is a problem that must solve.
Summary of the invention
The object of the invention is to, for the problems referred to above, propose a kind of salinization soil salt ion content monitoring method based on hyperspectral technique, to realize the advantage to soil salt ion Quantitative Monitoring fast and accurately.
For achieving the above object, the technical solution used in the present invention is:
Based on a salinization soil salt ion content monitoring method for hyperspectral technique, comprise the following steps:
Step one: choose the representative region representing local salt marsh situation, the similar point of surface condition is chosen in same sample area, gather the pedotheque at upper soll layer 0-5cm thickness place, and mixed by the pedotheque of collection and to be encapsulated in valve bag rapidly afterwards, and record gathers the longitude and latitude of pedotheque point;
Step 2: the pedotheque fetched in step one is rejected the impurity beyond soil, and the pedotheque after rejecting is divided into three parts, 2mm sieve is crossed after first part of pedotheque A porphyrize of pedotheque, and be contained in valve bag by after the pedotheque numbering after sieving, then valve bag is put into Refrigerator store; Get second part of pedotheque B natural air drying of pedotheque; Second part of pedotheque C of pedotheque is placed in the baking oven of 105-110 DEG C and dries to constant weight;
Step 3: by the soil sieve of the pedotheque B after step 2 process and pedotheque C respectively by 2mm, 1mm, 0.5mm, 0.25mm and 0.15mm, the sample formed cross 2mm sieve after pedotheque A porphyrize after and pedotheque B after step 2 process and pedotheque C respectively by the soil sieve of 2mm, 1mm, 0.5mm, 0.25mm and 0.15mm after in the sample that formed, selected part sample adopts eight ions content in chemical gauging soil respectively, thus draws chemical assay numerical value;
Step 4: utilize EO-1 hyperion instrument to measure each pedotheque spectrum characteristic parameter, the sample that each pedotheque is formed after crossing 2mm sieve after being included in pedotheque A porphyrize and the pedotheque B after step 2 process and pedotheque C are respectively by 2mm, 1mm, 0.5mm, the sample formed after the soil sieve of 0.25mm and 0.15mm, the chemical assay numerical value contrast of integrating step three show that different dry condition soil spectrum reflectivity and dry condition are on the impact of forecast model, different-grain diameter soil sample on soil spectrum reflectivity and particle diameter on the impact of forecast model, complete the spectrometry condition screening of antecedent soil moisture situation and particle diameter composition,
Step 5: Different Light incident angle is set respectively, the spectrometry condition screening of antecedent soil moisture situation and particle diameter composition is completed to above-mentioned steps four and the sample determined carries out spectroscopic assay, the spectrum samples curve measured is carried out to the variance analysis of undulatory property, filter out optimum light source incidence angle;
Step 6: the result of screening according to step 4 and step 5, measure the spectrum characteristic parameter of pedotheque, by the smoothing denoising of original soil sample spectra values, carry out data transformation process to soil sample spectral reflectance data afterwards, the chemical analysis results in conjunction with soil salt ion filters out optimal data variation;
Step 7: utilize model construction of SVM method, on the basis of step 6 optimal data variation screening, builds soil salt ion appraising model and carries out external inspection to model, realizing the soil salt ion Quantitative Monitoring based on hyperspectral technique.
Preferably, the surface condition in described step one comprises around vegetation, soil color, roughness and quality water percentage, described quality water percentage <15%.
Preferably, in described step 3, selected part sample adopts in chemical gauging soil in eight ions content respectively, and described chemical method comprises, CO
3 2-and HCO
3 -dual-indicator neutralisation is adopted to measure; Cl
-adopt AgNO
3titration measuring; SO
4 2-eDTA indirect titration is adopted to measure; Ca
2+and Mg
2+adopt EDTA complexometric titration; Na
+and K
+adopt flame spectrophotometric determination.
Preferably, arrange Different Light incident angle respectively in described step 5, Different Light incident angle comprises 15 °, 25 °, 35 ° and 45 °.
Preferably, in step 4, by Ricoh's spectrum signature comparative analysis everywhere, the salt ion precision of forecasting model crossed after pedotheque C drying constructed by 0.15mm soil sieve is higher.
Preferably, in step 5, when pedotheque C crosses the sample of 0.15mm soil sieve to arrange light source incidence angle be 25 ° after drying, when the variance analysis of undulatory property is carried out to the spectrum samples curve measured, be the optimal incident angle in fact in mensuration process.
Preferably, in step 6, data transformation process comprises data transformation form in 12, after data transformation process, draw Na
+, K
+, Mg
2+and Cl
-ion adopts the process of logarithm first differential can increase significant correlation wave band quantity, Ca
2+adopt first differential can increase significant correlation wave band quantity, SO
4 2-and HCO
3 -ion adopts continuum Transformatin method can increase significant correlation wave band quantity.
Preferably, also comprise in step 7 and adopt training set cross validation and grid data service to carry out parameter optimization.
Technical scheme of the present invention has following beneficial effect:
Technical scheme of the present invention, higher precision of prediction is had to salt ion content each in soil, for the indoor measurement of salinization soil salt ion content provides new approach, also for quick, the non-destructive monitoring of feasible region soil salinization Harm Type provide new technological means.
Technical solution of the present invention compared with prior art has the following advantages:
1, invent and contrasted by previous experiments, determine the pedotheque adopting EO-1 hyperion to measure soil salt ion concentration, should carry out drying to constant weight in the baking oven of 105-110 DEG C process, measurement result is better than conventional wind desiceted soil process.
2, inventing by contrasting different-grain diameter composition spectral signature, determining the pedotheque adopting EO-1 hyperion to measure soil salt ion concentration, cross the measurement result optimum that 0.15 mm soil sieve obtains.
3, invent and contrasted by the Spectral characteristics of soil of different incident light source, determine into set light source as 25 ° of fitting precisions the highest.
4, invent the spectrum characteristic parameter after by contrast process, filter out the optimal transformation that each salt ion measures.
5, introduce support vector machine and build Soluble Salts In Salt-affected Soil ion concentration EO-1 hyperion inverse model, higher through testing accuracy.
6, invention assay method operation steps is simple, rationally, cost is low, can the real conditions of effective reflect soil salt ion composition, and can be salinization soil evaluation, improvement, improvement, crop-planting adjustment provides new data acquisition approach.
Below by drawings and Examples, technical scheme of the present invention is described in further detail.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the salinization soil salt ion content monitoring method based on hyperspectral technique described in the embodiment of the present invention;
Fig. 2 is the conditional filtering process flow diagram that soil salt ion spectra measures;
Fig. 3 is foundation and the inspection process figure of soil salt ion appraising model.
Embodiment
Below in conjunction with accompanying drawing, the preferred embodiments of the present invention are described, should be appreciated that preferred embodiment described herein is only for instruction and explanation of the present invention, is not intended to limit the present invention.
Embodiment one:
As shown in Figure 1, step one: choose the representative region representing local salt marsh situation, the similar point of surface condition is chosen in same sample area, gather the pedotheque at upper soll layer 0-5cm thickness place, and mixed by the pedotheque of collection and to be encapsulated in valve bag rapidly afterwards, and record gathers the longitude and latitude of pedotheque point;
Step 2: the pedotheque fetched in step one is rejected the impurity beyond soil, and the pedotheque after rejecting is divided into three parts, 2mm sieve is crossed after first part of pedotheque A porphyrize of pedotheque, and be contained in valve bag by after the pedotheque numbering after sieving, then valve bag is put into Refrigerator store; Get second part of pedotheque B natural air drying of pedotheque; Second part of pedotheque C of pedotheque is placed in the baking oven of 105-110 DEG C and dries to constant weight;
Step 3: by the soil sieve of the pedotheque B after step 2 process and pedotheque C respectively by 2mm, 1mm, 0.5mm, 0.25mm and 0.15mm, the sample formed cross 2mm sieve after pedotheque A porphyrize after and pedotheque B after step 2 process and pedotheque C respectively by the soil sieve of 2mm, 1mm, 0.5mm, 0.25mm and 0.15mm after in the sample that formed, selected part sample adopts eight ions content in chemical gauging soil respectively, thus draws chemical assay numerical value;
Step 4: utilize EO-1 hyperion instrument to measure each pedotheque spectrum characteristic parameter, the sample that each pedotheque is formed after crossing 2mm sieve after being included in pedotheque A porphyrize and the pedotheque B after step 2 process and pedotheque C are respectively by 2mm, 1mm, 0.5mm, the sample formed after the soil sieve of 0.25mm and 0.15mm, the chemical assay numerical value contrast of integrating step three show that different dry condition soil spectrum reflectivity and dry condition are on the impact of forecast model, different-grain diameter soil sample on soil spectrum reflectivity and particle diameter on the impact of forecast model, complete the spectrometry condition screening of antecedent soil moisture situation and particle diameter composition,
Step 5: Different Light incident angle is set respectively, the spectrometry condition screening of antecedent soil moisture situation and particle diameter composition is completed to above-mentioned steps four and the sample determined carries out spectroscopic assay, the spectrum samples curve measured is carried out to the variance analysis of undulatory property, filter out optimum light source incidence angle;
Step 6: the result of screening according to step 4 and step 5, measure the spectrum characteristic parameter of pedotheque, by the smoothing denoising of original soil sample spectra values, carry out data transformation process to soil sample spectral reflectance data afterwards, the chemical analysis results in conjunction with soil salt ion filters out optimal data variation;
Step 7: utilize model construction of SVM method, on the basis of step 6 optimal data variation screening, builds soil salt ion appraising model and carries out external inspection to model, realizing the soil salt ion Quantitative Monitoring based on hyperspectral technique.
Wherein, the surface condition in step one comprises around vegetation, soil color, roughness and quality water percentage, described quality water percentage <15%.
In step 3, selected part sample adopts in chemical gauging soil in eight ions content respectively, and described chemical method comprises, CO
3 2-and HCO
3 -dual-indicator neutralisation is adopted to measure; Cl
-adopt AgNO
3titration measuring; SO
4 2-eDTA indirect titration is adopted to measure; Ca
2+and Mg
2+adopt EDTA complexometric titration; Na
+and K
+adopt flame spectrophotometric determination.
Arrange Different Light incident angle respectively in step 5, Different Light incident angle comprises 15 °, 25 °, 35 ° and 45 °.
Preferably, in step 4, by Ricoh's spectrum signature comparative analysis everywhere, the salt ion precision of forecasting model crossed after pedotheque C drying constructed by 0.15mm soil sieve is higher.
In step 5, when pedotheque C crosses the sample of 0.15mm soil sieve to arrange light source incidence angle be 25 ° after drying, when the variance analysis of undulatory property is carried out to the spectrum samples curve measured, be the optimal incident angle in fact in mensuration process.
In step 6, data transformation process comprises data transformation form in 12, after data transformation process, show that Na+, K+, Mg2+ and Cl-ion adopts the process of logarithm first differential can increase significant correlation wave band quantity, Ca2+ adopts first differential can increase significant correlation wave band quantity, SO42-and HCO3-ion adopts continuum Transformatin method can increase significant correlation wave band quantity.
Also comprise in step 7 and adopt training set cross validation and grid data service to carry out parameter optimization.
Carry out drying to constant weight in the baking oven of 105-110 DEG C after process to pedotheque after selected, after crossing 0.15 mm soil sieve measurement result optimum, and determine into set light source as 25 ° of fitting precisions the highest.
Pedotheque is proceeded as follows:
step 1:same sampling representative region choose 5 surface conditions (around vegetation, soil color, roughness and quality water percentage <15%) similar some position, get 0-5cm pedotheque and mix and be encapsulated in rapidly in valve bag;
step 2:the fresh soil sample of fetching rejects the impurity such as plant residue, stone beyond soil, and the pedotheque after numbering is placed in the baking oven of 105-110 DEG C and dries to constant weight;
step 3:by the soil sieve of step 2 pedotheque by 0.15 mm, every part of pedotheque about 250 g is stand-by, wherein salinity eight ions content in selected part sample determination soil, CO
3 2-and HCO
3 -dual-indicator neutralisation is adopted to measure; Cl
-adopt AgNO
3titration measuring; SO
4 2-eDTA indirect titration is adopted to measure; Ca
2+and Mg
2+adopt EDTA complexometric titration; Na
+and K
+adopt flame spectrophotometric determination;
step 4:the pedotheque obtained in step 3 is carried out the measurement of soil spectrum numerical value, spectral measurement carries out in the darkroom of an energy controlled light condition.Getting pedotheque is positioned in the transparent sample-containing dish of radius 5 cm, dark 1.5 cm (thinking optically infinite thickness), soil filling unit weight about 1.4 gcm
-3.200 W Halogen lamp LEDs are adopted to be placed in target both sides, light source incidence angle 25 °, distance objective 30 cm, the sensor probe of probe employing 8 ° of field angle is placed in the vertical direction from soil sample surface 15 cm, the region of probe receiving spectrum is the circle of diameter 2.1 cm, be less than the area of sample-containing dish, the reflectance spectrum being soil that probe receives.First calibrate with blank before test, each soil sample gathers 10 curves of spectrum, obtains the actual reflected spectrum data of this soil sample after arithmetic mean;
step 5:use spectroscopic data process software ViewSpecPro to carry out splicing correction to the curve of spectrum that soil sample each in step 4 collects, then ask and calculate the spectral reflectivity of 10 curve of spectrum mean values as corresponding sample.Adopt the method for moving average original spectrum reflectivity (R) of noise as numerical analysis is smoothly removed to spectroscopic data, then to soil sample spectral reflectance data open radical sign process (
), logarithm process (lgR), process (1/R) reciprocal, original spectrum first differential (R') and second-order differential (R''), the process of first differential radical sign (
), the process of second-order differential radical sign (
), logarithm first differential process ((lgR) '), logarithm second-order differential process ((lgR) ' '), first differential reciprocal ((1/R) '), second-order differential reciprocal ((1/R) ' ') and continuum Transformatin (CR);
Continuum is removed and is defined as reflecting peak-to-peak linear connected portion in reflection spectrum curve.Continuum connects reflectance spectrum peak and broken line is greater than 180 degree at the exterior angle at peak value place, continuum remove namely with reflectance spectrum divided by continuum spectrum, following formula is:
。
Wherein, R
crfor the absorption spectrum after continuum removal, R is absorption spectrum, R
cfor the continuum value of absorption spectrum.
step 6:to each soil sample spectra values of conversion in step 5, carry out correlation analysis with the soil salt ion concentration measured in corresponding step 3, compare characteristic wavelengths quantity and coefficient R respectively;
。
Wherein, R is related coefficient,
,
for spectroscopic assay conversion numerical value and the mean value of different soils sample,
,
be respectively measured value and the mean value of pedotheque salt ion.
step 7:according to the analysis result of step 6, filter out the spectroscopic data variation best with soil salt eight-ions correlativity, support vector machine method is introduced to filtered out numerical value, setting SVM type is 4(and v-SVR), kernel function type is 2(and RBF), training set cross validation and grid data service (grid search) is adopted to carry out parameter optimization, determine the value of punishing parameter C and RBF core parameter g according to mean square deviation minimum principle, build Soluble Salts In Salt-affected Soil ion concentration EO-1 hyperion inverse model.
。
Wherein,
for mean square deviation.
step 8:carry out model accuracy evaluation to the result of step 7 gained, evaluating mainly contains modeling data collection coefficient of determination R
cal 2, standard error RMSEE, cross-validation R
cv 2with standard error RMSECV, external inspection data set coefficient of determination R
val 2with standard error RMSEP, and the ratio R PD of measured value standard deviation and normative forecast error.Wherein the coefficient of determination is larger, and more minimodel precision of prediction is higher for standard error, and RPD > 2.0 illustrates that model is suitable for estimating soil attribute, and during RPD < 1.4, model is unreliable; 1.4 < RPD < 2.0, the reliability of model can be improved by different modeling methods.
。
In formula, RMSE is standard error, and S is sample standard deviation, and SEP is normative forecast error,
for pattern die analog values,
for measured value,
for the mean value of measured value, n is sample group number.
Embodiment two:
Sampling concrete on the basis of embodiment one is as follows:
(1) pedotheque obtains: in order to obtain more natural soil sample salinity gradient, according to study area salinized farmland distribution situation, severe, moderate and slight salinized farmland upper soll layer 0 ~ 5 cm soil sample is gathered in 4 ~ October, same sampling location choose 5 surface conditions (around vegetation, soil color, roughness and quality water percentage <15%) similar some position, the longitude and latitude of sampling point is recorded with GPS, sample mix is evenly encapsulated in rapidly in valve bag, amounts to and gathers study area pedotheque 203 parts.
(2) the fresh soil sample of fetching rejects the impurity such as plant residue, stone beyond soil, be placed on dry to constant weight in the baking oven of 105-110 DEG C stand-by.
(3) the ground 0.15 mm sieve of the soil sample of drying, electronic balance takes 50 g pedotheques and mixes with 250 ml distilled water, after immersion and vibration make soil salt fully dissolve, carries out total salt quantity (mass method) and measures, CO
3 2-and HCO
3 -dual-indicator neutralisation is adopted to measure; Cl
-adopt AgNO
3titration measuring; SO
4 2-eDTA indirect titration is adopted to measure; Ca
2+and Mg
2+adopt EDTA complexometric titration; Na
+and K
+adopt flame spectrophotometric determination, according to its salt content numeric distribution and soil particle composition, choose the wide sample close with the content of organic matter of salt content codomain and amount to 135, wherein 90 soil samples are as modeling sample, remain 45 soil samples as test samples.Selected the descriptive statistic of soil sample salt content, eight-ions composition in table 1:
Table 1, pedotheque salinity descriptive statistic table:
。
(4) spectral signature pH-value determination pH: spectral measurement carries out in the darkroom of an energy controlled light condition.Getting pedotheque is positioned in the transparent sample-containing dish of radius 5 cm, dark 1.5 cm (thinking optically infinite thickness), soil filling unit weight about 1.4 gcm
-3.200 W Halogen lamp LEDs are adopted to be placed in target both sides, light source incidence angle 25 °, distance objective 30 cm, the sensor probe of probe employing 8 ° of field angle is placed in the vertical direction from soil sample surface 15 cm, the region of probe receiving spectrum is the circle of diameter 2.1 cm, be less than the area of sample-containing dish, the reflectance spectrum being soil that probe receives.First calibrate with blank before test, each soil sample gathers 10 curves of spectrum, obtains the actual reflected spectrum data of this soil sample after arithmetic mean.
(5) conversion of spectroscopic data and screening: use spectroscopic data process software ViewSpecPro to carry out splicing correction to the curve of spectrum that each soil sample collects, then ask and calculate the spectral reflectivity of 10 curve of spectrum mean values as corresponding sample.Adopt the method for moving average original spectrum reflectivity (R) of noise as numerical analysis is smoothly removed to spectroscopic data, then to soil sample spectral reflectance data open radical sign process (
), logarithm process (lgR), process (1/R) reciprocal, original spectrum first differential (R') and second-order differential (R''), the process of first differential radical sign (
), the process of second-order differential radical sign (
), logarithm first differential process ((lgR) '), logarithm second-order differential process ((lgR) ' '), first differential reciprocal ((1/R) '), second-order differential reciprocal ((1/R) ' ') and continuum Transformatin (CR), first differential, second-order differential adopt
originsoftware calculates, and continuum is removed and adopted
eNVIsoftware
continuum Removepower and energy.
(6) spectroscopic data after conversion and soil salt ion are done correlation analysis, calculate significant correlation wave band quantity, result shows in Table 2:Na
+, K
+, Mg
2+and Cl
-adopt the process of logarithm first differential can increase significant correlation wave band quantity, Ca
2+adopt first differential can increase significant correlation wave band quantity, SO
4 2-and HCO
3 -adopt continuum Transformatin method can increase significant correlation wave band quantity.
The conversion of table 2, different spectroscopic data and soil salt ion significant correlation wave band schedule of quantities:
。
(7) foundation of soil salt ion inverse model: on the basis to spectroscopic data conversion and screening, choose the variation many with soil salt ion significant correlation wave band, support vector machine (support vector machine, SVM) is adopted to build Soluble Salts In Salt-affected Soil ion concentration remote sensing estimation model.Setting SVM type is 4(and v-SVR), kernel function type is 2(and RBF), adopt training set cross validation and grid data service (grid search) to carry out parameter optimization, determine the value of punishing parameter C and RBF core parameter g according to mean square deviation minimum principle; Correlation computations adopts libsvm 3.11 tool box of Matlab R2012a software to realize.
(8) the precision evaluation parameter of model mainly contains modeling data collection coefficient of determination R
cal 2, standard error RMSEE, cross-validation R
cv 2with standard error RMSECV, external inspection data set coefficient of determination R
val 2with standard error RMSEP, and the ratio R PD of measured value standard deviation and normative forecast error.Wherein the coefficient of determination is larger, and more minimodel precision of prediction is higher for standard error, and RPD > 2.0 illustrates that model is suitable for estimating soil attribute, and during RPD < 1.4, model is unreliable; 1.4 < RPD < 2.0, the reliability of model can be improved by different modeling methods.Design parameter is as table 3.
Table 3, soil salt ion concentration inverse model parameter list:
。
As can be seen from Table 3, except soil salt CO
3 2-ion, other salt ions adopt spectrum transform data, utilize support vector machines method all to have higher precision to soil salt ion concentration simulation and forecast.The R that each salt ion model is set up
cal 2between 0.7097 ~ 0.8863, RMSEE numerical range is between 0.3985 ~ 3.2121; The R of cross validation
cv 2between 0.6123 ~ 0.7483, RMSECV numerical range is between 2.312 ~ 5.3185; Utilize 45 external samples to carry out external inspection to the model set up, also show higher precision of prediction, wherein Ca
2+and Mg
2+rPD be respectively 1.5502,1.4719, the RPD of other ions is all greater than 1.7, illustrates and utilizes the method can measure fast and accurately the salt ion in soil.
The present invention makes full use of hyperspectral technique in conjunction with data shift means and support vector machine (SVM) method, achieves salinization soil Na
+, K
+, Ca
2+, Mg
2+, Cl
-, SO
4 2-and HCO
3 -through cross validation and external inspection, the prediction of ion concentration, shows that precision of prediction is higher to the model set up.The inventive method is that the indoor measurement of salinization soil salt ion content provides new approach, also for quick, the non-destructive monitoring of feasible region soil salinization degree provide new means.
Last it is noted that the foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, although with reference to previous embodiment to invention has been detailed description, for a person skilled in the art, it still can be modified to the technical scheme described in foregoing embodiments, or carries out equivalent replacement to wherein portion of techniques feature.Within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (8)
1., based on a salinization soil salt ion content monitoring method for hyperspectral technique, it is characterized in that, comprise the following steps:
Step one: choose the representative region representing local salt marsh situation, the similar point of surface condition is chosen in same sample area, gather the pedotheque at upper soll layer 0-5cm thickness place, and mixed by the pedotheque of collection and to be encapsulated in valve bag rapidly afterwards, and record gathers the longitude and latitude of pedotheque point;
Step 2: the pedotheque fetched in step one is rejected the impurity beyond soil, and the pedotheque after rejecting is divided into three parts, 2mm sieve is crossed after first part of pedotheque A porphyrize of pedotheque, and be contained in valve bag by after the pedotheque numbering after sieving, then valve bag is put into Refrigerator store; Get second part of pedotheque B natural air drying of pedotheque; Second part of pedotheque C of pedotheque is placed in the baking oven of 105-110 DEG C and dries to constant weight;
Step 3: by the soil sieve of the pedotheque B after step 2 process and pedotheque C respectively by 2mm, 1mm, 0.5mm, 0.25mm and 0.15mm, the sample formed cross 2mm sieve after pedotheque A porphyrize after and pedotheque B after step 2 process and pedotheque C respectively by the soil sieve of 2mm, 1mm, 0.5mm, 0.25mm and 0.15mm after in the sample that formed, selected part sample adopts eight ions content in chemical gauging soil respectively, thus draws chemical assay numerical value;
Step 4: utilize EO-1 hyperion instrument to measure each pedotheque spectrum characteristic parameter, the sample that each pedotheque is formed after crossing 2mm sieve after being included in pedotheque A porphyrize and the pedotheque B after step 2 process and pedotheque C are respectively by 2mm, 1mm, 0.5mm, the sample formed after the soil sieve of 0.25mm and 0.15mm, the chemical assay numerical value contrast of integrating step three show that different dry condition soil spectrum reflectivity and dry condition are on the impact of forecast model, different-grain diameter soil sample on soil spectrum reflectivity and particle diameter on the impact of forecast model, complete the spectrometry condition screening of antecedent soil moisture situation and particle diameter composition,
Step 5: Different Light incident angle is set respectively, the spectrometry condition screening of antecedent soil moisture situation and particle diameter composition is completed to above-mentioned steps four and the sample determined carries out spectroscopic assay, the spectrum samples curve measured is carried out to the variance analysis of undulatory property, filter out optimum light source incidence angle;
Step 6: the result of screening according to step 4 and step 5, measure the spectrum characteristic parameter of pedotheque, by the smoothing denoising of original soil sample spectra values, carry out data transformation process to soil sample spectral reflectance data afterwards, the chemical analysis results in conjunction with soil salt ion filters out optimal data variation;
Step 7: utilize model construction of SVM method, on the basis of step 6 optimal data variation screening, builds soil salt ion appraising model and carries out external inspection to model, realizing the soil salt ion Quantitative Monitoring based on hyperspectral technique.
2. the salinization soil salt ion content monitoring method based on hyperspectral technique according to claim 1, it is characterized in that, surface condition in described step one comprises around vegetation, soil color, roughness and quality water percentage, described quality water percentage <15%.
3. the salinization soil salt ion content monitoring method based on hyperspectral technique according to claim 1, it is characterized in that, in described step 3, selected part sample adopts in chemical gauging soil in eight ions content respectively, and described chemical method comprises, CO
3 2-and HCO
3 -dual-indicator neutralisation is adopted to measure; Cl
-adopt AgNO
3titration measuring; SO
4 2-eDTA indirect titration is adopted to measure; Ca
2+and Mg
2+adopt EDTA complexometric titration; Na
+and K
+adopt flame spectrophotometric determination.
4. the salinization soil salt ion content monitoring method based on hyperspectral technique according to claim 1, it is characterized in that, arrange Different Light incident angle respectively in described step 5, Different Light incident angle comprises 15 °, 25 °, 35 ° and 45 °.
5. the salinization soil salt ion content monitoring method based on hyperspectral technique according to claim 4, it is characterized in that, in step 4, by Ricoh's spectrum signature comparative analysis everywhere, the salt ion precision of forecasting model crossed after pedotheque C drying constructed by 0.15mm soil sieve is higher.
6. the salinization soil salt ion content monitoring method based on hyperspectral technique according to claim 4, it is characterized in that, in step 5, when pedotheque C crosses the sample of 0.15mm soil sieve to arrange light source incidence angle is 25 ° after drying, when the variance analysis of undulatory property is carried out to the spectrum samples curve measured, it is the optimal incident angle in fact in mensuration process.
7. the salinization soil salt ion content monitoring method based on hyperspectral technique according to claim 3, is characterized in that, in step 6, data transformation process comprises data transformation form in 12, after data transformation process, draw Na
+, K
+, Mg
2+and Cl
-ion adopts the process of logarithm first differential can increase significant correlation wave band quantity, Ca
2+adopt first differential can increase significant correlation wave band quantity, SO
4 2-and HCO
3 -ion adopts continuum Transformatin method can increase significant correlation wave band quantity.
8., according to claim 17 arbitrary described salinization soil salt ion content monitoring methods based on hyperspectral technique, it is characterized in that, also comprise in step 7 and adopt training set cross validation and grid data service to carry out parameter optimization.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101769868A (en) * | 2008-12-29 | 2010-07-07 | 中国科学院沈阳应用生态研究所 | Soil moisture reflection spectrum characteristic analysis method |
| CN103954567A (en) * | 2014-04-15 | 2014-07-30 | 塔里木大学 | Soil salinity determining method based on continuum removal method |
| CN104048946A (en) * | 2014-06-16 | 2014-09-17 | 河南省农业科学院植物营养与资源环境研究所 | Indoor hyperspectral determination method for undisturbed soil |
| CN104697937A (en) * | 2015-03-18 | 2015-06-10 | 北京师范大学 | Technical method for high-spectrum identification of soil property |
-
2015
- 2015-06-11 CN CN201510318460.3A patent/CN104897592B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101769868A (en) * | 2008-12-29 | 2010-07-07 | 中国科学院沈阳应用生态研究所 | Soil moisture reflection spectrum characteristic analysis method |
| CN103954567A (en) * | 2014-04-15 | 2014-07-30 | 塔里木大学 | Soil salinity determining method based on continuum removal method |
| CN104048946A (en) * | 2014-06-16 | 2014-09-17 | 河南省农业科学院植物营养与资源环境研究所 | Indoor hyperspectral determination method for undisturbed soil |
| CN104697937A (en) * | 2015-03-18 | 2015-06-10 | 北京师范大学 | Technical method for high-spectrum identification of soil property |
Non-Patent Citations (2)
| Title |
|---|
| 王海江 等: "基于高光谱反射特性的土壤水盐状况预测模型研究", 《农业机械学报》 * |
| 赖宁: "基于高光谱数据的开都河流域下游绿洲盐渍化土壤表层含盐量估算模型对比研究", 《中国优秀硕士学位论文全文数据库 农业科技辑》 * |
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