A multi-frequency dual-polarization passive microwave surface temperature estimation method and device

Abstract

The present invention discloses a multi-frequency dual-polarization passive microwave surface temperature estimation method and device. The method includes: deriving a passive microwave radiation transfer equation according to the Rayleigh-Jeans approximation formula of Planck's law and the microwave radiation transfer mechanism; wherein, the microwave radiation is measured by the brightness temperature; selecting a brightness temperature combination of multi-frequency dual-polarization of passive microwave; constructing a passive microwave surface temperature estimation model based on the brightness temperature combination, and estimating the surface temperature through the estimation model. The multi-frequency dual-polarization passive microwave surface temperature estimation method and device of the present invention are simple and practical, and have a passive microwave surface temperature estimation method with a certain accuracy, thereby reducing the influence of microwave emissivity on the inversion accuracy of surface temperature.

Description

Technical Field

[0001] The present invention relates to the field of satellite communication, and particularly to a method and device for estimating the surface temperature of multi-frequency dual-polarized passive microwave. Background Art

[0002] The surface temperature is a key parameter for describing the interaction and energy balance between the land and the atmosphere at the regional and global scales. Since the surface temperature exhibits obvious heterogeneity in both the time domain and the space domain, it is difficult to obtain the surface temperature that is continuous in time on a large scale through limited ground observation stations. Therefore, the use of satellite remote sensing technology to obtain the spatio-temporal distribution of the surface temperature at the regional and global scales has received extensive attention.

[0003] Currently, satellite remote sensing for obtaining surface temperature information is mainly based on thermal infrared and passive microwave data. When there are clouds in the sensor's field of view, the radiation energy from the ground in the thermal infrared band cannot penetrate the clouds to reach the sensor. Therefore, the surface temperature information under cloudy conditions cannot be obtained based on thermal infrared data. Compared with thermal infrared data, passive microwave data is less affected by clouds and rain. Its wavelength is longer, and it has better penetration and anti-interference capabilities. It can penetrate clouds and even rain areas, making up for the deficiency of thermal infrared remote sensing affected by cloud cover. Therefore, using passive microwave data to obtain all-weather surface temperature information has great application prospects.

[0004] Currently, the main methods for retrieving the surface temperature of passive microwave include empirical statistical methods, physical mechanism methods, and machine learning methods. These methods mainly solve the surface temperature by making different assumptions and approximations of the microwave radiative transfer equation for the surface emissivity and atmospheric effect parameters. Among them, the spatio-temporal dynamic changes of the microwave surface emissivity are significant, which is an important factor affecting the accuracy of surface temperature retrieval. To improve the retrieval accuracy, usually, the surface temperature is retrieved by partitioning according to the surface cover type or the microwave index constructed from the brightness temperature (such as the microwave polarization difference index MPDI, polarization ratio index PR). However, these partitions do not fully consider the influence of various factors such as soil moisture, soil texture, and vegetation growth status on the microwave emissivity under different climate states. Therefore, there is an urgent need to propose a method for estimating the surface temperature of passive microwave considering climate partitioning. Summary of the Invention

[0005] The present invention provides a method and device for estimating the surface temperature of multi-frequency dual-polarized passive microwave, aiming to solve the problem that the common methods for retrieving the surface temperature of passive microwave in the prior art cannot estimate a more accurate surface temperature due to the differences in various factors such as soil moisture, soil texture, and vegetation growth status on the microwave emissivity under different climate states.

[0006] A method for estimating the surface temperature of multi-frequency dual-polarized passive microwave in the present invention, the method includes:

[0007] Derive the passive microwave radiation transfer equation based on the Rayleigh-Jeans approximation formula of Planck's law and the microwave radiation transfer mechanism; among them, the microwave radiation is measured by the microwave brightness temperature.

[0008] Select the microwave brightness temperature combination of passive microwave multi-frequency and dual polarization.

[0009] Construct a passive microwave land surface temperature estimation model based on the microwave brightness temperature combination, and estimate the land surface temperature through the estimation model.

[0010] Optionally, the method further includes:

[0011] Verify the accuracy of the estimated land surface temperature; among them, the accuracy verification includes: the land surface temperature under clear sky and cloudy conditions.

[0012] Optionally, the method further includes: constructing passive microwave land surface temperature estimation models for different climate zones respectively based on the classification indicators of climate zones.

[0013] Optionally, the classification indicators of the climate zones at least include: air temperature, precipitation, and vegetation.

[0014] Construct different climate zones based on the classification indicators of climate zones, including: constructing climate zones with different combinations of climate state, land surface cover, and months based on air temperature, precipitation, and vegetation.

[0015] Optionally, the microwave brightness temperature combination of passive microwave multi-frequency and dual polarization includes: the brightness temperature of the vertical and horizontal polarization channels in the Ku band, the brightness temperature of the vertical and horizontal polarization channels in the K band, the brightness temperature of the vertical channel in the Ka band, and the brightness temperature of the vertical channel in the W band.

[0016] Optionally, the microwave brightness temperature of the observation frequency includes: the upward radiation of the atmosphere, the soil emission radiation attenuated by vegetation and the atmosphere, the upward radiation of vegetation attenuated by the atmosphere, the downward radiation of vegetation reflected by the land surface and attenuated by the atmosphere, the downward radiation of the atmosphere reflected by the land surface and attenuated by the atmosphere and vegetation, and the cosmic background radiation.

[0017] Optionally, the passive microwave radiation transfer equation is:

[0018] T b =T ba↑ +T s ε s exp(-τ v -τ a )+T v (1 - ω)[1 - exp(-τ v )]exp(-τ a )+T v (1 - ω)(1 - ε s )[1 - exp(-τv )]exp(-τ v -τ a )+(T ba↓ +T sky )(1 - ε s )exp(-2τ v -τ a )

[0019] In the formula, T ba↑ is the upward atmospheric radiation, T s is the surface temperature detectable by microwaves at the observation frequency, ε s is the surface emissivity, τ v is the vegetation optical depth, τ a is the atmospheric optical depth, T v is the vegetation canopy temperature, ω is the vegetation single - scattering albedo, T sky is the cosmic background radiation, T ba↓ is the downward atmospheric radiation reflected by the surface and attenuated by the atmosphere and vegetation.

[0020] Optionally, the method further includes:

[0021] Performing spatio - temporal matching between the microwave brightness temperature and the satellite thermal infrared surface temperature or the site - measured surface temperature;

[0022] Performing fitting calculation on the multiple linear regression of the microwave brightness temperature after spatio - temporal matching and the satellite thermal infrared surface temperature or the site - measured surface temperature to obtain the estimation model coefficients and the model residual matrix.

[0023] A multi - frequency dual - polarization passive microwave surface temperature estimation device in the present invention, the device includes:

[0024] A first acquisition unit, configured to derive a passive microwave radiation transfer equation according to the Rayleigh - Jones approximation formula of Planck's law and the microwave radiation transfer mechanism; wherein, the microwave radiation is measured by the microwave brightness temperature;

[0025] A second acquisition unit, configured to select a combination of microwave brightness temperatures of passive microwave multi - frequency dual - polarization;

[0026] A calculation unit, configured to construct a passive microwave surface temperature estimation model based on the combination of microwave brightness temperatures and estimate the surface temperature through the estimation model.

[0027] A computer - readable storage medium in the present invention, the computer - readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the multi - frequency dual - polarization passive microwave surface temperature estimation method as described in any one of the above.

[0028] The method in the present invention is based on climate zoning and uses the brightness temperature of passive microwave multi-frequency dual-polarization channels to construct a passive microwave surface temperature estimation model considering climate zoning for different surface cover types and different months and times. The present invention proposes a simple and practical passive microwave surface temperature estimation method with a certain accuracy, fully considering the influence of various factors such as soil moisture, soil texture, and vegetation growth status on microwave emissivity under different climate states, thereby reducing the influence of microwave emissivity on the inversion accuracy of surface temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flowchart of the multi-frequency dual-polarization passive microwave surface temperature estimation method in an embodiment of the present invention;

[0030] Figure 2 is a structural diagram of the multi-frequency dual-polarization passive microwave surface temperature estimation device in an embodiment of the present invention;

[0031] Figure 3 is a comparison schematic diagram of the FY-3D / MWRI surface temperature and the MODIS surface temperature under clear sky conditions during the day in an embodiment of the present invention;

[0032] Figure 4 is a comparison schematic diagram of the FY-3D / MWRI surface temperature and the station surface temperature under cloudy conditions at night in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] The present invention will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. Additionally, it should be noted that for the sake of description, only parts related to the present invention are shown in the drawings, rather than all the structures.

[0034] It should be understood that in various embodiments herein, the magnitude of the serial numbers of the above processes does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments herein.

[0035] An embodiment of the present invention provides a multi-frequency dual-polarization passive microwave surface temperature estimation method, as Figure 1 shown, the method includes:

[0036] Step 100: Derive the passive microwave radiative transfer equation based on the Rayleigh-Jeans approximation formula of Planck's law and the microwave radiative transfer mechanism. Among them, microwave radiation is measured by microwave brightness temperature. Specifically, according to the microwave radiative transfer mechanism, the radiation intensity obtained by the sensor mainly includes: the energy of the emitted radiation from the surface reaching the sensor after attenuation by the propagation medium and the emitted radiation of the propagation medium itself on the propagation path. In the microwave spectral range, the passive microwave radiative transfer equation can be derived based on the Rayleigh-Jeans approximation formula of Planck's law, and microwave radiation is usually measured by microwave brightness temperature. Among them, the brightness temperature of passive microwave mainly comes from the contributions of the atmosphere, vegetation layer and soil layer.

[0037] Step 200: Select the microwave brightness temperature combination of passive microwave multi-frequency and dual polarization. Specifically, in the microwave spectral range, due to the longer wavelength and less influence of the atmosphere, there is a linear relationship between the brightness temperature and the surface temperature at different microwave frequencies. When the ground object is fixed, the surface temperature that can be detected at different frequencies is approximately equal, and the microwave brightness temperature and emissivity have different values at different frequencies and different polarizations. Therefore, in this step, the microwave brightness temperature combination of passive microwave multi-frequency and dual polarization is selected to determine the microwave brightness temperature at different frequencies and different polarizations in the follow-up.

[0038] Step 300: Construct a passive microwave surface temperature estimation model based on the microwave brightness temperature combination, and estimate the surface temperature through the estimation model. Specifically, based on the brightness temperature combination, regression fitting is used to calculate the parameters required by the model, and a passive microwave surface temperature estimation model is constructed on the passive microwave radiative transfer equation. The surface temperature can be estimated through the estimation model.

[0039] The frequency dual-polarization passive microwave surface temperature estimation method described in the embodiments of the present invention can simply estimate the surface temperature by constructing a passive microwave surface temperature estimation model using the microwave brightness temperature of the passive microwave multi-frequency and dual-polarization channels.

[0040] A multi-frequency dual-polarization passive microwave surface temperature estimation method described in the embodiments of the present invention. Preferably, the method further includes:

[0041] Perform accuracy verification on the estimated surface temperature; among them, the accuracy verification includes: surface temperature under clear sky and cloudy conditions. Specifically, under clear sky conditions, the thermal infrared surface temperature that does not participate in the calculation of model parameters is selected for spatio-temporal matching verification with the microwave surface temperature. Under cloudy conditions, due to the lack of satellite thermal infrared surface temperature data, the measured surface temperature of the stations that do not participate in the calculation of model parameters is selected for spatio-temporal matching verification. At the same time, since the surface temperature heterogeneity is stronger during the day, it is difficult for the station temperature data to correctly verify the large-scale passive microwave remote sensing data. Therefore, the nighttime data with slightly weaker temperature heterogeneity is selected for verification. Among them, the accuracy verification indicators are represented by the correlation coefficient and the mean deviation.

[0042] A multi-frequency dual-polarization passive microwave land surface temperature estimation method according to an embodiment of the present invention. Preferably, the method further includes: respectively constructing passive microwave land surface temperature estimation models for different climate zones based on classification indexes of climate zones. Specifically, the spatio-temporal dynamic variation of microwave emissivity is significant and is an important factor affecting the accuracy of land surface temperature inversion. To improve the inversion accuracy, considering the influence of various factors such as soil moisture, soil texture, and vegetation growth status under different climate states on microwave emissivity, the differences in soil moisture, soil texture, vegetation growth status, etc. under different climate states can be fully considered. Therefore, based on climate zones, using the microwave brightness temperature of multi-frequency dual-polarization channels, for different land surface cover types and different months and times, a passive microwave land surface temperature estimation model considering climate zones is constructed.

[0043] In a preferred embodiment, the model coefficients and the model residual matrix can be obtained through multiple linear regression fitting calculation of the microwave brightness temperature after spatio-temporal matching and the satellite thermal infrared land surface temperature or the measured land surface temperature at the station. When spatio-temporally matching the microwave brightness temperature and the thermal infrared land surface temperature, first resample the thermal infrared land surface temperature to the same spatial resolution as the microwave brightness temperature, and then select the data of the same spatial area within 15 minutes of the observation time of the two for matching. When spatio-temporally matching the microwave brightness temperature and the measured land surface temperature at the station, first perform spatial matching of the microwave brightness temperature and all stations within the microwave pixel, and then select the stations within 15 minutes of the observation time of the two to calculate the measured average value of the stations within the microwave pixel, and match the average value with the microwave brightness temperature value. Calculate the parameters required for the model through regression fitting, so as to establish a passive microwave land surface temperature estimation model considering climate zones.

[0044] A multi-frequency dual-polarization passive microwave land surface temperature estimation method according to an embodiment of the present invention. Preferably,

[0045] The classification indexes of the climate zones at least include: air temperature, precipitation, and vegetation;

[0046] Respectively constructing different climate zones based on the classification indexes of climate zones includes: constructing climate zones under different combinations of climate states, land surface cover, and months based on air temperature, precipitation, and vegetation.

[0047] Specifically, climate zones usually use elements such as air temperature, precipitation, and vegetation as classification indexes, and the estimation model can be expressed as:

[0048]

[0049] In the formula, C ki , L mi , T nirespectively represent the classifications of different climate states, land covers, and months, where n represents the number of classifications of climate states, land covers, and months; j is the product of the number of climate state classifications, land cover classifications, and month classifications, A is the model coefficient matrix corresponding to the microwave brightness temperature under each combination type, and TB f is the microwave brightness temperature matrix under each combination type, and B0 is the model residual matrix corresponding to the microwave brightness temperature channels under each combination type. In a preferred embodiment, the number of climate state classifications is 12, the number of land cover classifications is the IGBP land cover classification number which is 17, and the number of month classifications is 12.

[0050] A method for estimating multi-frequency dual-polarized passive microwave land surface temperature according to an embodiment of the present invention. Preferably, the microwave brightness temperature combinations of the passive microwave multi-frequency dual-polarization include: the brightness temperatures of the vertical and horizontal polarization channels in the Ku band, the brightness temperatures of the vertical and horizontal polarization channels in the K band, the brightness temperature of the vertical channel in the Ka band, and the brightness temperature of the vertical channel in the W band. In a specific embodiment, the existing international spaceborne passive microwave radiometers mainly have vertical (V) and horizontal (H) polarization channels in bands such as L, C, X, Ku, K, Ka, and W. In land surface temperature inversion, the channels used are generally selected to be neither too sensitive to the atmosphere nor too penetrative, and at the same time sensitive to land surface temperature. Therefore, the brightness temperature combinations selected in the embodiment of the present invention include: the brightness temperatures of the vertical and horizontal polarization channels in the Ku band, the brightness temperatures of the vertical and horizontal polarization channels in the K band, the brightness temperature of the vertical channel in the Ka band, and the brightness temperature of the vertical channel in the W band. The brightness temperatures of these 6 channels are respectively represented as TB Ku_V 、TB Ku_H 、TB K_V 、TB K_H 、TB Ka_V 、TB W_V 。

[0051] A method for estimating multi-frequency dual-polarized passive microwave land surface temperature according to an embodiment of the present invention. Preferably, the brightness temperature of the observation frequency includes: upward atmospheric radiation, soil emission radiation attenuated by vegetation and the atmosphere, upward vegetation radiation attenuated by the atmosphere, downward vegetation radiation reflected by the land surface and attenuated by the atmosphere, downward atmospheric radiation reflected by the land surface and attenuated by the atmosphere and vegetation, and cosmic background radiation.

[0052] A method for estimating multi-frequency dual-polarized passive microwave land surface temperature according to an embodiment of the present invention. Preferably, the passive microwave radiative transfer equation is:

[0053] T b =T ba↑ +T s ε s exp(-τ v -τ a) + T v (1 - ω)[1 - exp(-τ v )]exp(-τ a ) + T v (1 - ω)(1 - ε s )[1 - exp(-τ v )]exp(-τ v -τ a ) + (T ba↓ +T sky )(1 - ε s )exp(-2τ v -τ a )

[0054] In the formula, T b is the microwave brightness temperature of the sensor observation frequency, T ba↑ is the upward atmospheric radiation, T s is the surface temperature that can be detected by microwaves at the observation frequency, ε s is the surface emissivity, τ v is the vegetation optical depth, τ a is the atmospheric optical depth, T v is the vegetation canopy temperature, ω is the vegetation single-scattering albedo, T sky is the cosmic background radiation, T ba↓ is the downward atmospheric radiation reflected by the surface and attenuated by the atmosphere and vegetation, T sky is generally approximated to be 2.725 K.

[0055] Specifically, in passive microwave remote sensing, assuming that the atmosphere is a homogeneous non-scattering medium, the absorption and emission of the atmosphere are mainly affected by the atmospheric transmittance and the upward and downward atmospheric radiation, and the upward and downward atmospheric radiation can be approximately equal, that is T a is the equivalent atmospheric radiation. At the same time, assuming that the vegetation effect is not considered, the vegetation canopy temperature and the surface temperature are approximately equal, that is Then the passive microwave radiative transfer equation can be approximated as:

[0056] T b = T a + T s ε s exp(-τ a ) + (T a + T sky )(1 - ε s )exp(-τ a )

[0057] Therefore, based on the approximate passive microwave radiative transfer equation, the surface temperature calculation formula can be derived as:

[0058]

[0059] According to the formula for calculating the surface temperature derived from the above approximate passive microwave radiative transfer equation, if the atmospheric influence is not considered or after atmospheric influence correction, the microwave brightness temperature T of the sensor observation frequency b can be simplified to:

[0060] T b (f) = ε s (f) × T s

[0061] In the formula, ε s (f) is the emissivity at the observation frequency f. From the simplified microwave brightness temperature formula, it can be seen that there is a linear relationship between the microwave brightness temperature and the surface temperature at different microwave frequencies. When the ground object is fixed, the surface temperatures T that can be detected at different frequencies s are approximately equal, and the microwave brightness temperature T b and the emissivity ε s have different values at different frequencies and different polarizations. In view of the different sensitivities of different frequencies to the emissivity and atmospheric influence, the influence of atmospheric and emissivity changes on the surface temperature inversion is eliminated through the brightness temperature combination of multi-frequency dual polarization, so as to obtain a higher-precision surface temperature.

[0062] A specific embodiment of the present invention also provides a multi-frequency dual-polarization passive microwave surface temperature estimation device, as Figure 2 shown, the device includes:

[0063] The first acquisition unit 201 is used to derive the passive microwave radiative transfer equation according to the Rayleigh-Jeans approximation formula of Planck's law and the microwave radiative transfer mechanism; wherein, the microwave radiation is measured by the brightness temperature;

[0064] The second acquisition unit 202 is used to select the brightness temperature combination of passive microwave multi-frequency dual polarization;

[0065] The calculation unit 203 is used to construct a passive microwave surface temperature estimation model based on the brightness temperature combination and estimate the surface temperature through the estimation model.

[0066] An embodiment of the present invention also provides a computer-readable storage medium, and the computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the multi-frequency dual-polarization passive microwave surface temperature estimation method described in any one of the above specific embodiments.

[0067] The present invention also provides a specific embodiment. Taking the FY-3D / MWRI passive microwave land surface temperature estimation as an example, according to the microwave radiative transfer mechanism, based on the Rayleigh-Jeans approximation formula of Planck's law, the microwave brightness temperature is used to measure microwave radiation, and the passive microwave brightness temperature is expressed as a radiative transfer equation contributed by the atmosphere, vegetation layer and soil layer. Assuming that in passive microwave remote sensing, the upward and downward atmospheric radiations are approximately equal, and the vegetation canopy temperature and the land surface temperature are approximately equal, an approximate formula of the passive microwave radiative transfer equation is obtained. Based on the approximate passive microwave radiative transfer equation, a land surface temperature calculation formula is derived. Assuming that the atmospheric influence is not considered or corrected in passive microwave remote sensing, a simplified formula showing a linear relationship between the microwave brightness temperature and the land surface temperature at different microwave frequencies can be obtained. Considering the different sensitivities of emissivity and atmospheric influence to different frequencies, the influence of atmospheric and emissivity changes on land surface temperature inversion is eliminated through the combination of brightness temperatures of multi-frequency dual polarization, and thus the vertical and horizontal polarization brightness temperatures of the FY-3D / MWRI 18.7 GHz and 23.8 GHz bands, and the vertical polarization brightness temperatures of the 36.5 GHz and 89 GHz bands, which are neither too sensitive to the atmosphere, nor too penetrative, and are sensitive to the land surface temperature, are selected. Based on the Köppen climate classification, the globe is divided into 12 main climate types, and using the multi-frequency dual polarization channel brightness temperatures, a passive microwave land surface temperature estimation model considering climate classification is constructed for different land cover types and different months and times. The FY-3D / MWRI brightness temperatures from July 2018 to June 2019 are spatio-temporally matched with the MODIS land surface temperature. First, the MODIS land surface temperature is resampled to the same spatial resolution as the microwave brightness temperature, and then the data of the same spatial area with the observation times within 15 minutes of the two are selected for matching. The model coefficients and residual matrix are obtained through multiple linear regression fitting calculation of the microwave brightness temperature and the MODIS land surface temperature after spatio-temporal matching, and thus the FY-3D / MWRI land surface temperature estimation model considering climate classification is obtained. The MODIS land surface temperature on July 6, 2019 and the measured land surface temperature at the station are respectively spatio-temporally matched with the MWRI land surface temperature to verify the accuracy of the FY-3D / MWRI passive microwave land surface temperature under clear sky and cloudy conditions. The results show that for a multi-frequency dual polarization passive microwave land surface temperature estimation method considering climate classification provided by the present invention, the correlation coefficient is 0.9 and the mean bias is -2.18 K under clear sky during the day, as shown in Figure 3 shown. The correlation coefficient is 0.7 and the mean bias is -2.93 K under cloudy conditions at night, as shown in Figure 4 shown.

[0068] The method described in the embodiments of the present invention is based on climate zoning and uses the brightness temperature of passive microwave multi-frequency dual-polarization channels to construct a passive microwave land surface temperature estimation model considering climate zoning for different land cover types and different months and times. The present invention proposes a simple and practical passive microwave land surface temperature estimation method with a certain accuracy, fully considering the influence of various factors such as soil moisture, soil texture, and vegetation growth status on microwave emissivity under different climate states, so as to reduce the influence of microwave emissivity on the inversion accuracy of land surface temperature.

[0069] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0071] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0073] The foregoing description of the specific exemplary embodiments of the present invention is for purposes of illustration and exemplification. These descriptions are not intended to limit the invention to the precise forms disclosed, and it is apparent that many changes and variations are possible in light of the above teaching. The purpose of selecting and describing the exemplary embodiments is to explain the specific principles of the present invention and its practical applications, so that those skilled in the art can implement and utilize the various different exemplary embodiments of the present invention, as well as various different selections and changes. The scope of the present invention is intended to be defined by the claims and their equivalents.

Claims

1. A multi-frequency dual-polarization passive microwave land surface temperature estimation method, characterized in that, The method includes: Deriving the passive microwave radiative transfer equation according to the Rayleigh-Jeans approximation formula of Planck's law and the microwave radiative transfer mechanism; wherein, the microwave radiation is measured by the microwave brightness temperature. Selecting the microwave brightness temperature combination of passive microwave multi-frequency and dual-polarization. Constructing a passive microwave land surface temperature estimation model based on the microwave brightness temperature combination, and estimating the land surface temperature through the estimation model; wherein, passive microwave land surface temperature estimation models for different climate zones are respectively constructed based on the classification indexes of climate zones; the estimation model can be expressed as: where C ki , L mi , T ni respectively represent the classifications of different climate states, surface coverages, and months, n represents the number of classifications of climate states, surface coverages, and months; j is the product of the number of climate state classifications, the number of surface coverage classifications, and the number of month classifications, A is the model coefficient matrix corresponding to the microwave brightness temperature under each combination type, TB f is the microwave brightness temperature matrix under each combination type, and B0 is the model residual matrix corresponding to the microwave brightness temperature channel under each combination type.

2. A multi-frequency dual-polarization passive microwave surface temperature estimation method according to claim 1, characterized in that, The method further includes: Performing accuracy verification on the estimated land surface temperature; wherein, the accuracy verification includes: the land surface temperature under clear sky and cloudy conditions.

3. A multi-frequency dual-polarization passive microwave land surface temperature estimation method according to claim 1, wherein The classification indexes of the climate zones at least include: air temperature, precipitation and vegetation. Respectively constructing different climate zones based on the classification indexes of climate zones includes: constructing climate zones with different combinations of climate state, land surface cover and month based on air temperature, precipitation and vegetation.

4. A multi-frequency dual-polarization passive microwave surface temperature estimation method according to claim 1, characterized in that, The microwave brightness temperature combination of passive microwave multi-frequency and dual-polarization includes: the brightness temperature of the vertical and horizontal polarization channels in the Ku band, the brightness temperature of the vertical and horizontal polarization channels in the K band, the brightness temperature of the vertical channel in the Ka band, and the brightness temperature of the vertical channel in the W band.

5. A multi-frequency dual-polarization passive microwave surface temperature estimation method according to claim 1, characterized in that, The microwave brightness temperature of the observation frequency includes: the upward atmospheric radiation, the soil emission radiation attenuated by vegetation and the atmosphere, the upward vegetation radiation attenuated by the atmosphere, the downward vegetation radiation reflected by the land surface and attenuated by the atmosphere, the downward atmospheric radiation reflected by the land surface and attenuated by the atmosphere and vegetation, and the cosmic background radiation.

6. A multi-frequency dual-polarization passive microwave surface temperature estimation method according to claim 1, characterized in that The passive microwave radiative transfer equation is: T b = T ba↑ + T s ε s exp(-v v - τ a ) + T v (1 - ω)[1 - exp(-τ v )]exp(-τ a ) + T v (1 - ω)(1 - ε s )[1 - exp(-τ v )]exp(-τ v - τ a ) + (T ba↓ + T sky )(1 - ε s )exp(-2τ v - τ a ) Where, T b is the microwave brightness temperature of the sensor observation frequency, T ba↑ is the upward atmospheric radiation, T s is the surface temperature detectable by microwaves at the observation frequency, ε s is the surface emissivity, τ v is the vegetation optical depth, τ a is the atmospheric optical depth, T v is the vegetation canopy temperature, ω is the vegetation single-scattering albedo, T sky is the cosmic background radiation, T ba↓ is the downward atmospheric radiation reflected by the surface and attenuated by the atmosphere and vegetation.

7. A multi-frequency dual-polarization passive microwave surface temperature estimation method according to claim 1, characterized in that, The method further includes: Performing spatio-temporal matching on the microwave brightness temperature and the satellite thermal infrared land surface temperature or the in-situ measured land surface temperature. Performing fitting calculation on the multiple linear regression of the microwave brightness temperature and the satellite thermal infrared land surface temperature or the in-situ measured land surface temperature after spatio-temporal matching to obtain the estimation model coefficients and the model residual matrix.

8. A multi-frequency dual-polarization passive microwave surface temperature estimation device, characterized in that The device includes: A first acquisition unit, configured to derive the passive microwave radiative transfer equation according to the Rayleigh-Jeans approximation formula of Planck's law and the microwave radiative transfer mechanism; wherein, the microwave radiation is measured by the microwave brightness temperature. A second acquisition unit, configured to select the microwave brightness temperature combination of passive microwave multi-frequency and dual-polarization. A calculation unit, configured to construct a passive microwave land surface temperature estimation model based on the microwave brightness temperature combination, and estimate the land surface temperature through the estimation model; wherein, passive microwave land surface temperature estimation models for different climate zones are respectively constructed based on the classification indexes of climate zones; the estimation model can be expressed as: Wherein, C ki , L mi , T ni respectively represent the classifications of different climate states, land cover, and months, n represents the number of classifications of climate states, land cover, and months; j is the product of the number of climate state classifications, the number of land cover classifications, and the number of month classifications, A is the model coefficient matrix corresponding to the microwave brightness temperature under each combination type, TB f is the microwave brightness temperature matrix under each combination type, and B0 is the model residual matrix corresponding to the microwave brightness temperature channel under each combination type.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the multi-frequency dual-polarization passive microwave land surface temperature estimation method according to any one of claims 1 to 7.

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