Estimation method of soil-sediment organic carbon enrichment coefficient based on reflectance spectroscopy

Abstract

The present invention relates to a soil-sediment organic carbon enrichment factor estimation method based on reflectance spectroscopy, comprising: collecting river sediment samples and soil samples respectively, measuring the organic carbon content and calculating the measured organic carbon enrichment factor; measuring the sample reflectance spectrum to obtain reflectance, and screening the organic carbon key response bands of the sediment and soil reflectance spectra respectively; constructing an organic carbon enrichment factor spectral index based on the key response band, and constructing a soil-sediment organic carbon enrichment factor spectral estimation model based on the measured organic carbon enrichment factor and the spectral index; collecting sediment samples and soil samples to be tested, measuring the reflectance spectrum respectively, calculating the organic carbon enrichment factor spectral index and inputting it into the estimation model to obtain the soil-sediment organic carbon enrichment factor. The method of the present invention can quickly estimate the organic carbon enrichment factor of the soil-sediment in the basin, obtain the enrichment characteristics of local nutrients, and provide technical support for soil and water conservation, eutrophication and the realization of dual carbon goals.

Description

Technical Field

[0001] The present invention belongs to the field of remote sensing technology, and in particular relates to a method for estimating soil-sediment organic carbon enrichment coefficient based on reflectance spectroscopy. Background Art

[0002] Organic carbon in soil and sediment is a crucial component of ecosystem carbon storage, playing a key role in maintaining soil fertility, promoting plant growth, and enhancing the carbon sequestration function of ecosystems. The enrichment of soil organic carbon is primarily driven by a variety of environmental factors, including topography, land use patterns, hydrological characteristics, and human activities. Because sediment is a key carrier of organic carbon within a watershed, it accumulates organic matter during transport and is further deposited in riverbeds, lakes, or coastal areas. Therefore, sediment accumulation is a crucial factor in understanding changes in watershed carbon storage.

[0003] The degree of organic carbon enrichment is typically measured by the enrichment coefficient (EC), which refers to the ratio of the organic carbon concentration in sediment or soil particles to that in the surface soil. Traditional calculations of the sediment enrichment coefficient rely primarily on outdoor collection or laboratory analysis of soil samples, requiring significant human and material resources. With the rapid development of spectral technology, techniques such as Raman spectroscopy, near-infrared spectroscopy, and laser-induced spectroscopy have enabled rapid monitoring of soil nutrient content by extracting spectral information related to soil nutrients. Indoor hyperspectral data has been widely used by scholars both domestically and internationally to estimate soil physical and chemical properties. However, no research has yet been reported on hyperspectral estimation of the enrichment coefficient. Therefore, using field-measured hyperspectral technology to rapidly estimate the organic carbon enrichment coefficient of watershed soil and sediment and characterize the enrichment of local nutrients can provide technical support for soil and water conservation, eutrophication, and the achievement of dual carbon goals. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for estimating soil-sediment organic carbon enrichment coefficient based on reflectance spectroscopy.

[0005] To achieve the above object, the technical solutions adopted by the present invention are as follows:

[0006] A method for estimating soil-sediment organic carbon enrichment coefficient based on reflectance spectroscopy, the method comprising:

[0007] Collect river sediment samples and soil samples from the corresponding riverbanks, measure their organic carbon content, and calculate the soil-sediment organic carbon enrichment factor as the measured organic carbon enrichment factor;

[0008] Measure the reflectance spectrum of the sediment sample and soil sample to obtain the reflectance R rs, and screen the key response bands of sediment reflectance spectrum and soil reflectance spectrum respectively;

[0009] Constructing an organic carbon enrichment coefficient spectral index based on the key response bands of the sediment reflectance spectrum and the soil reflectance spectrum;

[0010] Constructing a soil-sediment organic carbon enrichment coefficient spectral estimation model based on the measured organic carbon enrichment coefficient and spectral index;

[0011] Collect sediment samples and soil samples to be tested, measure their reflectance spectra, calculate the organic carbon enrichment coefficient spectral index, and input it into the estimation model to obtain the soil-sediment organic carbon enrichment coefficient.

[0012] As a preferred embodiment, the key response bands are screened using an importance analysis method.

[0013] Furthermore, the key response bands are screened using a variety of importance analysis methods, including:

[0014] The organic carbon characteristic bands of sediment and soil samples were analyzed using a variety of importance analysis methods and ranked according to importance. Each method obtained several organic carbon characteristic bands of sediment and soil samples.

[0015] After performing different mathematical permutations and combinations on the organic carbon characteristic bands of the sediment samples and soil samples obtained by each method, the key response band combinations of organic carbon of the sediment samples and soil samples were screened out using the multiple importance analysis methods.

[0016] Different machine learning methods were combined with the band combination for testing, and the final key response bands of organic carbon in sediment samples and soil samples were determined based on the model accuracy.

[0017] Furthermore, the importance analysis method includes SKlearn importance analysis, Eli5 importance analysis, and SHAP method.

[0018] As a preferred embodiment, the organic carbon enrichment factor spectral index is a normalized spectral index constructed based on the key response bands of the sediment reflectance spectrum and the key response bands of the soil reflectance spectrum.

[0019] As a preferred embodiment, when calculating the spectral index, the calculation is based on the distance range and land use type classification, and models are constructed for soils of different distance ranges and different land use types respectively; the distance is the distance between the sediment sample and the corresponding soil sample.

[0020] As a preferred embodiment, the constructing of soil models for different distance ranges and different land use types includes:

[0021] The distances between the sediment samples and the corresponding soil samples are divided into different distance range groups according to the ranges of 0-1 km, 1-5 km, 5-10 km and 10-20 km, and the estimation models are constructed for the sample data of the different distance range groups respectively;

[0022] The sediment samples and the corresponding soil samples are divided into different land use type groups according to the land use type to which the soil samples belong, and the estimation models are constructed for the different land use type groups respectively.

[0023] As a preferred implementation, the model is constructed using random forest, SVM or XGBoost methods.

[0024] As a preferred embodiment, based on the average R 2 , RMSE or MRE to evaluate the model performance, and the model with the best performance was obtained as the final soil-sediment organic carbon enrichment coefficient spectral estimation model.

[0025] As a preferred embodiment, the collection point of the sediment sample is arranged in the river, and the collected sediment sample is river suspended sediment or bed sediment;

[0026] The soil sample collection points are arranged on both sides of the river channel, and the collected soil samples are surface soil.

[0027] As a preferred embodiment, the sediment samples and soil samples are air-dried and ground before measuring the reflectance spectrum.

[0028] As a preferred embodiment, the reflectivity is obtained by preprocessing the reflectance spectrum. R rs , the preprocessing includes:

[0029] Perform breakpoint correction on reflectance spectrum data;

[0030] Calculate the average value of several spectral reflectances measured for each sample as the spectral reflectance of the sample;

[0031] The reflectance spectrum curve is smoothed and denoised, and the noise edge bands at wavelengths of 350-399 nm and 2451-2500 nm are removed.

[0032] The method of the present invention can use indoor measured hyperspectral data to quickly obtain the soil-sediment organic carbon enrichment coefficient. By formulating standard sediment samples and testing their organic carbon and hyperspectral data, the soil organic carbon quality can be reversed, providing technical support for soil and water conservation, eutrophication and the realization of dual carbon goals.

[0033] It should be understood that all combinations of the foregoing concepts and the additional concepts described in more detail below, as long as such concepts are not mutually inconsistent, can be considered part of the inventive subject matter of this disclosure. In addition, all combinations of the claimed subject matter are considered part of the inventive subject matter of this disclosure.

[0034] The foregoing and other aspects, embodiments, and features of the present invention will be more fully understood from the following description in conjunction with the accompanying drawings. Other additional aspects of the present invention, such as features and / or beneficial effects of the exemplary embodiments, will become apparent from the following description or through practice of specific embodiments according to the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component shown in various figures may be represented by the same reference numeral. For the sake of clarity, not every component is labeled in every figure. Embodiments of various aspects of the present invention will now be described by way of example and with reference to the accompanying drawings, in which:

[0036] Figure 1 This is a flow chart of the soil-sediment organic carbon enrichment factor estimation method based on reflectance spectroscopy.

[0037] Figure 2 It is a box plot of the test results of sediment and soil organic matter (organic carbon in this application), where ac is the organic carbon content of three different types of soil covered on the banks of four rivers (Caoqiao River, Shaogang River, Taige Canal, and Yincun Port); d is the organic carbon content of sediment in the four rivers.

[0038] Figure 3 It is a box plot of measured organic carbon enrichment coefficient.

[0039] Figure 4 is the result of reflectance spectrum measurement, where a is the result of sediment reflectance spectrum measurement, b is the result of soil reflectance spectrum measurement.

[0040] Figure 5 These are the screening results of the key response bands (characteristic bands) of sediment and soil organic carbon, where a: bands screened by the SHAP method, b: bands screened by the Sklearn method, and c: bands screened by the Eli5 method.

[0041] Figure 6 These are the results of screening the key response bands (characteristic bands) of sediment and soil organic carbon combinations, where a is the optimal band combination screened by the SHAP method, b is the optimal band combination screened by the Sklearn method, and c is the optimal band combination screened by the Eli5 method.

[0042] Figure 7is the accuracy of the soil-sediment organic carbon enrichment coefficient spectral estimation model for different rivers.

[0043] Figure 8 It is the accuracy of the soil-sediment organic carbon enrichment coefficient spectral estimation model for soil source samples under different land use conditions.

[0044] Figure 9 is the accuracy of the soil-sediment organic carbon enrichment coefficient spectral estimation model at different distances.

[0045] In the aforementioned Figures 1-9, the coordinates, symbols or other expressions expressed in English are all well known in the art and will not be described in detail in this example. DETAILED DESCRIPTION

[0046] In order to better understand the technical content of the present invention, specific embodiments are given and described below with reference to the accompanying drawings.

[0047] Various aspects of the present invention are described in the embodiments of the present disclosure with reference to the accompanying drawings, in which many illustrative embodiments are shown. The embodiments of the present disclosure are not necessarily intended to include all aspects of the present invention. It should be understood that the various concepts and embodiments introduced above, as well as those described in more detail below, can be implemented in any of many ways, because the concepts and embodiments disclosed in the present invention are not limited to any embodiment. In addition, some aspects disclosed in the present invention can be used alone or in any appropriate combination with other aspects disclosed in the present invention.

[0048] According to the general definition of organic carbon enrichment coefficient (the ratio of the organic carbon concentration in sediment or soil particles to the organic carbon concentration in the surface soil), the soil-sediment organic carbon enrichment coefficient referred to in this application refers to the ratio of soil organic carbon content to sediment organic carbon content.

[0049] The key response band described in this application refers to the band in the reflectance spectrum that is sensitive to changes in organic carbon content.

[0050] Example 1

[0051] This embodiment takes the river sediment and surrounding soil of Caoqiao River, Shaogang Port, Taige Canal and Yincun Port in the Taihu Lake Basin as examples to further describe the technical solution of the present invention.

[0052] The process of estimating soil-sediment organic carbon enrichment coefficient shown in the embodiment is as follows: Figure 1 As shown, the specific steps include:

[0053] Step 1: Place several sediment samples in the river and set up soil sample collection points on both sides of the river. Measure the organic carbon content of the sediment and soil samples, and calculate the soil-sediment organic carbon enrichment factor as the measured organic carbon enrichment factor.

[0054] Traditional sediment source research in mountainous areas is based on collecting sediment source samples in the catchment area. However, it is impossible to accurately divide the catchment area in plain river network areas. Most studies use buffer zones as boundaries to study the impact of watersheds on water bodies, and the land use types for source sample collection are cultivated land, woodlands, and grasslands on both sides of the river.

[0055] This example uses samples collected in the previous study (CN117030627A) for calculation. Specifically, sediment and source samples were collected after the rainstorm event in July 2022. Among them, (1) the sediment samples are river bedload. A sediment column sampler is used to collect river sediment columns, and the surface flocculent sediment is peeled off. The bedload on four columns is collected at each sampling point and mixed together as the sediment sample of the sampling point. A total of 36 sediment samples were collected; (2) The source samples are soil samples. According to the land use classification of the Taihu Lake Basin, the surface 0-2 cm soil samples were collected using the quartering method. A total of 88 samples were collected, including 30 cultivated land soils, 28 forest soils, and 30 grassland soils. All samples were placed in self-sealing bags, kept cool and away from light, and brought back to the laboratory.

[0056] Sample pretreatment, including air drying, grinding, and sieving, ensures sample homogeneity and provides quality assurance for subsequent organic matter testing. The dry-burning method is a classic method for determining soil organic matter content. This method uses high-temperature roasting to directly oxidize organic matter in the soil and calculates the organic matter content based on the mass difference before and after the roasting. This method allows researchers to quickly obtain basic data on the sample's organic matter content, providing foundational information for subsequent spectral model construction and enrichment analysis.

[0057] Spectroscopy-assisted testing: Use near-infrared spectroscopy (NIR) to quickly predict and verify the organic matter content of samples.

[0058] The test results of the organic carbon content of sediment and soil in the sediment samples and source samples in this example are as follows: Figure 2 As shown in the figure, the organic carbon enrichment factor was calculated based on the measured sediment and soil organic carbon content as the measured organic carbon enrichment factor. The results are shown in the figure. Figure 3 shown.

[0059] The calculation method of measured organic carbon enrichment coefficient is:

[0060]

[0061] Where, Indicates the concentration of organic carbon in sediment (unit: g / kg ), It indicates the concentration of organic carbon in soil (unit: g / kg). The larger the value, the higher the enrichment of particulate organic carbon. This indicator reflects the enrichment of particulate organic carbon in a soil source sample. EC The larger the value, the higher the degree of organic carbon enrichment in the soil source sample.

[0062] Step 2: After the collected soil and sediment samples are air-dried and ground, the reflectance spectrum is measured indoors, and the reflectance R is obtained after pre-processing. rs , respectively screening key response bands, soil-sediment organic carbon enrichment coefficient spectral index;

[0063] Soil spectral measurements were conducted in a closed, dark laboratory. The spectrometer used a FieldSpec 3 portable optical spectrum analyzer. The probe had a 5-degree field of view (B = 2.5°) and was perpendicular to the sample, 15 cm from the sample (H), with a detection radius (R2) of 0.655 cm. The light source was a 50W halogen lamp, 15° from vertical (A), and 30 cm from the sample (L). The sample container was a small aluminum circular box with a radius (R1) of 2.5 cm and a depth of 1 cm, placed on a black automatic rotating platform. Before collecting sample spectra, dark current was removed, and absolute reflectance was obtained using a 40×40 cm diffuse reflectance standard white reference plate. During spectral acquisition, the sample surface was first smoothed with a ruler. The small circular box containing the soil sample was placed on the black automatic rotating platform. As the small circular box rotated at a constant speed for one revolution, the spectrometer collected 20 spectra at equal intervals. After collecting five sample spectra, the spectra were recalibrated using the reference white reference plate to minimize spectral errors.

[0064] The measured raw spectrum needs to undergo a series of preprocessing before it can be used for model construction: 1) The spectrometer consists of three sub-spectrometers, which receive reflection spectra in the three bands of 350-1100nm, 1000-1800nm, and 1700-2500nm respectively. Therefore, there will be breakpoints near 1000nm and 1800nm due to instrument conversion. The raw spectrum data is corrected for breakpoints using the spectral data processing software ViewSpecPro that comes with the ASD spectrometer ( Figure 4 ); 2) 20 spectra were measured for each sample, and the average reflectance of the 20 spectra was calculated; 3) The spectral curve at this time still had a certain amount of noise and steps, which needed to be smoothed and denoised through preprocessing to reduce noise interference. The convolution smoothing method, which is currently a widely used denoising method, was used to resample the spectral curve at intervals of 5 nm. 4) The noise edge bands (350-399nm and 2451-2500nm) were removed to obtain the processed reflectance spectrum R rs The reflectance spectrum measurement results are as follows: Figure 4 shown.

[0065] The key response bands were screened by using SKlearn importance analysis, Eli5 importance analysis and SHAP method to screen the key response bands of organic carbon content in sediment and soil respectively, and the screening results were obtained, such as Figure 5 As shown in the figure, the screening results (top five in importance) obtained by the SHAP method are 1920nm, 2420nm, 400nm, 1150nm, and 565nm for sediment, and 1890nm, 1750nm, 2400nm, 2225nm, and 1915nm for soil; the screening results (top five in importance) obtained by the SKlearn importance analysis method are 2225nm, 1750nm, 1890nm, 2330nm, and 1915nm for sediment, and 2240nm, 2375nm, 1150nm, 2200nm, and 1920nm for soil; the screening results (top five in importance) obtained by the Eli5 importance analysis method are 1920nm, 400nm, 2420nm, 1150nm, and 2200nm for sediment, and 2400nm, 2225nm, 1915nm, 1890nm, and 1885nm for soil.

[0066] Based on the independent single band screening results, the soil single band screening results obtained by the above three methods (the top five bands in importance) were paired with the top five sediment single bands in importance that were also screened. New band combinations were constructed using different mathematical forms such as addition, subtraction, multiplication, division, and normalization. The band combinations were tested using the three methods respectively. The results are as follows: Figure 6 As shown, the optimal band combination of the three methods is determined as follows: SHAP method: Soil 2400 +Sed 400 (i.e. the sum of the soil 2400nm and sediment 400nm bands); Eli5 method: Soil 1915 -Sed 2420 ;Sklearn method: Soil 1920 -Sed 565 .

[0067] Finally, the optimal band combinations screened by the three methods were input into the three machine learning models respectively, and the optimal results were determined to be the RF model and the Soil model. 1920 -Sed 565 The key response bands of sediment and soil organic carbon are finally determined to be 575nm and 1920nm respectively, and the constructed spectral index is Soil 1920 -Sed 565 .

[0068] Step 3: Construct a soil-sediment organic carbon enrichment coefficient spectral estimation model based on the measured organic carbon enrichment coefficient and the organic carbon enrichment coefficient spectral index.

[0069] RF, XGBoost and SVM models were used to construct soil-sediment organic carbon enrichment coefficient spectral estimation models respectively.

[0070] First, all data were mixed together for modeling, without distinguishing between soil land use types or the distance ranges of soil and sediment samples. Overall, RF performed best across all three land use types, with R² values around 0.86. SVM R² values were generally lower, ranging from 0.17 to 0.21, indicating that the SVM model fit was poor and could not fully capture the relationships between variables, potentially requiring parameter or method adjustment. XGBoost R² values were moderate, ranging from 0.32 to 0.50. While not as good as RF overall, it performed better than SVM, with particularly improved performance on grassland and woodland.

[0071] The data were divided into different groups according to soil types. In this embodiment, the data were divided into cultivated land, forest land and grassland according to soil types. Estimation models were constructed for the cultivated land group data (cultivated land soil samples and corresponding sediment samples), forest land group data and grassland group data respectively. The results are shown in Table 1 and Figure 7 As shown in the figure, the RF model performed best for cultivated land, with an R² of 0.87. XGBoost's R² was only 0.32, indicating poor performance. SVM performed the worst, with an R² of 0.17. The RF model remained optimal for grassland, with an R² of 0.86. XGBoost showed some improvement, reaching an R² of 0.50. SVM performed the weakest, with an R² of only 0.17. RF again achieved the highest value for woodland, with an R² of 0.87. XGBoost's R² was 0.46, second only to RF. SVM performed slightly better than grassland and cultivated land, but still weaker, with an R² of 0.21. RF was the optimal choice, with R² values close to 0.86 across all land use types, demonstrating stable and excellent performance, suitable for fitting the current data. XGBoost was the second-best choice. Although its overall performance was inferior to RF, it performed better for grassland and woodland.

[0072] Table 1 Accuracy of three models under different land use types

[0073] <![CDATA[R 2 ]]> arable land grassland woodland RF 0.87 0.86 0.87 Support Vector Machine 0.17 0.17 0.21 XGBoost 0.32 0.50 0.46

[0074] The sample data are divided into different groups according to the river. In this embodiment, the sampling points are divided into the Caoqiao River group (using the Caoqiao River sediment sample, i.e., the corresponding riverbank soil sample model), Yincun Port, Taige Canal and Shaogang Group. The results are as follows: Figure 8As shown in Table 2, RF's overall performance across all four river categories is close to 0.86-0.87, demonstrating very stable fitting. This is the best of the three models and well explains data variation. SVM's R² values across all categories are very low, only 0.17-0.19, barely explaining relationships between variables and potentially inappropriate for the current dataset. XGBoost's R² values range from 0.41-0.50, placing it between RF and SVM. While not as good as RF overall, it performs well (close to 0.50) for Yincun Port and the Taige Canal, demonstrating potential. Regarding model performance across categories, the RF model performs best for the Caoqiao River, with an R² of 0.86. XGBoost performs moderately well, with an R² of 0.47. SVM performs worst, with an R² of 0.19. RF remains the best for Yincun Port, with an R² of 0.87. XGBoost performs best in this category, with an R² of 0.50. SVM performs the worst, with an R² of 0.18. The RF model performed best for the Taige Canal, with an R² of 0.86. XGBoost performed second best, with an R² of 0.50. SVM performed worst, with an R² of 0.17. RF performed best for Hong Kong, with an R² of 0.87, the highest of the four categories. XGBoost degraded significantly, with an R² of 0.41, slightly worse than the other categories. SVM performed the weakest, with an R² of 0.17.

[0075] Table 2 Accuracy of the three models under different river sediment conditions

[0076] <![CDATA[R 2 ]]> Caoqiao River Yincun Port Taige Canal Burning Hong Kong RF 0.86 0.87 0.86 0.87 Support Vector Machine 0.19 0.18 0.17 0.17 XGBoost 0.47 0.50 0.50 0.41

[0077] The samples were classified according to the distance between the sediment sample and the corresponding soil sample, and the distance ranges were 0-1km, 1-5km, 5-10km, and 10-20km, and the models were constructed separately (only the distance was considered, and the soil type and the river of the sediment were not classified). The results are as follows Figure 9As shown in Table 3, RF performs consistently across all distance ranges, with R² values ranging from 0.86 to 0.88, making it suitable for fitting the full range of data. SVM's R² values are generally low, ranging from 0.13 to 0.21, indicating a weak fit for the data and an inability to adequately explain the relationships between variables. XGBoost performs best in the 0-1 km range, with an R² of 0.99, far exceeding other models. However, at longer distances (1-20 km), performance declines significantly, with R² values ranging from 0.34 to 0.50, indicating insufficient stability. Regarding model performance across different distance ranges, XGBoost performs exceptionally well in the 0-1 km range, achieving an R² of 0.99, a near-perfect fit. RF comes in second, with an R² of 0.88, maintaining excellent performance. SVM performs worst, with an R² of 0.13. RF continues to perform best in the 1-5 km range, with an R² of 0.86, demonstrating strong stability. XGBoost follows closely, with an R² of 0.50. SVM performed relatively poorly, with an R² of 0.21. RF performed best again for the 5-10 km range, with an R² of 0.8667. XGBoost performed moderately well, with an R² of 0.34. SVM performed the worst, with an R² of 0.17. RF performed the best for the 10-20 km range, with an R² of 0.86, demonstrating significant stability. XGBoost came in second, with an R² of 0.40, showing some improvement but still insufficient. SVM performed the worst, with an R² of 0.17.

[0078] Table 3 Different accuracies of three models at different distances

[0079] <![CDATA[R 2 ]]> 0-1km 1-5km 5-10km 10-20km RF 0.88 0.86 0.87 0.86 Support Vector Machine 0.13 0.21 0.17 0.17 XGBoost 0.99 0.50 0.34 0.40

[0080] While the present invention has been disclosed above with reference to preferred embodiments, this is not intended to limit the present invention. Persons skilled in the art will readily appreciate that various modifications and variations can be made without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the claims.

Claims

1. A method for estimating soil-sediment organic carbon enrichment coefficient based on reflectance spectroscopy, characterized in that: The method comprises: Collect river sediment samples and soil samples from the corresponding riverbanks, measure their organic carbon content, and calculate the soil-sediment organic carbon enrichment factor as the measured organic carbon enrichment factor; Measure the reflectance spectrum of the sediment sample and soil sample to obtain the reflectance R rs , and screened the key response bands of organic carbon in sediment reflectance spectrum and soil reflectance spectrum respectively; Constructing an organic carbon enrichment coefficient spectral index based on the key response bands of the sediment reflectance spectrum and the soil reflectance spectrum; Constructing a soil-sediment organic carbon enrichment coefficient spectral estimation model based on the measured organic carbon enrichment coefficient and spectral index; Collect sediment samples and soil samples to be tested, measure their reflectance spectra, calculate the organic carbon enrichment coefficient spectral index, and input it into the estimation model to obtain the soil-sediment organic carbon enrichment coefficient.

2. The estimation method according to claim 1, wherein: The key response bands were screened using the importance analysis method.

3. The estimation method according to claim 1 or 2, characterized in that: The key response bands were screened using a variety of importance analysis methods, including: The organic carbon characteristic bands of sediment and soil samples were analyzed using a variety of importance analysis methods and ranked according to importance. Each method obtained several organic carbon characteristic bands of sediment and soil samples. After performing different mathematical permutations and combinations on the organic carbon characteristic bands of the sediment samples and soil samples obtained by each method, the key response band combinations of organic carbon of the sediment samples and soil samples were screened out using the multiple importance analysis methods. Different machine learning methods were combined with the band combination for testing, and the final key response bands of organic carbon in sediment samples and soil samples were determined based on the model accuracy.

4. The estimation method according to claim 1, wherein: The organic carbon enrichment coefficient spectral index is a spectral index in different mathematical forms constructed based on the key response bands of the sediment reflectance spectrum and the key response bands of the soil reflectance spectrum; the different mathematical forms include addition, subtraction, multiplication, division and normalization forms.

5. The estimation method according to claim 1, wherein: When calculating the spectral index, the distance range and land use type classification are used to construct estimation models for soils of different distance ranges and different land use types; the distance is the distance between the sediment sample and the corresponding soil sample.

6. The estimation method according to claim 5, characterized in that The soil estimation models for different distance ranges and different land use types are constructed separately, including: The distances between the sediment samples and the corresponding soil samples are divided into different distance range groups according to the ranges of 0-1 km, 1-5 km, 5-10 km and 10-20 km, and the estimation models are constructed for the sample data of the different distance range groups respectively; The sediment samples and the corresponding soil samples are divided into different land use type groups according to the land use type to which the soil samples belong, and the estimation models are constructed for the different land use type groups respectively.

7. The estimation method according to claim 1, wherein: The estimation model is constructed using random forest, SVM or XGBoost methods.

8. The estimation method according to claim 1, wherein: Based on the average R 2 , RMSE or MRE to evaluate the model performance, and the model with the best performance was obtained as the final soil-sediment organic carbon enrichment coefficient spectral estimation model.

9. The estimation method according to claim 1, wherein: The collection points of the sediment samples are arranged in the river, and the collected sediment samples are river suspended sediment or bedload; The soil sample collection points are arranged on both sides of the river channel, and the collected soil samples are surface soil.

10. The estimation method according to claim 1, wherein: The sediment samples and soil samples were air-dried and ground, and then the reflectance spectra were measured.

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