Soil-plant carbon element tracking method and system based on isotope labeling

Abstract

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.

Description

Technical Field

[0001] This invention relates to the field of in-situ detection technology of soil carbon cycle, and in particular to a method and system for tracking soil-plant carbon elements based on isotope labeling. Background Technology

[0002] Isotope-labeled carbon tracking technology is an important means of studying the carbon cycle process in paddy fields. Existing technologies introduce carbon-13 labeled compounds into soil or plant systems and combine them with mass spectrometry or spectral analysis to detect the migration and transformation of carbon at the plant-soil interface. Conventional operations usually involve collecting mixed soil samples from the rhizosphere for batch determination, or indirectly assessing carbon allocation pathways through in vitro culture experiments.

[0003] However, due to the spatial heterogeneity of redox microregions in the rice rhizosphere, carbon exhibits drastically different migration behaviors in the root surface oxidative layer and the adjacent anaerobic layer. Because it is impossible to distinguish the carbon migration dynamics of different redox sites at the millimeter scale at the root-soil interface in situ, the carbon flux in the active area of ​​root tip exudates, the enriched area of ​​root microorganisms, and the mineral-bound area are mixed in the calculation, resulting in substantial misjudgment of the rhizosphere carbon source-sink pathway. This makes it difficult for isotope labeling data to truly reflect the migration mechanism of carbon at the micro-interface, thus restricting the accuracy of paddy field carbon cycle research. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a method and system for tracking soil-plant carbon elements based on isotope labeling.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: This invention provides the following technical solution: Soil-plant carbon tracking methods based on isotope labeling include: S1. Deploy a spatially encoded microprobe array at the interface between rice roots and soil in paddy fields. S2. Apply carbon-13 labeled compounds to rice plants and simultaneously start a spatially encoded microprobe array to continuously acquire the redox potential and carbon isotope abundance values ​​of grid nodes. S3. Based on the continuously acquired redox potential values, analyze the diurnal oscillation phase and delineate the spatial boundary between the root surface oxide layer and the adjacent anaerobic layer according to the significant phase difference abrupt change position between adjacent grid nodes. S4. In-situ Raman spectroscopy was used to detect the chemical bond vibration peak shifts of carbon-13 labeled compounds in the spatial boundary transition region. Based on the migration amount from the carbonyl bond characteristic peak to the carbon-carbon bond characteristic peak, the critical position of carbon valence state transition was determined to correct the spatial boundary. S5. Based on the corrected spatial boundary, the carbon isotope abundance values ​​of the grid nodes are partitioned and clustered to identify the hotspot boundaries of multiple carbon migration functional regions. S6. Calculate the carbon isotope flux gradient within each hotspot boundary to generate a spatial distribution map of carbon migration at the root-soil interface.

[0006] Furthermore, a spatially coded microprobe array is deployed at the interface between rice roots and soil in paddy fields, including: In paddy fields, the transition zone from the root tip exudate release area to the mineral-bound area is identified at the rice root system-soil interface. A spatially encoded microprobe array is deployed along the transition zone. The spatially encoded microprobe array is formed by multiple microprobe nodes arranged according to a preset two-dimensional grid coordinate system. Each microprobe node integrates a redox potential sensor and a carbon isotope sampling unit.

[0007] Furthermore, the redox potential sensor is realized through a potential difference detection component between a platinum electrode and a reference electrode; The carbon isotope sampling unit is achieved by encapsulating the cavity with a semi-permeable membrane and using a carbon-13 labeled compound specific adsorption material inside the cavity.

[0008] Furthermore, carbon-13 labeled compounds were applied to rice plants, and a spatially encoded microprobe array was simultaneously activated to continuously acquire redox potential values ​​and carbon isotope abundance values ​​at grid nodes, including: Carbon-13 labeled compounds were applied by injection into the stems of rice plants; The power supply module of the spatially encoded microprobe array is activated while applying the carbon-13 labeled compound; The redox potential value is continuously acquired by the redox potential sensor of the microprobe node, wherein the redox potential sensor is realized by the potential difference detection component between the platinum electrode and the reference electrode. Carbon isotope abundance values ​​are continuously captured by the carbon isotope sampling unit of the microprobe node, wherein the carbon isotope sampling unit is achieved by encapsulating the cavity with a semi-permeable membrane and a carbon-13 labeled compound specific adsorption material in the cavity. The redox potential and carbon isotope abundance values ​​are recorded synchronously at preset time intervals, and the data is associated with the two-dimensional coordinates of the grid nodes.

[0009] Furthermore, based on the continuously acquired redox potential values, the diurnal oscillation phase is analyzed. According to the locations of significant phase difference abrupt changes between adjacent grid nodes, the spatial boundary between the root surface oxide layer and the adjacent anaerobic layer is delineated, including: The diurnal rhythm signal dominated by rice root respiration was isolated from continuously acquired redox potential values; The stable phase state of each grid node in the diurnal rhythm signal is extracted by a lock-in amplifier circuit; Based on the stable phase state, the phase state differences of adjacent grid nodes are compared sequentially along the natural growth direction of rice roots. When the phase state difference between adjacent grid nodes reaches or exceeds the critical threshold, it is identified as a location of abrupt change at the redox interface. The locations of abrupt changes at the redox interface are sequentially connected to form a continuous spatial boundary line; The root surface oxidized layer region and the adjacent anaerobic layer region are divided according to the spatial boundary line.

[0010] Furthermore, in-situ Raman spectroscopy was used to detect the peak shifts of chemical bond vibrations in the carbon-13 labeled compound within the spatial boundary transition region. Based on the migration amount from the carbonyl bond characteristic peak to the carbon-carbon bond characteristic peak, the critical position of the carbon valence state transition was determined to correct the spatial boundary, including: Select detection points within the spatial boundary transition zone, with the detection points covering the boundary area between the root surface oxide layer and the adjacent anaerobic layer; In-situ scanning of the carbon-13 labeled compound within the detection point was performed using a Raman spectrometer to obtain the chemical bond vibration spectrum. Identify the wave values ​​of characteristic peak positions of carbonyl bonds and carbon-carbon bonds in the vibrational spectra of chemical bonds; Calculate the wavenumber shift from the carbonyl bond characteristic peak to the carbon-carbon bond characteristic peak; When the wavenumber shift exceeds the preset critical shift threshold, the corresponding detection point is marked as the critical position of carbon valence state transition; The boundary line of the spatial boundary transition zone is corrected based on the spatial coordinates of the critical position.

[0011] Furthermore, the spatial boundary transition zone is determined in the following way: The spatial boundary between the root surface oxide layer and the adjacent anaerobic layer is used as the central reference. Extend a first preset distance toward the root surface oxide layer and extend a second preset distance toward the adjacent anaerobic layer; The strip-shaped area within the first preset distance and the second preset distance is defined as the spatial boundary transition zone.

[0012] Furthermore, based on the corrected spatial boundaries, the carbon isotope abundance values ​​of the grid nodes are partitioned and clustered to identify hotspot boundaries of multiple carbon migration functional regions, including: The spatial domain is divided based on the corrected spatial boundary. Similarity clustering analysis was performed on the carbon isotope abundance values ​​of grid nodes within the spatial domain; Based on the clustering results, regions with high carbon isotope abundance values ​​were identified; these regions were then labeled as areas of active root tip exudates, microbial enrichment, and mineral binding. Extract the spatial outline boundaries of the active zone, microbial enrichment zone, and mineral-bound zone of root tip exudate; The spatial contour boundary is defined as the hotspot boundary of the carbon migration functional zone.

[0013] Furthermore, the carbon isotope flux gradient within each hotspot boundary is calculated to generate a spatial distribution map of carbon migration at the root-soil interface, including: Within the hotspot boundaries of the root tip exudate active zone, microbial enrichment zone, and mineral binding zone included in the carbon migration functional zone, the rate of change of carbon isotope abundance values ​​in the spatial direction of the grid nodes is calculated. The migration direction and intensity of carbon isotope flux in the active zone of root tip exudates, the microbial enrichment zone and the mineral-bound zone were determined based on the rate of change. The flux intensity of the active zone of root tip exudates, the microbial enrichment zone and the mineral-bound zone is mapped as a gradient color level; Construct a carbon isotope flux gradient distribution matrix based on the spatial coordinates of grid nodes; By superimposing the gradient color levels with the carbon isotope flux gradient distribution matrix, a spatial distribution map of carbon migration at the root-soil interface is generated.

[0014] On the other hand, the present invention provides a soil-plant carbon tracking system based on isotope labeling, comprising: The probe deployment module is used to deploy a spatially encoded microprobe array at the interface between rice roots and soil in paddy fields. The data acquisition module is used to apply carbon-13 labeled compounds to rice plants, simultaneously activate the spatially encoded microprobe array, and continuously acquire the redox potential values ​​and carbon isotope abundance values ​​of grid nodes. The boundary delineation module is used to analyze the diurnal oscillation phase based on continuously acquired redox potential values, and delineate the spatial boundary between the root surface oxide layer and the adjacent anaerobic layer according to the significant phase difference abrupt change positions between adjacent grid nodes. The boundary correction module is used to detect the chemical bond vibration peak shifts of carbon-13 labeled compounds in the spatial boundary transition region using in-situ Raman spectroscopy. Based on the amount of migration from the carbonyl bond characteristic peak to the carbon-carbon bond characteristic peak, the critical position of carbon valence state transition is determined to correct the spatial boundary. The clustering identification module is used to perform partitioning and clustering of the carbon isotope abundance values ​​of grid nodes based on the corrected spatial boundaries, and to identify the hotspot boundaries of multiple carbon migration functional regions. The map generation module is used to calculate the carbon isotope flux gradient within each hotspot boundary and generate a spatial distribution map of carbon migration at the root-soil interface.

[0015] The beneficial effects of this invention are: 1. By synergistically combining spatially encoded microprobe arrays with carbon-13 labeling, the redox potential oscillation phase and carbon isotope abundance dynamics at the rice root-soil interface in paddy fields are captured in situ at the millimeter scale. Specifically, the boundary of the root surface oxide layer is located based on the abrupt change in the diurnal oscillation phase difference, and the transition zone from carbonyl bonds to carbon-carbon bonds is corrected by Raman spectroscopy. This overcomes the boundary ambiguity limitations caused by traditional mixed sampling, improves the spatial delineation accuracy of the rhizosphere oxide layer and anaerobic layer, and avoids miscalculation of carbon flux in heterogeneous micro-regions.

[0016] 2. By utilizing zone clustering and flux gradient mapping guided by calibration boundaries, independent carbon flux quantification is achieved in the active area of ​​root tip exudates, the microbial enrichment area, and the mineral binding area. By identifying the migration direction and intensity of carbon isotope flux within the hotspot boundaries of functional zones, the spatial differentiation patterns of three pathways—active migration driven by root tip exudates, transformation migration dominated by microbial metabolism, and fixed migration controlled by mineral adsorption—are clarified. The analytical capability overcomes the static limitations of in vitro experiments and provides real in-situ process parameters for paddy field carbon cycle models. Attached Figure Description

[0017] Figure 1 This is a flowchart of the soil-plant carbon tracking method based on isotope labeling of the present invention; Figure 2 This is a schematic diagram of the structure of the soil-plant carbon tracking system based on isotope labeling of the present invention. Detailed Implementation

[0018] 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.

[0019] Example 1: Figure 1 This invention provides a soil-plant carbon tracking method based on isotope labeling, comprising: S1. Deploy a spatially encoded microprobe array at the interface between rice roots and soil in paddy fields. S2. Apply carbon-13 labeled compounds to rice plants and simultaneously start a spatially encoded microprobe array to continuously acquire the redox potential and carbon isotope abundance values ​​of grid nodes. S3. Based on the continuously acquired redox potential values, analyze the diurnal oscillation phase and delineate the spatial boundary between the root surface oxide layer and the adjacent anaerobic layer according to the significant phase difference abrupt change position between adjacent grid nodes. S4. In-situ Raman spectroscopy was used to detect the chemical bond vibration peak shifts of carbon-13 labeled compounds in the spatial boundary transition region. Based on the migration amount from the carbonyl bond characteristic peak to the carbon-carbon bond characteristic peak, the critical position of carbon valence state transition was determined to correct the spatial boundary. S5. Based on the corrected spatial boundary, the carbon isotope abundance values ​​of the grid nodes are partitioned and clustered to identify the hotspot boundaries of multiple carbon migration functional regions. S6. Calculate the carbon isotope flux gradient within each hotspot boundary to generate a spatial distribution map of carbon migration at the root-soil interface.

[0020] S1. Deploy a spatially encoded microprobe array at the interface between rice roots and soil in paddy fields, as follows: First, the transition zone between the root tip exudate release area and the mineral-bound area was identified using in-situ root staining. Specifically, representative rice plants were selected during the peak tillering stage, and the roots were stained using a 0.05% to 0.15% toluidine blue solution, for example, a 0.1% toluidine blue solution was continuously infused for 5 minutes. After staining, the color distribution was observed using a high-resolution rhizosphere microscopy system with magnification of 200x to 400x. In the root tip exudate release area, dark blue bands formed due to the binding of the stain with the carboxyl groups of organic acids, while in the mineral-bound area, light blue spots formed due to the coordination binding of the stain with iron and manganese oxides. The transition zone between these two areas was defined as a continuous region with a color gradient change rate greater than 50% of the chromaticity value per millimeter, and the measured width of this zone was 0.5 mm to 2 mm. The spatial coordinates of the transition zone were determined using a digital coordinate grid provided with the microscopy system, with the positioning error controlled within ±0.05 mm.

[0021] When deploying the spatially coded microprobe array along the identified transition zone, a preset two-dimensional grid coordinate system is generated based on the spatial coordinates of the transition zone. The arrangement rule of the preset two-dimensional grid coordinate system is as follows: with the center line of the transition zone as the reference axis, the row coordinate axis is set along the main growth direction of the rice root system, and the column coordinate axis is set perpendicular to the root system direction. The spatially coded microprobe array consists of multiple microprobe nodes, for example, 48 nodes are arranged in a 6-row × 8-column matrix, and the spatial position of each node strictly matches the preset two-dimensional grid coordinate system. The spacing between the row coordinate axis nodes is dynamically configured according to the rice growth stage: 1.0 mm at the tillering stage and 1.5 mm at the heading stage; the spacing between the column coordinate axis nodes is simultaneously set to 0.8 mm at the tillering stage and 1.2 mm at the heading stage. This spacing setting is based on publicly published agronomic experimental data showing that the average diameter of rice roots at the tillering stage is 1.0 ± 0.2 mm, increasing to 1.5 ± 0.3 mm at the heading stage. The implantation depth of all microprobe nodes is controlled to be 0.2 ± 0.05 mm below the root surface to ensure that the mesh completely covers the transition zone space.

[0022] Each microprobe node integrates a redox potential sensor via a potential difference detection component between a platinum electrode and a reference electrode. The platinum electrode uses 99.99% pure platinum wire with a diameter of 0.1 mm; the reference electrode is a solid-state Ag / AgCl electrode, with a fixed center-to-center distance of 0.5 mm. The potential difference detection component includes a gain-adjustable signal amplification circuit and an analog-to-digital converter module. The signal amplification circuit gain is set from 80 to 120 times, for example, 100 times; the analog-to-digital converter module has a 16-bit sampling accuracy and a range covering -500 mV to +500 mV. When the platinum electrode contacts the soil solution, the potential difference between the working electrode and the reference electrode is amplified and converted to output a redox potential value with a resolution of 0.1 mV.

[0023] The carbon isotope sampling unit utilizes a semi-permeable membrane-encapsulated cavity and a carbon-13 labeled compound-specific adsorbent material within the cavity. The cavity is injection-molded from polycarbonate, with its outer wall encapsulated by a semi-permeable membrane having an average pore size of 0.2 micrometers. This membrane allows organic molecules with a molecular weight less than 1000 Daltons to pass through. The cavity is filled with phenylboronic acid-modified silica gel as the adsorbent material. The gel is prepared by immersing silica microspheres with a particle size of 100 to 200 nanometers in an ethanol solution containing 3-aminopropyltriethoxysilane for 2 hours, followed by coupling with 4-carboxyphenylboronic acid at pH 8.5 for 12 hours. The resulting material exhibits an adsorption capacity of 0.12 to 0.18 micromoles of carbon-13 per milligram of gel, for example, 0.15 micromoles of carbon-13.

[0024] The dynamic configuration of the distance between microprobe nodes is implemented based on the physiological characteristics of rice roots: during the tillering stage, the row spacing is set to 0.8 mm to 1.2 mm, for example, 1.0 mm; during the heading stage, the row spacing increases to 1.2 mm to 1.8 mm, for example, 1.5 mm. The column spacing is simultaneously set at 0.8 times the row spacing. This configuration is mechanically adjusted through a retractable probe bracket. The stepper motor built into the bracket drives the node displacement according to preset growth stage parameters, with a displacement accuracy of ±0.01 mm. The method for verifying the integrity of grid coverage is as follows: after the layout is completed, a methylene blue solution with a concentration of 0.005 to 0.015 mol / L is injected into the transition zone, for example, 0.01 mol / L. The coverage rate is confirmed to be greater than 98% by the spatial overlap between the colored area and the grid node.

[0025] S2. Apply carbon-13 labeled compounds to rice plants, simultaneously activate a spatially encoded microprobe array, and continuously acquire redox potential values ​​and carbon isotope abundance values ​​of grid nodes. The specific implementation is as follows: First, the carbon-13 labeled compound is applied via stem injection into rice plants. The specific procedure is as follows: Select an injection point 5 to 10 cm above the ground at the base of the rice stem, for example, 8 cm. Using a micro-syringe, inject a carbon-13 labeled glucose solution with a concentration ranging from 8 mg / mL to 12 mg / mL, for example, 10 mg / mL, at a flow rate of 0.1 mL / min to 0.3 mL / min. The injection volume is dynamically adjusted based on the rice leaf area index (LAI): 0.4 mL is injected when the LAI is 3, and the injection volume increases by 0.1 mL for every 1 increase in LAI. Use a needle with a diameter of 0.3 mm to 0.5 mm, for example, a 0.4 mm diameter needle. Control the injection angle to be between 30 and 45 degrees with the stem axis, for example, a 40-degree angle. After injection, seal the puncture site with medical-grade silicone, allowing the seal to solidify for 20 to 40 seconds. The carbon-13 labeled compound used was glucose with a carbon-13 labeling rate greater than 98% and a molecular weight of 180 Daltons. The osmotic pressure of its aqueous solution was adjusted to 300 mmol / L to be isotonic with rice juice to avoid cell damage.

[0026] Simultaneously with the injection of the carbon-13 labeled compound, the power supply module of the spatially encoded microprobe array is activated. The power supply module consists of a lithium thionyl chloride battery pack and a voltage regulator circuit. Activation is achieved by sending a wireless start signal to the receiving unit of each microprobe node. The voltage regulator circuit outputs a 3.3V DC voltage, with voltage fluctuations controlled within ±0.05V. The power supply response time is within 100 milliseconds after injection initiation, powering on all nodes. Voltage stability is achieved through feedback regulation: when the output voltage deviates from 3.3V by more than 0.1V, the pulse width modulation duty cycle is automatically adjusted for compensation.

[0027] The redox potential (RPP) value is continuously acquired using a redox potential sensor at a microprobe node. The sensor operates as follows: a 0.1 mm diameter platinum electrode contacts the soil solution, forming a detection loop with an Ag / AgCl reference electrode 0.5 mm away. The potential difference detection component acquires the potential difference signal at a frequency of 5 to 20 times per second (e.g., 10 times per second), which is then amplified by a signal amplification circuit with a gain range of 80 to 120 times (e.g., 100x), and finally output as the RRP value via a 16-bit analog-to-digital converter. Continuous acquisition covers the period from 10 minutes before injection to 48 hours after injection. When the detected potential value exceeds the -500 mV to +500 mV range, a protection mode is automatically switched.

[0028] Carbon isotope abundance values ​​are continuously captured using a carbon isotope sampling unit with microprobe nodes. The sampling unit operates as follows: carbon-13 labeled compounds in the soil solution enter the chamber through a 0.2-micron pore size semi-permeable membrane and are specifically adsorbed by phenylboronic acid-modified silica gel. The adsorption process follows the principle of chemical equilibrium: each milligram of gel adsorbs 0.12 to 0.18 micromoles of carbon-13 at 25 degrees Celsius, for example, 0.15 micromoles. Abundance values ​​are detected using thermal desorption-gas chromatography: the adsorbate is thermally desorbed every 4 to 6 minutes at a temperature of 190 to 210 degrees Celsius, for example, 200 degrees Celsius; after separation by a chromatographic column, the peak area ratio of carbon-13 to carbon-12 is measured using a thermal conductivity detector. The carbon isotope abundance value is calculated using the formula: Abundance value = (carbon-13 peak area ÷ carbon-12 peak area) × 1000‰, with a measurement accuracy of 0.5‰.

[0029] Redox potential (RP) values ​​and carbon isotope abundance (CBE) values ​​are recorded synchronously at preset time intervals. The preset time intervals employ a dynamic adjustment strategy: 20 to 40 seconds (e.g., 30 seconds) are set within 0 to 1 hour post-injection; 4 to 6 minutes (e.g., 5 minutes) are set within 1 to 24 hours; and 10 to 20 minutes (e.g., 15 minutes) are set within 24 to 48 hours. Time synchronization is achieved by receiving GPS second pulse signals; all microprobe nodes trigger data recording on the rising edge of the second pulse. The recorded data packet contains four parts of information: a UTC-formatted timestamp, a spatially encoded string, an ROD value (in millivolts), and a CBE value (in parts per thousand).

[0030] The recorded data is associated with the two-dimensional coordinates of the grid nodes. The association method is as follows: a mapping table between spatial codes and two-dimensional coordinates is established, with each spatial code string corresponding to a unique position in a preset two-dimensional grid coordinate system. The coordinate system has the center of the transition zone as the origin (0,0), with the row coordinate axis increasing in millimeters along the root growth direction and the column coordinate axis increasing in millimeters perpendicular to the root direction. Data storage adopts a three-dimensional matrix structure: the first dimension is the time axis (Unix timestamp), the second dimension is the row coordinate value (floating-point number), and the third dimension is the column coordinate value (floating-point number). When the spatial code of a data packet is missing, it is filled by interpolation using the average coordinates of the three nearest nodes.

[0031] S3. Based on the continuously acquired redox potential values, analyze the diurnal oscillation phase. According to the significant phase difference abrupt change positions between adjacent grid nodes, delineate the spatial boundary between the root surface oxide layer and the adjacent anaerobic layer. The specific implementation is as follows: First, the diurnal rhythm signal dominated by rice root respiration was separated from continuously acquired redox potential (RP) values. Specifically, the time series of RRP values ​​from 24 to 48 hours after injection of the carbon-13 labeled compound was obtained, and the signal components in the frequency range of 0.8 Hz to 1.2 Hz were extracted using a fourth-order Butterworth bandpass filter. This band setting is based on the physiological cycle characteristics of rice root respiration: root aerobic respiration leads to periodic fluctuations in RRP, with a typical cycle of 20 to 30 hours, corresponding to frequencies of 0.8 Hz to 1.2 Hz. During filtering, the stopband attenuation was set to be greater than 40 dB, and the passband ripple to be less than 1 dB. The separated signal must meet the energy proportion condition: the power spectrum integral value in the 0.8 Hz to 1.2 Hz frequency band must account for more than 60% of the total power in the entire frequency band, for example, reaching 65%, to be considered a valid diurnal rhythm signal.

[0032] The stable phase state of each grid node in the circadian rhythm signal is extracted using a lock-in amplifier (LIA) circuit. The LIA circuit employs a quadrature demodulation structure: a 1.0 Hz standard square wave is used as the reference signal, and in-phase and quadrature multiplication operations are performed with the input signal. The two product signals are passed through a low-pass filter with a cutoff frequency of 0.1 Hz, outputting DC components I (in-phase component) and Q (quadrature component). The phase state value is obtained by calculating the arctangent function: the phase state value equals the arctangent value of the quotient of Q divided by I, expressed in degrees. To eliminate the influence of instantaneous fluctuations, phase data for 10 complete respiratory cycles (approximately 240 hours) are continuously collected, and the arithmetic mean is taken as the final stable phase state value, with an output range covering 0 to 360 degrees and an accuracy of 0.5 degrees.

[0033] Based on stable phase state values, the phase state differences of adjacent grid nodes are compared sequentially along the natural growth direction of rice roots. The natural growth direction of rice roots is defined as the row coordinate axis direction in a preset two-dimensional grid coordinate system. The comparison order starts from the minimum row coordinate value and proceeds row by row towards the maximum row coordinate value. The phase state difference is calculated as follows: the absolute difference between the stable phase state values ​​of two adjacent nodes is calculated. When this difference is greater than 180 degrees, 360 degrees is subtracted from this difference to obtain the actual phase state difference value. For example, if the phase state value of node G-03-05 is 355 degrees and that of node G-03-06 is 5 degrees, the absolute difference is 350 degrees, and the corrected phase state difference value is 10 degrees.

[0034] When the phase state difference between adjacent grid nodes reaches or exceeds a critical threshold, it is identified as a redox interface abrupt change location. The critical threshold is set within a range of 90 degrees ± 10 degrees, for example, 95 degrees. This threshold is determined based on the electrochemical properties of the rhizosphere interface: the difference in electron migration rate between the root surface oxide layer and the adjacent anaerobic layer leads to a phase difference abrupt change, and measured data show that the phase difference at the abrupt change point is greater than 80 degrees. The identification logic is as follows: in the increasing direction of the row coordinates, if the phase state difference between the current node and the previous node is greater than or equal to the critical threshold for the first time, the coordinates of the current node are recorded as the abrupt change location. For example, at row coordinate 3.2 mm, if the phase state difference jumps from 70 degrees to 105 degrees, then the coordinate point (3.2, Y) is marked as the abrupt change location.

[0035] The identified redox interface abrupt change locations are sequentially connected to form a continuous spatial boundary line. The connection method is as follows: the coordinates of all abrupt change locations are arranged in ascending order of row coordinates, and a piecewise cubic polynomial interpolation algorithm is used to generate a smooth curve. The interpolation process requires that the curve monotonically increases between adjacent points, with a rate of change of curvature of less than 0.5 degrees per millimeter. The spatial boundary line is ultimately represented as an ordered sequence of coordinate points, with the distance between adjacent points not exceeding 0.1 millimeters. For example, interpolation of 8 abrupt change locations generates a boundary line containing 80 points, with the point set stored in the format [(x1,y1), (x2,y2), ..., (x80,y80)].

[0036] The root surface oxidized layer region and the adjacent anaerobic layer region are divided according to the spatial boundary line. The division rule is as follows: using the spatial boundary line as a reference, the coordinates of the grid nodes are substituted into the spatial topology discrimination function to calculate the shortest distance d from the node to the boundary line and determine the orientation of the node relative to the boundary line. When the node is located on the boundary line closer to the negative normal side of the root system, it is marked as the root surface oxidized layer region; when the node is located on the boundary line farther from the root system on the positive normal side, it is marked as the adjacent anaerobic layer region. Finally, the region classification labels of all grid nodes are output, and the label data is mapped to the preset two-dimensional grid coordinates.

[0037] S4. In-situ Raman spectroscopy is used to detect the shifts of chemical bond vibration peaks in the transition region of carbon-13 labeled compounds within the spatial boundary. Based on the migration amount from the carbonyl bond characteristic peak to the carbon-carbon bond characteristic peak, the critical position of carbon valence state transition is determined to correct the spatial boundary. The specific implementation is as follows: First, the spatial extent of the transition zone is determined. Using the spatial boundary line between the generated root surface oxidized layer and the adjacent anaerobic layer as the central baseline, a first predetermined distance is extended towards the root surface oxidized layer, and a second predetermined distance is extended towards the adjacent anaerobic layer. The first predetermined distance is determined by the thickness of the rice root oxidized layer: 0.3 to 0.5 times the root diameter is used as the extension value. For example, if the root diameter is 1.0 mm during the tillering stage, the first predetermined distance is set to 0.4 mm. The second predetermined distance is determined by the chemical gradient of the anaerobic layer: 0.8 to 1.2 times the distance to the inflection point of the redox potential change rate is used. For example, if the maximum potential change rate is 0.25 mm from the boundary, the second predetermined distance is set to 0.3 mm. The band-shaped area enclosed by the two extension distances is defined as the spatial boundary transition zone, with a total width of 0.5 mm to 0.9 mm. The setting of the extension distance is based on the characteristics of the rhizosphere chemical gradient: the active electron transfer on the root surface oxidized layer side leads to drastic chemical changes, requiring a larger coverage area; the reaction on the anaerobic layer side is slower, requiring a smaller coverage area. The geometry of the spatial boundary transition zone is represented by a vector polygon, with vertex coordinates generated by sampling at equal intervals from the central baseline, with a sampling interval of 0.05 mm.

[0038] Detection points were selected within the spatial boundary transition zone, covering the interface between the root surface oxide layer and the adjacent anaerobic layer. The selection method was as follows: along the normal direction of the spatial boundary line, detection grids were laid out in a strip-shaped area at 0.1 mm intervals. For example, on a 10 mm long boundary segment, 5 columns were placed towards the oxide layer (covering a first preset distance of 0.4 mm) and 3 columns towards the anaerobic layer (covering a second preset distance of 0.3 mm) with a step size of 0.1 mm, for a total of 80 detection points. The spatial coordinates of the detection points were determined using a laser interferometric positioning system with a positioning accuracy of ±0.005 mm. The selection density was adjusted based on the diffusion kinetics of the carbon-13 labeled compound: a diffusion coefficient greater than 1 × 10⁻⁻⁻⁶. 6 When the density is 1 square centimeters per second, the dot spacing is reduced to 0.05 millimeters; when it is less than this value, the spacing is maintained at 0.1 millimeters.

[0039] In-situ scanning of the carbon-13 labeled compound at each detection point was performed using Raman spectroscopy. A 785 nm wavelength laser light source was used, with a power range of 10 mW to 50 mW, e.g., 20 mW; the spectral acquisition range was 400 wavenumbers to 1800 wavenumbers; and the integration time was 5 to 10 seconds, e.g., 8 seconds. The probe tip was kept at a 0.5 mm vertical distance from the soil interface, and three-dimensional precise positioning was achieved using a piezoelectric ceramic micro-motion platform. Three spectral data were collected for each detection point, and the arithmetic mean was taken after removing outliers with a dispersion value greater than 5%. The scanning environment was controlled as follows: temperature 25 ± 1 degrees Celsius, relative humidity 80% ± 3%, and argon atmosphere protection to prevent oxidation interference.

[0040] The wavenumbers of characteristic peaks of carbonyl and carbon-carbon bonds in chemical bond vibrational spectra were identified. The carbonyl bond characteristic peak was identified in the range of 1630 to 1680 wavenumbers, with the following criteria: peak intensity greater than 10 times the baseline noise and half-width at half-maximum (WHM) less than 25 wavenumbers. The carbon-carbon bond characteristic peak was identified in the range of 1080 to 1120 wavenumbers, with the following criteria: peak intensity greater than 8 times the noise and half-width at half-maximum (WHM) less than 20 wavenumbers. Peak position determination was performed using Lorentz curve fitting: using spectral data as input, an iterative optimization algorithm was used to fit a function curve, and the peak position was taken as the characteristic peak wavenumber, with the fitting residual controlled within 1 wavenumber. For example, the fitted peak number was 1655.3 for the carbonyl bond and 1105.7 for the carbon-carbon bond.

[0041] Calculate the wavenumber shift from the carbonyl bond characteristic peak to the carbon-carbon bond characteristic peak. The wavenumber shift is defined as the absolute difference between the wavenumbers of the carbonyl bond characteristic peak and the carbon-carbon bond characteristic peak. The calculation process is as follows: read the wavenumbers of the two characteristic peaks from the stored spectral data table, and take the absolute value of the difference as the wavenumber shift. For example, if the carbonyl bond characteristic peak has a wavenumber of 1655.3 and the carbon-carbon bond characteristic peak has a wavenumber of 1105.7, then the wavenumber shift = |1655.3 - 1105.7| = 549.6 wavenumbers. The calculation result is stored in the detection point attribute table with one decimal place.

[0042] When the wavenumber migration exceeds a preset critical migration threshold, the corresponding detection point is marked as a critical position for carbon valence state transition. The critical migration threshold is set in the range of 500 to 550 wavenumbers, for example, 520 wavenumbers. This threshold is determined based on the theory of carbon valence state transformation: when carbonyl carbon (valence state +2) transitions to alkyl carbon (valence state -1), the theoretical value of the characteristic peak shift is greater than 500 wavenumbers. The marking logic is as follows: if the wavenumber migration of a detection point reaches or exceeds the critical migration threshold for the first time, then the coordinates of that point are recorded as a critical position. For example, if a migration of 535.2 wavenumbers is detected at coordinates (2.15, 1.82), exceeding the 520 wavenumber threshold, then it is marked as a critical position.

[0043] The boundary line of the spatial boundary transition zone is corrected based on the spatial coordinates of all critical positions. The correction method is as follows: the set of critical position points is sorted according to the direction of the spatial boundary line, and a new boundary line is generated using weighted least squares. The weighting function is set to a Gaussian distribution: critical positions within 0.3 mm of the current fitted point are given high weights, and those beyond this distance have decreasing weights. The new boundary line must meet smoothness constraints: the radius of curvature is greater than 2 mm, and the rate of change of curvature is less than 0.1 per millimeter. The correction result is output as the vertex coordinates of the new spatial boundary transition zone polygon, while also recording the average offset between the old and new boundaries. For example, on a 12 cm boundary segment, the corrected boundary moves an average of 0.18 mm towards the anaerobic layer.

[0044] In step S3, the rhizosphere interface abrupt changes are identified by analyzing the diurnal oscillation phase difference of the redox potential, overcoming the limitation of traditional electrochemical detection relying on static thresholds and significantly improving the accuracy of the boundary delineation between the root surface oxide layer and the anaerobic layer. Combined with step S4, in-situ Raman spectroscopy is used to detect the critical point of carbon valence state transitions, achieving boundary correction at the chemical bond scale and solving the problem of blurred transition zones. This forms a dual verification mechanism, accurately characterizing the dynamic boundary of the rhizosphere chemical gradient and improving the accuracy of boundary localization.

[0045] S5. Based on the corrected spatial boundaries, the carbon isotope abundance values ​​of the grid nodes are partitioned and clustered to identify hotspot boundaries of multiple carbon migration functional regions. The specific implementation is as follows: First, the spatial domain is divided based on the corrected boundary line of the transition zone. Specifically, the spatial boundary line is extended 0.4 mm to 0.6 mm towards the root surface oxide layer to form the inner boundary (e.g., 0.5 mm); and extended 0.7 mm to 0.9 mm towards the adjacent anaerobic layer to form the outer boundary (e.g., 0.8 mm). The area enclosed by these two extended boundaries is defined as the target spatial domain, with a total width of 1.1 mm to 1.5 mm. The inclusion criteria for grid nodes within the spatial domain are: the distance from the node to the inner boundary is greater than or equal to 0 mm, and the distance to the outer boundary is less than or equal to 0 mm. Node coordinates and carbon isotope abundance values ​​are extracted from step S2, and data association is achieved through spatial coding. For example, a 12 square millimeter spatial domain contains 150 effective grid nodes, with a node density of 12.5 nodes per square millimeter.

[0046] Similarity clustering analysis is performed on the carbon isotope abundance values ​​of grid nodes within the spatial domain. The clustering algorithm employs density clustering, with the following steps: setting the neighborhood radius parameter to 0.15 mm to 0.25 mm (e.g., 0.2 mm); and the minimum sample size parameter to 4 to 6 nodes (e.g., 5 nodes). The similarity criterion is: when the absolute difference in carbon isotope abundance values ​​between two nodes is less than 3‰ and the spatial Euclidean distance is less than 0.2 mm, they are considered similar node pairs. The clustering iteration process includes: randomly selecting an unvisited node as the starting point; retrieving all neighboring nodes that meet the similarity criteria; if the number of neighboring nodes is greater than or equal to 5, expanding the clustering range until no new nodes are added; and outputting clusters with a number of nodes greater than or equal to 5. Each cluster is assigned a unique identifier.

[0047] High-aggregation regions of carbon isotope abundance were identified based on clustering results. High-aggregation regions needed to meet three conditions simultaneously: the average carbon isotope abundance within the cluster was greater than 15‰. The 15‰ threshold was set as follows: before carbon-13 labeling in step S2, the measured background carbon isotope abundance in the rice rhizosphere was 10‰ ± 0.8‰ (n=50). Taking 1.5 times the background value, i.e., 15‰, as the threshold ensured statistical significance (p<0.01) and effectively eliminated interference from natural abundance fluctuations. For example, in preliminary experiments, clusters with an abundance below 14‰ were verified by mass spectrometry to be unlabeled carbon background, while regions with an abundance above 15‰ showed enrichment of carbon-13 labels; the standard deviation of abundance within the cluster was less than 2‰, ensuring data consistency; and the cluster regions were spatially continuous, with the maximum node spacing less than 0.5 mm. For example, two highly clustered regions were identified: region A had an average abundance of 18.3‰ (standard deviation 1.1‰) and region B had an average abundance of 16.5‰ (standard deviation 1.6‰), while region C was excluded due to a standard deviation of 2.5‰.

[0048] High-aggregation areas were categorized into three functional zones: active root tip exudate zones, microbial enrichment zones, and mineral-bound zones. The categorization rules were based on a combination of spatial location and abundance characteristics: zones with a centroid less than 0.5 mm from the root surface and an average abundance greater than 18‰ were categorized as active root tip exudate zones; zones with a centroid located within the spatial boundary transition zone and an average abundance between 15‰ and 18‰ were categorized as microbial enrichment zones; and zones with a centroid greater than 1.0 mm from the root surface and an average abundance greater than 15‰ were categorized as mineral-bound zones. The categorized data was written to the attribute fields of the spatial database; for example, node cluster CL-02 was categorized as a "microbial enrichment zone."

[0049] The spatial contour boundaries of the active areas of root tip exudates, microbial enrichment areas, and mineral-bound areas were extracted. The extraction method involved calculating the convex hull of the node coordinate set for each functional area to generate the minimum convex polygon boundary. The convex hull algorithm steps were as follows: The node with the smallest ordinate was identified as the reference point P0; the polar angles of the remaining nodes relative to P0 were calculated; the nodes were sorted in ascending order of polar angle; and the node stack was scanned counterclockwise, removing nodes that caused concave angles. The polygon boundaries were smoothed using B-spline curve interpolation with a tension coefficient of 0.2 to 0.4, for example, a tension coefficient of 0.3. The final boundary point spacing was controlled within 0.1 mm.

[0050] The spatial outline boundary is defined as the hotspot boundary of the carbon migration functional zone. Hotspot boundary data is stored as topologically closed polygon vectors, with each polygon associated with an attribute table recording: functional zone type, average abundance value, area value, and centroid coordinates. A spatial mapping relationship is established between the hotspot boundaries and a preset two-dimensional grid coordinate system, with a mapping error of less than 0.01 mm. The final output includes a spatial distribution map containing the three types of hotspot boundaries and generates a boundary coordinate data file.

[0051] S6. Calculate the carbon isotope flux gradient within each hotspot boundary to generate a spatial distribution map of carbon migration at the root-soil interface. The specific implementation is as follows: First, within the hotspot boundaries of the root tip exudate active zone, microbial enrichment zone, and mineral-bound zone, the rate of change of carbon isotope abundance values ​​of grid nodes in the spatial direction is calculated. The specific calculation method is as follows: For any grid node in the spatial coordinate system, the data of adjacent nodes in a preset two-dimensional grid coordinate system are obtained. In the x-axis direction, the rate of change = (carbon isotope abundance value of the adjacent node to the right - carbon isotope abundance value of the adjacent node to the left) / (2 × node spacing in the x-direction). In the y-axis direction, the rate of change = (carbon isotope abundance value of the adjacent node above - carbon isotope abundance value of the adjacent node below) / (2 × node spacing in the y-direction). The node spacing is taken from the measured grid parameter values, for example, row spacing of 0.8 mm to 1.2 mm and column spacing of 0.6 mm to 1.0 mm. For boundary nodes, when adjacent nodes are missing, a one-sided difference method is used: the leftmost node uses (abundance value of the node to the right - current node abundance value) / node spacing. The calculation results are stored in thousands per millimeter, retaining two decimal places of precision.

[0052] The migration direction and intensity of carbon isotope flux in the active areas of root tip exudates, microbial enrichment areas, and mineral-bound areas are determined based on the rate of change. The migration direction is calculated using vector synthesis: the rate of change in the x-direction is taken as the horizontal component, and the rate of change in the y-direction as the vertical component; the azimuth of the synthesized vector is the migration direction. The azimuth is calculated as the arctangent of the quotient of the rate of change in the y-direction divided by the rate of change in the x-direction. The flux intensity is equal to the magnitude of the rate of change vector, calculated as the square root of the flux intensity (the square of the rate of change in the x-direction plus the square of the rate of change in the y-direction). The flux intensity is converted to micromoles per square meter per second, and the conversion factor is determined experimentally: the relationship between the abundance gradient and the actual flux is measured on a standard sample with known flux, yielding a conversion factor ranging from 0.2 to 0.3, for example, 0.25.

[0053] The flux intensity of the root tip exudate active zone, microbial enrichment zone, and mineral-bound zone is mapped to a gradient color scale. The color scale mapping rule is as follows: the flux intensity range from 0 μmol / m² / s to 2.0 μmol / m² / s is linearly divided into 256 levels, with 0 values ​​corresponding to blue (RGB:0,0,255), 2.0 values ​​corresponding to red (RGB:255,0,0), and intermediate values ​​calculated proportionally by interpolation. A differentiated coding scheme for functional zones is implemented: green triangle markers are superimposed on the color scale for the root tip exudate active zone, yellow circular markers for the microbial enrichment zone, and purple square markers for the mineral-bound zone. The marker size is 10% of the node spacing; for example, a marker diameter of 0.1 mm is used when the node spacing is 1.0 mm. The color mapping data is stored as a lookup table, with the flux intensity value as the index key.

[0054] A carbon isotope flux gradient distribution matrix is ​​constructed based on the spatial coordinates of grid nodes. The matrix construction method is as follows: the row and column indices of a predefined two-dimensional grid coordinate system are used as the matrix row and column numbers, and the matrix element values ​​are the flux intensity values ​​corresponding to the grid nodes. The matrix resolution is consistent with the grid density, for example, an 8x10 matrix. Data missing handling: when a grid node has no valid data, inverse distance weighted interpolation is used to fill in the missing data. The weight is calculated as 1 divided by the square of the distance, and the weighted average of the four nearest neighboring nodes is taken. The final matrix is ​​stored as a two-dimensional floating-point array, arranged in row-major order, and also records the coordinates of the matrix origin and the node spacing parameters.

[0055] A spatial distribution map of carbon migration at the root-soil interface is generated by overlaying gradient color levels with a carbon isotope flux gradient distribution matrix. The overlay process involves: traversing each element of the flux gradient distribution matrix and obtaining the basic color level by consulting a color lookup table based on the element value; then overlaying corresponding shape markers according to the marked functional zone type; and finally synthesizing an RGB three-channel image. The map coordinate system is completely consistent with the preset two-dimensional grid coordinate system, with a scale bar set to 1 mm corresponding to 100 pixels. Map auxiliary elements include: direction indicators (root growth direction arrows, 5 mm in length); color level legends (gradient bars from 0 to 2.0 micromoles per square meter per second); and functional zone marker legends (triangles - active root tip exudate zone, circles - microbial enrichment zone, squares - mineral-bound zone).

[0056] Example 2: Figure 2 A schematic diagram of the soil-plant carbon tracking system based on isotope labeling of the present invention is shown. The soil-plant carbon tracking system based on isotope labeling includes: The probe deployment module is used to deploy a spatially encoded microprobe array at the interface between rice roots and soil in paddy fields. The data acquisition module is used to apply carbon-13 labeled compounds to rice plants, simultaneously activate the spatially encoded microprobe array, and continuously acquire the redox potential values ​​and carbon isotope abundance values ​​of grid nodes. The boundary delineation module is used to analyze the diurnal oscillation phase based on continuously acquired redox potential values, and delineate the spatial boundary between the root surface oxide layer and the adjacent anaerobic layer according to the significant phase difference abrupt change positions between adjacent grid nodes. The boundary correction module is used to detect the chemical bond vibration peak shifts of carbon-13 labeled compounds in the spatial boundary transition region using in-situ Raman spectroscopy. Based on the amount of migration from the carbonyl bond characteristic peak to the carbon-carbon bond characteristic peak, the critical position of carbon valence state transition is determined to correct the spatial boundary. The clustering identification module is used to perform partitioning and clustering of the carbon isotope abundance values ​​of grid nodes based on the corrected spatial boundaries, and to identify the hotspot boundaries of multiple carbon migration functional regions. The map generation module is used to calculate the carbon isotope flux gradient within each hotspot boundary and generate a spatial distribution map of carbon migration at the root-soil interface.

[0057] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0058] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0059] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0060] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0061] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0062] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0063] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0064] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0065] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0066] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A soil-plant carbon tracking method based on isotope labeling, characterized in that, include: S1. Deploy a spatially encoded microprobe array at the interface between rice roots and soil in paddy fields. S2. Apply carbon-13 labeled compounds to rice plants and simultaneously start a spatially encoded microprobe array to continuously acquire the redox potential and carbon isotope abundance values ​​of grid nodes. S3. Based on the continuously acquired redox potential values, analyze the diurnal oscillation phase and delineate the spatial boundary between the root surface oxide layer and the adjacent anaerobic layer according to the significant phase difference abrupt change position between adjacent grid nodes. S4. In-situ Raman spectroscopy was used to detect the chemical bond vibration peak shifts of carbon-13 labeled compounds in the spatial boundary transition region. Based on the migration amount from the carbonyl bond characteristic peak to the carbon-carbon bond characteristic peak, the critical position of carbon valence state transition was determined to correct the spatial boundary. S5. Based on the corrected spatial boundary, the carbon isotope abundance values ​​of the grid nodes are partitioned and clustered to identify the hotspot boundaries of multiple carbon migration functional regions. S6. Calculate the carbon isotope flux gradient within each hotspot boundary to generate a spatial distribution map of carbon migration at the root-soil interface.

2. The method for soil-plant carbon tracking based on isotope labeling according to claim 1, characterized in that, A spatially encoded microprobe array was deployed at the interface between rice roots and soil in paddy fields, including: In paddy fields, the transition zone from the root tip exudate release area to the mineral-bound area is identified at the rice root system-soil interface. A spatially encoded microprobe array is deployed along the transition zone. The spatially encoded microprobe array is formed by multiple microprobe nodes arranged according to a preset two-dimensional grid coordinate system. Each microprobe node integrates a redox potential sensor and a carbon isotope sampling unit.

3. The method for soil-plant carbon tracking based on isotope labeling according to claim 2, characterized in that, The redox potential sensor is realized through a potential difference detection component between a platinum electrode and a reference electrode; The carbon isotope sampling unit is achieved by encapsulating the cavity with a semi-permeable membrane and using a carbon-13 labeled compound specific adsorption material inside the cavity.

4. The method for soil-plant carbon tracking based on isotope labeling according to claim 1, characterized in that, A carbon-13 labeled compound was applied to rice plants, and a spatially encoded microprobe array was simultaneously activated to continuously acquire redox potential values ​​and carbon isotope abundance values ​​at grid nodes, including: Carbon-13 labeled compounds were applied by injection into the stems of rice plants; The power supply module of the spatially encoded microprobe array is activated while applying the carbon-13 labeled compound; The redox potential value is continuously acquired by the redox potential sensor of the microprobe node, wherein the redox potential sensor is realized by the potential difference detection component between the platinum electrode and the reference electrode. Carbon isotope abundance values ​​are continuously captured by the carbon isotope sampling unit of the microprobe node, wherein the carbon isotope sampling unit is achieved by encapsulating the cavity with a semi-permeable membrane and a carbon-13 labeled compound specific adsorption material in the cavity. The redox potential and carbon isotope abundance values ​​are recorded synchronously at preset time intervals, and the data is associated with the two-dimensional coordinates of the grid nodes.

5. The method for soil-plant carbon tracking based on isotope labeling according to claim 1, characterized in that, Based on continuously acquired redox potential values, the diurnal oscillation phase is analyzed. According to the locations of significant phase difference abrupt changes between adjacent grid nodes, the spatial boundary between the root surface oxide layer and the adjacent anaerobic layer is delineated, including: The diurnal rhythm signal dominated by rice root respiration was isolated from continuously acquired redox potential values; The stable phase state of each grid node in the diurnal rhythm signal is extracted by a lock-in amplifier circuit; Based on the stable phase state, the phase state differences of adjacent grid nodes are compared sequentially along the natural growth direction of rice roots. When the phase state difference between adjacent grid nodes reaches or exceeds the critical threshold, it is identified as a location of abrupt change at the redox interface. The locations of abrupt changes at the redox interface are sequentially connected to form a continuous spatial boundary line; The root surface oxidized layer region and the adjacent anaerobic layer region are divided according to the spatial boundary line.

6. The method for soil-plant carbon tracking based on isotope labeling according to claim 1, characterized in that, In-situ Raman spectroscopy was used to detect the peak shifts of chemical bond vibrations in carbon-13 labeled compounds within the spatial boundary transition region. Based on the migration amount from the carbonyl bond characteristic peak to the carbon-carbon bond characteristic peak, the critical position of the carbon valence state transition was determined to correct the spatial boundary, including: Select detection points within the spatial boundary transition zone, with the detection points covering the boundary area between the root surface oxide layer and the adjacent anaerobic layer; In-situ scanning of the carbon-13 labeled compound within the detection point was performed using a Raman spectrometer to obtain the chemical bond vibration spectrum. Identify the wave values ​​of characteristic peak positions of carbonyl bonds and carbon-carbon bonds in the vibrational spectra of chemical bonds; Calculate the wavenumber shift from the carbonyl bond characteristic peak to the carbon-carbon bond characteristic peak; When the wavenumber shift exceeds the preset critical shift threshold, the corresponding detection point is marked as the critical position of carbon valence state transition; The boundary line of the spatial boundary transition zone is corrected based on the spatial coordinates of the critical position.

7. The method for soil-plant carbon tracking based on isotope labeling according to claim 6, characterized in that, The spatial boundary transition zone is determined in the following way: The spatial boundary between the root surface oxide layer and the adjacent anaerobic layer is used as the central reference. Extend a first preset distance toward the root surface oxide layer and extend a second preset distance toward the adjacent anaerobic layer; The strip-shaped area within the first preset distance and the second preset distance is defined as the spatial boundary transition zone.

8. The method for soil-plant carbon tracking based on isotope labeling according to claim 1, characterized in that, Based on the corrected spatial boundaries, the carbon isotope abundance values ​​of the grid nodes are partitioned and clustered to identify hotspot boundaries of multiple carbon migration functional regions, including: The spatial domain is divided based on the corrected spatial boundary. Similarity clustering analysis was performed on the carbon isotope abundance values ​​of grid nodes within the spatial domain; Based on the clustering results, regions with high carbon isotope abundance values ​​were identified; these regions were then labeled as areas of active root tip exudates, microbial enrichment, and mineral binding. Extract the spatial outline boundaries of the active zone, microbial enrichment zone, and mineral-bound zone of root tip exudate; The spatial contour boundary is defined as the hotspot boundary of the carbon migration functional zone.

9. The method for soil-plant carbon tracking based on isotope labeling according to claim 1, characterized in that, Calculate the carbon isotope flux gradient within each hotspot boundary to generate a spatial distribution map of carbon migration at the root-soil interface, including: Within the hotspot boundaries of the root tip exudate active zone, microbial enrichment zone, and mineral binding zone included in the carbon migration functional zone, the rate of change of carbon isotope abundance values ​​in the spatial direction of the grid nodes is calculated. The migration direction and intensity of carbon isotope flux in the active zone of root tip exudates, the microbial enrichment zone and the mineral-bound zone were determined based on the rate of change. The flux intensity of the active zone of root tip exudates, the microbial enrichment zone and the mineral-bound zone is mapped as a gradient color level; Construct a carbon isotope flux gradient distribution matrix based on the spatial coordinates of grid nodes; By superimposing the gradient color levels with the carbon isotope flux gradient distribution matrix, a spatial distribution map of carbon migration at the root-soil interface is generated.

10. A soil-plant carbon tracking system based on isotope labeling, used to implement the soil-plant carbon tracking method based on isotope labeling as described in any one of claims 1-9, characterized in that, include: The probe deployment module is used to deploy a spatially encoded microprobe array at the interface between rice roots and soil in paddy fields. The data acquisition module is used to apply carbon-13 labeled compounds to rice plants, simultaneously activate the spatially encoded microprobe array, and continuously acquire the redox potential values ​​and carbon isotope abundance values ​​of grid nodes. The boundary delineation module is used to analyze the diurnal oscillation phase based on continuously acquired redox potential values, and delineate the spatial boundary between the root surface oxide layer and the adjacent anaerobic layer according to the significant phase difference abrupt change positions between adjacent grid nodes. The boundary correction module is used to detect the chemical bond vibration peak shifts of carbon-13 labeled compounds in the spatial boundary transition region using in-situ Raman spectroscopy. Based on the amount of migration from the carbonyl bond characteristic peak to the carbon-carbon bond characteristic peak, the critical position of carbon valence state transition is determined to correct the spatial boundary. The clustering identification module is used to perform partitioning and clustering of the carbon isotope abundance values ​​of grid nodes based on the corrected spatial boundaries, and to identify the hotspot boundaries of multiple carbon migration functional regions. The map generation module is used to calculate the carbon isotope flux gradient within each hotspot boundary and generate a spatial distribution map of carbon migration at the root-soil interface.

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