An adaptive method and model for obtaining microwave land surface emissivity at different spatial scales

Abstract

The present application relates to the field of atmospheric science and remote sensing technology, and particularly relates to an adaptive method and model for obtaining microwave surface emissivity of different spatial scales. The method comprises: screening observation pixels of various vegetation and bare land types based on a land cover classification database; removing the influence of cold air background, atmosphere and land surface temperature to obtain an instantaneous emissivity dataset; establishing an emissivity semi-empirical model of various single land surface types through regression analysis; determining the ground footprint of the observation pixel and matching the land cover grid to extract the position, land surface type and area proportion; combining the geographic coordinates to extract the snow depth and atmospheric parameters from the reanalysis data to screen clear sky and snow-free pixels; calculating the microwave emissivity of different land surface types in each grid according to the model input parameters; fusing the area proportion and antenna directional diagram normalized radiation power to calculate the comprehensive weight coefficient, and finally obtaining the microwave emissivity of the target grid.

Description

Technical Field

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

[0002] The land surface is complex and variable, and the interaction between electromagnetic waves and land surface components is exceptionally complex, making it difficult to establish a perfect emissivity model. Currently, commonly used emissivity models for the land surface can be divided into physical models and semi-empirical models.

[0003] Physical models can be further divided into numerical simulation methods and analytical models. Numerical simulation methods are difficult to apply to emissivity studies on large spatial scales due to their high computational cost and complex input parameters. Analytical models approximate the scattering field of rough surfaces to a certain extent, but they only consider the influence of surface geometric roughness on the scattering field and ignore volume scattering, so they are only suitable for situations with high soil moisture.

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

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

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

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

[0008] Step 2: Remove the contributions of cold air background and atmosphere 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 influencing factors and microwave surface emissivity, establish semi-empirical models of emissivity for multiple single land surface types through linear regression;

[0010] Step 4: Determine the ground footprint of the observed 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 land surface type and its corresponding area percentage;

[0011] Step 5: Based on the geographic coordinates of the ground footprints, the nearest neighbor matching method is used to obtain the snow depth and atmospheric parameters in the climate reanalysis database to obtain ground footprints under clear skies and without snow or ice coverage.

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

[0013] Step 7: Calculate the comprehensive weighting coefficient by combining the area proportion of different land surface types in each target grid and the normalized antenna pattern radiated 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 within 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, and combining the atmospheric absorption coefficient model and radiative transfer equation, removing the contribution of cold air background and atmosphere from the observed pixel brightness temperature data, and stripping away the influence of land surface temperature to obtain the instantaneous microwave emissivity dataset.

[0017] As an improvement to the above method, 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, and combining the atmospheric absorption coefficient model and the radiative transfer equation, removing the contribution of cold air background and atmosphere from the observed pixel brightness temperature data, and stripping away the influence of land surface temperature to obtain the instantaneous microwave emissivity dataset.

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

[0019] As an improvement to the above method, the influencing factors in step 3 include: normalized vegetation index, soil moisture and skin temperature; step 3 specifically includes: based on the correlation between the influencing factors and microwave surface emissivity, performing linear regression step by step from large to small, thereby establishing multiple semi-empirical models of emissivity for 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, surface type, and corresponding area percentage of the target grid of the ground footprint; 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 parameter is equal to or lower than the preset unit, the nearest neighbor method is directly used to match the input parameter; when the resolution of the input parameter is higher than the preset unit, it is first resampled to the preset unit and then 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] By combining the area proportion of different land surface types in each target grid and the normalized antenna pattern radiated power, the comprehensive weighting coefficient is calculated:

[0024]

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

[0026] 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 within the target grid and their corresponding comprehensive weighting coefficients:

[0028]

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

[0030] To achieve another objective of this invention, this 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 observation pixels of multiple vegetation types and bare land types from the acquired observation brightness temperature data;

[0032] The dataset module is used to remove the contribution of cold air background and atmosphere 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 semi-empirical models of emissivity for multiple single land surface types based on the correlation between influencing factors and microwave surface emissivity through linear regression.

[0034] The matching module is used to determine the ground footprint of the observed pixels and match the ground footprint with the grid of the land cover classification database to obtain the location, land surface type and corresponding area percentage of the target grid of the ground footprint.

[0035] The ground footprint module is used to obtain snow depth and atmospheric parameters from the climate reanalysis database based on the geographical coordinates of the ground footprints and the location of the target grid. This allows for the acquisition of ground footprints under clear skies and without snow or ice cover.

[0036] The input module is used to match the input parameters of the semi-empirical model of emissivity obtained by the module with preset unit matching model, and substitute the matched input parameters into the semi-empirical model of emissivity of each single land surface type to calculate the microwave emissivity of each type of land surface in each target grid of the ground footprint.

[0037] The comprehensive weighting module is used to calculate the comprehensive weighting coefficient by combining the area proportion of different land surface types in each target grid and the normalized antenna pattern radiated 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 within the target grid and their corresponding comprehensive weight coefficients.

[0039] Compared to existing technologies, the advantages of this invention are as follows: The adaptive method and model for obtaining microwave surface emissivity at different spatial scales takes the ground area corresponding to the antenna's main beam as the research object. For observation data that meets the model's application conditions, the corresponding ground emissivity can be directly calculated without resampling brightness temperature and with a more comprehensive consideration of footprint size. This invention also comprehensively considers the influence of antenna pattern and the area proportion of each type, proposing a comprehensive weighting coefficient for each land surface type, thus overcoming the shortcomings of previous models. Attached Figure Description

[0040] Figure 1 A flowchart of an adaptive method for obtaining microwave surface emissivity at different spatial scales, provided for embodiments of the invention. Detailed Implementation

[0041] The technical solutions provided by the present invention will be further illustrated below with reference to the embodiments.

[0042] Example 1

[0043] The adaptive method for obtaining microwave surface emissivity at different spatial scales provided in this embodiment, such as Figure 1 As shown, it includes:

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

[0045] Step 2: Remove the contributions of cold air background and atmosphere 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 influencing factors and microwave surface emissivity, establish semi-empirical models of emissivity for multiple single land surface types through linear regression;

[0047] Step 4: Determine the ground footprint of the observed 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 land surface type and its corresponding area percentage;

[0048] Step 5: Based on the geographic coordinates of the ground footprints, the nearest neighbor matching method is used to obtain the snow depth and atmospheric parameters in the climate reanalysis database to obtain ground footprints under clear skies and without snow or ice coverage.

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

[0050] Step 7: Calculate the comprehensive weighting coefficient by combining the area proportion of different land surface types in each target grid and the normalized antenna pattern radiated 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 within the target grid and the corresponding comprehensive weight coefficient.

[0052] Specifically, the observed brightness temperature data in step 1 includes: existing satellite-borne, airborne, and surface microwave observation brightness temperature data 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 atmospheric temperature and humidity profiles, land surface temperature and surface pressure from the climate reanalysis database as auxiliary parameters, and combining atmospheric absorption coefficient models and radiative transfer equations, removing the contribution of cold air background and atmosphere from the observed pixel brightness temperature data, and removing the influence of land surface temperature to obtain an instantaneous microwave emissivity dataset.

[0054] Specifically, step 2 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, and combining the atmospheric absorption coefficient model and the radiative transfer equation, removing the contribution of cold air background and atmosphere from the observed pixel brightness temperature data, and removing the influence of land surface temperature to obtain the instantaneous microwave emissivity dataset.

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

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

[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, surface type, and corresponding area percentage of the target grid of the ground footprint; 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 parameter is equal to or lower than a preset unit, the nearest neighbor method is directly used to match the input parameter; when the resolution of the input parameter is higher than the preset unit, it is first resampled to the preset unit and then 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, step 7 includes:

[0060] The comprehensive weighting coefficient is calculated 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 iC is the normalized radiated power of the i-th grid; i,j represents the percentage of the area occupied by the j-th land surface type in the i-th grid; M represents the number of grids contained within the footprint, and N is the number of land surface types studied in this paper. In this embodiment, N is 13.

[0063] Specifically, 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 within the target grid and their corresponding comprehensive weighting coefficients:

[0065]

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

[0067] The following description, in conjunction with the accompanying drawings, further illustrates this embodiment. Figure 1 As shown, the technical solution provided in this embodiment includes the following:

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

[0069] First, acquire 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 obtained through screening.

[0071] Then, using the atmospheric temperature and humidity profile of ERA5, land surface skin temperature (or effective temperature of bare land), surface pressure, etc. as auxiliary parameters, and combining the MPM93 atmospheric absorption coefficient model and radiative transfer equation, the contribution of cold air background and atmosphere is removed from the observed brightness temperature, and the influence of land surface temperature is stripped away, thus obtaining the instantaneous microwave emissivity dataset.

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

[0073] (2) Establish an adaptive emissivity model for hybrid pixels.

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

[0075] Then, for the qualified observation pixels, the input parameters of the model are matched in units of 0.05 degrees and substituted into the emissivity models of each single type to calculate the emissivity of each land surface type in each grid. For parameters with a resolution equal to or lower than 0.05 degrees (NDVI, skin temperature, soil moisture, and soil temperature), the nearest neighbor method is directly used to match the input parameters; for surface parameters with a resolution higher than 0.05 degrees (topographic slope and percentage of sandy soil), they are first resampled to 0.05 degrees, and then the parameters are matched according to the nearest neighbor rule.

[0076] Finally, by combining the area proportion of each type in each grid and the normalized radiative power of each grid, the comprehensive weight coefficient of each land surface type in each grid is calculated, and the emissivity of the target grid is expressed as the sum of the products of the emissivity of the land surface types contained in that 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 observation pixels of multiple vegetation types and bare land types from the acquired observation brightness temperature data;

[0080] The dataset module is used to remove the contribution of cold air background and atmosphere 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 semi-empirical models of emissivity for multiple single land surface types based on the correlation between influencing factors and microwave surface emissivity through linear regression.

[0082] The matching module is used to determine the ground footprint of the observed pixels and match the ground footprint with the grid of the land cover classification database to obtain the location, land surface type and corresponding area percentage of the target grid of the ground footprint.

[0083] The ground footprint module is used to obtain snow depth and atmospheric parameters from the climate reanalysis database based on the geographical coordinates of the ground footprints and the location of the target grid. This allows for the acquisition of ground footprints under clear skies and without snow or ice cover.

[0084] The input module is used to match the input parameters of the semi-empirical model of emissivity obtained by the module with preset unit matching model, and substitute the matched input parameters into the semi-empirical model of emissivity of each single land surface type to calculate the microwave emissivity of each type of land surface in each target grid of the ground footprint.

[0085] The comprehensive weighting module is used to calculate the comprehensive weighting coefficient by combining the area proportion of different land surface types in each target grid and the normalized antenna pattern radiated 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 within the target grid and their corresponding comprehensive weight coefficients.

[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 the research object, analyzing the coverage and energy contribution of different surface types within the grid. This research method is of great significance for basic theoretical research; however, since satellite footprints cannot perfectly match the grid, they cannot be directly applied to satellite observation data. Resampling the observation data to a specified grid size would increase the computational cost and reduce processing speed. Furthermore, a few studies use the ground area corresponding to the antenna's ground resolution as the research object, but the nominal resolution of a radiometer is the ground area corresponding to a 3dB beamwidth, which is approximately 1 / 6 of the actual footprint area. Therefore, this method may overlook some important ground information. The model proposed in this invention uses the ground area corresponding to the antenna's main beam as the research object. For observation data that meets the model's application conditions, its corresponding ground emissivity can be directly calculated without resampling for brightness temperature and more comprehensively considers footprint size.

[0089] Secondly, previous studies typically calculated the emissivity of mixed pixels by weighting the area percentage of each land surface type, rarely considering the role of antenna patterns. This method has little impact on gridded observation data, but may introduce significant errors for models focusing on 3dB footprints or main beam footprints. Therefore, this invention comprehensively considers the influence of antenna patterns and the area percentage of each type, proposing a comprehensive weighting coefficient for each land surface type to overcome the shortcomings of previous models.

[0090] This embodiment comprehensively considers the area proportion of different land surface types within a pixel and the influence of antenna patterns to establish an adaptive land surface microwave emissivity model oriented towards the observed pixel. Compared to traditional models that, for ease of calculation and analysis, typically simulate microwave emissivity over artificially defined ground areas and do not consider the influence of antenna receiving characteristics on the received signal, making such methods difficult to directly apply to satellite observation data with real-time changes in observation position and scanning angle, this embodiment proposes a fast and adaptive emissivity calculation model oriented towards the observed pixel, capable of directly calculating the microwave emissivity of any given observed pixel.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand 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 all such modifications or substitutions should be covered within the scope of 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, select observation pixels of multiple vegetation types and bare land types from the acquired observation brightness temperature data; Step 2: Remove the contributions of cold air background and atmosphere 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 influencing factors and microwave surface emissivity, establish semi-empirical models of emissivity for multiple single land surface types through linear regression; Step 4: Determine the ground footprint of the observed 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 land surface type and its corresponding area percentage; Step 5: Based on the geographic coordinates of the ground footprints, the nearest neighbor matching method is used to obtain the snow depth and atmospheric parameters in the climate reanalysis database to obtain ground footprints under clear skies and without snow or ice coverage. Step 6: Match the input parameters of the semi-empirical emissivity model obtained in Step 3 with a preset unit, and substitute the matched input parameters into the semi-empirical emissivity model of emissivity for each single land surface type to calculate the microwave emissivity of each type of land surface within each target grid of the ground footprint. Step 7: Calculate the comprehensive weighting coefficient by combining the area proportion of different land surface types in each target grid and the normalized antenna pattern radiated 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 within 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 includes: existing satellite-borne, airborne, and surface microwave observation brightness temperature data at the target frequency; the land cover classification database in step 1 uses 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, Step 2 specifically includes: using at least the atmospheric temperature and humidity profile, land surface temperature and surface pressure from the climate reanalysis database as auxiliary parameters, and combining the atmospheric absorption coefficient model and radiative transfer equation, removing the contribution of cold air background and atmosphere from the observed pixel brightness temperature data, and stripping away the influence of land surface temperature to obtain the instantaneous microwave emissivity dataset.

4. The adaptive method for obtaining microwave surface emissivity at different spatial scales according to claim 1, characterized in that, 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, and combining the atmospheric absorption coefficient model and the radiative transfer equation, removing the contribution of cold air background and atmosphere from the observed pixel brightness temperature data, and stripping away the influence of land surface temperature to obtain the 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 uses ERA5 climate reanalysis data; the atmospheric absorption coefficient model uses the 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 in step 3 include: normalized vegetation index, soil moisture and skin temperature; step 3 specifically includes: based on the correlation between the influencing factors and microwave surface emissivity, performing linear regression step by step from large to small, thereby establishing multiple semi-empirical models of emissivity for a single land surface type.

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, surface type, and corresponding area percentage of the target grid of the ground footprint; wherein, 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 parameter is equal to or lower than the preset unit, the nearest neighbor method is directly used to match the input parameter; when the resolution of the input parameter is higher than the preset unit, it is first resampled to the preset unit and then 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, Step 7 specifically includes: By combining the area proportion of different land surface types in each target grid and the normalized antenna pattern radiated power, the comprehensive weighting coefficient is calculated: Among them, W i,j P represents the comprehensive weighting coefficient for the j-th land surface type in the i-th grid. i C is the normalized radiated power of the i-th grid; i,j represents the percentage of area occupied by the j-th land surface type in the i-th grid; M represents the number of grids contained within the footprint, and N is the number of land surface types studied; 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 within the target grid and their corresponding comprehensive weighting coefficients: Where the subscripts f and p represent frequency and polarization, respectively; Emi (f,p) This 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 observation pixels of multiple vegetation types and bare land types from the acquired observation brightness temperature data; The dataset module is used to remove the contribution of cold air background and atmosphere 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 semi-empirical models of emissivity for multiple single land surface types based on the correlation between influencing factors and microwave surface emissivity through linear regression. The matching module is used to determine the ground footprint of the observed pixels and match the ground footprint with the grid of the land cover classification database to obtain the location, land surface type and corresponding area percentage of the target grid of the ground footprint. The ground footprint module is used to obtain snow depth and atmospheric parameters from the climate reanalysis database based on the geographical coordinates of the ground footprints and the location of the target grid. This allows for the acquisition of ground footprints under clear skies and without snow or ice cover. The input module is used to match the input parameters of the semi-empirical model of emissivity obtained by the module with preset unit matching model, and substitute the matched input parameters into the semi-empirical model of emissivity of each single land surface type to calculate the microwave emissivity of each type of land surface in each target grid of the ground footprint. The integrated weighting module is used to calculate the integrated weighting 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 within the target grid and their corresponding comprehensive weight coefficients.

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