Threshold-adaptive freeze-thaw discrimination method and device

Abstract

The invention provides a threshold-adaptive freeze-thaw discrimination method and device, and the method comprises the steps: determining a classic freeze-thaw discrimination function, carrying out the conversion of the classic freeze-thaw discrimination function, obtaining a threshold-adaptive target freeze-thaw discrimination function, carrying out the fitting of a second-order Bezier curve based on a preset number of target control points, and obtaining a threshold-adaptive target freeze-thaw discrimination function; adjusting a preset initial dynamic parameter of the target freeze-thaw discrimination function to obtain a new dynamic parameter, calculating a global long-time-sequence day-by-day freeze-thaw result based on the target freeze-thaw discrimination function, and obtaining a global freeze-thaw stable region and a global freeze-thaw alternate salient region based on the global long-time-sequence day-by-day freeze-thaw result, and for the global freeze-thaw stable region and the global freeze-thaw alternate salient region, selecting respective corresponding regional difference processing strategies, and processing parameters of the target freeze-thaw discrimination functions based on the regional difference processing strategies so as to perform freeze-thaw discrimination based on respective target freeze-thaw discrimination functions.

Description

Technical Field

[0001] This application relates to the field of microwave remote sensing monitoring, and more specifically, to a threshold-adaptive freeze-thaw discrimination method and apparatus. Background Technology

[0002] Surface freeze-thaw refers to the physical geological process and phenomenon of soil freezing and thawing due to temperatures dropping below zero and rising above zero. Because the freeze-thaw process involves the alternation of phases between liquid water and solid ice in the soil, it is highly unique among numerous surface processes. Even when this freeze-thaw phase alternation occurs only in a very thin surface soil layer, it can cause a series of complex abrupt changes in surface process trajectories, significantly impacting surface hydrological processes, particularly runoff. Remote sensing, especially microwave remote sensing, offers all-weather, all-time capabilities and can reflect the differences in soil dielectric properties caused by changes in water phases during freeze-thaw processes. Therefore, it has unparalleled mechanistic advantages in monitoring surface freeze-thaw conditions.

[0003] Currently, traditional soil freeze-thaw status monitoring algorithms mainly include threshold discrimination algorithms (TA) and freeze-thaw discriminant function algorithms (DFA). The core of the threshold discrimination algorithm is to select microwave signals that are sensitive to the soil freeze-thaw status as criteria, and select a specific value within the criterion range as the classification threshold to achieve accurate discrimination of the freeze-thaw status through a threshold comparison mechanism. The core of the freeze-thaw discriminant function algorithm is to use Fisher discriminant analysis to classify the data.

[0004] However, traditional soil freeze-thaw status monitoring algorithms mainly rely on actual observation data from local or global regions. Due to significant differences in geographical environment, climate conditions, and surface characteristics across different regions, thresholds set based on local data may fail to accurately determine freeze-thaw status in other regions. This leads to adaptability issues during spatial migration, making it difficult to effectively cope with complex situations at different spatial scales and to make judgments applicable to arbitrary regions and seasons. Furthermore, the large number of parameters involved results in generally low computational efficiency. Summary of the Invention

[0005] This application provides a threshold-adaptive freeze-thaw discrimination method and apparatus. By transforming the classical freeze-thaw discrimination function, a threshold-adaptive target freeze-thaw discrimination function is obtained. Based on a preset number of target control points, a second-order Bézier curve is fitted to adjust the preset initial dynamic parameters of the target freeze-thaw discrimination function, resulting in new dynamic parameters. Global long-term daily freeze-thaw results are calculated based on the target freeze-thaw discrimination function to dynamically adjust its parameters, reducing the number of parameters and improving computational efficiency. A dynamic threshold adjustment mechanism is introduced, automatically adjusting the discrimination threshold based on global long-term daily freeze-thaw results obtained from satellite observation data. This improves the accuracy of freeze-thaw state determination in complex environments, solves the adaptability problem during spatial migration, and can effectively cope with complex situations at different spatial scales. It is applicable to discrimination in any region and season.

[0006] In a first aspect, embodiments of this application provide a threshold-adaptive freeze-thaw discrimination method, the method comprising: A classic freeze-thaw discrimination function is determined and transformed to obtain a threshold-adaptive target freeze-thaw discrimination function; wherein, the parameters of the target freeze-thaw discrimination function include preset fixed parameters and dynamic parameters; The second-order Bézier curve is fitted based on a preset number of target control points, and the initial dynamic parameters of the target freeze-thaw discrimination function are adjusted to obtain new dynamic parameters. The global long-term daily freeze-thaw results are calculated based on the target freeze-thaw discriminant function, and the global freeze-thaw stability zone and the global freeze-thaw significant alternation zone are obtained based on the global long-term daily freeze-thaw results. For the global freeze-thaw stability zone and the global freeze-thaw alternation zone, a corresponding regional difference processing strategy is selected. The parameters of the target freeze-thaw discriminant function are processed based on the regional difference processing strategy to perform freeze-thaw discrimination based on the respective target freeze-thaw discriminant function.

[0007] In one possible implementation, the transformation of the classical freeze-thaw discriminant function to obtain a threshold-adaptive target freeze-thaw discriminant function includes: The target discriminant coefficient vector is determined based on a preset discriminant formula; wherein, the discriminant formula represents the discriminant criterion as equal to the ratio of the projected inter-class distance to the intra-class variance; and the target discriminant coefficient vector represents a set of optimal discriminant coefficients that maximize the discriminant criterion. In response to the discrimination criterion reaching its maximum value, the system of equations in which the partial derivatives of the discrimination criterion with respect to the target discrimination coefficient vector are equal to zero is solved to obtain the corresponding optimal discrimination function; A two-dimensional discriminant function is constructed based on the preset key classification indicators and the optimal discriminant function, and the two-dimensional discriminant function is transformed to obtain the corresponding target freeze-thaw discriminant function.

[0008] In one possible implementation, the fitting of a second-order Bézier curve based on a preset number of target control points, and the adjustment of the preset initial dynamic parameters of the target freeze-thaw discriminant function to obtain new dynamic parameters, include: Based on the target control point, determine the first and second derivatives of the parametric equation of the second-order Bézier curve, and determine the curvature of the second-order Bézier curve based on the first and second derivatives. Calculate the curvature maxima of the second-order Bézier curve, and substitute the curvature maxima into the target freeze-thaw discrimination function under the fixed parameters. Adjust the preset initial dynamic parameters of the target freeze-thaw discrimination function to obtain new dynamic parameters.

[0009] In one possible implementation, the step of calculating global long-term daily freeze-thaw results based on the target freeze-thaw discriminant function, and obtaining global freeze-thaw stability zones and global freeze-thaw alternation significant regions based on the global long-term daily freeze-thaw results, includes: For different satellites and different orbits, separate fixed parameters and dynamic parameters are determined for each satellite and each orbit, and the target freeze-thaw discrimination function is obtained based on the fixed parameters and the dynamic parameters. The global soil freeze-thaw state is determined based on the target freeze-thaw discriminant function, and the global long-term daily freeze-thaw results are obtained. The number of thawing days and the number of freezing days are obtained based on the global long-term daily freeze-thaw results. The number of thawing days and the number of freezing days constitute the total number of days. A first proportion of the number of thawing days to the total number of days and a second proportion of the number of freezing days to the total number of days are determined, and the area where the first proportion is greater than a preset first threshold is determined as a global freeze-thaw stability zone, and the area where the second proportion is greater than a preset second threshold is determined as a global freeze-thaw significant alternation zone.

[0010] In one possible implementation, selecting corresponding regional difference processing strategies for the global freeze-thaw stability zone and the global freeze-thaw alternation significant regions includes: For the global freeze-thaw stability zone, the original fixed and dynamic parameters of the target freeze-thaw discrimination function remain unchanged; For the aforementioned regions with significant global freeze-thaw cycles, while keeping the original fixed parameters of the target freeze-thaw discriminant function unchanged, the dynamic parameters are dynamically optimized based on the second-order Bézier curve.

[0011] In one possible implementation, the method further includes: Soil temperature observation data at target depths of multiple observation networks distributed in the Northern Hemisphere with frequent freeze-thaw transitions, from the International Soil Moisture Network, were selected; wherein, the multiple observation networks cover different geographical environments and characterize the freeze-thaw active areas under different geographical environments; Based on the soil temperature observation data of the observation network, the accuracy of the target freeze-thaw discriminant function and the classical freeze-thaw discriminant function in monitoring soil freeze-thaw status is compared; wherein, the accuracy includes at least the overall accuracy, the thawing state accuracy, and the freezing state accuracy.

[0012] In one possible implementation, the method further includes: Select a target observation network from the multiple observation networks, and determine the freeze-thaw state classification of the target freeze-thaw discriminant function and the classical freeze-thaw discriminant function at the same pixel location in the target observation network; The first accuracy of the target freeze-thaw discriminant function in this pixel is determined based on the freeze-thaw state classification of the target freeze-thaw discriminant function, and the second accuracy of the classical freeze-thaw discriminant function in this pixel is determined based on the freeze-thaw state classification of the classical freeze-thaw discriminant function. Based on a comparison of the first accuracy and the second accuracy, the threshold adjustment capabilities of the target freeze-thaw discriminant function and the classical freeze-thaw discriminant function at the pixel level are evaluated.

[0013] Secondly, embodiments of this application also provide a threshold-adaptive freeze-thaw discrimination device, the device comprising: The first acquisition module is used to determine the classical freeze-thaw discrimination function and transform the classical freeze-thaw discrimination function to obtain a threshold-adaptive target freeze-thaw discrimination function; wherein, the parameters of the target freeze-thaw discrimination function include preset fixed parameters and dynamic parameters; The second acquisition module is used to fit a second-order Bézier curve based on a preset number of target control points, adjust the preset initial dynamic parameters of the target freeze-thaw discrimination function, and obtain new dynamic parameters. The third acquisition module is used to calculate the global long-term daily freeze-thaw results based on the target freeze-thaw discriminant function, and to obtain the global freeze-thaw stability zone and the global freeze-thaw significant alternation zone based on the global long-term daily freeze-thaw results. The processing module is used to select the corresponding regional difference processing strategy for the global freeze-thaw stability zone and the global freeze-thaw significant alternation zone, and process the parameters of the target freeze-thaw discriminant function based on the regional difference processing strategy, so as to perform freeze-thaw discrimination based on the respective target freeze-thaw discriminant function.

[0014] In one possible implementation, the first acquisition module is specifically used for: The target discriminant coefficient vector is determined based on a preset discriminant formula; wherein, the discriminant formula represents the discriminant criterion as equal to the ratio of the projected inter-class distance to the intra-class variance; and the target discriminant coefficient vector represents a set of optimal discriminant coefficients that maximize the discriminant criterion. In response to the discrimination criterion reaching its maximum value, the system of equations in which the partial derivatives of the discrimination criterion with respect to the target discrimination coefficient vector are equal to zero is solved to obtain the corresponding optimal discrimination function; A two-dimensional discriminant function is constructed based on the preset key classification indicators and the optimal discriminant function, and the two-dimensional discriminant function is transformed to obtain the corresponding target freeze-thaw discriminant function.

[0015] In one possible implementation, the second acquisition module is specifically used for: Based on the target control point, determine the first and second derivatives of the parametric equation of the second-order Bézier curve, and determine the curvature of the second-order Bézier curve based on the first and second derivatives. Calculate the curvature maxima of the second-order Bézier curve, and substitute the curvature maxima into the target freeze-thaw discrimination function under the fixed parameters. Adjust the preset initial dynamic parameters of the target freeze-thaw discrimination function to obtain new dynamic parameters.

[0016] In one possible implementation, the third acquisition module is specifically used for: For different satellites and different orbits, separate fixed parameters and dynamic parameters are determined for each satellite and each orbit, and the target freeze-thaw discrimination function is obtained based on the fixed parameters and the dynamic parameters. The global soil freeze-thaw state is determined based on the target freeze-thaw discriminant function, and the global long-term daily freeze-thaw results are obtained. The number of thawing days and the number of freezing days are obtained based on the global long-term daily freeze-thaw results. The number of thawing days and the number of freezing days constitute the total number of days. A first proportion of the number of thawing days to the total number of days and a second proportion of the number of freezing days to the total number of days are determined, and the area where the first proportion is greater than a preset first threshold is determined as a global freeze-thaw stability zone, and the area where the second proportion is greater than a preset second threshold is determined as a global freeze-thaw significant alternation zone.

[0017] In one possible implementation, the third acquisition module is specifically used for: For the global freeze-thaw stability zone, the original fixed and dynamic parameters of the target freeze-thaw discrimination function remain unchanged; For the aforementioned regions with significant global freeze-thaw cycles, while keeping the original fixed parameters of the target freeze-thaw discriminant function unchanged, the dynamic parameters are dynamically optimized based on the second-order Bézier curve.

[0018] In one possible implementation, the device further includes: The selection module is used to select soil temperature observation data at target depths from multiple observation networks distributed in the International Soil Moisture Network in regions of frequent freeze-thaw transition in the Northern Hemisphere; wherein, the multiple observation networks cover different geographical environments and characterize areas of active freeze-thaw transition under different geographical environments; The comparison module is used to compare the accuracy of the target freeze-thaw discriminant function and the classical freeze-thaw discriminant function in monitoring soil freeze-thaw status based on soil temperature observation data from the observation network; wherein the accuracy includes at least the overall accuracy, the thawing state accuracy, and the freezing state accuracy.

[0019] In one possible implementation, the device further includes: The first determining module is used to select a target observation network from the plurality of observation networks and determine the freeze-thaw state classification of the target freeze-thaw discriminant function and the classical freeze-thaw discriminant function at the same pixel location in the target observation network; The second determining module is used to determine the first accuracy of the target freeze-thaw discriminant function in the pixel based on the freeze-thaw state classification of the target freeze-thaw discriminant function, and to determine the second accuracy of the classical freeze-thaw discriminant function in the pixel based on the freeze-thaw state classification of the classical freeze-thaw discriminant function. The evaluation module is used to evaluate the threshold control capabilities of the target freeze-thaw discriminant function and the classical freeze-thaw discriminant function at the pixel level by comparing the first accuracy and the second accuracy.

[0020] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the threshold adaptive freeze-thaw discrimination method described in any of the first aspects.

[0021] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, performs the steps of the threshold adaptive freeze-thaw discrimination method described in any one of the first aspects.

[0022] This application provides the aforementioned threshold-adaptive freeze-thaw discrimination method and apparatus. It determines the classical freeze-thaw discrimination function and transforms it to obtain a threshold-adaptive target freeze-thaw discrimination function. It then fits a second-order Bézier curve based on a preset number of target control points, adjusts the preset initial dynamic parameters of the target freeze-thaw discrimination function to obtain new dynamic parameters, calculates global long-term daily freeze-thaw results based on the target freeze-thaw discrimination function, and obtains global freeze-thaw stability zones and regions with significant freeze-thaw alternation based on these results. For each of the global freeze-thaw stability zones and regions with significant freeze-thaw alternation, it selects a corresponding regional difference processing strategy and processes the parameters of the target freeze-thaw discrimination function based on the regional difference processing strategy to perform freeze-thaw discrimination based on their respective target freeze-thaw discrimination functions. This application transforms the classical freeze-thaw discriminant function to obtain a threshold-adaptive target freeze-thaw discriminant function. Based on a preset number of target control points, it uses second-order Bézier curves to adjust the preset initial dynamic parameters of the target freeze-thaw discriminant function, obtaining new dynamic parameters. Furthermore, it calculates global long-term daily freeze-thaw results based on the target freeze-thaw discriminant function to dynamically adjust its parameters, reducing the number of parameters and improving computational efficiency. It introduces a dynamic threshold adjustment mechanism, automatically adjusting the discriminant threshold based on global long-term daily freeze-thaw results obtained from satellite observation data. This improves the accuracy of freeze-thaw state determination in complex environments, solves the adaptability problem during spatial migration, and can effectively cope with complex situations at different spatial scales, applicable to discrimination in any region and season. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart of the first threshold-adaptive freeze-thaw discrimination method provided in the embodiments of this application is shown; Figure 2 A schematic diagram of the threshold-adaptive freeze-thaw discrimination process is shown; Figure 3 A schematic diagram of scatter point density for soil freeze-thaw state classification based on ADFA is shown. Figure 4 A schematic diagram of scatter point density for soil freeze-thaw state classification based on DFA is shown. Figure 5 This paper shows a schematic diagram of a threshold-adaptive freeze-thaw discrimination device provided in an embodiment of this application. Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0026] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0027] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0028] Freeze-thaw cycles are a physical geological process and phenomenon characterized by the freezing and thawing of soil layers due to temperatures dropping below or rising above zero degrees Celsius. Because the freeze-thaw process involves the alternation of liquid water and solid ice in the soil, it is unique among various surface processes. Even when this freeze-thaw phase alternation occurs only in a thin surface soil layer, it can cause a series of complex abrupt changes in surface process trajectories, significantly impacting surface hydrological processes, particularly runoff. Remote sensing, especially microwave remote sensing, offers all-weather, all-time capabilities and can reflect the differences in soil dielectric properties caused by changes in water phase during freeze-thaw cycles. Therefore, it has unparalleled mechanistic advantages in monitoring surface freeze-thaw conditions.

[0029] Currently, traditional soil freeze-thaw status monitoring algorithms mainly include threshold discrimination algorithms (TA) and freeze-thaw discriminant function algorithms (DFA). The core of the threshold discrimination algorithm is to select microwave signals that are sensitive to the soil freeze-thaw status as criteria, and select a specific value within the criterion range as the classification threshold to achieve accurate discrimination of the freeze-thaw status through a threshold comparison mechanism. The core of the freeze-thaw discriminant function algorithm is to use Fisher discriminant analysis to classify the data.

[0030] However, traditional soil freeze-thaw status monitoring algorithms mainly rely on actual observation data from local or global regions. Due to significant differences in geographical environment, climate conditions, and surface characteristics across different regions, thresholds set based on local data may fail to accurately determine freeze-thaw status in other regions. This leads to adaptability issues during spatial migration, making it difficult to effectively cope with complex situations at different spatial scales and to make judgments applicable to arbitrary regions and seasons. Furthermore, the large number of parameters involved results in generally low computational efficiency.

[0031] Based on this, this application provides a threshold-adaptive freeze-thaw discrimination method and apparatus. By transforming the classical freeze-thaw discrimination function to obtain a threshold-adaptive target freeze-thaw discrimination function, and by fitting a second-order Bézier curve based on a preset number of target control points to adjust the preset initial dynamic parameters of the target freeze-thaw discrimination function, new dynamic parameters are obtained. Furthermore, global long-term daily freeze-thaw results are calculated based on the target freeze-thaw discrimination function to dynamically adjust the parameters of the target freeze-thaw discrimination function, reducing the number of parameters and improving computational efficiency. A dynamic threshold adjustment mechanism is introduced, automatically adjusting the discrimination threshold based on global long-term daily freeze-thaw results obtained from satellite observation data. This improves the accuracy of freeze-thaw state determination in complex environments, solves the adaptability problem during spatial migration, and can effectively cope with complex situations at different spatial scales, applicable to discrimination in any region and season.

[0032] like Figure 1 As shown in the figure, this application provides a threshold-adaptive freeze-thaw discrimination method, the method comprising: S101. Determine the classical freeze-thaw discrimination function and transform it to obtain the target freeze-thaw discrimination function with threshold adaptation.

[0033] S102. Fit a second-order Bézier curve based on a preset number of target control points, adjust the preset initial dynamic parameters of the target freeze-thaw discrimination function, and obtain new dynamic parameters.

[0034] S103. Calculate the global long-term daily freeze-thaw results based on the target freeze-thaw discriminant function, and obtain the global freeze-thaw stability zone and the global freeze-thaw significant alternation zone based on the global long-term daily freeze-thaw results.

[0035] S104. For global freeze-thaw stability zones and global areas with significant freeze-thaw alternation, select their respective regional difference processing strategies, process the parameters of the target freeze-thaw discriminant function based on the regional difference processing strategies, and perform freeze-thaw discrimination based on their respective target freeze-thaw discriminant functions.

[0036] In the aforementioned threshold-adaptive freeze-thaw discrimination method, a threshold-adaptive target freeze-thaw discrimination function is obtained by transforming the classical freeze-thaw discrimination function. Based on a preset number of target control points, a second-order Bézier curve is fitted to adjust the preset initial dynamic parameters of the target freeze-thaw discrimination function, resulting in new dynamic parameters. Global long-term daily freeze-thaw results are calculated based on the target freeze-thaw discrimination function to dynamically adjust its parameters, reducing the number of parameters and improving computational efficiency. A dynamic threshold adjustment mechanism is introduced, automatically adjusting the discrimination threshold based on global long-term daily freeze-thaw results obtained from satellite observation data. This improves the accuracy of freeze-thaw state determination in complex environments, solves the adaptability problem during spatial migration, and can effectively cope with complex situations at different spatial scales, applicable to discrimination in any region and season.

[0037] The exemplary embodiments described above will be explained below: S101. Determine the classical freeze-thaw discrimination function and transform it to obtain the target freeze-thaw discrimination function with threshold adaptation.

[0038] It should be noted that the classical freeze-thaw discriminant function is the traditional freeze-thaw discriminant function, and the corresponding formula is as follows (1): (1) The classical freeze-thaw discriminant function f serves as the discriminant function, determining the freeze-thaw state of the soil based on its value: when f ≤ 0, the soil is considered to be in a thawed state; when f > 0, it is considered to be in a frozen state. The coefficients A, B, and C in the classical freeze-thaw discriminant function are parameters of the discriminant function, and their values ​​vary with different combinations of freeze-thaw criteria. X and Y represent two criteria.

[0039] In this embodiment, the target freeze-thaw discriminant function is the threshold-adaptive target freeze-thaw discriminant function (ADFA) obtained by transforming the classical freeze-thaw discriminant function. The parameters of the target freeze-thaw discriminant function include preset fixed parameters and dynamic parameters. That is, a set of initial fixed parameters and dynamic parameters are preset for the parameters of the target freeze-thaw discriminant function. The threshold-adaptive target freeze-thaw discriminant function is obtained by transforming the classical freeze-thaw discriminant function for subsequent processing.

[0040] Optionally, when transforming the classic freeze-thaw discriminant function to obtain the threshold-adaptive target freeze-thaw discriminant function, the target discriminant coefficient vector of the discriminant formula is determined based on the preset discriminant formula; in response to the discriminant criterion reaching its maximum value, the system of equations in which the partial derivative of the discriminant criterion with respect to the target discriminant coefficient vector is solved to obtain the corresponding optimal discriminant function; a two-dimensional discriminant function is constructed based on the preset key classification indicators and the optimal discriminant function, and the two-dimensional discriminant function is transformed to obtain the corresponding target freeze-thaw discriminant function.

[0041] The discriminant formula represents the discriminant criterion as the ratio of the projected inter-class distance to the intra-class variance; the target discriminant coefficient vector represents the optimal set of discriminant coefficients that maximizes the discriminant criterion. Furthermore, the optimal discriminant function represents the discriminant score, determined by the discriminant coefficients and preset classification indicators (feature variables of the samples). The discriminant score serves as the quantitative standard for sample classification. The classification indicators represent the feature variables of the samples, and include at least a first classification indicator and a second classification indicator. The first classification indicator represents the soil liquid water content, and the second classification indicator represents the soil temperature.

[0042] Here, this application uses Fisher discriminant analysis as an example for description, but this does not constitute a limitation on the discriminant method. Fisher discriminant analysis is a classic dimensionality reduction classification technique. Its core lies in constructing an optimal linear projection to map high-dimensional feature data to a low-dimensional space (usually one-dimensional) for effective classification. This Fisher discriminant method aims to determine a set of optimal discriminant coefficients that maximize the distance between different categories after projection while minimizing the dispersion of samples within each category.

[0043] The mathematical principle of Fisher's discrimination can be expressed as follows (2), that is, finding the discriminant coefficient vector (c1, c2, ..., c n This allows the discrimination criterion Q to reach its maximum value.

[0044] (2) Where D represents the projected inter-class distance and V represents the intra-class variance.

[0045] When the discriminant criterion Q is maximized, the optimal discriminant function (formula (3)) can be obtained by solving the system of equations in which its partial derivatives with respect to the coefficient vector are equal to zero: (3) Where D(t) represents the discriminant score, a quantitative standard used for sample classification; c1 to c n The discriminant coefficients represent the importance weights of each feature variable in the classification; x1 to x n These are the preset classification indicators, i.e., the characteristic variables of the samples.

[0046] Next, for example, this application selected QE, which characterizes the liquid water content of the soil, and TbV36.5, which characterizes the surface soil temperature, as key classification indicators. Combined with the above-mentioned optimal discriminant function formula (3), a two-dimensional Fisher discriminant function (formula 4) was constructed: (4) Among them, FTI (Freeze-thaw Index) is the freeze-thaw index.

[0047] Finally, based on the two-dimensional Fisher discriminant function (Equation 4), mathematical derivation and formal simplification are performed to obtain the corresponding target freeze-thaw discriminant function (Equation (5)), thus determining the target freeze-thaw discriminant functions with fixed parameters and dynamic parameters: (5) Where K represents a fixed parameter and B(b) represents a dynamic parameter.

[0048] It can be seen that, as Figure 2 As shown, this application transforms the classical freeze-thaw discriminant function (Formula (1)) FTI=AX+BY+C into a threshold-adaptive target freeze-thaw discriminant function (Formula (5)). That is, FTI = K*X + Y + B.

[0049] Therefore, this application simplifies the traditional classical freeze-thaw discriminant function formula FTI=AX+BY+C to the form FTI=KX+Y+B, reducing the number of parameters, improving computational efficiency, and retaining the model's core predictive capabilities. This simplification optimizes the computation process and makes the model easier to understand and apply, laying the foundation for subsequent adaptive optimization.

[0050] It should be noted that the target freeze-thaw discriminant function can be constructed based on statistical classification methods other than Fisher's discriminant analysis. For example, other statistical classification or dimensionality reduction methods besides Fisher's discriminant analysis (such as principal component analysis combined with classifiers, logistic regression, support vector machines (SVM), and other variants of linear discriminant analysis) can be used to construct the target freeze-thaw discriminant function for freeze-thaw discrimination. These methods can also learn from the input features and build a discriminant model to distinguish between frozen and thawed states. Based on this, different mechanisms can be designed to achieve adaptive adjustment of the threshold.

[0051] S102. Fit a second-order Bézier curve based on a preset number of target control points, adjust the preset initial dynamic parameters of the target freeze-thaw discrimination function, and obtain new dynamic parameters.

[0052] In this embodiment, the target control points correspond to different soil freeze-thaw states. The first target control point represents the completely frozen soil state, the second target control point represents the completely thawed soil state, and the third target control point represents the soil freeze-thaw transition process. A second-order Bézier curve simulates the nonlinear relationship between soil surface temperature and soil emissivity during the freeze-thaw process. A second-order Bézier curve is generated using a preset number of target control points, and the curve is fitted based on these control points. The preset initial dynamic parameters of the target freeze-thaw discrimination function are adjusted to obtain new dynamic parameters for subsequent processing. For example, such as... Figure 2 As shown.

[0053] Optionally, when fitting a second-order Bézier curve based on a preset number of target control points, adjusting the preset initial dynamic parameters of the target freeze-thaw discriminant function, and obtaining new dynamic parameters, the first and second derivatives of the parametric equation of the second-order Bézier curve are determined based on the target control points, and the curvature of the second-order Bézier curve is determined based on the first and second derivatives; the curvature maxima of the second-order Bézier curve are calculated, and the curvature maxima are substituted into the target freeze-thaw discriminant function under fixed parameters, adjusting the preset initial dynamic parameters of the target freeze-thaw discriminant function to obtain new dynamic parameters.

[0054] It should be noted that Bézier curves, based on the mathematical principles of Bernstein polynomials, can generate smooth, continuous curves using a finite number of control points, possessing intuitive geometric meaning and favorable mathematical properties. Given control points P0, P1, P2, ... Pi, any point P(t) on the Bézier curve can be represented by formula 6):

[0055] in, Let be the i-th Bernstein polynomial of order n. It is the number of combinations.

[0056] Continuing on, the second-order Bézier curve used in this application can be composed of three control points and three target control points. , and Define, where, control point (Representing a completely frozen soil state), control point (Representing the state of complete soil thawing) and control points (Representing the soil freeze-thaw transition process), such as Figure 2 As shown, , and These represent the freezing period, freeze-thaw period, and thawing period, respectively. The parametric equation of this curve is shown in Equation 9, and its first derivative is determined (represented by Equation 10). For a second-order Bézier curve on the plane, its curvature κ can be calculated using Equation 11, where... It is the second derivative:

[0057] It should be noted that this application uses the QE of soil liquid water content and TbV36.5, which characterizes surface soil temperature, as key indicators to simulate the nonlinear relationship between soil surface temperature and soil emissivity during soil freeze-thaw processes. Secondly, three target control points are selected. , and To achieve the fitting of the second-order Bézier curve, the curvature maxima of the second-order Bézier curve are calculated. Finally, using the curvature maxima as the key "inflection point" of the freeze-thaw transition, the coordinates of the curvature maxima are substituted into the target freeze-thaw discriminant function formula 5 with a fixed parameter K to obtain the dynamic parameter B. For example, as... Figure 2 As shown.

[0058] Therefore, this application uses Fisher's discriminant method to determine globally unified K and B parameters, providing a consistent benchmark for the discrimination process. Fisher's discriminant method, as a classic statistical method, can maximize inter-class distance and minimize intra-class variance, ensuring the accuracy and stability of freeze-thaw state discrimination.

[0059] Optionally, the relationship between the dynamic parameters and the fixed parameters and various environmental features is learned based on a preset machine learning algorithm; the optimal discrimination threshold of the dynamic parameters of the target freeze-thaw discrimination function in different regions or conditions is predicted, and the dynamic parameters are dynamically adjusted. These environmental features include, for example, topography, vegetation cover, and historical freeze-thaw patterns.

[0060] It should be noted that, in addition to using second-order Bézier curves to adjust and optimize the dynamic parameters of the target freeze-thaw discriminant function, this application can also use machine learning algorithms (such as decision trees, random forests, gradient boosting models, or shallow neural networks) to directly learn and predict the optimal discrimination threshold (referring to dynamic parameter B) in different regions or conditions. This method does not directly rely on Bézier curves for threshold optimization, but rather uses a data-driven approach to allow the model to automatically learn the complex relationship between the threshold and various environmental features, thereby achieving dynamic adjustment of the discrimination threshold.

[0061] In addition, other mathematical curve fitting techniques or function approximation methods besides second-order Bézier curves can be used to simulate or realize the dynamic changes of the discrimination threshold, such as higher-order polynomial regression, spline functions (such as cubic splines), B-spline curves or other nonlinear regression models. These methods can also smoothly or gradually adjust the discrimination threshold according to changes in regional characteristics or environmental parameters.

[0062] S103. Calculate the global long-term daily freeze-thaw results based on the target freeze-thaw discriminant function, and obtain the global freeze-thaw stability zone and the global freeze-thaw significant alternation zone based on the global long-term daily freeze-thaw results.

[0063] In this embodiment of the application, the global freeze-thaw stability zone includes a stable thawing zone and a stable freezing zone; based on the target freeze-thaw discriminant function obtained in step S101 and the dynamic parameters of the target freeze-thaw discriminant function obtained in step S102, the global long-term daily freeze-thaw results are calculated, and the global freeze-thaw stability zone and the global freeze-thaw significant alternation zone are obtained based on the global long-term daily freeze-thaw results.

[0064] In some implementations, for different satellites and different orbits, separate fixed and dynamic parameters are determined for each satellite and each orbit, and a target freeze-thaw discriminant function is obtained based on the fixed and dynamic parameters. The global soil freeze-thaw state is determined based on the target freeze-thaw discriminant function, resulting in global long-term daily freeze-thaw results. The number of thawing days and the number of frozen days are then obtained based on these global long-term daily freeze-thaw results. A first proportion of thawing days to the total number of days and a second proportion of frozen days to the total number of days are determined. Regions with the first proportion greater than a preset first threshold are defined as global freeze-thaw stable zones, and regions with the second proportion greater than a preset second threshold are defined as global freeze-thaw alternation significant zones. The number of thawing days and the number of frozen days constitute the total number of days.

[0065] For example, this application used three years of ascending and descending orbit data from FY-3B, FY-3C, and FY-3D to analyze the changes in global soil freeze-thaw conditions. For different satellites and different orbits, each satellite and each orbit has separate fixed parameters K and B, as shown in Table 1 below: Table 1 Optimization parameters and study time

[0066] Continuing, this application utilizes the fixed parameter K and dynamic parameter B of different orbits FY-3B, FY-3C, and FY-3D. First, it employs Fisher discriminant analysis to determine the global soil freeze-thaw state. Based on its global long-term daily freeze-thaw results, it obtains the number of thawing days and freezing days, determining the first proportion of thawing days to the total number of days and the second proportion of freezing days. When the proportion of freezing or thawing days exceeds 90%, it is considered a globally stable freeze-thaw zone; otherwise, it is considered a region with significant global freeze-thaw alternation. Figure 2 As shown.

[0067] S104. For global freeze-thaw stability zones and global areas with significant freeze-thaw alternation, select their respective regional difference processing strategies, process the parameters of the target freeze-thaw discriminant function based on the regional difference processing strategies, and perform freeze-thaw discrimination based on their respective target freeze-thaw discriminant functions.

[0068] In this embodiment, the regional difference processing strategy refers to different processing strategies for global freeze-thaw stability zones and global freeze-thaw alternation zones. Specifically, it is a strategy to adjust the parameters of the target freeze-thaw discriminant function. The corresponding regional difference processing strategies are selected for global freeze-thaw stability zones and global freeze-thaw alternation zones, and the parameters of the target freeze-thaw discriminant function are processed based on the regional difference processing strategies to perform freeze-thaw discrimination based on their respective target freeze-thaw discriminant functions.

[0069] Optionally, when selecting corresponding regional difference processing strategies for global freeze-thaw stable regions and global regions with significant freeze-thaw alternation, for global freeze-thaw stable regions, the original fixed parameters and dynamic parameters of the target freeze-thaw discriminant function are kept unchanged; for global regions with significant freeze-thaw alternation, while keeping the original fixed parameters of the target freeze-thaw discriminant function unchanged, the dynamic parameters are dynamically optimized based on the second-order Bézier curve.

[0070] It should be noted that, as Figure 2 As shown, for the global freeze-thaw stability zone, K and B remain constant, while for the global freeze-thaw alternation zone, K remains constant and B changes. That is, B needs to be dynamically optimized according to the second-order Bézier curve, thus realizing the regional adaptive optimization of the model parameters.

[0071] Therefore, this application implements a differentiated processing strategy to address regional differences: for global freeze-thaw stability zones (e.g., where the proportion of thawed or frozen days exceeds 90%), the parameters determined by Fisher's discriminant method are directly applied to ensure model stability and prediction consistency; for global freeze-thaw alternation zones, the K value is kept constant, but the B value is dynamically optimized using a second-order Bézier curve to achieve accurate adaptation to local characteristics.

[0072] This application provides a threshold-adaptive freeze-thaw discrimination method. It determines the classical freeze-thaw discrimination function and transforms it to obtain a threshold-adaptive target freeze-thaw discrimination function. Based on a preset number of target control points, it performs second-order Bézier curve fitting, adjusts the preset initial dynamic parameters of the target freeze-thaw discrimination function to obtain new dynamic parameters, calculates global long-term daily freeze-thaw results based on the target freeze-thaw discrimination function, and obtains global freeze-thaw stability zones and regions with significant freeze-thaw alternation based on these results. For each of the global freeze-thaw stability zones and regions with significant freeze-thaw alternation, it selects a corresponding regional difference processing strategy and processes the parameters of the target freeze-thaw discrimination function based on the regional difference processing strategy to perform freeze-thaw discrimination based on their respective target freeze-thaw discrimination functions. The threshold-adaptive freeze-thaw discrimination method of this application transforms the classical freeze-thaw discrimination function to obtain a threshold-adaptive target freeze-thaw discrimination function. Based on a preset number of target control points, a second-order Bézier curve is fitted to adjust the preset initial dynamic parameters of the target freeze-thaw discrimination function, resulting in new dynamic parameters. Global long-term daily freeze-thaw results are calculated based on the target freeze-thaw discrimination function to dynamically adjust its parameters, reducing the number of parameters and improving computational efficiency. A dynamic threshold adjustment mechanism is introduced, automatically adjusting the discrimination threshold based on global long-term daily freeze-thaw results obtained from satellite observation data. This improves the accuracy of freeze-thaw state determination in complex environments, solves the adaptability problem during spatial migration, and can effectively cope with complex situations at different spatial scales. It is applicable to discrimination in any region and season.

[0073] Furthermore, soil temperature observation data at target depths were selected from multiple observation networks distributed in the Northern Hemisphere's regions with frequent freeze-thaw transitions, as part of the International Soil Moisture Network. Based on the soil temperature observation data from these observation networks, the accuracy of the target freeze-thaw discriminant function and the classical freeze-thaw discriminant function in monitoring soil freeze-thaw status was compared. These multiple observation networks cover different geographical environments, representing active freeze-thaw regions under different geographical conditions. The accuracy rates included at least overall accuracy, thawing state accuracy, and freezing state accuracy.

[0075] These monitoring results clearly demonstrate the seasonal variation patterns of global soil freeze-thaw states. During the Northern Hemisphere winter, large areas of high latitude regions such as North America and northern Eurasia are frozen, closely matching seasonal climate characteristics. In the Northern Hemisphere summer, most of these areas thaw, with only permafrost regions like Greenland and northern Siberia remaining frozen, accurately reflecting the global distribution of permafrost. In the Southern Hemisphere, the adaptive threshold freeze-thaw discrimination algorithm also demonstrates excellent monitoring capabilities. In the Southern Hemisphere summer (January), most areas of South America, southern Africa, and Australia show a red thawing state, perfectly consistent with the Southern Hemisphere summer climate characteristics. Partial thawing is also observed at the edges of the Antarctic ice sheet, accurately reflecting the impact of the Southern Hemisphere summer on the polar cryosphere. In the Southern Hemisphere winter (August), high-latitude regions, especially the Andes Mountains in South America, show a distinct blue freezing state. This phenomenon fully reflects the combined influence of altitude and latitude on soil temperature; the Andes Mountains, due to their significant high altitude, exhibit extensive soil freezing during the Southern Hemisphere winter. Meanwhile, the frozen area around Antarctica has also expanded, consistent with winter climatic conditions. It is noteworthy that, compared to the Northern Hemisphere, the Southern Hemisphere has a smaller and more unevenly distributed land area, resulting in a unique spatial pattern of freeze-thaw conditions. Comparison of monitoring results from six satellites at different orbits reveals a highly consistent spatial distribution pattern across the sub-maps, confirming that the adaptive threshold-based target freeze-thaw discrimination algorithm effectively eliminates systematic differences between multi-source satellite data while preserving accurate information about the surface freeze-thaw status.

[0076] Specifically, for example, to verify the accuracy of the threshold-adaptive target freeze-thaw discriminant function (ADFA) in soil freeze-thaw state monitoring, this application selected seven observation networks (ARM, MAQU, NGARI, CTP_SMTMN, NAQU, RISMA, and FMI) distributed in areas with frequent freeze-thaw transitions in the Northern Hemisphere from the International Soil Moisture Network as validation benchmarks. These observation networks cover geographical locations including the United States, the Tibetan Plateau of China, Canada, and Finland, representing active freeze-thaw regions under different climatic and geographical conditions. Using 5cm soil temperature observation data from these networks, a systematic comparison was made between the adaptive threshold target freeze-thaw discriminant function and the classical freeze-thaw discriminant function (DFA), comprehensively evaluating the performance advantages of the target freeze-thaw discriminant function in capturing the dynamic process of soil freeze-thaw, especially its adaptability in complex areas with frequent freeze-thaw transitions. Table 2 below shows the average accuracy of soil freeze-thaw state discrimination using different models in different satellite orbits. These results are based on a comprehensive dataset from the seven international soil moisture observation networks.

[0077] Table 2 Comparison of ADFA and DFA Validation Results

[0078] As can be seen from Table 2, the target freeze-thaw discriminant function ADFA exhibits significant performance advantages across various indicators.

[0079] Specifically, in terms of overall accuracy, ADFA's performance ranged from 0.8132 to 0.8579, significantly higher than DFA's 0.7337 to 0.8195. Notably, ADFA achieved its highest overall accuracy of 0.8579 when processing FY-3D satellite descent data. Breaking it down further, ADFA excelled particularly in thawing accuracy, reaching a high level of 0.8749 to 0.9244, while DFA's performance on the same metric (0.6422 to 0.8762) was relatively weaker, especially in FY-3D satellite ascent data, where DFA's thawing accuracy was only 0.6422, far lower than ADFA's 0.8749. Regarding frozen accuracy, the performance differences between ADFA and DFA exhibited a complex pattern. In FY-3B satellite data, the freeze-state accuracy of DFA (0.8768 and 0.8440) was slightly higher than that of ADFA (0.7116 and 0.7231); however, in FY-3D data, the freeze-state accuracy of ADFA (0.7088 and 0.7613) was slightly lower than that of DFA (0.8215 and 0.8103). This phenomenon may indicate that the two algorithms have their own preferences and adaptive differences when handling different freeze-thaw process characteristics. However, considering the significant advantage of ADFA in overall accuracy and thaw accuracy, this slight difference in freeze-state accuracy does not affect its overall performance superiority. From the perspective of regional adaptability, ADFA's adaptive mechanism is particularly advantageous for handling complex and variable freeze-thaw transition regions. The seven observation networks selected in this study (ARM, MAQU, NGARI, CTP_SMTMN, NAQU, RISMA, and FMI) are distributed in the active freeze-thaw region of the Northern Hemisphere, covering different geographical environments such as the United States, the Tibetan Plateau of China, Canada, and Finland. The high accuracy achieved by ADFA in these regions demonstrates its adaptability to different freeze-thaw patterns. In contrast, DFA performs relatively poorly in these complex areas, especially in the border regions where freeze-thaw transitions are frequent.

[0080] Furthermore, a target observation network is selected from multiple observation networks to determine the freeze-thaw state classification of the target freeze-thaw discriminant function and the classical freeze-thaw discriminant function at the same pixel location in the target observation network. Based on the freeze-thaw state classification of the target freeze-thaw discriminant function, a first accuracy of the target freeze-thaw discriminant function at that pixel is determined, and based on the freeze-thaw state classification of the classical freeze-thaw discriminant function, a second accuracy of the classical freeze-thaw discriminant function at that pixel is determined. Based on the comparison of the first accuracy and the second accuracy, the threshold adjustment capabilities of the target freeze-thaw discriminant function and the classical freeze-thaw discriminant function at the pixel level are evaluated.

[0081] It should be noted that, in order to more intuitively demonstrate the threshold control capability of ADFA compared to DFA at the pixel level, this application selects a target observation network from the aforementioned multiple observation networks. For example, the Finnish FMI monitoring network is selected as the target observation network. Based on this, the first accuracy of the target freeze-thaw discriminant function at this pixel and the second accuracy of the classical freeze-thaw discriminant function at this pixel are determined and compared to evaluate the threshold control capability of the target freeze-thaw discriminant function and the classical freeze-thaw discriminant function at the pixel level.

[0082] Continuing on, Figure 3 and Figure 4 This demonstrates the freeze-thaw state classification results of the target freeze-thaw discriminant function and the classical freeze-thaw discriminant function at the same pixel location (e.g., row_21 col_797) in the Finnish FMI monitoring network. Figure 3 and Figure 4 The data points in the data represent the actual freeze-thaw state determined by measured soil temperature data. The color distinguishes between freezing and thawing, and the color intensity reflects the density of the data points.

[0083] right Figure 3 and Figure 4 By comparison, it can be seen that ADFA uses three key control points ( , and The ADFA (Advanced Graphical Function) accurately captures the gradual change process of freeze-thaw states by fitting second-order Bézier curves, especially excelling in the freeze-thaw critical region (KA value approximately 250-260). It achieves adaptive discrimination function thresholding, overcoming the limitations of the fixed threshold of the DFA (Discrete Acid Function). Specifically, in terms of accuracy, the ADFA achieves an overall accuracy of 89.39% for this pixel, with a frozen state accuracy of 90.53% and a thawing state accuracy of 88.59%. The corresponding accuracy rates for the DFA are 85.00%, 93.77%, and 78.83%, respectively. It is noteworthy that although the DFA has a slight advantage in frozen state identification (93.77% / 90.53%), it significantly lags behind the ADFA in thawing state identification (78.83% / 88.59%) and overall accuracy (85.00% / 89.39%). This phenomenon is consistent with the trend observed in the tabular analysis of this application, further confirming the superiority of the ADFA in handling complex and variable freeze-thaw processes.

[0084] In addition, from Figure 3 and Figure 4 It can also be seen that the trend of the ADFA fitting curve closely matches the data distribution characteristics. In the high KA value region (approximately 270-290), the curve tends to be flat, which is consistent with the relatively stable thawing state characteristics of this region; while in the region with KA values ​​of approximately 230-250, the curve exhibits a moderate slope, reflecting the distribution characteristics of the frozen state. It is worth noting that in the freeze-thaw transition region (KA value approximately 250-260), ADFA, through the special design of the control point fitting curve, can distinguish between frozen and thawing data to the greatest extent, achieving accurate characterization of this complex region. In contrast, DFA, unable to adjust the position of the discriminant function, struggles to accurately distinguish between intertwined frozen and thawing data, leading to a higher misclassification rate. In summary, ADFA, by introducing second-order Bézier curves and a key control point mechanism, achieves fine-tuning of freeze-thaw state discrimination at the pixel level, demonstrating significant advantages, especially in complex regions with frequent freeze-thaw transitions.

[0085] In summary, this application has the following key points and technical effects: 1. Algorithm Simplification and Parameter Determination: This paper improves the traditional freeze-thaw discriminant algorithm by using the Fisher discriminant method and second-order Bézier curves, proposing a threshold-adaptive freeze-thaw discriminant algorithm (ADFA). The traditional FTI=AX+BY+C formula is simplified to FTI=KX+Y+B, reducing the number of parameters and improving computational efficiency while retaining the model's core predictive capabilities, laying the foundation for subsequent adaptive optimization. The Fisher discriminant method is used to determine globally unified basic parameters K and B. Leveraging their property of maximizing inter-class distance and minimizing intra-class variance, the accuracy and stability of freeze-thaw state discrimination are ensured, providing a consistent benchmark for the discrimination process.

[0086] 2. Regional Difference Handling Strategy: For regions with relatively stable freeze-thaw conditions (e.g., where the percentage of thawed or frozen days exceeds 90%), the parameters determined by Fisher's discriminant method are directly applied to ensure model stability and predictive consistency. For regions with frequent and significant freeze-thaw changes, the K value is kept constant, and the B value is dynamically optimized using a second-order Bézier curve to achieve accurate adaptation to local characteristics and solve the problem of inaccurate threshold setting caused by regional differences in existing algorithms.

[0087] 3. Fisher Discriminant Analysis (FSA) for Parameter Determination: As a classic dimensionality reduction classification technique, Fisher discriminant analysis constructs an optimal linear projection to map high-dimensional feature data to a low-dimensional space for effective classification. It determines a set of optimal discriminant coefficients that maximize the distance between different categories and minimize the dispersion of samples within each category after projection. QE, representing soil liquid water content, and TbV36.5, representing surface soil temperature, are selected as key classification indicators. A two-dimensional Fisher discriminant function is constructed, and based on this, mathematical derivation and simplification yield expressions for the fixed parameters K and B.

[0088] 4. Determining Dynamic Parameters Using Second-Order Bézier Curves: Utilizing the mathematical principles and properties of second-order Bézier curves, a smooth, continuous curve is generated using a finite number of control points to simulate the nonlinear relationship between soil surface temperature and soil emissivity during the freeze-thaw process. Three control points representing the completely frozen, completely thawed, and freeze-thaw transition states are selected to fit the second-order Bézier curve. The maximum curvature point of the curve is calculated, and this point is used as the key "inflection point" of the freeze-thaw transition. With a fixed parameter K, the dynamic parameter B is obtained by substituting it into the formula.

[0089] Therefore, this application achieves: (1) parameter simplification and improved computational efficiency, that is, the traditional freeze-thaw discriminant function (FTI=AX+BY+C) is simplified to the form FTI=KX+Y+B, reducing the number of parameters, while retaining the core predictive ability of the model, optimizing the calculation process, and making it easier to understand and apply; (2) threshold adaptation to regional differences, adopting the strategy of "global benchmark + regional dynamic adjustment", using Fisher's discriminant method to determine globally unified K and B basic parameters, and using second-order Bézier curves to dynamically optimize the B value for regions with frequent freeze-thaw changes, effectively solving the problem of threshold mismatch caused by regional geographical and climatic differences in traditional algorithms; (3) higher discrimination accuracy, through FY-3B, FY-3C, FY-3D Verification by satellite data and observation data from the International Soil Moisture Network showed that its overall accuracy (0.8132-0.8579) was significantly higher than that of the traditional freeze-thaw discriminant algorithm (DFA, 0.7337-0.8195), especially in the identification of thawing state (accuracy 0.8749-0.9244), and it could accurately capture the state changes in the critical area of ​​freeze-thaw transition; (4) Setting separate fixed parameters for different satellites and different orbital data can effectively eliminate the system differences between multi-source satellite data, while preserving the real information of the surface freeze-thaw state, and is suitable for freeze-thaw monitoring of multi-source microwave remote sensing data; (5) It has a wide range of applications, and can be stably applied to stable areas where the freeze-thaw state accounts for more than 90%, and can also be accurately adapted to complex areas where freeze-thaw alternation is frequent, which can meet the needs of freeze-thaw monitoring at the global and regional scales.

[0090] Therefore, this application proposes a threshold-adaptive freeze-thaw discriminant algorithm based on Bézier curves and Fisher discriminant analysis. By introducing a dynamic threshold adjustment mechanism, the algorithm can automatically adjust the discrimination threshold according to the characteristics of satellite observations, thereby improving the accuracy of freeze-thaw determination in complex environments.

[0091] Reference Figure 5 As shown, this application provides a threshold-adaptive freeze-thaw discrimination device, the device comprising: The first acquisition module 501 is used to determine the classical freeze-thaw discrimination function and transform the classical freeze-thaw discrimination function to obtain the target freeze-thaw discrimination function with threshold adaptation; wherein, the parameters of the target freeze-thaw discrimination function include preset fixed parameters and dynamic parameters.

[0092] The second acquisition module 502 is used to fit a second-order Bézier curve based on a preset number of target control points, adjust the preset initial dynamic parameters of the target freeze-thaw discrimination function, and obtain new dynamic parameters.

[0093] The third acquisition module 503 is used to calculate the global long-term daily freeze-thaw results based on the target freeze-thaw discriminant function, and to obtain the global freeze-thaw stability zone and the global freeze-thaw significant alternation zone based on the global long-term daily freeze-thaw results.

[0094] The processing module 504 is used to select the corresponding regional difference processing strategy for global freeze-thaw stable zones and global areas with significant freeze-thaw alternation, and process the parameters of the target freeze-thaw discriminant function based on the regional difference processing strategy, so as to perform freeze-thaw discrimination based on the respective target freeze-thaw discriminant function.

[0095] In one possible implementation, the first acquisition module is specifically used for: The target discriminant coefficient vector is determined based on the preset discriminant formula; where the discriminant formula represents the discriminant criterion as equal to the ratio of the projected inter-class distance to the intra-class variance; the target discriminant coefficient vector represents the set of optimal discriminant coefficients that maximize the discriminant criterion. In response to the discrimination criterion reaching its maximum value, the system of equations in which the partial derivatives of the discrimination criterion with respect to the target discrimination coefficient vector are equal to zero is solved to obtain the corresponding optimal discrimination function; A two-dimensional discriminant function is constructed based on preset key classification indicators and the optimal discriminant function, and the two-dimensional discriminant function is transformed to obtain the corresponding target freeze-thaw discriminant function.

[0096] In one possible implementation, the second acquisition module is specifically used for: The first and second derivatives of the parametric equations of the second-order Bézier curve are determined based on the target control points, and the curvature of the second-order Bézier curve is determined based on the first and second derivatives. Calculate the curvature maxima of the second-order Bézier curve, and substitute these maxima into the target freeze-thaw discriminant function with fixed parameters. Adjust the preset initial dynamic parameters of the target freeze-thaw discriminant function to obtain new dynamic parameters.

[0097] In one possible implementation, the third acquisition module is specifically used for: For different satellites and different orbits, separate fixed parameters and dynamic parameters are determined for each satellite and each orbit, and the target freeze-thaw discrimination function is obtained based on the fixed parameters and dynamic parameters; The global soil freeze-thaw state is determined based on the target freeze-thaw discriminant function, and the global long-term daily freeze-thaw results are obtained. The number of thawing days and the number of freezing days are obtained based on the global long-term daily freeze-thaw results. The number of thawing days and the number of freezing days constitute the total number of days. The first proportion of thawing days to the total number of days and the second proportion of freezing days to the total number of days are determined. Areas with the first proportion greater than a preset first threshold are defined as global freeze-thaw stability zones, and areas with the second proportion greater than a preset second threshold are defined as global freeze-thaw significant alternation zones.

[0098] In one possible implementation, the third acquisition module is specifically used for: For the global freeze-thaw stability zone, the original fixed and dynamic parameters of the target freeze-thaw discrimination function remain unchanged; For regions with significant global freeze-thaw cycles, while keeping the original fixed parameters of the target freeze-thaw discriminant function unchanged, the dynamic parameters are dynamically optimized based on the second-order Bézier curve.

[0099] In one possible implementation, the device further includes: The selection module is used to select soil temperature observation data at target depths from multiple observation networks distributed in the International Soil Moisture Network in regions of frequent freeze-thaw transition in the Northern Hemisphere; among them, multiple observation networks cover different geographical environments and characterize areas of active freeze-thaw transition under different geographical environments; The comparison module is used to compare the accuracy of the target freeze-thaw discriminant function and the classical freeze-thaw discriminant function in monitoring soil freeze-thaw status based on soil temperature observation data from the observation network; wherein the accuracy includes at least the overall accuracy, the accuracy of the thawing state, and the accuracy of the frozen state.

[0100] In one possible implementation, the device further includes: The first determination module is used to select the target observation network from multiple observation networks and determine the freeze-thaw state classification of the target freeze-thaw discriminant function and the classical freeze-thaw discriminant function at the same pixel location in the target observation network. The second determining module is used to determine the first accuracy of the target freeze-thaw discriminant function in the pixel based on the freeze-thaw state classification of the target freeze-thaw discriminant function, and to determine the second accuracy of the classical freeze-thaw discriminant function in the pixel based on the freeze-thaw state classification of the classical freeze-thaw discriminant function. The evaluation module is used to evaluate the threshold control capabilities of the target freeze-thaw discriminant function and the classical freeze-thaw discriminant function at the pixel level by comparing the first accuracy and the second accuracy.

[0101] This application provides the aforementioned threshold-adaptive freeze-thaw discrimination device, which determines the classical freeze-thaw discrimination function and transforms it to obtain a threshold-adaptive target freeze-thaw discrimination function. It then performs second-order Bézier curve fitting based on a preset number of target control points, adjusts the preset initial dynamic parameters of the target freeze-thaw discrimination function to obtain new dynamic parameters, calculates global long-term daily freeze-thaw results based on the target freeze-thaw discrimination function, and obtains global freeze-thaw stability zones and global areas with significant freeze-thaw alternation based on these results. For each of the global freeze-thaw stability zones and areas with significant freeze-thaw alternation, it selects a corresponding regional difference processing strategy and processes the parameters of the target freeze-thaw discrimination function based on the regional difference processing strategy to perform freeze-thaw discrimination based on their respective target freeze-thaw discrimination functions. The threshold-adaptive freeze-thaw discrimination device of this application transforms the classical freeze-thaw discrimination function to obtain a threshold-adaptive target freeze-thaw discrimination function. Based on a preset number of target control points, it adjusts the preset initial dynamic parameters of the target freeze-thaw discrimination function by fitting a second-order Bézier curve, obtaining new dynamic parameters. Furthermore, it calculates global long-term daily freeze-thaw results based on the target freeze-thaw discrimination function to dynamically adjust the parameters of the target freeze-thaw discrimination function, reducing the number of parameters and improving computational efficiency. It introduces a dynamic threshold adjustment mechanism, automatically adjusting the discrimination threshold based on global long-term daily freeze-thaw results obtained from satellite observation data. This improves the accuracy of freeze-thaw state determination in complex environments, solves the adaptability problem during spatial migration, and can effectively cope with complex situations at different spatial scales. It is applicable to discrimination in any region and season.

[0102] like Figure 6 As shown in the embodiment of this application, an electronic device 600 includes a processor 601, a memory 602, and a bus. The memory 602 stores machine-readable instructions executable by the processor 601. When the electronic device is running, the processor 601 communicates with the memory 602 via the bus. The processor 601 executes the machine-readable instructions to perform the steps of the above-described threshold adaptive freeze-thaw discrimination method.

[0103] Specifically, the memory 602 and processor 601 mentioned above can be general-purpose memory and processor, without any specific limitations. When the processor 601 runs the computer program stored in the memory 602, it can execute the above-mentioned threshold adaptive freeze-thaw discrimination method.

[0104] Corresponding to the above-described threshold-adaptive freeze-thaw discrimination method, this application embodiment also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the above-described threshold-adaptive freeze-thaw discrimination method.

[0105] The electronic device and storage medium provided in this application, by transforming the classical freeze-thaw discriminant function to obtain a threshold-adaptive target freeze-thaw discriminant function, and by fitting a second-order Bézier curve based on a preset number of target control points to adjust the preset initial dynamic parameters of the target freeze-thaw discriminant function, new dynamic parameters are obtained. Furthermore, global long-term daily freeze-thaw results are calculated based on the target freeze-thaw discriminant function to dynamically adjust the parameters of the target freeze-thaw discriminant function, reducing the number of parameters, improving computational efficiency, and introducing a dynamic threshold adjustment mechanism. The discriminant threshold is automatically adjusted based on global long-term daily freeze-thaw results obtained from satellite observation data, improving the accuracy of freeze-thaw state determination in complex environments, solving the adaptability problem during spatial migration, effectively coping with complex situations at different spatial scales, and applicable to discrimination in any region and season.

[0106] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces; the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0107] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0108] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0109] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, 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, ROM, RAM, magnetic disks, or optical disks.

[0110] The above are merely specific embodiments 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 technical scope 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.

Claims

1. A threshold-adaptive freeze-thaw discrimination method, characterized in that, The method includes: Step S1: Determine the classical freeze-thaw discrimination function and transform the classical freeze-thaw discrimination function to obtain the threshold-adaptive target freeze-thaw discrimination function; wherein, the parameters of the target freeze-thaw discrimination function include preset fixed parameters and dynamic parameters; Step S2: Fit a second-order Bézier curve based on a preset number of target control points, and adjust the preset initial dynamic parameters of the target freeze-thaw discriminant function to obtain new dynamic parameters; Step S3: Calculate the global long-term daily freeze-thaw results based on the target freeze-thaw discriminant function, and obtain the global freeze-thaw stability zone and the global freeze-thaw significant alternation zone based on the global long-term daily freeze-thaw results; Step S4: For the global freeze-thaw stability zone and the global freeze-thaw significant alternation zone, select the corresponding regional difference processing strategy, process the parameters of the target freeze-thaw discriminant function based on the regional difference processing strategy, and perform freeze-thaw discrimination based on the target freeze-thaw discriminant function.

2. The threshold-adaptive freeze-thaw discrimination method according to claim 1, characterized in that, In step S1, the transformation of the classical freeze-thaw discriminant function to obtain a threshold-adaptive target freeze-thaw discriminant function includes: The target discriminant coefficient vector is determined based on a preset discriminant formula; wherein, the discriminant formula represents the discriminant criterion as equal to the ratio of the projected inter-class distance to the intra-class variance; and the target discriminant coefficient vector represents a set of optimal discriminant coefficients that maximize the discriminant criterion. In response to the discrimination criterion reaching its maximum value, the system of equations in which the partial derivatives of the discrimination criterion with respect to the target discrimination coefficient vector are equal to zero is solved to obtain the corresponding optimal discrimination function; A two-dimensional discriminant function is constructed based on the preset key classification indicators and the optimal discriminant function, and the two-dimensional discriminant function is transformed to obtain the corresponding target freeze-thaw discriminant function.

3. The threshold-adaptive freeze-thaw discrimination method according to claim 1, characterized in that, In step S2, the second-order Bézier curve is fitted based on a preset number of target control points, and the preset initial dynamic parameters of the target freeze-thaw discriminant function are adjusted to obtain new dynamic parameters, including: Based on the target control point, determine the first and second derivatives of the parametric equation of the second-order Bézier curve, and determine the curvature of the second-order Bézier curve based on the first and second derivatives. Calculate the curvature maxima of the second-order Bézier curve, and substitute the curvature maxima into the target freeze-thaw discrimination function under the fixed parameters. Adjust the preset initial dynamic parameters of the target freeze-thaw discrimination function to obtain new dynamic parameters.

4. The threshold-adaptive freeze-thaw discrimination method according to claim 1, characterized in that, Step S3 includes: For different satellites and different orbits, separate fixed parameters and dynamic parameters are determined for each satellite and each orbit, and the target freeze-thaw discrimination function is obtained based on the fixed parameters and the dynamic parameters. The global soil freeze-thaw state is determined based on the target freeze-thaw discriminant function, and the global long-term daily freeze-thaw results are obtained. The number of thawing days and the number of freezing days are obtained based on the global long-term daily freeze-thaw results. The number of thawing days and the number of freezing days constitute the total number of days. A first proportion of the number of thawing days to the total number of days and a second proportion of the number of freezing days to the total number of days are determined, and the area where the first proportion is greater than a preset first threshold is determined as a global freeze-thaw stability zone, and the area where the second proportion is greater than a preset second threshold is determined as a global freeze-thaw significant alternation zone.

5. The threshold-adaptive freeze-thaw discrimination method according to claim 1, characterized in that, In step S4, selecting corresponding regional difference processing strategies for the global freeze-thaw stability zone and the global freeze-thaw alternation significant region includes: For the global freeze-thaw stability zone, the original fixed and dynamic parameters of the target freeze-thaw discrimination function remain unchanged; For the regions with significant global freeze-thaw cycles, while keeping the original fixed parameters of the target freeze-thaw discriminant function unchanged, the dynamic parameters are dynamically optimized based on the second-order Bézier curve.

6. The threshold-adaptive freeze-thaw discrimination method according to claim 1, characterized in that, The method further includes: Soil temperature observation data at target depths of multiple observation networks distributed in the Northern Hemisphere with frequent freeze-thaw transitions, from the International Soil Moisture Network, were selected; wherein, the multiple observation networks cover different geographical environments and characterize the freeze-thaw active areas under different geographical environments; The accuracy of the target freeze-thaw discriminant function and the classical freeze-thaw discriminant function in monitoring soil freeze-thaw status is compared based on the soil temperature observation data of the observation network; wherein the accuracy includes at least the overall accuracy, the thawing state accuracy, and the freezing state accuracy.

7. The threshold-adaptive freeze-thaw discrimination method according to claim 6, characterized in that, The method further includes: Select a target observation network from the multiple observation networks, and determine the freeze-thaw state classification of the target freeze-thaw discriminant function and the classical freeze-thaw discriminant function at the same pixel location in the target observation network; The first accuracy of the target freeze-thaw discriminant function in this pixel is determined based on the freeze-thaw state classification of the target freeze-thaw discriminant function, and the second accuracy of the classical freeze-thaw discriminant function in this pixel is determined based on the freeze-thaw state classification of the classical freeze-thaw discriminant function. Based on a comparison of the first accuracy and the second accuracy, the threshold adjustment capabilities of the target freeze-thaw discriminant function and the classical freeze-thaw discriminant function at the pixel level are evaluated.

8. A threshold-adaptive freeze-thaw discrimination device, characterized in that, The device includes: The first acquisition module is used to determine the classical freeze-thaw discrimination function and transform the classical freeze-thaw discrimination function to obtain a threshold-adaptive target freeze-thaw discrimination function; wherein, the parameters of the target freeze-thaw discrimination function include preset fixed parameters and dynamic parameters; The second acquisition module is used to fit a second-order Bézier curve based on a preset number of target control points, adjust the preset initial dynamic parameters of the target freeze-thaw discrimination function, and obtain new dynamic parameters. The third acquisition module is used to calculate the global long-term daily freeze-thaw results based on the target freeze-thaw discriminant function, and to obtain the global freeze-thaw stability zone and the global freeze-thaw significant alternation zone based on the global long-term daily freeze-thaw results. The processing module is used to select the corresponding regional difference processing strategy for the global freeze-thaw stability zone and the global freeze-thaw significant alternation zone, and process the parameters of the target freeze-thaw discriminant function based on the regional difference processing strategy, so as to perform freeze-thaw discrimination based on the respective target freeze-thaw discriminant function.

9. An electronic device, characterized in that, include: The device includes a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the threshold-adaptive freeze-thaw discrimination method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the threshold-adaptive freeze-thaw discrimination method as described in any one of claims 1 to 7.

Images