A method for remote sensing of surface temperature-based near-surface geotemperature

Abstract

The application provides a surface temperature-based surface ground temperature remote sensing measurement method, which comprises the following steps: expanding radiation terms of bare soil and vegetation at a target pixel into Taylor series at a remote sensing surface temperature respectively, establishing a remote sensing radiation equation of the remote sensing surface temperature by taking remote sensing vegetation coverage as a weight; solving remote sensing bare soil temperature and remote sensing vegetation temperature at the target pixel by combining the remote sensing radiation equation, a relationship equation of the remote sensing surface temperature and a remote sensing component temperature; expanding radiation terms of bare soil and vegetation at a simulation grid into Taylor series at a simulation surface temperature respectively, establishing a simulation radiation equation of the simulation surface temperature by taking simulation resolution vegetation coverage as a weight; solving simulation bare soil temperature and simulation vegetation temperature at the simulation grid by combining the simulation radiation equation, a relationship equation of the simulation surface temperature and a simulation component temperature; and estimating average surface ground temperature by using a correlation model between the simulation bare soil temperature and simulation surface ground temperature and the remote sensing bare soil temperature.

Description

Technical Field

[0001] The present invention relates to a remote sensing measurement method for surface average ground temperature, and in particular to a remote sensing measurement method for surface average ground temperature by means of a surface and underground soil temperature correlation pattern in process model simulation. Background Art

[0002] Surface ground temperature plays an important role in ground-air energy exchange, soil physical environment, ecosystem maintenance, and water circulation. It is a key indicator for monitoring ecosystem primary production capacity, crop growth and development, and surface water and heat flux. It is also the variable that most directly and closely influences global climate change, so measuring surface ground temperature is of great significance. However, current surface ground temperature measurements are limited to point observations and process model simulations. Point observations have very limited spatial coverage and cannot monitor surface ground temperature over large areas. While process model simulations, driven by forced data, can simulate surface ground temperature over a large area based on the soil temperature diffusion equation, their spatial resolution is typically very low and cannot yet accurately depict the spatial distribution of surface ground temperature. How to efficiently and quickly measure surface ground temperature over a large area has become a technical challenge in the fields of ecological environment, climate change, and crop health.

[0003] Thermal infrared remote sensing, with its many advantages, including a wide field of view, high precision, and short time period, provides fundamental data for extracting information such as surface temperature, surface radiation, and surface heat. However, thermal infrared remote sensing can only invert the surface temperature of the Earth's surface skin. The limited representation of thermal infrared electromagnetic waves at the depth of the Earth's surface prevents it from being directly used to measure surface ground temperature. Given that thermal infrared remote sensing records rich radiation information from the Earth's surface skin, the only viable approach currently is to monitor large-scale surface ground temperature with high spatial resolution using remote sensing thermal infrared bands. To implement this approach, the correlation between bare soil temperature and surface ground temperature must first be resolved. This correlation can be combined with remote sensing surface temperature to measure surface ground temperature over a large area with high spatial resolution.

[0004] The land surface process model uses the soil temperature heat diffusion formula to describe soil temperature changes caused by the transmission of solar radiation from the surface to the surface. It uses the Crank-Nicholson formula to solve for the temperature of each soil layer, including the surface ground temperature. The forecast results well reflect the physical consistency of bare soil temperature and surface ground temperature, revealing the correlation pattern between bare soil temperature and surface ground temperature at each simulation time. This provides a reference for understanding the coupling relationship between bare soil temperature and surface ground temperature at the spatial resolution of remote sensing and provides a basic criterion for unlocking the correlation pattern between bare soil temperature and surface ground temperature at the spatial resolution of remote sensing thermal infrared bands. Therefore, using process model simulation data, remote sensing measurements of surface ground temperature over a large area with high spatial resolution can be achieved.

[0005] However, to achieve this goal, the process chain for remote sensing surface ground temperature measurement still requires solving the problem of decomposing the mixed surface temperature in the remote sensing inversion pixels and the process model simulation grid. This is because the highly heterogeneous local surface usually contains two components: bare soil and vegetation. This results in the surface temperature in remote sensing inversion and the surface temperature simulated by the process model being the combined effect of the bare soil temperature and vegetation temperature within the surface covered by their respective spatial resolutions. The connection between vegetation temperature and surface ground temperature is weaker than that between bare soil temperature and surface ground temperature. Therefore, to measure surface ground temperature through thermal infrared remote sensing, it is necessary to separate the bare soil temperature from the surface temperature in remote sensing inversion and process model simulation. Then, using the simulated bare soil temperature and simulated surface ground temperature, remote sensing measurement of surface ground temperature over a large area with high spatial resolution can be achieved.

[0006] To this end, a surface ground temperature remote sensing measurement method based on ground surface temperature was invented. Summary of the Invention

[0007] In order to solve the above technical problems, the present invention provides a surface ground temperature remote sensing measurement method based on ground surface temperature.

[0008] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0009] A surface ground temperature remote sensing measurement method based on ground surface temperature comprises the following steps:

[0010] S1: The radiation terms of bare soil and vegetation at the target pixel are expanded into Taylor series at the remote sensing surface temperature, and the remote sensing radiation equation of remote sensing surface temperature is established with remote sensing vegetation coverage as the weight;

[0011] S2: Combine the S1 remote sensing radiation equation and the relationship equation between remote sensing surface temperature and remote sensing component temperature to solve the remote sensing bare soil temperature and remote sensing vegetation temperature at the target pixel;

[0012] S3: The radiation terms of bare soil and vegetation at the simulation grid are expanded into Taylor series at the simulated surface temperature, and the simulated radiation equation for the simulated surface temperature is established with the simulated resolution vegetation coverage as the weight;

[0013] S4: Combine S3 to simulate the radiation equation and the relationship equation between the simulated surface temperature and the simulated component temperature to solve the simulated bare soil temperature and simulated vegetation temperature at the simulation grid;

[0014] S5: Estimate the average surface ground temperature using the correlation model between the simulated bare soil temperature and the simulated surface ground temperature and the remotely sensed bare soil temperature.

[0015] Furthermore, the S1 includes the following steps:

[0016] S11: Expand the radiation term of the bare soil component at the target pixel into a Taylor series at the remote sensing surface temperature, specifically:

[0017]

[0018] Where σ is the Stefan-Boltzmann constant, ε s is the emissivity of the bare soil component at the target pixel, F v is the remote sensing vegetation coverage, T O Indicates the remote sensing surface temperature at the target pixel (K), T s represents the remote sensing bare soil temperature of the bare soil component at the target pixel (K), represents the error in the Taylor series expansion;

[0019] S12: Expand the radiation term of the vegetation component at the target pixel into a Taylor series at the remote sensing surface temperature, specifically:

[0020]

[0021] Where, ε v is the emissivity of the vegetation component at the target pixel, T v represents the remote sensing vegetation temperature of the vegetation component at the target pixel (K), represents the error in the Taylor series expansion;

[0022] S13: The remote sensing radiation equation for the remote sensing surface temperature is established using remote sensing vegetation coverage as the weight. Specifically:

[0023]

[0024] Where, ε O is the emissivity of the ground surface at the target pixel.

[0025] Furthermore, the step S2 includes the following steps:

[0026] S21: Establish the relationship equation between remotely sensed surface temperature and remotely sensed component temperature, specifically:

[0027]

[0028] S22: Combine the S13 remote sensing radiation equation and the S21 equation for the relationship between remote sensing surface temperature and remote sensing component temperature to solve the T at the target pixel using the root-finding formula of the quartic equation. s and T v .

[0029] Furthermore, the step S3 includes the following steps:

[0030] S31: Expand the radiation term of the bare soil component at the simulation grid into a Taylor series at the simulated surface temperature, specifically:

[0031]

[0032] Where, E s is the emissivity of the bare soil component at the simulated grid, f v To simulate the vegetation coverage at grid spatial resolution, τ s represents the simulated bare soil temperature (K) of the bare soil component in the simulated surface temperature at the simulation grid, τ O represents the simulated surface temperature at the simulation grid (K), ζ s represents the error in the Taylor series expansion;

[0033] S32: Expand the radiation term of the vegetation component at the simulation grid into a Taylor series at the simulated surface temperature, specifically:

[0034]

[0035] Where, E v is the emissivity of vegetation components at the simulated grid, τ v represents the simulated vegetation temperature of the vegetation component in the simulated surface temperature at the simulation grid (K), ζ v represents the error in the Taylor series expansion;

[0036] S33: The simulated radiation equation for simulating the surface temperature is established with the simulated resolution vegetation coverage as the weight, specifically:

[0037]

[0038] Where, E O is the surface emissivity at the simulation grid.

[0039] Furthermore, the S4 includes the following steps:

[0040] S41: Establish the relationship equation between the simulated surface temperature and the simulated component temperature, specifically:

[0041]

[0042] S42: Combine S33 to simulate the radiation equation and S41 to simulate the relationship between surface temperature and component temperature and use the root-finding formula of the quartic equation to solve the τ at the simulation grid. s and τ v .

[0043] Furthermore, the step S5 includes the following steps:

[0044] S51: Establish a correlation model between the simulated bare soil temperature and the simulated surface ground temperature, specifically:

[0045] M=Model(τ s ,τ ↓ )

[0046] τ ↓ =M(τ s )

[0047] Where, τ ↓ To simulate the surface ground temperature at the simulated grid, Model represents the establishment of τ s and τ ↓ The mathematical method of the correlation model between the simulated bare soil temperature and the simulated surface ground temperature is established;

[0048] S52: Using M and T s Estimate the average surface ground temperature, specifically:

[0049] T ↓ =M(T s )

[0050] Where, T ↓ is the estimated average surface temperature, based on T s Remote sensing measurement results of the average surface ground temperature.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1) Currently, there is no method for measuring surface ground temperature over a large area with high spatial resolution. This invention fills this gap by using thermal infrared remote sensing technology.

[0053] 2) The present invention proposes a component radiation Taylor series expansion method for surface mixed temperature decomposition;

[0054] 3) The present invention proposes a method for expanding the remote sensing surface temperature depth information to simulate the correlation model between the simulated bare earth temperature and the simulated surface ground temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A flow chart of the method in the embodiment provided by the present invention; DETAILED DESCRIPTION

[0056] To more clearly illustrate the technical features, basic concepts, and beneficial effects of the present invention, the following detailed description of the objectives and technical features of the present invention is provided in conjunction with the accompanying drawings used in the embodiments. It should be noted that the accompanying drawings illustrate only some embodiments of the present invention and should not be construed as limiting the scope of the present invention. Other relevant drawings may be obtained by a person of ordinary skill in the art without inventive effort.

[0057] like Figure 1 As shown, the technical process in the embodiment provided by the present invention includes the following steps:

[0058] S1 acquires remote sensing images including visible light band and thermal infrared band, total atmospheric column water content products, land surface emissivity data and process model simulation and preprocessing;

[0059] S2 expands the radiation terms of bare soil and vegetation at the target pixel into Taylor series at the remote sensing surface temperature, and establishes the remote sensing radiation equation of remote sensing surface temperature with remote sensing vegetation coverage as the weight;

[0060] S3 combines the S2 remote sensing radiation equation and the relationship equation between remote sensing surface temperature and remote sensing component temperature to solve the remote sensing bare soil temperature and remote sensing vegetation temperature at the target pixel;

[0061] S4 expands the radiation terms of bare soil and vegetation at the simulation grid into Taylor series at the simulated surface temperature, and establishes the simulated radiation equation for the simulated surface temperature with the simulated resolution vegetation coverage as the weight;

[0062] S5 combines S4 to simulate the radiation equation and the relationship equation between the simulated surface temperature and the simulated component temperature to solve the simulated bare soil temperature and simulated vegetation temperature at the simulation grid;

[0063] S6 estimates the average surface ground temperature using the correlation model between simulated bare soil temperature and simulated surface ground temperature and remotely sensed bare soil temperature.

[0064] In S1, the remote sensing image comprising a visible light band and a thermal infrared band is obtained. Exemplarily, the remote sensing image can be selected from Landsat 5, 7, or 8 (https: / / ladsweb.modaps.eosdis.nasa.gov / ) or HJ-1B satellite (https: / / www.gscloud.cn / search) or Terra satellite or Aqua satellite of the Earth Observation System (EOS) (http: / / modis.gsfc.nasa.gov / );

[0065] In S1, the total atmospheric column water content product can be selected, for example, from the Total Column Water Vapor dataset in the reanalysis data of the National Centers for Environmental Prediction of the United States. The dataset covers the period from 1948 to the present, has a temporal resolution of 6 hours, a spatial resolution of 2.5°, and global coverage, and can be downloaded from https: / / ladsweb.modaps.eosdis.nasa.gov / ;

[0066] In S1, the land surface emissivity data may be the ASTER GEDv3 dataset, which is the average value of the surface emissivity derived from all clear-sky ASTER images between 2000 and 2008 using a temperature emissivity separation algorithm, with an assessment accuracy of ~0.01, a spatial resolution of 100 m, and global coverage.

[0067] In said S1, the process model simulation, exemplarily, may be the near-real-time equal latitude and longitude grid fusion analysis product of the China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0), with a time span from January 19, 2017 to the present, covering the Asian region (0-65°N, 60-160°E), 0.0625°×0.0625° spatial resolution, 1 hour time resolution, and soil temperature analysis products vertically divided into 5 layers: 5, 10, 40, 100, and 200 cm; or may be the ERA5 reanalysis dataset, with a time resolution of hourly, a spatial resolution of 0.1°, a time span from 1950 to the present, and global coverage, including 0-7 cm surface ground temperature, 7-28 cm, 28-100 cm, and 100-289 cm soil temperature;

[0068] In S1, the preprocessing includes vegetation coverage calculation, surface emissivity calculation, thermal infrared band surface temperature inversion, process model simulation of surface ground temperature spatiotemporal fusion and spatial upscaling;

[0069] The vegetation coverage calculation is preferably:

[0070]

[0071]

[0072] Where NDVI is the normalized difference vegetation index, soil is the NDVI value of the pixel with completely bare soil or no vegetation cover. veg is the NDVI value of the fully covered vegetation pixel, FVC is the vegetation coverage, NIR represents the near infrared band of the selected remote sensing image, and R represents the red light band of the selected remote sensing image;

[0073] The surface emissivity calculation is preferably:

[0074] ε=FVCε veg +(1-FVC)ε bare

[0075]

[0076] Where, ε 13a and ε 14a are the surface emissivity of bands 13 and 14 in the ASTER GEDv3 product, and FVC a represents the vegetation coverage calculated using the near-infrared and red bands of the ASTER image, and ε is the surface emissivity obtained in the thermal infrared band;

[0077] The thermal infrared band surface temperature inversion is preferably a statistical single window algorithm, specifically:

[0078]

[0079] Where LST is the inverted surface temperature of the remote sensing thermal infrared band (K), Tb is the brightness temperature of the remote sensing thermal infrared band (K), and A i , B i , C i Represents the coefficient of the statistical single-window algorithm, which is determined by the atmospheric column water vapor content at the time of remote sensing image acquisition in the Total Column Water Vapor dataset;

[0080] The process model simulates the spatiotemporal fusion of surface ground temperature. The spatiotemporal fusion refers to the preprocessing when the time of the process model simulation is inconsistent with the time of remote sensing image acquisition. For example, if the CLDAS-V2.0 near-real-time product is used, its time span is from January 19, 2017 to the present. If remote sensing images before 2017 are used, the time span of the CLDAS-V2.0 near-real-time product needs to be extended to the time of remote sensing image acquisition with the help of the ERA5 reanalysis dataset with a longer time span. Specifically:

[0081]

[0082]

[0083] Where (x, y) represents the simulation grid position in the process model simulation, subscript O represents the process model simulation, τ O_C_b represents the simulated surface temperature at the base time b of the CLDAS-V2.0 near-real-time product, τ O_E_p and τ O_E_b They represent the simulated surface temperature at time p and time b corresponding to the remote sensing image acquisition in the ERA5 reanalysis dataset, respectively. W represents the size of the rectangular window centered at (x, y). ij represents the weight of the grid position (i, j) in the rectangular window with similar surface temperature to that at (x, y) in spatiotemporal fusion, τ O represents the simulated surface temperature of the CLDAS-V2.0 near-real-time product at time p obtained using the spatiotemporal fusion algorithm; τ ↓_C_b represents the simulated surface ground temperature at time b of the CLDAS-V2.0 near-real-time product, τ ↓_E_p and τ ↓_E_b They represent the simulated surface ground temperature at time p and time b of the ERA5 reanalysis dataset, respectively. Indicates the weight of the grid position (i, j) in the rectangular window with similar surface ground temperature to that at (x, y) in spatiotemporal fusion, τ↓ represents the simulated surface ground temperature of the CLDAS-V2.0 near-real-time product at time p obtained using the spatiotemporal fusion algorithm;

[0084] The spatial upscaling includes spatially upscaling the vegetation cover and surface emissivity of the remote sensing spatial resolution to the process model simulation spatial resolution. The upscaling model can be selected as one of local averaging, nearest neighbor method, bilinear interpolation or median sampling.

[0085] In S2, the radiation terms of bare soil and vegetation at the target pixel are expanded into Taylor series at the remote sensing surface temperature, and a remote sensing radiation equation of the remote sensing surface temperature is established with remote sensing vegetation coverage as a weight, including the following steps:

[0086] S21: Expand the radiation term of the bare soil component at the target pixel into a Taylor series at the remote sensing surface temperature, specifically:

[0087]

[0088] Where σ is the Stefan-Boltzmann constant, ε s is the emissivity of the bare soil component at the target pixel, F v is the remote sensing vegetation coverage, T O Indicates the remote sensing surface temperature at the target pixel (K), T s represents the remote sensing bare soil temperature of the bare soil component at the target pixel (K), represents the error in the Taylor series expansion;

[0089] S22: Expand the radiation term of the vegetation component at the target pixel into a Taylor series at the remote sensing surface temperature, specifically:

[0090]

[0091] Where, ε v is the emissivity of the vegetation component at the target pixel, T v represents the remote sensing vegetation temperature of the vegetation component at the target pixel (K), represents the error in the Taylor series expansion;

[0092] S23: The remote sensing radiation equation for remote sensing surface temperature is established with remote sensing vegetation coverage as the weight. Specifically:

[0093]

[0094] Where, ε O is the emissivity of the ground surface at the target pixel.

[0095] In the S3, the joint S2 remote sensing radiation equation, the remote sensing surface temperature and the remote sensing component temperature relationship equation solve the remote sensing bare soil temperature and the remote sensing vegetation temperature at the target pixel, comprising the following steps:

[0096] S31: Establish the remote sensing surface temperature and the remote sensing component temperature relationship equation, specifically:

[0097]

[0098] S32: Joint S23 remote sensing radiation equation, S31 remote sensing surface temperature and remote sensing component temperature relationship equation using a quartic equation root formula to solve the target pixel T s and T v ;

[0099] Exemplary, a quartic equation root formula can be selected using one of the Ferrari method, permutation group method, Tianheng formula or Ferrari method root formula to solve.

[0100] In the S4, the radiation term of the bare soil and vegetation at the simulation grid is expanded into a Taylor series at the simulated surface temperature, and a simulation radiation equation of the simulated surface temperature is established by taking the simulation resolution vegetation coverage as the weight, comprising the following steps:

[0101] S41: The radiation term of the bare soil component at the simulation grid is expanded into a Taylor series at the simulated surface temperature, specifically:

[0102]

[0103] In the formula, E s is the specific radiation of the bare soil component at the simulation grid, f v is the spatial resolution vegetation coverage of the simulation grid, τ s represents the simulated bare soil temperature (K) of the bare soil component in the simulated surface temperature at the simulation grid, τ O represents the simulated surface temperature (K) at the simulation grid, ζ s represents the error of the Taylor series expansion formula;

[0104] S42: The radiation term of the vegetation component at the simulation grid is expanded into a Taylor series at the simulated surface temperature, specifically:

[0105]

[0106] In the formula, E v is the specific radiation of the vegetation component at the simulation grid, τ v represents the simulated vegetation temperature (K) of the vegetation component in the simulated surface temperature at the simulation grid, ζ v represents the error of the Taylor series expansion formula;

[0107] S43: The simulated radiation equation for simulating the surface temperature is established with the simulated resolution vegetation coverage as the weight, specifically:

[0108]

[0109] Where, E O is the surface emissivity at the simulation grid.

[0110] In S5, the step of combining the radiation simulation equation and the relationship equation between the simulated surface temperature and the simulated component temperature in S4 to solve the simulated bare soil temperature and the simulated vegetation temperature at the simulation grid includes the following steps:

[0111] S51: Establish the relationship equation between the simulated surface temperature and the simulated component temperature, specifically:

[0112]

[0113] S52: Combine S43 to simulate the radiation equation and S51 to simulate the relationship between surface temperature and component temperature, and use the root-finding formula of the quartic equation to solve the τ at the simulation grid. s and τ v .

[0114] In S6, estimating the average surface ground temperature using the correlation model between the simulated bare soil temperature and the simulated surface ground temperature and the remotely sensed bare soil temperature includes the following steps:

[0115] S61: Establish a correlation model between the simulated bare soil temperature and the simulated surface ground temperature, specifically:

[0116] M=Model(τ s ,τ ↓ )

[0117] τ ↓ =M(τ s )

[0118] Where, τ ↓ To simulate the surface ground temperature at the simulated grid, Model represents the establishment of τ s and τ ↓ A mathematical method for the correlation model between the simulated bare soil temperature and the simulated surface ground temperature. Exemplarily, the mathematical method can be selected from least squares regression, random forest or support vector regression. M represents the established correlation model between the simulated bare soil temperature and the simulated surface ground temperature.

[0119] S62: Using M and T s Estimate the average surface ground temperature, specifically:

[0120] T ↓ =M(T s )

[0121] Where, T ↓ is the estimated average surface temperature, based on T s The surface average ground temperature remote sensing measurement result. The surface ground temperature remote sensing measurement method based on ground surface temperature proposed in the present invention has the following advantages:

[0122] 1) With the help of process model simulation, high spatial resolution remote sensing measurement of surface ground temperature over a large area was achieved, filling the gap in remote sensing measurement of surface ground temperature;

[0123] 2) By using the Taylor expansion of the radiation energy of the surface components, a closed equation for the decomposition of the surface mixed temperature is constructed, which avoids the calculation of the surface temperature variability with vegetation cover;

[0124] 3) Given that process model simulations can currently achieve global coverage, the present invention has the potential to estimate global surface temperature with high spatial resolution.

[0125] It should be noted that based on the preferred embodiments given in the present invention, ordinary technicians in this field can make various modifications and changes to the present invention. However, within the spirit and principles of the present invention, any modifications, equivalent substitutions, improvements, etc. made cannot depart from the scope of protection of the technical solutions of the various embodiments of the present invention.

Claims

1. A surface ground temperature remote sensing measurement method based on ground surface temperature, characterized in that: The steps include: S1: Expand the radiation terms of bare soil and vegetation at the target pixel into Taylor series at the remote sensing surface temperature, and establish the remote sensing radiation equation of remote sensing surface temperature with remote sensing vegetation coverage as the weight, including the following steps: S11: Expand the radiation term of the bare soil component at the target pixel into a Taylor series at the remote sensing surface temperature, specifically: Where σ is the Stefan-Boltzmann constant, ε s is the emissivity of the bare soil component at the target pixel, F v is the remote sensing vegetation coverage, T O Indicates the remote sensing surface temperature at the target pixel (K), T s represents the remote sensing bare soil temperature of the bare soil component at the target pixel (K), represents the error in the Taylor series expansion; S12: Expand the radiation term of the vegetation component at the target pixel into a Taylor series at the remote sensing surface temperature, specifically: Where, ε v is the emissivity of the vegetation component at the target pixel, T v represents the remote sensing vegetation temperature of the vegetation component at the target pixel (K), represents the error in the Taylor series expansion; S13: The remote sensing radiation equation for the remote sensing surface temperature is established using remote sensing vegetation coverage as the weight. Specifically: Where, ε O is the surface emissivity at the target pixel; S2: Combine the S1 remote sensing radiation equation and the relationship equation between remote sensing surface temperature and remote sensing component temperature to solve the remote sensing bare soil temperature and remote sensing vegetation temperature at the target pixel; S3: The radiation terms of bare soil and vegetation at the simulation grid are expanded into Taylor series at the simulated surface temperature, and the simulated radiation equation for the simulated surface temperature is established with the simulated resolution vegetation coverage as the weight; S4: Combine S3 to simulate the radiation equation and the relationship equation between the simulated surface temperature and the simulated component temperature to solve the simulated bare soil temperature and simulated vegetation temperature at the simulation grid; S5: Estimate the average surface ground temperature using the correlation model between the simulated bare soil temperature and the simulated surface ground temperature and the remotely sensed bare soil temperature.

2. The method according to claim 1, characterized in that Said S2 comprises the following steps: S21: Establish the relationship equation between remotely sensed surface temperature and remotely sensed component temperature, specifically: S22: Combine the S13 remote sensing radiation equation and the S21 equation for the relationship between remote sensing surface temperature and remote sensing component temperature to solve the T at the target pixel using the root-finding formula of the quartic equation. s and T v .

3. The method according to claim 1, characterized in that The S3 includes the following steps: S31: Expand the radiation term of the bare soil component at the simulation grid into a Taylor series at the simulated surface temperature, specifically: Where, E s is the emissivity of the bare soil component at the simulated grid, f v To simulate the vegetation coverage at grid spatial resolution, τ s represents the simulated bare soil temperature (K) of the bare soil component in the simulated surface temperature at the simulation grid, τ O represents the simulated surface temperature at the simulation grid (K), ζ s represents the error in the Taylor series expansion; S32: Expand the radiation term of the vegetation component at the simulation grid into a Taylor series at the simulated surface temperature, specifically: Where, E v is the emissivity of vegetation components at the simulated grid, τ v represents the simulated vegetation temperature of the vegetation component in the simulated surface temperature at the simulation grid (K), ζ v represents the error in the Taylor series expansion; S33: The simulated radiation equation for simulating the surface temperature is established with the simulated resolution vegetation coverage as the weight, specifically: Where, E O is the surface emissivity at the simulation grid.

4. The method according to claim 1, wherein The S4 comprises the following steps: S41: Establish the relationship equation between the simulated surface temperature and the simulated component temperature, specifically: S42: Combine S33 to simulate the radiation equation and S41 to simulate the relationship between surface temperature and component temperature and use the root-finding formula of the quartic equation to solve the τ at the simulation grid. s and τ v .

5. The method according to claim 1, wherein The S5 comprises the following steps: S51: Establish a correlation model between the simulated bare soil temperature and the simulated surface ground temperature, specifically: M=Model(τ s ,t ↓ ) t ↓ =M(τ s ) Where, τ ↓ To simulate the surface ground temperature at the simulated grid, Model represents the establishment of τ s and τ ↓ The mathematical method of the correlation model between the simulated bare soil temperature and the simulated surface ground temperature is represented by M. S52: Using M and T s Estimate the average surface ground temperature, specifically: T ↓ =M(T s ) Where, T ↓ is the estimated average surface temperature, based on T s Remote sensing measurement results of the average surface ground temperature.

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