950 results about "Land utilization" patented technology

The present invention belongs to the technical field of land change detection. Disclosed are a land change detection method combining matrix decomposition and adaptive propagation, and a system. A deep learning semantic segmentation model combining matrix decomposition and adaptive propagation is used for detecting a land change. The model is of a twin double-branch structure, which can receive both an input of a pre-temporal remote sensing image data set and an input of a post-temporal remote sensing image data set and can share network parameters, and which has the advantage of end-to-end feature learning. In the present invention, an attention method based on matrix decomposition is used to replace a conventional attention method based on manual design; moreover, in view of adaptive propagation technology for pixel offsets of neighboring points, a deep learning twin differential semantic segmentation network is established to realize automatic and precise land utilization / land coverage change detection on the basis of high-resolution optical data.

The invention relates to the technical field of natural resource monitoring and spatial information processing, in particular to a method and a system for feeding back land utilization change based on a land spatio-temporal model. The method comprises the following steps: deploying multi-source land monitoring equipment, carrying out collaborative data acquisition and standardization processing, and constructing a land space-time reference data set; performing triple mapping on the land space-time reference data set to obtain a land semantic association graph; constructing a land utilization knowledge graph based on the land semantic association graph; constructing a land change detection initial model by using the land utilization knowledge graph; meanwhile, in a high-frequency change scene, such as an urban and rural ecologic zone or an ecological sensitive area, a traditional model is slow in response to short-term land utilization disturbance, automatic adjustment cannot be carried out through deviation feedback between historical errors and model output, and the reliability of the model in actual application scenes such as resource regulation and control is limited.

A 100-meter gridded spatialization method for carbon emissions of different land use types based on multi-source heterogeneous data is provided, including the following steps: S1, fitting Luojia-1A Satellite nighttime light data year by year with defense meteorological satellite program-operational linescan system (DMSP-OLS) and national polar-orbiting partnership-visible infrared imaging radiometer (NPP-VIIRS) fused 1-kilometer gridded nighttime light data, performing additive fusion on previous light data and current light data, introducing a time inertia weight factor to improve particle swarm optimization-back propagation (BP) neural network algorithm, and forming 100-meter nighttime light data correctable on a long time series; and S2, simulating a complex nonlinear dynamic changing relationship between multilevel 100-meter gridded data of different land use types in different industries and energy carbon emissions in different industries based on the 100-meter nighttime light data, and establishing a 100-meter spatialization inversion model for energy carbon emissions of different land use types in different industries.

The invention proposes a land utilization monitoring method and system based on remote sensing and big data, and relates to the technical field of land monitoring, and the method comprises the steps: dividing sub-regions, and obtaining the multi-temporal and multi-resolution remote sensing data of the sub-regions; performing feature extraction and classification on the remote sensing data based on a deep learning model to generate a land utilization classification map; based on the dual-temporal difference attention network, identifying a change area and constructing a change driving factor library fusing meteorological data and human activity data; detecting an abnormal area based on the driving factor library, and generating an abnormal type label and an attribution analysis report in combination with a dynamic early warning threshold; performing visual rendering on the monitoring result, and outputting an abnormal region early warning map and a disposal suggestion; high-precision feature extraction and classification are realized, change areas and driving factors are deeply analyzed, abnormal areas are effectively detected and early warning is performed, and the accuracy, timeliness and practicability of land utilization monitoring are improved.

The invention discloses a risk assessment method based on soil heavy metal pollution pathway analysis, and particularly relates to the technical field of land risk assessment. Heavy metal pollutant data and geographical location information of a target area are collected to generate a basic data set; constructing a heavy metal migration model based on the basic data set, and predicting a pollutant migration path and concentration change in combination with the comprehensive characteristics of the regional environment to identify a first high-risk region; a second high-risk area is identified by analyzing retention and release behaviors of pollutants in the microfractures in combination with quantitative soil microfracture connectivity characteristics and pollution migration behaviors of a dynamic entropy model; union processing is carried out on the first high-risk area and the second high-risk area to generate a comprehensive high-risk area; heavy metal concentration and land utilization changes in different time periods of the high-risk area are analyzed and synthesized through a multi-dimensional data fusion algorithm, and cumulative risk distribution is generated; and comparing the accumulated risk distribution with enterprise land use planning, evaluating potential environmental responsibility and generating a risk evaluation report.

The invention belongs to the technical field of electric digital data processing, and discloses a dynamic remote sensing monitoring method and system based on land utilization. The method comprises the steps of obtaining a multi-source heterogeneous time sequence data set, and generating standardized spatio-temporal data through preprocessing; extracting space-time correlation features based on the dynamic graph convolutional network, and generating dynamic mode data through multi-dimensional feature fusion; performing classification decision and state transition analysis on the dynamic mode data, identifying a state change area and generating a dynamic analysis report; constructing an interactive visualization engine to map a space-time thermodynamic diagram and a trend graph; and in combination with the incremental learning optimization model, outputting a long-term evolution prediction result. Through the dynamic graph convolutional network and the adaptive cross-modal alignment technology, the problems that multi-source data space-time correlation modeling efficiency is low and the noise suppression capability is insufficient are solved, the dynamic mode recognition precision is remarkably improved, real-time interaction analysis and long-term prediction are supported, and the method is suitable for accurate decision making of industrial monitoring, traffic planning and other scenes.

The invention discloses a water and soil loss dynamic risk assessment system based on a remote sensing image, and relates to the field of water and soil loss risk assessment. The invention discloses a water and soil loss dynamic risk assessment system based on remote sensing images. The system comprises a data acquisition and preprocessing module, a dynamic feature extraction and analysis module, a hierarchical risk assessment model and a risk grading and modeling module, the method further comprises the following steps: acquiring a remote sensing image and historical soil erosion map data of a specific administrative region in a specific period through a data acquisition and preprocessing module; the dynamic feature extraction and analysis module is used for extracting land utilization types, vegetation coverage and quarterly change features of topographic factors through the acquired data; according to the method, a three-layer model architecture is arranged, static historical data, human activity disturbance and social exposure are decoupled and quantified, and the evaluation dimension is improved, so that the accuracy of an evaluation conclusion is improved.

The invention relates to the technical field of environment management, in particular to a land coverage change monitoring method based on fusion of a remote sensing image and a geographic information system, which comprises the following steps: S1, data acquisition: acquiring multi-temporal remote sensing and auxiliary data; s2, data preprocessing: preprocessing the collected remote sensing and auxiliary data; s3, land feature classification and land cover change identification: classifying land cover types, and performing land cover change identification in combination with the spatial texture features of the remote sensing image data; and S4, result output and analysis: performing fusion and analysis on the terrain classification and land cover change identification result and the existing land cover data in the geographic information system, and evaluating the change trend of land utilization. According to the method, different types of land coverage can be identified carefully, and particularly, when complex remote sensing data is processed, the classification precision and the accuracy of change identification are remarkably improved.

The invention relates to a land utilization type intelligent identification method and system based on remote sensing data. The method comprises the following steps: acquiring multi-source remote sensing data; based on a preset land utilization characteristic model, analyzing and processing the multi-source remote sensing data to obtain corresponding land utilization characteristics in the land utilization characteristic model; based on a land utilization characteristic knowledge base, matching and analyzing the land utilization characteristics to obtain a matching result; and according to a matching result, determining a land utilization type corresponding to the matching result, and outputting the land utilization type as an identification result. By adopting the method, multi-dimensional information complementation can be realized, space-time coverage is enhanced, and data reliability is improved; a high-precision classification result is provided, dynamic monitoring is supported, and an application scene is expanded; the defect of a single data source is overcome, manual dependence is reduced, the operation period is shortened, the cost is reduced, the complex scene recognition capability is improved, and space-time continuous dynamic monitoring and fine management support is provided for the fields of ecological assessment and the like.

The embodiment of the invention provides an intelligent optimization system and method for a photovoltaic power station design scheme, and the system comprises a data input module which receives and analyzes input data which specifically comprises topographic data, meteorological data and equipment parameters, and transmits the input data to a data processing module; the data processing module is used for carrying out standardization processing and cleaning processing on the input data, complementing missing values and obtaining a constructed high-precision terrain and meteorological model; and the optimization algorithm module adopts a multi-objective optimization algorithm, takes the power generation efficiency, the economic cost and the land utilization rate as optimization objectives, dynamically adjusts the layout, the inclination angle and the line spacing of the photovoltaic modules based on the input data, and obtains an intelligent optimization result and a related report of the design scheme of the photovoltaic power station. The generating capacity and the cost of the intelligent optimization result of the photovoltaic power station design scheme are predicted through a high-precision terrain and meteorological model; and the result output module is used for outputting an intelligent optimization result and a related report of the photovoltaic power station design scheme.

The invention relates to the field of geographic information technology and natural resource management, and discloses an automatic analysis system for land utilization flow change in land change investigation. The system comprises an object-level spatiotemporal spectrum tree reconstruction and data initialization module, a topological potential energy field construction module based on evolution entropy, a multi-scale geometric difference extraction and morphological analysis module, a variable stiffness topological adsorption and decoupling module based on a stiffness ratio, and a flow damping filtering and event serialization module driven by semantic rules. The system constructs a topological potential energy field by using a historical state vector and semantic stiffness, performs one-way adsorption, adjustment or retention on a difference polygon according to relative stiffness, and cleans invalid traffic based on a damping matrix. According to the method, through physical field simulation and semantic logic verification, high-frequency disturbance misjudgment is effectively eliminated, pattern spot boundary correction is ensured to conform to the legal weight, and the authenticity and logic self-consistency of change survey data are improved.

The invention relates to the technical field of urban planning, in particular to an industrial space suitability optimization method and system, and the method comprises the steps: collecting geographic information data, industrial data, industrial service related data, enterprise information and infrastructure data as a multi-source data set, associating the multi-source data set with geographic space coordinates, and inputting the geographic space coordinates into a database; the adjusted weight coefficient is applied to the LSTM-ARIMA hybrid model, and updated future industry prediction data is obtained; generating optimized industrial land layout scheme data based on a preset parametric modeling rule; determining planning scale data selected by a user, and obtaining a unified database associated with cross-level data; and comparing the land utilization change condition and the enterprise activity state data with data in the unified database, and when it is detected that data updating lag exists, updating the latest data after verification and prediction filling to the unified database. According to the invention, the adaptability and the intelligent level of urban industrial space planning can be improved.

The invention relates to the technical field of land utilization change evaluation, in particular to a multi-scene land gradient utilization carbon emission prediction method and system and a readable storage medium. A multi-scenario land gradient utilization carbon emission prediction coupling model under three scenarios of historical development trend, future climate change and policy planning effect is considered to comprehensively evaluate the influence of land gradient utilization change on carbon emission under different development paths. And a decision basis is provided for formulating scientific and reasonable regional space planning, a carbon emission reduction policy and a sustainable development strategy. The invention aims to solve the problem of how to improve the simulation accuracy of the carbon emission management regulation simulation system.

A dynamic real estate valuation system based on multiparametric market indicators, which includes the following: a valuation engine configured to generate real-time results for property valuation; a multitude of distributed data ingestion and processing units configured to capture heterogeneous data sources, including historical property transaction data, real-time property listings, zoning and land use records, macroeconomic indicators, geospatial data, environmental sensor outputs, and sentiment-derived metrics; a model orchestration control unit comprising a stack of machine learning models, wherein the models include at least a gradient boosting decision tree model, a long-short-term memory (LSTM) time series forecaster, and an enhancement learning module that iteratively optimizes the model parameters based on the observed evaluation accuracy; a data contextualization controller configured to apply dynamic weighting to each input parameter based on the geographic, temporal, and market context by executing decay functions and location-specific rule matrices; a physical property valuation terminal (PVT) that includes an edge processing unit (EPU), geolocation circuitry, secure communication interfaces and a touch-based user interface; a valuation book subsystem configured to hash the valuation output, timestamp, and signatures of the input record into a blockchain-based distributed ledger; wherein the system is designed to continuously recalibrate its valuation results by comparing the predicted valuations with the actual sales or rental prices, and wherein the physical terminal is designed to produce a legally certifiable valuation document with embedded provenance data.

The invention discloses an attention-based deep learning urban flood affected population evaluation method and system, and the method comprises the following steps: collecting rainfall, flood water depth, terrain, pipe network and satellite image data of a research region, carrying out the preprocessing, and constructing a first data set and a second data set; and training and verifying the first deep learning model and the second deep learning model respectively. Inputting the first data set test set into the first model, and outputting a flood water depth prediction map; and inputting the second data set test set into the second model, and outputting a land utilization segmentation map. The two maps are superposed to obtain matching data of the flood water depth and the land utilization category, the number of exposed people is calculated by adopting an exposed population formula after land utilization reassignment, the population disaster rate is calculated by utilizing a preset flood disaster loss curve in the flood water depth, the number of exposed people and the population disaster rate are multiplied to obtain the number of affected people, and the number of affected people is calculated. And completing urban flood disaster population evaluation. According to the scheme, the accuracy, the reliability and the effectiveness of urban flood disaster population evaluation can be remarkably improved.

The invention discloses a watershed pollution prediction method based on multi-source data fusion, and belongs to the technical field of watershed pollution prediction. According to the method, water quality, runoff, land utilization and meteorological data sequences are firstly constructed, and then features of the sequences are extracted and spliced to form a fusion feature vector. And forming a training set by using a plurality of fusion feature vectors, inputting the training set into a random forest regression model for training, performing performance evaluation on the trained model by using a verification set, and finally using the model meeting evaluation conditions to predict the drainage basin pollution degree. According to the invention, through multi-source data fusion and scientific model training and evaluation processes, the accuracy of drainage basin pollution prediction is effectively improved.

The invention provides a full-life-cycle dynamic monitoring system and method for global land comprehensive improvement, and the method comprises the steps: firstly collecting and fusing multi-source heterogeneous data related to global land comprehensive improvement through a multi-source data fusion module, and constructing a data bottom plate with unified space-time reference and semantic association; then the intelligent monitoring module realizes land utilization type change identification, project progress tracking and index dynamic monitoring by utilizing a preset business rule and a machine learning model based on the data base plate, obtains monitoring data in real time and generates early warning information; and finally, the decision support module visually displays the monitoring data and the early warning information, and provides interaction service and decision support for the user at the same time. According to the invention, multi-source heterogeneous data can be efficiently integrated, whole-process dynamic monitoring, intelligent effect evaluation and risk early warning of a global land comprehensive improvement project are realized, and powerful support is provided for management decision.

The invention discloses an intelligent optimization method and system for territorial space planning based on a multi-objective evolutionary algorithm, and relates to the technical field of territorial space planning, and the method comprises the steps: collecting multi-source basic data needed by territorial space planning, and carrying out the optimization of the territorial space planning based on ecological environment bearing capacity and construction land development suitability indexes; constructing a nonlinear mapping model to quantitatively evaluate the development risk of each grid unit, constructing a multi-objective optimization model covering economic, ecological and social benefits by taking the area or quantity of each land type as a decision variable, and calculating the development risk of each grid unit in combination with the initial land utilization state and developable spatial data. The FLUS model is adopted to carry out simulation expansion on the urban development boundary, and the planning scale and the land use growth trend are responded in real time by dynamically adjusting the conversion rule in the simulation process. According to the method, the data integration accuracy and the model response flexibility are remarkably improved, the scientificity and the intelligent level of planning decision making are enhanced, and powerful technical support is provided for regional sustainable development.

The invention discloses a multi-dimensional rural settlement space reconstruction service system and method, and the system comprises a sensing layer which monitors environment parameters in real time, and develops a mobile terminal APP to collect subjective sensing data; performing cleaning, fusion and standardization processing on multi-source heterogeneous data, constructing a rural settlement digital twin base of the heritage area, and performing traceability and tamper-resistant processing on data acquisition; the collaboration layer is used for generating a land utilization conflict degree thermodynamic diagram and evaluating the business state symbiosis degree of the business; quantifying the collaboration degree of each dimension, and identifying protection and development collaboration regions; simulating spatial evolution trends under different reconstruction schemes, and generating a candidate scheme library; the optimization layer is used for constructing a settlement three-dimensional digital twinborn body based on a BIM + GIS technology, designing a'protection-development-sharing 'three-gear adjusting knob, and generating a seasonal adaptability space configuration scheme by applying a genetic algorithm; and a continuous layer: establishing a five-dimensional monitoring index library, predicting a reconstruction effect by applying an LSTM neural network, and dynamically adjusting an optimization strategy according to a monitoring result.

The invention provides a remote sensing-based land utilization carbon emission evaluation method, device and equipment and a medium, and the method comprises the steps: carrying out the radiometric calibration and spatial registration of obtained multi-source remote sensing data, and obtaining a standardized raster data set; generating a land utilization classification grid map based on the standardized grid data set; generating a land change vector layer through a continuous multi-stage land utilization classification grid map; generating a dynamic evaluation unit vector boundary according to the land utilization classification grid map and the land change vector layer; correcting a vegetation carbon absorption coefficient and an industrial emission coefficient in real time based on remote sensing inversion data, and constructing a spatial carbon transfer model; and inputting the land utilization classification grid map, the dynamic evaluation unit vector boundary and the spatial carbon transfer model into a layered mixed carbon emission evaluation model to obtain a carbon emission intensity distribution map. By adopting the scheme, the carbon emission under different land utilization modes can be accurately evaluated, and the scientificity and policy applicability of regional carbon balance evaluation are improved.

The invention relates to the technical field of data analysis, in particular to a territorial space planning data analysis system for ecological restoration, which comprises an interference parameter acquisition module, an ecological disturbance calculation module, a restoration area identification module, a priority level judgment module and a site selection stability screening module. According to the method, the accuracy and efficiency of ecological restoration are improved by capturing various disturbance factors of an ecological sensitive area and dynamically evaluating ecological degradation, and the signal of ecological degradation can be recognized earlier by comprehensively monitoring the change trend of earth surface coverage and the dynamic state of a water body and quickly responding to the subtle change of an ecological environment; ecological service function indexes are adopted for inter-year comparison, a long-term view angle about the health state of an ecological system is provided, prediction accuracy is improved, ecological restoration work can be implemented in a more targeted mode, and the lasting effect of the ecological restoration work is ensured through continuous monitoring of land utilization changes.

A water and soil loss pattern spot landing system based on land utilization information weak constraint comprises a land utilization information acquisition module, a factor evaluation and analysis module, a time sequence analysis and pattern spot landing module and a data integration and visualization module. The factor evaluation and analysis module is used for calculating a water and soil loss erosion modulus and evaluating and analyzing influence factors, the time sequence analysis and pattern spot landing module is used for analyzing water and soil loss changes and generating landing pattern spots, and the data integration and visualization module is used for data integration and pattern spot visualization display. According to the water and soil loss pattern spot landing system based on the land utilization information weak constraint, an erosion modulus calculation algorithm based on data fusion and pixel unmixing is proposed to calculate an erosion modulus, and a water and soil loss pattern spot change analysis algorithm based on time sequence classification is proposed to analyze pattern spot change.

The invention discloses an agricultural land utilization monitoring management system and method based on remote sensing, and belongs to the technical field of remote sensing image processing. Multi-temporal remote sensing images are acquired, and a land surface energy fluctuation spectrogram is constructed; extracting disturbance characteristics through small-scale grid slices to form a multi-dimensional disturbance characteristic tensor; identifying an abnormal evolution region by using a sparse volume accumulation algorithm, and outputting a preliminary screening identification graph; inputting the region with the continuous evolution characteristic into a time sequence attention mechanism inversion network, estimating a crop growth state trajectory, matching an agricultural planting mode library, and generating a candidate land utilization behavior probability distribution diagram; in combination with regional consistency optimization, a historical planting period and meteorological disturbance data, calculating a purpose change confidence score, and outputting early warning information and a monitoring report; the agricultural land dynamic change identification precision and management capability can be effectively improved.

The invention discloses an urban rail transit station site selection multi-objective optimization method and a related device, and belongs to the crossing field of urban planning and traffic engineering, and the method comprises the following steps: according to preprocessed urban road network data, screening crossing points of urban three-level roads and roads above the three-level roads as candidate stations; the coordinates of the candidate sites are coded; based on the preprocessed crowd travel origin and destination data, POI data, building data and land utilization data, quantifying the service people flow, facility accessibility and land development intensity of each candidate site, and forming a feature matrix of each candidate site; and constructing a multi-target traffic station site selection model, screening candidate stations meeting distance constraints through a greedy search algorithm according to the feature matrix, inputting the candidate stations to NSGA-II, and solving a multi-target optimization function of the multi-target traffic station site selection model to obtain an optimal planning result. The method can solve the problems that in the prior art, the target is single during site selection, and the inter-site distance constraint is not considered.

The invention discloses a land utilization dynamic change monitoring method and system based on multi-modal remote sensing data, and relates to the technical field of multi-modal remote sensing data analysis. The system comprises a data acquisition and preprocessing module, a consistency baseline construction module, a real-time data analysis module and an abnormality diagnosis and processing module. The data acquisition and preprocessing module realizes space-time alignment of multi-source data; the consistency baseline construction module divides land utilization types based on geographic features and establishes a baseline database; the real-time data analysis module screens suspected abnormal areas through difference indexes; the abnormity diagnosis and processing module distinguishes data quality abnormity and emergency abnormity based on the characteristic association relation, the data quality abnormity triggers data restoration and verification, and the emergency abnormity marks a high-priority area and outputs position information. According to the system, through multi-modal data fusion and correlation analysis, high-precision monitoring and abnormal intelligent classification of land utilization changes are realized.

The invention discloses a water quality prediction method based on machine learning and an SWAT model, and belongs to the technical field of water environment simulation and water quality early warning. The method comprises the steps that S1, hydrology, water quality, meteorology, land utilization types, pollution sources and spatial elevation multi-temporal data are collected from multiple channels in a drainage basin, and noise reduction, normalization and missing value filling preprocessing methods are adopted for original data; s2, performing sensitivity analysis on the nonlinear relationship between the SWAT model parameters and the output by adopting SVR, and screening out key parameters with remarkable influence; s3, on the basis of the water quality monitoring data, constructing a water quality time sequence prediction model by adopting an LSTM model; and S4, constructing a self-learning mechanism based on the LSTM and the SVR model, and retraining the model by adjusting a learning time window and regularly utilizing latest data. The method is excellent in the aspects of model efficiency, prediction precision and application adaptability, and has remarkable engineering application value and popularization potential.

The invention relates to the technical field of urban space information processing, in particular to an urban functional area fine classification method based on multi-modal data collaboration. According to the method, POI data, AOI data, land utilization data, remote sensing images and other multi-source heterogeneous data are fused, a multi-dimensional feature system is constructed, and an urban functional area classification model covering a single functional area, a composite functional area, a mixed functional area and a non-functional area is provided. According to the method, firstly, block-level basic units are constructed based on an open street map (OSM), then coordinate transformation and function classification are carried out on POI and AOI data, features such as POI density, AOI area proportion and land utilization structure are extracted respectively, and finally, function type identification is carried out on urban block units according to set classification rules. Compared with the prior art, the method has the advantages that through multi-source information fusion and discrimination logic optimization, the accuracy and applicability of urban functional area recognition are remarkably improved, and the method is suitable for scenes such as urban planning, land utilization evaluation and urban management.

The invention discloses an ecological system monitoring method based on an INVEST model, and belongs to the technical field of ecological system monitoring, and the method comprises the steps: S1, building a unified ecological monitoring data frame through satellite remote sensing, an Internet of Things sensor and a ground survey multi-source data collection and cleaning algorithm; s2, dynamically adjusting INVEST model parameters based on regional ecological characteristics and a machine learning algorithm, according to the method, ecological system services are quantitatively evaluated by using an INVEST model core module, key ecological parameters are extracted in combination with a remote sensing inversion technology, the problems that traditional ground survey is high in cost and limited in coverage range are solved, and the method is suitable for large-scale popularization and application. The remote sensing data preprocessing and classification algorithm improves the efficiency and precision of surface feature information extraction, the PLUS model simulates the land utilization change scene and CMIP6 climate scene input, the INVEST model is combined to quantify the future ecosystem service function, and the limitation that the traditional static assessment cannot cope with the dynamic environment change is solved.

The invention discloses a watershed non-point source pollution load dynamic measurement and calculation method and system based on a distributed process model, and the method comprises the steps: firstly collecting the meteorological, hydrological, water quality, soil and terrain, land utilization, vegetation coverage, atmospheric settlement and surface pollution source data of a target watershed, and constructing a basic database of the target watershed; performing integration processing on different types of data in the basic database of the target drainage basin by adopting geospatial interpolation, spatial resampling and frequency matching; according to the invention, a function of coupling a runoff-sediment-nitrogen and phosphorus physical process on the basis of a distributed hydrological model and combining social economic development driving factors, land utilization change and land pollution source dynamic change to carry out space-time quantitative measurement and calculation on the drainage basin non-point source pollution load under a change scene is realized; the method not only can better capture and predict the dynamic change of the input data, but also avoids the separation of social economic development driving factors, land utilization change and land pollution source dynamic change, and is suitable for wide popularization and use.

The invention relates to the technical field of environmental monitoring, and discloses a method for reconstructing carbon dioxide concentration enhancement based on multi-source satellite data, which comprises the following operation steps: collecting environmental information such as carbon dioxide dry air column concentration, nitrogen dioxide total column concentration, population density, land utilization, weather and the like to construct an environmental database; pre-processing the collected multi-source data to ensure time-space consistency; according to the population density of the region, the total column concentration of nitrogen dioxide and the land utilization type, setting a threshold value, and dividing the target region into a discharge region and a background region; constructing a background concentration reconstruction model, inverting the carbon dioxide background concentration, and calculating a residual error with actually measured carbon dioxide to obtain a carbon dioxide concentration enhanced sample; based on the carbon dioxide concentration enhanced sample, a concentration enhanced reconstruction model is constructed, and high-resolution carbon dioxide concentration enhanced spatial-temporal distribution is obtained. According to the method, high-frequency monitoring of the carbon source in the region can be realized spatially, and powerful support is provided for dynamic tracking of carbon emission.