Self-adaptive method and model for acquiring microwave surface emissivity of different spatial scales

Abstract

The invention relates to the technical field of atmospheric science and remote sensing, in particular to a self-adaptive method and model for acquiring microwave surface emissivity of different spatial scales. The method comprises the following steps: screening observation pixels of various vegetation and bare land types based on a land coverage classification database; removing the influence of cold air background, atmosphere and land surface temperature, and obtaining an instantaneous emissivity data set; establishing emissivity semi-empirical models of various single land surface types through regression analysis; determining a ground footprint of an observation pixel, matching a land coverage grid, and extracting a position, a ground surface type and an area proportion; combining geographic coordinates, extracting snow depth and atmospheric parameters from the reanalysis data, and screening clear sky snow-free pixels; according to model input parameters, calculating microwave emissivity of different land surface types in each grid; and fusing the area proportion and the normalized radiation power of the antenna pattern, calculating a comprehensive weight coefficient, and finally obtaining the microwave emissivity of the target grid.

Description

Technical Field

[0001] The present invention relates to the fields of atmospheric science and remote sensing technology, and in particular to an adaptive method and model for obtaining microwave surface emissivity at different spatial scales. Background Art

[0002] The land surface is complex and ever-changing, and the interactions between electromagnetic waves and land surface components are extremely complex, making it difficult to establish a comprehensive emissivity model. Currently, commonly used surface emissivity models can be divided into physical models and semi-empirical models.

[0003] Physical models can be further divided into numerical simulations and analytical models. Numerical simulations are difficult to apply to large-scale emissivity studies due to their high computational cost and complex input parameters. Analytical models provide a certain degree of approximation for the scattered field from rough surfaces, but they only consider the effect of the surface's geometric roughness on the scattered field and ignore volume scattering, making them only applicable to conditions with high soil moisture.

[0004] In contrast, semi-empirical models have relatively simple calculation formulas and fewer input parameters, making them more suitable for data analysis and geophysical parameter inversion. However, existing semi-empirical models are generally only applicable to the spatial scale corresponding to the data source and suffer from large calculation errors when processing pixels containing mixed land cover types. Summary of the Invention

[0005] The purpose of the present invention is to overcome the above-mentioned defects of the prior art and thus provide an adaptive method and model for obtaining microwave surface emissivity at different spatial scales.

[0006] To solve the above technical problems, the technical solution of the present invention provides an adaptive method for obtaining microwave surface emissivity at different spatial scales, including:

[0007] Step 1: Based on the land cover classification database, the observation pixels of various vegetation types and bare land types are screened from the acquired observation brightness temperature data;

[0008] Step 2: Remove the cold sky background and atmospheric contribution from the observed pixel brightness temperature data, and remove the influence of land surface temperature to obtain the instantaneous microwave emissivity dataset;

[0009] Step 3: Based on the correlation between the influencing factors and microwave surface emissivity, establish multiple semi-empirical emissivity models for single land surface types through linear regression;

[0010] Step 4: Determine the ground footprint of the observation pixel and match the ground footprint with the grid of the land cover classification database to obtain the location of the target grid of the ground footprint, the surface type and its corresponding area ratio;

[0011] Step 5: For the target grid location of the ground footprint, use the nearest neighbor matching method to obtain the snow depth and atmospheric parameters in the climate reanalysis database based on the geographic coordinates of the ground footprint to obtain the ground footprint under clear sky and without ice and snow cover;

[0012] Step 6: Match the input parameters of the emissivity semi-empirical model obtained in step 3 with the preset units, and substitute the matched input parameters into the emissivity semi-empirical model of each single land surface type to calculate the microwave emissivity of each land surface type within each target grid of the ground footprint;

[0013] Step 7: Calculate the comprehensive weight coefficient based on the area proportion of different land surface types in each target grid and the normalized antenna pattern radiation power;

[0014] Step 8: Express the microwave emissivity of the target grid as the sum of the products of the emissivity of all land surface types in the target grid and the corresponding comprehensive weight coefficient.

[0015] As an improvement to the above method, the observed brightness temperature data in step 1 includes: existing satellite-borne, airborne and surface microwave observed brightness temperature data at the target frequency; the land cover classification database in step 1 adopts the MODIS land cover classification product MCD12C1.

[0016] As an improvement to the above method, step 2 specifically includes: using at least the atmospheric temperature and humidity profile, land surface temperature and surface pressure in the climate reanalysis database as auxiliary parameters, combining the atmospheric absorption coefficient model and the radiation transfer equation, removing the contribution of the cold sky background and the atmosphere from the observed pixel brightness temperature data, and stripping off the influence of the land surface temperature to obtain the instantaneous microwave emissivity data set.

[0017] As an improvement to the above method, step 2 specifically includes: using at least the atmospheric temperature and humidity profiles, the effective temperature of bare land, and the surface pressure in the climate reanalysis database as auxiliary parameters, combining the atmospheric absorption coefficient model and the radiation transfer equation, removing the contribution of the cold sky background and the atmosphere from the observed pixel brightness temperature data, and stripping off the influence of the land surface temperature to obtain the instantaneous microwave emissivity dataset.

[0018] As an improvement to the above method, the climate reanalysis database adopts ERA5 climate reanalysis data; and the atmospheric transmission model adopts the MPM93 atmospheric absorption coefficient model.

[0019] As an improvement to the above method, the influencing factors of step 3 include: normalized vegetation index, soil moisture and surface temperature; step 3 specifically includes: based on the correlation between the influencing factors and the microwave surface emissivity, linear regression is performed step by step from large to small, thereby establishing multiple semi-empirical models of the emissivity of a single land surface type.

[0020] As an improvement to the above method, step 4 specifically includes: determining the ground footprint of the observed pixel based on its geographic coordinates, frequency, and scanning angle, and matching the ground footprint with the grid of the land cover classification database to obtain the location of the target grid of the ground footprint, the surface type, and its corresponding area ratio; wherein, the land cover classification database samples the MODIS land cover classification product MCD12C1.

[0021] As an improvement to the above method, in step 6, when the resolution of the input parameters is equal to or lower than the preset unit, the nearest neighbor method is directly used to match the input parameters; when the resolution of the input parameters is higher than the preset unit, it is first resampled to the preset unit, and then the input parameters are matched according to the nearest neighbor rule; wherein, the input parameters include: normalized vegetation index, skin temperature, soil moisture and soil temperature, percentage of terrain slope and percentage of sandy soil; the preset unit is 0.05 degrees.

[0022] As an improvement to the above method, step 7 specifically includes:

[0023] Combined with the area proportion of different land surface types in each target grid and the normalized antenna pattern radiation power, the comprehensive weight coefficient is calculated:

[0024]

[0025] Among them, W i,j represents the comprehensive weight coefficient of the jth land surface type in the i-th grid, P i is the normalized radiation power of the i-th grid; C i,j is the percentage of area occupied by the jth land surface type in the i-th grid; M represents the number of grids contained in the footprint, and N is the number of land surface types studied;

[0026] The step 8 specifically includes:

[0027] The microwave emissivity Emi of the target grid (f,p) It is expressed as the sum of the products of the emissivity of all land surface types in the target grid and the corresponding comprehensive weight coefficient:

[0028]

[0029] Wherein, the subscripts f and p represent frequency and polarization respectively; Emi (f,p) Represents the emissivity of the j-th land surface type in the i-th grid.

[0030] To achieve another object of the present invention, the present invention also provides an adaptive model for obtaining microwave surface emissivity at different spatial scales, including

[0031] The acquisition module, based on the land cover classification database, is used to filter and obtain observation pixels of various vegetation types and bare land types from the acquired observation brightness temperature data;

[0032] The dataset module is used to remove the cold sky background and atmospheric contribution from the observed pixel brightness temperature data, and to remove the influence of land surface temperature to obtain the instantaneous microwave emissivity dataset;

[0033] The model building module establishes multiple semi-empirical emissivity models of single land surface types through linear regression based on the correlation between influencing factors and microwave surface emissivity;

[0034] The matching module is used to determine the ground footprint of the observation pixel and match the ground footprint with the grid of the land cover classification database to obtain the location of the target grid of the ground footprint, the surface type and its corresponding area ratio;

[0035] The ground footprint module is used to obtain the snow depth and atmospheric parameters from the climate reanalysis database based on the geographic coordinates of the ground footprint target grid using the nearest neighbor matching method to obtain the ground footprint under clear sky and without ice and snow cover;

[0036] A substitution module is used to match the input parameters of the emissivity semi-empirical model obtained by the model establishment module with preset units, and substitute the matched input parameters into the emissivity semi-empirical model of each single land surface type to calculate the microwave emissivity of each type of land surface within each target grid of the ground footprint;

[0037] A comprehensive weight module is used to calculate the comprehensive weight coefficient by combining the area proportion of different land surface types in each target grid and the normalized antenna pattern radiation power; and

[0038] The microwave emissivity module is used to obtain the microwave emissivity of the target grid, where the microwave emissivity of the target grid is expressed as the sum of the products of the emissivity of all land surface types in the target grid and the corresponding comprehensive weight coefficient.

[0039] Compared to existing technologies, the present invention's adaptive method and model for obtaining microwave surface emissivity at different spatial scales focuses on the ground range corresponding to the antenna's main beam. For observational data that meets the model's application requirements, the corresponding ground emissivity can be directly calculated without resampling brightness temperature and more comprehensively considering footprint size. The present invention also comprehensively considers the influence of antenna patterns and the proportion of each type of area, proposing comprehensive weighting coefficients for each land surface type, addressing the shortcomings of previous models. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flowchart of an adaptive method for obtaining microwave surface emissivity at different spatial scales provided in an embodiment of the invention. DETAILED DESCRIPTION

[0041] The technical solution provided by the present invention is further illustrated below with reference to embodiments.

[0042] Example 1

[0043] The adaptive method for obtaining microwave surface emissivity at different spatial scales provided in this embodiment is as follows: Figure 1 Shown, including:

[0044] Step 1: Based on the land cover classification database, the observation pixels of various vegetation types and bare land types are screened from the acquired observation brightness temperature data;

[0045] Step 2: Remove the cold sky background and atmospheric contribution from the observed pixel brightness temperature data, and remove the influence of land surface temperature to obtain the instantaneous microwave emissivity dataset;

[0046] Step 3: Based on the correlation between the influencing factors and microwave surface emissivity, establish multiple semi-empirical emissivity models for single land surface types through linear regression;

[0047] Step 4: Determine the ground footprint of the observation pixel and match the ground footprint with the grid of the land cover classification database to obtain the location of the target grid of the ground footprint, the surface type and its corresponding area ratio;

[0048] Step 5: For the target grid location of the ground footprint, use the nearest neighbor matching method to obtain the snow depth and atmospheric parameters in the climate reanalysis database based on the geographic coordinates of the ground footprint to obtain the ground footprint under clear sky and without ice and snow cover;

[0049] Step 6: Match the input parameters of the emissivity semi-empirical model obtained in step 3 with the preset units, and substitute the matched input parameters into the emissivity semi-empirical model of each single land surface type to calculate the microwave emissivity of each land surface type within each target grid of the ground footprint;

[0050] Step 7: Calculate the comprehensive weight coefficient based on the area proportion of different land surface types in each target grid and the normalized antenna pattern radiation power;

[0051] Step 8: Express the microwave emissivity of the target grid as the sum of the products of the emissivity of all land surface types in the target grid and the corresponding comprehensive weight coefficient.

[0052] Specifically, the observed brightness temperature data in step 1 includes: observed brightness temperature data of existing satellite-borne, airborne and surface microwaves at the target frequency; the land cover classification database in step 1 adopts the MODIS land cover classification product MCD12C1.

[0053] Specifically, step 2 includes: using at least the atmospheric temperature and humidity profile, land surface temperature and surface pressure in the climate reanalysis database as auxiliary parameters, combining the atmospheric absorption coefficient model and the radiation transfer equation, removing the contribution of the cold sky background and the atmosphere from the observed pixel brightness temperature data, and stripping off the influence of the land surface temperature to obtain the instantaneous microwave emissivity data set.

[0054] Specifically, step 2 includes: using at least the atmospheric temperature and humidity profile, the effective temperature and surface pressure of bare land in the climate reanalysis database as auxiliary parameters, combining the atmospheric absorption coefficient model and the radiation transfer equation, removing the contribution of the cold sky background and the atmosphere from the observed pixel brightness temperature data, and stripping off the influence of the land surface temperature to obtain the instantaneous microwave emissivity data set.

[0055] Specifically, the climate reanalysis database adopts ERA5 climate reanalysis data; the atmospheric transport model adopts MPM93 atmospheric absorption coefficient model.

[0056] Specifically, the influencing factors of step 3 include: normalized vegetation index, soil moisture and skin temperature; step 3 specifically includes: based on the correlation between the influencing factors and the microwave surface emissivity, linear regression is performed step by step from large to small, thereby establishing multiple semi-empirical models of emissivity of single land surface types.

[0057] Specifically, step 4 includes: determining the ground footprint of the observed pixel based on its geographic coordinates, frequency and scanning angle, and matching the ground footprint with the grid of the land cover classification database to obtain the location of the target grid of the ground footprint, the surface type and its corresponding area ratio; wherein, the land cover classification database samples the MODIS land cover classification product MCD12C1.

[0058] Specifically, in step 6, when the resolution of the input parameters is equal to or lower than the preset unit, the nearest neighbor method is directly used to match the input parameters; when the resolution of the input parameters is higher than the preset unit, it is first resampled to the preset unit, and then the input parameters are matched according to the nearest neighbor rule; wherein, the input parameters include: normalized vegetation index, skin temperature, soil moisture and soil temperature, percentage of terrain slope and percentage of sandy soil; the preset unit is 0.05 degrees.

[0059] Specifically, the step 7 includes:

[0060] Calculate the comprehensive weight coefficient by combining the area proportion of different land surface types in each target grid and the normalized antenna pattern radiation power;

[0061]

[0062] Among them, P iis the normalized radiation power of the i-th grid; C i,j is the percentage of area occupied by the jth land surface type in the i-th grid; M represents the number of grids contained in the footprint, and N is the number of land surface types studied in this paper. In this embodiment, N is 13.

[0063] Specifically, the step 8 includes:

[0064] The microwave emissivity of the target grid is expressed as the sum of the products of the emissivity of all land surface types in the target grid and the corresponding comprehensive weight coefficient:

[0065]

[0066] Wherein, the subscripts f and p represent frequency and polarization respectively; W i,j Represents the comprehensive weight coefficient of the jth land surface type in the i-th grid; Emi (f,p) represents the emissivity of the jth land surface type in the ith grid; M represents the number of grids contained in the footprint, and N is the number of land surface types studied.

[0067] The following further describes this embodiment with reference to the accompanying drawings. Figure 1 As shown, the technical solution provided in this embodiment includes the following contents:

[0068] (1) Establish an emissivity model for a single land surface type.

[0069] First, obtain existing satellite-borne, airborne, and surface microwave brightness temperature data at the target frequency.

[0070] Then, based on the MODIS land cover classification product MCD12C1, relatively "pure" observation pixels of various vegetation types and bare land types were screened.

[0071] Then, using ERA5's atmospheric temperature and humidity profiles, land surface temperature (or effective temperature of bare land), surface pressure, etc. as auxiliary parameters, combined with the MPM93 atmospheric absorption coefficient model and radiation transfer equation, the cold sky background and atmospheric contributions are removed from the observed brightness temperature and the influence of land surface temperature is stripped off, thus obtaining the instantaneous microwave emissivity dataset.

[0072] Finally, taking NDVI, soil moisture, and skin temperature as influencing factors, linear regression was performed step by step from large to small according to the correlation between the three influencing factors and emissivity to establish multiple semi-empirical models of surface emissivity.

[0073] (2) Establish an adaptive emissivity model of mixed pixels.

[0074] First, the ground footprint of the observed pixel is determined based on its geographic coordinates (longitude and latitude), frequency, and scanning angle. This footprint is then matched to the MCD12C1 grid to obtain the target grid, determining its location, surface type, and corresponding percentage. For each target grid, the nearest neighbor matching method is used to obtain snow depth and total vertical liquid water content (TCLW) from the ERA5 reanalysis data based on its geographic coordinates (longitude and latitude).

[0075] Then, for each observation pixel that meets the criteria, the model input parameters were matched at a 0.05-degree grid and then applied to each single-type emissivity model to calculate the emissivity of each land surface type within each grid. For parameters with a resolution equal to or lower than 0.05 degrees (NDVI, surface temperature, soil moisture, and soil temperature), the input parameters were directly matched using the nearest neighbor method. For surface parameters with a resolution higher than 0.05 degrees (topography slope and percentage of sandy soil), the parameters were first resampled to 0.05 degrees and then matched using the nearest neighbor method.

[0076] Finally, the comprehensive weight coefficient of each land surface type in each grid is calculated based on the area proportion of each type in each grid and the normalized radiant power of each grid. The emissivity of the target grid is expressed as the sum of the products of the emissivity of the land surface types contained in the grid and the corresponding comprehensive weight coefficient.

[0077] Example 2

[0078] This embodiment provides an adaptive model for obtaining microwave surface emissivity at different spatial scales, including

[0079] The acquisition module, based on the land cover classification database, is used to filter and obtain observation pixels of various vegetation types and bare land types from the acquired observation brightness temperature data;

[0080] The dataset module is used to remove the cold sky background and atmospheric contribution from the observed pixel brightness temperature data, and to remove the influence of land surface temperature to obtain the instantaneous microwave emissivity dataset;

[0081] The model building module establishes multiple semi-empirical emissivity models of single land surface types through linear regression based on the correlation between influencing factors and microwave surface emissivity;

[0082] The matching module is used to determine the ground footprint of the observation pixel and match the ground footprint with the grid of the land cover classification database to obtain the location of the target grid of the ground footprint, the surface type and its corresponding area ratio;

[0083] The ground footprint module is used to obtain the snow depth and atmospheric parameters from the climate reanalysis database based on the geographic coordinates of the ground footprint target grid using the nearest neighbor matching method to obtain the ground footprint under clear sky and without ice and snow cover;

[0084] A substitution module is used to match the input parameters of the emissivity semi-empirical model obtained by the model establishment module with preset units, and substitute the matched input parameters into the emissivity semi-empirical model of each single land surface type to calculate the microwave emissivity of each type of land surface within each target grid of the ground footprint;

[0085] A comprehensive weight module is used to calculate the comprehensive weight coefficient by combining the area proportion of different land surface types in each target grid and the normalized antenna pattern radiation power; and

[0086] The microwave emissivity module is used to obtain the microwave emissivity of the target grid, where the microwave emissivity of the target grid is expressed as the sum of the products of the emissivity of all land surface types in the target grid and the corresponding comprehensive weight coefficient.

[0087] The model proposed in this invention has at least the following two improvements compared to previous models:

[0088] First, to facilitate calculation and analysis, most existing methods use a fixed-size latitude and longitude grid (e.g., 0.25° × 0.25°) as their research object, analyzing the coverage and energy contribution of different surface types within the grid. This approach is of great significance for fundamental theoretical research, but because the satellite footprint does not perfectly match the grid, it cannot be directly applied to satellite observation data. Resampling the observation data to a specified grid size increases the computational cost and reduces processing speed. Furthermore, a small number of studies use the ground range corresponding to the antenna's ground resolution as their research object. However, the nominal resolution of a radiometer is the ground range corresponding to a 3dB beamwidth, which is approximately one-sixth the area of ​​the actual footprint. Therefore, this method may overlook some important ground information. The model proposed in this paper uses the ground range corresponding to the antenna's main beam as its research object. For observation data that meet the model's application requirements, the corresponding ground emissivity can be directly calculated, eliminating the need for brightness temperature resampling and more comprehensively considering the footprint size.

[0089] Secondly, previous studies typically use the percentage of area occupied by each land surface type within the mixed pixel as a weight to calculate the emissivity of the mixed pixel, rarely considering the role of the antenna pattern. This approach has little impact on gridded observational data, but can introduce significant errors in models that focus on 3dB footprints or main beam footprints. Therefore, this invention comprehensively considers the influence of the antenna pattern and the area percentage of each type, and proposes a comprehensive weight coefficient for each land surface type, addressing the shortcomings of previous models.

[0090] This embodiment comprehensively considers the area proportions of different land surface types within a pixel and the influence of antenna patterns to establish an adaptive land surface microwave emissivity model for observation pixels. Compared to traditional models, which typically simulate microwave emissivity based on artificially demarcated ground areas for ease of calculation and analysis, and fail to consider the impact of antenna reception characteristics on the received signal, this approach is difficult to directly apply to satellite observation data with real-time changes in observation position, scanning angle, and other parameters. The fast, adaptive emissivity calculation model for observation pixels proposed in this embodiment can directly calculate the microwave emissivity of any given observation pixel.

[0091] Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions to the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention and are intended to be encompassed by the claims of the present invention.

Claims

1. An adaptive method for obtaining microwave surface emissivity at different spatial scales, comprising: Step 1: Based on the land cover classification database, the observation pixels of various vegetation types and bare land types are screened from the acquired observation brightness temperature data; Step 2: Remove the cold sky background and atmospheric contribution from the observed pixel brightness temperature data, and remove the influence of land surface temperature to obtain the instantaneous microwave emissivity dataset; Step 3: Based on the correlation between the influencing factors and microwave surface emissivity, establish multiple semi-empirical emissivity models for single land surface types through linear regression; Step 4: Determine the ground footprint of the observation pixel and match the ground footprint with the grid of the land cover classification database to obtain the location of the target grid of the ground footprint, the surface type and its corresponding area ratio; Step 5: For the target grid location of the ground footprint, use the nearest neighbor matching method to obtain the snow depth and atmospheric parameters in the climate reanalysis database based on the geographic coordinates of the ground footprint to obtain the ground footprint under clear sky and without ice and snow cover; Step 6: Match the input parameters of the emissivity semi-empirical model obtained in step 3 with the preset units, and substitute the matched input parameters into the emissivity semi-empirical model of each single land surface type to calculate the microwave emissivity of each land surface type within each target grid of the ground footprint; Step 7: Calculate the comprehensive weight coefficient based on the area proportion of different land surface types in each target grid and the normalized antenna pattern radiation power; Step 8: Express the microwave emissivity of the target grid as the sum of the products of the emissivity of all land surface types in the target grid and the corresponding comprehensive weight coefficient.

2. The adaptive method for obtaining microwave surface emissivity at different spatial scales according to claim 1, characterized in that: The observed brightness temperature data in step 1 include: observed brightness temperature data of existing satellite-borne, airborne and surface microwaves at the target frequency; the land cover classification database in step 1 adopts the MODIS land cover classification product MCD12C1.

3. The adaptive method for obtaining microwave surface emissivity at different spatial scales according to claim 1, characterized in that: The step 2 specifically includes: using at least the atmospheric temperature and humidity profile, land surface temperature and surface pressure in the climate reanalysis database as auxiliary parameters, combining the atmospheric absorption coefficient model and the radiation transfer equation, removing the contribution of the cold sky background and the atmosphere from the observed pixel brightness temperature data, and stripping off the influence of the land surface temperature, to obtain the instantaneous microwave emissivity data set.

4. The adaptive method for obtaining microwave surface emissivity at different spatial scales according to claim 1, characterized in that: The step 2 specifically includes: using at least the atmospheric temperature and humidity profile, the effective temperature of bare land, and the surface pressure in the climate reanalysis database as auxiliary parameters, combining the atmospheric absorption coefficient model and the radiation transfer equation, removing the contribution of the cold sky background and the atmosphere from the observed pixel brightness temperature data, and stripping off the influence of the land surface temperature, to obtain an instantaneous microwave emissivity dataset.

5. The adaptive method for obtaining microwave surface emissivity at different spatial scales according to claim 3 or 4, characterized in that: The climate reanalysis database adopts ERA5 climate reanalysis data; the atmospheric transmission model adopts MPM93 atmospheric absorption coefficient model.

6. The adaptive method for obtaining microwave surface emissivity at different spatial scales according to claim 1, characterized in that: The influencing factors of step 3 include: normalized vegetation index, soil moisture and surface temperature; step 3 specifically includes: based on the correlation between the influencing factors and the microwave surface emissivity, linear regression is performed step by step from large to small, thereby establishing multiple semi-empirical models of emissivity of single land surface types.

7. The adaptive method for obtaining microwave surface emissivity at different spatial scales according to claim 1, characterized in that: Step 4 specifically includes: determining the ground footprint of the observed pixel based on its geographic coordinates, frequency, and scanning angle, and matching the ground footprint with the grid of the land cover classification database to obtain the location of the target grid of the ground footprint, the surface type, and its corresponding area percentage; among which, the land cover classification database samples the MODIS land cover classification product MCD12C1.

8. The adaptive method for obtaining microwave surface emissivity at different spatial scales according to claim 1, characterized in that: In step 6, when the resolution of the input parameters is equal to or lower than the preset unit, the nearest neighbor method is directly used to match the input parameters; when the resolution of the input parameters is higher than the preset unit, it is first resampled to the preset unit, and then the input parameters are matched according to the nearest neighbor rule; wherein, the input parameters include: normalized vegetation index, skin temperature, soil moisture and soil temperature, percentage of terrain slope and percentage of sandy soil; the preset unit is 0.05 degrees.

9. The adaptive method for obtaining microwave surface emissivity at different spatial scales according to claim 1, characterized in that: The step 7 specifically includes: Combined with the area proportion of different land surface types in each target grid and the normalized antenna pattern radiation power, the comprehensive weight coefficient is calculated: Among them, W i,j represents the comprehensive weight coefficient of the jth land surface type in the i-th grid, P i is the normalized radiation power of the i-th grid; C i,j is the percentage of area occupied by the jth land surface type in the i-th grid; M represents the number of grids contained in the footprint, and N is the number of land surface types studied; The step 8 specifically includes: The microwave emissivity Emi of the target grid (f,p) It is expressed as the sum of the products of the emissivity of all land surface types in the target grid and the corresponding comprehensive weight coefficient: Wherein, the subscripts f and p represent frequency and polarization respectively; Emi (f,p) Represents the emissivity of the j-th land surface type in the i-th grid.

10. An adaptive model for obtaining microwave surface emissivity at different spatial scales, including The acquisition module, based on the land cover classification database, is used to filter and obtain observation pixels of various vegetation types and bare land types from the acquired observation brightness temperature data; The dataset module is used to remove the cold sky background and atmospheric contribution from the observed pixel brightness temperature data, and to remove the influence of land surface temperature to obtain the instantaneous microwave emissivity dataset; The model building module establishes multiple semi-empirical emissivity models of single land surface types through linear regression based on the correlation between influencing factors and microwave surface emissivity; The matching module is used to determine the ground footprint of the observation pixel and match the ground footprint with the grid of the land cover classification database to obtain the location of the target grid of the ground footprint, the surface type and its corresponding area ratio; The ground footprint module is used to obtain the snow depth and atmospheric parameters from the climate reanalysis database based on the geographic coordinates of the ground footprint target grid using the nearest neighbor matching method to obtain the ground footprint under clear sky and without ice and snow cover; A substitution module is used to match the input parameters of the emissivity semi-empirical model obtained by the model establishment module with preset units, and substitute the matched input parameters into the emissivity semi-empirical model of each single land surface type to calculate the microwave emissivity of each type of land surface within each target grid of the ground footprint; The comprehensive weight module is used to calculate the comprehensive weight coefficient by combining the area proportion of different land surface types in each target grid and the normalized antenna pattern radiation power; and The microwave emissivity module is used to obtain the microwave emissivity of the target grid, where the microwave emissivity of the target grid is expressed as the sum of the products of the emissivity of all land surface types in the target grid and the corresponding comprehensive weight coefficient.

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