Urbanization agricultural soil quality monitoring method based on minimum data set

Abstract

The invention discloses an urbanization agricultural soil quality monitoring method based on a minimum data set, and belongs to the field of soil quality monitoring, and the urbanization agricultural soil quality monitoring method comprises the following steps: setting a soil sampling site according to soil monitoring requirements, and obtaining soil quality index data according to a collected soil sample; utilizing a principal component analysis method to obtain a minimum data set of soil quality index data; and processing through a soil scoring function, cross validation and a spatial interpolation method to obtain a visual soil quality monitoring result. According to the method, the change condition of the urbanized agricultural soil can be known only by measuring a small amount of data by utilizing the minimum data set, so that data redundancy is avoided while the monitoring accuracy is ensured, and the research cost and period of soil quality monitoring are further reduced; meanwhile, through a spatial interpolation method, the cost and time consumption of soil quality monitoring can be reduced, and rapid monitoring of soil quality changes is achieved.

Description

Technical Field

[0001] The invention belongs to the field of soil quality monitoring, and in particular relates to an urbanized agricultural soil quality monitoring method based on a minimum data set. Background Art

[0002] Soil is an important component of terrestrial ecosystems. Soil quality (SQ) has a vital impact on long-term sustainable agricultural development and environmental quality. Accurate evaluation of soil quality is crucial to human living environment, agricultural management and government decision-making. However, with the continuous acceleration of social development and urbanization, problems such as improper management of agricultural soils have become increasingly serious, resulting in declining soil fertility, heavy metal pollution and water pollution, leading to soil quality degradation, reduced crop quality and food yields. Therefore, the soil quality problem in urbanized areas has attracted widespread attention around the world, and its evaluation results provide a theoretical basis for subsequent improvement and restoration policies.

[0003] Based on this, 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. These indicators affect soil functions and are sensitive to changes in the soil environment. Although choosing more comprehensive indicators can more truly reflect soil quality, it will significantly increase the difficulty of data acquisition and analysis and the cost and cycle of evaluation, and cannot be used in soil environments that require rapid monitoring. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides an urbanized agricultural soil quality monitoring method based on a minimum data set, which solves the problems of long agricultural soil quality monitoring cycle and high research cost in urbanized areas.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a method for monitoring the quality of urbanized agricultural soil based on a minimum data set, comprising the following steps:

[0006] S1: According to soil monitoring requirements, set up urbanized agricultural soil sampling sites and collect soil samples;

[0007] S2: Obtain soil quality index data based on soil samples;

[0008] S3: Based on the soil quality index data, the principal component analysis method is used to screen and obtain the minimum data set of soil quality index data;

[0009] S4: Based on the minimum data set, the soil scoring function is used to convert the membership function to obtain the soil index score of the minimum data set;

[0010] S5: Based on the soil indicator scores of the minimum data set, cross-validation and spatial interpolation methods are used to obtain visualized soil quality monitoring results.

[0011] The beneficial effects of the present invention are as follows: the present invention utilizes a minimum data set and only requires the measurement of relatively small amounts of data to understand changes in urbanized agricultural soils, thereby ensuring monitoring accuracy while avoiding data redundancy, thereby reducing the research cost and cycle of soil quality monitoring; at the same time, the present invention predicts soil quality at unknown points based on actual sampling locations through a spatial interpolation method, without the need to add additional sampling locations, thereby reducing the cost and time consumption of soil quality monitoring, achieving rapid monitoring of soil quality changes, and expanding a regional soil quality index database, thereby providing urbanized areas with dynamic changes in agricultural soil environments and providing a basis for the formulation of decisions in response to soil environmental changes.

[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 include organic matter, total nitrogen, total phosphorus, total potassium, available nitrogen and available phosphorus;

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

[0015] The basic indicator data of agricultural soil include pH, moisture and soil particle size.

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

[0017] S301: standardizing the soil quality index data and performing principal component analysis to obtain the principal components of the soil quality index data;

[0018] S302: retaining the principal components of the soil quality index data whose eigenvalues ​​are greater than a set eigenvalue threshold, and obtaining the soil index principal components;

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

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

[0021]

[0022] Among them, N ik is the comprehensive load (Norm value) of the ith variable on the first k principal components with eigenvalues ​​≥ 1; N iis the comprehensive load of the i-th principal component in the soil index principal components, u i is the load on the i-th principal component of the soil index principal component, λ i is the eigenvalue of the i-th principal component.

[0023] Further: the main components that meet the set requirements include:

[0024] 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 soil indexes stored in the same principal component PC are subjected to correlation analysis and Norm value comparison.

[0025] For indicators with correlation coefficients reaching or exceeding 0.5, the indicators with higher Norm values ​​are 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] Among them, 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 environmental index scoring function is as follows:

[0031]

[0032] Among them, N(x2) is the scoring function of agricultural soil environmental indicators, U2 is the highest critical value among agricultural soil environmental indicators, L2 is the lowest critical value among agricultural soil environmental indicators, and x2 is the agricultural soil environmental indicator data. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Flowchart of a method for monitoring soil quality in urbanized agriculture based on a minimum data set. DETAILED DESCRIPTION

[0034] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0035] Example 1

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

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

[0038] S2: Obtain soil quality index data based on soil samples;

[0039] S3: Based on the soil quality index data, the principal component analysis method is used to screen and obtain the minimum data set of soil quality index data;

[0040] S4: Based on the minimum data set, the soil scoring function is used to convert the membership function to obtain the soil index score of the minimum data set;

[0041] S5: Based on the soil indicator scores of the minimum data set, cross-validation and spatial interpolation methods are used to obtain visualized soil quality monitoring results.

[0042] In one embodiment of the present invention, in S1, different regions set up soil sampling sites according to different soil monitoring standards and research purposes. The sampling depth of the sampling sites can be 0 to 30 cm, and the sampling points are arranged using the X-type method. Each sampling point is arranged with five sub-sample points. Each sub-sample point collects about 1.5 kg of soil. After the soil is fully mixed, it is bagged using the quartering method, and numbered and registered. After air-drying, impurities such as plant roots and stones are removed to obtain soil samples.

[0043] In one embodiment of the present invention, the soil quality index data includes agricultural soil fertility index data, agricultural soil environment index data and agricultural soil basic index data;

[0044] Agricultural soil fertility index data include soil fertility quality indicators such as organic matter, total nitrogen, total phosphorus, total potassium, available nitrogen and available phosphorus;

[0045] Agricultural soil environmental index data include soil environmental quality indicators such as chromium, cadmium, mercury, lead and heavy metal elements;

[0046] The basic indicator data of agricultural soil include basic soil properties such as pH, moisture and soil particle size.

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

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

[0049] S302: retaining the principal components of the soil quality index data whose eigenvalues ​​are greater than a set eigenvalue threshold, and obtaining the soil index principal components;

[0050] S303: According to the factor loading and comprehensive loading of the principal components of the soil indicators, the principal components that meet the set requirements are retained to obtain the minimum data set of soil quality indicator data.

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

[0052]

[0053] Among them, N ik is the comprehensive load (Norm value) of the ith variable on the first k principal components with eigenvalues ​​≥ 1; N i is the comprehensive load of the i-th principal component in the soil index principal components, u i is the load on the i-th principal component of the soil index principal component, λ i is the eigenvalue of the i-th principal component.

[0054] The main components that meet the set requirements include:

[0055] 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 soil indexes stored in the same principal component PC are subjected to correlation analysis and Norm value comparison.

[0056] In one embodiment of the present invention, 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 larger the agricultural soil fertility index score, the better, the smaller the agricultural soil environment index score, the better, and the agricultural soil basic index score is formed by a combination of the two functions. Within the appropriate range, the closer to the optimal value, the better the function is, and outside the appropriate range, the less the better the function is.

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

[0058]

[0059] Among them, 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;

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

[0061]

[0062] Among them, N(x2) is the scoring function of agricultural soil environmental indicators, U2 is the highest critical value among agricultural soil environmental indicators, L2 is the lowest critical value among agricultural soil environmental indicators, and x2 is the agricultural soil environmental indicator data.

[0063] The agricultural soil basic index scoring function is an optimal range scoring function formed by combining two functions. For example, the agricultural soil basic index scoring function may include an index scoring function in which the closer to the optimal value within the appropriate range, the better the index is, and an index scoring function in which the less the index is, the better the index is beyond the appropriate range.

[0064] In one embodiment of the present invention, in S5, according to the soil index score of the minimum data set, the spatial interpolation method is used to visualize it, supplement the data of the unsampled location, and cross-validate the visualization result; wherein the interpolation method includes: kriging (K), inverse distance weighting (IDW) and radial basis function (RBF) interpolation method, etc.; according to different soil monitoring standards and research purposes, a suitable spatial interpolation method is selected, and multiple data such as root mean square error, mean square error, standard deviation are used to cross-validate the interpolation visualization result, and different functions and parameters of the interpolation method are continuously adjusted to achieve the optimal effect. Through the interpolation method, the present invention can supplement the soil index data of the unsampled location area, expand the soil index data in the study area, and quickly build a database of the sampling location area.

[0065] The beneficial effects of the present invention are as follows: the present invention utilizes a minimum data set and only requires the measurement of relatively small amounts of data to understand changes in urbanized agricultural soils, thereby ensuring monitoring accuracy while avoiding data redundancy, thereby reducing the research cost and cycle of soil quality monitoring; at the same time, the present invention predicts soil quality at unknown points based on actual sampling locations through a spatial interpolation method, without the need to add additional sampling locations, thereby reducing the cost and time consumption of soil quality monitoring, achieving rapid monitoring of soil quality changes, and expanding a regional soil quality index database, thereby providing urbanized areas with dynamic changes in agricultural soil environments and providing a basis for the formulation of decisions in response to soil environmental changes.

[0066] Example 2

[0067] In one embodiment of the present invention, a method for monitoring agricultural soil quality in urbanization based on a minimum data set of the present invention is used to sample and monitor agricultural soil in four cities, which are respectively recorded as City A, City B, City C, and City D. As urban construction and population migration increase in the four cities, soil quality is significantly affected by urbanization and industrialization.

[0068] Each urban soil sample was collected 400 m around the determined sampling point. 2 The X-shaped sampling method was used on agricultural land (20m×20m), and 5 sub-sample points were evenly arranged. The soil depth was 0-20cm. The soil collected from each sub-sample point was mixed evenly and divided into four bags. The mass of each bag of soil was 1.5-2kg. It was packed into plastic bags, numbered and registered. After processing, soil samples were obtained. The soil quality index data obtained by testing are shown in Table 1;

[0069] Table 1

[0070]

[0071]

[0072] According to data correlation and Norm value, the minimum data set was screened out, including organic matter, total potassium, available potassium, moisture content, nickel and cobalt; the soil scoring function was used to convert the membership of the minimum data set, and interpolation was performed through cross-validation. Finally, it was visualized through RBF-SWT radial basis function-tension spline interpolation method and RBF-IM radial basis function-inverse quadratic curve interpolation method to obtain visualized soil quality monitoring results, complete the urban agricultural soil quality monitoring, and supplement the soil quality indicator database of the region.

[0073] Due to the complexity of human activities in urbanized areas, agricultural soils are greatly affected. By comparing the population migration dynamics map, factory site selection map, field fertilization survey results and other factors in the region, it is believed that: the content of alkaline nitrogen, quick-acting potassium and effective phosphorus in the region is high, but the organic matter content is not at a high level, indicating that this phenomenon may be caused by the aggregation of crops and the long-term application of nitrogen, phosphorus, potassium and other fertilizers during human farming; the high content of cadmium is mainly affected by industrial production in human activities, and its distribution area is a concentrated area of ​​industrial activities; and the low content of mercury is the result of the combined influence of its special volatility and low background value.

[0074] The beneficial effects of the present invention are as follows: the present invention utilizes a minimum data set and only requires the measurement of relatively small amounts of data to understand changes in urbanized agricultural soils, thereby ensuring monitoring accuracy while avoiding data redundancy, thereby reducing the research cost and cycle of soil quality monitoring; at the same time, the present invention predicts soil quality at unknown points based on actual sampling locations through a spatial interpolation method, without the need to add additional sampling locations, thereby reducing the cost and time consumption of soil quality monitoring, achieving rapid monitoring of soil quality changes, and expanding a regional soil quality index database, thereby providing urbanized areas with dynamic changes in agricultural soil environments and providing a basis for the formulation of decisions in response to soil environmental changes.

Claims

1. A method for monitoring the quality of urbanized agricultural soil 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: Obtain soil quality index data based on soil samples; S3: Based on the soil quality index data, the principal component analysis method is used to screen and obtain the minimum data set of soil quality index data; S4: Based on the minimum data set, the soil scoring function is used to convert the membership function to obtain the soil index score of the minimum data set; S5: Based on the soil indicator scores of the minimum data set, cross-validation and spatial interpolation methods are used to obtain visualized soil quality monitoring results.

2. The method for monitoring the quality of urbanized agricultural soil based on a minimum data set according to claim 1, characterized in that: The soil quality index data include 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 chromium, cadmium, mercury, lead and heavy metal elements; The basic indicator data of agricultural soil include pH, moisture and soil particle size.

3. The method for monitoring the quality of urbanized agricultural soil 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, and obtaining the soil index principal components; S303: According to the factor loading and comprehensive loading of the principal components of the soil indicators, the principal components that meet the set requirements are retained to obtain the minimum data set of soil quality indicator data.

4. The method for monitoring the quality of urbanized agricultural soil based on a minimum data set according to claim 3, characterized in that: The expression of the comprehensive load is as follows: Among them, N ik is the comprehensive load (Norm value) of the ith variable on the first k principal components with eigenvalues ​​≥ 1; N i is the comprehensive load of the i-th principal component in the soil index principal components, u i is the load on the i-th principal component of the soil index principal component, λ i is the eigenvalue of the i-th principal component.

5. The method for monitoring the quality of urbanized agricultural soil based on a minimum data set according to claim 3, characterized in that: 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 soil indexes stored in the same principal component PC are subjected to correlation analysis and Norm value comparison. For indicators with correlation coefficients reaching or exceeding 0.5, the indicators with higher Norm values ​​are retained.

6. The method for monitoring the quality of urbanized agricultural soil based on a minimum data set according to claim 1, characterized in that: 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: Among them, 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; The expression of the agricultural soil environmental index scoring function is as follows: Among them, N(x2) is the scoring function of agricultural soil environmental indicators, U2 is the highest critical value among agricultural soil environmental indicators, L2 is the lowest critical value among agricultural soil environmental indicators, and x2 is the agricultural soil environmental indicator data.

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