Method for constructing land space multi-element comprehensive observation network

Abstract

The present application relates to the technical field of geographic information science, and discloses a land space multi-element comprehensive observation network construction method, comprising: acquiring characteristic parameters of remote sensing satellites and performance parameters of sensors in a target land space, and determining satellite observation plans and sensor layout schemes; using a consistency checking algorithm to perform multi-source data fusion on the characteristic parameters of remote sensing satellites and the performance parameters of sensors, and realizing dynamic linkage analysis among multiple elements; based on a feature matching and weight distribution algorithm, aggregating multi-source data and eliminating redundant features; performing radiation reference alignment on the multi-source data, constructing a unified space-time reference frame to coordinate the consistency of time stamps, spatial coordinates, spectral bands, observation angles and polarization characteristics, and integrating the consistency coordination results to generate a multi-dimensional data cube. The present application significantly improves the data comprehensiveness and timeliness, and provides a scientific basis for land space planning, ecological protection and disaster warning.

Description

Technical Field

[0001] This invention relates to the field of geographic information science and technology, and more specifically, to a method for constructing a multi-element integrated observation network for national land space. Background Technology

[0002] With the acceleration of globalization and urbanization, the development and utilization of national land space face increasingly complex challenges. Traditional national land space monitoring technologies mainly rely on single data sources, such as ground observation stations or satellite remote sensing, which suffer from limited coverage, lagging data updates, and insufficient correlation of elements. For example, landform monitoring may not be able to be analyzed synchronously with changes in vegetation cover or water resources, and the impacts of climate conditions and human activities are difficult to fully incorporate, leading to one-sided monitoring results. These limitations are particularly evident in areas with complex terrain, such as mountains, wetlands, or high-altitude regions, where traditional methods struggle to achieve high-precision dynamic observation. Furthermore, single-element monitoring methods cannot meet the multidimensional needs of modern national land space planning, ecological protection, and disaster early warning. In recent years, with the rapid development of sensor technology, remote sensing, and big data analysis, there is an urgent need for a new technology that integrates multi-source data and achieves comprehensive observation to improve the scientific nature and decision-making efficiency of national land space management.

[0003] While some studies have attempted to improve monitoring capabilities through multi-source data fusion, significant technical bottlenecks remain. First, ensuring spatiotemporal-spectral-angular-polarization consistency in data acquisition is difficult, leading to conflicts and redundancy during data fusion. Second, insufficient sensor layout and satellite characteristic analysis limit the stability and adaptability of observation networks in complex environments. For example, existing systems often experience a decrease in accuracy and coverage when processing data under terrain obstruction or severe weather conditions. Third, traditional methods lack effective correlation constraints and complementary feature aggregation mechanisms, making it difficult to achieve dynamic correlation analysis among multiple elements. These problems make it difficult for existing technologies to meet the comprehensive needs of land resource management, environmental protection, and urban development planning. Therefore, developing a technology for constructing a multi-element integrated observation network for land space that integrates multiple data acquisition methods, optimizes data processing workflows, and possesses modular scalability has become a crucial issue that urgently needs to be addressed. Summary of the Invention

[0004] In response to the problems in related technologies, this invention proposes a method for constructing a multi-element integrated observation network for national land space, in order to overcome the aforementioned technical problems existing in existing related technologies.

[0005] This invention provides solutions to several shortcomings of traditional land space monitoring technologies. First, it addresses the limited coverage and outdated updates of single data sources by integrating remote sensing, ground observation stations, UAVs, and social sensing to achieve comprehensive monitoring of multiple elements. Second, it addresses the issue of decreased observation accuracy in complex terrain and variable environments by optimizing satellite characteristic analysis, sensor feature analysis, and data fusion processes to ensure data stability and accuracy under harsh conditions. Furthermore, to address the lack of consistency and correlation analysis of multi-source data in traditional methods, this invention introduces conflict resolution, correlation constraints, and feature aggregation techniques to achieve consistency across time, space, spectrum, angle, and polarization, and constructs a data cube. Finally, it addresses the inability of existing technologies to meet dynamic, multi-dimensional needs by using modular design and intelligent analysis to adapt to the monitoring requirements of different regions, providing efficient support for land space management.

[0006] Therefore, the specific technical solution adopted by the present invention is as follows:

[0007] Methods for constructing a multi-element integrated observation network for national spatial data include:

[0008] To acquire the characteristic parameters of remote sensing satellites and the performance parameters of sensors in the target land space, to achieve monitoring of multi-source data, and to determine the satellite observation plan and sensor layout scheme based on the terrain and coverage requirements of the observation area, combined with the working environment and applicable scenarios of the sensors.

[0009] The consistency verification algorithm is used to fuse multi-source data of characteristic parameters of remote sensing satellites and performance parameters of sensors, and dynamic linkage analysis between multiple elements is realized based on the correlation model and constraints between multi-source data; based on feature matching and weight allocation algorithms, multi-source data is aggregated and redundant features are removed.

[0010] By using radiometric correction and standardization techniques, radiometric benchmarks are aligned to multi-source data. Based on the aligned radiometric benchmarks, a unified spatiotemporal benchmark framework is constructed to coordinate the consistency of timestamps, spatial coordinates, spectral bands, observation angles, and polarization characteristics. The consistency coordination results are then integrated to generate a multidimensional data cube.

[0011] Furthermore, the acquisition of characteristic parameters of remote sensing satellites and performance parameters of sensors in the target land space, enabling multi-source data monitoring, and determining the satellite observation plan and sensor layout scheme based on the terrain and coverage requirements of the observation area, combined with the working environment and applicable scenarios of the sensors, includes:

[0012] The goal is to acquire the characteristic parameters of remote sensing satellites and the performance parameters of sensors in the target territory to achieve monitoring of multi-source data. The characteristic parameters of remote sensing satellites include orbital parameters, imaging resolution, band range and revisit period, while the performance parameters of sensors include sensor data from ground observation stations, UAVs and social sensing devices.

[0013] Based on the terrain and coverage requirements of different observation areas, the visibility and revisit capability of remote sensing satellites over target areas within a specified time window are determined to clarify the satellite observation plan; based on multi-objective optimization algorithms, the optimal sensor layout scheme is determined by combining sensor performance parameters, working environment and applicable scenarios.

[0014] Furthermore, the determination of the optimal sensor layout scheme based on a multi-objective optimization algorithm, combined with the sensor's performance parameters, operating environment, and applicable scenarios, includes:

[0015] Based on the sensor's spatial resolution, sampling frequency, and data format characteristics, and combined with the sensor's working environment and usage scenarios, the initial layout scheme of the sensor is determined.

[0016] Based on the functional zoning of national land space, the coverage density requirements are determined, a weighted optimization model is constructed, and the observation priorities in the initial layout scheme are ranked according to the results of the weighted optimization model.

[0017] Based on a multi-objective optimization algorithm and considering constraints, the globally optimal scheduling of the observation plan is determined, and the optimal sensor layout scheme is determined based on the globally optimal scheduling. The expression for the objective function of the weighted optimization model is:

[0018]

[0019] In the formula, This represents the comprehensive optimization objective function value. Indicates coverage rate. Indicates data acquisition latency. , These represent the weighting coefficients for coverage and data acquisition latency, respectively.

[0020] Furthermore, the method utilizes a consistency verification algorithm to fuse multi-source data of the characteristic parameters of remote sensing satellites and the performance parameters of sensors, and realizes dynamic linkage analysis among multiple elements based on the correlation model and constraints between the multi-source data; based on feature matching and weight allocation algorithms, the method aggregates multi-source data and removes redundant features, including:

[0021] The characteristic parameters of remote sensing satellites, the performance parameters of sensors, and the standardized reference data already existing in historical databases are integrated, and a consistency check algorithm is used to align the characteristic parameters of remote sensing satellites and the performance parameters of sensors in order to identify and resolve temporal, spatial, and spectral conflicts between data.

[0022] Construct a correlation model among landform, vegetation cover, and water resources, define spatiotemporal correlation rules and physical constraints, and realize dynamic linkage analysis among multiple elements; extract complementary features from each data source, and use feature matching and weight allocation algorithms to aggregate complementary features; identify redundant information in multi-source data, and use principal component analysis dimensionality reduction technology to optimize aggregation.

[0023] Furthermore, the expression for the consistency check algorithm is:

[0024]

[0025] In the formula, Represents a consistency measure. Indicates the number of samples. Represents observation data, Indicates reference data. This represents the tolerance threshold.

[0026] Furthermore, the expression for aggregation is:

[0027]

[0028] In the formula, Indicates aggregation features, , These represent remote sensing satellite features and ground features, respectively. , These represent the weights of remote sensing satellite features and ground features, respectively.

[0029] Furthermore, the process of using radiometric correction and standardization techniques to align multi-source data with a radiometric reference, and constructing a unified spatiotemporal reference framework based on the aligned radiometric reference to coordinate the consistency of timestamps, spatial coordinates, spectral bands, observation angles, and polarization characteristics, and integrating the consistency coordination results to generate a multidimensional data cube includes:

[0030] By using radiometric correction and standardization techniques, radiometric reference alignment is performed on multi-source data to eliminate deviations caused by differences in illumination or sensor sensitivity.

[0031] Based on the aligned radiation reference, a unified spatiotemporal reference framework is constructed to coordinate the consistency of timestamps, spatial coordinates, spectral bands, observation angles and polarization characteristics, and interpolation and projection transformation techniques are used to supplement and resample multidimensional data.

[0032] By integrating and coordinating data such as timestamps, spatial coordinates, spectral bands, observation angles, and polarization characteristics, a multidimensional data cube is generated. The length and width of this multidimensional data cube are spatial resolution dimensions, and the third dimension is multi-temporal, multi-spectral, multi-angle, and multi-polarization layers.

[0033] Furthermore, the radiation correction formula is as follows:

[0034]

[0035] In the formula, This indicates the corrected radiation value. This represents the original radiation value. , These represent the reference and the original mean, respectively. This indicates the offset.

[0036] Furthermore, the interpolation formula is:

[0037]

[0038] In the formula, This represents the interpolation result. Let represent the planar spatial coordinates of the points to be interpolated, and n represent the total number of known sampling points involved in the interpolation calculation. Indicates the first Known point values, Indicates the point to be interpolated With the The distance from a known point to the point to be interpolated Indicates the point to be interpolated With the The distance from a known point to the point to be interpolated Indicates the first The weights of the known point values.

[0039] The beneficial effects of this invention are as follows:

[0040] 1) This invention, through satellite characteristic analysis, sensor feature analysis, conflict resolution, correlation constraints, complementary feature aggregation, and redundant feature aggregation and elimination, ensures that observation accuracy and coverage remain stable in complex terrain and variable environments. Compared with traditional single-element monitoring methods, this invention significantly improves data comprehensiveness and timeliness, providing a scientific basis for land spatial planning, ecological protection, and disaster early warning.

[0041] 2) This invention is applicable to scenarios such as land and resources management, environmental protection, urban development planning, and disaster early warning. In land and resources management, it is used to monitor changes in land use and resource development; in environmental protection, it is used to track ecosystem health and changes in vegetation cover; in urban development planning, it is used to analyze the impact of urban expansion on landforms and water resources; and in disaster early warning, it is used to monitor climate anomalies and geological activities in real time. It has strong promotional value and practicality.

[0042] 3) This invention also supports modular expansion, allowing for the addition of specific observation modules based on the needs of different regions, further enhancing its adaptability and functionality. Through the implementation of this technology, systematic management and intelligent analysis of multi-dimensional land space data can be achieved, promoting the realization of sustainable development goals. It can be widely applied to government departments, research institutions, and enterprises, providing technical support for the achievement of sustainable development goals. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a schematic diagram illustrating the principle of the method for constructing a multi-element integrated observation network for territorial space according to an embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram of the framework of the method for constructing a multi-element integrated observation network for territorial space according to an embodiment of the present invention;

[0046] Figure 3 This is a schematic diagram of constructing a land space shape flow data cube by combining remote sensing and crowdsourced data in the land space multi-element integrated observation network construction method according to an embodiment of the present invention;

[0047] Figure 4 This is a schematic diagram illustrating an example of data cube construction for land topography monitoring in the method for constructing a multi-element integrated observation network for land space according to an embodiment of the present invention. Detailed Implementation

[0048] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention. The components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0049] According to an embodiment of the present invention, a method for constructing a multi-element integrated observation network for national spatial data is provided.

[0050] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments. Figure 2 and Figure 3 This invention presents key technologies and an overall framework for constructing a multi-element integrated observation network for national land space. The observation network constructed by this invention can conduct multi-element monitoring of national land at different spatial scales (national, regional, and local scales) and functional spaces (urban, agricultural, and ecological spaces). For the entire national scope, a spatial layer observation system composed of multiple remote sensing satellites is used to achieve macroscopic, full-coverage, and periodic monitoring, primarily for understanding the overall pattern of land use, ecological security patterns, national land change trends, and climate element monitoring. Focusing on mesoscale regions such as provinces, economic zones, river basins, and ecological corridors, data fusion from satellite observations, UAV supplementary observations, and regional ground observation stations is combined to achieve medium-to-high resolution and high-frequency dynamic monitoring. Primarily targeting small-scale areas such as counties, townships, and key functional zones, ultra-high resolution and high-frequency local monitoring is conducted using ground observation stations, mobile monitoring vehicles, and social sensing devices (such as crowdsourced mobile terminals) to meet the needs of emergency response, refined supervision, and governance.

[0051] like Figure 1 As shown, the method for constructing this multi-element integrated observation network for national land space includes the following steps:

[0052] S1. Obtain the characteristic parameters of remote sensing satellites and the performance parameters of sensors in the target land space, realize the monitoring of multi-source data, and determine the satellite observation plan and sensor layout scheme according to the terrain and coverage requirements of the observation area, combined with the working environment and applicable scenarios of the sensors.

[0053] The process of acquiring characteristic parameters of remote sensing satellites and performance parameters of sensors in the target land space, realizing multi-source data monitoring, and determining the satellite observation plan and sensor layout scheme based on the terrain and coverage requirements of the observation area, combined with the working environment and applicable scenarios of the sensors, includes:

[0054] S11. Acquire the characteristic parameters of remote sensing satellites and the performance parameters of sensors in the target land space to achieve monitoring of multi-source data. The characteristic parameters of remote sensing satellites include orbital parameters, imaging resolution, band range and revisit period. The performance parameters of sensors include sensor data from ground observation stations, UAVs and social sensing devices.

[0055] S12. Based on the terrain and coverage requirements of different observation areas, determine the visibility and revisit capability of remote sensing satellites over the target area within a specified time window to clarify the satellite observation plan; based on a multi-objective optimization algorithm, determine the optimal sensor layout scheme by combining sensor performance parameters, operating environment, and applicable scenarios; specifically including:

[0056] Satellite Characteristic Analysis: First, the characteristics of the remote sensing satellites used are analyzed, including orbital parameters (e.g., orbital altitude approximately 500-800 km, inclination 98°), imaging resolution (0.5-30 m), spectral range (visible to thermal infrared, 0.4-12.5 μm), and revisit period (5-16 days). Based on the terrain and coverage requirements of different regions, the satellite observation plan is optimized to ensure data acquisition coverage of over 95% and timeliness within 24 hours.

[0057] Specifically, satellite characteristic analysis involves analyzing the sensor characteristics of remote sensing satellites, including orbital parameters, imaging resolution, spectral range, and revisit period. For different observation areas, the satellite's visibility and revisit capability within a specified time window are determined by combining parameters such as orbital altitude h, period T, and revisit period R. The orbital period can be calculated using the following formula:

[0058]

[0059] in, Let be the average radius of the Earth, h be the orbital altitude, and μ be the Earth's gravitational constant. Based on the dynamic monitoring needs of the region, the revisit period R is designed to ensure coverage of over 95% and time resolution controlled within 24 hours.

[0060] Sensor Feature Analysis: Feature analysis is performed on sensors from ground observation stations, drones, and social sensing devices to evaluate their spatial resolution (0.1-5m), sampling frequency (1Hz to 1kHz), and data format characteristics. Based on the sensor's operating environment and applicable scenarios, an optimal layout scheme is designed to increase coverage to 90% and maximize the complementarity of multi-source data.

[0061] Specifically, in sensor feature analysis, coverage density requirements are determined based on national land spatial functional zoning (such as ecological protection red lines, basic farmland protection areas, urban construction areas, and high-risk areas for natural disasters), and a weighted optimization model is constructed to prioritize observations. The optimization model can employ a multi-objective function:

[0062]

[0063] In the formula, This represents the comprehensive optimization objective function value. Indicates coverage rate. Indicates data acquisition latency. , The weighting coefficients for coverage and data acquisition latency are respectively (set according to task requirements); a scheduling optimization algorithm based on the Constraint Satisfaction Problem (CSP) is adopted, combined with heuristic search and multi-objective optimization (NSGA-II algorithm) to perform global optimal scheduling of the observation plan, and the optimal layout scheme of the sensors is determined based on the global optimal scheduling.

[0064] S2. Use the consistency check algorithm to fuse multi-source data on the characteristic parameters of remote sensing satellites and the performance parameters of sensors, and realize dynamic linkage analysis between multiple elements based on the correlation model and constraints between multi-source data; aggregate multi-source data based on feature matching and weight allocation algorithms, and remove redundant features.

[0065] The process involves using a consistency check algorithm to fuse multi-source data on the characteristic parameters of remote sensing satellites and the performance parameters of sensors, and realizing dynamic linkage analysis among multiple elements based on the correlation model and constraints between the multi-source data. The aggregation of multi-source data and the removal of redundant features based on feature matching and weight allocation algorithms include:

[0066] S21. Integrate the characteristic parameters of remote sensing satellites, the performance parameters of sensors, and the standardized reference data already in the historical database, and use a consistency check algorithm to align the characteristic parameters of remote sensing satellites and the performance parameters of sensors in order to identify and resolve temporal, spatial, and spectral conflicts between data.

[0067] Specifically, in the multi-source data fusion process, satellite remote sensing data, ground observation station data, UAV low-altitude imagery data, social perception data (such as crowdsourced monitoring and mobile terminal perception data), and standardized reference data already existing in historical databases are integrated. Temporal, spatial (±10m), or spectral (±2%) conflicts between data are identified and resolved. A consistency check algorithm is used to adjust the alignment parameters of data from different sources, eliminating inconsistencies caused by equipment differences or environmental changes, ensuring a data fusion success rate of 98%. The expression for the consistency check algorithm is:

[0068]

[0069] In the formula, Represents a consistency measure. Indicates the number of samples. Represents observation data, Indicates reference data. This represents the tolerance threshold, the value of which is determined based on the data characteristics, such as 1 day for time conflict, 30 meters for spatial conflict, and 2% for spectral conflict;

[0070] If, after consistency verification, a data unit does not meet the tolerance threshold condition (i.e., C exceeds ∈), a weighted correction is performed, combining data from surrounding areas or adjacent time periods, and outliers are corrected through weighted interpolation or time series completion methods. If the data quality score is lower than the system's set threshold, the data is marked as an anomaly and temporarily excluded from subsequent analysis; if necessary, the integrated observation network is requested to re-observe or supplement data collection.

[0071] S22. Construct a correlation model between landform, vegetation cover, and water resources; define spatiotemporal correlation rules and physical constraints; realize dynamic linkage analysis among multiple elements; extract complementary features from each data source; use feature matching and weight allocation algorithms to aggregate complementary features; identify redundant information in multi-source data; and use principal component analysis dimensionality reduction technology to optimize aggregation.

[0072] Specifically, a correlation model is established between elements such as landform, vegetation cover, and water resources. Spatiotemporal correlation rules (e.g., Δt≤1h, Δx≤50m, where Δt represents the temporal difference between different satellite observations, and Δx represents the spatial location difference between different satellite observations) and physical constraints (e.g., Normalized Difference Vegetation Index (NDVI)>0.2) are defined. These constraints are used to optimize the data processing workflow, enabling dynamic linkage analysis between multiple elements and improving the comprehensiveness of monitoring by over 85%.

[0073] Complementary features from various data sources are extracted, such as the high spatial coverage of satellites (>90%) and the high temporal resolution of ground stations (>95%). These features are then aggregated using feature matching and a weight allocation algorithm (weight ratio 1:2). This ensures the complementary advantages of data from different sources, enhancing the overall performance of the observation network by approximately 20%. The aggregation expression is as follows:

[0074]

[0075] In the formula, Indicates aggregation features, , These represent remote sensing satellite features and ground features, respectively. , These represent the weights of remote sensing satellite features and ground features, respectively.

[0076] Redundant information in multi-source data is identified, and principal component analysis (PCA) is used for dimensionality reduction and optimization aggregation to reduce data redundancy to below 30% while improving processing efficiency by approximately 15%. The stable contribution of redundant features under complex terrain is ensured, maintaining observation accuracy error below 5%. The PCA dimensionality reduction formula is as follows:

[0077]

[0078] In the formula, To reduce the dimensionality of the data, For the front There are one principal component, where T is the number of bands. This is the original data.

[0079] S3. Using radiometric correction and standardization techniques, radiometric reference alignment is performed on multi-source data. Based on the aligned radiometric reference, a unified spatiotemporal reference framework is constructed to coordinate the consistency of timestamps, spatial coordinates, spectral bands, observation angles and polarization characteristics. The consistency coordination results are then integrated to generate a multidimensional data cube.

[0080] The process of using radiometric correction and standardization techniques to align multi-source data with a radiometric reference, constructing a unified spatiotemporal reference framework based on the aligned radiometric reference to coordinate the consistency of timestamps, spatial coordinates, spectral bands, observation angles, and polarization characteristics, and integrating the consistency coordination results to generate a multidimensional data cube includes:

[0081] S31. Using radiometric correction and standardization techniques, radiometric reference alignment is performed on multi-source data to eliminate deviations caused by differences in illumination or sensor sensitivity.

[0082] Specifically, through radiometric correction (correction coefficient R²>0.95) and standardization, multi-source data are aligned to a radiometric reference, eliminating biases caused by differences in illumination (±10%) or sensor sensitivity, laying the foundation for subsequent consistency analysis. The radiometric correction formula is as follows:

[0083]

[0084] In the formula, This indicates the corrected radiation value. This represents the original radiation value. , These represent the reference and the original mean, respectively. Indicates the offset;

[0085] S32. Based on the aligned radiation reference, a unified spatiotemporal reference framework is constructed to coordinate the consistency of timestamps, spatial coordinates, spectral bands, observation angles and polarization characteristics, and interpolation and projection transformation techniques are used to supplement and resample multidimensional data.

[0086] Specifically, based on the radiometric benchmark, a unified spatiotemporal benchmark framework is constructed to coordinate the consistency of timestamps (error <1s), spatial coordinates (error <5m), spectral bands (overlap rate >90%), observation angles (±5°), and polarization characteristics (correlation coefficient >0.9), achieving spatiotemporal-spectral-angle-polarization consistency of the spatiotemporal benchmark. Interpolation and projection transformation techniques are used to ensure that the multidimensional data integration error is <3%.

[0087] In this embodiment, a unified spatiotemporal reference framework is constructed for the precise alignment and integration of multi-source, multi-scale, and multi-temporal data. A standard system is established to regulate the correspondence and consistency of various observation data across time and space dimensions (including spatial coordinate system, time system, spectral system, observation angle, polarization state, etc.). This framework enables strict registration of data from different observation platforms (such as satellites, ground-based systems, UAVs, and social sensing) within the same reference frame. To ensure the uniformity of spatial alignment, the projection systems used by different source data are first standardized and converted to the main projection system UTM. Spatial transformation mainly includes preliminary coarse registration, performing overall geometric coarse alignment, and initially eliminating large-scale displacements. Then, a fine projection transformation is performed, introducing high-precision ground control points (GCPs) and spatial reference points, using affine transformation (for flat areas), quadratic polynomial transformation, or cubic polynomial transformation (for mountains and hills). During the data fusion process after the projection transformation, a weighted inverse distance squared interpolation algorithm is used to supplement and resample observations for areas with different resolutions or sparse voids. The interpolation formula is:

[0088]

[0089] In the formula, This represents the interpolation result. These represent the planar spatial coordinates of the points to be interpolated (i.e., the raster or location points whose values ​​need to be estimated), typically expressed in geographic coordinates or projected coordinate systems (such as UTM). n represents the total number of known sampling points involved in the interpolation calculation. Indicates the first Known point values, Indicates the point to be interpolated With the The distance from a known point to the point to be interpolated Indicates the point to be interpolated With the The distance from a known point to the point to be interpolated The weight represents the value of a known point. The weight is inversely proportional to the distance, with the weight increasing as the distance increases. The core idea of ​​this algorithm is based on the assumption of local spatial continuity, that is, the observations of nearby points are closer. The contribution is dynamically adjusted through a weighting mechanism to achieve refined local compensation.

[0090] S33. Integrate the consistent and coordinated timestamps, spatial coordinates, spectral bands, observation angles and polarization characteristics of the data to generate a multi-dimensional data cube. The length and width of the multi-dimensional data cube are the spatial resolution dimensions, and the third dimension is multi-temporal, multi-spectral, multi-angle and multi-polarization layers.

[0091] Specifically, the spatiotemporal-spectral-angular-polarization data, after consistency processing, are integrated to generate a multidimensional data cube (example size: 1000×1000×50 pixels, where the length and width are spatial resolution dimensions, and the third dimension represents multiple temporal, multispectral, multi-angle, and multi-polarization layers). This cube supports comprehensive querying, automatic analysis, and visualization of dynamic changes in national land space. It supports dynamic querying and visualization analysis, achieving a data utilization rate of 90%, and provides data support for national land space planning, ecological protection, and disaster early warning.

[0092] The multidimensional data cube integrates spatiotemporal, spectral, angular, and polarization multidimensional observation data to provide comprehensive data support for dynamic changes in national land space. Specifically, it is applied to optimizing the pattern of national land use, monitoring the quality of ecosystems and detecting abnormal changes, as well as identifying natural disaster risks and making emergency response decisions, providing quantitative and real-time decision-making basis for national land space planning, ecological protection, and disaster early warning.

[0093] To better illustrate the above-mentioned technical solution of the present invention, the following will describe the data cube construction process for land topography monitoring with examples, such as... Figure 4 As shown, the three devices used are all remote sensing satellites, including ICESat-2, TerraSAR-X / TanDEM-X, and ALOS PRISM. Each satellite collects and supplements data for different elements to meet the needs of comprehensive observation of multiple elements of national land space. The ICESat-2 satellite uses LiDAR ranging technology for high-precision surface elevation measurement. The TerraSAR-X / TanDEM-X satellite uses Synthetic Aperture Radar (SAR) imaging, enabling all-weather, all-time observation. The ALOS PRISM satellite, launched by Japan, is an Earth observation satellite that uses a stereo imaging sensor to generate high-precision digital surface models (DSM) and digital elevation models (DEM). Data from all three satellites can be acquired. Using data from these three different satellite sources, this invention can achieve virtual constellation networking and the construction of data cubes for national land topography monitoring. The specific implementation includes the following steps:

[0094] The first step is satellite characteristic analysis: Characteristic analysis is conducted on ICESat-2 (orbital altitude approximately 500km, photon footprint diameter 10.3~11.2m), TerraSAR-X / TanDEM-X (elevation accuracy 2m), and ALOS PRISM (stereo imaging resolution 12.5m) to evaluate their orbital parameters, revisit periods (e.g., 91 days for ICESat-2), and spectral range. For land topographic monitoring needs (e.g., the Loess Plateau region), a clear observation plan is defined to ensure 95% coverage.

[0095] The three instruments used are all remote sensing satellites, including ICESat-2, TerraSAR-X / TanDEM-X, and ALOSPRISM, each collecting and supplementing data for different elements to meet the needs of comprehensive observation of multiple elements of national land space. The ICESat-2 satellite uses LiDAR ranging technology for high-precision surface elevation measurement. The TerraSAR-X / TanDEM-X satellites use Synthetic Aperture Radar (SAR) imaging, enabling all-weather, all-time observation.

[0096] The second step is sensor characteristic analysis: analyzing the spaceborne LiDAR sensor (sampling frequency 10kHz, vertical accuracy 0.1m), SAR sensor (high penetration, suitable for cloud areas), and optical stereo sensor (spatial resolution 2m, stereo parallax 0.5m). Based on the complexity of the terrain, one LiDAR point cloud sampling point is deployed every 20km² in plateau areas, with additional SAR coverage added in mountainous regions, achieving 90% data complementarity in the designed layout.

[0097] The third step is conflict resolution: using a consistency check algorithm, the spatial coordinate deviation (±15m) and spatial error <5m between ICESat-2 and TanDEM-X are detected. Then, the observation time phase conflict is identified, and the time stamp deviation between TerraSAR and ALOS PRISM is identified (set to within 1 year due to the slow terrain update speed).

[0098] The fourth step is complementary feature aggregation: extracting high vertical accuracy (0.1m) from LiDAR, penetration (penetration rate >80%) from SAR, and stereo parallax (0.5m) from optical stereo. A feature matching algorithm (weight ratio 1:1:1) is used for aggregation, combined with terrain slope and aspect data, improving complementarity by 25% and generating a preliminary terrain dataset.

[0099] The fifth step is radiation benchmark alignment: radiation correction (R²=0.95) is performed on the multi-source data, and the LiDAR reflectivity (±5%) and SAR echo intensity (±8%) are standardized to eliminate differences between illumination and sensors, laying the foundation for consistency analysis.

[0100] Step 6: Achieving Spatiotemporal-Spectral-Angle-Polarization Consistency in Spatiotemporal Reference: Constructing a unified spatiotemporal reference framework to coordinate timestamps (error < 1 year), spatial coordinates (error < 5m), spectral bands (overlap rate 90%), observation angles (±3°), and polarization characteristics (correlation coefficient 0.92). Projection transformation (UTM coordinate system) is used to ensure integration error < 3%.

[0101] Step 7: Constructing the Data Cube: Integrating consistent data to generate a 1000×1000×4 pixel data cube containing three corresponding data products after satellite consistency verification: SRTM DEM (for providing regional basic elevation information), Glacier Elevation Change Rate, and TanDEM-X DEM (for high-precision landform characterization), along with their associated Mask Layers (for marking anomalous areas such as clouds, image holes, and low-confidence points, providing a basis for subsequent data removal and analysis; and providing a quality layer for the generated data cube to exclude low-confidence data such as clouds, image holes, and anomalous areas). The cube was successfully applied to optimal terrain retrieval for Loess Plateau topographic change monitoring in a test conducted on May 23, 2024, at 08:47 AM BST. It integrated the optimal terrain segments from public DEMs, demonstrating advantages in rugged terrain and densely vegetated areas. Data utilization reached 90%, and dynamic queries were supported (response time <5s).

[0102] This data cube merges remote sensing data from different sources to form multiple bands (consistent in time, space, spectrum, angle, and polarization, all on a single spatial reference). This invention enables dynamic analysis of the entire land topography, providing support for land and resources management.

[0103] In summary, by employing the technical solutions described above, this invention ensures that observation accuracy and coverage remain stable in complex terrain and variable environments through satellite characteristic analysis, sensor feature analysis, conflict resolution, correlation constraints, complementary feature aggregation, and redundant feature aggregation and elimination. Compared to traditional single-element monitoring methods, this invention significantly improves data comprehensiveness and timeliness, providing a scientific basis for land spatial planning, ecological protection, and disaster early warning.

[0104] Furthermore, this invention is applicable to scenarios such as land and resources management, environmental protection, urban development planning, and disaster early warning. In land and resources management, it is used to monitor changes in land use and resource development; in environmental protection, it is used to track ecosystem health and changes in vegetation cover; in urban development planning, it is used to analyze the impact of urban expansion on landforms and water resources; and in disaster early warning, it is used to monitor climate anomalies and geological activity in real time. It has strong promotional value and practicality.

[0105] Furthermore, this invention supports modular expansion, allowing for the addition of specific observation modules to meet the needs of different regions, further enhancing its adaptability and functionality. The implementation of this technology enables systematic management and intelligent analysis of multi-dimensional land spatial data, promoting the achievement of sustainable development goals. It can be widely applied to government departments, research institutions, and enterprises, providing technical support for the realization of sustainable development goals.

[0106] In this invention, the above description is only a preferred embodiment of the invention and is not intended to limit the invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the protection scope of the invention.

Claims

1. A method for constructing a national land space multi-element comprehensive observation network, characterized in that, include: To acquire the characteristic parameters of remote sensing satellites and the performance parameters of sensors in the target land space, to achieve monitoring of multi-source data, and to determine the satellite observation plan and sensor layout scheme based on the terrain and coverage requirements of the observation area, combined with the working environment and applicable scenarios of the sensors. The consistency verification algorithm is used to fuse multi-source data of characteristic parameters of remote sensing satellites and performance parameters of sensors, and dynamic linkage analysis between multiple elements is realized based on the correlation model and constraints between multi-source data; based on feature matching and weight allocation algorithms, multi-source data is aggregated and redundant features are removed. Using radiometric correction and standardization techniques, radiometric reference alignment is performed on multi-source data. Based on the aligned radiometric reference, a unified spatiotemporal reference framework is constructed to coordinate the consistency of timestamps, spatial coordinates, spectral bands, observation angles and polarization characteristics. The consistency coordination results are then integrated to generate a multidimensional data cube. The sensor layout scheme includes: Based on the sensor's spatial resolution, sampling frequency, and data format characteristics, and combined with the sensor's working environment and usage scenarios, the initial layout scheme of the sensor is determined. Based on the functional zoning of national land space, the coverage density requirements are determined, a weighted optimization model is constructed, and the observation priorities in the initial layout scheme are ranked according to the results of the weighted optimization model. Based on a multi-objective optimization algorithm and combined with constraints, the global optimal schedule of the observation plan is determined, and the optimal layout scheme of the sensors is determined based on the global optimal schedule. The objective function of the weighted optimization model is expressed as: In the formula, represents the comprehensive optimization objective function value, represents the coverage, represents the data acquisition delay, , respectively represent the weight coefficients of the coverage and the data acquisition delay.

2. The method of claim 1, wherein the method further comprises: The process of acquiring characteristic parameters of remote sensing satellites and performance parameters of sensors in the target land space to achieve multi-source data monitoring, and determining the satellite observation plan and sensor layout scheme based on the terrain and coverage requirements of the observation area, combined with the working environment and applicable scenarios of the sensors, includes: Acquire the characteristic parameters of remote sensing satellites and the performance parameters of sensors in the target land space to achieve monitoring of multi-source data. The characteristic parameters of remote sensing satellites include orbital parameters, imaging resolution, band range and revisit period, and the performance parameters of sensors include sensor data from ground observation stations, UAVs and social sensing devices. Based on the terrain and coverage requirements of different observation areas, the visibility and revisit capability of remote sensing satellites over target areas within a specified time window are determined to clarify the satellite observation plan; based on multi-objective optimization algorithms, the optimal sensor layout scheme is determined by combining sensor performance parameters, working environment and applicable scenarios.

3. The method of claim 1, wherein, The method utilizes a consistency check algorithm to perform multi-source data fusion on the characteristic parameters of remote sensing satellites and the performance parameters of sensors, and realizes dynamic linkage analysis between multiple elements based on the correlation model and constraints between the multi-source data. Based on feature matching and weight allocation algorithms, multi-source data is aggregated, and redundant features are removed, including: The characteristic parameters of remote sensing satellites, the performance parameters of sensors, and the standardized reference data already existing in historical databases are integrated, and a consistency check algorithm is used to align the characteristic parameters of remote sensing satellites and the performance parameters of sensors in order to identify and resolve temporal, spatial, and spectral conflicts between data. Construct a correlation model among landform, vegetation cover, and water resources, define spatiotemporal correlation rules and physical constraints, and realize dynamic linkage analysis among multiple elements; extract complementary features from each data source, and use feature matching and weight allocation algorithms to aggregate complementary features; identify redundant information in multi-source data, and use principal component analysis dimensionality reduction technology to optimize aggregation.

4. The method of claim 3, wherein the method further comprises: The expression for the consistency check algorithm is: In the formula, Represents a consistency measure. Indicates the number of samples. Represents observation data, Indicates reference data. This represents the tolerance threshold.

5. The method of claim 3, wherein the method further comprises: The expression for aggregation is: In the formula, Indicates aggregation features, , These represent remote sensing satellite features and ground features, respectively. , These represent the weights of remote sensing satellite features and ground features, respectively.

6. The method of claim 1, wherein the method further comprises: The process utilizes radiometric correction and standardization techniques to align multi-source data with a radiometric reference. Based on the aligned radiometric reference, a unified spatiotemporal reference framework is constructed to coordinate the consistency of timestamps, spatial coordinates, spectral bands, observation angles, and polarization characteristics. The consistency coordination results are then integrated to generate a multidimensional data cube, including: By using radiometric correction and standardization techniques, radiometric reference alignment is performed on multi-source data to eliminate deviations caused by differences in illumination or sensor sensitivity. Based on the aligned radiation reference, a unified spatiotemporal reference framework is constructed to coordinate the consistency of timestamps, spatial coordinates, spectral bands, observation angles and polarization characteristics, and interpolation and projection transformation techniques are used to supplement and resample multidimensional data. By integrating and coordinating data such as timestamps, spatial coordinates, spectral bands, observation angles, and polarization characteristics, a multidimensional data cube is generated. The length and width of this multidimensional data cube are spatial resolution dimensions, and the third dimension is multi-temporal, multi-spectral, multi-angle, and multi-polarization layers.

7. The method of claim 6, wherein the method further comprises: The radiation correction formula is: wherein represents the corrected radiance value, represents the original radiance value, , respectively represent the reference and original mean values, represents the offset.

8. The method of claim 6, wherein the method further comprises: The interpolation formula is: In the formula, This represents the interpolation result. Let represent the planar spatial coordinates of the points to be interpolated, and n represent the total number of known sampling points involved in the interpolation calculation. Indicates the first Known point values, Indicates the point to be interpolated With the The distance from a known point to the point to be interpolated Indicates the point to be interpolated With the The distance from a known point to the point to be interpolated Indicates the first The weights of the known point values.

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