Multi-temporal surface water heat flux collaborative inversion method, device and medium

Abstract

The present application belongs to the technical field of surface water heat flux inversion, in particular to a multi-temporal surface water heat flux collaborative inversion method, device and medium. The method comprises: calculating the change rate of surface radiation with vegetation coverage by using multi-temporal remote sensing surface temperature; realizing multi-temporal remote sensing surface temperature collaborative decomposition; solving the joint balance equation of multi-temporal water heat flux and its components; and inverting multi-temporal surface sensible heat flux, water heat flux and its components. The present application realizes multi-temporal surface temperature joint decomposition based on surface temperature and vegetation coverage time series information, thereby proposing a new idea of multi-temporal surface water heat flux joint cutting sharing reference temperature, establishing multi-temporal balance equation considering surface water heat flux and its components, overcoming the deficiency that current remote sensing model is only suitable for single temporal surface water heat flux inversion, improving the efficiency of surface water heat flux remote sensing estimation, and providing technical services for climate change, agricultural production, ecological construction, water resources management and the like.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of surface water heat flux inversion, in particular to a multi-temporal surface water heat flux collaborative inversion method, device and medium. BACKGROUND

[0002] Surface water heat flux is the link between land water balance and energy balance, and is the physical process of water absorption of land and ocean by solar radiation to the atmosphere, which plays an important role in the distribution of water and heat in the earth system. In addition, surface water heat flux has a huge impact on parameters such as air temperature and wind speed of the earth's climate system, thereby causing the climate to be very sensitive to the response of the surface water heat flux of land and ocean. The land water resources on which human beings depend are closely related to surface water heat flux, and the intensity of water heat flux at the regional scale directly regulates the amount of water resources in the region, thereby affecting the social and economic development of the region. Therefore, the estimation of surface water heat flux is of great significance to climate change, agricultural production, ecological construction, water resources management, etc. Remote sensing technology can well observe the spatial heterogeneity of the land surface, and has gradually become the main method for estimating surface water heat flux.

[0003] At present, the remote sensing model of surface water heat flux mainly includes remote sensing model based on energy balance, Penman model and empirical model. In the case of ignoring horizontal advection, the remote sensing model based on energy balance represents the surface water heat flux as the difference between the available energy of the surface and the sensible heat flux, which is divided into single-source model and double-source model. The disadvantage of single-source model is the contradiction between the canopy aerodynamic temperature required by the model and the remote sensing radiation temperature. The double-source model usually cannot obtain soil temperature and vegetation temperature, and needs to assume additional constraints when solving the surface water heat flux. In the Penman model, the surface water vapor resistance factor involves the surface soil adsorption force and the biological physical characteristics of vegetation, which is difficult to obtain by remote sensing inversion. The empirical model predicts the surface water heat flux through the regression relationship between the surface parameters obtained by remote sensing inversion and the observed value of the surface water heat flux, and the physical basis is relatively weak.

[0004] The remote sensing model of surface water heat flux is developed based on single temporal remote sensing data, which can only be used to estimate the surface water heat flux at a single time phase. The remote sensing estimation of single temporal surface water heat flux has limited application value for production practice, while the remote sensing estimation result of multi-temporal surface water heat flux is more meaningful. In order to utilize the multi-temporal remote sensing time series information to collaboratively invert the surface water heat flux, fully tap the physical meaning of surface vegetation coverage for surface temperature decomposition, and deeply understand the physical coupling law between multi-temporal surface water heat flux, a multi-temporal surface water heat flux collaborative inversion method, device and medium are proposed to realize the joint inversion of multi-temporal remote sensing and multi-temporal surface water heat flux, and thus the present application is proposed. SUMMARY

[0005] The present application aims to provide a multi-temporal surface water heat flux collaborative inversion method, device and medium, so as to overcome the deficiency that the existing surface water heat flux remote sensing model is only suitable for single temporal surface water heat flux estimation.

[0006] To achieve the above technical purpose, the technical scheme adopted by the present application is:

[0007] In a first aspect, the present application provides a multi-temporal surface water heat flux collaborative inversion method, comprising the following steps:

[0008] S1: calculating the change rate of surface radiation with vegetation coverage using multi-temporal remote sensing surface temperature;

[0009] S2: realizing multi-temporal remote sensing surface temperature collaborative decomposition using the change rate of S1;

[0010] S3: solving the joint balance equation of multi-temporal surface water heat flux and its components established based on S2;

[0011] S4: inverting multi-temporal surface sensible heat flux and its components based on the solution of S3;

[0012] S5: inverting multi-temporal surface water heat flux and its components based on S4.

[0013] In one embodiment, S1: calculating the change rate of surface radiation with vegetation coverage using multi-temporal remote sensing surface temperature, specifically:

[0014]

[0015] In the formula, n represents the nth time phase of multi-temporal remote sensing surface temperature, T n represents the n time phase remote sensing surface temperature (K), ε n represents the n time phase surface emissivity, f n represents the n time phase vegetation coverage, f m represents the m time phase vegetation coverage, ε m represents the m time phase surface emissivity, represents the ε m bare soil emissivity in the middle, represents the ε m vegetation emissivity in the middle, represents the ε n bare soil emissivity in the middle, represents the ε n vegetation emissivity in the middle, represents the change rate of n time phase vegetation coverage compared with m time phase vegetation coverage, represents the change rate of n time phase surface radiation compared with m time phase surface radiation, represents the change rate of surface radiation with vegetation coverage.

[0016] In one embodiment, the S2: multi-temporal remote sensing land surface temperature is cooperatively decomposed by using the S1 rate of change, specifically:

[0017]

[0018]

[0019] In the formula, n-time T n The bare soil temperature (K) in the middle, n-time T n The vegetation temperature (K) in the middle.

[0020] In one embodiment, the S3: solving the joint balance equation of multi-temporal land surface water and heat flux and its components based on S2, including the following steps:

[0021] S31: establishing the joint balance equation of multi-temporal land surface water and heat flux and its components, specifically:

[0022]

[0023] In the formula, N represents the number of time phases of the remote sensing image of the selected study area, 1≤n≤N, R n n-time surface incident radiation (W / m 2 ), R nS n-time bare soil incident radiation (W / m 2 ), R nV n-time vegetation incident radiation (W / m 2 ), G n n-time soil heat flux (W / m 2 ), T n n-time land surface temperature (K), f n n-time vegetation coverage, T ds The bare soil temperature in the pixel when the soil water content is 0, T dv The vegetation temperature in the pixel when the soil water content is 0, T d The land surface temperature of the pixel when the soil water content is 0, f d The vegetation coverage corresponding to T ds and T dv , ε d The land surface emissivity corresponding to T ds and T dv , The rate of change of land surface emissivity with vegetation coverage when the soil water content is 0, T ws The bare soil temperature in the pixel when the soil water content is 1, Twv T represents the vegetation temperature in a pixel when the soil moisture content is 1. w f represents the surface temperature of a pixel when the soil moisture content is 1. w Indicates the corresponding T ws and T wv vegetation cover, ε w Indicates the corresponding T ws and T wv The surface emissivity, This represents the rate of change of surface emissivity with vegetation cover when the soil moisture content is 1.

[0024] S32: Solve for the bare soil temperature and vegetation temperature in pixels when the soil moisture content is 0 and 1, specifically:

[0025]

[0026]

[0027]

[0028] In the formula, 1≤n≤N, N represents the number of multi-phase periods, and j represents the number of iterations. and Let represent the results of the j-th and j+1-th iterations, respectively, with the superscript ' indicating transpose.

[0029] In one embodiment, S4: the decomposition and inversion of multi-temporal surface apparent heat flux and its components based on S3, specifically includes:

[0030]

[0031] In the formula, 1≤n≤N, H n The surface sensible heat flux (W / m²) represents phase n. 2 ), H nS This represents the sensible heat flux of bare soil in the surface sensible heat flux at time n (W / m²). 2 ), H nV This represents the vegetation sensible heat flux (W / m²) in the surface sensible heat flux at time n. 2 ).

[0032] In one embodiment, S5: retrieving multi-temporal surface water heat fluxes and their components based on S4, specifically includes:

[0033]

[0034] In the formula, 1≤n≤N, LE n Represents the surface water heat flux (W / m³) in time phase n. 2 ), LE nS This represents the bare soil water and heat flux in the surface water and heat flux at time n (W / m²).2 ), LE nV represents the vegetation water heat flux (W / m 2 ) in the n time phase surface water heat flux.

[0035] In a second aspect, an embodiment of the present application provides a multi-time phase surface water heat flux cooperative inversion device, which is used to execute the multi-time phase surface water heat flux cooperative inversion method in the first aspect, and comprises a change rate module, a decomposition module, a balance module, a sensible heat module and a water heat module.

[0036] The change rate module is used to execute the change rate of the surface radiation with the vegetation coverage calculated by using the multi-time phase remote sensing surface temperature in the first aspect.

[0037] The decomposition module is used to execute the multi-time phase remote sensing surface temperature cooperative decomposition realized by the change rate of the surface radiation with the vegetation coverage calculated by using the multi-time phase remote sensing surface temperature in the first aspect.

[0038] The balance module is used to execute the joint balance equation of the multi-time phase water heat flux and the components thereof established based on the multi-time phase remote sensing surface temperature cooperative decomposition in the first aspect.

[0039] The sensible heat module is used to execute the multi-time phase surface sensible heat flux and the components thereof in the first aspect.

[0040] The water heat module is used to execute the multi-time phase surface water heat flux and the components thereof in the first aspect.

[0041] In a third aspect, an embodiment of the present application provides a multi-time phase surface water heat flux cooperative inversion computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the steps of the multi-time phase surface water heat flux cooperative inversion method in the first aspect.

[0042] Compared with the prior art, the present application has the following beneficial effects:

[0043] 1) Based on the time sequence information of the multi-time phase remote sensing surface temperature and the vegetation coverage, the change rate of the multi-time phase remote sensing surface temperature with the vegetation coverage is used to realize the joint decomposition of the multi-time phase surface temperature.

[0044] 2) A modeling method for establishing the balance equation between the multi-time phase surface water heat flux and the components thereof is proposed, which takes into account the multi-time phase balance of the surface water heat flux and the components thereof.

[0045] 3) The multi-time phase surface water heat flux remote sensing cooperative inversion is realized, which overcomes the limitation that the traditional remote sensing model is only suitable for single-time phase water heat flux inversion, and improves the efficiency of the remote sensing inversion water heat flux.

[0046] 4) The multi-temporal surface sensible heat flux remote sensing collaborative inversion is realized, the deficiency that the traditional remote sensing model is only suitable for single temporal phase sensible heat flux inversion is made up, and the efficiency of remote sensing inversion sensible heat flux is improved;

[0047] 5) The physical understanding of the difference and connection between the surface temperature component change rate and the surface temperature temporal phase change rate is deepened, and the further development of surface temperature decomposition is promoted. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 A multi-temporal surface water heat flux collaborative inversion method flowchart provided by the embodiment of the application;

[0049] Figure 2 Another multi-temporal surface water heat flux collaborative inversion method flowchart provided by the embodiment of the application;

[0050] Figure 3 A multi-temporal surface water heat flux collaborative inversion device flowchart provided by the embodiment of the application. DETAILED DESCRIPTION

[0051] In one embodiment, as shown in Figure 1 , Figure 1 A multi-temporal surface water heat flux collaborative inversion method flowchart provided by the embodiment of the application includes the following steps:

[0052] S1: calculating the change rate of surface radiation with vegetation coverage by using multi-temporal remote sensing surface temperature;

[0053] S2: realizing multi-temporal remote sensing surface temperature collaborative decomposition by using the change rate of S1;

[0054] S3: solving the joint balance equation of multi-temporal surface water heat flux and components based on S2;

[0055] S4: inverting multi-temporal surface sensible heat flux and components based on the solution of S3;

[0056] S5: inverting multi-temporal surface water heat flux and components based on S4.

[0057] Based on the above embodiment, further, as shown in Figure 1 , the S1: calculating the change rate of surface radiation with vegetation coverage by using multi-temporal remote sensing surface temperature, one implementation is specifically:

[0058]

[0059] In the formula, n represents the nth time phase of multi-temporal remote sensing surface temperature, 1≤n≤N, N represents the total number of time phases, T n represents the n time phase remote sensing surface temperature (K), and εn represents the n-time phase surface emissivity, f n represents the n-time phase vegetation coverage, f m represents the m-time phase vegetation coverage, ε m represents the m-time phase surface emissivity, represents ε m the bare soil emissivity in the n-time phase, represents ε m the vegetation emissivity in the n-time phase, represents ε n the bare soil emissivity in the m-time phase, represents ε n the vegetation emissivity in the m-time phase, represents the rate of change of the n-time phase vegetation coverage relative to the m-time phase vegetation coverage, represents the rate of change of the n-time phase surface radiation relative to the m-time phase surface radiation, represents the rate of change of the surface radiation with the vegetation coverage.

[0060] In the formula, the N-time phase remote sensing surface temperature {T n} 1≤n≤N The thermal infrared band of the multi-time phase remote sensing image can be used for inversion, and the open source product of the multi-time phase remote sensing surface temperature can be downloaded from the Internet. For example, the thermal infrared band of the multi-time phase remote sensing image can be selected as Landsat 8TIRS band 10, which has a spatial resolution of 100 meters, a return period of 16 days, and an inversion algorithm can be selected as a single channel algorithm. The open source product of the multi-time phase remote sensing surface temperature can be selected as the MOD11A1 product with a global coverage of 1 kilometer per day;

[0061] In the formula, the N-time phase surface emissivity {ε n} 1≤n≤N MOD11A1 product with a global coverage of 1 kilometer per day, which provides global data containing surface temperature and emissivity values of each pixel in the form of swath and grid;

[0062] In the formula, the N-time phase vegetation coverage {f n}1 ≤ n ≤ N can be calculated from the surface reflectance of the first band red light and the fourth band near infrared in the MYD09GA with 1 kilometer per day, specifically:

[0063]

[0064] In the formula, respectively represent the surface reflectance of the first band red light and the fourth band near infrared in the n-time phase MYD09GA, VI n represents the n-time phase vegetation index, VImin and VI max respectively represent the minimum and maximum values of the n-time phase vegetation index VI n f n represents the vegetation coverage of the n-time phase;

[0065] In the formula,

[0066] In the formula, can be obtained from the ε m , ε n of the m-time phase and the n-time phase, and specifically:

[0067]

[0068] In the formula, and can be obtained by the least square method respectively using the ε m , ε n and f m , f n of the m-time phase and the n-time phase of the study area.

[0069] Based on the above embodiment, further, as shown in Figure 1 , the S2: achieving multi-time phase remote sensing surface temperature collaborative decomposition by using the S1 change rate, and one implementation manner is specifically:

[0070]

[0071] In the formula, represents the bare soil temperature (K) in the n-time phase T n represents the vegetation temperature (K) in the n-time phase T n

[0072] Based on the above embodiment, further, as shown in Figure 2 , the S3: solving the joint balance equation of multi-time phase surface water and heat flux and components based on S2, and one implementation manner includes the following steps:

[0073] S31: establishing the joint balance equation of multi-time phase surface water and heat flux and components, and specifically:

[0074]

[0075] In the formula, N represents the number of time phases of the remote sensing image of the selected study area, 1≤n≤N, R n represents the n-time phase surface incident radiation (W / m 2 ), R nS represents the n-time phase surface bare soil incident radiation (W / m 2 ), R nV ​​Represents the incident radiation from surface vegetation at time n (W / m²). 2 ), G n Represents soil heat flux at time n (W / m²) 2 ), T n f represents the surface temperature (K) at time n. n T represents the vegetation cover at time n. ds T represents the temperature of bare soil in a pixel when the soil moisture content is 0. dv T represents the vegetation temperature in a pixel when the soil moisture content is 0. d f represents the surface temperature of a pixel when the soil moisture content is 0. d Indicates the corresponding T ds and T dv vegetation cover, ε d Indicates the corresponding T ds and T dv The surface emissivity, T represents the rate of change of surface emissivity with vegetation cover when soil moisture content is 0. ws T represents the temperature of bare soil in a pixel when the soil moisture content is 1. wv T represents the vegetation temperature in a pixel when the soil moisture content is 1. w f represents the surface temperature of a pixel when the soil moisture content is 1. w Indicates the corresponding T ws and T wv vegetation cover, ε w Indicates the corresponding T ws and T wv The surface emissivity, This represents the rate of change of surface emissivity with vegetation cover when the soil moisture content is 1.

[0076] In the formula, the {S n} 1≤n≤N 、{S nS} 1≤n≤N 、{S nV} 1≤n≤N 、{G n} 1≤n≤N The relevant algorithms in the references can be used to invert the selected remote sensing surface temperature data in conjunction with meteorological data. The surface emissivity and vegetation cover at the point of highest surface temperature from multi-temporal remote sensing data of the study area can be estimated using the least squares method. The surface emissivity and vegetation cover at the lowest point of the surface temperature in the multi-temporal remote sensing of the study area can be calculated using the least squares method.

[0077] S32: Solve for the bare soil temperature and vegetation temperature in pixels when the soil moisture content is 0 and 1, specifically:

[0078]

[0079]

[0080] wherein, 1≤n≤N, N represents the number of multi-time phases, j represents the number of iterations, andrespectively represent the jth and j+1th iteration results;

[0081] The iteration stopping condition can be set as that the maximum value of the relative error of the difference between two iterations in the eight variables is less than a preset threshold, and an example can be:

[0082]

[0083] wherein, ξ represents the preset threshold, and the mathematical operator represents any given, but not limited to this, and a person of ordinary skill in the art can also set it according to the specific circumstances.

[0084] Based on the above embodiment, further, as Figure 2 indicated, the S4: based on the S3, the multi-time phase surface sensible heat flux and components thereof are inversed, and one implementation manner is specifically:

[0085]

[0086] wherein, 1≤n≤N, H n represents the n-time phase surface sensible heat flux (W / m 2 ), H nS represents the n-time phase surface sensible heat flux (W / m 2 ), H nV represents the n-time phase surface sensible heat flux (W / m 2 ).

[0087] Based on the above embodiment, further, as Figure 2 indicated, the S5: based on the S4, the multi-time phase surface water heat flux and components thereof are inversed, and one implementation manner is specifically:

[0088]

[0089] wherein, 1≤n≤N, LE n represents the n-time phase surface water heat flux (W / m 2 ), LE nS represents the n-time phase surface water heat flux (W / m 2 ), LE nV represents the n-time phase surface water heat flux (W / m 2 ).

[0090] In summary, the multi-temporal surface water heat flux collaborative inversion method can be realized.

[0091] In one embodiment, as shown in Figure 3 A multi-temporal surface water heat flux collaborative inversion device is provided for performing the multi-temporal surface water heat flux collaborative inversion method described in the above embodiments, comprising: a rate of change module 1, a decomposition module 2, a balance module 3, a sensible heat module 4, and a water heat module 5.

[0092] The rate of change module 1 is used to calculate the rate of change of surface radiation with vegetation coverage using multi-temporal remote sensing surface temperature, as described in the above embodiments.

[0093] The decomposition module 2 is used to achieve multi-temporal remote sensing surface temperature collaborative decomposition using the rate of change of surface radiation with vegetation coverage calculated using multi-temporal remote sensing surface temperature, as described in the above embodiments.

[0094] The balance module 3 is used to solve the joint balance equation of multi-temporal water heat flux and its components established based on multi-temporal remote sensing surface temperature collaborative decomposition, as described in the above embodiments.

[0095] The sensible heat module 4 is used to invert multi-temporal surface sensible heat flux and its components, as described in the above embodiments.

[0096] The water heat module 5 is used to invert multi-temporal surface water heat flux and its components, as described in the above embodiments.

[0097] The multi-temporal surface water heat flux collaborative inversion method proposed by the present application has the following advantages:

[0098] 1) A multi-temporal surface temperature component solving method is constructed using the time series information of multi-temporal remote sensing surface temperature and vegetation coverage, and multi-temporal soil temperature and vegetation temperature are obtained.

[0099] 2) A new idea of multi-temporal surface water heat flux partitioning based on shared reference temperature of multi-temporal surface temperature is proposed, which opens up a new path for multi-temporal surface heat flux remote sensing inversion.

[0100] 3) A multi-temporal surface water heat flux and its component balance modeling method is proposed, and a joint balance equation between multi-temporal surface water heat flux and its components is established.

[0101] 4) Remote sensing collaborative inversion of multi-temporal surface water heat flux, sensible heat flux and its components is realized, and the inversion efficiency of traditional remote sensing model is improved.

[0102] 5) It can provide technical services for multi-temporal surface water heat flux estimation for climate change response, agricultural production, ecological construction, water resource management, etc.

[0103] The implementation process and technical effects of the multi-time-surface water heat flux cooperative inversion device and the medium embodiment and the method embodiment are similar. For similarities, refer to the corresponding places of the method embodiment. It should be noted that a person of ordinary skill in the art can polish, modify, and improve the present application without creative labor, but these are within the protection scope of the present application.

Claims

1. A method for coordinated inversion of multi-temporal surface water and heat fluxes, characterized in that, Includes the following steps: S1: Calculate the rate of change of surface radiation with vegetation cover using multi-temporal remote sensing of surface temperature; S2: Utilizing the rate of change of S1 to achieve coordinated decomposition of multi-temporal remote sensing surface temperature; S3: Solve the joint equilibrium equations for multi-temporal surface water heat fluxes and their components established based on S2, specifically including: S31: Establish the joint equilibrium equations for multi-temporal surface water heat fluxes and their components, specifically: In the formula, N represents the number of time phases of the selected remote sensing images of the study area, 1≤n≤N, R n This represents the incident radiation at the Earth's surface during phase n, with units of W / m². 2 R nS This represents the incident radiation of bare soil at time n, expressed in W / m². 2 R nV This represents the incident radiation from surface vegetation at time n, expressed in W / m². 2 G n This represents the soil heat flux at time n, expressed in W / m³. 2 T n f represents the surface temperature at time n, in Kelvin (K). n T represents the vegetation cover at time n. ds T represents the temperature of bare soil in a pixel when the soil moisture content is 0. dv T represents the vegetation temperature in a pixel when the soil moisture content is 0. d f represents the surface temperature of a pixel when the soil moisture content is 0. d Indicates the corresponding T ds and T dv vegetation cover, ε d Indicates the corresponding T ds and T dv The surface emissivity, T represents the rate of change of surface emissivity with vegetation cover when soil moisture content is 0. ws T represents the temperature of bare soil in a pixel when the soil moisture content is 1. wv T represents the vegetation temperature in a pixel when the soil moisture content is 1. w f represents the surface temperature of a pixel when the soil moisture content is 1. w Indicates the corresponding T ws and T wv vegetation cover, ε w Indicates the corresponding T ws and T wv The surface emissivity, This represents the rate of change of surface emissivity with vegetation cover when the soil moisture content is 1. S32: Solve for the bare soil temperature and vegetation temperature in the pixel when the soil moisture content is 0 and 1; S4: Based on the solution inversion of S3, the multi-temporal apparent heat flux and its components are retrieved, specifically: In the formula, H n This represents the surface sensible heat flux for the nth time phase, expressed in W / m³. 2 H nS This represents the bare soil sensible heat flux within the n-phase surface sensible heat flux obtained from the solution, in units of W / m³. 2 H nV This represents the vegetation sensible heat flux within the n-phase surface sensible heat flux obtained from the solution, in units of W / m². 2 ; S5: Based on the inversion of S3, multi-temporal surface water and heat fluxes and their components are retrieved, specifically: In the formula, LE n This represents the surface water heat flux at time n, in W / m³. 2 LE nS This represents the surface water and heat flux in bare soil during time n, expressed in W / m³. 2 LE nV This represents the vegetation hydrothermal flux in the surface hydrothermal flux at time n, expressed in W / m². 2 .

2. The method according to claim 1, characterized in that, Specifically, S1 is: In the formula, n represents the nth time phase of multi-temporal remote sensing of land surface temperature, and T n ε represents the remotely sensed land surface temperature at time n, in Kelvin (K). n f represents the surface emissivity at time n. n f represents the vegetation cover at time n. m ε represents the vegetation cover at time phase m. m This represents the surface emissivity at time m. Represents ε m Emissivity of bare soil Represents ε m Medium vegetation specific radiation rate Represents ε n Emissivity of bare soil ε n Medium vegetation specific radiation rate This represents the rate of change of vegetation cover at time n compared to vegetation cover at time m. This represents the rate of change of surface radiation at time n relative to surface radiation at time m. This represents the rate of change of surface radiation with vegetation cover.

3. The method according to claim 2, characterized in that, Specifically, S2 is: In the formula, Represents n-phase T n Temperature of bare soil in Kelvin (K). Represents n-phase T n Temperature in the vegetation zone, in Kelvin (K).

4. The method according to claim 1, characterized in that, S32: Solving for the bare soil temperature and vegetation temperature in pixels when the soil moisture content is 0 and 1, specifically: In the formula, and Let represent the results of the j-th and j+1-th iterations, respectively, where j represents the iteration number and the superscript ' denotes transpose.

5. A multi-temporal surface water heat flux collaborative inversion device, characterized in that, The method for performing the multi-temporal surface water and heat flux collaborative inversion method according to claim 1 includes: a rate of change module, a decomposition module, a balance module, a sensible heat module, and a hydrothermal module; The rate of change module is used to perform the calculation of the rate of change of surface radiation with vegetation cover using multi-temporal remote sensing surface temperature as described in claim 2. The decomposition module is used to perform the multi-temporal remote sensing surface temperature collaborative decomposition as described in claim 3, which uses the rate of change of surface radiation with vegetation cover calculated by multi-temporal remote sensing surface temperature. The balance module is used to execute the joint balance equation of multi-temporal hydrothermal flux and its components established based on the multi-temporal remote sensing surface temperature co-decomposition as described in claim 4. The sensible heat module is used to perform the inversion of multi-temporal surface sensible heat flux and its components as described in claim 1. The hydrothermal module is used to perform the inversion of multi-temporal surface water and heat fluxes and their components as described in claim 1.

6. A computer-readable storage medium for multi-temporal surface water and heat flux collaborative inversion, wherein a computer program is stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-temporal surface water heat flux collaborative inversion method according to any one of claims 1-4.

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