202 results about "Carbon cycle" patented technology

The invention discloses an intelligent measurement and calculation method and system for forest carbon reserves, and relates to the technical field of carbon reserve measurement and calculation, and the system obtains a forest electronic distribution diagram through a GIS technology and surveying and mapping data, collects various carbon reserve related data, and then carries out the correction and denoising of the collected data through a FLAASH algorithm and other preprocessing technologies; the AI sampling optimization model extracts forest features based on a convolutional neural network CNN, and dynamically adjusts a sampling area; secondary acquisition is carried out, data preprocessing is carried out, the total carbon reserve of the forest is estimated by calculating the carbon reserve of each grid, and the forest succession stage, the carbon sink change and the ecological carbon cycle are further analyzed; and finally, in combination with the dynamic influence coefficient of the carbon reserves and a preset threshold value Q, judging the change trend of the carbon reserves, triggering early warning and giving a coping strategy.

The invention discloses an operation balance optimization method for a waste incineration thermal power plant. Efficient and environment-friendly waste treatment and green energy recovery are achieved through regulation and control of core parameters. Comprising an intelligent control system, a carbon cyclic utilization technology and a flue gas purification technology, an optimization model taking comprehensive net income maximization and carbon emission intensity minimization as targets is constructed, a garbage heat value, incinerator temperature, steam parameters, power generation efficiency, station service power consumption rate and the like are selected as core parameters, an operation balance optimization target function is constructed, and the comprehensive net income maximization and the carbon emission intensity minimization are realized. And through an intelligent control system, key parameters such as the incinerator temperature, the excess air coefficient and the flue gas treatment temperature are monitored and adjusted in real time, it is ensured that garbage is fully combusted in the incinerator, harmful gas emission is reduced, and the power generation efficiency is improved. Compared with the prior art, efficient operation of the waste incineration thermal power plant and maximum utilization of green energy are achieved, and remarkable economic and environmental benefits are achieved.

The invention discloses a multi-modal data fused karst forest soil carbon cycle feature extraction method. The method comprises the following steps: acquiring soil physical and chemical protection component carbon data, bioactive component carbon data, isotopic tracing data and the like of different succession stages of a karst forest to establish multi-source heterogeneous data; integrating the obtained multi-source heterogeneous data into a'physical-chemical-biological-isotope 'four-dimensional feature space through feature tensor decomposition; constructing a carbon saturation capacity prediction hybrid optimization model, and outputting lt; the maximum carbon sequestration capacity and component carbon proportion of 53 [mu] m microaggregates; the method comprises the following steps: performing mineralization culture on soil in different succession stages of the karst forest, analyzing a dynamic proportion of exogenous carbon to original soil carbon and an input / output path of soil carbon in combination with a 13C / 15N double-standard-sample time-varying calibration curve, and calculating a carbon stability disturbance threshold. According to the method, collaborative analysis of multi-source data is realized, and the temporal-spatial resolution and the model precision of carbon cycle feature extraction are remarkably improved.

The invention discloses a multi-model coupled regional terrestrial ecosystem carbon reserve estimation method, which is suitable for the field of carbon cycle and carbon reserve estimation. The method comprises the following steps: firstly, collecting remote sensing data, and then estimating overground, underground and soil annual carbon density mean values of a regional terrestrial ecosystem by adopting an ARIMA model; the method comprises the following steps: clustering regions with different carbon densities by adopting a CatBoost model, dividing the carbon densities of a regional ecological system into low, medium and high levels, and avoiding the influence of spatial heterogeneity among different density levels on subsequent model training; an RNN model is constructed to evaluate the carbon density conditions of different levels of unit grids, and interannual dynamic carbon density parameters of different types of ecosystems are obtained based on multi-source data; and finally, calculating the carbon reserves of the unit grids, analyzing the annual average change trend and the spatial distribution condition of the carbon reserves, and realizing accurate evaluation of the regional terrestrial ecosystem carbon sink. The method is simple in step and good in using effect, and the change condition of the carbon sink capacity in the corresponding time can be obtained according to the sampling time interval of the input data.

The invention discloses a rhizosphere deposited carbon flux real-time monitoring method adopting a sensor array, and relates to the technical field of soil carbon cycle monitoring, and the method comprises the steps: determining a layered sampling depth and a horizontal concentric circle partition range based on the three-dimensional distribution characteristics of a target plant root system, and mapping the layered sampling depth and the horizontal concentric circle partition range to a preset three-dimensional grid coordinate system; gas sensors and environmental parameter sensors are deployed according to root system partitions, and a multi-dimensional sensor array is constructed. Through the three-dimensional grid coordinate system and the root system density gradient model, accurate spatial mapping of rhizosphere carbon flux is realized, high matching of sensor layout and root system biomass distribution is ensured by utilizing division of vertical layering and horizontal concentric circle partition, and the gas sensor and the environmental parameter sensor are cooperatively deployed, so that the reliability of the system is improved. The device can capture instantaneous changes such as root exudate pulse and microbial respiration, generates a continuous and consistent carbon flux data set, and provides a high-resolution dynamic observation basis for researching plant-soil-microbial interaction.

The invention discloses a regional GPP assimilation inversion system fusing foundation-multi-source satellite chlorophyll fluorescence, and belongs to the technical field of terrestrial ecosystem carbon cycle monitoring. The signal unmixing and simulation generation module is used for unmixing the total fluorescence of the mixed pixels into pure chlorophyll fluorescence (SIF) and driving an ecological model to generate SIF and GPP simulation values based on environmental stress data; the state component decoupling module is used for decoupling the total GPP analog value into a light response fast component, a light response slow component and a light response memory component; the state evolution prediction module is used for predicting dynamic evolution of the slow component and the memory component; and the self-adaptive assimilation inversion module is used for performing inversion on the region GPP by taking the slow component and the memory component as state variables, taking the SIF observation value as observation data and combining a prediction result of the state evolution prediction module. According to the method, systematic deviation caused by model simplification or data singleness in a traditional method is overcome.

The invention relates to the technical field of forestry investigation, and discloses a forestry investigation system for carbon sink forest management, which comprises a data acquisition module, a data processing module, a data integration module, a model construction and analysis module, a decision and early warning module, an output module and an implementation monitoring module. Through multi-dimensional index investigation, information can be obtained from multiple levels of an ecological system, the carbon cycle process and ecological functions of the carbon sink forest are comprehensively known, more comprehensive data support is provided for scientific management, in addition, more variables influencing carbon sink are introduced, so that the model better conforms to the actual situation, the accuracy and generalization ability of the model are enhanced, and the method is suitable for popularization and application. According to the method, carbon reserve changes of the carbon sink forest under different conditions can be better predicted, finally, effect prediction of multiple management schemes is provided for managers through scene simulation and model analysis, scientific and reasonable management decision making is assisted, and meanwhile, an early warning mechanism can find potential risks in time and guarantee stable development of the carbon sink forest.

The invention relates to the technical field of forest wetland carbon sink monitoring and evaluation, and particularly discloses a forest wetland carbon sink intelligent monitoring and evaluation system and method. According to the method, a vegetation carbon sequestration potential evaluation result is obtained by analyzing vegetation structure feature data of forest, water and soil composite ecology of the forest wetland, a water-soil-carbon cooperative evaluation result is obtained by analyzing specific hydrological-soil carbon cycle key elements of the forest wetland, and environmental disturbance response factors are obtained by combining various environmental disturbance factors. Through multi-source data fusion and intelligent modeling, a carbon sink quantitative evaluation value is output, and abnormal feedback is triggered; the problems that in the prior art, adaptability to a forest wetland compound ecological system is insufficient, evaluation refinement is insufficient, and dynamic response is lacked are solved. According to the method, accurate and dynamic monitoring and evaluation of forest wetland carbon sink are realized, carbon sink short plates can be accurately positioned, optimization support is provided, and technical support is provided for protection, restoration and optimization management of forest wetland carbon sink resources.

The invention provides a method for determining the influence of a straw returning mode on tobacco-rice rotation soil carbon emission, and belongs to the technical field of soil carbon emission. The method comprises the steps that test plots are divided, and different straw returning treatment groups are arranged; installing a static gas tank measuring system and regularly collecting a gas sample; measuring the emission of CO2 and N2O and the concentration of CH4 by using a gas chromatographic method, and calculating the carbon emission flux of the soil; analyzing a straw decomposition process by applying a soil microorganism kinetic equation; analyzing the dynamic change of a soil carbon library based on a carbon recurrent neural network model; constructing a carbon flow network topological structure by using a minimum spanning tree algorithm and identifying key nodes; according to the method, comprehensive influences of environmental factors, crop growth parameters and microbial community structures on carbon emission are integrated through a multi-head cross attention mechanism, accurate quantification and prediction of straw returning carbon emission are achieved, and a scientific basis is provided for optimizing a farmland carbon management strategy and promoting conversion from a farmland carbon source to a carbon sink.

The invention relates to a numerical mode simulation method for CO2 flux of high-resolution rice, wheat and corn, which comprises the following steps: calculating a vegetation index and a land surface water index, and carrying out inversion to obtain key vegetation parameters of a land ecosystem; the meteorological data and the topographic data are processed, meteorological field initial and boundary condition files are generated, and CO2 chemical initial and boundary condition files are generated; integrating the gridded artificial source CO2 emission data file; applying the optimized photosynthetic and respiratory action parameters to a photosynthetic and respiratory action calculation formula of the terrestrial ecosystem, and constructing a CO2 flux simulation system of the crop system; outputting the CO2 flux of the rice, the wheat and the corn in the simulation area. The method has the advantages that CO2 flux seasonal change characteristics in the key phenological period in the growth process of rice, wheat and corn are simulated, carbon cycle characteristics in the growth process of crops can be better reflected, and more reliable data support is provided for ecological meteorological research.

The invention discloses a carbon sink calculation method based on a space-time fusion technology and flux data, and the method comprises the steps: carrying out the space-time fusion synthesis of a remote sensing image, calculating a leaf area index and chlorophyll concentration data with high space-time resolution, and improving the time and space resolution of carbon sink data; calculating daily net primary productivity data by using a light energy utilization rate model based on the leaf area index data and the meteorological data; correcting parameters of the light energy utilization rate model based on the daily net primary productivity data and the daily flux data; migrating the corrected parameters of the light energy utilization rate model to a flux-free site area based on the ratio of the chlorophyll concentrations to obtain the migrated parameters of the light energy utilization rate model; the migrated parameters of the light energy utilization rate model are transmitted back to the light energy utilization rate model, the net primary productivity is calculated, and the carbon sink data are calculated based on the net primary productivity, so that the carbon sink calculation method is higher in applicability, and reliable technical support is provided for carbon cycle research of an ecological system.

The invention provides a carbon reduction effect evaluation method and system for a healthy agricultural product production process. According to the method, by tracking carbon migration paths of chemical fertilizer application, organic fertilizer degradation and soil respiration and combining matching analysis of chemical reaction chain data and an agricultural carbon emission database, a carbon retention area and methane generation and distribution characteristics in the production process are accurately identified. Modeling is carried out on the concentration of an intermediate product by applying a dynamic compensation engine, a soil pore optimization scheme and an organic fertilizer gradient feeding strategy are generated, and conversion of degradation residues to a stable carbon form is accelerated by regulating and controlling a micro-aerobic environment. Finally, a carbon cycle equilibrium map is constructed, greenhouse gas emission reduction monitoring and effect quantitative evaluation in the whole production period are achieved, and the soil carbon sequestration capacity is synchronously improved. According to the technical scheme provided by the invention, the agricultural production carbon cycle efficiency can be optimized.

The invention discloses a lake and reservoir future carbon income and expenditure scenario prediction accounting method based on climate change influence, and relates to the technical field of ecological environment monitoring and carbon cycle research, and the method comprises the steps: S1, collecting climate data, social economic data and lake and reservoir ecological data of a target lake and reservoir and a surrounding region, and carrying out the cleaning and missing value supplementation of the data; according to the method, the social economy-climate coupling model is constructed, social economy factors such as population growth and economic development and climate change factors are cooperatively incorporated into lake and reservoir carbon income and expenditure prediction, comprehensive simulation of lake and reservoir carbon income and expenditure multi-driving factors is realized, the fitting degree of a prediction result and an actual situation is improved, and the prediction efficiency is improved. By setting multiple groups of social economy-climate scene combinations, lake and reservoir carbon income and expenditure prediction results under different development paths are output, a carbon income and expenditure accounting sub-model based on a process mechanism is embedded, and model parameters are verified and optimized in combination with historical data, so that the accuracy and reliability of lake and reservoir carbon income and expenditure prediction accounting are further improved.

The invention discloses an ecological carbon sink monitoring method based on data processing, and relates to the technical field of ecological carbon sink monitoring, and the method comprises the following steps: S1, obtaining the current physiological parameters of each plant in vegetation of a to-be-monitored region, and determining the vegetation influence degree of the to-be-monitored region; s2, collecting the carbon emission of the to-be-monitored area in the historical time period; and S3, determining the final carbon emission at the next moment according to the vegetation influence degree of the to-be-monitored area and the carbon emission at the historical time period. According to the method, the complexity and the collaboration of the carbon circulation of the ecological system are comprehensively reflected, and the complex mechanism of the carbon circulation of the ecological system is precisely restored, so that the monitoring result can better reflect the real ecological nature of carbon sink.

The invention discloses a method for detecting soil active organic carbon based on fluorescence spectrometry, and belongs to the technical field of soil detection. The method comprises the following steps: sequentially drying, sieving and grinding fresh soil to obtain a soil sample, and sequentially performing active organic carbon extraction, purification, decoloration and pH regulation on the soil sample to obtain a soil liquid to be detected. The method comprises the following steps: adding fluorescein into a soil liquid to be detected to form a soil internal standard detection liquid, obtaining a soil fluorescence spectrum through three-dimensional fluorescence spectrum scanning, carrying out background deduction and correction coefficient correction, and calculating the active organic carbon concentration of the soil by using tryptophan, tyrosine and SMP fluorescence concentration standard curves. According to the method, an internal standard method is combined with a fluorescence spectrum technology, and through multi-step sample pretreatment and spectrum correction, the detection accuracy and the anti-interference capability are improved, the active organic carbon concentration of the soil can be rapidly and accurately determined, and an effective means is provided for soil carbon cycle research and quality evaluation.

The invention discloses a soil organic carbon dynamic monitoring system based on model fusion, which belongs to the technical field of soil monitoring and ecological environment information, and comprises the following steps: S1, obtaining multi-source data related to soil organic carbon monitoring, and preprocessing to obtain a standardized data set; s2, generating mechanism characteristics with mechanism property and space-time relevance through the carbon cycle process model; s3, constructing an artificial intelligence prediction model, and enabling the artificial intelligence prediction model to learn a mapping relation between the soil organic carbon and multi-source input so as to realize rapid inversion of the soil organic carbon; s4, fusing the carbon cycle process model and the artificial intelligence prediction model to obtain a fusion model; s5, realizing long-term continuous monitoring through dynamic inversion, operation state monitoring and abnormity self-repairing, and sliding window increment updating; and S6, generating a standardized result for supporting multi-field long-term application and system continuous optimization. By adopting the system, high-precision and high-efficiency multi-scale dynamic monitoring of soil organic carbon is realized.

The invention discloses a multi-source remote sensing reflectivity fusion method based on orthogonal matching pursuit and physical constraint, and the method comprises the steps: taking a multi-source remote sensing image as a data basis, and constructing a multiband sparse dictionary through time sequence alignment and overlapping sampling; then, an orthogonal matching pursuit algorithm introducing physical constraints is utilized to gradually select an image atom which is most matched with a target date in a sparse reconstruction process, and physical consistency of a reconstruction result in the aspects of hydrological conditions, phenological characteristics, spectral reflectivity and the like is ensured; and finally, through residual error return correction, day-by-day seamless 30-meter six-waveband reflectivity reconstruction is realized. According to the method, the stability and the physical interpretability of a reconstruction result are remarkably improved while efficient calculation is kept, continuous, multi-band and high-resolution time sequence data generation can be achieved in complex ecological environments such as lake wetlands, and high-precision remote sensing data support is provided for hydrological process simulation, ecological environment evaluation and carbon cycle research.

The invention discloses a satellite-borne sunlight-induced chlorophyll fluorescence spectrum detection imaging system, and belongs to the technical field of spectral imaging. The system includes a telescope optical system, a field-of-view separator assembly, a hyperspectral spectrometer system, and a hyperspectral spectrometer system. A vegetation chlorophyll signal from the earth surface is condensed by the telescope optical system and then is separated into a first view field object signal and a second view field object signal by the view field separator assembly, and chlorophyll fluorescence information and vegetation photochemical index information are synchronously acquired by the hyper-spectral spectrograph system and the hyperspectral spectrograph system respectively. According to the invention, synchronous high-precision detection of chlorophyll fluorescence and vegetation physiological parameters is realized, dynamic spectrum sampling is supported to match fluorescence peak valley characteristics, and an advanced remote sensing technical means is provided for global vegetation photosynthesis and carbon cycle research.

The invention relates to the technical field of environmental science, discloses a wetland carbon cycle model construction and optimization method and system, and aims to solve the problems of complex wetland carbon cycle process, insufficient model precision generalization, difficulty in coordination of multi-carbon component management decisions and the like. The method comprises the following steps: acquiring preprocessed multi-source wetland data, and constructing multi-scale spatial-temporal characteristic representation; synergistically simulating the dynamic state of the carbon components of the wetland through a mechanism fusion network; and based on a simulation result, a management strategy is generated through multi-objective optimization, scene prediction is performed, and a response is evaluated. The system comprises a data acquisition and preprocessing module, a multi-scale spatial-temporal feature coding module, a mechanism fusion network module, a multi-target optimization decision module and a scene prediction visualization module. According to the technical scheme, a high-precision and high-generalization model can be constructed, deep learning and a physical mechanism are fused, multi-target optimization decision and scene prediction capabilities are provided, multi-carbon components are effectively simulated and optimized, and the wetland carbon management precision and efficiency are improved.

The invention relates to the technical field of land management and carbon circulation, and discloses a soil organic carbon estimation method based on a random forest and a lightweight gradient elevator. Soil organic carbon estimation is used as an entry point of carbon balance treatment and climate regulation, multiple machine learning model technologies are integrated, influence factors of soil data are mined and analyzed, and a soil organic carbon estimation model set is constructed and evaluated and analyzed, so that the soil organic carbon is estimated. A set of space-time adaptability model evaluation technical method integrating multi-source data and incorporating climatic factors is innovatively developed. According to the method provided by the invention, the generalization ability and the stability of the machine learning evaluation model are optimized, so that the precision and the efficiency of soil organic carbon estimation are improved, and a more accurate and efficient application method is provided for soil organic carbon estimation under different regions and environment conditions.

The invention discloses a soil-plant carbon element tracking method and system based on isotope labeling, particularly relates to the technical field of soil carbon cycle in-situ detection, and is used for solving the technical problem that a carbon migration path in a rice rhizosphere heterogeneous micro region cannot be distinguished in the prior art. The method comprises the following steps: arranging a spatial coding microprobe array on a rice root system and a soil interface of a rice field, synchronously applying a C13 labeled compound, and continuously obtaining a grid node oxidation-reduction potential and a carbon isotope abundance value; dividing the boundary of a root surface oxidation layer and an anaerobic layer based on day and night oscillation phase difference mutation; detecting peak displacement migration from carbonyl bonds to carbon-carbon bonds in a boundary transition region by adopting an in-situ Raman spectrum, and correcting a space boundary; partitioning and clustering carbon isotope abundance values according to the corrected boundary, and identifying hot spot boundaries of a root tip secretion active region, a microorganism enrichment region and a mineral binding region; calculating the carbon isotope flux gradient in each region and generating a spatial distribution map; in-situ analysis of a rhizosphere carbon migration path is realized, and rice field carbon cycle research precision is improved.

The invention provides a power generation system and method based on carbonation calcination cycle coupling thermochemical energy storage. The power generation system comprises a carbonate calcination reactor, a COturbine power generation unit, a carbonation reactor, a thermochemical energy storage unit, a heat exchange network and a control system. According to the invention, industrial waste heat or renewable energy electric power is utilized to drive carbonate calcination to generate high-temperature COs to push the turbine to generate power; meanwhile, a calcined product CaO / MgO enters a carbonation reactor to absorb COO in smoke or the atmosphere, carbon sequestration circulation is completed, high-temperature heat needed by calcination is supplied by high-grade heat energy stored by a thermochemical energy storage unit in the charging stage, and the heat energy comes from surplus electric power or system power generation tail gas waste heat; the thermochemical energy storage unit releases heat to drive calcination in the discharging stage, and power grid peak regulation and energy time shifting are achieved. The problems of intermittency and carbon emission of traditional thermal power / wind and light power generation are solved, and integration of efficient, low-carbon and schedulable power output and carbon capture is achieved.

The invention discloses a method for analyzing sensitivity of terrestrial ecosystem carbon cycle to drought, and relates to the field of carbon neutralizer.The method comprises the steps that terrestrial ecological data are collected and preprocessed, a standardized soil humidity index is calculated, and a drought event is recognized through a three-dimensional clustering method; drought characteristics are extracted, the correlation degree of the standardized land ecological data and the drought characteristics is calculated by using a Pearson's correlation method, and importance degree sorting is performed on the standardized land ecological data in combination with an interpretable machine learning method to obtain main climate driving factors; analyzing the influence degree of the drought characteristics on the change trend of the main climate driving factors by using a multi-source linear regression method; and analyzing the influence degree of the main climate-driven factors on the carbon cycle during the drought period by using the partial correlation coefficient between the main climate-driven factors and the carbon flux. The invention discloses the change rule of the land carbon sink driven by different climates.

The invention discloses an NEP estimation method based on feature selection and machine learning, and belongs to the technical field of ecological environment monitoring and carbon cycle research, and the method comprises the steps: obtaining remote sensing image data and environment factors of a target region, and carrying out the preprocessing of the obtained remote sensing image data, and obtaining remote sensing factors; based on the remote sensing factor and the environmental factor, obtaining a net ecosystem productivity NEP feature influencing multi-source data fusion of the target area, performing screening, obtaining a preset number of key factors, and performing training through an extreme gradient lifting regression algorithm; analyzing the training result of the extreme gradient lifting regression algorithm by adopting a Shapril addition interpretation method; the extreme gradient lifting regression algorithm is verified through the evaluation indexes, and target area NEP estimation is completed; according to the method, high-precision estimation and driving mechanism analysis of the NEP of the complex mountain ecosystem are realized by fusing multi-source data and a machine learning technology, and clear mechanism explanation is provided for ecological research.

The invention relates to the technical field of grassland ecological monitoring, and discloses a grassland biomass estimation method based on multi-modal phenotype fusion and moisture content correction. The method comprises the following steps: setting a 0.5 m * 0.5 m quadrat in a grassland research area, synchronously acquiring a multi-spectral image of an unmanned aerial vehicle, multi-view structural data of a ZED 2i depth camera and ground actual measurement data, extracting structural features such as a multi-view vegetation projection volume index and 10 vegetation indexes after preprocessing, and constructing an XGBoost moisture content inversion model; and constructing a biomass initial model by using a pure structure, a pure spectrum and fusion characteristics, introducing a moisture content correction formula to correct deviation, and screening an optimal model through Monte Carlo cross validation. According to the method, multi-source data are effectively fused, moisture interference is stripped, the grassland biomass estimation precision is improved, and reliable technical support is provided for grassland ecological monitoring, carbon cycle evaluation and resource management.

The invention relates to a circulation quality data evaluation method, system and equipment for carbon circulation, and relates to the field of carbon circulation quality evaluation, and the method comprises the steps: carrying out the carbon emission prediction of a coal mining subsidence land, and obtaining the predicted carbon emission; scale change prediction of soil, vegetation and water is carried out according to the collapse characteristic information; soil physicochemical property parameters, vegetation type parameters and water body component parameters are collected, and the predicted carbon sequestration amount is obtained through calculation in combination with the change scale parameters of soil, vegetation and a water body; and calculating according to the predicted carbon sequestration amount and the predicted carbon emission amount to obtain a carbon cycle quality evaluation result. According to the method, the technical problem that the carbon cycle quality cannot be effectively and quantitatively evaluated due to the fact that the carbon emission and the carbon sequestration capacity cannot be accurately predicted because the special geographical environment characteristics and the change rule of the coal mining subsidence land are not fully considered in a traditional evaluation method can be solved; the scientificity, accuracy and reliability of carbon emission and carbon sequestration capability prediction can be remarkably improved, and accurate quantitative evaluation of carbon cycle quality is realized.

The invention relates to the technical field of man-machine interaction, in particular to a soil science exhibition and interactive experience integrated system which comprises the steps that a dynamic change curve of the carbon release rate of a soil sample is captured; detecting the dynamic change of the isotope in the decomposition process through a mass spectrometer, and generating carbon decomposition path data; adopting a machine learning algorithm to construct a soil carbon sink prediction model of the soil carbon sink capacity; a user is supported to input different environmental parameters to predict carbon fixation and carbon release of soil in future time; the whole process from plant residue decomposition to carbon storage to carbon release is displayed; through a visual interface, a user is supported to adjust environmental parameters through gesture interaction, and dynamic changes of carbon circulation under different environmental conditions are observed; according to the invention, layered dynamic display and interactive dynamic response of soil carbon circulation are realized, a complex carbon circulation process is concrete, and a brand new mode is provided for scientific exhibition, education and scientific research application of soil.

The invention discloses a high-precision determination method for determining iron-bound organic carbon delta 13C in soil, and belongs to the technical field of determination of the content of carbon isotope in soil. A liquid extracting solution is dropped into a tin boat and freeze-dried, an extracting solution of the iron-bound organic carbon is converted into a solid form, and the content of the iron-bound organic carbon delta 13C in the soil is determined. The problem that 13C abundance of a liquid sample is difficult to directly measure is solved, the isotope characterization method of the metal mineral binding state organic carbon is provided for the first time, the problems that mass spectrum signals of the liquid sample are unstable and form mixed measurement errors are large are avoided, accurate analysis (RSD is smaller than or equal to 1%) of form-divided delta 13C is achieved, and a key technical support is provided for research of a soil carbon circulation mechanism.

The invention provides a high spatial resolution vegetation productivity method and system based on flux observation footprint, and relates to the technical field of ecological system carbon cycle process and model simulation, the method comprises the steps of calculating the flux footprint of each station, and determining the observation range of a vortex motion related system, and micrometeorological parameters comprise the height of an observation tower and the rough length; extracting land satellite high-resolution normalized vegetation index data matched with a 16-day period in the range of each station; utilizing the vegetation index data to drive and optimize a parameter framework based on a vortex motion correlation light energy utilization rate model; in combination with site carbon flux data, a Markov chain Monte Carlo method is adopted to invert model key parameters for optimization, and finally a model optimization parameterization result is obtained. The method has the beneficial effects that the accurate verification of the observation and remote sensing high-resolution GPP on the pixel scale based on the flux footprint is realized, and the problem that the current medium-resolution GPP cannot consider the parameter optimization and verification scheme of the flux footprint is solved.

The invention discloses a land type adjustment and simulation method based on carbon neutralization constraint, and the method comprises the following steps: obtaining land data of a target region, and calculating the carbon flux of each classification space; constructing a carbon profit and loss weight function based on the carbon flux, the ecological benefit and the economic benefit as an overall constraint condition; reversely deducing each land space area corresponding to a preset time node on the basis of a Markov chain model, and taking the land space areas as stage constraint conditions; performing NSGA-III optimization solution based on the constraint condition to obtain a corresponding land use adjustment scheme; an improved PLUS model is adopted to simulate a land use adjustment scheme, the carbon cost of land conversion is incorporated into a land utilization simulation process, and a genetic algorithm is utilized to optimize model parameters to obtain a land space simulation result of a target year. According to the method, a carbon cycle mechanism, a carbon sink optimization target and a land utilization decision full process are coupled, a closed loop of carbon constraint-land simulation-policy assessment is realized, and scientific decision support is provided for regional carbon emission reduction and ecological protection.