Soil organic carbon distribution prediction method based on remote sensing index

Abstract

The invention relates to the technical field of remote sensing detection, in particular to a soil organic carbon distribution prediction method based on a remote sensing index, and the method comprises the steps: extracting a correlation coefficient matrix of any two-waveband spectrums under a plurality of sensitive indexes based on the obtained organic carbon content and soil hyperspectral data in a soil sample; determining an optimal dual-band sensitive hyperspectral index according to the plurality of correlation coefficient matrixes; performing cross-regional verification on the plurality of optimal sensitive indexes, and selecting the optimal sensitive index with the optimal robustness as the organic carbon sensitive hyperspectral index; and performing broadband spectrum resampling on the hyperspectral data, calculating an organic carbon sensitive broadband index after resampling according to the optimal hyperspectral index correlation coefficient matrix, and completing soil organic carbon spatial and temporal distribution characteristic evaluation based on remote sensing data based on the index. The method provided by the invention has good reproducibility and popularization capability in cross-region and cross-time corresponding application.

Description

Technical Field

[0001] This invention belongs to the field of remote sensing detection technology, and in particular relates to a method for predicting the distribution of soil organic carbon based on remote sensing indices. Background Technology

[0002] Existing remote sensing methods for quantitative estimation of soil organic carbon (SOC) have a recognized limitation in model universality, making it extremely difficult to achieve cross-temporal and cross-regional replication. Although the development of satellite remote sensing technology and its integration with machine learning algorithms over the past 20 years has led to the development of numerous techniques, key technical bottlenecks remain in large-scale dynamic remote sensing monitoring of soil physicochemical properties. Soil itself is a complex system, and the transmission of electromagnetic waves within it exhibits strong uncertainty. Complex "reciprocal effects" exist between different functional groups; for example, SOC spectral characteristic bands can be weakened or shifted due to the involvement of iron oxides. Therefore, it is extremely difficult to effectively predict (and invert) soil spectral characteristics using physical theoretical models. The modeling-validation strategy is currently a widely adopted technique for quantitative inversion of soil physicochemical properties, and multiple linear models such as partial least squares regression have been proven to effectively achieve quantitative analysis of organic carbon content based on soil spectra. With the development of machine learning technology, algorithms such as artificial neural networks, random forests, and support vector machines have also been introduced to achieve quantitative estimation of SOC content at local scales. However, the modeling-validation inversion process is highly dependent on ground sampling and the model has poor cross-regional universality, making it unsuitable for large-scale, high-time-sensitivity SOC monitoring. Its main technical bottlenecks are as follows: (1) Traditional remote sensing quantitative estimation models for soil parameters based on statistical processes are highly sample-dependent and cannot be widely used across regions; (2) Statistical models such as least squares regression, as well as machine learning or deep learning models such as artificial neural networks, cannot provide explicit mathematical expressions for specific bands and are extremely difficult to promote and apply. Summary of the Invention

[0003] In view of this, the present invention aims to provide a method for predicting soil organic carbon distribution based on remote sensing indices. By combining data mining algorithms to construct soil remote sensing indices, and through cross-regional and cross-temporal verification, a novel organic carbon-sensitive broadband remote sensing index is proposed. This provides a reliable and easily promoted technical method for monitoring arable land soil quality and conducting agricultural ecological research, filling the gap in cross-regional and cross-temporal monitoring techniques for arable land SOC.

[0004] To achieve the above objectives, the technical solution created by this invention is implemented as follows: A method for predicting soil organic carbon distribution based on remote sensing indices, comprising: S1: Obtain the organic carbon content and soil hyperspectral data from soil samples, and calculate multiple sensitivity indices between any two spectral bands; based on the relationship between organic carbon content and soil hyperspectral data, extract the correlation coefficient matrix between each sensitivity index and soil organic carbon content. S2: Based on the multiple correlation coefficient matrices obtained in step S1, determine the two optimal spectral bands for each sensitivity index, and the optimal sensitivity index corresponding to the two optimal spectral bands; S3: Cross-regional verification was performed on multiple optimal sensitivity indices, and the optimal sensitivity index with the best robustness was selected as the organic carbon sensitive hyperspectral index; S4: Based on the spectral response function of the remote sensing data, perform broadband resampling of the soil hyperspectral data in step S1, calculate the correspondence of the resampled broadband in the correlation coefficient matrix in step S2, and extract the organic carbon sensitive broadband remote sensing index based on the organic carbon sensitive hyperspectral index in step S3. S5: Using the organic carbon-sensitive broadband remote sensing index from step S4, predict the distribution of organic carbon in the remote sensing data of the area to be detected.

[0005] Furthermore, in step S1: using the Pearson correlation analysis algorithm, the correlation coefficient is calculated using the following formula: ; Where, r i,j X represents the correlation coefficient between the spectral bands of the i-th and j-th bands of the soil sample. i,j Let Y represent the sensitivity index between the spectral values ​​of the i-th band and the j-th band, and let Y represent the organic carbon content. The obtained correlation coefficients are arranged in band order to form a correlation coefficient matrix.

[0006] Furthermore, the sensitivity indices are the difference index, the ratio index, and the normalization index.

[0007] Furthermore, in step S2: the two spectral bands corresponding to the largest correlation coefficient in the correlation coefficient matrix are selected as the two optimal spectral bands; each sensitivity index under the two optimal spectral bands is the corresponding optimal sensitivity index.

[0008] Furthermore, between steps S2 and S3, the following step is also included: verifying the optimal spectral band determined in step S2 using the ICO algorithm and / or SPA algorithm. This includes: calculating each sensitivity index for any two spectral bands in the soil hyperspectral data, constructing a spectral index set corresponding to each sensitivity index, with a size of N×N×n, where N represents the total number of bands in the soil hyperspectral data and n represents the number of soil samples; taking a sub-spectral index set of size N×n corresponding to any band in each spectral index set, and using the organic carbon content of the corresponding sample for calculation using the ICO algorithm and / or SPA algorithm, so that the optimal sub-spectral index set is selected from the N sub-spectral index sets, and the band spectra corresponding to the optimal sub-spectral index set form the verification band spectrum set; if the optimal band spectrum determined in step S2 is in the band spectrum set, it indicates that the optimal band spectrum determined in step S2 is correct.

[0009] Furthermore, in step S4, the broadband spectral resampling of the soil hyperspectral data includes: setting a moving average window based on the full width at half maximum (FWHM) of the spectral response function of the remote sensing sensor, and resampling the spectral data within the effective spectral range of the soil hyperspectral data to obtain broadband spectral data.

[0010] Furthermore, step S4 also includes: expanding the resampled wideband spectral data based on the regression fitting transformation equation.

[0011] Furthermore, the organic carbon-sensitive broadband remote sensing index is as follows: RSI=R SWIR / R NIR ; Wherein, RSI represents the organic carbon sensitive broadband remote sensing index, R SWIR R represents the spectral reflectance in the shortwave infrared band of the remote sensing data of the area to be detected. NIR This represents the spectral reflectance in the near-infrared band of the remote sensing data of the area to be detected.

[0012] Compared with the prior art, the present invention can achieve the following beneficial effects: The soil organic carbon distribution prediction method based on remote sensing indices described in this invention utilizes indices with clear physical meanings and concise expressions. By combining soil organic carbon spectral characteristics with multi-temporal remote sensing data, it can rapidly and quantitatively evaluate the spatiotemporal distribution characteristics of soil organic carbon over large areas. This allows for the development of multi-year, cross-regional, and large-scale soil organic carbon remote sensing indices, generating large-scale spatial distribution maps of soil organic carbon with resolutions exceeding 10 meters, and enabling precise tracking and dynamic analysis of the spatiotemporal variation trends of soil organic carbon. Furthermore, the method provided by this invention has good cross-regional applicability and a simpler expression (ratio of shortwave infrared to near-infrared spectrum) compared to traditional models. It is also applicable to multiple types of satellite remote sensing data (with both shortwave and near-infrared channels), demonstrating strong scalability. Attached Figure Description

[0013] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 A schematic flowchart of the method for predicting soil organic carbon distribution based on remote sensing indices as described in the embodiments of the present invention; Figure 2 A flowchart illustrating the method for predicting soil organic carbon distribution based on remote sensing indices as described in an embodiment of the present invention; Figure 3 A schematic diagram illustrating the process of obtaining the correlation coefficient matrix in step S1 of the present invention embodiment; Figure 4 The correlation image under the ratio index described in the embodiment of the present invention; Figure 5 A schematic diagram illustrating the verification process of the optimal band spectrum described in the embodiments of the present invention; Figure 6 The result diagram of the cross-regional verification described in the embodiment of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not constitute a limitation thereof.

[0015] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0016] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0017] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0018] The invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] like Figure 1 and Figure 2 As shown in the embodiments of the present invention, the method for predicting soil organic carbon distribution based on remote sensing indices includes: S1: Obtain the organic carbon content and soil hyperspectral data from soil samples, and calculate multiple sensitivity indices between any two spectral bands; based on the relationship between organic carbon content and soil hyperspectral data, extract the correlation coefficient matrix between each sensitivity index and soil organic carbon content.

[0020] In this embodiment of the invention, the organic carbon content and hyperspectral data of soil samples were acquired in a standardized laboratory environment. Specifically, the soil samples were sieved through a 0.075mm standard soil sieve, dried at 50°C for 24 hours, and then subjected to standardized spectral measurements. During the spectral measurements, ambient light interference needed to be minimized; therefore, the measurements were performed in a darkroom. An ASD portable hyperspectral analyzer was used with a high-density reflectance probe (2901±10°%K, 4.05W halogen lamp light source) to collect the spectral reflectance data of the soil samples (401nm-2400nm). The soil sample was placed in a clean petri dish on a metal stand. The probe was positioned as close to the soil sample surface as possible, but not in direct contact to avoid contamination. The instrument and probe were preheated for at least 30 minutes before the spectral measurements began. The instrument was calibrated every 30 minutes using a standard diffuse reflectance reference white plate after the measurements started. Each soil sample was measured five times, and the average value was used as the spectral data for that sample. Standardized spectral measurements were performed on 17,730 soil samples from different regions to construct a hyperspectral index. The organic carbon content of all soil samples was measured using the potassium dichromate titration method to analyze the organic carbon reflectance spectral characteristics. In this embodiment, 10,000 of the 17,730 soil samples were selected as calculation samples, and the remaining soil samples were used as validation samples, divided into seven aliquots according to their distribution area. This invention acquires hyperspectral data of soil samples under controlled laboratory conditions, strictly controlling the soil sample processing and preparation process, laboratory environmental conditions, and spectral measurement operation specifications to ensure the quality of spectral data acquisition and greatly reduce data errors introduced into further spectral analysis. It effectively solves the technical problem that current soil spectral analysis technology is difficult to achieve under standardized conditions for soil sample collection, preparation, and spectral testing during basic data acquisition, and the resulting spectral variation is a major factor restricting model sharing and cross-application.

[0021] In some embodiments, the process of obtaining the correlation coefficient matrix is ​​as follows: Figure 3 As shown, it includes: Using the Pearson correlation analysis algorithm, the correlation coefficient is calculated using the following formula: ; Where, r i,j X represents the correlation coefficient between the i-th and j-th bands of a soil sample. i,j Let Y represent the sensitivity index between the spectral values ​​of the i-th band and the j-th band, and let Y represent the organic carbon content. The obtained correlation coefficients are arranged in band order to form a correlation coefficient matrix.

[0022] By traversing the full-band soil spectral data from 401 to 2400 nm, the sensitivity index of any two spectral bands is calculated. Then, based on correlation analysis, the correlation coefficient between any sensitivity index and organic carbon content is calculated, and a three-dimensional correlation coefficient matrix is ​​constructed, thereby realizing the analysis and extraction of organic carbon sensitive spectral indices.

[0023] Specifically, in some embodiments, the sensitivity index is the difference index SSDI. i,j =R i -R j SSRI (Short-Term Ratio Index) i,j =R i / R j and normalized index SSNI i,j =(R i -R j ) / (R i +R j ), where R i and R j These represent the spectral reflectance of the i-th and j-th bands within the 401nm-2400nm wavelength range, respectively. In this embodiment of the invention, the 401nm-2400nm wavelength range is divided into 1nm band intervals, resulting in 2000 spectral bands. It is understandable that for the SSDI (Spectral Reflectance Difference Index)... i,j The corresponding correlation coefficient for: ; For the ratio index SSRI i,j The corresponding correlation coefficient for: ; For the normalized index SSNI i,j The corresponding correlation coefficient for: ; In this embodiment of the invention, based on the Pearson correlation coefficient, the correlation coefficients of the sensitivity indices of any two bands and the organic carbon content are filled into the corresponding positions of the correlation coefficient matrix. By traversing all bands (401nm-2400nm), a full-band correlation coefficient matrix of size 2000×2000 can be obtained.

[0024] Based on the correlation coefficient matrix of the ratio index, the embodiments of the present invention provide, as follows: Figure 4 The correlation graph shown is under the ratio index. Figure 4 Figures (a) through (e) show the changes in the correlation index with sample sizes of 100, 500, 1000, 5000, and 10000, respectively. Figure 4The colors, from blue to red, represent the transition from negative correlation to positive correlation. Figure 4 It can be seen that as the sample size included in the analysis increases, the high-low distribution of the correlation coefficient between the dual-band index and organic carbon content gradually converges and stabilizes. This distribution characterizes the correlation coefficient relationship between any dual-band index (difference index, ratio index, and normalized index) and organic carbon content in the 1 nm interval spectral range. Specifically, the correlation coefficient matrix characterizes the significant response of the dual-band spectral index to organic carbon content in the near-infrared and short-wave infrared ranges. As organic carbon content increases, the reflectance in the near-infrared band decreases more rapidly due to changes in organic matter, associated minerals, or structure, while the decrease in the short-wave infrared band is relatively slower or less affected. As organic carbon content increases, the ratio of the short-wave infrared band to the near-infrared band increases, resulting in a positive correlation between the sensitivity index and organic carbon content.

[0025] S2: Based on the multiple correlation coefficient matrices obtained in step S1, determine the two optimal spectral bands for each sensitivity index, and the optimal sensitivity index corresponding to the two optimal spectral bands. In some embodiments, the two spectral bands corresponding to the largest correlation coefficient in the correlation coefficient matrix are selected as the two optimal spectral bands; each sensitivity index under the two optimal spectral bands is the corresponding optimal sensitivity index. In this embodiment of the invention, step S2 specifically includes: for each sensitivity index among the difference index, ratio index, and normalization index, selecting the two spectral bands corresponding to the largest correlation coefficient in the correlation coefficient matrix as the two optimal spectral bands; each sensitivity index under the two optimal spectral bands is the corresponding optimal sensitivity index. Specifically, taking the ratio index as an example, the largest correlation coefficient in the correlation coefficient matrix corresponding to the ratio index (e.g., the largest correlation coefficient in the ratio index) is selected as the largest correlation coefficient in the correlation coefficient matrix (e.g., the largest correlation coefficient in the ratio index) is selected as the largest correlation coefficient in the correlation coefficient matrix. Figure 4 (e) The two bands corresponding to the point with the deepest red color (i.e., λ) i =1406, λ j =1281) represents two optimal spectral bands; based on the two optimal spectral bands λ i =1406 and λ j =1281 Calculate the optimal sensitivity index corresponding to the ratio index, that is: R-SSRI=R 1406 / R 1281 ; Where R-SSRI represents the optimal sensitivity index corresponding to the ratio index, R 1406 and R 1281 These represent the spectral reflectance at wavelengths of 1406 nm and 1281 nm, respectively. Using the above method, the optimal sensitivity indices R-SSDI and R-SSNI corresponding to the difference index and normalized index can be obtained as follows: R-SSDI=R 1399 -R 1353 ; R-SSNI=(R 1406 -R 1281 ) / (R 1406 +R 1281 ).

[0026] In some embodiments, after step S2, the algorithm further includes: using the ICO (Interval Combination Optimization) algorithm and / or the SPA (Successive Projection Algorithm) algorithm to verify the optimal band spectrum determined in step S2. The SPA algorithm is a typical forward optimization algorithm based on the successive projection strategy. When extracting spectral feature bands, the successive projection algorithm starts from one band and iteratively selects the band data with the lowest correlation to the starting band spectral data to form a data subset. Then, stepwise multiple regression modeling and verification are performed based on this data subset. Since the data subset selected by the algorithm consists of band data with the lowest correlation, the SPA algorithm can effectively reduce collinearity between feature bands and is a major algorithm for constructing remote sensing indices. The ICO algorithm is a spectral feature band analysis method. This algorithm uses a soft contraction followed by local retrieval to achieve spectral interval combination optimization, and has the advantages of fewer parameters, soft contraction, and fast convergence. Similar to the SPA algorithm, the ICO algorithm is also based on multi-band spectral data combination, using a multi-band combination selection strategy that optimizes the model accuracy during multiple regression modeling and validation. Unlike the SPA algorithm, ICO employs spectral interval analysis, replacing the point-by-point analysis strategy used in algorithms like SPA. This significantly reduces the computational burden of soft contraction strategies during the optimization process and effectively mitigates the risk of overfitting, making it a primary algorithm for remote sensing index optimization. This invention further validates the optimal band spectrum determined in step S2 using two algorithms to ensure the accuracy of the optimal band spectrum.

[0027] The verification process for the optimal band spectrum is as follows: Figure 5 As shown, the specific steps include: calculating each sensitivity index for any two spectral bands in the soil hyperspectral data, constructing a spectral index set corresponding to each sensitivity index, with a size of N×N×n, where N represents the total number of bands in the soil hyperspectral data (N=2000 in this embodiment), and n represents the number of soil samples (n=10000 in this embodiment); within each spectral index set, taking a sub-spectral index set IDX of size N×n corresponding to any band, and using the organic carbon content of the corresponding sample for calculation using the ICO algorithm and / or SPA algorithm, so that the optimal sub-spectral index set Opt is selected from the N sub-spectral index sets IDX. IDX Optimal subspectral index set OptIDX The corresponding band spectrum constitutes the verification band spectrum set; if the optimal band spectrum determined in step S2 is in the band spectrum set, it indicates that the optimal band spectrum determined in step S2 is correct.

[0028] S3: Cross-regional verification was performed on multiple optimal sensitivity indices, and the optimal sensitivity index with the best robustness was selected as the organic carbon sensitive hyperspectral index.

[0029] In this embodiment of the invention, seven validation soil samples from different regions were used to conduct cross-regional validation of the optimal sensitivity indices corresponding to the difference index, ratio index, and normalized index, respectively. The stability of the three optimal sensitivity indices in cross-regional application was tested, and the test results are as follows: Figure 6 As shown, Figure 6 Figures (a) to (f) show the accuracy performance of the optimal difference index (R-SSDI), optimal ratio index (R-SSRI), and optimal normalized index (R-SSNI) in seven test sample sets, respectively. The seven validation soil samples came from different regions, and the validation results were obtained in... Figure 6 The results are represented by scatter plots of different colors and fitting results. The results show that the optimal sensitivity index corresponding to the ratio index has the best robustness in cross-regional verification and is the organic carbon sensitive hyperspectral index selected in the embodiments of this invention.

[0030] S4: Based on the spectral response function of the remote sensing data, the soil hyperspectral data from step S1 is resampled in a wideband. The correspondence between the resampled wideband data and the correlation coefficient matrix in step S2 is calculated. Based on the organic carbon-sensitive hyperspectral index from step S3, the organic carbon-sensitive wideband remote sensing index is extracted. This invention utilizes the spectral response function of the remote sensing sensor to perform wideband resampling of soil hyperspectral data, matching the sampling rate of the soil hyperspectral data in the laboratory environment with the remote sensing data. This maps the hyperspectral data from the laboratory environment to the remote sensing data, thereby achieving the transfer from measured data in the laboratory environment to remote sensing data. The organic carbon-sensitive wideband remote sensing index obtained in step S4 reflects the distribution of soil organic carbon when applied to the remote sensing data, thus enabling prediction of soil organic carbon distribution based on the remote sensing index. In some embodiments, wideband spectral resampling of soil hyperspectral data includes: setting a moving average window based on the full width at half maximum (FWHM) of the spectral response function of the remote sensing sensor, and resampling the spectral data within the effective spectral range of the soil hyperspectral data to obtain wideband spectral data. Step S4 also includes: expanding the resampled wideband spectral data based on the regression fitting transformation equation.

[0031] To verify the effectiveness of the method provided by this invention, this embodiment uses publicly available Landsat, Sentinel, and MODIS remote sensing data to predict the distribution of organic carbon content in a provincial demonstration area. Step S4 specifically includes: using the full width at half maximum (FWHM) of the spectral response function of the remote sensing sensors corresponding to the Landsat, Sentinel, and MODIS data, respectively, setting a moving average window, and using spectral resampling technology. The moving average window is used to resample hyperspectral data within the effective spectral range of different remote sensing data, and then the hyperspectral data is converted to multispectral data based on a regression fitting transformation equation; based on the scale-transformed spectral data, the correlation coefficient matrix between the broadband spectral index and organic carbon content is reconstructed; according to the organic carbon-sensitive hyperspectral index in step S3, the organic carbon-sensitive broadband remote sensing index is extracted. At this point, the following results are obtained: The organic carbon-sensitive broadband remote sensing index for Landsat remote sensing data is: RSI Landsat =R B7 / R B4 ; Among them, RSI Landsat R represents the organic carbon-sensitive broadband remote sensing index of Landsat remote sensing data. B7 R represents the spectral reflectance of band 7 in Landsat remote sensing data, corresponding to the shortwave infrared band in Landsat remote sensing data. B4 This represents the spectral reflectance of band 4 in Landsat remote sensing data, corresponding to the near-infrared band in Landsat remote sensing data. The organic carbon-sensitive broadband remote sensing index for Sentinel remote sensing data is: RSI Sentinel =R B12 / R B8 ; Among them, RSI Sentinel R represents the organic carbon-sensitive broadband remote sensing index of Sentinel remote sensing data. B12 R represents the spectral reflectance of band 12 in Sentinel remote sensing data, corresponding to the shortwave infrared band in Sentinel remote sensing data. B8 This represents the spectral reflectance of band 4 in Sentinel remote sensing data, corresponding to the near-infrared band in Sentinel remote sensing data. The organic carbon-sensitive broadband remote sensing index for MODIS remote sensing data is: RSI MODISl =R B5 / R B2 ; Among them, RSI MODISlR represents the organic carbon-sensitive broadband remote sensing index of MODIS remote sensing data. B5 R represents the spectral reflectance of band 5 in MODIS remote sensing data, corresponding to the shortwave infrared band in MODIS remote sensing data. B2 This represents the spectral reflectance of the second band in MODIS remote sensing data, corresponding to the near-infrared band in MODIS remote sensing data. Based on the above three remote sensing data, the organic carbon sensitive broadband remote sensing index can be summarized as follows: RSI=R SWIR / R NIR ; Wherein, RSI represents the Ratio Soil Index, which is a broadband remote sensing index sensitive to organic carbon. SWIR R represents the spectral reflectance in the shortwave infrared band of the remote sensing data of the area to be detected. NIR This represents the spectral reflectance in the near-infrared band of the remote sensing data of the area to be detected.

[0032] S5: Using the organic carbon-sensitive broadband remote sensing index from step S4, predict the distribution of organic carbon in the remote sensing data of the area to be detected.

[0033] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0034] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for predicting soil organic carbon distribution based on remote sensing indices, characterized by, The method comprises the following steps: S1: Obtain the organic carbon content in the soil sample and the soil hyperspectral data, calculate a plurality of sensitive indexes between any two waveband spectrums, and extract a correlation coefficient matrix of each sensitive index and the soil organic carbon content based on the relationship between the organic carbon content and the soil hyperspectral data; S2: Determine two optimal spectral bands under each sensitive index and the optimal sensitive index corresponding to the two optimal spectral bands according to the plurality of correlation coefficient matrices obtained in step S1; S3: Perform cross-region verification on the plurality of optimal sensitive indexes, and select the optimal sensitive index with the best robustness as the organic carbon sensitive hyperspectral index; S4: Perform wide-band resampling on the soil hyperspectral data in step S1 according to the spectral response function of the remote sensing data, calculate the corresponding relationship of the resampled wide-band in the correlation coefficient matrix in step S2, extract the organic carbon sensitive wide-band remote sensing index according to the organic carbon sensitive hyperspectral index in step S3; S5: Use the organic carbon sensitive wide-band remote sensing index in step S4 to predict the organic carbon distribution of the remote sensing data of the region to be detected.

2. The remote sensing index-based soil organic carbon distribution prediction method according to claim 1, characterized in that, In step S1: The correlation coefficient is calculated by using the Pearson correlation analysis algorithm through the following formula: ； wherein r i,j represents the correlation coefficient under the spectrum of the i-th wave band and the j-th wave band of the soil sample, X i,j represents the sensitive index between the i-th wave band and the j-th wave band spectrum, Y represents the organic carbon content; The obtained correlation coefficient is arranged in the order of the waveband to form a correlation coefficient matrix. 3.The remote-sensing index-based method for predicting soil organic carbon distribution according to claim 1, wherein, The sensitive index is a difference index, a ratio index and a normalized index.

4. The remote sensing index-based prediction method of soil organic carbon distribution according to claim 1, characterized in that, In step S2: The two spectral bands corresponding to the maximum correlation coefficient in the correlation coefficient matrix are selected as the two optimal spectral bands; and each sensitive index under the two optimal spectral bands is the corresponding optimal sensitive index.

5. The remote sensing index-based prediction method of soil organic carbon distribution according to claim 1, characterized in that, Between step S2 and step S3, the ICO algorithm and / or the SPA algorithm are further used to verify the optimal spectral bands determined in step S2, including: Each sensitive index is calculated for any two spectral bands in the soil hyperspectral data, and a spectral index set corresponding to each sensitive index is constructed, and the size of the spectral index set is N×N×n, wherein N represents the total number of wavebands in the soil hyperspectral data, and n represents the number of soil samples; In each spectral index set, a sub-spectral index set with a size of N×n corresponding to any one waveband is taken, and the ICO algorithm and / or the SPA algorithm are calculated by introducing the organic carbon content of the corresponding sample, so that the optimal sub-spectral index set is selected from the N sub-spectral index sets, and the waveband spectrum corresponding to the optimal sub-spectral index set constitutes a verification waveband spectrum set; If the optimal waveband spectrum determined in step S2 is in the waveband spectrum set, it is indicated that the optimal waveband spectrum determined in step S2 is correct.

6. The remote sensing index-based soil organic carbon distribution prediction method according to claim 1, characterized in that, In step S4, the wide-band spectral resampling on the soil hyperspectral data comprises: According to the full width at half maximum of the spectral response function of the remote sensing sensor, a moving average window is set, and the spectral data in the effective spectral interval in the soil hyperspectral data is resampled to obtain wide-band spectral data.

7. The remote sensing index-based soil organic carbon distribution prediction method according to claim 6, characterized in that, Step S4 further comprises: extending the resampled wide-band spectral data based on a regression fitting conversion equation.

8. The remote sensing index-based soil organic carbon distribution prediction method according to claim 1, characterized in that, The organic carbon sensitive wide-band remote sensing index is as follows: RSI = R SWIR / R NIR ; wherein RSI represents an organic carbon sensitive wide-band remote sensing index, R SWIR represents the spectral reflectance of the short-wave infrared band in the remote sensing data of the region to be detected, NIR represents the spectral reflectance of the near-infrared band in the remote sensing data of the region to be detected.

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