CN117269078A - Method for determining cation exchange capacity of soil - Google Patents
Method for determining cation exchange capacity of soil Download PDFInfo
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- CN117269078A CN117269078A CN202311551246.3A CN202311551246A CN117269078A CN 117269078 A CN117269078 A CN 117269078A CN 202311551246 A CN202311551246 A CN 202311551246A CN 117269078 A CN117269078 A CN 117269078A
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- 239000002689 soil Substances 0.000 title claims abstract description 90
- 238000005341 cation exchange Methods 0.000 title claims abstract description 75
- 238000000034 method Methods 0.000 title claims abstract description 44
- 230000003595 spectral effect Effects 0.000 claims abstract description 49
- 238000005070 sampling Methods 0.000 claims abstract description 46
- 238000002310 reflectometry Methods 0.000 claims abstract description 28
- 229930002875 chlorophyll Natural products 0.000 claims description 39
- 235000019804 chlorophyll Nutrition 0.000 claims description 39
- ATNHDLDRLWWWCB-AENOIHSZSA-M chlorophyll a Chemical compound C1([C@@H](C(=O)OC)C(=O)C2=C3C)=C2N2C3=CC(C(CC)=C3C)=[N+]4C3=CC3=C(C=C)C(C)=C5N3[Mg-2]42[N+]2=C1[C@@H](CCC(=O)OC\C=C(/C)CCC[C@H](C)CCC[C@H](C)CCCC(C)C)[C@H](C)C2=C5 ATNHDLDRLWWWCB-AENOIHSZSA-M 0.000 claims description 39
- 238000012360 testing method Methods 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 239000000126 substance Substances 0.000 claims description 4
- 230000003750 conditioning effect Effects 0.000 claims description 2
- 239000000758 substrate Substances 0.000 claims 1
- 238000011835 investigation Methods 0.000 abstract description 11
- 238000005259 measurement Methods 0.000 abstract description 4
- 238000012937 correction Methods 0.000 description 13
- 238000003066 decision tree Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 7
- 238000007637 random forest analysis Methods 0.000 description 7
- 230000008901 benefit Effects 0.000 description 5
- 230000005855 radiation Effects 0.000 description 5
- USFZMSVCRYTOJT-UHFFFAOYSA-N Ammonium acetate Chemical compound N.CC(O)=O USFZMSVCRYTOJT-UHFFFAOYSA-N 0.000 description 3
- 239000005695 Ammonium acetate Substances 0.000 description 3
- 229940043376 ammonium acetate Drugs 0.000 description 3
- 235000019257 ammonium acetate Nutrition 0.000 description 3
- 230000001419 dependent effect Effects 0.000 description 3
- 208000027066 STING-associated vasculopathy with onset in infancy Diseases 0.000 description 2
- 238000009614 chemical analysis method Methods 0.000 description 2
- 238000002790 cross-validation Methods 0.000 description 2
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- 230000007935 neutral effect Effects 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- DGAQECJNVWCQMB-PUAWFVPOSA-M Ilexoside XXIX Chemical compound C[C@@H]1CC[C@@]2(CC[C@@]3(C(=CC[C@H]4[C@]3(CC[C@@H]5[C@@]4(CC[C@@H](C5(C)C)OS(=O)(=O)[O-])C)C)[C@@H]2[C@]1(C)O)C)C(=O)O[C@H]6[C@@H]([C@H]([C@@H]([C@H](O6)CO)O)O)O.[Na+] DGAQECJNVWCQMB-PUAWFVPOSA-M 0.000 description 1
- 230000002378 acidificating effect Effects 0.000 description 1
- UKIIOGOSUQFLMB-UHFFFAOYSA-L barium(2+) sulfuric acid dichloride Chemical compound [Cl-].[Ba+2].S(O)(O)(=O)=O.[Cl-] UKIIOGOSUQFLMB-UHFFFAOYSA-L 0.000 description 1
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- ADNRGADCCSPMSK-UHFFFAOYSA-N diazanium;acetate;chloride Chemical compound [NH4+].[NH4+].[Cl-].CC([O-])=O ADNRGADCCSPMSK-UHFFFAOYSA-N 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000003337 fertilizer Substances 0.000 description 1
- 238000005048 flame photometry Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000002386 leaching Methods 0.000 description 1
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- 229910052708 sodium Inorganic materials 0.000 description 1
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- 239000002681 soil colloid Substances 0.000 description 1
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Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/55—Specular reflectivity
Abstract
The embodiment of the invention belongs to the technical field of soil parameter measurement, and particularly relates to a method for determining soil cation exchange capacity, which comprises the following steps: obtaining the ground object spectral reflectivity of the region to be detected; acquiring cation exchange capacity of soil at a plurality of sampling points in a region to be detected; determining vegetation parameters of the region to be detected according to the ground object spectral reflectivity of the region to be detected; and determining the cation exchange amount of the soil in the area to be detected according to the vegetation parameters of the area to be detected and the cation exchange amount of the soil at the sampling point. By using the method for determining the cation exchange capacity of the soil, disclosed by the embodiment of the application, the cation exchange capacity can be calculated by utilizing aviation hyperspectrum under the condition that the soil is not exposed, so that investigation work can be carried out in the growing period of crops and is not limited by the condition of bare soil on the earth surface.
Description
Technical Field
The embodiment of the invention belongs to the technical field of soil parameter measurement, and particularly relates to a method for determining soil cation exchange capacity.
Background
The cation exchange capacity of the soil refers to the total amount of various cations absorbed by the soil colloid, is an important index for evaluating the fertilizer retaining capacity of the soil, and is an important basis for improving the soil and reasonably fertilizing. The traditional cation exchange capacity investigation method generally collects soil samples at fixed points, discrete data are obtained after chemical analysis, and finally a parameter distribution map of the whole area is obtained through data interpolation. However, the conventional method has the limitation factors of large workload, long period, data incomplete coverage and the like. At present, although the method for inverting the cation exchange capacity by utilizing the aviation hyperspectrum has the advantages of rapidness, no damage and full data coverage, the application precondition is that the earth surface of the region to be detected is in a bare earth state and is easy to be restricted by earth surface conditions.
Disclosure of Invention
In view of the above problems, the present application provides a method for determining a cation exchange amount of soil, which aims to calculate the cation exchange amount by using aviation hyperspectral under a non-bare state of soil and improve the working efficiency of the aviation hyperspectral technology in soil quality investigation.
The embodiment of the application provides a method for determining the cation exchange capacity of soil, which comprises the following steps: obtaining the ground object spectral reflectivity of the region to be detected; acquiring cation exchange capacity of soil at a plurality of sampling points in a region to be detected; determining vegetation parameters of the region to be detected according to the ground object spectral reflectivity of the region to be detected; and determining the cation exchange amount of the soil in the area to be detected according to the vegetation parameters of the area to be detected and the cation exchange amount of the soil at the sampling point.
According to the method for determining the cation exchange capacity of the soil, provided by the embodiment of the application, the cation exchange capacity of a plurality of sampling points and vegetation parameters of a region to be detected can be obtained, so that the cation exchange capacity can be calculated by utilizing aviation hyperspectrum under the non-bare state of the soil, investigation work can be carried out in the growing period of crops, the restriction of bare earth conditions on the earth surface is avoided, the application space of aviation hyperspectral technology in the work of investigating the cation exchange capacity of the soil is expanded, and the working efficiency of the aviation hyperspectral technology in soil quality investigation is effectively improved.
Drawings
Other objects and advantages of the present invention will become apparent from the following description of embodiments of the present invention, which is to be read in connection with the accompanying drawings, and may assist in a comprehensive understanding of the present invention.
Fig. 1 is a flowchart of a method for determining a cation exchange amount of soil according to an embodiment of the present application.
Fig. 2 is a flow chart of acquiring cation exchange capacity of soil at a plurality of sampling points in a region to be measured according to an embodiment of the present application.
FIG. 3 is a flow chart for determining the cation exchange capacity of soil in a test area provided in an embodiment of the present application.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to the drawings of the embodiments of the present application. It will be apparent that the described embodiments are one embodiment of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without the benefit of the present disclosure, are intended to be within the scope of the present application based on the described embodiments.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which this application belongs. If, throughout, reference is made to "first," "second," etc., the description of "first," "second," etc., is used merely for distinguishing between similar objects and not for understanding as indicating or implying a relative importance, order, or implicitly indicating the number of technical features indicated, it being understood that the data of "first," "second," etc., may be interchanged where appropriate. If "and/or" is present throughout, it is meant to include three side-by-side schemes, for example, "A and/or B" including the A scheme, or the B scheme, or the scheme where A and B are satisfied simultaneously. Furthermore, for ease of description, spatially relative terms, such as "above," "below," "top," "bottom," and the like, may be used herein merely to describe the spatial positional relationship of one device or feature to another device or feature as illustrated in the figures, and should be understood to encompass different orientations in use or operation in addition to the orientation depicted in the figures.
The inventors of the present application found that, when measuring the cation exchange amount of soil in a region to be measured using aviation hyperspectral, the cation exchange amount cannot be obtained reversely if the soil is covered with crops. Because most of the farmland surface time of the region to be detected is in a state of being covered by crops in one year, the bare soil period is short, and therefore, although the aviation hyperspectral technology has the advantages of high investigation efficiency, full data coverage and the like compared with the traditional method, the window period for carrying out work is short, and the whole investigation period can be greatly prolonged once missing.
For this reason, an embodiment of the present application provides a method for determining a cation exchange amount of soil, referring to fig. 1, which includes the following steps S10 to S40.
Step S10: and obtaining the ground object spectral reflectivity of the region to be detected.
Step S20: and acquiring cation exchange capacity of soil at a plurality of sampling points in the region to be detected.
Step S30: and determining vegetation parameters of the region to be detected according to the ground object spectral reflectivity of the region to be detected.
Step S40: and determining the cation exchange amount of the soil in the area to be detected according to the vegetation parameters of the area to be detected and the cation exchange amount of the soil at the sampling point.
According to the determining method, vegetation parameters of the area to be detected are obtained through aviation hyperspectrum, then soil cation exchange amount data of a plurality of sampling points in the area to be detected are combined, and the soil cation exchange amount of the area to be detected is obtained through indirect inversion, so that the influence of surface coverage on aviation hyperspectral inversion cation exchange amount is weakened, the aviation hyperspectral inversion cation exchange amount is utilized in a non-bare soil state, investigation work can be carried out in a crop growing period, and the working efficiency of an aviation hyperspectral technology in soil quality investigation is improved.
In some embodiments, in step S10, obtaining the feature spectral reflectance of the area to be measured includes: collecting hyperspectral data of a region to be detected; and processing the hyperspectral data to obtain the ground feature spectral reflectivity of the region to be detected. In some embodiments, an aviation hyperspectral sensor may be used to collect hyperspectral data of crops in a region to be measured, and the hyperspectral data obtained may be processed to obtain accurate ground object spectral reflectance data.
In some embodiments, correction processing can be performed on the obtained hyperspectral data to eliminate errors and influences caused by factors such as atmosphere and illumination, so as to obtain accurate ground object spectral reflectance data. Optionally, radiation correction, geometric correction, atmospheric correction, and the like may be performed on the acquired hyperspectral data to obtain the ground object spectral reflectance. The radiation correction, the geometric correction and the atmospheric correction are processing modes for the aviation hyperspectral remote sensing image, and the radiation correction aims to eliminate or correct the brightness distortion of the aviation hyperspectral remote sensing image caused by radiation errors; the purpose of geometric correction is to eliminate or correct the geometric error of the aviation hyperspectral remote sensing image; the atmospheric correction aims to eliminate the influence of factors such as atmosphere, illumination and the like on the ground reflection.
For example, for a farm in northeast China, hyperspectral data for that region may be collected using an aviation hyperspectral sensor first to determine the amount of soil cation exchange for that farm. Specifically, the type of sensor for collecting hyperspectral data can be a CASI/SASI aviation hyperspectral sensor, and the wave band range is 350-2450nm. And performing radiation correction, geometric correction and atmospheric correction on the obtained hyperspectral data to finally obtain the ground object spectral reflectance data.
Fig. 2 shows an embodiment for obtaining cation exchange capacity of soil at a plurality of sampling points in an area to be measured, referring to fig. 2, in step S20, including: step S201, collecting soil samples of sampling points; step S202, performing chemical analysis on the soil sample to determine the cation exchange capacity of the soil sample. In this embodiment, the soil cation exchange amount of a plurality of sampling points in the area to be measured is obtained by chemical analysis, so that the soil cation exchange amount of the whole area is determined as a reference.
In some embodiments, in step S201, when collecting the soil samples of the sampling points, a plurality of sampling points may be disposed in the area to be measured, so that the plurality of sampling points are uniformly distributed in the area to be measured, so as to obtain the soil samples at different positions in the area to be measured.
In some embodiments, the method for determining the cation exchange capacity of the soil can be ammonium acetate centrifugal exchange method, neutral ammonium acetate leaching method, ammonium chloride-ammonium acetate centrifugal exchange method, barium chloride-sulfuric acid forced exchange method, sodium acetate-flame photometry method, isotope tracing method and the like, and is mainly selected according to the pH value of the soil in the area to be detected.
For example, for the farm in northeast China, when determining the cation exchange capacity of the soil, sampling points can be distributed in the region to be tested of the farm in northeast China and soil samples can be collected, wherein the area of the region to be tested is 500km 2 The density of the sampling points can be set to be 1/5 km 2 100 sampling points are distributed in total, soil samples are collected at the sampling points, and then the cation exchange capacity of the soil samples at the sampling points is determined by a chemical analysis method, wherein the chemical analysis method can be an ammonium acetate centrifugal exchange method, and the method is suitable for measuring the cation exchange capacity of acidic and neutral soil.
In some embodiments, in step S30, the vegetation parameters of the area to be measured include chlorophyll content and leaf area index of the vegetation.
In some embodiments, determining vegetation parameters of the area under test includes: acquiring chlorophyll content and leaf area indexes of vegetation at a plurality of sampling points; and determining the chlorophyll content and the leaf area index of the region to be detected according to the chlorophyll content and the leaf area index of the sampling point and the ground feature spectral reflectivity of the region to be detected. According to the embodiment, the chlorophyll content and the leaf area index of the whole area are obtained through inversion by means of the chlorophyll content and the leaf area index of the plurality of sampling points and the ground feature spectral reflectivity of the whole area, so that the full coverage of vegetation parameter data is realized.
In some embodiments, when acquiring the chlorophyll content and the leaf area index of the vegetation at the plurality of sampling points, the chlorophyll content and the leaf area index of the ground vegetation may be measured synchronously when the hyperspectral data acquisition of the area to be measured is performed in step S10, where the measurement position is consistent with the sampling points laid when the soil sample is acquired in step S201. Since the chlorophyll content and leaf area index of the vegetation are in dynamic change, in the embodiment, the chlorophyll content, leaf area index and hyperspectral are synchronously measured, so that the timeliness consistency of hyperspectral data and ground vegetation data can be ensured, and the subsequent inversion precision is ensured.
In some embodiments, chlorophyll content may be measured at each sampling point using a chlorophyll meter, and leaf area index may be measured at each sampling point using a plant canopy analyzer, both data being field measurements.
In some embodiments, determining the chlorophyll content and the leaf area index of the area to be measured from the chlorophyll content and the leaf area index of the sampling point and the ground object spectral reflectance of the area to be measured includes: constructing a change relation model of chlorophyll content, leaf area index and ground object spectral reflectivity according to the chlorophyll content, leaf area index and ground object spectral reflectivity corresponding to the sampling points; and determining the chlorophyll content and the leaf area index of the region to be detected according to the ground feature spectral reflectivity of the region to be detected based on the change relation model of the chlorophyll content, the leaf area index and the ground feature spectral reflectivity. In the embodiment, the chlorophyll content and the leaf area index of the area to be detected are obtained by inversion through constructing the change relation model of the chlorophyll content, the leaf area index and the ground object spectral reflectivity, so that the data acquisition efficiency and accuracy are improved.
In some embodiments, when the change relation model of the chlorophyll content, the leaf area index and the ground object spectral reflectance is constructed, the ground object spectral reflectance corresponding to the sampling point obtained in the step S10 may be used as a dependent variable, and the chlorophyll content and the leaf area index of the sampling point obtained in the step S30 are respectively used as independent variables to respectively construct the change relation model of the chlorophyll content, the leaf area index and the ground object spectral reflectance.
In some embodiments, an inversion model of chlorophyll content, leaf area index, and ground object spectral reflectance may be constructed, by which the relationship of chlorophyll content/leaf area index to ground object spectral reflectance is described. Alternatively, the modeling method employs a random forest method. The random forest method belongs to an integrated learning method in machine learning, is an algorithm for integrating a plurality of decision trees based on an integrated learning idea, and is characterized in that a basic unit of the random forest method is a decision tree, and a built forest is the integration of the decision tree. Wherein each decision tree is a classifier, and for an input sample, N decision trees will obtain N classification voting results. And the random forest integrates all classification voting results, and the class with the largest voting frequency is designated as the final output, namely the final model prediction result.
In this embodiment, when an inversion model of chlorophyll content, leaf area index and ground object spectral reflectance is constructed, the chlorophyll content, leaf area index and corresponding ground object spectral reflectance of each sampling point are taken as one sample, n samples are randomly selected as training data from n samples for the samples of n sampling points, then a classifier is constructed, and finally the classifier obtained by training is combined to obtain a random forest model, so that the overall classification effect is improved, the prediction result is reliable, and the prediction speed is high. Wherein, the random forest method only needs to determine 2 parameters: i.e. the number of decision trees and the number of variables needed to create the branches. Specifically, when building a random forest model, the number of decision trees may be set to 5000, and the number of variables required to create decision branches is set to 3.
In some embodiments, when determining the chlorophyll content and the leaf area index of the area to be measured, the feature spectral reflectance data of the area to be measured obtained in step S10 may be substituted into the constructed relation model of the chlorophyll content, the leaf area index and the feature spectral reflectance to obtain the chlorophyll content and the leaf area index data of the area to be measured, so as to determine the vegetation parameter of the area to be measured.
In some embodiments, the vegetation parameters further comprise a vegetation index. In step S30, determining the vegetation parameters of the area to be measured further includes: and determining the vegetation index of the region to be detected according to the ground object spectral reflectivity of the region to be detected. According to the method, the vegetation index of the area to be detected is calculated through the ground object spectral reflectance, so that vegetation parameter data of more comprehensive ground vegetation are obtained, and the influence of ground surface coverage on inversion of soil cation exchange capacity by using aviation hyperspectrum is weakened to the greatest extent.
In some embodiments, the vegetation index may include at least one of: normalized vegetation index, ratio vegetation index, vertical vegetation index, differential vegetation index, soil conditioning vegetation index, photochemical vegetation index, and atmospheric resistance vegetation index.
In some embodiments, when determining the vegetation index of the area to be measured according to the ground object spectral reflectivities of the area to be measured, at least one of the 7 vegetation indexes may be determined according to the ground object spectral reflectivities of different wavelengths of the area to be measured. Specifically, the expression for calculating the vegetation index is as follows:
NDVI=（R 810 -R 680 ）/（R 810 +R 680 ）；
RVI= R 810 /R 680 ；
PVI=；
DVI= R 810 -R 680 ；
SAVI=；
PRI=(R 570 -R 531 )/(R 570 +R 531 )；
ARVI=；
wherein, NDVI represents normalized vegetation index, RVI represents ratio vegetation index, PVI represents vertical vegetation index, DVI represents difference vegetation index, SAVI represents soil adjustment vegetation index, PRI represents photochemical vegetation index, ARVI represents atmospheric resistance vegetation index; r is R x Representing the spectral reflectance of features having a wavelength of X nanometers, e.g. R 810 Ground object spectral reflectance at 810 nm wavelength; l is soil regulating factor, is a parameter which varies with vegetation densityThe value range of the number L is 0-1, when the vegetation coverage is very high, L=1, when the vegetation coverage is very low, L=0, and when no prior information of the vegetation coverage exists, the value of L is usually 0.5.
Optionally, the vegetation parameters of the area to be measured may include: chlorophyll content, leaf area index, normalized vegetation index, ratio vegetation index, vertical vegetation index, differential vegetation index, soil adjustment vegetation index, photochemical vegetation index, atmospheric resistance vegetation index, 9 vegetation parameter data in total.
Fig. 3 shows an embodiment of determining the cation exchange amount of soil in a region to be measured, referring to fig. 3, in step S40, including: step S401, constructing a change relation model of cation exchange capacity and vegetation parameters according to the vegetation parameters at the sampling points and the cation exchange capacity of soil; step S402, determining the cation exchange amount of soil in the area to be detected according to the vegetation parameters in the area to be detected based on the change relation model of the cation exchange amount and the vegetation parameters. In the embodiment, by constructing a change relation model of vegetation parameters and cation exchange amount, the cation exchange amount of a whole region can be obtained by inversion, and the method can be developed in the growing period of crops and is not limited by the earth surface bare soil condition.
In some embodiments, in step S401, the cation exchange amount of the soil at the sampling point obtained in step S202 may be used as a dependent variable, the vegetation parameter corresponding to the sampling point obtained in step S30 is used as an independent variable, a model of the change relation between the cation exchange amount of the soil and the vegetation parameter is constructed, and the model is verified to ensure the accuracy of the model and the validity of the result. The independent variable can be the 9 vegetation parameters.
Alternatively, an inversion model of soil cation exchange capacity and vegetation parameters can be constructed by using a partial least square method, and the model is verified by using a cross verification mode. Setting the cation exchange capacity of soil as a dependent variable and vegetation parameters as independent variables as sample data during modeling; the number of principal components may be set to 10, regression equations of 10 principal components are obtained by partial least square calculation, respectively, and then cross-validation is performed. In the cross-validation process, for n total samples, one sample i is optionally removed, fitting is performed by the n-1 sample sets which remain, the value of the sample i removed before is estimated by using the obtained regression equation, and the estimation is performed on each sample by using the method. And finally selecting the principal component with highest accuracy of cross-verifying the cation exchange capacity from 10 principal components through modeling and cross-verifying, and taking a regression equation of the principal component as a final model.
In some embodiments, in step S402, the vegetation parameter data of the area to be measured obtained in step S30 may be input into the model to participate in the model operation based on the model of the relation between the cation exchange amount and the vegetation parameter constructed in step S401, so as to obtain the soil cation exchange amount of the area to be measured, so as to achieve the purpose of indirectly calculating the soil cation exchange amount of the area to be measured by using the aviation hyperspectral technology in the period of the soil in a non-bare soil state.
According to the embodiment of the application, the change relation model of the cation exchange amount and various vegetation parameters is established, and the soil cation exchange amount of the whole area is obtained through inversion from an indirect angle, so that investigation work can be carried out in the growing period of crops and is not limited by the bare earth condition of the earth surface, the application space of the aviation hyperspectral technology in the work of investigating the soil cation exchange amount is expanded, and the investigation efficiency is improved.
It should also be noted that, in the embodiments of the present invention, the features of the embodiments of the present invention and the features of the embodiments of the present invention may be combined with each other to obtain new embodiments without conflict.
The present invention is not limited to the above embodiments, but the scope of the invention is defined by the claims.
Claims (10)
1. A method for determining the cation exchange capacity of soil, comprising the steps of:
obtaining the ground object spectral reflectivity of the region to be detected;
acquiring cation exchange capacity of soil at a plurality of sampling points in the region to be detected;
determining vegetation parameters of the region to be detected according to the ground object spectral reflectivity of the region to be detected;
and determining the cation exchange amount of the soil in the area to be detected according to the vegetation parameters of the area to be detected and the cation exchange amount of the soil at the sampling point.
2. The method of claim 1, wherein the vegetation parameters comprise chlorophyll content and leaf area index of vegetation;
the determining the vegetation parameters of the area to be detected according to the ground object spectral reflectivity of the area to be detected comprises:
acquiring chlorophyll content and leaf area indexes of vegetation at the plurality of sampling points;
and determining the chlorophyll content and the leaf area index of the region to be detected according to the chlorophyll content and the leaf area index of the sampling point and the ground object spectral reflectivity of the region to be detected.
3. The method of claim 2, wherein the step of determining the position of the substrate comprises,
constructing a change relation model of the chlorophyll content, the leaf area index and the ground object spectral reflectivity according to the chlorophyll content and the leaf area index of the sampling point and the ground object spectral reflectivity corresponding to the sampling point;
and determining the chlorophyll content and the leaf area index of the region to be detected according to the ground feature spectral reflectivity of the region to be detected based on the change relation model of the chlorophyll content, the leaf area index and the ground feature spectral reflectivity.
4. The method of claim 1, wherein the vegetation parameter comprises a vegetation index;
the determining the vegetation parameters of the area to be detected according to the ground object spectral reflectivity of the area to be detected further comprises:
and determining a vegetation index of the region to be detected according to the ground feature spectral reflectivity of the region to be detected.
5. The method of claim 4, wherein the vegetation index of the test area is determined based on the different wavelength of the ground object spectral reflectivities of the test area.
6. The method of claim 4, wherein the vegetation index comprises at least one of:
normalized vegetation index, ratio vegetation index, vertical vegetation index, differential vegetation index, soil conditioning vegetation index, photochemical vegetation index, and atmospheric resistance vegetation index.
7. The method of any one of claims 1-6, wherein the determining the cation exchange amount of soil in the area under test based on the vegetation parameters of the area under test and the cation exchange amount of soil at the sampling point comprises:
constructing a change relation model of the cation exchange amount and the vegetation parameter according to the vegetation parameter at the sampling point and the cation exchange amount of soil;
and determining the cation exchange amount of the soil of the region to be detected according to the vegetation parameter of the region to be detected based on the change relation model of the cation exchange amount and the vegetation parameter.
8. The method of any one of claims 1-6, wherein the obtaining the ground object spectral reflectance of the area under test comprises:
collecting hyperspectral data of the region to be detected;
and processing the hyperspectral data to obtain the ground object spectral reflectance of the region to be detected.
9. The method of claim 8, wherein the hyperspectral data is corrected to obtain the feature spectral reflectance of the area under test.
10. The method of any one of claims 1-6, wherein said obtaining cation exchange capacity of soil at a plurality of sampling points within said area to be measured comprises:
collecting a soil sample of the sampling point;
and performing chemical analysis on the soil sample to determine the cation exchange capacity of the soil sample.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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