Urbanized agricultural soil quality monitoring method based on minimum data set

Abstract

The application discloses a kind of urbanization agricultural soil quality monitoring methods based on minimum data set, belong to soil quality monitoring field, comprising: according to soil monitoring requirement, set soil sampling site, and according to the soil sample obtained by collection, obtain soil quality index data, utilize principal component analysis method to obtain the minimum data set of soil quality index data;And by soil scoring function, cross validation and spatial interpolation method are handled, obtain visual soil quality monitoring result.The application utilizes minimum data set, only needs to determine less data to understand the change of urbanization agricultural soil, while ensuring the accuracy of monitoring, avoid data redundant, to reduce the research cost and period of soil quality monitoring;At the same time, by spatial interpolation method, the cost and time consumption of soil quality monitoring can be reduced, and the change of soil quality is monitored quickly.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of soil quality monitoring, and particularly relates to a method for monitoring soil quality of urbanized agricultural land based on a minimum data set. BACKGROUND

[0002] Soil is an important component of the terrestrial ecosystem. Soil quality (SQ) is crucial to long-term sustainable agricultural development and environmental quality. Accurate evaluation of soil quality is essential for human survival environment and agricultural management and government decision-making. However, with the acceleration of social development and urbanization, problems such as improper management of agricultural soil have become increasingly serious, leading to soil fertility decline, heavy metal pollution, and water pollution, resulting in soil quality degradation and reduced crop quality and food production. Therefore, soil quality problems in urbanized areas have attracted worldwide attention, and the evaluation results provide a theoretical basis for subsequent improvement and remediation policies.

[0003] Therefore, some people have proposed different soil quality indicators and evaluation methods to comprehensively evaluate soil quality. Soil quality indicators include physical indicators, chemical indicators, and biological indicators, which affect soil function and are sensitive to soil environmental changes. Although selecting more comprehensive indicators can more accurately reflect soil quality, it significantly increases the difficulty of data acquisition and analysis, and the cost and cycle of evaluation, which cannot be applied to soil environment that needs to be monitored quickly. SUMMARY

[0004] In view of the above problems in the prior art, the application provides a method for monitoring soil quality of urbanized agricultural land based on a minimum data set, which solves the problems of long monitoring cycle and high research cost of urbanized agricultural soil quality.

[0005] In order to achieve the above-mentioned purpose, the technical scheme adopted by the application is as follows: a method for monitoring soil quality of urbanized agricultural land based on a minimum data set, comprising the following steps:

[0006] S1: According to the soil monitoring requirements, set the sampling site of urbanized agricultural soil, and collect the soil samples;

[0007] S2: According to the soil samples, obtain soil quality indicator data;

[0008] S3: According to the soil quality indicator data, use principal component analysis method for screening to obtain the minimum data set of soil quality indicator data;

[0009] S4: According to the minimum data set, use soil scoring function to convert membership function to obtain soil indicator score of the minimum data set;

[0010] S5: According to the soil index score of the minimum data set, the cross-validation and spatial interpolation method are used for processing to obtain the visual soil quality monitoring result.

[0011] The beneficial effects of the present application are: the present application uses the minimum data set, and only a small amount of data needs to be determined to understand the change of the urbanized agricultural soil, which can avoid data redundancy while ensuring the accuracy of monitoring, thereby reducing the research cost and period of soil quality monitoring; at the same time, the present application predicts the soil quality of unknown points based on the actual sampling site through the spatial interpolation method, without the need to additionally increase the sampling site, which can reduce the cost and time consumption of soil quality monitoring, realize rapid monitoring of soil quality change, and expand the regional soil quality index database, provide the dynamic change of the agricultural soil environment in the urbanized area, and provide the basis for the decision-making of the soil environment change response.

[0012] Further: the soil quality index data includes agricultural soil fertility index data, agricultural soil environment index data and agricultural soil basic index data;

[0013] The agricultural soil fertility index data includes organic matter, total nitrogen, total phosphorus, total potassium, available nitrogen and available phosphorus;

[0014] The agricultural soil environment index data includes chromium, cadmium, mercury, lead and heavy metal elements;

[0015] The agricultural soil basic index data includes pH, moisture and soil particle size.

[0016] Further: the specific steps of S3 are as follows:

[0017] S301: The soil quality index data is standardized and principal component analysis is performed to obtain the principal components of the soil quality index data;

[0018] S302: The principal components of the soil quality index data whose eigenvalues are greater than the set eigenvalue threshold are retained to obtain the soil index principal components;

[0019] S303: According to the factor load and comprehensive load of the soil index principal components, the principal components of the soil index principal components that meet the set requirements are retained to obtain the minimum data set of the soil quality index data.

[0020] Further: the expression of the comprehensive load is as follows:

[0021]

[0022] Wherein, N ik is the comprehensive load (Norm value) of the i th variable on the first k principal components with eigenvalue ≥1; N iu is the comprehensive load of the i-th principal component of the soil index, i λ is the load on the i-th principal component of the soil index, i λ is the load on the i-th principal component of the soil index,

[0023] Further, the principal component meeting the set requirement includes:

[0024] If the factor load of the soil index principal component is greater than the set factor load threshold value and appears in multiple principal components PC, it is attributed to the principal component PC with low correlation between the soil index and other indexes. The soil indexes saved in the same principal component PC are analyzed for correlation and compared with the Norm value.

[0025] For indexes with a correlation coefficient reaching or exceeding 0.5, the index with a higher Norm value is retained.

[0026] Further, the soil scoring function includes an agricultural soil fertility index scoring function, an agricultural soil environment index scoring function, and an agricultural soil basic index scoring function.

[0027] The expression of the agricultural soil fertility index scoring function is as follows:

[0028]

[0029] Wherein, N(x1) is the agricultural soil fertility index scoring function, U1 is the highest critical value of the agricultural soil fertility index, L1 is the lowest critical value of the agricultural soil fertility index, and x1 is the agricultural soil fertility index data.

[0030] The expression of the agricultural soil environment index scoring function is as follows:

[0031]

[0032] Wherein, N(x2) is the agricultural soil environment index scoring function, U2 is the highest critical value of the agricultural soil environment index, L2 is the lowest critical value of the agricultural soil environment index, and x2 is the agricultural soil environment index data. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 A flow chart of a method for monitoring the quality of urbanized agricultural soil based on a minimum data set. DETAILED DESCRIPTION

[0034] The specific embodiments of the present application are described below to facilitate the understanding of the present application for those skilled in the art, but it should be clear that the present application is not limited to the scope of the specific embodiments, and for those skilled in the art, all the inventions utilizing the concept of the present application are within the scope of the present application as long as various changes are obvious within the spirit and scope of the present application defined and determined by the appended claims.

[0035] Embodiment 1

[0036] As Figure 1 shown, a minimum data set-based urbanized agricultural soil quality monitoring method comprises the following steps:

[0037] S1: According to the soil monitoring requirements, set the urbanized agricultural soil sampling site, and collect the soil samples;

[0038] S2: According to the soil samples, obtain the soil quality index data;

[0039] S3: According to the soil quality index data, utilize the principal component analysis method for screening, and obtain the minimum data set of the soil quality index data;

[0040] S4: According to the minimum data set, utilize the soil scoring function for membership function conversion, and obtain the soil index score of the minimum data set;

[0041] S5: According to the soil index score of the minimum data set, utilize the cross-validation and spatial interpolation method for processing, and obtain the visual soil quality monitoring result.

[0042] In an embodiment of the present application, in S1, different regions set the soil sampling site according to different soil monitoring standards and research purposes, the sampling depth of the sampling site can be 0-30cm, and the X-type method is used to arrange the sampling points, five sub-sampling points are arranged for each sampling point, about 1.5kg of soil is collected for each sub-sampling point, the soil is fully mixed, then the four-part method is used to bag, and the information is numbered and registered, after air drying, the impurities such as plant roots and stones are removed, and the soil samples are obtained.

[0043] In an embodiment of the present application, the soil quality index data comprises agricultural soil fertility index data, agricultural soil environment index data and agricultural soil basic index data;

[0044] The agricultural soil fertility index data comprises soil fertility quality indexes such as organic matter, total nitrogen, total phosphorus, total potassium, available nitrogen and available phosphorus;

[0045] The agricultural soil environment index data comprises soil environment quality indexes such as chromium, cadmium, mercury, lead and heavy metal elements;

[0046] The agricultural soil basic index data include soil basic properties such as pH, moisture and soil granularity.

[0047] In one embodiment of the present application, the specific steps of S3 are as follows:

[0048] S301: standardizing the soil quality index data and performing principal component analysis to obtain principal components of the soil quality index data;

[0049] S302: retaining principal components with eigenvalues greater than a set eigenvalue threshold value in the principal components of the soil quality index data to obtain soil index principal components;

[0050] S303: retaining principal components meeting set requirements in the soil index principal components according to factor loadings and comprehensive loadings of the soil index principal components to obtain a minimum data set of the soil quality index data.

[0051] The expression of the comprehensive loading is as follows:

[0052]

[0053] N ik is the comprehensive loading (Norm value) of the ith variable on the first k principal components with eigenvalues greater than or equal to 1; N i is the comprehensive loading of the ith principal component in the soil index principal components, u i is the loading on the ith principal component in the soil index principal components, λ i is the eigenvalue of the ith principal component.

[0054] The principal components meeting the set requirements include:

[0055] If the factor loading of the soil index principal component is greater than a set factor loading threshold value and appears in multiple principal components PC, the soil index principal component PC with low correlation to other indexes is attributed to the soil index, and correlation analysis and Norm value comparison are performed on the soil indexes saved in the same principal component PC.

[0056] In one embodiment of the present application, in S4, the soil scoring function includes an agricultural soil fertility index scoring function, an agricultural soil environment index scoring function and an agricultural soil basic index scoring function; the greater the agricultural soil fertility index score is, the better; the smaller the agricultural soil environment index score is, the better; the agricultural soil basic index score is formed by two functions, and the closer to the optimal value in the appropriate range, the better.

[0057] The expression of the agricultural soil fertility index scoring function is as follows:

[0058]

[0059] N(x1)=(x1-U1) / (L1-U1) wherein, N(x1) is an agricultural soil fertility index scoring function, U1 is the highest critical value in the agricultural soil fertility index, L1 is the lowest critical value in the agricultural soil fertility index, and x1 is the agricultural soil fertility index data;

[0060] The expression of the agricultural soil environment index scoring function is as follows:

[0061]

[0062] N(x2)=(x2-U2) / (L2-U2) wherein, N(x2) is an agricultural soil environment index scoring function, U2 is the highest critical value in the agricultural soil environment index, L2 is the lowest critical value in the agricultural soil environment index, and x2 is the agricultural soil environment index data.

[0063] The agricultural soil basic index scoring function is an optimal range scoring function formed by two functions. For example, the agricultural soil basic index scoring function can include an index scoring function that is better when closer to the optimal value within the suitable range and an index scoring function that is worse when exceeding the suitable range.

[0064] In one embodiment of the present application, in S5, the soil index scoring according to the minimum data set is visualized by using a spatial interpolation method, data of unsampled sites are supplemented, and the visualized results are cross-validated; wherein the interpolation method includes kriging (K), inverse distance weighting (IDW), and radial basis function (RBF) interpolation methods, etc.; a suitable spatial interpolation method is selected according to different soil monitoring standards and research purposes, and the interpolation visualized results are cross-validated by using multiple data such as root mean square error, mean square error, and standard deviation, and the different functions and parameters of the interpolation method are continuously adjusted to achieve the optimal effect. Through the interpolation method, the soil index data of the region of the unsampled sites can be supplemented, the soil index data within the research area is expanded, and the database of the region of the sampling sites is quickly constructed.

[0065] The present application has the following beneficial effects: the present application uses the minimum data set, and only a small amount of data needs to be determined to understand the change of the urbanized agricultural soil, the accuracy of the monitoring is ensured, the data is not redundant, and the research cost and period of the soil quality monitoring are reduced; meanwhile, the present application predicts the soil quality of the unknown points based on the actual sampling sites by using the spatial interpolation method, without the need of additional sampling sites, the cost and time consumption of the soil quality monitoring are reduced, the change of the soil quality is quickly monitored, the regional soil quality index database is expanded, the dynamic change of the agricultural soil environment in the urbanized area is provided, and the basis for the decision-making of the response to the change of the soil environment is provided.

[0066] Example 2

[0067] In one embodiment of the present application, a minimum data set-based urbanization agricultural soil quality monitoring method of the present application is used to sample and monitor agricultural soils in four cities, denoted as City A, City B, City C and City D, respectively. The urban construction and population migration in the four cities are increasing, and the soil quality is significantly affected by urbanization and industrialization.

[0068] Each city soil sample is collected around a determined sampling point within a radius of 400 m 2 An X-shaped sampling method is used on agricultural land (20 m x 20 m), and 5 sub-sample points are uniformly arranged. The soil depth is 0-20 cm. The soil collected from each sub-sample point is mixed uniformly and divided by the quarter method. Each bag of soil weighs 1.5-2 kg, is packed into a plastic bag, numbered and registered, processed to obtain soil samples, and the soil quality index data obtained by detection are shown in Table 1.

[0069] Table 1

[0070]

[0071]

[0072] According to the data correlation and Norm value, the minimum data set includes organic matter, total potassium, available potassium, water content, nickel and cobalt. The membership degree conversion of the minimum data set is performed by using a soil scoring function, and interpolation is performed by cross-validation. Finally, the RBF-SWT radial basis function-tension spline interpolation method and the RBF-IM radial basis function-inverse quadratic curve interpolation method are used for visualization to obtain the visual soil quality monitoring results, complete the urbanization agricultural soil quality monitoring, and supplement the regional soil quality index database.

[0073] Due to the complexity of human activities in urban areas, the agricultural soil is greatly affected. By comparing the regional population migration dynamic map, factory site selection map and field fertilization investigation results and other factors, it is considered that the alkali nitrogen, available potassium and available phosphorus content in the region is high, but the organic matter content is not at a high level, which indicates that the phenomenon may be caused by the aggregation of crops and the long-term application of nitrogen, phosphorus and potassium fertilizers during human farming. The high content of cadmium element is mainly affected by industrial production in human activities, and its distribution area is the industrial activity concentration area. The low content of mercury element is the result of the joint influence of its special volatility and low background value.

[0074] The application has the beneficial effects that: the application uses the minimum data set, and only needs to determine less data to understand the change of the urbanized agricultural soil, avoids the data redundancy while ensuring the monitoring accuracy, and further reduces the research cost and period of the soil quality monitoring; meanwhile, the application predicts the soil quality of unknown points based on the actual sampling sites through the spatial interpolation method, does not need to additionally increase the sampling sites, can reduce the cost and time consumption of the soil quality monitoring, realizes the rapid monitoring of the soil quality change, expands the regional soil quality index database, provides the dynamic change of the agricultural soil environment in the urbanized area, and provides the basis for the decision-making of the soil environment change response.

Claims

1. A method for monitoring urbanized agricultural soil quality based on a minimum data set, characterized in that: The following steps are involved: S1: According to soil monitoring requirements, set up urbanized agricultural soil sampling sites and collect soil samples; S2: obtaining soil quality index data based on the soil sample; the soil quality index data includes agricultural soil fertility index data, agricultural soil environment index data, and agricultural soil basic index data; The agricultural soil fertility index data include organic matter, total nitrogen, total phosphorus, total potassium, available nitrogen and available phosphorus; The agricultural soil environmental index data include heavy metal elements; The basic indicator data of agricultural soil include pH, moisture and soil particle size; S3: Based on the soil quality index data, principal component analysis is used to screen and obtain the minimum data set of soil quality index data; S4: Based on the minimum data set, using the soil scoring function to perform membership function conversion to obtain a soil index score of the minimum data set; the soil scoring function includes an agricultural soil fertility index scoring function, an agricultural soil environment index scoring function, and an agricultural soil basic index scoring function; The expression of the agricultural soil fertility index scoring function is as follows: in, is the scoring function of agricultural soil fertility index, It is the highest critical value among agricultural soil fertility indicators. It is the lowest critical value of agricultural soil fertility index. It is the agricultural soil fertility index data; The expression of the agricultural soil environmental index scoring function is as follows: in, is the scoring function for agricultural soil environmental indicators, It is the highest critical value among agricultural soil environmental indicators. It is the lowest critical value among agricultural soil environmental indicators. It is the agricultural soil environmental indicator data; S5: Based on the soil indicator scores of the minimum data set, cross-validation and spatial interpolation methods are used to process and obtain visual soil quality monitoring results.

2. The method for monitoring urbanized agricultural soil quality based on a minimum data set according to claim 1, characterized in that: The specific steps of S3 are as follows: S301: standardizing the soil quality index data and performing principal component analysis to obtain the principal components of the soil quality index data; S302: retaining the principal components of the soil quality index data whose eigenvalues ​​are greater than a set eigenvalue threshold, to obtain the principal components of the soil index; S303: According to the factor loads and comprehensive loads of the principal components of the soil indicators, the principal components that meet the set requirements are retained to obtain a minimum data set of soil quality indicator data.

3. The method for monitoring urbanized agricultural soil quality based on a minimum data set according to claim 2, characterized in that: The expression of the comprehensive load is as follows: in, For the The comprehensive load (Norm value) of the variables on the first k principal components with eigenvalue ≥ 1; The first principal component of soil index The comprehensive load of the principal components, The first principal component of soil index The loadings on the principal components, For the The eigenvalues ​​of the principal components.

4. The method for monitoring urbanized agricultural soil quality based on a minimum data set according to claim 2, wherein: The main components that meet the set requirements include: If the factor loading of the principal component of the soil index is greater than the set factor loading threshold and appears in multiple principal component PCs, it belongs to the principal component PC with low correlation between the soil index and other indicators. The correlation analysis and Norm value comparison are performed on the soil indexes stored in the same principal component PC. For indicators with correlation coefficients reaching or exceeding 0.5, the indicators with higher Norm values ​​are retained.

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