A soil moisture content and bulk density in-situ monitoring system and method coupling active thermal pulse method and time series analysis method

Abstract

The application discloses a kind of coupling active heat pulse method and time series analysis method's soil moisture content and bulk density in situ monitoring method, and the soil bulk thermal capacity C S , thermal diffusivity α, thermal conductivity λ are obtained by active heat pulse probe, and the soil bulk thermal capacity C S , thermal diffusivity α, thermal conductivity λ are measured and recorded in situ continuously, corresponding time series data set is established, characteristic decomposition is carried out using time series analysis method, decomposition result is matched corresponding with the change characteristics of soil bulk density and moisture content respectively, and the inversion model of soil moisture content and bulk density is established, so as to realize only relying on soil thermal characteristic monitoring, obtain in situ monitoring result of soil bulk density and moisture content.The application realizes that only single heat pulse probe is used to the in situ continuous monitoring of soil moisture content and bulk density, expands in situ monitoring technology of soil bulk density, and provides a new idea for in situ monitoring of soil bulk density.

Description

Technical Field

[0001] This invention relates to an in-situ monitoring device and method for soil moisture content and bulk density using a coupled active thermal pulse method and a time series analysis method, belonging to the field of soil research technology. Background Technology

[0002] Soil physical properties such as water content, heat content, and bulk density are important indicators of soil quality. In recent years, with the continuous innovation and improvement of my country's farmland protection policies and the introduction of various new tillage and planting systems, there is an urgent need to understand the changing characteristics of these soil properties in order to conduct technical evaluations and optimization adjustments. Among these, soil moisture content is a crucial component of water resources and is inextricably linked to crop growth and development, as well as solute transport in the soil. Soil bulk density reflects the soil structure under natural conditions and is related to crop root growth, soil water and fertilizer retention capacity, and aeration. However, current in-situ monitoring technologies are lacking, especially for monitoring structural characteristics such as soil bulk density.

[0003] Changes in soil water, heat, and structural properties are essentially a three-phase change problem involving the soil's solid, liquid, and gas phases. The gradation, cementation, and compaction of solid particles determine soil bulk density and porosity, which in turn directly affect saturated water content, water-holding capacity, and air content. Therefore, monitoring the three phases of soil requires utilizing the differences in physical properties among the solid, liquid, and gas phases, and obtaining this information through electromagnetic, optical, high-energy particle, and thermal detection methods.

[0004] Currently, there are various methods for monitoring soil moisture, which can be categorized into point-scale and large-scale remote sensing methods based on the monitoring scale. Point-scale methods include the ring-drying method, dielectric sensor (time domain reflectometer, frequency domain reflectometer) method, neutron method, and tensiometer method, etc.; large-scale remote sensing methods mainly utilize near-Earth unmanned aerial vehicles (UAVs), high-altitude spacecraft, and satellite methods. Among these, large-scale remote sensing methods are limited by factors such as weather and reversion cycles, making it difficult to obtain high temporal resolution soil moisture data. Within the point-scale method, considering factors such as monitoring cost and ease of operation, probe-based moisture monitoring methods are the primary approach. These methods are simple to operate, have relatively low instrument costs, and can obtain high temporal resolution soil moisture monitoring results. Therefore, probe-based moisture monitoring methods are currently widely used.

[0005] However, there are relatively few methods for monitoring soil bulk density. Currently, the ring-drying method is the main approach. While this method provides accurate results, it causes significant soil disturbance and is a non-in-situ measurement method. Another method is the "thermal pulse-TDR" method, which simultaneously measures thermal and electromagnetic properties. This method obtains the soil's heat capacity through thermal pulses and combines it with soil moisture content obtained from TDR to calculate the soil bulk density. The thermal pulse-TDR method combines two monitoring methods, enabling in-situ monitoring of soil bulk density. It is evident that the development of measurement technologies for solid phase properties such as bulk density has been much slower. On one hand, soil bulk density changes much more slowly than moisture, leading many short-term studies to ignore changes in bulk density. More importantly, if soil moisture content and bulk density are considered simultaneously, it is impossible to obtain two unknown quantities simultaneously through a single monitoring result.

[0006] How to decompose target variables (water content, bulk density, etc.) from apparent observation data such as dielectric constant and heat capacity is a major problem in soil three-phase measurement research. Currently, there are two possible solutions to the problem of multiple unknowns in soil three-phase measurement: (1) Adding additional monitoring technologies. By adding other sensors, independent monitoring of two or more types of information during the three-phase change process of soil can be achieved, and soil moisture content and bulk density can be solved by data fusion. For example, thermal pulse-TDR integrates TDR and thermal pulse technology. By simultaneously measuring soil moisture content, temperature and thermal properties, the moisture content is obtained using TDR, and the soil bulk density is obtained by combining the thermal pulse process data. However, the idea of ​​integrating multi-source monitoring technologies still has some problems: the time periodicity of the data is not considered. The change period of soil bulk density is long, and it is difficult to accurately reflect the actual change of soil bulk density using only short-term data, even if two technologies are used; it increases the technical complexity and increases the monitoring cost.

[0007] (2) By utilizing the differences in the changing cycles of soil properties through dynamic time series monitoring, even based on a single monitoring technology, simultaneous monitoring of multiple variables can be achieved. During the three-phase change of soil, there are significant differences in the rate of change over time. Specifically, the gas and liquid phases of soil change rapidly, measured in hours or seconds; while under no human disturbance, the solid phase of soil changes over a longer period, measured in days or even months. Therefore, by monitoring the time series of changes in a specific characteristic of soil, such as electromagnetic, thermal, or optical properties, analysis can decompose "short-term rapid fluctuation" variables and "long-term trend" variables. That is, based on the differences in the changing cycles of water and bulk density, soil moisture content and bulk density can be obtained, such as... Figure 1 As shown.

[0008] Currently, soil thermal property measurement is the most feasible technique for time-series monitoring of soil water, heat, and structural properties in situ. This is because soil heat capacity and thermal conductivity are both additive composite functions of soil moisture content and bulk density, resulting in a well-defined time-series model. Furthermore, among the electromagnetic, optical, high-energy particle, and thermal property sensors mentioned above, temperature sensors offer the highest accuracy, the simplest structure, and the lowest cost. Additionally, thermal sensors come in various forms, facilitating data integration and assimilation with monitoring data from sensor networks, thermal infrared remote sensing, and other sources.

[0009] The thermal pulse method is a method for determining soil thermal properties. This method provides a heat source in the form of pulses by heating a resistance wire, and uses thermocouples at fixed positions on both sides of the heating wire to measure changes in soil temperature. Based on the relationship between soil temperature and time, and combined with the law of thermal conduction, the soil's thermal diffusivity, volumetric heat capacity, and thermal conductivity are calculated. The thermal pulse method can obtain in-situ soil thermal properties, which are also affected by soil bulk density, moisture content, and other factors. In practical applications, people are more concerned with changes in soil moisture content and bulk density; therefore, it is necessary to separate moisture content and bulk density from other thermal properties such as soil heat capacity and thermal conductivity.

[0010] Time series analysis is a data mining method that extracts useful information and patterns from large amounts of noisy, random time series data. Through algorithmic models, complex time series data is decomposed into certain periodic, trend, and abrupt change characteristics, thereby revealing the patterns of change in observed values ​​over time, enabling influencing factor analysis and trend prediction.

[0011] Soil thermal properties, such as heat capacity and thermal conductivity, are all affected by soil moisture content and bulk density. Under natural conditions, these two exhibit distinctly different characteristics: moisture content changes rapidly in the short term due to precipitation and irrigation, while bulk density shows a long-term creep trend over time. Soil heat capacity and thermal conductivity are both additive composite functions of bulk density and moisture content, and these two parameters are also independent to some extent; therefore, they can be decomposed using time series analysis.

[0012] The active heat source-based thermal pulse method can monitor soil thermal properties in situ, but it cannot analyze soil bulk density and moisture content. Time series analysis can analyze thermal property parameters, thereby decomposing the fluctuations, trends, residuals, and other characteristics of thermal properties. Combining these two techniques, the active heat source-based thermal pulse method is used to obtain a time series dataset of soil thermal properties. Time series analysis is then used for feature decomposition, and the decomposition results are matched with the variation characteristics of soil bulk density and moisture content. This allows for in-situ monitoring of soil bulk density and moisture content solely based on monitoring soil thermal properties. Due to advancements in monitoring technology and the rise of big data analytics, there are currently few studies that use only single thermal property data to solve for soil bulk density and moisture content. Summary of the Invention

[0013] The purpose of this invention is to provide an in-situ monitoring system and method for soil moisture content and bulk density that combines an emerging big data algorithm with an active thermal pulse method and a time series analysis method.

[0014] The technical solution adopted in this invention is as follows: A soil moisture and bulk density in-situ monitoring system coupled with active thermal pulse method and time series analysis method, comprising: The soil thermal properties monitoring system consists of an active thermal pulse probe, a probe control module, a power supply system, and a support frame. The probes are buried underground in layers according to the measurement requirements. The probe control module and the power supply system are fixed on the support frame. The probe control module is used to control the heating time, heating amount, and sampling frequency of the heating wire. The power supply system supplies power to the probes and the control module. A remote data transmission system is used to transmit soil thermal properties measured by thermal pulse probes to a data processing center; The data processing center is used to receive and process the data obtained from the soil thermal properties monitoring system, and to calculate and output the soil moisture content and bulk density information of the observation points.

[0015] The active thermal pulse probe includes a heating resistance wire and thermocouples located on both sides of the heating resistance wire. The distance between the two thermocouples and the heating resistance wire is equal. The two thermocouples are set up to simultaneously measure the change in soil temperature on both sides of the heating wire. According to formulas (2)-(4), the soil thermal diffusivity, heat capacity and thermal conductivity on both sides of the heating wire are obtained respectively. The average value of the results is taken as the result of the thermal pulse measurement at that point to ensure data stability.

[0016] This invention also discloses a method for monitoring soil moisture content and bulk density by coupling an active thermal pulse method with time series analysis. The method is implemented using the aforementioned active thermal pulse in-situ soil moisture content and bulk density monitoring system, and its steps include: (1) Establish a soil thermal characteristic parameter monitoring system based on active thermal pulse. The thermal pulse probe is buried in the soil to be tested, and the above-ground part is equipped with a probe control module and a power supply system. The probe control module is used to control the probe heating time, heating amount and sampling frequency, and the power supply system ensures that the probe can work stably.

[0017] (2) The soil thermal diffusivity α and volumetric heat capacity C were measured using an active thermal pulse probe. S Thermal conductivity λ. According to the law of heat conduction, in an isothermal homogeneous medium, the heat pulse released by a linear heat source radiates radially in all directions. Assuming the soil under test is an isothermal homogeneous medium within a certain spatial range, the change in soil temperature at a certain point within it with respect to an event can be expressed as: (1) Where ΔT is the change in soil temperature (K); r is the vertical distance between the thermocouple and the linear heat source (m); t is time (s); t0 is the heating duration of the heat pulse (s); and α is the soil thermal diffusivity (m²). 2 ·s -1 ); It is an exponential integral; Q is the intensity of the linear heat source, defined as... q is the amount of heat (W·m) released per unit length of heating wire per unit time. -1 ), C S It is the soil volumetric heat capacity (J·cm) -3 ·K).

[0018] After the heating wire stops heating, heat is gradually transferred to the thermocouples on both sides in the soil. The thermocouples will reach a maximum temperature after heating stops, and this time is defined as t. m Taking the partial differential of t with respect to equation (1) and setting the result to zero, we obtain the expression for α: (2) Substituting (2) into (1) yields the volumetric heat capacity C. S The expression: (3) Where ΔT m It is in t m At time t0, the maximum temperature change (K) at a distance r from the heat source; q, r, and t0 are constants. Soil thermal conductivity λ (W·m) -1 ·K -1 It is the product of the thermal diffusivity and the volumetric heat capacity: (4) (3) Establish the soil thermal diffusivity α and the volumetric heat capacity C SA time-series dataset of thermal conductivity λ was developed. Using an active thermal pulse probe, long-term in-situ monitoring data of soil volumetric heat capacity, thermal diffusivity, and thermal conductivity were acquired to establish a continuous and stable time-series dataset of soil thermal characteristic parameters. The monitoring frequency ranged from 30 min / time to 1 day / time to ensure more sensitive capture of the changing patterns of soil thermal characteristic parameters and improve the accuracy of the monitoring results.

[0019] (4) Use time series analysis methods (applicable but not limited to ARIMA, STL and other algorithms) to decompose the time series dataset of soil heat capacity, thermal conductivity and thermal diffusivity, and decompose the original time series data into "trend", "season" and "residual" terms.

[0020] (5) Using the correlation between the decomposition results and soil moisture content θ, an inversion model for soil moisture content θ is established. Under natural conditions, soil moisture content changes rapidly in the short term with precipitation, irrigation, etc., and also exhibits a certain periodicity over a longer observation period (this periodicity is consistent with the precipitation cycle and irrigation cycle). Therefore, soil moisture content corresponds to the "period and residual" term in the decomposition results. By using the measured values ​​of soil moisture content at the corresponding time points and the "period and residual" term, a correlation is established to build an inversion model for soil moisture content and the decomposition results of thermal characteristic parameters.

[0021] (6) Using the decomposition results and soil bulk density ρ b The correlation between them was established to determine the soil bulk density ρ. b The inversion model shows that under natural conditions, soil bulk density first increases and then tends to stabilize after tillage. Among common soil thermal property parameters, soil heat capacity ρ... b Soil thermal conductivity λ has a relatively clear relationship with water content and bulk density, and is therefore used as the decomposition object. The principles of establishing a soil bulk density inversion model using soil heat capacity and soil thermal conductivity are explained below.

[0022] Soil heat capacity is the sum of the heat capacities of the solid, liquid, and gas phases in the soil. However, since the heat capacity of air is much smaller than that of the solid and liquid phases, its influence is usually ignored. Soil heat capacity is expressed as: (5) In the formula, C S It is the soil volumetric heat capacity (J·cm) −3 ·K −1 ); C W It is the volumetric heat capacity of water (4.18 J·cm³). −3 ·K −1 ); C b It is the mass heat capacity of the solid phase (J·g) −1 ·K−1 ); θ is the volumetric water content (cm³). 3 ·cm −3 ); ρ b Soil bulk density (g·cm³) −3 According to formula (5), it can be seen that the effects of water content and bulk density on soil heat capacity are independent and superimposed, so time series analysis can be used to decompose them.

[0023] Soil thermal conductivity has also been found to be related to soil moisture content and bulk density. (6) In the formula, λ is the thermal conductivity of the soil; dry is the thermal conductivity of the soil sample when dry; for a given soil, this is related to bulk density. a and b are soil solid phase parameters; for a given soil, a and b are constants. For a given soil, the effects of soil moisture content and bulk density on thermal conductivity are independent and additive, therefore, they can be decomposed using time series analysis.

[0024] Based on the relationship formulas (5)-(6) between bulk density, soil moisture content and soil thermal properties parameters during the bulk density variation period, and combined with the moisture content results in step (5), the soil bulk density is calculated. The measured and calculated values ​​of soil bulk density at the corresponding time points are used to establish a correlation and establish an inversion model between soil bulk density and decomposition results.

[0025] This invention uses active thermal pulse technology to obtain soil thermal capacity, thermal conductivity and other parameters to establish a time series dataset of soil thermal characteristic parameters. Combined with big data analysis methods of time series analysis, it decomposes and obtains soil moisture content and bulk density, realizing in-situ continuous monitoring of soil moisture content and bulk density using only thermal pulse probes. This expands the in-situ monitoring technology of soil bulk density and provides a new approach for in-situ monitoring of soil bulk density. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of an active thermal pulse probe.

[0027] Figure 2 This is a diagram of an in-situ monitoring system for soil moisture content and bulk density based on an active heat source.

[0028] Figure 3 The decomposition results are based on the ARIMA model.

[0029] Figure 4 To compare the heat capacity and thermal conductivity decomposition results based on the ARIMA model with the water content monitoring values, where Figure 5 a) Heat capacity - trend term - moisture content; b) Heat capacity - residual term - moisture content; c) Thermal conductivity - trend term - moisture content; d) Thermal conductivity - residual term - moisture content.

[0030] Figure 5 This represents the variation of soil bulk density over time under natural conditions.

[0031] Figure 6 This shows the changes in soil heat capacity, moisture content, and bulk density over time. Detailed Implementation

[0032] The present invention is further illustrated below by way of examples, but is not intended to limit the invention. Specific instruments or reagents used in the embodiments of the present invention are provided below. However, it should be understood that these are merely exemplary and are not intended to limit the invention. Materials of the same or similar type, model, quality, properties, or function as the reagents and instruments described below can be used to implement the present invention. Unless otherwise specified, the experimental methods used in the following examples are conventional methods. Unless otherwise specified, the materials and reagents used in the following examples are commercially available. Example

[0033] like Figure 1-2 As shown, a soil moisture and bulk density in-situ monitoring system based on active thermal pulse probe coupled with time series analysis includes: The soil thermal property monitoring system consists of an active thermal pulse probe 1, a probe control module 2, a solar power supply system 3, and a support frame 4. The probes are buried underground in layers as needed (in this embodiment, they are deployed at depths of 1, 3, 5, and 10 cm below the surface). The probe control module and the solar power supply system are fixed to the support frame. The probe control module controls the heating time, heating amount, and sampling frequency of the heating wire. The solar power supply module supplies power to the probes and the control module. Figure 1-2 As shown; A remote data transmission system is used to transmit soil thermal properties measured by thermal pulse probes to a data processing center; Data processing center 5 is used to receive and process the data obtained by the soil thermal properties monitoring system, and to calculate and output the soil moisture content and bulk density information of the observation points.

[0034] Example 2 The method for monitoring soil moisture content and bulk density based on active thermal pulse probe coupled with time series analysis includes the following steps: A. Based on the various parts of the system in Example 1, a soil moisture content and bulk density monitoring system based on active thermal pulse is constructed, including: probe control module, solar power supply system, remote data transmission system, and support frame, and thermal pulse probes are buried at 5cm, 10cm, 20cm and 40cm from the ground surface respectively.

[0035] B. Measure the soil thermal diffusivity α and volumetric heat capacity using an active thermal pulse probe.S Thermal conductivity λ. According to the law of heat conduction, in an isothermal homogeneous medium, the heat pulse released by a linear heat source radiates radially in all directions. Assuming the soil under test is an isothermal homogeneous medium within a certain spatial range, the change in soil temperature at a certain point within it with respect to an event can be expressed as: (1) Where ΔT is the change in soil temperature (K); r is the vertical distance between the thermocouple and the linear heat source (m); t is time (s); t0 is the heating duration of the heat pulse (s); and α is the soil thermal diffusivity (m²). 2 ·s -1 ); It is an exponential integral; Q is the intensity of the linear heat source, defined as... q is the amount of heat (W·m) released per unit length of heating wire per unit time. -1 ), C S It is the soil volumetric heat capacity (J·cm) -3 ·K).

[0036] After the heating wire stops heating, heat is gradually transferred to the thermocouples on both sides through the soil. The thermocouples will reach a maximum temperature after heating stops, and this time is defined as t. m Taking the partial differential of t with respect to equation (1) and setting the result to zero, we obtain the expression for α: (2) Substituting (2) into (1) yields the volumetric heat capacity C. S The expression: (3) Where ΔT m It is in t m At time t0, the maximum temperature change (K) at a distance r from the heat source; q, r, and t0 are constants. Soil thermal conductivity λ (W·m) -1 ·K -1 It is the product of the thermal diffusivity and the volumetric heat capacity: (4) C. Establish the soil thermal diffusivity α and the volumetric heat capacity C. S A time-series dataset of thermal conductivity λ was obtained. Using an active thermal pulse probe, with a monitoring frequency of 30 min / time, long-term in-situ monitoring data of soil volumetric heat capacity, thermal diffusivity, and thermal conductivity were acquired to establish a continuous and stable time-series dataset of soil thermal characteristic parameters.

[0037] D. Using time series analysis methods (applicable but not limited to ARIMA, STL, etc.), decompose the time series dataset of soil heat capacity, thermal conductivity, and thermal diffusivity. Decompose the original time series data into "trend," "season," and "residual" terms. The decomposition results of soil heat capacity and thermal conductivity are as follows: Figure 3 As shown.

[0038] E. Utilizing the correlation between the decomposition results and soil moisture content θ, an inversion model for soil moisture content θ is established. Under natural conditions, soil moisture content exhibits rapid short-term changes with precipitation and irrigation, and also shows a certain periodic pattern over a longer observation period (this pattern corresponds to the precipitation cycle and irrigation cycle). Therefore, soil moisture content corresponds to the "period and residual" term in the decomposition results. By establishing a correlation between the measured soil moisture content at corresponding time points and the "period and residual" term, an inversion model of soil moisture content and thermal characteristic parameter decomposition results is established. For example... Figure 4 As shown in a and 4b, the changes in heat capacity follow a similar trend to those in water content. When water content fluctuates significantly, heat capacity shows a clear response, and the peaks and valleys of the residual term are basically consistent with those of water content. The decomposition results of thermal conductivity are as follows... Figure 4 c, 4d, the thermal conductivity increases with increasing water content, which conforms to the change pattern in formula (6), and the residual term of thermal conductivity also responds when the water content changes abruptly. Therefore, the time series decomposition results of soil heat capacity and thermal conductivity can be used to describe the change of water content over time.

[0039] F. Utilizing the decomposition results and soil bulk density ρ b The correlation between them was established to determine the soil bulk density ρ. b The inversion model. Under natural conditions, soil bulk density first increases and then tends to stabilize after tillage, such as... Figure 5 As shown. Among common soil thermal property parameters, soil heat capacity and thermal conductivity have relatively clear relationships with water content and bulk density, and are therefore included as decomposition targets.

[0040] Soil heat capacity is the sum of the heat capacities of the solid, liquid, and gas phases in the soil. However, since the heat capacity of air is much smaller than that of the solid and liquid phases, its influence is usually ignored. Soil heat capacity is expressed as: (5) In the formula, C S It is the soil volumetric heat capacity (J·cm) −3 ·K −1 ); C W It is the volumetric heat capacity of water (4.18 J·cm³). −3 ·K −1 ); C bIt is the mass heat capacity of the solid phase (J·g) −1 ·K −1 ); θ is the volumetric water content (cm³). 3 ·cm −3 ); ρ b Soil bulk density (g·cm³) −3 According to formula (5), it can be seen that the effects of water content and bulk density on soil heat capacity are independent and superimposed, so time series analysis can be used to decompose them.

[0041] Soil thermal conductivity has also been found to be related to soil moisture content and bulk density. (6) In the formula, λ is the thermal conductivity of the soil; dry is the thermal conductivity of the soil sample when dry; for a given soil, this is related to bulk density. a and b are soil solid phase parameters; for a given soil, a and b are constants. For a given soil, the effects of soil moisture content and bulk density on thermal conductivity are independent and additive, therefore, they can be decomposed using time series analysis.

[0042] In the bulk density variation segment, based on the relationship formulas (5)-(6) between bulk density, soil moisture content, and soil thermal property parameters, and combined with the moisture content results in step E, the soil bulk density is calculated. A correlation is established between the measured and calculated soil bulk density values ​​at corresponding time points to obtain the correlation between soil bulk density and decomposition results. The soil thermal capacity-moisture content-bulk density relationship is as follows: Figure 6 As shown.

Claims

1. A method for in-situ monitoring of soil moisture content and bulk density using a coupled active thermal pulse method and time series analysis, wherein the method is based on measuring soil thermal characteristic parameters such as soil heat capacity, thermal diffusivity, and thermal conductivity to solve for soil moisture content and bulk density, characterized in that... The steps include: (1) The soil thermal diffusivity α and volumetric heat capacity C were measured using an active thermal pulse probe. S Thermal conductivity λ: An active thermal pulse probe is buried in the soil, and a heating resistance wire is controlled to heat the soil for a time t0. Based on the soil temperature measured by the thermocouple, the volumetric heat capacity C of the soil is calculated. S Thermal diffusivity α, thermal conductivity λ （2） （3） （4） Where: α is the soil thermal diffusivity (m) 2 ·s -1 ); C S Soil heat capacity (MJ·m) -3 ·K -1 λ is the soil thermal conductivity (W·m). -1 ·K -1 ); r is the perpendicular distance (m) between the thermocouple and the linear heat source, t is time (s), and t0 is the heating duration of the heat pulse (s); t m q is the time (s) when the thermocouple reaches its maximum temperature after the heating wire stops heating; q is the amount of heat (W·m) released per unit length of heating wire per unit time. -1 Ei(x) is the exponential integral, representing the integral over x; ΔT m It is in t m At time t0, the maximum temperature change (K) at a distance r from the heat source; for a given thermal pulse probe, q, r, and t0 are all constants. (2) Establish the soil thermal diffusivity α and the volumetric heat capacity C S Time-series dataset of thermal conductivity λ: Using a thermal pulse probe, the thermal diffusivity α and volumetric heat capacity C of soil were analyzed. S In-situ continuous measurement and recording of thermal conductivity λ was conducted to establish a corresponding time series dataset. (3) Use time series analysis to decompose the time series dataset of soil heat capacity, thermal conductivity and thermal diffusivity, and decompose the original time series data into "trend", "season" and "residual" terms; (4) Using the correlation between the decomposition results and soil moisture content θ, an inversion model of soil moisture content θ is established: the soil moisture content under natural conditions corresponds to the "period and residual" term in the decomposition results. Using the correlation between the measured value of soil moisture content at the corresponding time point and the "period and residual" term, an inversion model of soil moisture content and thermal characteristic parameter decomposition results is established. (5) Using the decomposition results and soil bulk density ρ b The correlation between them was established to determine the soil bulk density ρ. b The inversion model: Under natural conditions, soil bulk density first increases and then tends to stabilize after tillage. During the bulk density change period, the soil bulk density is calculated based on the relationship formulas (5)-(6) between bulk density, soil moisture content and soil thermal characteristic parameters, and combined with the soil moisture content results in step (4). Using the correlation between the measured and calculated values ​​of soil bulk density at the corresponding time points, an inversion model between soil bulk density and the decomposition results of thermal characteristic parameters is established, where the soil heat capacity C S The expressions for soil thermal conductivity λ in relation to water content and bulk density are as follows: （5） （6） Among them, C S It is the soil volumetric heat capacity (J·cm) −3 ·K −1 ); C W It is the volumetric heat capacity of water (4.18 J·cm³). −3 ·K −1 ); C b It is the mass heat capacity of the solid phase (J·g) −1 ·K −1 ); θ is the volumetric water content (cm³). 3 ·cm −3 ); ρ b Soil bulk density (g·cm³) −3 ); λ is the thermal conductivity of the soil; λ dry is the thermal conductivity of the soil sample when dry. For a given soil, this is related to the bulk density. a and b are solid phase parameters of the soil. For a given soil, a and b are constants.

2. The method for in-situ monitoring of soil moisture content and bulk density according to claim 1, characterized in that... In step (2), the monitoring frequency of soil thermal characteristic parameters is fixed and ranges from 30 min / time to 1 day / time.

3. A soil moisture and bulk density in-situ monitoring system coupled with active thermal pulse method and time series analysis method, used to implement the soil moisture and bulk density in-situ monitoring method as described in claim 1 or 2, characterized in that... include: The soil thermal properties monitoring system consists of an active thermal pulse probe, a probe control module, a power supply system, and a support frame. The probe is buried in the soil according to the actual monitoring needs. The probe control module and the power supply system are fixed on the support frame. The probe control module is used to control the heating time, heating amount, and sampling frequency of the heating resistance wire. The power supply system ensures that the probe can work stably. A remote data transmission system is used to transmit soil thermal properties measured by thermal pulse probes to a data processing center; The data processing center is used to receive and process the data obtained from the soil thermal properties monitoring system, and to calculate and output the soil moisture content and bulk density information of the observation points.

4. The soil moisture content and bulk density in-situ monitoring system according to claim 3, characterized in that... The active thermal pulse probe includes a heating resistance wire and thermocouples located on both sides of the heating resistance wire. The distance between the two thermocouples and the heating resistance wire is equal to ensure the accuracy of the monitoring results.

Images