Method, system and equipment for dynamically monitoring soil carbon loss and storage medium

Abstract

The invention discloses a dynamic monitoring method, system and device for soil carbon loss and a storage medium, and relates to the technical field of environment monitoring, and the method comprises the steps: obtaining soil parameters through a multi-mode sensing array and a runoff monitoring device which are arranged in a target soil area, the soil parameters comprise a soil gaseous parameter, conductivity, a soil solid parameter and a liquid carbon parameter; calculating a carbon loss amount generated by soil respiration according to the soil gas state parameter, and taking the carbon loss amount as a respiration carbon flux; according to the conductivity, the soil solid parameters and a preset soil organic carbon content relation model, calculating the carbon loss amount of soil erosion migration as erosion carbon flux; calculating the carbon loss amount of soil leaching loss according to the liquid carbon parameters, and taking the carbon loss amount as leaching carbon flux; and performing weighted summation on the respiration carbon flux, the erosion carbon flux and the leaching carbon flux to obtain the total carbon loss of the soil in the target area. A more comprehensive carbon income and expenditure evaluation method is provided by establishing a soil total carbon loss calculation framework covering'gas-solid-liquid 'three phases.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a method, system, equipment and storage medium for dynamic monitoring of soil carbon loss. Background Art

[0002] Existing soil carbon monitoring technologies primarily include traditional experimental analysis, remote sensing and model estimation, soil cuttings, and runoff plots. Traditional experimental analysis, through stratified sampling and laboratory measurements (such as potassium dichromate oxidation or elemental analysis), obtains high-precision data suitable for soil quality assessment and long-term carbon storage studies. Remote sensing and model estimation utilizes satellite data and meteorological models for large-scale carbon sink analysis. The soil cutting method uses probes to collect soil gas or pore water, enabling fixed-point carbon flux monitoring. The runoff plot method quantifies carbon losses through erosion and leaching by collecting runoff and sediment.

[0003] However, all of these technologies have significant limitations: Traditional experimental analysis methods require destructive sampling and long sampling cycles, making it difficult to dynamically capture changes in carbon flux; remote sensing technology has low spatial resolution, unable to reflect microtopographic differences or distinguish carbon loss pathways; runoff plot methods have insufficient temporal resolution and struggle to interpret vertical carbon migration; and while cutting methods support continuous monitoring, the vertical data accuracy is limited and requires frequent calibration. Therefore, a technical solution with high spatial and temporal resolution, low cost, and the ability to dynamically monitor soil carbon fluxes and distinguish loss pathways is urgently needed. Summary of the Invention

[0004] The main purpose of the present invention is to provide a method, system, equipment and storage medium for dynamic monitoring of soil carbon loss. By synchronously acquiring gaseous, solid and liquid carbon parameters through a multimodal sensor array and runoff monitoring equipment, the limitation of traditional methods that only monitor a single carbon loss path is broken through. The carbon fluxes of respiration (gaseous), erosion migration (solid) and leaching loss (liquid) are calculated separately to clarify the quantitative relationship between different carbon loss pathways. By integrating the three types of carbon fluxes, a total soil carbon loss calculation framework covering the "gas-solid-liquid" three phases is established, providing a more comprehensive carbon budget assessment method.

[0005] In order to achieve the above objectives, the embodiments of the present application provide the following technical solutions:

[0006] According to a first aspect of an embodiment of the present application, a method for dynamically monitoring soil carbon loss is provided, the method comprising:

[0007] Acquiring soil parameters by deploying a multimodal sensing array and runoff monitoring equipment in a target soil area, wherein the soil parameters include soil gas parameters, electrical conductivity, soil solid parameters, and liquid carbon parameters;

[0008] Calculating the carbon loss caused by soil respiration according to the soil gaseous parameters as the respiratory carbon flux;

[0009] Calculating the amount of carbon lost due to soil erosion and migration as erosion carbon flux based on the electrical conductivity, soil solid parameters, and a preset soil organic carbon content relationship model;

[0010] Calculating the amount of carbon loss due to soil leaching according to the liquid carbon parameters as the leaching carbon flux;

[0011] The respiratory carbon flux, the erosion carbon flux and the leaching carbon flux are weighted and summed to obtain the total soil carbon loss in the target area.

[0012] Optionally, the soil gaseous parameters include target gas concentration data; and calculating the carbon loss caused by soil respiration based on the soil gaseous parameters includes:

[0013] Calculating the target gas concentration gradient in the vertical direction according to the target gas concentration data measured by the multimodal sensing array;

[0014] Determine the diffusion coefficient of the target gas in the soil based on soil temperature and humidity data;

[0015] Calculate the convection rate of the target gas in the soil based on soil pore characteristics and environmental parameters;

[0016] Multiplying the vertical gradient of the target gas concentration by the diffusion coefficient to obtain a diffusion term flux;

[0017] Multiplying the target gas concentration data by the convection rate to obtain a convection term flux;

[0018] Adding the diffusion term flux and the convection term flux to obtain the total flux of the target gas per unit area;

[0019] The total flux of the target gas is integrated over time and space to obtain the carbon loss caused by soil respiration.

[0020] Optionally, calculating the amount of carbon loss due to soil erosion migration based on the electrical conductivity, soil solid parameters, and a preset soil organic carbon content relationship model includes:

[0021] Inputting the electrical conductivity into a preset soil organic carbon content relationship model to invert and obtain the soil organic carbon content;

[0022] Multiplying the soil organic carbon content by sediment mass data to obtain the carbon loss due to soil erosion and migration; the sediment mass data is obtained through runoff monitoring equipment;

[0023] The spatial distribution of carbon loss due to soil erosion and migration is corrected according to the terrain slope parameter.

[0024] Optionally, calculating the amount of carbon loss due to soil leaching based on the liquid carbon parameter includes:

[0025] When the rainfall reaches a set threshold, the runoff monitoring device is activated so that the runoff monitoring device collects dissolved organic carbon concentration data in the runoff at set time intervals;

[0026] The runoff velocity is measured in real time by the runoff monitoring device, and the runoff volume is calculated according to the set time interval;

[0027] Matching the dissolved organic carbon concentration data with the runoff at corresponding time points;

[0028] The matched dissolved organic carbon concentration data and runoff were time-integrated to calculate the cumulative leaching carbon flux as the carbon loss due to soil leaching.

[0029] Optionally, performing a weighted summation of the respiratory carbon flux, the erosion carbon flux, and the leaching carbon flux to obtain the total soil carbon loss in the target area includes:

[0030] Determining weight coefficients of the respiratory carbon flux, the erosion carbon flux, and the leaching carbon flux respectively according to the soil type and environmental characteristics of the target area;

[0031] Converting the respiratory carbon flux, the erosion carbon flux, and the leaching carbon flux into standard flux values of the same dimension;

[0032] multiplying the standard flux values of the respiratory carbon flux, the erosion carbon flux, and the leaching carbon flux by corresponding weight coefficients to obtain a weighted flux value;

[0033] All weighted flux values are spatially summed, and the spatial summation results within the set time period are temporally integrated to obtain the total soil carbon loss in the target area.

[0034] Optionally, the deployment process of the multimodal sensor array includes:

[0035] Determine the installation location of each sensor array according to the preset grid layout plan;

[0036] The multi-parameter sensing array is vertically pressed into the soil to a set depth; the multi-parameter sensing array includes: a gaseous carbon monitoring layer, a spectral sensor and a liquid carbon monitoring layer; the gaseous carbon monitoring layer includes a target gas sensor for measuring soil respiration flux; the solid carbon monitoring layer includes a spectral sensor for inverting soil organic carbon content; the liquid carbon monitoring layer includes a soil moisture sensor for evaluating carbon leaching conditions.

[0037] Optionally, the deployment process of the runoff monitoring equipment includes:

[0038] Determine the installation location of the runoff trough according to the terrain slope;

[0039] Install multiple layers of filtration devices in sequence;

[0040] Equipped with dissolved carbon analyzer and sediment weighing device.

[0041] According to a second aspect of an embodiment of the present application, a soil carbon loss dynamic monitoring system is provided, the system comprising:

[0042] a parameter acquisition module, configured to acquire soil parameters, including soil gas parameters, electrical conductivity, soil solid parameters, and liquid carbon parameters, through a multimodal sensing array and runoff monitoring equipment deployed in a target soil area;

[0043] a respiratory carbon flux module, configured to calculate the carbon loss caused by soil respiration based on the soil gaseous parameters as the respiratory carbon flux;

[0044] an erosion carbon flux module, configured to calculate the amount of carbon lost by soil erosion migration as the erosion carbon flux based on the electrical conductivity, soil solid parameters, and a preset soil organic carbon content relationship model;

[0045] a leaching carbon flux module, configured to calculate the amount of carbon lost by soil leaching according to the liquid carbon parameter as the leaching carbon flux;

[0046] The total soil carbon loss module is used to perform weighted summation on the respiratory carbon flux, the erosion carbon flux and the leaching carbon flux to obtain the total soil carbon loss in the target area.

[0047] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect above.

[0048] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which computer-readable instructions are stored. The computer-readable instructions can be executed by a processor to implement the method described in the first aspect above.

[0049] In summary, the embodiments of the present application provide a method, system, device and storage medium for dynamic monitoring of soil carbon loss. Soil parameters are obtained through a multimodal sensing array and runoff monitoring equipment deployed in the target soil area, and the soil parameters include soil gaseous parameters, electrical conductivity, soil solid parameters and liquid carbon parameters; the carbon loss caused by soil respiration is calculated based on the soil gaseous parameters as a respiratory carbon flux; the carbon loss due to soil erosion migration is calculated based on the electrical conductivity, soil solid parameters and a preset soil organic carbon content relationship model as an erosion carbon flux; the carbon loss due to soil leaching is calculated based on the liquid carbon parameters as a leaching carbon flux; the respiratory carbon flux, the erosion carbon flux and the leaching carbon flux are weighted and summed to obtain the total soil carbon loss in the target area. Gaseous, solid and liquid carbon parameters are synchronously obtained through a multimodal sensing array and runoff monitoring equipment, breaking through the limitation of traditional methods that only monitor a single carbon loss path. Separate calculations of carbon fluxes from respiration (gaseous), erosion (solid), and leaching (liquid) clarify the quantitative relationships between these different carbon loss pathways. By weighted integration of these three carbon fluxes, a framework for calculating total soil carbon loss across the "gas-solid-liquid" phases was established, providing a more comprehensive carbon budget assessment method. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0051] The structures, proportions, sizes, etc. illustrated in this specification are intended solely to complement the contents disclosed herein and to facilitate understanding and reading by persons skilled in the art. They are not intended to limit the conditions under which the present invention may be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportions, or adjustments in sizes, without affecting the efficacy and objectives of the present invention, shall remain within the scope of the technical contents disclosed herein.

[0052] Figure 1 A schematic flow chart of a method for dynamic monitoring of soil carbon loss provided in an embodiment of the present application;

[0053] Figure 2 Schematic diagram of the soil carbon loss dynamic monitoring stage provided in the embodiment of the present application;

[0054] Figure 3 A schematic diagram of a soil carbon loss dynamic monitoring system provided in an embodiment of the present application;

[0055] Figure 4 A structural diagram of an electronic device provided in an embodiment of the present application is shown;

[0056] Figure 5 A diagram showing a computer-readable storage medium provided in an embodiment of the present application.

[0057] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0059] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0060] In addition, the terms "first," "second," and so on, used in this disclosure are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referenced. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this disclosure, "plurality" means at least two, such as two or three, unless otherwise specifically defined.

[0061] In the present invention, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will be able to understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0062] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0063] Figure 1 A method for dynamically monitoring soil carbon loss provided by an embodiment of the present application is shown, the method comprising:

[0064] Step 101: Acquire soil parameters by deploying a multimodal sensing array and runoff monitoring equipment in a target soil area, wherein the soil parameters include soil gas parameters, electrical conductivity, soil solid parameters, and liquid carbon parameters;

[0065] Step 102: Calculating the carbon loss caused by soil respiration based on the soil gaseous parameters as respiratory carbon flux;

[0066] Step 103: Calculate the carbon loss due to soil erosion and migration based on the electrical conductivity, soil solid parameters, and a preset soil organic carbon content relationship model as erosion carbon flux;

[0067] Step 104: Calculate the amount of carbon loss due to soil leaching based on the liquid carbon parameter as the leaching carbon flux;

[0068] Step 105: performing weighted summation on the respiratory carbon flux, the erosion carbon flux, and the leaching carbon flux to obtain the total soil carbon loss in the target area.

[0069] In one possible implementation, in step 102, the soil gaseous parameters include target gas concentration data; and calculating the carbon loss caused by soil respiration based on the soil gaseous parameters includes:

[0070] The target gas concentration gradient in the vertical direction is calculated based on the target gas concentration data measured by the multimodal sensor array; the diffusion coefficient of the target gas in the soil is determined based on the soil temperature and humidity data; the convection rate of the target gas in the soil is calculated based on the soil pore characteristics and environmental parameters; the vertical gradient of the target gas concentration is multiplied by the diffusion coefficient to obtain the diffusion term flux; the target gas concentration data is multiplied by the convection rate to obtain the convection term flux; the diffusion term flux and the convection term flux are added to obtain the total target gas flux per unit area; the total target gas flux is integrated over time and space to obtain the carbon loss caused by soil respiration.

[0071] The soil gaseous parameters obtained by the multimodal sensing array can accurately quantify the amount of carbon loss caused by soil respiration. Specifically, by measuring the target gas concentration data and calculating its vertical concentration gradient, combined with the diffusion coefficient determined by the soil temperature and humidity data and the convection rate calculated based on the soil pore characteristics and environmental parameters, the diffusion term flux and the convection term flux are calculated separately. After adding the two to obtain the total flux of the target gas per unit area, the carbon loss caused by soil respiration is finally obtained by time and space integration. This method can accurately reflect the gas transfer mechanism in the soil respiration process and provide reliable data support for the assessment of soil carbon cycle.

[0072] In a possible implementation, in step 103, calculating the carbon loss due to soil erosion migration based on the electrical conductivity, soil solid parameters, and a preset soil organic carbon content relationship model includes:

[0073] The electrical conductivity is input into a preset soil organic carbon content relationship model to invert the soil organic carbon content; the soil organic carbon content is multiplied by the sediment mass data to obtain the carbon loss due to soil erosion and migration; the sediment mass data is obtained through runoff monitoring equipment; and the spatial distribution of the carbon loss due to soil erosion and migration is corrected based on the terrain slope parameter.

[0074] Through the coordinated analysis of conductivity parameters and solid soil parameters, the amount of carbon loss due to soil erosion migration can be accurately quantified. Specifically, by inputting the real-time monitored conductivity data into the preset soil organic carbon content relationship model, accurate soil organic carbon content data is inverted, and then multiplied by the sediment mass data obtained by the runoff monitoring equipment to obtain the basic erosion carbon loss. The spatial distribution correction is further combined with the terrain slope parameters to obtain the erosion carbon loss, which effectively solves the deviation problem caused by terrain factors in the traditional erosion carbon loss assessment. This method realizes the full-chain calculation of erosion carbon loss from microscopic parameter measurement to macroscopic spatial distribution, providing a reliable quantitative basis for soil carbon cycle research and erosion prevention and control.

[0075] In a possible implementation, in step 104, calculating the amount of carbon loss due to soil leaching based on the liquid carbon parameter includes:

[0076] When the rainfall reaches a set threshold, the runoff monitoring device is started so that the runoff monitoring device collects dissolved organic carbon concentration data in the runoff at set time intervals; the runoff velocity is measured in real time by the runoff monitoring device, and the runoff volume is calculated at the set time intervals; the dissolved organic carbon concentration data is matched with the runoff volume at the corresponding time point; the matched dissolved organic carbon concentration data and runoff volume are time-integrated to calculate the cumulative leaching carbon flux as the carbon loss due to soil leaching.

[0077] Through intelligent runoff monitoring and dynamic integration methods, accurate measurement of soil leaching carbon loss can be achieved. Specifically, the system automatically starts the monitoring process under rainfall trigger conditions, synchronously collects runoff dissolved organic carbon concentration and runoff volume data, ensures data consistency through strict time matching, and uses a time integration algorithm to calculate the cumulative leaching carbon flux. This method innovatively solves key technical problems such as discontinuous sampling and data asynchrony in traditional leaching carbon monitoring, and realizes a complete quantitative chain from instantaneous monitoring to cumulative calculation, providing a reliable dynamic monitoring solution for evaluating soil carbon loss driven by water. This technology is particularly suitable for the accurate measurement of leaching carbon loss under different rainfall intensities, and provides important data support for the prevention and control of agricultural non-point source pollution and soil carbon management decisions.

[0078] In a possible implementation, in step 105, weighted summing of the respiratory carbon flux, the erosion carbon flux, and the leaching carbon flux is performed to obtain the total soil carbon loss in the target area, including:

[0079] The weight coefficients of the respiratory carbon flux, the erosion carbon flux and the leaching carbon flux are determined respectively according to the soil type and environmental characteristics of the target area; the respiratory carbon flux, the erosion carbon flux and the leaching carbon flux are uniformly converted into standard flux values of the same dimension; the standard flux values of the respiratory carbon flux, the erosion carbon flux and the leaching carbon flux are multiplied by the corresponding weight coefficients to obtain weighted flux values; all weighted flux values are spatially summed, and the spatial summation results within a set time period are time-integrated to obtain the total soil carbon loss in the target area.

[0080] Through the dynamic weighted integration method of multi-source carbon fluxes, an accurate assessment of the total amount of regional soil carbon loss is achieved. Specifically, the system intelligently assigns weight coefficients to each carbon flux based on soil type and environmental characteristics, ensures the comparability of data from different carbon loss pathways through standardized processing, and adopts a dual calculation framework of "spatial summation-time integration" to ultimately obtain a total carbon loss amount that is representative in time and space. This method innovatively solves key technical problems such as subjective path weight allocation and inconsistent spatiotemporal scales in traditional carbon assessments, and realizes a complete calculation chain from single-point monitoring to regional assessment, from instantaneous measurement to long-term accumulation. This technology is particularly suitable for the comprehensive assessment of carbon losses in different ecosystems, providing a scientific quantitative tool for regional carbon budget accounting and soil carbon management, and plays an important supporting role in achieving the "dual carbon" goals.

[0081] In a possible implementation, in step 101, the deployment process of the multimodal sensor array includes:

[0082] The installation position of each sensor array is determined according to a preset grid layout plan; the multi-parameter sensor array is vertically pressed into the soil to a set depth; the multi-parameter sensor array includes: a gaseous carbon monitoring layer, a spectral sensor and a liquid carbon monitoring layer; the gaseous carbon monitoring layer includes a target gas sensor for measuring soil respiration flux; the solid carbon monitoring layer includes a spectral sensor for inverting soil organic carbon content; the liquid carbon monitoring layer includes a soil moisture sensor for evaluating carbon leaching conditions.

[0083] In one possible implementation, in step 101, the process of deploying the runoff monitoring equipment includes:

[0084] Determine the installation location of the runoff trough according to the terrain slope; install multiple-stage filtration devices in sequence; configure a dissolved carbon analyzer and a sediment weighing device.

[0085] Through an intelligent monitoring network layout plan, a multi-dimensional monitoring system for soil carbon loss is constructed. Specifically, the system adopts a grid layout strategy to deploy a multi-parameter sensor array, and synchronously monitors gaseous (target gas sensor), solid (spectral sensor) and liquid (water sensor) carbon parameters through a vertical layered design; at the same time, the layout of runoff monitoring equipment is optimized based on terrain characteristics, and multi-stage filtering and precision detection units are configured. This method innovatively solves the technical bottlenecks of traditional carbon monitoring, such as single parameters and insufficient spatial representativeness, and realizes the construction of a three-dimensional monitoring network of "vertical layering-horizontal grid", providing reliable infrastructure support for the comprehensive assessment of soil carbon loss. This technology is particularly suitable for carbon cycle research under complex terrain conditions, and provides a standardized implementation plan for building a space-ground integrated carbon monitoring system.

[0086] The following describes the method provided in the embodiments of this application in detail. To address the shortcomings of existing soil carbon monitoring technologies, an in-situ, multi-dimensional, high-resolution soil carbon loss dynamic monitoring system is proposed. Through multi-sensor fusion, intelligent algorithm optimization, and a collaborative monitoring mechanism, the system achieves simultaneous quantification of gaseous (respiration), solid (erosion), and liquid (leaching) carbon losses. The system consists of:

[0087] 1. Multimodal rod sensing array – enabling high-resolution, non-destructive in-situ monitoring;

[0088] Layered embedded sensors (20cm spacing): Utilizing a corrosion-resistant titanium alloy rod, these sensors integrate CO2, near-infrared spectroscopy (NIR), soil tension, conductivity, and temperature and humidity sensors, enabling high-resolution vertical monitoring (0.2cm level) of soil depths from 0 to 2m. Real-time SOC inversion using NIR replaces traditional laboratory analysis, reducing destructive sampling and improving data timeliness (second-level response vs. 72 hours). A CO2 sensor (±1.5% accuracy) dynamically monitors soil respiration carbon flux, combining temperature and humidity data from the temperature and humidity sensors to modify the diffusion model (Fick's law) for improved calculation accuracy. This overcomes the limitations of traditional destructive sampling, enabling long-term continuous observation and reducing manual intervention. With a vertical resolution of 0.2cm, it accurately captures carbon migration patterns in different soil layers. Combined with conductivity data from the conductivity sensor, SOC content is inverted with an error of <±8%, surpassing the traditional penetration method (which is prone to drift and requires frequent calibration).

[0089] 2. Intelligent runoff collaborative monitoring module - quantifying carbon losses from erosion and leaching;

[0090] Three-stage filtration system (1mm→0.45μm→0.22μm): Separates sediment, dissolved organic carbon (DOC) and colloidal carbon to improve detection accuracy. UV-fluorescence spectrometer: Simultaneously measures DOC and particulate organic carbon (POC) to avoid the low resolution problem of the traditional runoff cell method. Weighing sensor (±1g accuracy): Quantifies the mass of eroded sediment, and combines the conductivity-SOC calibration model to accurately calculate erosion carbon loss. Flow rate sensor, quantifies real-time runoff velocity, calculates runoff volume based on runoff time, and calculates leached carbon loss. Real-time carbon loss monitoring under rainfall events (data updated every 5 minutes) is achieved to solve the problem of low time resolution of the traditional runoff cell method. It can distinguish between eroded carbon (POC) and leached carbon (DOC), making up for the defect that remote sensing technology cannot distinguish loss paths.

[0091] 3. Multi-source data fusion and intelligent algorithm optimization - improve model accuracy;

[0092] Transfer learning SOC inversion model: Pre-trained on the World Soil Information Service database and optimized with local monitoring data to reduce small sample bias. Lateral carbon flux correction factor: Introduces a compensation algorithm from a PNAS study to correct traditional models' overestimation of microtopographic carbon losses. Dynamic diffusion-convection equation: Incorporates temperature and humidity data to correct CO2 flux calculations in real time, improving the accuracy of respiratory carbon loss estimates. Compared to remote sensing models (resolution >100m), this system can capture differences in carbon loss caused by microtopography. Through algorithm optimization, the overall carbon loss calculation error is reduced to <10%, surpassing single sensor or model approaches.

[0093] 4. Grid deployment and real-time data transmission – supporting carbon trading verification;

[0094] 5×5 grid layout (50cm spacing): Covers a wider monitoring area and improves spatial representativeness. 15-minute / 5-minute high-frequency data upload: Supports cloud storage and analysis, meeting the real-time verification requirements of carbon trading scenarios. Provides high-temporal and spatial resolution carbon flux data, providing a reliable basis for carbon footprint accounting and ecological restoration projects. Reduces traditional laboratory analysis costs (reducing manual sampling and testing workload by 80%).

[0095] Table 1

[0096]

[0097]

[0098] The experimental data in Table 1 proves that this application has the following advantages: Resolution breakthrough: vertical 0.2cm-level monitoring + horizontal 50cm grid deployment, far exceeding remote sensing (>100m) and runoff cell method (low integration). Real-time breakthrough: 15-minute data update, dynamic capture of carbon flux changes, to make up for the shortcomings of traditional methods (long sampling intervals, inability to monitor instantly). Cost breakthrough: 80% reduction in manual sampling and laboratory analysis costs, suitable for long-term, large-scale deployment. Through multi-sensor fusion + intelligent algorithms + collaborative monitoring, a technical closed loop for soil carbon loss monitoring has been constructed, providing highly reliable data support for scenarios such as carbon trading and ecological governance, and promoting the industry to develop towards precision and intelligence.

[0099] The embodiment of this application adopts an in-situ, multi-dimensional, high-resolution dynamic monitoring method, and realizes the full path quantification of soil carbon loss in gaseous (CO2), solid (SOC), and liquid (DOC / POC) through multi-sensor collaboration and intelligent algorithm modeling. Figure 2 As shown, the solution is divided into five stages: deployment preparation, data collection, model calculation, dynamic optimization, and application expansion to ensure the accuracy, continuity, and scalability of monitoring.

[0100] Phase 1: Hardware Installation and Environmental Calibration: Complete the monitoring system's hardware installation, calibration, and environmental adaptability testing to ensure reliable sensor data.

[0101] Step 1.1: Monitoring point site selection and grid planning

[0102] 1. Site selection criteria: highly representative (e.g., farmland, forest, wetland, and other typical ecosystems); relatively flat terrain (slope <5°), avoiding extreme erosion and interference; and far from human interference (e.g., roads, buildings).

[0103] 2. Grid layout: A 5×5 grid (50cm spacing) is used to cover a 5m×5m area; a runoff monitoring trough (3m in diameter) is arranged at the center point for liquid carbon loss monitoring.

[0104] Step 1.2: Multimodal Pole Sensor Array Installation

[0105] 1. Hardware composition: titanium alloy rod (outer diameter 2cm, length 1.2m), corrosion-resistant; layered sensors (one layer embedded every 20cm): CO2 sensor (SenseAir K30, ±1.5% accuracy) - monitors respiratory carbon loss; near-infrared spectrum probe (400-2500nm) - inverts SOC content; soil tension sensor (0-200kPa) - monitors water migration; soil electrical conductivity sensor - corrects the inverted SOC content; soil temperature and humidity sensor - corrects the soil respiratory diffusion model.

[0106] 2. Installation method: Use the static pressure method to insert the rod vertically into the soil to avoid disturbing the original soil; install a rain cover on the top of the rod to prevent data drift.

[0107] Step 1.3: Runoff Monitoring Module Debugging

[0108] 1. Three-stage filtration system: 1mm screen (intercepts coarse particles) → 0.45μm filter membrane (captures POC) → 0.22μm filter membrane (separates DOC); equipped with automatic backwash function to prevent clogging.

[0109] 2. Detection unit: UV-fluorescence spectrometer: real-time measurement of DOC / POC concentration; high-precision weighing sensor (±1g): quantification of eroded sediment mass; runoff velocity sensor: quantification of runoff velocity.

[0110] Step 1.4: Environmental parameter calibration

[0111] 1. Conductivity-SOC model calibration: collect local soil samples, measure SOC content in the laboratory, and establish the conductivity-SOC regression equation (R 2 >0.9);

[0112] 2. Near-infrared spectroscopy model transfer learning: Pre-trained based on the World Soil Information Service (WoSIS) database, and then calibrated using local data to reduce small sample bias.

[0113] Phase II: Data collection; achieving continuous monitoring of soil carbon loss with high temporal and spatial resolution, ensuring data integrity and consistency.

[0114] Step 2.1: In-situ sensor data acquisition

[0115] 1. Monitoring frequency: CO2 concentration, temperature and humidity: collected every 15 minutes; SOC content (near-infrared spectrum), conductivity: scanned once an hour; soil tension, weighing sensor, runoff velocity sensor: real-time monitoring, triggered recording (such as rainfall events).

[0116] 2. Data transmission: LoRa wireless transmission and low-power wide area network (LPWAN) are used to ensure long-term operation in the field.

[0117] Step 2.2: Runoff Carbon Loss Monitoring

[0118] 1. Trigger condition: Automatically start when rainfall > 5mm / h;

[0119] 2. Monitoring parameters: DOC / POC concentration (UV-fluorescence spectrum, recorded every 5 minutes); sediment mass (weighing sensor, real-time recording); runoff (calculated by flow velocity sensor).

[0120] Step 2.3: Quality Control and Anomaly Detection

[0121] The 3σ principle was used to eliminate outliers (e.g., a sudden increase in CO2 concentration may be caused by animal interference); near-infrared spectral data needed to be regularly calibrated with standard samples (e.g., humic acid).

[0122] The third stage: model calculation - dynamic quantification of carbon loss; based on multi-source data, build gaseous, solid and liquid carbon loss calculation models to achieve accurate accounting.

[0123] Step 3.1: Calculation of respiratory carbon loss (CO2 flux model)

[0124]

[0125] D represents the diffusion coefficient of CO2 in soil (affected by porosity and temperature); V represents the convection velocity (reflecting the speed of gas migration); and C represents the CO2 concentration gradient. By combining real-time temperature and humidity data, the accuracy of Fick's diffusion law and convection equations is optimized.

[0126] Step 3.2: Calculation of carbon loss due to erosion (conductivity-SOC inversion):

[0127] C erosion = M sediment × SOC content

[0128] The SOC content is inverted through the conductivity-organic carbon calibration model; M sediment is the sediment mass weighed by the runoff monitoring equipment.

[0129] Step 3.3: Calculation of leaching carbon loss (DOC dynamic integration):

[0130] C leaching = ∑(DOCi×Qi)

[0131] DOCi is the DOC concentration of the i-th sampling, and Qi is the runoff at the corresponding time point.

[0132] Step 3.4: Transverse carbon flux correction (PNAS model optimization):

[0133] The total carbon loss is integrated across three pathways: vertical stratification (respiration), lateral erosion, and leaching, and is calculated using the following formula:

[0134] C total = C respiration + C erosion + C leaching - β·C transverse

[0135] Introducing terrain slope and soil texture weight factors to reduce model overestimation problems:

[0136] β is the correction factor for lateral carbon loss (0.1-0.3, adjusted according to slope). C is the topographically driven carbon loss based on the PNAS model.

[0137] Phase 4: Dynamic Optimization – Machine Learning and Long-Term Calibration: Improve the model’s adaptability in complex environments through transfer learning and real-time feedback.

[0138] Step 4.1: Transfer learning to optimize SOC inversion:

[0139] 1. Pre-trained model: WoSIS Global Soil Database;

[0140] 2. Local fine-tuning: Combine the rod spectrum and conductivity data to optimize the inversion accuracy (error <±5%).

[0141] Step 4.2: Adaptive carbon flux prediction:

[0142] An LSTM neural network is used to integrate historical meteorological data (rainfall and temperature) to predict carbon loss trends in the next seven days.

[0143] Step 4.3: System self-diagnosis and maintenance:

[0144] The rod sensor is calibrated in situ every quarter (such as the CO2 sensor is verified with standard gas); the runoff trough filter membrane is replaced every month to prevent biofilm interference.

[0145] The embodiments of this application can be widely used in agricultural, forestry, wetland and other ecosystems to provide high-precision and verifiable soil carbon data support for the carbon neutrality strategy.

[0146] In summary, the embodiment of the present application provides a method for dynamic monitoring of soil carbon loss, which obtains soil parameters by using a multimodal sensor array and runoff monitoring equipment deployed in the target soil area, wherein the soil parameters include soil gaseous parameters, electrical conductivity, soil solid parameters and liquid carbon parameters; the carbon loss caused by soil respiration is calculated based on the soil gaseous parameters as a respiratory carbon flux; the carbon loss due to soil erosion and migration is calculated based on the electrical conductivity, soil solid parameters and a preset soil organic carbon content relationship model as an erosion carbon flux; the carbon loss due to soil leaching loss is calculated based on the liquid carbon parameters as a leaching carbon flux; the respiratory carbon flux, the erosion carbon flux and the leaching carbon flux are weighted and summed to obtain the total soil carbon loss in the target area. Gaseous, solid and liquid carbon parameters are obtained synchronously by using a multimodal sensor array and a runoff monitoring device, breaking through the limitation of traditional methods that only monitor a single carbon loss path. The carbon fluxes of respiration (gaseous), erosion migration (solid) and leaching loss (liquid) are calculated separately to clarify the quantitative relationship between different carbon loss pathways. By weighted integration of the three types of carbon fluxes, a calculation framework for total soil carbon loss covering the "gas-solid-liquid" phases is established, providing a more comprehensive carbon budget assessment method.

[0147] Based on the same technical concept, the embodiment of the present application also provides a soil carbon loss dynamic monitoring system, such as Figure 3 As shown, the system includes:

[0148] Parameter acquisition module 301, configured to acquire soil parameters through a multimodal sensing array and runoff monitoring equipment deployed in a target soil area, wherein the soil parameters include soil gas parameters, electrical conductivity, soil solid parameters, and liquid carbon parameters;

[0149] A respiratory carbon flux module 302 is used to calculate the carbon loss caused by soil respiration based on the soil gaseous parameters as the respiratory carbon flux;

[0150] an erosion carbon flux module 303 for calculating the carbon loss due to soil erosion migration as the erosion carbon flux based on the electrical conductivity, soil solid parameters, and a preset soil organic carbon content relationship model;

[0151] a leaching carbon flux module 304 for calculating the amount of carbon lost by soil leaching according to the liquid carbon parameter as the leaching carbon flux;

[0152] The total soil carbon loss module 305 is used to perform weighted summation on the respiratory carbon flux, the erosion carbon flux, and the leaching carbon flux to obtain the total soil carbon loss in the target area.

[0153] The present application also provides an electronic device corresponding to the method provided in the above embodiment. Figure 4, which shows an electronic device provided by some embodiments of the present application. The electronic device 20 may include: a processor 200, a memory 201, a bus 202, and a communication interface 203. The processor 200, the communication interface 203, and the memory 201 are connected via the bus 202. The memory 201 stores a computer program executable on the processor 200. When the processor 200 executes the computer program, it executes the method provided by any of the aforementioned embodiments of the present application.

[0154] The memory 201 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The system network element and at least one other network element are connected via at least one physical port (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0155] The bus 202 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 201 is used to store programs. The processor 200 executes the programs upon receiving execution instructions. The methods disclosed in any of the aforementioned embodiments of the present application may be applied to or implemented by the processor 200.

[0156] The processor 200 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor 200 or by software instructions. The above processor 200 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of this application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory 201 , and the processor 200 reads the information in the memory 201 and completes the steps of the above method in combination with its hardware.

[0157] The electronic device provided in the embodiments of the present application and the method provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, operated or implemented by them.

[0158] The present application also provides a computer-readable storage medium corresponding to the method provided in the above embodiment. Figure 5 The computer-readable storage medium shown is a CD 30 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the method provided by any of the aforementioned embodiments is executed.

[0159] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made by using the contents of the present invention description and drawings under the concept of the present invention, or direct / indirect application in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for dynamic monitoring of soil carbon loss, characterized in that: The method comprises: Acquiring soil parameters by deploying a multimodal sensing array and runoff monitoring equipment in a target soil area, wherein the soil parameters include soil gas parameters, electrical conductivity, soil solid parameters, and liquid carbon parameters; Calculating the carbon loss caused by soil respiration according to the soil gaseous parameters as the respiratory carbon flux; Calculating the amount of carbon lost due to soil erosion and migration as erosion carbon flux based on the electrical conductivity, soil solid parameters, and a preset soil organic carbon content relationship model; Calculating the amount of carbon loss due to soil leaching according to the liquid carbon parameters as the leaching carbon flux; The respiratory carbon flux, the erosion carbon flux and the leaching carbon flux are weighted and summed to obtain the total soil carbon loss in the target area.

2. The method according to claim 1, wherein The soil gaseous parameters include target gas concentration data; and calculating the carbon loss caused by soil respiration based on the soil gaseous parameters includes: Calculating the target gas concentration gradient in the vertical direction according to the target gas concentration data measured by the multimodal sensing array; Determine the diffusion coefficient of the target gas in the soil based on soil temperature and humidity data; Calculate the convection rate of the target gas in the soil based on soil pore characteristics and environmental parameters; Multiplying the vertical gradient of the target gas concentration by the diffusion coefficient to obtain a diffusion term flux; Multiplying the target gas concentration data by the convection rate to obtain a convection term flux; Adding the diffusion term flux and the convection term flux to obtain the total flux of the target gas per unit area; The total flux of the target gas is integrated over time and space to obtain the carbon loss caused by soil respiration.

3. The method according to claim 1, wherein Calculate the amount of carbon lost due to soil erosion and migration based on the electrical conductivity, soil solid parameters, and a preset soil organic carbon content relationship model, including: Inputting the electrical conductivity into a preset soil organic carbon content relationship model to invert and obtain the soil organic carbon content; Multiplying the soil organic carbon content by sediment mass data to obtain the carbon loss due to soil erosion and migration; the sediment mass data is obtained through runoff monitoring equipment; The spatial distribution of carbon loss due to soil erosion and migration is corrected according to the terrain slope parameter.

4. The method according to claim 1, wherein Calculate the amount of carbon loss due to soil leaching based on the liquid carbon parameters, including: When the rainfall reaches a set threshold, the runoff monitoring device is activated so that the runoff monitoring device collects dissolved organic carbon concentration data in the runoff at set time intervals; The runoff velocity is measured in real time by the runoff monitoring device, and the runoff volume is calculated according to the set time interval; Matching the dissolved organic carbon concentration data with the runoff at corresponding time points; The matched dissolved organic carbon concentration data and runoff were time-integrated to calculate the cumulative leaching carbon flux as the carbon loss due to soil leaching.

5. The method according to claim 1, wherein The total soil carbon loss in the target area is obtained by weighted summing of the respiratory carbon flux, the erosion carbon flux, and the leaching carbon flux, including: Determining weight coefficients of the respiratory carbon flux, the erosion carbon flux, and the leaching carbon flux respectively according to the soil type and environmental characteristics of the target area; Converting the respiratory carbon flux, the erosion carbon flux, and the leaching carbon flux into standard flux values of the same dimension; multiplying the standard flux values of the respiratory carbon flux, the erosion carbon flux, and the leaching carbon flux by corresponding weight coefficients to obtain a weighted flux value; All weighted flux values are spatially summed, and the spatial summation results within the set time period are temporally integrated to obtain the total soil carbon loss in the target area.

6. The method according to claim 1, wherein The deployment process of the multimodal sensor array includes: Determine the installation location of each sensor array according to the preset grid layout plan; The multi-parameter sensing array is vertically pressed into the soil to a set depth; the multi-parameter sensing array includes: a gaseous carbon monitoring layer, a spectral sensor and a liquid carbon monitoring layer; the gaseous carbon monitoring layer includes a target gas sensor for measuring soil respiration flux; the solid carbon monitoring layer includes a spectral sensor for inverting soil organic carbon content; the liquid carbon monitoring layer includes a soil moisture sensor for evaluating carbon leaching conditions.

7. The method according to claim 1, wherein The deployment process of the runoff monitoring equipment includes: Determine the installation location of the runoff trough according to the terrain slope; Install multiple layers of filtration devices in sequence; Equipped with dissolved carbon analyzer and sediment weighing device.

8. A soil carbon loss dynamic monitoring system, characterized in that: The system comprises: a parameter acquisition module, configured to acquire soil parameters, including soil gas parameters, electrical conductivity, soil solid parameters, and liquid carbon parameters, through a multimodal sensing array and runoff monitoring equipment deployed in a target soil area; a respiratory carbon flux module, configured to calculate the carbon loss caused by soil respiration based on the soil gaseous parameters as the respiratory carbon flux; an erosion carbon flux module, configured to calculate the amount of carbon lost by soil erosion migration as the erosion carbon flux based on the electrical conductivity, soil solid parameters, and a preset soil organic carbon content relationship model; a leaching carbon flux module, configured to calculate the amount of carbon lost by soil leaching according to the liquid carbon parameter as the leaching carbon flux; The total soil carbon loss module is used to perform weighted summation on the respiratory carbon flux, the erosion carbon flux and the leaching carbon flux to obtain the total soil carbon loss in the target area.

9. An electronic device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Computer-readable instructions are stored thereon, and the computer-readable instructions can be executed by a processor to implement the method according to any one of claims 1 to 7.

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