CN113420412B - Soil organic carbon content continuous depth distribution extraction method based on imaging spectrum - Google Patents
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Abstract
The invention discloses a soil organic carbon content continuous depth distribution extraction method based on imaging spectrum, which relates to the technical field of metering soil science. According to the technical scheme, the transformation from the punctiform reflection spectrum of the soil to the planar image spectrum is realized, the defect caused by larger sampling depth interval is avoided, the high-space and high-spectrum-resolution soil profile refinement data are provided, technical support is provided for obtaining the continuous depth change characteristics of the soil in the vertical direction of the organic carbon, and the data acquisition capability is greatly improved.
Description
Technical Field
The invention relates to the technical field of metering soil science, in particular to a soil organic carbon content continuous depth distribution extraction method based on imaging spectrum.
Background
The soil is the largest organic carbon reservoir in the land ecological system, and the global soil organic carbon reserves are estimated to be about 1500Pg and 2 times of the atmospheric carbon reservoir, the land vegetation carbon reservoir is 2-4 times, the soil organic carbon releases carbon to the atmosphere in the form of greenhouse gases such as carbon dioxide and the like, the climate change caused by the carbon release to the atmosphere has great influence on human living environment and sustainable development of socioeconomic performance, and the reduction of the carbon dioxide content in the atmosphere by increasing the fixation of the soil organic carbon becomes an effective measure with long-term benefit. Therefore, the research of the organic carbon in the soil is not only an important foundation for sustainable utilization of soil resources, but also has important significance for the research of soil carbon circulation and global climate change.
The difference of the vertical distribution patterns of the soil organic carbon on the profile influences the soil carbon dynamics, which becomes an important content of the research of the soil carbon library in the last 10 years, the traditional soil organic carbon measuring method has the defects of long period, high cost, pollutant emission and the like, meanwhile, the number of the profile samples is limited by a strip sampling method of a generation layer/soil layer or a certain depth interval, the depth distribution rule of the organic carbon is not accurately reflected, the uncertainty of the estimation result of the organic carbon on the profile is caused, and the traditional soil reflection spectrum technology has the advantages of short time, low cost, no pollution, no damage and the like when the organic carbon measurement is carried out, but is still insufficient for inverting the continuous depth distribution characteristics of the soil profile, and the space drawing of the soil organic carbon cannot be directly realized.
Disclosure of Invention
The invention aims to provide an imaging spectrum-based soil organic carbon content continuous depth distribution extraction method to solve the problems in the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the soil organic carbon content continuous depth distribution extraction method based on imaging spectrum comprises the following steps:
step A, respectively collecting complete section soil samples in each sampling point of a target area, and collecting a preset number of soil section strip samples in the collection depth of the complete section soil samples;
step B, respectively preprocessing each collected soil profile strip sample aiming at each sampling point, removing impurities in the soil profile strip sample, naturally airing the sample, grinding and sieving, measuring the organic carbon content of soil in each preprocessed soil profile strip sample, and further obtaining the organic carbon content range of the soil profile strip sample in a target area;
step C, respectively aiming at each sampling point, acquiring imaging spectrum data of a complete soil profile sample to form complete profile imaging, further obtaining spectrum data of the complete profile imaging, preprocessing the spectrum data of the complete profile imaging, extracting a line average value of the spectrum data of the complete profile imaging as line average spectrum data, then obtaining spectrum data of soil profile strip samples with different depths according to the sampling depth of each soil profile strip sample by utilizing the line average spectrum data, and further obtaining line average spectrum data of all the complete profile soil samples in a target area and spectrum data of all the soil profile strip samples;
step D, obtaining a soil organic carbon content prediction model based on the spectral data of the soil profile strip sample in the target area obtained in the step C;
and E, inverting the organic carbon content of different depths in the complete section of the soil based on the soil organic carbon content prediction model obtained in the step D and combining the line average spectrum data obtained in the step C to obtain a continuous depth distribution curve of the organic carbon content in the complete section of the soil, thereby obtaining the distribution and change rule of the organic carbon content of the soil.
Further, in the step B, the organic carbon content of soil in the section strip sample is measured by adopting a potassium dichromate oxidation-external heating method, so that the range and the average value of the organic carbon content in the section strip sample are obtained.
Further, in the step C, the obtained whole section sample is air-dried at room temperature, imaging spectrum data is collected by using a INFINITY V E hyperspectral imager in a darkroom, the spectrum range is 383.53 nm-1050.26 nm, and the number of wave bands is 256.
Further, after the acquired imaging spectrum data are removed from vegetation root systems, section surface cracks and shadow pixels in the spectrum data, band data in a spectrum range of 411.64 nm-999.15 nm are reserved, and the preprocessing of the spectrum data of the complete section imaging of the soil is completed.
In the step D, all the soil profile strip samples in the target area are randomly divided into a modeling sample set and a verification sample set, spectral data in the modeling sample set is used as input, a predicted value of the organic carbon content of the soil profile strip samples is used as output, a soil organic carbon content prediction model is constructed through a partial least squares regression analysis method, spectral data in the verification sample set is used, and the accuracy of the soil organic carbon content prediction model is tested in combination with the range of the organic carbon content of the soil profile strip samples in the target area obtained in the step B.
Further, the determination coefficient R of the soil organic carbon content prediction model is calculated 2 And checking the accuracy of the soil organic carbon content prediction model according to the formula:
calculating a determination coefficient R of a soil organic carbon content prediction model 2 And a root mean square error RMSE, where y i For the measured value of the organic carbon content of the soil in the ith soil profile strip sample of the n soil profile strip samples in the target area,
for the predicted value of soil organic carbon content in the ith soil profile strip sample, +.>
Is the average value of samples of the organic carbon content of the soil;
wherein R is 2 The greater the calculated value of (c) and the smaller the calculated value of RMSE, the higher the accuracy of the soil organic carbon content prediction model.
In the step E, the organic carbon content of different depths in the complete section of the soil is inverted by using the line average spectrum data, and the organic carbon content of a preset unit depth is extracted to form a continuous depth distribution curve of the organic carbon content in the complete section of the soil.
Compared with the prior art, the method for extracting the soil organic carbon content continuous depth distribution based on the imaging spectrum has the following technical effects:
1. according to the technical scheme, the problem that the traditional soil organic carbon laboratory determination method is time-consuming and labor-consuming is solved, a INFINITY V E hyperspectral imager is utilized for collecting a spectrum curve for each pixel, and the organic carbon content of each row of a complete section of soil is inverted by constructing a soil organic carbon content prediction model, so that a soil organic carbon content continuous depth distribution extraction method is provided, high-space and high-spectral-resolution soil section refinement data are provided, and the data acquisition capacity is greatly improved;
2. the invention provides the transformation from the punctiform reflection spectrum of the soil to the reflection spectrum of the planar image by collecting the soil sample with the complete section and each soil section strip sample, thereby avoiding the defect caused by larger sampling depth interval, solving the problem that the existing soil reflection spectrum technology can not reflect the space variation information of the soil in detail, and providing technical support for obtaining the vertical continuous depth variation characteristics of the organic carbon of the soil and laying a theoretical foundation for the space drawing of the soil attribute because the section attribute presents the contradiction between the step variation and the continuous variation of the soil attribute based on the sampling of the occurrence layer or the sampling of the fixed depth interval.
Drawings
FIG. 1 is a flow chart of a method for extracting soil organic carbon content continuous depth distribution according to an exemplary embodiment of the present invention;
FIG. 2 is a schematic diagram of the results of cross-validation of modeled samples in accordance with an exemplary embodiment of the present invention;
FIG. 3 is a schematic diagram of a prediction result of a verification sample according to an exemplary embodiment of the present invention;
FIG. 4 is a schematic representation of a soil integrity profile organic carbon content continuous depth profile in accordance with an exemplary embodiment of the present invention.
Detailed Description
For a better understanding of the technical content of the present invention, specific examples are set forth below, along with the accompanying drawings.
Aspects of the invention are described herein with reference to the drawings, in which there are shown many illustrative embodiments. Embodiments of the present invention are not limited to those shown in the drawings. It is to be understood that this invention is capable of being carried out by any of the various concepts and embodiments described above and as such described in detail below, since the disclosed concepts and embodiments are not limited to any implementation. Additionally, some aspects of the disclosure may be used alone or in any suitable combination with other aspects of the disclosure.
The method for extracting the continuous depth distribution of the organic carbon content in the soil according to the exemplary embodiment of the invention shown in fig. 1 is combined to solve the problem that the conventional method for measuring the organic carbon in the soil in a laboratory and the reflection spectrum cannot reflect the soil information in detail, and the implementation of the invention is more specifically described by collecting soil samples with different sections, constructing a soil organic carbon content prediction model, further obtaining a continuous depth distribution curve of the organic carbon content in the soil, obtaining the distribution condition and the change rule of the organic carbon content in the soil and combining with fig. 1-4.
The soil organic carbon content continuous depth distribution extraction method represented by the exemplary flow chart shown in fig. 1 comprises the following steps:
step A, respectively acquiring two sets of soil sampling sample data in a sampling point of a target area, wherein the soil sampling sample data comprises a complete section sample and each section strip sample with the same depth as the complete section sample;
taking Fan Gongdi of Dongtai city of Jiangsu province and soil organic carbon content continuous depth distribution extraction of the coastal tidal saline soil of the east region as an example, the invention samples according to a field soil description and sampling manual, and acquires 11 complete section soil samples with depth of 1m, and simultaneously, 110 section strip soil samples with depth of 0-5cm, 5-10cm, 10-15cm, 15-20cm, 20-30cm, 30-40cm, 40-50cm, 50-60cm, 60-80cm and 80-100cm are acquired according to each section.
And B, preprocessing the collected section strip sample aiming at each sampling point, removing impurities in the section strip sample, naturally airing the sample, grinding and sieving the sample, measuring the organic carbon content of soil in the preprocessed section strip sample by adopting a potassium dichromate oxidation-external heating method to obtain the range of the organic carbon content in the section strip sample between 1.00g/kg and 17.03g/kg, and obtaining the average value of the organic carbon content of 4.67g/kg.
And C, aiming at each sampling point, air-drying the acquired complete section sample at room temperature, reducing errors caused by different water contents of soil as much as possible, ensuring that the spectral data of each section are measured under the same soil humidity condition, using 4 halogen lamps as light sources to ensure the stability and consistency of illumination conditions, acquiring imaging spectral data by using a INFINITY V E hyperspectral imager under a darkroom, acquiring the imaging spectral data with the spectral range of 383.53 nm-1050.26 nm and the band number of 256, removing surrounding wood frames and tape pixels from the acquired imaging spectral data to form complete section imaging, further obtaining the spectral data of the complete section imaging, removing vegetation root systems, section surface cracks and shadow pixels in the spectral data, simultaneously removing larger bands with larger errors at two ends, reserving 230 band data in the spectral range of 411.64 nm-999.15 nm, sampling line number of 3600 lines, completing the pretreatment of the spectral data of the complete section imaging, extracting the line average value of the complete section imaging spectral data as line average spectral data, and then obtaining the spectral data of the soil sample of the complete section sample according to the depth of each section strip sample, namely obtaining the spectral data of the line average line and the spectral data of the two samples of the soil sample of the step C.
Step D, based on the soil organic carbon spectrum data in the section strip samples obtained in the step C, randomly dividing the section strip samples obtained in the step C into a modeling sample set and a verification sample set, taking every 3 section strip soil samples as an example in the embodiment, continuously taking the first 2 soil section strip samples into the modeling sample set, taking the 3 rd soil section strip samples as the verification sample set, namely 74 modeling sample sets, 36 verification sample sets, taking the soil spectrum data in the modeling sample set as input and the predicted value of the soil organic carbon content as output in the Unscrambler software, constructing a soil organic carbon content prediction model by using but not limited to using a partial least square regression analysis method, obtaining a soil organic carbon content prediction model, and using the spectrum data in the verification sample set to test the precision of the soil organic carbon content prediction model;
calculating a determination coefficient R of a soil organic carbon content prediction model 2 And checking the accuracy of the soil organic carbon content prediction model according to the formula:
calculating a determination coefficient R of a soil organic carbon content prediction model 2 And a root mean square error RMSE, where y i Is the actual measurement value of the organic carbon content of the soil,
is the predicted value of the organic carbon content of the soil, < ->
The average value of samples of the organic carbon content of the soil is obtained, and n is the number of the samples;
wherein R is 2 The larger the calculated value of (2) is, the smaller the calculated value of RMSE is, which shows that the higher the accuracy of the soil organic carbon content prediction model is;
FIG. 2 is a cross-validation of modeling samples using partial least squares regression analysis to model the decision coefficient R in accordance with an embodiment of the present invention 2 For 0.81, the modeling root mean square error RMSE is 1.61g/kg, FIG. 3 is a result of a validated sample prediction using a partial least squares regression analysis method, prediction decision coefficient R according to an embodiment of the present invention 2 The predicted root mean square error RMSE was 0.67 and 1.83g/kg. In a comprehensive view, the spectrum estimation model for constructing the soil organic carbon content by adopting the partial least square regression analysis method can be found to have higher modeling precision and higher prediction capability.
And E, based on the soil organic carbon content estimation model obtained in the step D, combining the line average spectrum data obtained in the step C, inverting the organic carbon content of each line of a complete section of the soil by using the line average spectrum data of a certain sampling point, extracting the organic carbon content of each centimeter of depth to reduce data redundancy, forming a continuous depth distribution curve of the organic carbon content in the complete section of the soil to obtain the distribution and change rule of the organic carbon content of the soil, and FIG. 4 is a continuous depth distribution curve of the organic carbon content of the complete section of the soil per centimeter of the embodiment of the invention, and can show that the organic carbon content of the soil shows continuous change along with the depth, thereby perfectly displaying the continuous variation characteristics and rules of the organic carbon content of the soil, and being superior to the 'ladder' -shaped variation characteristics of the organic carbon content of the section generated on the basis of fixed depth interval sampling.
While the invention has been described with reference to preferred embodiments, it is not intended to be limiting. Those skilled in the art will appreciate that various modifications and adaptations can be made without departing from the spirit and scope of the present invention. Accordingly, the scope of the invention is defined by the appended claims.
Claims (5)
1. The method for extracting the soil organic carbon content continuous depth distribution based on the imaging spectrum is characterized by comprising the following steps of:
step A, respectively collecting complete soil profile samples in all sampling points of a target area, and collecting a preset number of soil profile strip samples in the collection depth of the complete profile soil samples;
step B, respectively preprocessing each collected soil profile strip sample aiming at each sampling point, removing impurities in the soil profile strip sample, naturally airing the sample, grinding and sieving, measuring the organic carbon content of soil in each preprocessed soil profile strip sample, and further obtaining the organic carbon content range of the soil profile strip sample in a target area;
step C, respectively collecting imaging spectrum data of a complete soil profile sample aiming at each sampling point to form complete profile imaging, further obtaining spectrum data of the complete profile imaging, preprocessing the spectrum data of the complete profile imaging, extracting a line average value of the spectrum data of the complete profile imaging as the line average spectrum data of the complete profile imaging, then obtaining spectrum data of soil profile strip samples with different depths according to the sampling depths of each soil profile strip sample by utilizing the line average spectrum data, and further obtaining line average spectrum data of all the complete profile soil samples in a target area and spectrum data of all the soil profile strip samples;
step D, obtaining a soil organic carbon content prediction model based on the spectral data of the soil profile strip sample in the target area obtained in the step C;
in the step D, randomly dividing all soil profile strip samples in a target area into a modeling sample set and a verification sample set, taking spectral data in the modeling sample set as input, taking a predicted value of the organic carbon content of the soil profile strip samples as output, constructing a soil organic carbon content prediction model by a partial least squares regression analysis method, and checking the accuracy of the soil organic carbon content prediction model by using spectral data in the verification sample set and combining the range of the organic carbon content of the soil profile strip samples in the target area obtained in the step B;
and (3) testing the accuracy of the soil organic carbon content prediction model, and according to the formula:
calculating a determination coefficient R of a soil organic carbon content prediction model 2 And a root mean square error RMSE, where y i For the measured value of the organic carbon content of the soil in the ith soil profile strip sample of the n soil profile strip samples in the target area,
for the predicted value of soil organic carbon content in the ith soil profile strip sample, +.>
Is the average value of samples of the organic carbon content of the soil;
wherein R is 2 The larger the calculated value of (2) is, the smaller the calculated value of RMSE is, which shows that the higher the accuracy of the soil organic carbon content prediction model is;
and E, inverting the organic carbon content of different depths in the complete section of the soil based on the soil organic carbon content prediction model obtained in the step D and combining the line average spectrum data obtained in the step C to obtain a continuous depth distribution curve of the organic carbon content in the complete section of the soil, thereby obtaining the distribution and change rule of the organic carbon content of the soil.
2. The method for continuously and deeply extracting the organic carbon content in the soil based on the imaging spectrum according to claim 1, wherein the step B further comprises the step of measuring the organic carbon content in the soil profile strip sample by adopting a potassium dichromate oxidation-external heating method to obtain the range and the average value of the organic carbon content in the soil profile strip sample.
3. The method for extracting the continuous depth distribution of the organic carbon content of the soil based on the imaging spectrum according to claim 1, wherein in the step C, the obtained complete soil profile sample is air-dried at room temperature, imaging spectrum data are collected in a darkroom by using a INFINITY V E hyperspectral imager, the spectrum range is 383.53 nm-1050.26 nm, and the number of wave bands is 256.
4. The method for extracting the continuous depth distribution of the organic carbon content of the soil based on the imaging spectrum according to claim 3, wherein after the acquired imaging spectrum data are used for eliminating vegetation roots, section surface cracks and shadow pixels in the spectrum data, band data in a spectrum range of 411.64 nm-999.15 nm are reserved, and the pretreatment of the spectrum data of the complete section imaging of the soil is completed.
5. The method for extracting the continuous depth distribution of the organic carbon content in the soil based on the imaging spectrum according to claim 1, wherein in the step E, the organic carbon content of different depths in the complete soil profile is inverted by using line average spectrum data, and the organic carbon content of a preset unit depth is extracted to form a continuous depth distribution curve of the organic carbon content in the complete soil profile imaging.
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