Soil data fusion method and system based on geospatial relationship
By constructing a data fusion buffer and calculating the distance weight adjustment coefficient in the soil survey, the problem of data mutation caused by differences in county-level models was solved, the natural transition and spatial continuity of soil attribute data were realized, and the reliability of the soil survey results was improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-30
- Publication Date
- 2026-04-10
AI Technical Summary
In existing soil survey technologies, data abrupt changes at administrative boundaries caused by differences in county-level models affect the spatial continuity and consistency of soil attribute data, leading to distorted agricultural management decisions.
By constructing a data fusion buffer, the distance weight adjustment coefficient is calculated based on the geometric centroids of the target area and adjacent areas, and the soil attribute raster data is adjusted to achieve a natural transition.
It effectively eliminated data abrupt changes at administrative boundaries, improved the spatial continuity and consistency of soil survey results, and provided reliable data support for agricultural management.
Smart Images
Figure CN121834692A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of soil information monitoring, and in particular to a method and system for soil data fusion based on geospatial relationships. Background Technology
[0002] Soil surveys are important national surveys that comprehensively understand the quantity, quality, and distribution of soil resources. Their results are of vital importance for ensuring national food security, guiding precision fertilization in agriculture, controlling soil erosion, and promoting ecological civilization.
[0003] Currently, the process of generating soil survey results typically involves the following steps: Each county uses soil sampling data from its own area, combined with environmental explanatory variables such as remote sensing, to independently construct spatial inference models of soil properties (such as pH value, organic matter content, etc.), thereby generating high-resolution (e.g., 30-meter) soil property raster data within each county. Finally, these county-level data are integrated step by step into city-level, provincial-level, and even national-level data products through spatial stitching.
[0004] However, this independent modeling and stitching method based on counties has significant technical drawbacks: due to differences in the environmental variables, data sources (such as Landsat, Sentinel, and other remote sensing images), and even algorithm models used by each county during modeling, the generated soil attribute raster data from each county exhibit inherent systematic differences in numerical distribution and spatial trends. When raster data from adjacent counties are directly stitched together based on geographic coordinates, false, discontinuous data abrupt changes or hard boundaries may occur at administrative boundaries.
[0005] This boundary discontinuity problem severely disrupts the natural gradual change pattern of soil property spatial distribution, resulting in a large number of errors in the spliced data results and seriously restricting its application effect. For example, when conducting cross-regional soil fertility evaluation, precision agricultural fertilization planning, or large-scale soil and water loss risk assessment based on such data, data mutations at the boundary will lead to distorted analysis results and even incorrect decision-making, failing to provide reliable data support for agricultural production and ecological protection. Summary of the Invention
[0006] Based on this, the purpose of this application is to provide a soil data fusion method and system based on geospatial relationships, which can effectively eliminate data abrupt changes at administrative boundaries, ensure the spatial continuity and consistency of soil survey results, and guarantee agricultural production efficiency and ecological protection effects.
[0007] The objective of this application can be achieved through the following technical solutions: In a first aspect, embodiments of this application provide a soil data fusion method based on geospatial relationships, comprising the following steps: acquiring soil attribute raster data of a target administrative region; constructing a data fusion buffer of a preset scale based on the boundary of the target administrative region; acquiring the geometric centroid of the target administrative region and the geometric centroid of at least one other administrative region adjacent to the target administrative region; for each raster cell in the data fusion buffer, performing the following operations: calculating the spatial distance from the geometric centroid of the target administrative region and the geometric centroid of each other administrative region to the raster cell; acquiring the distance weight adjustment coefficient of each administrative region to the raster cell based on the spatial distance; adjusting the soil attribute raster data of the target administrative region in the raster cell using the distance weight adjustment coefficient, and acquiring the adjusted soil attribute raster data.
[0008] Secondly, embodiments of this application provide a soil data fusion system based on geospatial relationships. The soil data fusion system includes: a soil attribute raster data acquisition unit for acquiring soil attribute raster data of a target administrative region; a buffer construction unit for constructing a data fusion buffer of a preset scale based on the boundary of the target administrative region; a geometric centroid calculation unit for acquiring the geometric centroid of the target administrative region and the geometric centroid of at least one other administrative region adjacent to the target administrative region; and a raster cell fusion adjustment unit for performing the following operations on each raster cell within the data fusion buffer: calculating the spatial distance from the geometric centroid of the target administrative region and the geometric centroid of each of the other administrative regions to the raster cell; acquiring a distance weight adjustment coefficient for each administrative region relative to the raster cell based on the spatial distance; and adjusting the soil attribute raster data of the target administrative region in that raster cell using the distance weight adjustment coefficient to obtain adjusted soil attribute raster data.
[0009] Compared to existing technologies, the method described in this application introduces the concept of a data fusion buffer and dynamically calculates a distance weight adjustment coefficient for each pixel within the buffer based on the spatial distance between the geometric centroids of the target area and adjacent areas. This allows for local adjustments to the raster data of the target administrative region itself, rather than the simple direct splicing of traditional technologies. Therefore, the method described in this application effectively overcomes the problem of data abrupt changes at administrative boundaries caused by model heterogeneity and data source differences. It achieves a natural and smooth transition of soil attribute raster data at the junctions, significantly improving the spatial continuity and consistency of soil survey results, and providing a reliable data foundation for large-scale, high-precision soil resource assessment and agricultural management decisions.
[0010] To better understand and implement this application, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description
[0011] Figure 1 A flowchart illustrating a soil data fusion method based on geospatial relationships provided in this application; Figure 2 A flowchart illustrating the steps involved in obtaining soil attribute raster data in the geospatial relationship-based soil data fusion method provided in this application; Figure 3 A schematic diagram of the data fusion buffer established in the geospatial relationship-based soil data fusion method provided in this application; Figure 4 A flowchart illustrating the steps involved in performing operations on raster cells in the geospatial relationship-based soil data fusion method provided in this application. Figure 5 A schematic diagram of the structure of the soil data fusion system based on geospatial relationships provided in this application. Detailed Implementation
[0012] This application provides a method and system for soil data fusion based on geospatial relationships. To make the objectives, technical solutions, and effects of this application clearer and more explicit, the following detailed description is provided with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining this application and are not intended to limit this application.
[0013] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.
[0014] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.
[0015] The invention will be further explained below with reference to the accompanying drawings and the description of the embodiments.
[0016] Example 1 Please refer to Figure 1 , Figure 1 A flowchart illustrating a soil data fusion method based on geospatial relationships provided in this application. The method includes the following steps: S10: Obtain soil attribute raster data for the target administrative region; S20: Based on the boundary of the target administrative region, construct a data fusion buffer of a preset scale; S30: Obtain the geometric centroid of the target administrative region and the geometric centroid of at least one other administrative region adjacent to the target administrative region; S40: For each raster cell in the data fusion buffer, perform the following operations: Calculate the geometric centroid of the target administrative region and the spatial distance from the geometric centroid of each of the other administrative regions to the raster cell; Based on the spatial distance, obtain the distance weight adjustment coefficient of each administrative region to the raster cell; The soil attribute raster data of the target administrative region in this raster cell is adjusted using the distance weight adjustment coefficient to obtain the adjusted soil attribute raster data.
[0017] Compared to existing technologies, the technical solution of this application establishes a complete spatial boundary-joining technology system by constructing a data fusion buffer based on the boundary of the target administrative region and innovatively introducing a dynamic weight adjustment algorithm based on multi-source geospatial parameters (including centroid distance of administrative regions, regional area, and pixel affiliation). Specifically, in the technical solution of this application, the basic spatial weights are first obtained by calculating the centroid distance, then the weight distribution is adjusted by coupling the regional area factor, and finally the pixel affiliation factor is introduced to achieve fine-grained correction of the weight coefficients, forming a comprehensive weight adjustment coefficient with clear geographical significance.
[0018] Therefore, the technical solution of this application realizes adaptive spatial correction of soil attribute raster data, effectively eliminates the administrative boundary effect caused by model heterogeneity and data source differences, significantly improves the spatial continuity and regional consistency of soil survey results, and thus provides key technical support for establishing a high-precision, seamless national-scale soil database. It has important practical value for promoting precision agriculture practices, optimizing soil resource management and ensuring national food security.
[0019] For step S10: Obtain soil attribute raster data for the target administrative region.
[0020] The resolution of the soil attribute raster data is 30 meters; the target administrative region refers to the administrative division unit that serves as the core object of the current spatial boundary processing. It is the area covered by the soil attribute raster data that needs to be smoothed at the boundary, and is usually a county-level administrative unit.
[0021] Please refer to Figure 2 , Figure 2 A flowchart illustrating the steps involved in obtaining soil attribute raster data in the geospatial relationship-based soil data fusion method provided in this application.
[0022] In one embodiment, step S10 includes: S101: Collect target attribute analysis data of soil sampling points within the target administrative region, and simultaneously obtain remote sensing environmental interpretation variable data that match the location of the soil sampling points.
[0023] The target attribute analysis data are soil attribute data obtained through laboratory chemical analysis methods, such as the content of water-soluble bicarbonate in the soil; the remote sensing environmental interpretation variable data include vegetation index, surface temperature, topographic index, etc., obtained by processing remote sensing images acquired by Landsat series satellites and Sentinel series satellites.
[0024] In an optional specific embodiment, in step S101, the layout of the soil sampling points needs to follow the principle of spatial balance in order to effectively capture and represent the spatial variation characteristics of soil properties within the target administrative region.
[0025] S102: Using machine learning or geostatistical methods, establish a nonlinear mapping model between the target attribute analysis data and the remote sensing environmental explanatory variable data.
[0026] In one embodiment, the machine learning method is a random forest algorithm or a gradient boosting tree algorithm; the geostatistical method is a kriging interpolation method or a regression kriging method; and the nonlinear mapping relationship model is used to characterize the complex relationship between soil property data and remote sensing environmental explanatory variable data.
[0027] In an optional specific embodiment, step S102, the specific process of building a model using the random forest algorithm includes: using remote sensing environmental explanatory variables as features and soil attribute data as labels, performing corresponding model training until the model converges to the predetermined accuracy requirement.
[0028] S103: Input the remote sensing environmental interpretation variable data corresponding to the target administrative region into the nonlinear mapping relationship model to obtain the soil attribute raster data of the target administrative region.
[0029] In one optional specific embodiment, the environmental variable value corresponding to each raster cell obtained from the remote sensing environmental interpretation variable data corresponding to the target administrative region is input into the trained nonlinear mapping relationship model, thereby directly outputting the predicted value of the soil attribute of the raster cell, and finally converging into the soil attribute raster data of the administrative region.
[0030] For step S20: Based on the boundary of the target administrative region, construct a data fusion buffer of a preset scale.
[0031] In one embodiment, step S20 includes: S201: Using the boundary line of the target administrative region as a reference, extend a region with a preset width into the inner part of the target administrative region to form the data fusion buffer.
[0032] Wherein, the boundary line is the boundary line of the polygon vector data of the target administrative region; the inner extension refers to the construction of a buffer within the polygon to which the target administrative region belongs; the preset value can be set according to the smooth transition range required for edge processing; the data fusion buffer includes several raster pixels of preset scale.
[0033] For a more intuitive understanding, please refer to Figure 3 .like Figure 3 As shown, the data fusion buffer is a strip-shaped area formed by extending a certain width (such as 9000 meters) into the boundary of the target administrative region (county A). At the same time, the raster cells in this area will be the objects of subsequent edge-joining processing operations.
[0034] In one specific embodiment, the preset value is 9000 meters. This setting allows for a sufficiently wide processing band to be formed inside the administrative boundary, ensuring that the boundary cell values can achieve a sufficient gradient.
[0035] In an optional specific embodiment, in step S201, the data fusion buffer can be generated by using the buffer analysis tool in the geographic information system software, with the boundary line as the input line feature, setting a single-sided buffer distance of 9000 meters, and generating the data fusion buffer through spatial calculation.
[0036] In one optional embodiment, when the resolution of the soil attribute raster data is 30 meters, the 9000-meter-wide data fusion buffer corresponds to a strip-shaped area of 300 raster cells in the raster data. This configuration allows each raster cell in the data fusion buffer to perform spatial relationship calculations within a sufficiently large neighborhood, effectively ensuring the spatial continuity and natural smoothness of the edge-joining effect.
[0037] In one optional specific embodiment, the data fusion buffer is constructed only to the inner side of the target administrative region, which can ensure that all edge processing operations are completed within the data range of the target administrative region, avoiding the involvement of the original data of adjacent administrative regions. This simplifies the data processing flow while ensuring the clarity of responsibilities in the data processing process.
[0038] For step S30: Obtain the geometric centroid of the target administrative region and the geometric centroid of at least one other administrative region adjacent to the target administrative region.
[0039] The geometric centroid refers to the geometric center of the polygonal vector surface corresponding to the administrative region, also known as the centroid or centroid. It is a specific geographical coordinate that represents the spatial distribution center of the administrative region and is used as a reference point for subsequent spatial distance calculations.
[0040] In one embodiment, the step of obtaining the geometric centroid of the target administrative region includes: S301: Define the target administrative region as a polygon set in a preset coordinate system, and obtain its corresponding vertices, wherein the geographical coordinates of the vertices are recorded as follows: ( x 0, y 0), ( x 1, y 1), ..., ( x m-1 , y m-1 ), and the last vertex coincides with the first vertex, that is ( x m , y m )=( x 0, y 0).
[0041] In one embodiment, the preset coordinate system is a geographic coordinate system or a projected coordinate system; the polygon is a vector boundary polygon of the target administrative region.
[0042] In one optional specific embodiment, the vertices are obtained by reading the vector boundary file of the target administrative region, wherein the vector boundary file is in shapefile format or GeoJSON format.
[0043] S302: Calculate the vector area (Area) of the target administrative region according to the polygon vector area calculation formula: , Where Area is the vector area of the target administrative region. m The number of vertices, (x i , y i ) is the first polygon of the corresponding polygon i The geographical coordinates of vertices, in the order of . i =0,1,…, m 1.
[0044] In one embodiment, the vector area is a directed area, which is positive when the vertices are arranged in a counterclockwise order and negative when they are arranged in a clockwise order.
[0045] S303: Based on the formula for calculating the centroid coordinates of a polygon, and combined with the vector area of the target administrative region, calculate the centroid coordinates of the geometric centroid of the target administrative region. O x ,O y ): , in, O x Let x be the x-coordinate of the centroid of the target administrative region. O y The ordinate is the centroid coordinate of the target administrative region.
[0046] In one optional specific embodiment, the geometric centroids of the adjacent administrative regions are obtained by the same calculation method, or directly obtained from a pre-established spatial database of administrative region centroids.
[0047] For step S40: Perform an operation on each raster cell within the data fusion buffer.
[0048] Please refer to Figure 4 , Figure 4 A flowchart illustrating the steps involved in performing operations on raster cells in the geospatial relationship-based soil data fusion method provided in this application.
[0049] In one embodiment, step S40 includes: S401: Calculate the geometric centroid of the target administrative region and the spatial distance from the geometric centroid of each of the other administrative regions to the raster cell.
[0050] In one embodiment, the calculation of spatial distance is directly related to the distance decay principle in geography, that is, the influence intensity between geographical entities weakens as the distance increases. The geometric centroid, as the spatial representative point of the administrative region, directly determines the degree of spatial decay of the region's influence on the pixel by its distance from the pixel.
[0051] S402: Based on the spatial distance, obtain the distance weight adjustment coefficient of each administrative region to the raster pixel.
[0052] In one embodiment, the distance weight adjustment coefficient is calculated based on an inverse distance weighting model; the inverse distance weighting model is used to simulate the scale effect and spatial dependence of geographical phenomena in spatial analysis, and the range and intensity of spatial influence can be precisely controlled by setting a distance attenuation coefficient.
[0053] S403: Adjust the soil attribute raster data of the target administrative region in the raster cell using the distance weight adjustment coefficient, and obtain the adjusted soil attribute raster data.
[0054] In one embodiment, the operation of adjusting the original pixel value (the soil attribute raster data of the raster pixel) through the weight adjustment coefficient is essentially a simulation of the spatial superposition effect of multi-source geographic influences, so that the boundary pixel value can transition naturally, which conforms to the basic law of continuous and gradual change of geographic phenomena.
[0055] In one embodiment, the step of calculating the geometric centroid of the target administrative region and the spatial distance from the geometric centroid of each of the other administrative regions to the raster cell includes: S4011: Calculate the geometric centroid of the target administrative region and the spatial distance from the geometric centroid of each of the other administrative regions to the raster cell, according to the spatial distance calculation formula: , in, D ij For the first i The geometric centroid of each administrative region to the first j The spatial distance between grid cells; O xi , O yi ) is the first i The centroid coordinates of the geometric centroids of each administrative region; B xj , B yj ) is the first in the data fusion buffer j The geographic coordinates of the center point of each raster cell.
[0056] In one embodiment, by applying the Euclidean distance formula, the accuracy of spatial proximity measurement is ensured, providing a reliable distance benchmark for subsequent spatial weight calculation. This is the basis for quantifying the spatial relationships between geographic entities in spatial analysis.
[0057] In one optional specific embodiment, the unit of spatial distance is preferably ten kilometers, and the spatial distance can be calculated through the spatial analysis function of GIS software to ensure calculation efficiency and accuracy.
[0058] In one embodiment, the spatial distance D ij Spatial distance matrix can be constructed D : , Wherein, the spatial distance matrix D Each element in D ij Indicates the first i The geometric centroid of each administrative region to the first j The spatial distance between the centers of each raster cell.
[0059] In one embodiment, the step of obtaining the distance weight adjustment coefficient of each administrative region to the raster cell based on the spatial distance includes: S4021: Obtain the distance weight adjustment coefficient for each administrative region relative to the raster pixel according to the distance weight adjustment coefficient acquisition formula: , in, L i For the first i Distance weight adjustment coefficients for each administrative region to the raster cells; D i Indicates the first i The spatial distance from the centroid coordinates of the geometric centroid of an administrative region to the raster cell; β This is the preset distance attenuation coefficient.
[0060] In one optional specific embodiment, the distance attenuation coefficient β The value ranges from 1 to 3, with a preferred value of 2. By adjusting this distance attenuation coefficient, the degree of influence of spatial distance on the weight can be controlled.
[0061] In one optional specific embodiment, when the spatial distance D i When it is less than the preset limit threshold, for D i Setting a minimum value avoids extreme values in weight calculations and ensures numerical stability.
[0062] In one embodiment, the step of adjusting the soil attribute raster data of the target administrative region in that raster cell using the distance weight adjustment coefficient, and obtaining the adjusted soil attribute raster data includes: S4031: Calculate the adjusted soil attribute raster data based on the adjustment formula for the first soil attribute raster data: , in, V orginal The soil attribute raster data for the target administrative region in this raster cell. V new1 For the corresponding adjusted soil property raster data, n The number of administrative regions.
[0063] In one optional embodiment, the adjustment operation is performed independently on each raster cell within the data fusion buffer, supporting parallel computing and improving the processing efficiency of large-scale raster data.
[0064] In another alternative specific embodiment, when When, the adjustment operation degenerates into a weighted average; when At the same time, the adjustment operation produces a scaling effect, enabling more flexible data fusion.
[0065] To further improve the rationality of the boundary alignment results, this application also provides an optimization step based on area weights. Since, given the same spatial distance, administrative regions with larger areas typically have richer sample data and more stable statistical characteristics, their soil attribute data generally have higher overall representativeness and reliability. Therefore, they should be given greater influence in boundary alignment calculations.
[0066] After completing the initial adjustment based on distance weights, the area factor can be introduced for further adjustment using the following steps: After the step of adjusting the soil attribute raster data of the target administrative region in the raster cell using the distance weight adjustment coefficient and obtaining the adjusted soil attribute raster data, the method further includes the following step: S501: Obtain the area weight adjustment coefficient for each administrative region of the raster pixel according to the area weight adjustment coefficient acquisition formula: , in, S i For the first i The area of each administrative region for n The sum of the areas of all administrative regions A i For the first i The area weight adjustment coefficient of each administrative region for the raster cell.
[0067] In one embodiment, the area weight adjustment coefficient is a smoothed coefficient; the calculation of the area weight adjustment coefficient is based on the principle of scale effect in geography, which quantifies the differences in data representativeness of different administrative regions through area proportion, so that administrative regions with larger areas can obtain a contribution commensurate with their size in the process of bordering.
[0068] S502: Based on the adjustment formula for the second soil attribute raster data, perform a second adjustment on the adjusted soil attribute raster data to obtain the second soil attribute raster data: , in, V new1 For the adjusted soil property raster data, V new2 This is the raster data for the second soil attribute.
[0069] In this embodiment, the second soil attribute raster data adjustment formula corrects the distance weight by area weight, thereby achieving the coupling of spatial proximity and regional representativeness. This allows the edge-joining results to take into account both the geographical distance decay law and the administrative unit scale effect, significantly improving the geographical rationality of data fusion.
[0070] In one optional specific embodiment, the area data of each administrative region can be obtained from the statistical bulletin on the area of administrative divisions published by the state, or by calculating the projected area of the vector polygon corresponding to the target administrative region using GIS software.
[0071] In another optional specific embodiment, the introduction of area weight can effectively avoid the excessive influence of data from small administrative areas on the raster cells of the boundary (data fusion buffer), ensuring that the edge-joining results are more consistent with the overall distribution trend of soil properties in large areas.
[0072] In a specific embodiment, taking the border processing of the target administrative region county A and its adjacent administrative region county B as an example, the application of area weight is explained: The area of county A is set to 600 square kilometers, and the area of county B is set to 400 square kilometers; for a specific raster cell within the data fusion buffer, the distance weight adjustment coefficient from the geometric centroid of county A to that raster cell is calculated through steps S401 and S402. L A The distance weighting adjustment factor from the geometric centroid of county B to the raster cell is 0.6. L B The value is 0.4; at this point, the area weight adjustment coefficient for county A is calculated and obtained. A A The area weight adjustment coefficient for County B is 0.6. A B If V is 0.4; then when Vnew1 When V equals 20, the calculation yields V. new2 It is 10.4.
[0073] It should be noted that specific parameter conditions were set in this embodiment to clearly demonstrate the impact of the area weight adjustment coefficient. In practical applications, based on the inventive concept of this application, those skilled in the art can adjust the distance attenuation coefficient β or perform appropriate smoothing of the area ratio (such as taking the logarithm) to control the adjustment range within a more reasonable range (e.g., within ±20%), so as to ensure that the edge-joining results highlight the representativeness of the area while not destroying the original spatial continuity pattern of the soil properties.
[0074] Furthermore, although weight allocation based on the original area ratio is effective in most cases, those skilled in the art will recognize that when the area differences between the participating administrative regions are too large (the area differences between different counties are extremely large or extremely small), the area weight adjustment coefficient may excessively dominate the adjustment result. To improve the robustness and rationality of the soil data fusion method described in this application under all geographical scenarios, this application also provides the following two optimized embodiments: In a preferred embodiment, to mitigate the impact of extreme area differences in administrative regions, the area weight adjustment coefficient can be smoothed, and the formula for obtaining the area weight adjustment coefficient can be replaced with: , in, S i For the first i The area of each administrative region c This is a preset constant (usually set to 1) used to ensure that when S i Extremely low computational stability.
[0075] This logarithmic transformation is based on the principle of diminishing marginal effects in geography. It can effectively compress the relative influence of extreme area values, make the weight distribution smoother, and avoid the influence of small areas being excessively suppressed or the influence of large areas being excessively amplified, thereby ensuring the gradualness and rationality of the edge connection results.
[0076] In another preferred embodiment, to exclude the insignificant influence of remote administrative regions and improve computational efficiency and the local reasonableness of the results, an effective influence radius can be set. Therefore, the soil data fusion method further includes a step: filtering the administrative regions participating in the calculation based on a preset distance threshold.
[0077] Specifically, this refers to: preset distance threshold. D max (For example, 50 kilometers), before calculation, if the first... i Spatial distance from the geometric centroid of an administrative region to the target raster cellD i > D max If the weight coefficients of each item in the administrative region are set to zero, they will not be included in subsequent calculations.
[0078] Therefore, by introducing spatial influence boundaries, it is ensured that the edge calculation is contributed only by administrative regions that have a significant spatial relationship with the target pixel, avoiding interference from irrelevant administrative regions, making the calculation results more stable and reliable, and conforming to the basic principles of the first law of geography.
[0079] In another embodiment, although the distance- and area-based weighting adjustment in this application can effectively improve the boundary abruptness problem of soil attribute raster data, it still has limitations: it treats the influence of the administrative region to which the target raster cell belongs and adjacent regions equally. However, in actual geographic space, a raster cell clearly belongs to a specific administrative region, and its corresponding measured attribute value should have higher confidence and dominance in the soil data fusion process. Therefore, to avoid excessive dilution of the data characteristics of the administrative region to which the raster cell belongs by adjacent administrative regions during the edge joining process, it is necessary to introduce a "subordination" factor as a key correction term. This factor aims to distinguish the primary and secondary influences, ensuring that the edge joining result is smooth while remaining faithful to the original data characteristics of the administrative region to which the raster cell belongs, thereby further improving the rationality and reliability of the fusion result.
[0080] Therefore, this application also provides some soil data fusion steps that can be used in the aforementioned geospatial relationship-based soil data fusion method to further adjust the soil attribute raster data. After adjusting the soil attribute raster data of the target administrative region in the raster cell using the distance weight adjustment coefficient to obtain the adjusted soil attribute raster data, the application further includes the following steps: S601: Obtain the preset subordinate relationship correction factor O i : , in, C primary For the first i The enhancement coefficient set when the administrative region is the administrative region to which the raster pixel belongs. C neighbor For the first i The attenuation coefficient set when an administrative region is another administrative region adjacent to the administrative region to which the raster cell belongs, and C primary Greater than C neighbor .
[0081] In one specific embodiment, the enhancement coefficient C primary The value range is from 1.0 to 2.0, and the weakening coefficient is... C neighbor The value range is from 0.5 to 1.0, and preferably satisfies the following conditions: C primary + C neighbor >2.0, to create a smooth effect with moderate stretching at the boundaries of the joined data.
[0082] S602: Based on the adjustment formula for the third soil attribute raster data, perform a second adjustment on the adjusted soil attribute raster data to obtain the third soil attribute raster data: , in, V new1 For the adjusted soil property raster data, V new3 The third soil attribute raster data, n The number of administrative regions.
[0083] In another optional specific embodiment, the introduction of the subordinate relationship factor effectively ensures that during the edge-joining process, even if a cell in a data fusion buffer is geometrically closer to the centroid of an adjacent administrative region, its final adjustment value will still tend to retain the data characteristics of the administrative region to which it belongs due to its administrative affiliation. This solves the logical contradiction of affiliation that may be caused by simply relying on geometric distance and significantly improves the administrative management applicability of the results.
[0084] Furthermore, those skilled in the art should understand that the core of this invention lies in the fundamental concept of adaptively adjusting boundary cell values by introducing weight adjustment coefficients based on geospatial relationships (such as distance, area, and administrative affiliation). The adjustment schemes based on distance, area, and affiliation described in this application are all specific implementations of this core concept. Without departing from the principles of this invention, those skilled in the art can freely choose to combine or mix one or more of the above factors according to actual application scenarios and effect requirements. Such transformations and combinations based on the same inventive concept should be considered to fall within the protection scope sought by this invention.
[0085] Example 2 Please refer to Figure 5This application also provides a soil data fusion system based on geospatial relationships to implement the steps of the soil data fusion method based on geospatial relationships described in the above embodiments. The soil data fusion system based on geospatial relationships includes: a soil attribute raster data acquisition unit 1001, a buffer construction unit 1002, a geometric centroid calculation unit 1003, and a raster cell fusion adjustment unit 1004.
[0086] The soil attribute raster data acquisition unit 1001 is used to acquire soil attribute raster data of the target administrative region; The buffer construction unit 1002 is used to construct a data fusion buffer of a preset scale based on the boundary of the target administrative region; The geometric centroid calculation unit 1003 is used to obtain the geometric centroid of the target administrative region and the geometric centroid of at least one other administrative region adjacent to the target administrative region. The raster cell fusion adjustment unit 1004 is used to perform the following operations on each raster cell in the data fusion buffer: calculate the geometric centroid of the target administrative region and the spatial distance from the geometric centroid of each of the other administrative regions to the raster cell; based on the spatial distance, obtain the distance weight adjustment coefficient of each administrative region to the raster cell; and use the distance weight adjustment coefficient to adjust the soil attribute raster data of the target administrative region in the raster cell to obtain the adjusted soil attribute raster data.
[0087] It should be noted that the above-described embodiment of the soil data fusion system based on geospatial relationships is only illustrated by the division of the above-described functional modules when implementing the soil data fusion method based on geospatial relationships. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.
[0088] Furthermore, the soil data fusion system based on geospatial relationships provided in the above embodiments and the soil data fusion method based on geospatial relationships in Embodiment 1 belong to the same concept. The implementation process is detailed in the method embodiment, namely Embodiment 1, and will not be repeated here.
[0089] The embodiments described above are merely examples of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and this application also intends to include these modifications and variations.
Claims
1. A soil data fusion method based on geospatial relationships, characterized in that, Includes the following steps: Obtain soil attribute raster data for the target administrative region; Based on the boundaries of the target administrative region, a data fusion buffer of a preset scale is constructed; Obtain the geometric centroid of the target administrative region and the geometric centroid of at least one other administrative region adjacent to the target administrative region; For each raster cell within the data fusion buffer, perform the following operations: Calculate the geometric centroid of the target administrative region and the spatial distance from the geometric centroid of each of the other administrative regions to the raster cell; Based on the spatial distance, obtain the distance weight adjustment coefficient of each administrative region to the raster cell; The soil attribute raster data of the target administrative region in this raster cell is adjusted using the distance weight adjustment coefficient to obtain the adjusted soil attribute raster data.
2. The soil data fusion method based on geospatial relationships according to claim 1, characterized in that, The resolution of the soil property raster data is 30 meters; The steps to obtain soil attribute raster data for the target administrative region include: Collect target attribute analysis data of soil sampling points within the target administrative region, and simultaneously obtain remote sensing environmental interpretation variable data that match the location of the soil sampling points; A nonlinear mapping model between the target attribute analysis data and the remote sensing environmental explanatory variable data is established using machine learning or geostatistical methods. The remote sensing environmental interpretation variable data corresponding to the target administrative region are input into the nonlinear mapping relationship model to obtain the soil attribute raster data of the target administrative region.
3. The soil data fusion method based on geospatial relationships according to claim 1, characterized in that, The steps for constructing a data fusion buffer of a preset scale based on the boundaries of the target administrative region include: Using the boundary line of the target administrative region as a reference, an area with a preset width is extended inwards from the inside of the target administrative region to form the data fusion buffer.
4. The soil data fusion method based on geospatial relationships according to claim 1, characterized in that, The steps for obtaining the geometric centroid of the target administrative region include: The target administrative region is defined as a polygon set in a preset coordinate system, and its corresponding vertices are obtained. The geographical coordinates of the vertices are recorded as follows: ( x 0, y 0), ( x 1, y 1), ..., ( x m-1 , y m-1 ), and the last vertex coincides with the first vertex, that is ( x m , y m )=( x 0, y 0); Calculate the vector area (Area) of the target administrative region using the polygon vector area calculation formula: ， Where Area is the vector area of the target administrative region. m The number of vertices, ( x i , y i ) is the first polygon of the corresponding polygon i The geographical coordinates of vertices, in the order of . i =0,1,…, m 1; Based on the formula for calculating the centroid coordinates of a polygon, and combined with the vector area of the target administrative region, the centroid coordinates of the geometric centroid of the target administrative region are calculated. O x ,O y ): ， in, O x Let x be the x-coordinate of the centroid of the target administrative region. O y The ordinate is the centroid coordinate of the target administrative region.
5. The soil data fusion method based on geospatial relationships according to claim 4, characterized in that, The steps of calculating the geometric centroid of the target administrative region and the spatial distance from the geometric centroid of each of the other administrative regions to the raster cell include: Based on the spatial distance calculation formula, calculate the geometric centroid of the target administrative region and the spatial distance from the geometric centroid of each of the other administrative regions to the raster cell: ， in, D ij For the first i The geometric centroid of each administrative region to the first j The spatial distance between grid cells; O xi , O yi ) is the first i The centroid coordinates of the geometric centroids of each administrative region; B xj , B yj ) is the first in the data fusion buffer j The geographic coordinates of the center point of each raster cell.
6. The soil data fusion method based on geospatial relationships according to claim 5, characterized in that, The step of obtaining the distance weight adjustment coefficient of each administrative region to the raster cell based on the spatial distance includes: Based on the distance weight adjustment coefficient acquisition formula, the distance weight adjustment coefficient for each administrative region to the raster cell is obtained: ， in, L i For the first i Distance weight adjustment coefficients for each administrative region to the raster cells; D i Indicates the first i The spatial distance from the centroid coordinates of the geometric centroid of an administrative region to the raster cell; β This is the preset distance attenuation coefficient.
7. The soil data fusion method based on geospatial relationships according to claim 6, characterized in that, The steps of adjusting the soil attribute raster data of the target administrative region in this raster cell using the distance weight adjustment coefficient and obtaining the adjusted soil attribute raster data include: Calculate the adjusted soil attribute raster data based on the adjustment formula for the first soil attribute raster data: ， in, V orginal The soil attribute raster data for the target administrative region in this raster cell. V new1 For the corresponding adjusted soil property raster data, n The number of administrative regions.
8. The soil data fusion method based on geospatial relationships according to claim 7, characterized in that, After the step of adjusting the soil attribute raster data of the target administrative region in the raster cell using the distance weight adjustment coefficient and obtaining the adjusted soil attribute raster data, the method further includes the following step: Based on the formula for obtaining the area weight adjustment coefficient, the area weight adjustment coefficient for each administrative region to the raster cell is obtained: ， in, S i For the first i The area of each administrative region for n The sum of the areas of all administrative regions A i For the first i The area weight adjustment coefficient of each administrative region for the raster cell; Based on the adjustment formula for the second soil attribute raster data, the adjusted soil attribute raster data is further adjusted to obtain the second soil attribute raster data: ， in, V new1 For the adjusted soil property raster data, V new2 This is the raster data for the second soil attribute.
9. The soil data fusion method based on geospatial relationships according to claim 7, characterized in that, After the step of adjusting the soil attribute raster data of the target administrative region in the raster cell using the distance weight adjustment coefficient and obtaining the adjusted soil attribute raster data, the method further includes the following step: Get the preset subordinate relationship correction factor O i : ， in, C primary For the first i The enhancement coefficient set when the administrative region is the administrative region to which the raster pixel belongs. C neighbor For the first i The attenuation coefficient set when an administrative region is another administrative region adjacent to the administrative region to which the raster cell belongs, and C primary Greater than C neighbor ; Based on the adjustment formula for the third soil attribute raster data, the adjusted soil attribute raster data is further adjusted to obtain the third soil attribute raster data: ， in, V new1 For the adjusted soil property raster data, V new3 The third soil attribute raster data, n The number of administrative regions.
10. A soil data fusion system based on geospatial relationships, characterized in that, The soil data fusion system includes: The soil attribute raster data acquisition unit is used to acquire soil attribute raster data for the target administrative region. A buffer construction unit is used to construct a data fusion buffer of a preset scale based on the boundary of the target administrative region; A geometric centroid calculation unit is used to obtain the geometric centroid of the target administrative region and the geometric centroid of at least one other administrative region adjacent to the target administrative region; The raster cell fusion adjustment unit is used to perform the following operations on each raster cell in the data fusion buffer: calculate the geometric centroid of the target administrative region and the spatial distance from the geometric centroid of each of the other administrative regions to the raster cell; based on the spatial distance, obtain the distance weight adjustment coefficient of each administrative region to the raster cell; and use the distance weight adjustment coefficient to adjust the soil attribute raster data of the target administrative region in the raster cell to obtain the adjusted soil attribute raster data.