A surface temperature decomposition method based on ternary space

Abstract

The application provides a land surface temperature decomposition method based on a ternary space, comprising the following steps: S1, establishing a ternary space with vegetation coverage as an x-axis, land surface temperature as a y-axis and specific radiation as a z-axis; S2, dividing the ternary space into a plurality of subspaces along the y-axis land surface temperature and the z-axis specific radiation, and calculating the change rate of land surface temperature with vegetation coverage in each subspace; S3, dividing the ternary space into a plurality of subspaces along the y-axis land surface temperature, and calculating the change rate of specific radiation with vegetation coverage in each subspace; S4, determining the subspace to which the vegetation temperature belongs when the vegetation is fully covered based on S3, and calculating the change rate of vegetation temperature with vegetation coverage in the subspace; S5, determining the subspace to which the soil temperature belongs when the soil is fully covered based on S3, and calculating the change rate of soil temperature with vegetation coverage in the subspace; and S6, decomposing remote sensing land surface temperature into vegetation temperature and soil temperature based on the results of S2-S5. The proposed ternary space and subspace mode can effectively improve the component temperature decomposition precision.

Description

Technical Field

[0001] The present invention relates to a method for decomposing remotely sensed surface temperature components, and in particular to a method for decomposing surface temperature using spatial relations of multivariate surface parameters. Background Art

[0002] Surface evapotranspiration (ET) is a crucial medium for the exchange of energy, moisture, and momentum between land and air. It is the sum of soil evaporation at the bare soil-atmosphere interface and vegetation transpiration at the vegetation-atmosphere interface. It is crucial in numerous fields, including hydrology, ecology, meteorology, and agriculture. Currently, the only viable approach is to develop regional or global-scale ET models using remotely sensed thermal infrared land surface temperatures. However, due to the high heterogeneity of the Earth's surface, remotely sensed land surface temperatures are typically the combined temperatures of soil and vegetation within the pixel coverage area. Soil and vegetation component temperatures are the predictive variables for remotely sensing soil evaporation and vegetation transpiration. Therefore, component temperature decomposition of remotely sensed land surface temperature is essential for remotely sensing soil and vegetation water and heat fluxes.

[0003] At present, there are three main remote sensing measurement methods for surface component temperature: 1) Based on the directionality of thermal radiation and emissivity, an inversion model is established using multi-angle or multi-band thermal infrared remote sensing; 2) The multispectral band mixed pixel unmixing model is transplanted to the thermal infrared band; 3) Based on the scattered space of vegetation cover and surface temperature, the surface temperature of four characteristic points (extremely dry bare soil, extremely wet bare soil, extremely dry vegetation, and extremely wet vegetation) is obtained to realize mixed temperature decomposition. The first method has a solid physical foundation and is relatively comprehensive. However, due to the scarcity of satellite-borne multi-angle thermal infrared information and the inconsistency of the objects observed in this multi-angle data, it is difficult to generalize in practical applications. The second method is simple and easy to implement, enriching the spatial detail of thermal infrared surface temperature. However, strictly speaking, it falls into the category of spatial downscaling and cannot obtain soil and vegetation temperature information within the pixel. The third method reduces the mixed temperature decomposition to the spatial relationship between surface temperature and four extreme characteristic points. Although widely used in surface evapotranspiration measurements, the existence and identification of these four extreme characteristic points remain controversial. The virtual soil moisture contours in the scattered point space also differ from their actual existence. The difficulty in decomposing surface temperature lies in the close coupling between surface temperature and surface emissivity, which influence each other. Surface temperature decomposition also implies that surface emissivity should be decomposed accordingly, but the three current methods do not yet account for this phenomenon.

[0004] The contribution of soil and vegetation temperature to land surface temperature varies with vegetation cover. The feedback relationship between land surface temperature and surface emissivity becomes complex due to varying vegetation cover, and the decomposition of land surface temperature is also closely related to vegetation cover. To clarify the pathways linking the decomposition of surface emissivity and land surface temperature, to integrate the contribution of surface emissivity to land surface temperature decomposition as it varies with vegetation cover, and to deepen understanding of the relationship between temperature, radiation, and underlying surface, a surface temperature decomposition method based on ternary space was developed. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a surface temperature decomposition method based on ternary space.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A surface temperature decomposition method based on ternary space includes the following steps:

[0008] S1: Establish a ternary space with vegetation cover as the x-axis, surface temperature as the y-axis, and emissivity as the z-axis;

[0009] S2: Divide the ternary space into several subspaces along the y-axis surface temperature and the z-axis emissivity, and calculate the rate of change of surface temperature with vegetation cover in each subspace;

[0010] S3: Divide the ternary space into several subspaces along the y-axis of the surface temperature, and calculate the rate of change of the emissivity with vegetation cover in each subspace;

[0011] S4: Based on S3, determine the subspace to which the vegetation temperature belongs when the vegetation is fully covered, and calculate the rate of change of the vegetation temperature with vegetation coverage in the subspace;

[0012] S5: Based on S3, determine the subspace to which the soil temperature belongs when the soil is fully covered, and calculate the rate of change of soil temperature with vegetation coverage in the subspace;

[0013] S6: Decompose the remotely sensed surface temperature into vegetation temperature and soil temperature based on the results of S2-S5.

[0014] Furthermore, the S1 is specifically:

[0015] Using the remote sensing products of vegetation cover, surface temperature and emissivity, the three parameters of each pixel are constructed into a triplet (vegetation cover, surface temperature, emissivity), and then the triplet of all pixels in the study area is positioned in the ternary space.

[0016] Furthermore, the S2 is specifically:

[0017]

[0018] Where (x, y) represents the pixel location in the study area, T m (x,y) represents the surface temperature at (x,y), T mmax 、T mmin They represent the maximum and minimum surface temperatures in the study area, respectively. I represents the number of subspaces that the ternary space is divided into by the surface temperature along the y-axis. Indicates that the study area satisfies T mmin +(i-1)*binT m ≤T m (x,y)≤T mmin +i*binT m The set of locations where the surface temperature of the condition is located, ε m (x,y) represents the surface emissivity at (x,y), ε mmax , ε mmin They represent the maximum surface emissivity and the minimum surface emissivity in the study area respectively, J represents the number of subspaces divided by the surface emissivity along the z-axis. Indicates that the study area satisfies ε mmin +(j-1)*binε m ≤ε m (x,y)≤ε mmin +j*binε m The set of locations where the surface emissivity of the condition is located, Indicates simultaneous satisfaction and The location set, f(x,y) represents the vegetation coverage at (x,y), Γ represents the use of Calculate the surface temperature and vegetation cover at each location The rate of change of subspace surface temperature with vegetation cover κ ij mathematical model.

[0019] Furthermore, the S3 is specifically:

[0020]

[0021] Where K represents the number of subspaces that the ternary space is divided into along the y-axis by the surface temperature. Indicates that the study area satisfies T mmin +(k-1)*BinT m ≤T m (x,y)≤T mmin +k*BinT m The set of locations where the surface temperature of the conditions is located, M represents the use of The surface emissivity and vegetation coverage at each location are calculated The rate of change of subspace surface emissivity with vegetation cover νk mathematical model.

[0022] Furthermore, the S4 includes the following steps:

[0023] S41: Use the rate of change of surface emissivity in S3 with vegetation cover to obtain vegetation emissivity, specifically:

[0024] ε v =ε m +(1-f)v k

[0025]

[0026] Where, T m represents the surface temperature to be decomposed in the study area, Indicates T m The identification number of the subspace of the ternary space divided by the surface temperature along the y-axis, represents rounding up, f represents the vegetation coverage of the location where the surface temperature to be decomposed belongs in the study area, Indicates the data obtained from S3 The rate of change of subspace surface emissivity with vegetation cover, ε m , ε v They represent the surface emissivity and vegetation emissivity of the location where the surface temperature to be decomposed belongs in the study area;

[0027] S42: Based on ε v Determine the vegetation temperature subspace when vegetation is fully covered, and calculate the rate of change of vegetation temperature with vegetation coverage in this subspace, specifically:

[0028]

[0029] F v ={(x,y)|f(x,y)>0.6}

[0030]

[0031] Where, Represents ε v The subspace of the ternary space divided by the surface emissivity, F v is the set of locations where vegetation is fully covered in the study area, 0.6 is the threshold of vegetation coverage when vegetation is fully covered, Indicates the use of The mathematical model of the surface temperature and vegetation cover at each location in the equation is used to obtain the rate of change β of vegetation temperature with vegetation cover.

[0032] Furthermore, the step S5 includes the following steps:

[0033] S51: Use the S3 surface emissivity change rate with vegetation cover to obtain soil emissivity, specifically:

[0034]

[0035] Where, ε s Indicates the soil emissivity at the location where the surface temperature to be decomposed belongs in the study area;

[0036] S52: Based on ε s Determine the soil temperature subspace when bare soil is fully covered, and calculate the rate of change of soil temperature with vegetation coverage in this subspace, specifically:

[0037]

[0038] F s ={(x,y)|f(x,y)<0.3}

[0039]

[0040] Where, Represents ε s The subspace of the ternary space divided by the surface emissivity, F s is the set of locations where bare soil is fully covered in the study area, 0.3 is the threshold of vegetation coverage when bare soil is fully covered, Indicates the use of The mathematical model of the surface temperature and vegetation cover at each location in the soil temperature change rate α is obtained.

[0041] Furthermore, the S6 is specifically as follows:

[0042]

[0043] Where, T v 、T s Decomposed surface temperature T m The obtained vegetation temperature and soil temperature, the iteration end condition is two adjacent T v and T s The absolute value of the difference is less than the preset threshold, and the initial value of the iteration can be set to T m .

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1) A ternary space and subspace-based model was proposed to quantify the rate of change of surface temperature with vegetation cover;

[0046] 2) Integrate the effect of surface emissivity on the decomposition of surface temperature along with the change rate of vegetation cover;

[0047] 3) Compensates for the lack of temperature decomposition of vegetation temperature and bare soil temperature with the rate of change of vegetation cover. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flow chart of the method in the embodiment provided by the present invention; DETAILED DESCRIPTION

[0049] The technical features, implementation steps and beneficial effects of the present invention are described in detail below in conjunction with the embodiments and the accompanying drawings. However, those skilled in the art should understand that the following embodiments are only preferred embodiments of the present invention, not all, and are not intended to limit the scope of the present invention.

[0050] Please refer to Figure 1 , Figure 1 The technical flow chart in the embodiment provided by the present invention includes the following steps:

[0051] S1: Obtain remote sensing products of three parameters: surface temperature, vegetation cover and surface emissivity in the study area;

[0052] S2: Establish a ternary space with vegetation cover as the x-axis, surface temperature as the y-axis, and emissivity as the z-axis;

[0053] S3: Divide the ternary space into several subspaces along the surface temperature on the y-axis and the emissivity on the z-axis, and calculate the rate of change of surface temperature with vegetation cover in each subspace;

[0054] S4: Divide the ternary space into several subspaces along the y-axis of the surface temperature, and calculate the change rate of the specific emissivity with vegetation cover in each subspace;

[0055] S5: Based on S4, determine the subspace to which the vegetation temperature belongs when the vegetation is fully covered, and calculate the rate of change of the vegetation temperature with vegetation coverage in the subspace;

[0056] S6: Based on S4, determine the subspace to which the soil temperature belongs when the soil is fully covered, and calculate the rate of change of soil temperature with vegetation coverage in the subspace;

[0057] S7: Decompose the remotely sensed surface temperature into vegetation temperature and soil temperature based on the results of S3-S6.

[0058] In S1, there are two ways to obtain the surface temperature remote sensing product of the study area: 1) through remote sensing image inversion; 2) downloading the surface temperature remote sensing product;

[0059] The remote sensing image is exemplarily selected from a Landsat 8 satellite or a HJ-1B satellite having a visible light and a thermal infrared wave band. For the Landsat 8 satellite remote sensing image, the inversion method can be selected as a single-channel algorithm or a multi-channel algorithm; for the HJ-1B satellite remote sensing image, the inversion method can be selected as a radiation transfer equation method, a single-channel correction algorithm of Qin Zhihao or a single-channel correction algorithm;

[0060] The land surface temperature remote sensing product can be downloaded from https: / / ladsweb.modaps.eosdis.nasa.gov / as MOD11A1, which includes the MODIS Terra land surface temperature / emissivity, and is provided in a global data product including the land surface temperature and emissivity values of each pixel in a swath and grid format, is raster data organized in a tile manner, is projected as a sinusoidal projection, has a daily time resolution, a 1km spatial resolution, and global coverage; the tile manner of the MOD11A1 data can be processed by using a HEG (HDF-EOS To GeoTIFF Conversion Tool) tool so as to reformat, reproject, and perform splicing and cutting on the HDF-EOS object by a user;

[0061] In the S1, the vegetation coverage remote sensing product of the research area is obtained by firstly obtaining the vegetation index of the research area, and secondly obtaining the vegetation coverage product according to the relationship between the vegetation index and the vegetation coverage. The vegetation index can be calculated by using the near-infrared wave band and the red wave band of the downloaded remote sensing image of the research area according to the normalized difference vegetation index formula, or can be downloaded from https: / / ladsweb.modaps.eosdis.nasa.gov / as 16-day synthesized 250m L3 data products, which have global coverage, are projected as integerized sinusoidal sinusoidal projections, divide the global image data into 36 columns x 18 rows of grids, each grid represents a file storage area, and the file position row and column number is recorded starting from 0, and the 16d 250m NDVI data is obtained by using HEG to extract a sub-data set, splice, project a grid, convert a unit, and cut;

[0062] The relationship between the vegetation coverage and the vegetation index is preferably:

[0063]

[0064] In the formula, f is the vegetation coverage degree converted from the vegetation index VI, VI s and VI v are the NDVI values of the soil and the vegetation full coverage, respectively, VI s representative value is 0.3, and VI vThe representative value is 0.8;

[0065] In S1, the surface emissivity remote sensing product for the study area may be the ASTER GEDv3 product, which is the average surface emissivity between 2000 and 2008 and is obtained by separating surface temperature and surface emissivity based on all clear-sky ASTER images during that period. The product has an assessment accuracy of 0.01, a spatial resolution of 100 m, and global coverage. The ASTER GEDv3 product is adjusted based on the vegetation cover of the study area to take into account the impact of temporal changes in vegetation on the surface emissivity. The preferred product is:

[0066]

[0067] Where, ε 13A is the surface emissivity of band 13 in the ASTER GEDv3 product, ε 14A is the surface emissivity of band 14 in the ASTER GEDv3 product, f A represents the mean vegetation coverage of the ASTER image, and ε is the surface emissivity remote sensing product of the study area.

[0068] In S2, the ternary space is established with vegetation coverage as the x-axis, surface temperature as the y-axis, and emissivity as the z-axis, specifically:

[0069] Using the remote sensing products of vegetation cover, surface temperature and emissivity, the three parameters of each pixel are constructed into a triplet (vegetation cover, surface temperature, emissivity), and then the triplet of all pixels in the study area is positioned in the ternary space.

[0070] In S3, the ternary space along the y-axis surface temperature and the z-axis emissivity is divided into several subspaces, and the rate of change of the surface temperature with vegetation cover is calculated in each subspace, specifically:

[0071]

[0072]

[0073] Where (x, y) represents the pixel location in the study area, T m (x,y) represents the surface temperature at (x,y), T mmax 、T mmin They represent the highest and lowest surface temperatures in the study area respectively, I represents the number of subspaces divided by the ternary space along the y-axis surface temperature, Indicates that the study area satisfies T mmin +(i-1)*binT m ≤T m (x,y)≤T mmin+i*binT m a set of locations where the surface temperature satisfies the condition, ε m (x,y) represents the surface emissivity at (x,y), ε mmax (x,y) represents the surface emissivity at (x,y), ε mmin respectively represent the maximum surface emissivity and the minimum surface emissivity in the study area, J represents the number of subspaces into which the three-dimensional space of the surface emissivity along the z-axis is divided, represent a set of locations in the study area where the surface temperature satisfies the condition, T mmin +(j-1)*binε m ≤ε m (x,y)≤ε mmin +j*binε m a set of locations where the surface emissivity satisfies the condition, ε represent a set of locations that satisfy both and , f(x,y) represents the vegetation coverage at (x,y), and Γ represents a mathematical model for obtaining the rate of change of the surface temperature with respect to the vegetation coverage in each location in ; subspace; ij

[0074] For example, the mathematical model Γ can be one of a least squares method, a support vector regression, a random forest, an artificial neural network, or deep learning.

[0075] In S4, the three-dimensional space is divided into a plurality of subspaces along the y-axis surface temperature, and the rate of change of the emissivity with respect to the vegetation coverage is obtained in each subspace. Specifically, the following is performed.

[0076]

[0077] where K represents the number of subspaces into which the three-dimensional space is divided along the y-axis surface temperature, represent a set of locations in the study area where the surface temperature satisfies the condition, T mmin +(k-1)*BinT m ≤T m (x,y)≤T mmin +k*BinT m a set of locations where the surface temperature satisfies the condition, T represent a set of locations that satisfy both and k , M represents a mathematical model for obtaining the rate of change of the surface emissivity with respect to the vegetation coverage in each location in ;

[0078] For example, the mathematical model M can be one of a least squares method, a support vector regression, a random forest, an artificial neural network, or deep learning, and can be the same as or different from Γ.

[0079] In S5, the sub-space of vegetation temperature when the vegetation is fully covered is determined based on S4, and the rate of change of vegetation temperature with vegetation coverage is calculated in the sub-space, including the following steps:

[0080] S51: Obtain vegetation emissivity using the rate of change of land surface emissivity with vegetation coverage in S4, specifically:

[0081]

[0082] In the formula, T m represents the land surface temperature to be decomposed in the study area, represents T m the identification number of the sub-space of the three-dimensional space of land surface temperature along the y-axis, represents the upward rounding, and f represents the vegetation coverage of the location to which the land surface temperature to be decomposed in the study area belongs, represents the obtained in S4 the rate of change of land surface emissivity with vegetation coverage in the sub-space, ε m , and ε v respectively represent the land surface emissivity and the vegetation emissivity of the location to which the land surface temperature to be decomposed in the study area belongs.

[0083] S52: Determine the sub-space of vegetation temperature when the vegetation is fully covered based on ε v , and calculate the rate of change of vegetation temperature with vegetation coverage in the sub-space, specifically:

[0084]

[0085] F v = {(x, y) | f(x, y) > 0.6}

[0086]

[0087] In the formula, F represents the sub-space of the three-dimensional space of land surface emissivity to which ε v belongs, F v is a set composed of locations in the study area where the vegetation is fully covered, and 0.6 is a threshold value of vegetation coverage when the vegetation is fully covered, represents a mathematical model for calculating the rate of change of vegetation temperature with vegetation coverage β using the land surface temperature and vegetation coverage at each location in .

[0088] For example, the mathematical model may be one of least squares regression, ridge regression, random forest, or support vector regression, and may be the same as or different from Γ and M.

[0089] In S6, determining the subspace to which the soil temperature belongs when the soil is fully covered based on S4, and obtaining the rate of change of the soil temperature with vegetation coverage in the subspace include the following steps:

[0090] S61: Use the S4 surface emissivity change rate with vegetation cover to obtain soil emissivity, specifically:

[0091]

[0092] Where, ε s Indicates the soil emissivity at the location where the surface temperature to be decomposed belongs in the study area;

[0093] S62: Based on ε s Determine the soil temperature subspace when bare soil is fully covered, and calculate the rate of change of soil temperature with vegetation coverage in this subspace, specifically:

[0094]

[0095] F s ={(x,y)|f(x,y)<0.3}

[0096]

[0097] Where, Represents ε s The subspace of the ternary space divided by the surface emissivity, F s is the set of locations where bare soil is fully covered in the study area, 0.3 is the threshold of vegetation coverage when bare soil is fully covered, Indicates the use of The mathematical model of the rate of change of soil temperature with vegetation cover α is obtained by comparing the surface temperature and vegetation cover at each location in the equation;

[0098] Exemplary mathematical model It can be selected as one of linear regression, polynomial regression, least squares regression, random forest or support vector regression, which can be used with Γ, M, Same or different.

[0099] In S7, the remote sensing surface temperature is decomposed into vegetation temperature and soil temperature based on the results of S3-S6, specifically:

[0100]

[0101] Where, T v 、T s Decomposed surface temperature T m The obtained vegetation temperature and soil temperature, the iteration end condition is two adjacent T v and T sIf the absolute value of the difference between the two values is less than a preset threshold, the iteration initial value can be set as T m .

[0102] The surface temperature decomposition method based on ternary space provided by the application has the following characteristics:

[0103] 1) The contribution of the change rate of the surface temperature and the component temperature of the vegetation coverage to the surface temperature decomposition is quantified;

[0104] 2) The error caused by neglecting the surface emissivity decomposition is compensated by the change rate of the surface reflectivity with the vegetation coverage;

[0105] 3) The ternary space framework for explaining the temperature-emissivity-underground surface physical connection is proposed, and the ternary parameter physical connection is revealed more deeply;

[0106] 4) No auxiliary variable is needed, the input data requirement is simple, the operation is convenient, and the execution is easy.

[0107] It should be noted that the application also has other embodiments, and those skilled in the art can obtain other various embodiments by referring to the embodiments of the application without creative labor, but the obtained embodiments are within the protection scope of the technical solutions of the embodiments of the application.

Claims

1. A surface temperature decomposition method based on ternary space, characterized in that: The steps include: S1: Using the remote sensing products of vegetation cover, surface temperature and emissivity, the three parameters of each pixel are transformed into a triplet of vegetation cover, surface temperature and emissivity. A ternary space is established with vegetation cover as the x-axis, surface temperature as the y-axis and emissivity as the z-axis. Then, the triplet of all pixels in the study area is positioned in the ternary space. S2: Divide the ternary space into several subspaces along the y-axis surface temperature and the z-axis emissivity. Calculate the rate of change of surface temperature with vegetation cover in each subspace. Specifically: Where (x, y) represents the pixel location in the study area, T m (x,y) represents the surface temperature at (x,y), T mmax 、T mmin They represent the highest and lowest surface temperatures in the study area respectively, I represents the number of subspaces divided by the ternary space along the y-axis surface temperature, Indicates that the study area satisfies T mmin +(i-1)*binT m ≤T m (x,y)≤T mmin +i*binT m The set of locations where the surface temperature of the condition is located, ε m (x,y) represents the surface emissivity at (x,y), ε mmax , ε mmin They represent the maximum surface emissivity and the minimum surface emissivity in the study area respectively, J represents the number of subspaces divided by the surface emissivity along the z-axis. Indicates that the study area satisfies ε mmin +(j-1)*binε m ≤ε m (x,y)≤ε mmin +j*binε m The set of locations where the surface emissivity of the condition is located, Indicates simultaneous satisfaction and The location set, f(x,y) represents the vegetation coverage at (x,y), Γ represents the use of Calculate the surface temperature and vegetation cover at each location The rate of change of subspace surface temperature with vegetation cover κ ij Mathematical model of S3: Divide the ternary space into several subspaces along the y-axis of the surface temperature, and calculate the rate of change of the emissivity with vegetation cover in each subspace; S4: Based on S3, determine the subspace to which the vegetation temperature belongs when the vegetation is fully covered, and calculate the rate of change of the vegetation temperature with vegetation coverage in the subspace; S5: Based on S3, determine the subspace to which the soil temperature belongs when the soil is fully covered, and calculate the rate of change of soil temperature with vegetation coverage in the subspace; S6: Decompose the remotely sensed surface temperature into vegetation temperature and soil temperature based on the results of S2-S5.

2. The method according to claim 1, characterized in that The S3 is specifically: Where K represents the number of subspaces that the ternary space is divided into along the y-axis by the surface temperature. Indicates that the study area satisfies T mmin +(k-1)*BinT m ≤T m (x,y)≤T mmin +k*BinT m The set of locations where the surface temperature of the conditions is located, M represents the use of The surface emissivity and vegetation coverage at each location are calculated The rate of change of subspace surface emissivity with vegetation cover ν k mathematical model.

3. The method according to claim 1, characterized in that The S4 comprises the following steps: S41: Use the rate of change of surface emissivity in S3 with vegetation cover to obtain vegetation emissivity, specifically: Where, T m represents the surface temperature to be decomposed in the study area, Indicates T m The identification number of the subspace of the ternary space divided by the surface temperature along the y-axis, represents rounding up, f represents the vegetation coverage of the location where the surface temperature to be decomposed belongs in the study area, Indicates the data obtained from S3 The rate of change of subspace surface emissivity with vegetation cover, ε m , ε v They represent the surface emissivity and vegetation emissivity of the location where the surface temperature to be decomposed belongs in the study area; S42: Based on ε v Determine the vegetation temperature subspace when vegetation is fully covered, and calculate the rate of change of vegetation temperature with vegetation coverage in this subspace, specifically: F v ={(x,y)|f(x,y)>0.6} Where, Represents ε v The subspace of the ternary space divided by the surface emissivity, F v is the set of locations where vegetation is fully covered in the study area, 0.6 is the threshold of vegetation coverage when vegetation is fully covered, Indicates the use of The mathematical model of the surface temperature and vegetation cover at each location in the equation is used to obtain the rate of change β of vegetation temperature with vegetation cover.

4. The method according to claim 1, wherein The S5 comprises the following steps: S51: Use the S3 surface emissivity change rate with vegetation cover to obtain soil emissivity, specifically: Where, ε s Indicates the soil emissivity at the location where the surface temperature to be decomposed belongs in the study area; S52: Based on ε s Determine the soil temperature subspace when bare soil is fully covered, and calculate the rate of change of soil temperature with vegetation coverage in this subspace, specifically: F s ={(x,y)|f(x,y)<0.3} Where, Represents ε s The subspace of the ternary space divided by the surface emissivity, F s is the set of locations where bare soil is fully covered in the study area, 0.3 is the threshold of vegetation coverage when bare soil is fully covered, Indicates the use of The mathematical model of the surface temperature and vegetation cover at each location in the soil temperature change rate α is obtained.

5. The method according to claim 1, wherein Said S6 is specifically: Where, T v 、T s Decomposed surface temperature T m The obtained vegetation temperature and soil temperature, the iteration end condition is two adjacent T v and T s The absolute value of the difference is less than the preset threshold, and the initial value of the iteration can be set to T m .

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