A method and system for estimating the spatiotemporal variability and carbon sequestration potential of soil organic carbon.

Abstract

This invention discloses a method and system for estimating the spatiotemporal variability and carbon sequestration potential of soil organic carbon, relating to the fields of soil ecology and agricultural environmental management. The method acquires multi-source spatiotemporal data and performs standardized preprocessing, constructs a locally optimized process model and couples it with a cellular automata model, and after parameter calibration and accuracy verification, sets up multiple scenarios to simulate future soil organic carbon storage and calculates carbon sequestration potential, finally outputting the results in a visualized form. The corresponding system includes modules for data acquisition and preprocessing, core model computation, result display and application, and integrates decision support functions. This invention solves the problems of non-standardized data integration, neglect of carbon spatial migration, and insufficient practicality of results in existing technologies, improving the scientific rigor and reliability of the estimation, and providing precise support for regional soil carbon sequestration management and sustainable agricultural development.

Description

Technical Field

[0001] This invention relates to the field of home kitchen technology, specifically to a method and system for estimating the spatiotemporal variability of soil organic carbon and its carbon sequestration potential. Background Technology

[0002] Soil organic carbon is a core component of soil ecosystems. Its spatiotemporal variability directly correlates with soil fertility maintenance, ecological environment quality, and regional carbon sequestration capacity, playing a crucial role in sustainable agricultural development and the achievement of "dual carbon" goals. Currently, spatiotemporal dynamic simulation and carbon sequestration potential assessment of soil organic carbon have become research hotspots in agricultural ecology and resource environment, widely applied in soil resource management, agricultural policy formulation, and ecological protection planning. With the development of remote sensing, big data, and model simulation technologies, estimation methods based on multi-source data and process models have gradually become mainstream. However, the need for standardized data integration, spatial process coupling, and practical application of results remains unmet, necessitating more systematic and precise technical solutions.

[0003] Existing soil organic carbon estimation techniques have significant limitations: multi-source spatiotemporal data are scattered and vary in format, lacking a unified standardized preprocessing procedure, resulting in poor data compatibility and affecting the reliability of the simulation basis; most process models only focus on dynamic changes within a single grid cell, failing to fully consider the spatial migration of organic carbon caused by soil erosion and deposition, leading to discrepancies between spatiotemporal variability simulations and actual ecological processes; some assessment methods only set up a single scenario, making it difficult to comprehensively quantify the carbon sequestration potential of different management strategies, and the results output is mostly based on raw data, lacking intuitive visualization and targeted decision-making recommendations, making it difficult to directly adapt to the dual needs of scientific research analysis and management practice. Summary of the Invention

[0004] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a method and system for estimating the spatiotemporal variability and carbon sequestration potential of soil organic carbon. This method solves the problems of scattered sources and different formats of multi-source time-varying data, the lack of a unified standardized preprocessing process, which leads to poor data compatibility and affects the reliability of the simulation basis.

[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a method for estimating the spatiotemporal variability and carbon sequestration potential of soil organic carbon, comprising the following steps: S1. Acquire multi-source spatiotemporal data of the target area. The multi-source spatiotemporal data includes at least soil baseline data, environmental driving data, and human activity data. The soil baseline data comes from the national soil census dataset, historical soil profile data, and field sampling data. The environmental driving data includes long-term remote sensing vegetation index data, meteorological data, and high-precision topographic data. The human activity data includes land use / cover change data and agricultural management measures data. S2. Standardize and preprocess the multi-source spatiotemporal data obtained in S1, including coordinate system and projection transformation, spatial interpolation to fill missing values, resampling to a uniform grid resolution, and constructing a spatiotemporally matched raster database. S3. Construct a process-based dynamic model of soil organic carbon, wherein the model is a DNDC model or RothC model that has undergone parameter localization optimization; the target area is spatially discretized into grid cells, each cell runs the model independently, and the input driving data is the time-series dynamic data corresponding to that cell obtained in S2; S4. The model constructed in S3 was calibrated using measured soil organic carbon data from some historical periods, and the model accuracy was verified using another set of independent historical measured data. S5. Based on the model after calibration and verification in S4, set a baseline scenario and at least one optimized scenario to represent different management strategies, simulate the soil organic carbon storage at a specific time point in the future, and define the difference between the soil organic carbon storage of the optimized scenario and the baseline scenario as the carbon sequestration potential under that scenario. S6. Output and visualize the spatiotemporal variation simulation results of soil organic carbon obtained in S3 and the carbon sequestration potential estimation results obtained in S5 in the form of spatial distribution maps, time series curves and structured statistical reports.

[0006] Preferably, the soil background data includes the organic carbon content, bulk density, pH value, clay fraction, and total nitrogen content of the 0-30cm soil layer; the remote sensing data in the environmental driving data includes NDVI, EVI index, and MODISNPP products extracted from Landsat and Sentinel series satellite images, and the meteorological data includes daily temperature, precipitation, and photosynthetically active radiation; the agricultural management measures data include tillage methods, irrigation patterns, fertilizer application rates, straw return ratio, and green manure planting area.

[0007] Preferably, the agricultural management measures data specifically include tillage methods, application rates of organic and chemical fertilizers, straw return ratio, irrigation system, and green manure planting status.

[0008] Preferably, based on the process model in S3, a cellular automata model is coupled to simulate the spatial migration of soil organic carbon between grid cells caused by soil erosion and deposition.

[0009] Preferably, the model accuracy verification of S4 needs to meet the following criteria: coefficient of determination R² ≥ 0.65, root mean square error RMSE ≤ 15%.

[0010] Preferably, the optimized scenario in S5 includes one or more combined measures among returning all straw to the field, applying organic fertilizer, implementing conservation tillage, and planting green manure.

[0011] A system for estimating the spatiotemporal variability and carbon sequestration potential of soil organic carbon includes: Data acquisition and preprocessing module: used to automatically or semi-automatically acquire, clean, transform and integrate the multi-source spatiotemporal data from distributed databases and file systems, and store it in the spatiotemporal database management system; Core model computation module: It has a built-in localized optimized DNDC model and / or RothC model, and is equipped with a task scheduler to support parallel computation on multi-core processors or computing clusters to complete model calibration, verification and multi-scenario simulation. Results Display and Application Module: Developed based on the WebGIS platform, it provides interactive map browsing, querying, and statistical analysis functions, and can generate spatial distribution maps of carbon sequestration potential, trend maps of changes, and data reports that meet the standards.

[0012] Preferably, the results display and application module also integrates decision support functions, which can classify the potential levels according to administrative units or land use types based on the carbon sequestration potential simulation results, and output targeted management measure recommendation reports.

[0013] (III) Beneficial Effects This invention provides a method and system for estimating the spatiotemporal variability of soil organic carbon and its carbon sequestration potential. It has the following beneficial effects: 1. This invention solves the problems of scattered data sources and inconsistent formats in traditional methods by integrating multi-source spatiotemporal data and performing standardized preprocessing, providing a unified and standardized basic data support for subsequent simulations. At the same time, by adopting a locally optimized process model coupled with a cellular automata model, it not only ensures the professionalism of soil organic carbon dynamic simulation, but also fills the gap in traditional models that ignore spatial migration processes, making spatiotemporal variation simulation more in line with actual scenarios.

[0014] 2. The method and system of this invention achieve accurate assessment of carbon sequestration potential through multi-scenario simulation and output the results in a visual format, making complex data easier to understand and apply. The system's integrated decision support function can specifically classify potential levels and provide management suggestions, offering direct reference for relevant departments to formulate ecological protection and agricultural development strategies. It balances professionalism and practicality, meeting the precise analysis needs of scientific research while adapting to decision-making scenarios in actual management, thus contributing to the efficient utilization and sustainable development of regional soil carbon sink resources. Attached Figure Description

[0015] Figure 1 This is a flowchart of a method and system for estimating the spatiotemporal variability of soil organic carbon and its carbon sequestration potential, as proposed in this invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1: like Figure 1 As shown in the figure, this invention provides a method for estimating the spatiotemporal variability of soil organic carbon and its carbon sequestration potential, characterized by the following steps: S1. Acquire multi-source spatiotemporal data of the target area. The multi-source spatiotemporal data includes at least soil baseline data, environmental driving data, and human activity data. Soil baseline data comes from the national soil census dataset, historical soil profile data, and field sampling data. Environmental driving data includes long-term remote sensing vegetation index data, meteorological data, and high-precision topographic data. Human activity data includes land use / cover change data and agricultural management data. The soil baseline data integrates the 1980 national soil census, 2010 profile data, and 2020 field sampling (1 sample / 10 km²). The environmental driving data includes Landsat 8 imagery from 2010 to 2020, MODISNPP products, daily meteorological station data, and ASTERGDEM topographic data. The human activity data includes land use data classified by Sentinel-2 imagery and regional routine agricultural management records.

[0018] S2. Standardize and preprocess the multi-source spatiotemporal data acquired in S1, including coordinate system and projection transformation, spatial interpolation to fill missing values, resampling to a unified grid resolution, and constructing a spatiotemporally matched raster database; unify the WGS84 coordinate system + UTM50N projection, fill missing values ​​in meteorological data using Kriging interpolation, resample to a 1km×1km grid, and construct an annual raster database using ArcGIS.

[0019] S3. Construct a process-based dynamic model of soil organic carbon, which is either a DNDC model or a RothC model with localized parameter optimization; discretize the target area into grid cells, and run the model independently in each cell. The input driving data is the time-series dynamic data corresponding to that cell obtained in S2; using the locally optimized DNDC model, divide the area into 5000 grid cells, run the model independently in each cell, and couple a cellular automata model to simulate carbon migration.

[0020] S4. The model constructed in S3 was calibrated using measured soil organic carbon data from some historical periods, and the model accuracy was verified using another set of independent historical measured data. The parameters were calibrated using 60% of the sampling data from 2010 to 2015 and verified using 40% of the data from 2016 to 2020. The results showed R²=0.72 and RMSE=12.3%.

[0021] S5. Based on the model calibrated and verified in S4, set a baseline scenario and at least one optimized scenario to represent different management strategies, simulate soil organic carbon storage at a specific future time point, and define the difference between the soil organic carbon storage of the optimized scenario and the baseline scenario as the carbon sequestration potential under that scenario; set a baseline scenario for conventional management and two optimized scenarios to simulate carbon storage in 2030 and calculate the carbon sequestration potential (3.2 t / hm² for a single measure and 6.8 t / hm² for a combination of measures).

[0022] S6. Output and visualize the spatial and temporal variation simulation results of soil organic carbon obtained in S3 and the carbon sequestration potential estimation results obtained in S5 in the form of spatial distribution maps, time series curves and structured statistical reports, and generate spatial distribution maps, classification time series curves and statistical reports containing a list of high potential areas.

[0023] Soil baseline data includes organic carbon content, bulk density, pH value, clay fraction, and total nitrogen content in the 0-30cm soil layer; environmental driving data includes remote sensing data such as NDVI, EVI index, and MODISNPP products extracted from Landsat and Sentinel series satellite images; meteorological data includes daily temperature, precipitation, and photosynthetically active radiation; and agricultural management data includes tillage methods, irrigation patterns, fertilizer application rates, straw return ratio, and green manure planting area. (1) Soil baseline data (0-30cm soil layer): average organic carbon content 12.5g / kg, bulk density 1.35g / cm³, pH value 7.8, clay fraction 22%, total nitrogen content 1.1g / kg.

[0024] (2) Environmental driving data: Landsat 8 NDVI / EVI index and MODIS NPP product (500m resolution) were extracted from remote sensing data; meteorological data included daily average temperature of 14.2℃, average precipitation of 580mm and average photosynthetically active radiation of 1200μmol / m²·s.

[0025] (3) Agricultural management measures data: conventional tillage (rotary tillage 15cm), flood irrigation, fertilizer application rate of 180kg / hm², straw return rate of 30%, and green manure planting area of ​​5%.

[0026] The specific data on agricultural management measures include tillage methods, the amount of organic and chemical fertilizers applied, the proportion of straw returned to the field, irrigation systems, and the planting of green manure. (1) Tillage methods: rotary tillage (conventional), no-tillage (optimized scenario); organic fertilizer application rate 15t / hm² (optimized scenario); chemical fertilizer application rate 180kg / hm² (conventional), 150kg / hm² (precision fertilization scenario).

[0027] (2) Straw return ratio: 30% (baseline), 100% (full return scenario); Irrigation system: flood irrigation (conventional), drip irrigation (optimized scenario).

[0028] (3) Green manure planting situation: the planting area accounts for 5% (baseline) and 20% (optimized scenario), and the planting variety is milkvetch.

[0029] Based on the process model in S3, a cellular automata model is coupled to simulate the spatial migration of soil organic carbon between grid cells caused by soil erosion and deposition. (1) Cellular automata model rules: Soil erosion rate in areas with slope > 15° is 0.5t / hm²·a, and deposition rate in low-lying depositional areas is 0.3t / hm²·a.

[0030] (2) Carbon migration simulation: Based on the terrain slope and land use type, the migration coefficients are set as follows: 0.8 for cultivated land, 0.3 for forest land and 0.1 for construction land, to simulate the organic carbon exchange process between grids.

[0031] The accuracy verification of the S4 model must meet the following standards: coefficient of determination R² ≥ 0.65, root mean square error RMSE ≤ 15%; (1) Validation data: measured soil organic carbon data from 2016 to 2020 from 20 independent sampling points.

[0032] (2) Verification results: The coefficient of determination R² = 0.72 and the root mean square error RMSE = 12.3%, which meets the accuracy requirements of R² ≥ 0.65 and RMSE ≤ 15%.

[0033] The optimization scenarios in S5 include one or more combined measures such as returning all straw to the field, applying organic fertilizer, implementing conservation tillage, and planting green manure.

[0034] Example 2: like Figure 1 As shown, this embodiment of the invention provides a system for estimating the spatiotemporal variability of soil organic carbon and its carbon sequestration potential, comprising: Data acquisition and preprocessing module: used to automatically or semi-automatically acquire, clean, transform and integrate multi-source spatiotemporal data from distributed databases and file systems, and store it in the spatiotemporal database management system; Core model computation module: Built-in localized optimized DNDC model and / or RothC model, configured with a task scheduler, supporting parallel computation on multi-core processors or computing clusters to complete model calibration, verification and multi-scenario simulation; Results Display and Application Module: Developed based on the WebGIS platform, it provides interactive map browsing, querying, and statistical analysis functions, and can generate spatial distribution maps of carbon sequestration potential, trend maps of changes, and data reports that meet the standards.

[0035] Data acquisition and preprocessing module: It connects to the National Soil Survey Database, NASAEarthData, and China Meteorological Data Network via API, uses Python Pandas to clean outliers, and GDAL to perform format conversion, and stores the data in a PostgreSQL+PostGIS spatiotemporal database.

[0036] Core model computation module: Built-in Python version of localized DNDC model, supports switching with RothC model; based on Celery framework, configured task scheduler, 8-core processor parallel operation, 5000 grid simulation time ≤ 4 hours.

[0037] Results Display and Application Module: Based on the Leaflet framework, this module develops a WebGIS that supports map zooming, layer switching, and grid data querying; it also provides regional statistics and trend analysis functions, and generates Excel-format reports.

[0038] The results display and application module also integrates decision support functions, which can classify potential levels according to administrative units or land use types based on the carbon sequestration potential simulation results, and output targeted management measure recommendation reports.

[0039] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for estimating soil organic carbon spatiotemporal variability and carbon sequestration potential, characterized in that, The method comprises the following steps: S1, acquiring multi-source spatio-temporal data of a target region, wherein the multi-source spatio-temporal data at least comprises soil background data, environmental driving data and human activity data, wherein the soil background data is derived from national soil survey data sets, historical soil profile data and field sampling data, the environmental driving data comprises long-time series remote sensing vegetation index data, meteorological data and high-precision terrain data, and the human activity data comprises land use / cover change data and agricultural management measure data; S2, standardizing and pre-processing the multi-source spatio-temporal data acquired in S1, comprising coordinate system and projection conversion, spatial interpolation to fill in missing values, resampling to a unified grid resolution, and constructing a grid database matched in time and space; S3, constructing a process-based soil organic carbon dynamic model, wherein the model is a DNDC model or a RothC model which has been optimized in parameters localization; discretizing the target region into grid cells, independently running the model for each cell, and inputting the time-series dynamic data corresponding to the cell obtained in S2 as driving data; S4, calibrating the model constructed in S3 by using measured soil organic carbon data in some historical period, and verifying the accuracy of the model by using another part of independent historical measured data; S5, based on the calibrated and verified model in S4, setting baseline scenarios and at least one optimization scenario representing different management strategies, simulating soil organic carbon reserves at a specific future time point, and defining the difference between the soil organic carbon reserves in the optimization scenario and the baseline scenario as the carbon sequestration potential in the scenario; S6, outputting and visualizing the results of the soil organic carbon spatio-temporal variation simulation in S3 and the carbon sequestration potential estimation results in S5 in the form of spatial distribution maps, time series curves and structured statistical reports.

2. The method according to claim 1, wherein: The soil background data comprises organic carbon content, bulk density, pH value, clay fraction and total nitrogen content in the 0-30cm soil layer; the remote sensing data in the environmental driving data comprises NDVI and EVI indexes extracted from Landsat and Sentinel satellite images and MODIS NPP products, and the meteorological data comprises daily air temperature, precipitation and photosynthetically active radiation; the agricultural management measure data comprises tillage methods, irrigation modes, fertilizer application rates, straw returning rates and green manure planting areas.

3. The method according to claim 1, wherein: The agricultural management measure data specifically comprises tillage methods, organic fertilizer and chemical fertilizer application rates, straw returning rates, irrigation systems and green manure planting conditions.

4. The method according to claim 1, wherein: On the basis of the process model in S3, a cellular automaton model is coupled to simulate the spatial migration of soil organic carbon between grid cells caused by soil erosion and deposition.

5. The method of claim 1, wherein: The model accuracy verification in S4 needs to meet the following standards: determination coefficient R²≥0.65, and root mean square error RMSE≤15%.

6. The method for estimating soil organic carbon spatiotemporal variability and carbon sequestration potential according to claim 1, characterized in that: The optimization scenario in S5 comprises one or more combined measures of returning all straw to the field, applying organic fertilizer, implementing conservation tillage and planting green manure.

7. A soil organic carbon estimation system for implementing the method of any one of claims 1 to 6, characterized by, The method comprises: a data acquisition and preprocessing module for automatically or semi-automatically acquiring, cleaning, converting and integrating the multi-source spatio-temporal data from distributed databases and file systems, and storing the data in a spatio-temporal database management system; Core model operation module: built-in the localized optimized DNDC model and / or RothC model, configured with a task scheduler, supporting parallel operation on multi-core processors or computing clusters to complete model calibration, verification and multi-scenario simulation; Result display and application module: developed based on WebGIS platform, providing interactive map browsing, query, statistical analysis functions, and can generate carbon sequestration potential spatial distribution map, change trend chart and standard data report.

8. The system of claim 7, wherein, The result display and application module also integrates decision support function, which can divide the potential level according to the carbon sequestration potential simulation results, by administrative unit or land use type, and output targeted management measures suggestion report.

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