Method and device for determining critical value of soil moisture change, and storage medium

Abstract

The application provides a soil humidity change critical value determination method and device and a storage medium, relates to the technical field of remote sensing and data processing, and is used for improving the stability and accuracy of determining the soil humidity change critical value. The method comprises the following steps: obtaining target region to-be-analyzed data. According to the to-be-analyzed data, the soil evaporation efficiency for characterizing the restriction degree of soil moisture on the surface evaporation process is determined. According to a first segmented relationship model between the soil evaporation efficiency and the soil humidity, a first critical soil humidity is determined. According to a second segmented relationship model between the surface temperature change amplitude and the soil humidity, a second critical soil humidity is determined. The second critical soil humidity is the soil humidity corresponding to the second turning point of the surface temperature change amplitude with the soil humidity change in the soil drying process. According to a preset fusion rule, the first critical soil humidity and the second critical soil humidity are fused to generate a soil humidity change critical threshold distribution of the target region.

Description

Technical Field

[0001] This invention relates to the fields of remote sensing and data processing technology, and in particular to a method, apparatus and storage medium for determining the critical value of soil moisture change. Background Technology

[0002] Soil moisture is a key state variable connecting land surface water cycle, energy balance, and climate feedback. Accurately understanding how soil moisture affects surface evapotranspiration is fundamental for drought monitoring, vegetation water stress assessment, and land-atmosphere interaction research. Studies show that as soil moisture gradually decreases from a sufficient state, surface evapotranspiration transitions from an energy-limited state (evapotranspiration mainly determined by meteorological conditions such as solar radiation) to a water-limited state (evapotranspiration mainly determined by soil water supply capacity). The critical value of soil moisture change corresponding to this state transition is an important parameter characterizing the surface evapotranspiration response and can provide a basis for parameterization of hydrological, ecological, and climate models.

[0003] Existing technologies for identifying this critical point mainly include methods such as piecewise regression of evaporation fraction and soil moisture based on flux tower observations, inflection point detection methods based on diurnal variation information of land surface temperature, and diagnostic methods based on land surface model parameterization. However, these methods generally have high requirements for input data quality, sample coverage conditions, and specific weather processes. For example, they usually require continuous periods without precipitation or high-quality observation samples as support, which leads to problems such as unstable results, poor spatial continuity, and insufficient robustness when applied to large areas.

[0004] Therefore, there is an urgent need to provide a technical solution that can effectively identify the critical threshold of soil moisture change while ensuring the stability and accuracy of the results, so as to at least solve the problems of insufficient accuracy and spatial applicability in determining the critical point of soil moisture change in the existing technology. Summary of the Invention

[0005] This invention provides a method, apparatus, and storage medium for determining the critical value of soil moisture change, in order to at least solve the technical problem in the prior art that the high requirements for input data and specific sample conditions lead to insufficient stability and accuracy in the determination results of the critical threshold of soil moisture change.

[0006] To achieve the above objectives, the present invention provides a method for determining the critical value of soil moisture change, comprising the following steps: Acquire the data to be analyzed for the target area. The data to be analyzed should include at least the changes in soil moisture and surface temperature. Based on the data to be analyzed, determine the soil evaporation efficiency, which characterizes the degree to which soil moisture limits the surface evaporation process; Based on the first piecewise relationship model between soil evaporation efficiency and soil moisture, the first critical soil moisture is determined, where the first critical soil moisture is the soil moisture corresponding to the first inflection point in the first piecewise relationship model where soil evaporation efficiency transitions from the plateau region to the linearly decreasing region. Based on the second piecewise relationship model between the magnitude of surface temperature change and soil moisture, the second critical soil moisture is determined. The second critical soil moisture is the soil moisture corresponding to the second inflection point of the change in the magnitude of surface temperature change with soil moisture during the soil drying process. The first and second critical soil moisture are fused according to the preset fusion rules to generate the critical threshold distribution of soil moisture change in the target area.

[0007] In one possible implementation, based on the data to be analyzed, soil evaporation efficiency, used to characterize the degree to which soil moisture limits the surface evaporation process, is determined, including: Based on the data to be analyzed, determine the actual soil evaporation and the potential soil evaporation. The ratio of actual soil evaporation to potential soil evaporation is defined as soil evaporation efficiency.

[0008] In another possible implementation, the data to be analyzed also includes surface meteorological data and surface vegetation data. Based on the data to be analyzed, the actual soil evaporation and potential soil evaporation are determined, including: Based on surface meteorological data, surface vegetation data, and a pre-set evapotranspiration calculation model, the actual soil evapotranspiration is determined. The evapotranspiration calculation model is used to decompose the total surface evapotranspiration into vegetation transpiration and soil evapotranspiration. Based on surface meteorological data and a pre-set evapotranspiration calculation model, and by setting the resistance parameter used to characterize the inhibitory effect of soil surface moisture on the evaporation process to a minimum value, the potential soil evaporation is determined.

[0009] In another possible implementation, a first critical soil moisture level is determined based on a first piecewise relationship model between soil evaporation efficiency and soil moisture, including: Construct a sample point set relating soil evaporation efficiency and soil moisture; Soil moisture is divided into intervals, and a statistical representative value of soil evaporation efficiency is determined for each moisture interval to obtain a set of statistical relationships. The first segmented relationship model is fitted based on the statistical relationship set. The soil moisture corresponding to the first inflection point that minimizes the fitting error of the first segmented relationship model is taken as the first critical soil moisture.

[0010] In another possible implementation, the first segmented relationship model includes a first segment and a second segment. The first segment is used to describe the linear decreasing relationship between soil evaporation efficiency and soil moisture when soil moisture is less than the first inflection point, and the second segment is used to describe the plateau relationship between soil evaporation efficiency and soil moisture when soil moisture is greater than or equal to the first inflection point.

[0011] In another possible implementation, the first and second critical soil moisture contents are fused to generate a critical threshold distribution of soil moisture change in the target area, including: Quality control is performed on the first critical soil moisture, which includes at least one of the following: determining whether the number of samples used to determine the first critical soil moisture is sufficient, whether the fitting of the first piecewise relationship model is converged, and whether the first critical soil moisture is within the preset moisture range. If the first critical soil moisture passes quality control, then the first critical soil moisture will be used as the final critical soil moisture of the target pixel. If the first critical soil moisture fails to pass quality control, the second critical soil moisture will be used as the final critical soil moisture for the target pixel. The target area includes target pixels, and the distribution of critical thresholds for soil moisture change includes the final critical soil moisture.

[0012] The present invention also provides a device for determining the critical value of soil moisture change, comprising the following modules: The acquisition module is used to acquire the data to be analyzed in the target area. The data to be analyzed includes at least the changes in soil moisture and surface temperature. The processing module is used to determine the soil evaporation efficiency, which characterizes the degree to which soil moisture limits the surface evaporation process, based on the data to be analyzed. The processing module is also used to determine the first critical soil moisture based on the first piecewise relationship model between soil evaporation efficiency and soil moisture, wherein the first critical soil moisture is the soil moisture corresponding to the first inflection point in the first piecewise relationship model where soil evaporation efficiency transitions from the plateau region to the linearly decreasing region. The processing module is also used to determine the second critical soil moisture based on the second piecewise relationship model between the change range of surface temperature and soil moisture. The second critical soil moisture is the soil moisture corresponding to the second inflection point of the change range of surface temperature with soil moisture during the soil drying process. The processing module is also used to fuse the first critical soil moisture and the second critical soil moisture according to the preset fusion rules to generate the critical threshold distribution of soil moisture change in the target area.

[0013] In one possible implementation, the processing module is specifically used for: Based on the data to be analyzed, determine the actual soil evaporation and the potential soil evaporation. The ratio of actual soil evaporation to potential soil evaporation is defined as soil evaporation efficiency.

[0014] In another possible implementation, the data to be analyzed also includes surface meteorological data and surface vegetation data. The processing module is specifically used for: Based on surface meteorological data, surface vegetation data, and a pre-set evapotranspiration calculation model, the actual soil evapotranspiration is determined. The evapotranspiration calculation model is used to decompose the total surface evapotranspiration into vegetation transpiration and soil evapotranspiration. Based on surface meteorological data and a pre-set evapotranspiration calculation model, and by setting the resistance parameter used to characterize the inhibitory effect of soil surface moisture on the evaporation process to a minimum value, the potential soil evaporation is determined.

[0015] In another possible implementation, the processing module is specifically used for: Construct a sample point set relating soil evaporation efficiency and soil moisture; Soil moisture is divided into intervals, and a statistical representative value of soil evaporation efficiency is determined for each moisture interval to obtain a set of statistical relationships. The first segmented relationship model is fitted based on the statistical relationship set. The soil moisture corresponding to the first inflection point that minimizes the fitting error of the first segmented relationship model is taken as the first critical soil moisture.

[0016] In another possible implementation, the first segmented relationship model includes a first segment and a second segment. The first segment is used to describe the linear decreasing relationship between soil evaporation efficiency and soil moisture when soil moisture is less than the first inflection point, and the second segment is used to describe the plateau relationship between soil evaporation efficiency and soil moisture when soil moisture is greater than or equal to the first inflection point.

[0017] In another possible implementation, the processing module is specifically used for: Quality control is performed on the first critical soil moisture, which includes at least one of the following: determining whether the number of samples used to determine the first critical soil moisture is sufficient, whether the fitting of the first piecewise relationship model is converged, and whether the first critical soil moisture is within the preset moisture range. If the first critical soil moisture passes quality control, then the first critical soil moisture will be used as the final critical soil moisture of the target pixel. If the first critical soil moisture fails to pass quality control, the second critical soil moisture will be used as the final critical soil moisture for the target pixel. The target area includes target pixels, and the distribution of critical thresholds for soil moisture change includes the final critical soil moisture.

[0018] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the methods described above for determining the critical value of soil moisture change.

[0019] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for determining the critical value of soil moisture change as described above.

[0020] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements a method for determining any of the above-described soil moisture change thresholds.

[0021] The method, apparatus, and storage medium for determining the critical value of soil moisture change provided by this invention introduce soil evaporation efficiency, which characterizes the degree to which soil moisture limits the surface evaporation process, and establishes a first piecewise relationship model between evaporation efficiency and soil moisture. This enables the stable identification of the first critical soil moisture level when the evaporation process shifts from energy-limited to water-limited, reducing reliance on direct evapotranspiration observation data. Simultaneously, by utilizing a second piecewise relationship model between surface temperature change and soil moisture, a second critical soil moisture level is extracted from the surface thermal state response during soil drying, forming a supplementary estimation pathway independent of the evaporation efficiency path. Based on this, by fusing the first and second critical soil moisture levels to generate the critical threshold distribution of the target area, the surface temperature path can be effectively supplemented when the evaporation efficiency path fails due to insufficient data or model instability, thereby improving the spatial coverage, accuracy, and stability of the soil moisture change critical value estimation. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is one of the flowcharts illustrating the method for determining the critical value of soil moisture change provided by the present invention.

[0024] Figure 2 This is the second flowchart illustrating the method for determining the critical value of soil moisture change provided by the present invention.

[0025] Figure 3 This is the third flowchart illustrating the method for determining the critical value of soil moisture change provided by the present invention.

[0026] Figure 4 This is a schematic diagram of the device for determining the critical value of soil moisture change provided by the present invention.

[0027] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0029] The terms “comprising” and “having”, and any variations thereof, used in the description of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or modules is not limited to the steps or modules listed, but may optionally include other steps or modules not listed, or may optionally include other steps or modules inherent to such processes, methods, products, or apparatus.

[0030] Furthermore, in this invention, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present concepts in a concrete manner.

[0031] Soil moisture is a key state variable in the land surface water cycle and land-atmosphere interaction. Its changes directly affect evapotranspiration (ET), vegetation water stress, and surface energy distribution patterns, playing a fundamental role in drought evolution, heat wave formation, and climate feedback studies. Under certain climatic and underlying surface conditions, the ET process can exhibit two control mechanisms: energy-limited and water-limited. When soil moisture decreases from relatively abundant to a certain level, the response characteristics of ET to soil moisture change; this inflection point is the critical threshold for soil moisture change. With the development of remote sensing and reanalysis data, existing techniques often identify this threshold based on evaporative fraction (EF), energy distribution indicators, or surface thermal state characteristics, using piecewise regression or inflection point detection during drought decline or continuous drying processes.

[0032] However, existing technologies are heavily reliant on high temporal resolution ET or energy flux observation data and are susceptible to remote sensing inversion errors, cloud cover, and regional heterogeneity. For example, the EF soil moisture segmentation method based on flux tower observations has limited spatial representativeness, and site extrapolation suffers from scale mismatch and energy closure errors. Methods based on drying rates are sensitive to precipitation and noise processing and exhibit poor stability in humid or energy-constrained areas.

[0033] To address the aforementioned technical problems, this invention provides a method for determining the critical value of soil moisture change. This method introduces soil evaporation efficiency, which characterizes the degree to which soil moisture limits the surface evaporation process, and establishes a first piecewise relationship model between evaporation efficiency and soil moisture. This allows for the stable identification of the first critical soil moisture level when the evaporation process shifts from energy-limited to water-limited, reducing reliance on direct evapotranspiration observation data. Simultaneously, using a second piecewise relationship model between surface temperature change and soil moisture, a second critical soil moisture level is extracted from the surface thermal state response during soil drying, forming a supplementary estimation pathway independent of the evaporation efficiency path. Based on this, by fusing the first and second critical soil moisture levels to generate the critical threshold distribution of the target area, the surface temperature path can be effectively supplemented when the evaporation efficiency path fails due to insufficient data or model instability, thereby improving the spatial coverage, accuracy, and stability of the soil moisture change critical value estimation.

[0034] The execution entity of the method for determining the critical value of soil moisture change provided by this invention can be a device for determining the critical value of soil moisture change, which can be an electronic device. Furthermore, the device can also be the central processing unit (CPU) of the electronic device, or a module within the electronic device for determining the critical value of soil moisture change. This invention uses the example of a device for determining the critical value of soil moisture change executing the method for determining the critical value of soil moisture change to illustrate the method provided by this invention.

[0035] The electronic device can be a terminal, server, workstation, or other device with data processing capabilities. A terminal can be a device with transceiver functionality. Terminals can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water (such as ships); and they can be deployed in the air (e.g., on airplanes, balloons, and satellites). Terminals include handheld devices, vehicle-mounted devices, wearable devices, or computing devices with wireless communication capabilities. For example, a terminal can be a mobile phone, tablet computer, or computer with wireless transceiver capabilities. Terminal devices can also be virtual reality (VR) terminal devices, augmented reality (AR) terminal devices, wireless terminals in industrial control, wireless terminals in autonomous driving, wireless terminals in telemedicine, wireless terminals in smart grids, wireless terminals in smart cities, wireless terminals in smart homes, and so on.

[0036] The following is combined Figures 1 to 5 The present invention describes a method, apparatus, and storage medium for determining the critical value of soil moisture change.

[0037] Figure 1 This is one of the flowcharts illustrating the method for determining the critical value of soil moisture change provided by the present invention, such as... Figure 1 As shown, the method includes the following: Step 101: Obtain the data to be analyzed in the target area.

[0038] The data to be analyzed includes at least the variation in soil moisture and surface temperature.

[0039] It should be understood that the target area can be any land region globally, and in practical applications, the pixel is usually used as the basic unit of analysis. That is, the target area can be a single pixel, or the target area can include multiple pixels.

[0040] Soil moisture is used to indicate the volumetric water content of the top or shallow soil, characterizing the soil's water supply status. Surface temperature variation is used to characterize the temporal fluctuations in the surface thermal state.

[0041] For example, the surface temperature variation can be the difference between daytime and nighttime surface temperatures obtained from remote sensing data, i.e., the diurnal variation of surface temperature. This surface temperature variation is closely related to the cooling effect of surface evapotranspiration: the more abundant the soil moisture, the stronger the evapotranspiration cooling effect, and the smaller the diurnal variation of surface temperature. Conversely, when soil moisture is deficient, the evapotranspiration cooling effect weakens, and the diurnal variation of surface temperature increases.

[0042] In one possible implementation, long-term multi-source remote sensing and reanalysis data covering global land areas can be acquired. This data includes at least one of the following: surface meteorological data (including surface temperature), vegetation index or leaf area index, surface or shallow soil moisture, saturated vapor pressure difference, near-surface wind speed, and surface albedo. Subsequently, the long-term multi-source remote sensing and reanalysis data undergoes preprocessing, including resampling to a unified spatial grid, temporal scale unification and alignment, and removing data with severe cloud cover or poor inversion quality based on quality control indicators to obtain the data to be analyzed. The magnitude of surface temperature variation can be calculated from the daytime and nighttime surface temperatures in the dataset.

[0043] Step 102: Determine the soil evaporation efficiency based on the data to be analyzed.

[0044] Soil evaporation efficiency is a dimensionless indicator used to characterize the degree to which current soil moisture conditions limit the surface evaporation process.

[0045] In this step, based on the data to be analyzed (including but not limited to soil moisture, surface meteorological data, surface vegetation data, etc.), the actual soil evaporation and potential soil evaporation are estimated separately using a physical model, and the ratio of the two is determined as the soil evaporation efficiency.

[0046] The following is a detailed introduction to determining soil evaporation efficiency based on the data to be analyzed, including step one and step two.

[0047] Step 1: Based on the data to be analyzed, determine the actual soil evaporation and the potential soil evaporation.

[0048] In this embodiment of the invention, the data to be analyzed also includes surface meteorological data and surface vegetation data.

[0049] For example, surface meteorological data may include at least one of the following: net radiation, air density, specific heat capacity of air at constant pressure, saturated vapor pressure, actual vapor pressure, latent heat of vaporization of water, air temperature, wind speed, etc. Net radiation may include net radiation absorbed by the soil surface and net radiation absorbed by the vegetation canopy. Surface vegetation data may include leaf area index.

[0050] In a preferred implementation, the actual soil evaporation is determined based on surface meteorological data, surface vegetation data, and a preset evapotranspiration calculation model. The evapotranspiration calculation model is used to decompose the total surface evapotranspiration into vegetation transpiration and soil evaporation.

[0051] It should be understood that total surface evapotranspiration includes vegetation transpiration and soil evaporation. This invention obtains the actual soil evaporation by decomposition, which can more accurately determine the soil evaporation and thus the evaporation efficiency.

[0052] For example, the preset evapotranspiration calculation model can be an evapotranspiration decomposition model based on the Penman-Monteith energy balance formula.

[0053] In one possible design, the actual soil evaporation rate satisfies the following formula.

[0054] Formula 1.

[0055] in, This represents the actual soil evaporation. Net radiation absorbed by the soil surface. The slope of the saturated water vapor pressure curve. air density, The specific heat capacity of air at constant pressure. The saturated vapor pressure, This is the actual water vapor pressure. The latent heat of vaporization of water, This is the constant of the wet and dry meter. For soil heat flux, The aerodynamic drag from the soil surface to the reference height, It represents soil surface resistance, used to reflect the inhibitory effect of changes in soil surface moisture content on the evaporation process.

[0056] Optionally, the actual vegetation transpiration rate satisfies the following formula 2.

[0057] Formula 2.

[0058] in, This represents the actual vegetation transpiration. The net radiation absorbed by the vegetation canopy. The aerodynamic drag from the canopy to the reference height, It represents the surface resistance of the canopy, used to characterize the regulatory role of vegetation stomata in water exchange.

[0059] In one possible implementation, the potential soil evapotranspiration is determined based on surface meteorological data and a pre-defined evapotranspiration calculation model, with the resistance parameter characterizing the inhibitory effect of soil surface moisture on the evaporation process set to a minimum value. The potential evapotranspiration component characterizes the maximum allowable evaporation capacity of the atmosphere under the same meteorological conditions, i.e., the atmospheric evaporation demand level.

[0060] For example, assuming that soil moisture conditions do not limit the evapotranspiration process, the potential vegetation transpiration (e.g., ) can be calculated by minimizing both the vegetation canopy resistance and soil surface resistance. ) and potential soil evaporation (e.g. ).

[0061] For example, the resistance parameter (i.e., soil surface resistance) used to characterize the inhibitory effect of soil surface moisture on the evaporation process is set to a minimum value, which is usually 0, representing that water vapor can diffuse from the soil surface into the atmosphere without hindrance.

[0062] Understandably, by employing a unified evapotranspiration calculation model to decompose total surface evapotranspiration into vegetation transpiration and soil evaporation, and calculating actual soil evaporation separately, it is possible to effectively distinguish the differential impacts of vegetation canopy stomatal regulation and soil surface water supply on the evapotranspiration process. This avoids masking the true limiting effect of soil moisture on the evaporation process by mixing the response characteristics of different evapotranspiration components. Simultaneously, when calculating potential soil evaporation, the resistance parameter characterizing the inhibitory effect of soil surface water on the evaporation process is set to a minimum value, thereby obtaining the maximum evaporation capacity allowed by the atmosphere under the same meteorological conditions, i.e., the atmospheric evaporation demand level. Based on this, the ratio of actual soil evaporation to potential soil evaporation is used as the soil evaporation efficiency, which can objectively reflect the degree of constraint of soil moisture on the surface evaporation process.

[0063] Step 2: Determine the soil evaporation efficiency as the ratio of actual soil evaporation to potential soil evaporation.

[0064] Soil evaporation efficiency is used to reflect the degree to which changes in soil moisture constrain the soil evaporation process.

[0065] It should be understood that the actual soil evaporation is compared with the theoretically maximum soil evaporation that may occur under the same meteorological conditions (i.e., atmospheric evaporation demand). A soil evaporation efficiency greater than or equal to a preset soil evaporation efficiency threshold indicates sufficient soil moisture, with the evaporation process primarily controlled by atmospheric demand. A soil evaporation efficiency less than the preset threshold indicates insufficient soil moisture, which significantly inhibits evaporation.

[0066] It should be noted that the preset soil evaporation efficiency threshold can be 0.9, 0.88, 0.8, etc., and the embodiments of the present invention do not limit it.

[0067] Optionally, vegetation transpiration efficiency can be determined based on actual vegetation transpiration and potential vegetation transpiration. Vegetation transpiration efficiency reflects the regulatory effect of vegetation water status on the transpiration process.

[0068] It is understandable that by determining the actual soil evaporation and the potential soil evaporation separately, and using the ratio of the two as the soil evaporation efficiency, the influence of differences in atmospheric evaporation demand in different regions on the analysis of the evaporation process can be effectively eliminated, so that the soil evaporation efficiency index can objectively reflect the true degree of constraint of soil moisture on the surface evaporation process.

[0069] Step 103: Determine the first critical soil moisture based on the first piecewise relationship model between soil evaporation efficiency and soil moisture.

[0070] The first critical soil moisture is the soil moisture corresponding to the first turning point in the transition of soil evaporation efficiency from the plateau region to the linearly decreasing region in the first piecewise relationship model.

[0071] It should be understood that the physical relationship between soil evaporation efficiency and soil moisture can help identify the critical point at which the evaporation mechanism shifts. According to the principles of surface processes, when soil moisture is high, soil evaporation efficiency is mainly influenced by atmospheric evaporation demand and remains relatively stable with little change in soil moisture. When soil moisture decreases below a certain threshold, water supply becomes the primary limiting factor, and soil evaporation efficiency decreases linearly with decreasing soil moisture. The inflection point between these two regions is the first critical soil moisture level.

[0072] Therefore, the first piecewise relationship model is defined as a piecewise function used to describe the first inflection point when soil evaporation efficiency changes from a plateau region to a linearly decreasing region as soil moisture changes.

[0073] In one possible implementation, the first segmented relationship model includes a first segment and a second segment. The first segment describes the linear decreasing relationship between soil evaporation efficiency and soil moisture when soil moisture is below a first inflection point. The second segment describes the plateau relationship between soil evaporation efficiency and soil moisture when soil moisture is greater than or equal to the first inflection point. The soil moisture corresponding to the first inflection point is the first critical soil moisture.

[0074] By using statistical analysis and piecewise regression techniques to fit the observed soil evaporation efficiency and soil moisture data into a model, the inflection point that minimizes the overall model fitting error can be determined. The soil moisture corresponding to this inflection point is the first critical soil moisture.

[0075] For example, the first segmented relationship model can be fitted by iterating through the candidate inflection points and using the weighted least squares method, with the minimum weighted squared residuals as the criterion for determining the first inflection point.

[0076] It should be noted that the embodiments of the present invention do not limit the area corresponding to the first critical soil moisture determined in step 103. For example, the first critical soil moisture may correspond to the target area, or it may correspond to one or more pixels in the target area. The following description uses the first critical soil moisture corresponding to one pixel.

[0077] Step 104: Determine the second critical soil moisture based on the second piecewise relationship model between the daily variation of surface temperature and soil moisture.

[0078] It should be understood that there is also a segmented relationship between the diurnal variation of surface temperature and soil moisture. During a prolonged period of dry soil without rain, the initial stage is characterized by sufficient soil moisture, a strong evapotranspiration cooling effect, and a relatively small diurnal variation in surface temperature that increases slowly as soil moisture decreases. Once soil moisture drops to a certain critical value, the evapotranspiration cooling effect weakens, and the diurnal variation in surface temperature increases sharply as soil moisture decreases. This point of abrupt change in response characteristic is the second inflection point.

[0079] Therefore, the second piecewise relationship model is used to describe the transition relationship between surface temperature variation and soil moisture variation during soil drying. This transition point is identified, and the corresponding soil moisture value is used as the second critical soil moisture value.

[0080] Based on the second piecewise relationship model between the magnitude of surface temperature change and soil moisture, the second critical soil moisture is determined, including steps a-e.

[0081] Step a: Based on the soil moisture time series in the data to be analyzed, identify continuous soil drying processes. A soil drying process refers to an event in which soil moisture shows an overall downward trend over a continuous period of time without precipitation or sudden disturbances.

[0082] Step b: Set screening conditions for the identified soil drying process and retain valid drying events that meet the preset conditions; the screening conditions include at least one of the following: duration threshold, soil moisture change range threshold, and trend consistency threshold.

[0083] Step c: Construct a sample point set relating the effective dry event surface temperature change range and soil moisture. The sample point set contains multiple sample pairs, each consisting of the soil moisture value and the surface temperature change range value at the same time.

[0084] Step d: Divide the soil moisture into intervals, divide the range of soil moisture values ​​into multiple continuous intervals, and determine the statistical representative value of the surface temperature change range in each interval to obtain a set of statistical relationships.

[0085] Step e: Fit the second segmented relationship model based on the statistical relationship set, and take the soil moisture corresponding to the second inflection point with the smallest fitting error of the second segmented relationship model as the second critical soil moisture.

[0086] In one possible implementation, the second segmented relationship model includes a third segment and a fourth segment. The third segment describes the relationship that when soil moisture is greater than or equal to the second inflection point, the magnitude of surface temperature change increases slowly with the change in soil moisture; the fourth segment describes the relationship that when soil moisture is less than the second inflection point, the magnitude of surface temperature change increases rapidly with the change in soil moisture.

[0087] in, This refers to the soil moisture corresponding to the second inflection point, i.e., the second critical soil moisture. , The linear fitting parameters for the change in surface temperature with soil moisture during the water-limited stage; , These are the linear fitting parameters for the variation of surface temperature with soil moisture during the energy-limited stage.

[0088] It should be noted that both segments in the second piecewise relationship model are linear segments (rather than a plateau and a linear decline), and their physical meaning is the turning point where the surface thermal state response changes from a slow change to a rapid change. Therefore, a two-segment linear regression model is used for fitting, and all candidate turning points are traversed, with the second turning point determined by minimizing the weighted squared residuals.

[0089] For example, for any candidate inflection point For soil moisture less than and greater than or equal to We perform weighted linear regression on the two intervals and calculate the weighted sum of squared residuals. We then iterate through all candidate inflection points and determine the soil moisture value corresponding to the candidate point that minimizes the weighted sum of squared residuals as the second critical soil moisture value.

[0090] For example, daytime and nighttime surface temperatures are acquired based on multi-source remote sensing data, and the diurnal variation of surface temperature at the pixel scale is calculated. This variation is used to characterize the changes in surface thermal state at the diurnal scale. Due to the cooling effect of evapotranspiration, when soil moisture is sufficient, the cooling effect of evapotranspiration is strong, and the diurnal variation of surface temperature is small; as soil moisture gradually decreases, the cooling effect weakens, and the diurnal variation increases accordingly. Secondly, at the pixel scale, continuous soil drying processes are identified based on soil moisture time series, i.e., events where soil moisture shows an overall decreasing trend over a continuous period of time without precipitation or sudden disturbances. To ensure the reliability of the analysis, further screening conditions such as duration, variation amplitude, and trend consistency are set for the identified drying processes, retaining only valid drying events that meet the preset conditions.

[0091] In the selected effective drying process, a statistical relationship was established between the diurnal variation of surface temperature and the corresponding soil moisture. A piecewise regression method was then used to fit this statistical relationship, identifying the inflection point where the response changes from gradual to significant. The soil moisture corresponding to this inflection point represents the location where the surface thermal state's response to soil moisture undergoes a sudden change, and this location is defined as the second critical soil moisture for that pixel. This second critical soil moisture is primarily used as a supplementary estimation result in areas or time periods where it is difficult to stably obtain critical thresholds based on soil evaporation efficiency methods, thereby improving the spatial coverage and robustness of the critical soil moisture estimation results.

[0092] It should be noted that the embodiments of the present invention do not limit the area corresponding to the second critical soil moisture determined in step 104. For example, the second critical soil moisture may correspond to the target area, or it may correspond to one or more pixels in the target area. The following description uses the second critical soil moisture corresponding to one pixel.

[0093] Step 105: Combine the first critical soil moisture and the second critical soil moisture to generate the critical threshold distribution of soil moisture change in the target area.

[0094] In one possible implementation, a first critical soil moisture level and a second critical soil moisture level are fused according to a preset fusion rule to generate a critical threshold distribution of soil moisture change in the target area. For example, the first critical soil moisture level can be quality controlled, such as by determining whether the number of samples used in the calculation is sufficient or whether the piecewise regression model has stably converged. For pixels that pass the quality control, the first critical soil moisture level is directly used as the final critical soil moisture level. For pixels that do not pass the quality control, the second critical soil moisture level is used as the final critical soil moisture level. Finally, a spatial distribution map covering the entire target area and containing the final critical soil moisture level for each pixel is generated, i.e., the critical threshold distribution of soil moisture change.

[0095] In this embodiment of the invention, the fusion rule is as follows: firstly, quality control is performed on the first critical soil moisture; if the first critical soil moisture passes the quality control, then the first critical soil moisture is taken as the final critical soil moisture of the target pixel; if the first critical soil moisture fails the quality control, then the second critical soil moisture is taken as the final critical soil moisture of the target pixel. The quality control includes at least one of the following: determining whether the number of samples used to determine the first critical soil moisture is sufficient, whether the fitting of the first segmented relationship model converges, and whether the first critical soil moisture is within the humidity range.

[0096] In another possible implementation, a first confidence level for the first critical soil moisture and a second confidence level for the second critical soil moisture are determined. Based on the first and second confidence levels, a first fusion weight and a second fusion weight for the first and second critical soil moisture are determined, respectively. The first and second critical soil moisture are then weighted and summed according to the first and second fusion weights to obtain the final critical soil moisture for the target pixel. Here, the target region includes the target pixel, and the distribution of critical thresholds for soil moisture change includes the final critical soil moisture. The first confidence level is greater than the second confidence level.

[0097] In other words, the final data is generated through the first and second critical soil moisture levels. Since the first path based on physical mechanisms (soil evaporation efficiency) is theoretically more direct and robust, its estimation results are preferred. However, due to limitations in data quality and sample size, the first path may not yield stable and reliable results in some areas. In such cases, the estimation results from the second path (diurnal variation of surface temperature) can serve as a valuable supplement.

[0098] The following section introduces the first confidence level, the second confidence level, the first fusion weight, and the second fusion weight.

[0099] In one possible implementation, a first confidence level for the first critical soil moisture and a second confidence level for the second critical soil moisture are determined. The first confidence level characterizes the reliability of the first critical soil moisture obtained based on a soil evaporation efficiency path, and the second confidence level characterizes the reliability of the second critical soil moisture obtained based on a land surface temperature variation amplitude path.

[0100] For example, the first confidence level is determined based on at least one of the following: the number of bins used to fit the first piecewise relation model, the fitting residuals of the first piecewise relation model, and the significance level of the descent slope in the first piecewise relation model. The more bins, the smaller the fitting residuals, and the more significant the descent slope, the higher the first confidence level.

[0101] For example, the second confidence level is determined based on at least one of the following: the number of effective drying events used to determine the second critical soil moisture content, the fitting residuals of the second piecewise relationship model, and the significance level of the difference in slopes on both sides of the inflection point in the second piecewise relationship model. The more effective drying events, the smaller the fitting residuals, and the more significant the difference in slopes on both sides of the inflection point, the higher the second confidence level.

[0102] Based on the first confidence level and the second confidence level, a first fusion weight and a second fusion weight for the first critical soil moisture are determined, respectively. In one possible implementation, the first fusion weight is positively correlated with the first confidence level, the second fusion weight is positively correlated with the second confidence level, and the sum of the first fusion weight and the second fusion weight is 1.

[0103] For example, let the first fusion weight Second fusion weight ,in As the first confidence level, This represents the second confidence level.

[0104] Based on the first fusion weight and the second fusion weight, the first critical soil moisture and the second critical soil moisture are weighted and summed to obtain the final critical soil moisture of the target pixel.

[0105] For example, the final critical soil moisture satisfies the following formula.

[0106] in, The first critical soil moisture level, This is the second critical soil moisture level. The final critical soil moisture content is defined as follows: The target area includes target pixels, and the distribution of critical thresholds for soil moisture changes includes the final critical soil moisture content.

[0107] It should be noted that, since the soil evaporation efficiency pathway has a more direct physical mechanism, the first confidence level is higher than the second confidence level by default.

[0108] Based on the above technical solution, by integrating the first critical soil moisture and the second critical soil moisture, when the soil evaporation efficiency path results are unstable or missing, the surface temperature change amplitude path can be used to supplement them, thereby improving the spatial integrity and stability of the soil moisture change critical value estimation results.

[0109] Figure 2 This is the second flowchart illustrating the method for determining the critical value of soil moisture change provided by the present invention, as shown below. Figure 2 As shown, step 103 in this method includes the following: Step 201: Construct a sample point set relating soil evaporation efficiency and soil moisture.

[0110] For example, within a predetermined analysis period (e.g., a complete growing season or several years of observation data), the soil volumetric water content is obtained for each valid observation time for the target pixel. and the soil evaporation efficiency calculated in step 102 These constitute a sample point pair. The set of all sample point pairs constitutes the original sample point set for that pixel. .

[0111] in, Indicates the first Soil volumetric water content (i.e., soil moisture) at a given time. This represents the soil evaporation efficiency at the corresponding time point. The above sample pairs reflect the actual response of evaporation efficiency during changes in soil moisture under natural drying or intermittent precipitation conditions.

[0112] Step 202: Divide the soil moisture into intervals and determine the statistical representative value of soil evaporation efficiency in each moisture interval to obtain a set of statistical relationships.

[0113] It should be understood that since the evaporation efficiency of a single observation may be affected by short-term meteorological disturbances (such as instantaneous wind speed changes, cloud shadows, etc.), directly using the original sample points for modeling may introduce significant noise. To enhance the stability of the statistical relationship, this step performs binning of soil moisture.

[0114] For example, the soil moisture range from minimum to maximum is divided into fixed steps. The soil is divided into K consecutive intervals (i.e., bins). For the Kth bin, the corresponding soil moisture range is... ,in, This is the center value of the sub-bin. Then, the arithmetic mean of the soil evaporation efficiency of all sample points falling into the sub-bin is calculated and used as the representative value of the sub-bin.

[0115] Within each compartment, the evaporation efficiency of the compartment is obtained by taking the arithmetic mean of all evaporation efficiencies falling into that compartment using Formula 3.

[0116] Formula 3.

[0117] in, The evaporation efficiency of the compartment is the statistical representative value of the soil evaporation efficiency within the humidity range. For the first Number of samples in each soil moisture compartment.

[0118] Understandably, through the above processing, the original scatter plot is transformed into a discrete, smooth set of statistical relationships. Each point represents the average response state within a specific soil moisture range.

[0119] Step 203: Fit the segmented relationship model based on the statistical relationship set, and take the soil moisture corresponding to the first inflection point that minimizes the fitting error of the first segmented relationship model as the first critical soil moisture.

[0120] It should be noted that, according to the physical mechanism of surface evaporation, when soil moisture conditions are relatively sufficient, the evaporation process is mainly controlled by radiation and atmospheric conditions, and the evaporation efficiency does not change significantly with soil moisture. When soil moisture drops below a certain level, the evaporation process gradually becomes limited by water supply, and the evaporation efficiency decreases with soil moisture.

[0121] In this embodiment of the invention, the first piecewise relationship model describes the plateau and descending two-stage characteristics of evaporation efficiency as a function of soil moisture. The first piecewise relationship model satisfies the following formula four.

[0122] Formula 4.

[0123] in, The first critical soil moisture level, and The linear fitting parameter represents the change in evaporation efficiency with soil moisture during the water-limited stage (i.e., the declining stage). This represents the plateau value of evaporation efficiency during the energy-constrained phase (plateau phase). The piecewise model described above is used to characterize the differences in the evaporation efficiency response mechanism across different soil moisture ranges.

[0124] The fitting process employs piecewise regression based on weighted least squares. For any candidate inflection point... (usually from the center value of the bin) (Selected from the middle), divide all boxes into categories lower than and higher than or equal to Two areas. Below For the region, use weighted linear regression to fit the parameters. and The bin weight is the number of samples in that bin. In or above The region, platform value The weight of each bin is calculated using a weighted average method, and the weight of each bin is also equal to the number of samples in that bin. .

[0125] For example, parameter a satisfies the following formula five.

[0126] Formula 5.

[0127] in, This represents the weighted average soil moisture across all bins, where k is the k-th bin. This represents the weighted average evaporation efficiency of all compartments. Used to represent the bin weight of the k-th bin. This represents the statistical representative value of soil evaporation efficiency in the k-th compartment.

[0128] For example, parameter b satisfies the following formula six.

[0129] Formula Six.

[0130] Higher than or equal to For the region, the platform value p is calculated using a weighted average method, with the bin weight also equal to the number of samples in that bin. .

[0131] For example, platform value It satisfies the following formula seven.

[0132] Formula 7.

[0133] The platform value is used to characterize the stable level that evaporation efficiency can achieve under conditions where soil moisture is not a limiting factor.

[0134] To evaluate each candidate To assess the goodness of fit, a weighted sum of squared errors (SSE) is constructed as the error function. This error function satisfies Equation 8.

[0135] Formula 8.

[0136] Among them, in the candidate critical soil moisture The weighted squared residuals are used to measure the goodness of fit of the first segmented relationship model at different candidate inflection points.

[0137] The smaller the value of this error function, the better the piecewise model fits the actual data at the candidate inflection point. Therefore, by iterating through all candidates... (i.e., the center point of all sub-boxes) ), determine The candidate point that yields the minimum value is the soil moisture value corresponding to that candidate point, which is then determined as the final first critical soil moisture.

[0138] For example, the first critical soil moisture satisfies Formula Nine.

[0139] Formula Nine.

[0140] in, This is the first critical soil moisture level. Used to represent the value of the independent variable that makes the function reach its minimum value.

[0141] Based on the above technical solution, by constructing an original sample point set between soil evaporation efficiency and soil moisture, the dynamic response relationship between the two during natural wet-drying cycles can be fully captured. On this basis, soil moisture is divided into intervals, and a statistically representative value of evaporation efficiency within each interval is determined, effectively eliminating the impact of short-term meteorological disturbances and observation noise on single samples, enhancing the stability and representativeness of the statistical relationship. Furthermore, based on the statistical relationship set, a piecewise relationship model is fitted, and the soil moisture corresponding to the first turning point is objectively determined as the first critical soil moisture using the minimum fitting error as the criterion, avoiding the uncertainty caused by relying on empirical thresholds or subjective judgment. Moreover, it can stably and accurately identify the critical position where soil evaporation efficiency transitions from a plateau zone to a linearly decreasing zone under different climatic zones and underlying surface conditions, improving the objectivity and robustness of soil moisture change critical value estimation.

[0142] Figure 3 This is the third flowchart illustrating the method for determining the critical value of soil moisture change provided by the present invention, as shown below. Figure 3 As shown, step 105 of this method includes the following: Step 301: Perform quality control on the first critical soil moisture.

[0143] It should be understood that, in order to ensure the reliability of the final product, it is necessary to evaluate the first critical soil moisture result obtained based on the soil evaporation efficiency path (the first path).

[0144] In this embodiment of the invention, quality control includes, but is not limited to, one or more of the following: determining whether the number of samples used to determine the first critical soil moisture is sufficient (i.e., sample quantity sufficiency), whether the fitting of the first segmented relationship model is converged (model fitting convergence), and whether the first critical soil moisture is within the preset moisture range (threshold rationality).

[0145] The number of samples used to determine the first critical soil moisture content can be the number of bins.

[0146] For example, if the preset threshold for the number of bins is 10, and the number of bins in a pixel is 3, then the sample size is considered insufficient and the first critical soil moisture result is unreliable. If the number of bins in a pixel is 12, then the sample size is considered sufficient and the first critical soil moisture result is reliable.

[0147] Model fit convergence is used to determine whether a piecewise regression model stably converges to the first inflection point during the fitting process.

[0148] If the error function shows a flat or multi-valley distribution near the candidate inflection point, or if the slope of the falling segment obtained by fitting is not significant (e.g., close to zero), it indicates that the model has failed to effectively identify a clear inflection point, and the fitting result is unreliable.

[0149] Threshold rationality is used to check whether the first critical soil moisture is within a reasonably permissible range in a physically sense.

[0150] This eliminates obviously unreasonable results caused by data anomalies or model misuse. The critical soil moisture should generally lie between the wilting coefficient (permanent wilting point) and field capacity.

[0151] Step 302: The first critical soil moisture obtained through quality control is taken as the final critical soil moisture of the target pixel.

[0152] The target area includes target pixels, and the distribution of critical thresholds for soil moisture change includes the final critical soil moisture.

[0153] It should be understood that if the first critical soil moisture passes quality control, it indicates that the remote sensing and reanalysis data for that area are of good quality, the response relationship between soil evaporation efficiency and soil moisture is clear, and the estimation results of the first path are robust and reliable. Therefore, this first critical soil moisture is directly taken as the final critical soil moisture for that pixel.

[0154] In one possible implementation, if the first critical soil moisture passes quality control, then the first critical soil moisture is taken as the final critical soil moisture of the target pixel.

[0155] Step 303: For the first critical soil moisture that fails to pass quality control, the second critical soil moisture is taken as the final critical soil moisture of the target pixel.

[0156] It should be understood that if the first critical soil moisture fails quality control, for example in persistently wet or dry areas, or in areas with poor data quality, the first path cannot provide stable results. In this case, the result of the second estimation path is used as a supplement. The second critical soil moisture, based on the magnitude of surface temperature variation, is taken as the final estimation result for that pixel.

[0157] It should be noted that before adopting the second critical soil moisture, a basic rationality screening of the second critical soil moisture can also be carried out, such as eliminating extreme values ​​that are obviously beyond the physical range.

[0158] In one possible implementation, if the first critical soil moisture fails to pass quality control, the second critical soil moisture is used as the final critical soil moisture for the target pixel.

[0159] Based on the above technical solution, by performing quality control on the sufficiency of sample quantity and the convergence of model fitting for the first critical soil moisture, the reliability of estimation results based on soil evaporation efficiency can be effectively identified, ensuring that the first critical soil moisture with a clear physical mechanism and robust statistical relationship is prioritized as the final result. For pixels that fail quality control, the second critical soil moisture based on the diurnal variation of land surface temperature is automatically used as a supplement, avoiding spatial missing data due to insufficient data or fitting failure of a single path. This fusion strategy fully leverages the complementary advantages of the two estimation paths, improving the spatial coverage and overall robustness of the distribution of critical thresholds for soil moisture changes while ensuring the physical consistency of the results.

[0160] The apparatus for determining the critical value of soil moisture change provided by the present invention will be described below. The apparatus for determining the critical value of soil moisture change described below can be referred to in correspondence with the method for determining the critical value of soil moisture change described above. It is understood that, in order to achieve the above functions, the electronic device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the steps of the method for determining the critical value of soil moisture change described in conjunction with the embodiments disclosed in the present invention, the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a certain function is executed in a hardware or computer software-driven hardware manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0161] Figure 4 This is a schematic diagram of the device for determining the critical value of soil moisture change provided by the present invention, as shown below. Figure 4 As shown, the device for determining the critical value of soil moisture change includes the following modules: acquisition module 401 and processing module 402.

[0162] The acquisition module 401 is used to acquire the data to be analyzed in the target area. The data to be analyzed includes at least the changes in soil moisture and surface temperature. Processing module 402 is used to determine soil evaporation efficiency, which characterizes the degree to which soil moisture limits the surface evaporation process, based on the data to be analyzed. The processing module 402 is also used to determine the first critical soil moisture based on the first piecewise relationship model between soil evaporation efficiency and soil moisture, wherein the first critical soil moisture is the soil moisture corresponding to the first turning point in the first piecewise relationship model where soil evaporation efficiency transitions from the plateau region to the linearly decreasing region. The processing module 402 is also used to determine the second critical soil moisture based on the second segmented relationship model between the change range of surface temperature and soil moisture. The second critical soil moisture is the soil moisture corresponding to the second inflection point of the change range of surface temperature with soil moisture during the soil drying process. The processing module 402 is also used to fuse the first critical soil moisture and the second critical soil moisture according to the preset fusion rules to generate the critical threshold distribution of soil moisture change in the target area.

[0163] In one possible implementation, processing module 402 is specifically used for: Based on the data to be analyzed, determine the actual soil evaporation and the potential soil evaporation. The ratio of actual soil evaporation to potential soil evaporation is defined as soil evaporation efficiency.

[0164] In another possible implementation, the data to be analyzed also includes surface meteorological data and surface vegetation data. Processing module 402 is specifically used for: Based on surface meteorological data, surface vegetation data, and a pre-set evapotranspiration calculation model, the actual soil evapotranspiration is determined. The evapotranspiration calculation model is used to decompose the total surface evapotranspiration into vegetation transpiration and soil evapotranspiration. Based on surface meteorological data and a pre-set evapotranspiration calculation model, and by setting the resistance parameter used to characterize the inhibitory effect of soil surface moisture on the evaporation process to a minimum value, the potential soil evaporation is determined.

[0165] In another possible implementation, processing module 402 is specifically used for: Construct a sample point set relating soil evaporation efficiency and soil moisture; Soil moisture is divided into intervals, and a statistical representative value of soil evaporation efficiency is determined for each moisture interval to obtain a set of statistical relationships. The first segmented relationship model is fitted based on the statistical relationship set. The soil moisture corresponding to the first inflection point that minimizes the fitting error of the first segmented relationship model is taken as the first critical soil moisture.

[0166] In another possible implementation, the first segmented relationship model includes a first segment and a second segment. The first segment is used to describe the linear decreasing relationship between soil evaporation efficiency and soil moisture when soil moisture is less than the first inflection point, and the second segment is used to describe the plateau relationship between soil evaporation efficiency and soil moisture when soil moisture is greater than or equal to the first inflection point.

[0167] In another possible implementation, processing module 402 is specifically used for: Quality control is performed on the first critical soil moisture, which includes at least one of the following: determining whether the number of samples used to determine the first critical soil moisture is sufficient, whether the fitting of the first piecewise relationship model is converged, and whether the first critical soil moisture is within the preset moisture range. If the first critical soil moisture passes the quality control, then the first critical soil moisture is taken as the final critical soil moisture of the target pixel. If the first critical soil moisture fails to pass quality control, the second critical soil moisture will be used as the final critical soil moisture for the target pixel. The target area includes target pixels, and the distribution of critical thresholds for soil moisture change includes the final critical soil moisture.

[0168] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5As shown, the electronic device may include: a processor 510, a communications interface 520, a memory 530, and a communications bus 540, wherein the processor 510, the communications interface 520, and the memory 530 communicate with each other through the communications bus 540. The processor 510 can call logical instructions in the memory 530 to execute a method for determining the critical value of soil moisture change. This method includes: acquiring data to be analyzed in the target area, the data including at least soil moisture and surface temperature change amplitudes; determining soil evaporation efficiency, used to characterize the degree to which soil moisture limits the surface evaporation process, based on the data; determining a first critical soil moisture based on a first piecewise relationship model between soil evaporation efficiency and soil moisture, wherein the first critical soil moisture is the soil moisture corresponding to the first inflection point in the first piecewise relationship model where soil evaporation efficiency transitions from a plateau region to a linearly decreasing region; determining a second critical soil moisture based on a second piecewise relationship model between surface temperature change amplitude and soil moisture, wherein the second critical soil moisture is the soil moisture corresponding to the second inflection point where surface temperature change amplitude changes with soil moisture during soil drying; and fusing the first and second critical soil moisture according to a preset fusion rule to generate a critical threshold distribution of soil moisture change in the target area.

[0169] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part 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 the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0170] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for determining the critical value of soil moisture change provided by the above methods. The method includes: acquiring data to be analyzed in a target area, the data to be analyzed including at least soil moisture and surface temperature change amplitude; determining soil evaporation efficiency, used to characterize the degree of limitation of soil moisture on the surface evaporation process, based on the data to be analyzed; determining a first critical soil moisture based on a first piecewise relationship model between soil evaporation efficiency and soil moisture, wherein the first critical soil moisture is the soil moisture corresponding to the first inflection point in the first piecewise relationship model where soil evaporation efficiency transitions from a plateau area to a linearly decreasing area; determining a second critical soil moisture based on a second piecewise relationship model between surface temperature change amplitude and soil moisture, wherein the second critical soil moisture is the soil moisture corresponding to the second inflection point where the surface temperature change amplitude changes with soil moisture during the soil drying process; and fusing the first critical soil moisture and the second critical soil moisture according to a preset fusion rule to generate a critical threshold distribution of soil moisture change in the target area.

[0171] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a method for determining the critical value of soil moisture change provided by the methods described above. This method includes: acquiring data to be analyzed in a target area, the data including at least soil moisture and surface temperature variation amplitudes; determining soil evaporation efficiency, used to characterize the degree to which soil moisture limits the surface evaporation process, based on the data to be analyzed; determining a first critical soil moisture based on a first piecewise relationship model between soil evaporation efficiency and soil moisture, wherein the first critical soil moisture is the soil moisture corresponding to the first inflection point in the first piecewise relationship model where soil evaporation efficiency transitions from a plateau region to a linearly decreasing region; determining a second critical soil moisture based on a second piecewise relationship model between surface temperature variation amplitude and soil moisture, wherein the second critical soil moisture is the soil moisture corresponding to the second inflection point where surface temperature variation amplitude changes with soil moisture during soil drying; and fusing the first and second critical soil moisture according to a preset fusion rule to generate a critical threshold distribution of soil moisture change in the target area.

[0172] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0173] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0174] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining the critical value of soil moisture change, characterized in that, The method includes: Acquire the data to be analyzed for the target area, wherein the data to be analyzed includes at least: the variation range of soil moisture and surface temperature; Based on the data to be analyzed, determine the soil evaporation efficiency, which characterizes the degree to which soil moisture limits the surface evaporation process; Based on the first piecewise relationship model between the soil evaporation efficiency and the soil moisture, a first critical soil moisture is determined, wherein the first critical soil moisture is the soil moisture corresponding to the first inflection point in the first piecewise relationship model where the soil evaporation efficiency transitions from the plateau region to the linearly decreasing region. Based on the second piecewise relationship model between the surface temperature change range and the soil moisture, the second critical soil moisture is determined. The second critical soil moisture is the soil moisture corresponding to the second inflection point of the change range of surface temperature with the change of soil moisture during the soil drying process. The first critical soil moisture and the second critical soil moisture are fused according to a preset fusion rule to generate the critical threshold distribution of soil moisture change in the target area.

2. The method according to claim 1, characterized in that, Based on the data to be analyzed, determine the soil evaporation efficiency, which characterizes the degree to which soil moisture limits the surface evaporation process, including: Based on the data to be analyzed, determine the actual soil evaporation and the potential soil evaporation. The ratio of the actual soil evaporation to the potential soil evaporation is defined as the soil evaporation efficiency.

3. The method according to claim 2, characterized in that, The data to be analyzed also includes surface meteorological data and surface vegetation data; based on the data to be analyzed, the actual soil evaporation and potential soil evaporation are determined, including: Based on the surface meteorological data, the surface vegetation data, and the preset evapotranspiration calculation model, the actual soil evapotranspiration is determined. The evapotranspiration calculation model is used to decompose the total surface evapotranspiration into vegetation transpiration and soil evapotranspiration. Based on the surface meteorological data and the preset evapotranspiration calculation model, and by setting the resistance parameter used to characterize the inhibitory effect of soil surface moisture on the evaporation process to a minimum value, the potential soil evaporation is determined.

4. The method according to claim 1, characterized in that, Based on the first piecewise relationship model between the soil evaporation efficiency and the soil moisture, the first critical soil moisture is determined, including: Construct a sample point set relating the soil evaporation efficiency and the soil moisture; The soil moisture content is divided into intervals, and a statistical representative value of the soil evaporation efficiency within each moisture interval is determined to obtain a set of statistical relationships. The first segmented relationship model is fitted based on the statistical relationship set, and the soil moisture corresponding to the first inflection point with the smallest fitting error of the first segmented relationship model is taken as the first critical soil moisture.

5. The method according to claim 4, characterized in that, The first segmented relationship model includes a first segment and a second segment. The first segment is used to describe the linear decreasing relationship between soil evaporation efficiency and soil moisture when soil moisture is less than the first inflection point. The second segment is used to describe the plateau relationship between soil evaporation efficiency and soil moisture when soil moisture is greater than or equal to the first inflection point.

6. The method according to claim 1, characterized in that, The step of fusing the first critical soil moisture and the second critical soil moisture according to a preset fusion rule to generate the critical threshold distribution of soil moisture change in the target area includes: The quality control of the first critical soil moisture includes at least one of the following: determining whether the number of samples used to determine the first critical soil moisture is sufficient, whether the fitting of the first segmented relationship model has converged, and whether the first critical soil moisture is within a preset humidity range. If the first critical soil moisture passes the quality control, then the first critical soil moisture is taken as the final critical soil moisture of the target pixel. If the first critical soil moisture fails to pass the quality control, the second critical soil moisture will be used as the final critical soil moisture of the target pixel. The target area includes the target pixel, and the soil moisture change critical threshold distribution includes the final critical soil moisture.

7. A device for determining the critical value of soil moisture change, characterized in that, The device includes: The acquisition module is used to acquire data to be analyzed in the target area, and the data to be analyzed includes at least: the variation range of soil moisture and surface temperature. The processing module is used to determine the soil evaporation efficiency, which characterizes the degree to which soil moisture limits the surface evaporation process, based on the data to be analyzed. The processing module is further configured to determine a first critical soil moisture based on a first segmented relationship model between the soil evaporation efficiency and the soil moisture, wherein the first critical soil moisture is the soil moisture corresponding to the first turning point in the first segmented relationship model where the soil evaporation efficiency transitions from the plateau region to the descending region. The processing module is further configured to determine a second critical soil moisture based on a second piecewise relationship model between the surface temperature change range and the soil moisture. The second critical soil moisture is the soil moisture corresponding to the second inflection point of the change range of surface temperature with the change of soil moisture during the soil drying process. The processing module is further configured to fuse the first critical soil moisture and the second critical soil moisture according to a preset fusion rule to generate a critical threshold distribution of soil moisture change in the target area.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the method for determining the critical value of soil moisture change as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for determining the critical value of soil moisture change as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for determining the critical value of soil moisture change as described in any one of claims 1 to 6.

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