Method for evaluating quality of cultivated land in sandstorm saline-alkali area

Abstract

The invention discloses a sand-blown saline-alkali area cultivated land quality evaluation method, relates to the technical field of computer agriculture, and aims to solve the problem that an existing cultivated land quality evaluation method is inaccurate in a sand-blown saline-alkali area cultivated land evaluation result. The method comprises the following steps: 1, collecting research area data of cultivated land in a sand-blown saline-alkali area, wherein the data comprises field survey data, remote sensing image data and other auxiliary data; 2, calculating a remote sensing index; 3, constructing a farmland quality evaluation system in the sandstorm saline-alkali area; 4, determining the weight of each evaluation index of the index layer by adopting a consistent matrix method; 5, carrying out membership degree assignment on each evaluation index based on a membership function and a Delphi method; 6, adopting an accumulation method to obtain a cultivated land quality comprehensive index P; and 7, dividing the distribution interval of the cultivated land quality comprehensive index P into ten grades by adopting an equidistant method. The method is high in speed, good in stability, low in cost and capable of achieving large-area and long-time-sequence cultivated land quality evaluation in the sandstorm and saline-alkali area.

Description

Technical Field

[0001] The invention relates to the technical field of computer agriculture, and in particular to a method for evaluating the quality of cultivated land in a sandy and saline-alkali area. Background Art

[0002] The quality of cultivated land is directly related to crop yield and quality, which in turn affects the stability and development of the social economy. At present, the cultivated land area is facing obstacles and degradation problems (such as desertification and salinization), which has limited agricultural productivity. In-depth research and evaluation of changes in cultivated land quality are of great significance for guiding agricultural sustainable development strategies, and have a far-reaching impact on optimizing the allocation of cultivated land resources, guiding the adjustment of agricultural production structure, and improving the overall benefits of agricultural production.

[0003] Traditional evaluation of cultivated land quality mainly relies on experience and qualitative analysis based on factors such as soil fertility and topography. The national standard "Cultivated Land Quality Grade" implemented in 2016 clearly defines the evaluation indicators and methods of cultivated land quality. However, with the continuous changes in agricultural production conditions and the intensification of regional differences, the applicability of the national standard under certain special climatic and geographical conditions is limited. For example, in terrain conditions such as mountains and hills and areas with arid climates, these evaluation indicators and standards cannot accurately reflect the actual production capacity of local cultivated land. Therefore, more and more researchers have begun to adopt more flexible and diversified methods to evaluate cultivated land quality. At present, the framework of cultivated land quality evaluation is mainly divided into the Analytic Hierarchy Process (AHP) and the Pressure-State-Response (PSR) model. The Analytic Hierarchy Process determines the weights of each evaluation indicator through expert scoring and comparison. For example, Mesut Budak et al. (2024) used the AHP method to evaluate the land suitability for rapeseed production and used the mapping results to determine the most suitable areas for growing crops. The PSR model classifies indicators and conducts a comprehensive evaluation from three dimensions: environmental pressure, current status, and response measures. For example, Surendra Singh et al. combined the PSR model and the indicator method to construct a comprehensive agricultural sustainability index for 14 mainstream agricultural climate zones. With the development of social economy, the technical means of evaluating cultivated land quality have also been innovated. On the basis of traditional soil sampling and laboratory analysis, the introduction of more new technologies has significantly improved the efficiency and accuracy of the evaluation. Among them, the application of remote sensing technology (RS) and geographic information system (GIS) is particularly prominent. Through high-resolution satellite images and spatial analysis tools, researchers can dynamically monitor and evaluate cultivated land quality indicators over a large area. For example, Wang et al. selected slope, light and temperature production potential, NDVI, TVDI, and yield as evaluation indicators based on MODIS product data, and realized the evaluation of regional cultivated land quality in 2000 and 2005. In addition, for different regions and their natural environments, combining remote sensing and field survey data, constructing a suitable indicator system, and evaluating cultivated land quality can further improve the reliability of the evaluation results. For example, David et al. calculated the soil quality index based on the unique ecosystems in mountainous and hilly areas by combining GIS technology, thereby achieving soil quality assessment; Murat et al. evaluated the suitability of farmland in semi-arid areas by analyzing the spatial changes in soil properties based on the AHP method and GIS technology.

[0004] Although the current research on arable land quality evaluation methods and indicator systems has been increasingly improved worldwide, the existing arable land quality evaluation methods do not take into account indicators such as the degree of desertification, resulting in inaccurate evaluation results for arable land in sandy and saline-alkali areas. Summary of the invention

[0005] The invention aims to solve the problem that the existing method for evaluating the quality of cultivated land is inaccurate in evaluating the cultivated land in sandy and saline-alkali areas, and provides a method for evaluating the quality of cultivated land in sandy and saline-alkali areas.

[0006] The method for evaluating the quality of cultivated land in a sandy and saline-alkali area of ​​the present invention comprises the following steps:

[0007] Step 1: Collect the research area data of cultivated land in the sandy and saline-alkali areas, including field survey data, remote sensing image data and other auxiliary data;

[0008] Step 2: Calculate the remote sensing index according to the following formula; the remote sensing index is the normalized vegetation index NDVI, the structure-insensitive pigment index SIPI and the normalized difference moisture index NDMI:

[0009]

[0010] Where S1 represents the B11 band of Sentinel-2 and the B5 band of Landsat5;

[0011] Step 3: Construct a quality evaluation system for cultivated land in sandy and saline-alkali areas based on the data obtained in steps 1 and 2;

[0012] Step 4: Use the consistent matrix method to determine the weight of each evaluation indicator at the indicator layer;

[0013] Step 5: Assign membership to each evaluation index based on membership function and Delphi method;

[0014] Step 6: Use the accumulation method to obtain the comprehensive index P of cultivated land quality;

[0015] P=∑C i ×F i (4)

[0016] In the formula, C i represents the combined weight of the i-th evaluation index, F i It represents the membership degree of the i-th evaluation index. The higher the P value, the better the quality of cultivated land.

[0017] Step 7: Use the equal distance method to divide the distribution range of the comprehensive index P of cultivated land quality into ten levels, among which the first-level cultivated land has the highest quality and the tenth-level cultivated land has the lowest quality;

[0018] When the evaluation area contains multiple cultivated land quality assessment results, the average cultivated land quality grade R is used for evaluation:

[0019] R=[∑(D i ×S i )] / S 总 (5)

[0020] Where Di is the quality of the i-th level of cultivated land, Si is the i-th level of cultivated land area, and Stotal is the total cultivated land area.

[0021] Furthermore, the remote sensing image data in step 1 includes images of the bare soil period and the growing season, wherein the bare soil period images are used to invert soil parameters, and the growing season images are used to calculate remote sensing indices.

[0022] Furthermore, the other auxiliary data described in step one include digital elevation data, land use data, meteorology, soil moisture data, soil salinization degree and effective soil layer thickness.

[0023] Furthermore, the evaluation index system of cultivated land quality in windy and saline-alkali areas described in step 3 is composed of a target layer, a criterion layer and an indicator layer, wherein the criterion layer includes five categories: terrain conditions, plough layer properties, climate conditions, profile properties and remote sensing indexes, and the indicator layer is composed of 13 specific evaluation indicators, wherein the evaluation indicators of the indicator layer corresponding to terrain conditions are DEM and slope, the evaluation indicators of the indicator layer corresponding to plough layer properties are pH, soil texture and organic matter, the evaluation indicators of the indicator layer corresponding to climate conditions are sunshine hours and precipitation, the evaluation indicators of the indicator layer corresponding to profile properties are effective soil thickness, salinization degree and desertification degree, and the evaluation indicators of the indicator layer corresponding to remote sensing indexes are NDVI, SIPI and NDMI;

[0024] Furthermore, the specific method for determining the weight of each evaluation index of the index layer in step 4 is as follows: first, the weight value of the criterion layer relative to the target layer and the weight value of the index layer relative to the criterion layer are calculated, and then the weight of the index layer relative to the target layer is calculated based on the result; the indexes are compared pairwise to obtain the judgment matrix, and the eigenvector corresponding to the maximum eigenvalue of the judgment matrix is ​​the weight value λ max , determined by the consistency test algorithm with the help of the consistency index CI, the formula is as follows:

[0025]

[0026] Where n is the number of indicators.

[0027] Furthermore, the specific method for assigning the degree of membership to each evaluation index in step 5 is:

[0028] The evaluation indicators are divided into conceptual indicators and numerical indicators. For conceptual indicators, the Delphi method is used directly to determine the membership of the indicators. For numerical indicators, the membership functions are divided into linear, upper, lower and peak functions. The membership functions are fitted according to the upper and lower limits of the indicators, and then the corresponding membership degrees are evaluated based on the measured values.

[0029] Beneficial effects of the present invention:

[0030] The present invention provides a remote sensing-based farmland quality evaluation system and method for windy sandy saline-alkali areas. The calculation of the indicators in the algorithm is relatively simple and the calculation speed is fast. The algorithm is based on some real-time parameters of remote sensing and does not completely rely on empirical values. It has high accuracy and good stability. The method of the present invention has low cost and can achieve large-area and long-term series farmland quality evaluation in windy sandy saline-alkali areas, which is of great significance for guiding the sustainable development of agriculture in windy sandy saline-alkali areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is the algorithm flow chart of the present invention.

[0032] Figure 2 The invention discloses a farmland quality evaluation system.

[0033] Figure 3 The results of the cultivated land quality evaluation in the western Songnen Plain were achieved by using this method. DETAILED DESCRIPTION

[0034] The embodiments of the present invention are described in detail below. The following embodiments are implemented on the premise of the technical solution of the present invention, and detailed implementation schemes and specific operation processes are given, but the protection scope of the present invention is not limited to the following embodiments.

[0035] Embodiment 1:

[0036] Take the arable land quality evaluation system and method in the western sandy and saline-alkali area of ​​the Songnen Plain in 2022 as an example.

[0037] Step 1: Collect data on the study area, including field survey data, remote sensing image data and other auxiliary data;

[0038] The field survey was conducted from September to October 2022, and a total of 100 surface soil samples were obtained. The soil organic matter, sand, silt, clay, pH and other attribute data were obtained through laboratory measurements, and the darkroom spectra of these soil samples were measured using an ASD spectrometer. The classification of the degree of desertification of cultivated land and organic matter is modeled and inverted by combining bare soil images with field survey data. By selecting characteristic spectra and constructing remote sensing inversion models of soil organic matter, sand and clay, and applying them to remote sensing images, the results of soil organic matter, sand and clay in the study area were obtained.

[0039] Remote sensing image data include images of the bare soil period and the growing season. The bare soil period images are used to invert soil parameters, and the growing season images are used to calculate remote sensing indices. Images with cloud cover less than 10% are selected for splicing. The remote sensing indices are calculated using formulas (1) to (3): Normalized Difference Vegetation Index (NDVI), Structure Insensitive Pigment Index (SIPI) and Normalized Difference Moisture Index (NDMI). NDVI, SIPI and NDMI.

[0040]

[0041] In the formula, NIR represents the near infrared band of the remote sensing image, Red represents the red band of the remote sensing image, Blue represents the blue band of the remote sensing image, S1 represents the B11 band of Sentinel-2 and the B5 band of Landsat 5.

[0042] Other auxiliary data include: digital elevation data (DEM and slope can be extracted), land use data, meteorological and soil moisture data, soil salinization degree, and effective soil thickness. Other auxiliary data can be downloaded according to the data sources in Table 2.

[0043] Table 2 Data sources

[0044]

[0045] Step 2: Constructing a system for evaluating the quality of cultivated land in sandy and saline-alkali areas

[0046] The soil barrier factors are divided into soil salinization degree and soil desertification degree, and meteorological conditions and remote sensing indexes are further introduced to construct an evaluation index system for cultivated land quality in windy sandy saline-alkali areas (such as Figure 2 The system consists of a target layer, a criterion layer and an indicator layer. The criterion layer includes five categories: terrain conditions, tillage layer properties, climate conditions, profile properties and remote sensing indexes. The indicator layer consists of 13 specific indicators. The indicator layer corresponding to terrain conditions is DEM and slope, the indicator layer corresponding to tillage layer properties is pH, soil texture and organic matter, the indicator layer corresponding to climate conditions is sunshine hours and precipitation, the indicator layer corresponding to profile properties is effective soil thickness, salinization degree and desertification degree, and the indicator layer corresponding to remote sensing index is NDVI, SIPI and NDMI.

[0047] Step 3: Use the analytic hierarchy process to determine the weights of each evaluation index

[0048] The consistent matrix method is used to determine the weight of each indicator, and the results are shown in Table 3. The calculation order follows the principle of determining layer by layer from top to bottom, that is, first calculate the weight value of the criterion layer relative to the target layer, and the weight value of the indicator layer relative to the criterion layer, and then calculate the weight of the indicator layer relative to the target layer based on this result, which is called the indicator combination weight. During the calculation, the indicators are compared pairwise to obtain a consistent matrix (also called a judgment matrix). The eigenvector corresponding to the maximum eigenvalue of the judgment matrix is ​​the weight value λ max , determined by the consistency test algorithm with the help of the consistency index CI. The formula is as follows:

[0049]

[0050] Where n is the number of indicators. When CI = 0, it means that the judgment matrix has complete consistency. The closer CI is to 0, the better the consistency. When CI ≠ 0, the consistency ratio CR is used to measure the acceptability of the result:

[0051]

[0052] Where RI is the random consistency index. When CR < 0.10, the consistency of the judgment matrix is ​​considered acceptable, otherwise a new judgment matrix should be reconstructed until the judgment matrix meets the conditions.

[0053] Table 3 Weights of cultivated land quality evaluation indicators

[0054]

[0055] Step 4: Assign membership to each evaluation index based on membership function and Delphi method

[0056] The evaluation indicators are divided into conceptual indicators and numerical indicators. For conceptual indicators, the Delphi method is used directly to determine the membership of the indicators; for numerical indicators, the membership function is divided into linear, upper, lower and peak functions, and the membership function is fitted according to the upper and lower limits of the indicators, and then the corresponding membership is evaluated according to the measured values ​​(as shown in Table 4).

[0057] Table 4 Numerical indicators and membership functions

[0058]

[0059] Among them, y is the membership function, u is the measured value, c is the indicator, x is the coefficient, a and b are the upper and lower limits of the indicator respectively. The membership function is fitted according to the upper and lower limits of the indicator, and then the corresponding membership degree is evaluated according to the measured value (as shown in Table 5).

[0060] Table 5 Conceptual indicator membership

[0061]

[0062] Step 5: Use the accumulation method to obtain the comprehensive index P of cultivated land quality;

[0063] P=∑C i ×F i (4)

[0064] In the formula, C i represents the combined weight of the i-th evaluation index, F i It represents the membership degree of the i-th evaluation index. The higher the P value, the better the quality of cultivated land.

[0065] Step 6: Use the equal distance method to classify the quality of cultivated land (such as Figure 3 ).Depend on Figure 3 The quality grade of cultivated land can be obtained, which represents the quality of cultivated land; the lower the grade, the higher the quality of cultivated land. As can be seen from the figure, the quality of cultivated land in the middle of the study area is the worst, while the quality of cultivated land in the north and east is better.

[0066] The distribution range of the comprehensive index P of cultivated land quality is divided into ten levels by the equal distance method, among which the first-grade cultivated land has the highest quality and the tenth-grade cultivated land has the lowest quality.

[0067] When the evaluation area contains multiple cultivated land quality assessment results, the average cultivated land quality grade R is used for evaluation:

[0068] R=[∑(D i ×S i )] / S 总 (5)

[0069] Where Di is the quality of the i-th level of cultivated land, Si is the i-th level of cultivated land area, and Stotal is the total cultivated land area.

Claims

1. A method for evaluating the quality of cultivated land in sandy and saline-alkali areas, characterized in that: The method comprises the following steps: Step 1: Collect the research area data of cultivated land in the sandy and saline-alkali areas, including field survey data, remote sensing image data and other auxiliary data; Step 2: Calculate the remote sensing index according to the following formula; the remote sensing index is the normalized vegetation index NDVI, the structure-insensitive pigment index SIPI and the normalized difference moisture index NDMI: Where S1 represents the B11 band of Sentinel-2 and the B5 band of Landsat 5; Step 3: Construct a quality evaluation system for cultivated land in sandy and saline-alkali areas based on the data obtained in steps 1 and 2; Step 4: Use the consistent matrix method to determine the weight of each evaluation indicator at the indicator layer; Step 5: Assign membership to each evaluation index based on membership function and Delphi method; Step 6: Use the accumulation method to obtain the comprehensive index P of cultivated land quality; P=∑C i ×F i (4) In the formula, C i represents the combined weight of the i-th evaluation index, F i It represents the membership degree of the i-th evaluation index. The higher the P value, the better the quality of cultivated land. Step 7: Use the equal distance method to divide the distribution range of the comprehensive index P of cultivated land quality into ten levels, among which the first-level cultivated land has the highest quality and the tenth-level cultivated land has the lowest quality; When the evaluation area contains multiple cultivated land quality assessment results, the average cultivated land quality grade R is used for evaluation: R=[∑(D i ×S i )] / S 总 (5) Where Di is the quality of the i-th level of cultivated land, Si is the i-th level of cultivated land area, and Stotal is the total cultivated land area.

2. The method for evaluating the quality of cultivated land in sandy and saline-alkali areas according to claim 1, characterized in that: The remote sensing image data in step 1 include images of the bare soil period and the growing season, wherein the bare soil period images are used to invert soil parameters, and the growing season images are used to calculate remote sensing indices.

3. The method for evaluating the quality of cultivated land in sandy and saline-alkali areas according to claim 1 or 2, characterized in that: The other auxiliary data described in step 1 include digital elevation data, land use data, meteorology, soil moisture data, soil salinization degree and effective soil layer thickness.

4. The method for evaluating the quality of cultivated land in sandy and saline-alkali areas according to claim 1, characterized in that: The evaluation index system of cultivated land quality in sandy and saline-alkali areas described in step 3 consists of a target layer, a criterion layer and an indicator layer.

5. The method for evaluating the quality of cultivated land in sandy and saline-alkali areas according to claim 4, characterized in that: The criterion layer includes five categories: terrain conditions, tillage layer characteristics, climate conditions, profile characteristics and remote sensing index.

6. The method for evaluating the quality of cultivated land in sandy and saline-alkali areas according to claim 5, characterized in that: The index layer is composed of 13 specific evaluation indicators, among which the evaluation indicators of the index layer corresponding to terrain conditions are DEM and slope, the evaluation indicators of the index layer corresponding to tillage layer characteristics are pH, soil texture and organic matter, the evaluation indicators of the index layer corresponding to climate conditions are sunshine hours and precipitation, the evaluation indicators of the index layer corresponding to profile characteristics are effective soil layer thickness, salinization degree and desertification degree, and the evaluation indicators of the index layer corresponding to remote sensing index are NDVI, SIPI and NDMI.

7. The method for evaluating the quality of cultivated land in sandy and saline-alkali areas according to claim 1, characterized in that: The specific method for determining the weight of each evaluation index in the index layer in step 4 is as follows: first, calculate the weight value of the criterion layer relative to the target layer, and the weight value of the index layer relative to the criterion layer, and then calculate the weight of the index layer relative to the target layer based on this result; compare the indicators pairwise to obtain the judgment matrix, and the eigenvector corresponding to the maximum eigenvalue of the judgment matrix is ​​the weight value λ max , determined by the consistency test algorithm with the help of the consistency index CI, the formula is as follows: Where n is the number of indicators.

8. The method for evaluating the quality of cultivated land in sandy and saline-alkali areas according to claim 1, characterized in that: The specific method for assigning membership to each evaluation index in step 5 is: The evaluation indicators are divided into conceptual indicators and numerical indicators. For conceptual indicators, the Delphi method is used directly to determine the membership of the indicators. For numerical indicators, the membership functions are divided into linear, upper, lower and peak functions. The membership functions are fitted according to the upper and lower limits of the indicators, and then the corresponding membership degrees are evaluated based on the measured values.

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