CN114722350A - Underground surface temperature inversion and verification method for FY-3D passive microwave data cloud - Google Patents
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Abstract
The invention provides an FY-3D passive microwave data cloud-based surface temperature inversion and verification method, which comprises the following steps: acquiring FY-3D passive microwave data, and performing data preprocessing to extract dual-channel brightness temperatures of 18.7GHz and 23.8GHz vertical polarization channels; acquiring ERA5 atmospheric profile data, and performing data processing to extract atmospheric water vapor and liquid water content; based on the dual-channel brightness temperature of the 18.7GHz and 23.8GHz vertical polarization channels, the ground surface temperature under the cloud condition is estimated by adopting a dual-channel physical algorithm in combination with corresponding atmospheric water vapor and liquid water content data; and verifying and correcting the estimated earth surface temperature by utilizing the data of the earth surface temperature under the measured cloud of the station. According to the method, the influence of the atmospheric water vapor and the liquid water content in the cloud on the passive microwave radiation is quantified, the estimation precision of the cloud-to-ground surface temperature is improved, and the precision comparison verification of the ground surface data and the precision of the cloud-to-ground surface temperature of the FY-3D passive microwave data is realized.
Description
Technical Field
The invention relates to the technical field of quantitative remote sensing, in particular to an underground surface temperature inversion and verification method of FY-3D passive microwave data in a cloud.
Background
The surface temperature is a very important characteristic physical quantity for representing surface process change, is an indispensable important parameter for researching aspects such as substance and energy exchange, climate change and the like between the surface and the atmosphere, and relates to a plurality of basic subject researches and important application fields. How to quantitatively invert the surface temperature from the radiation information acquired from the remote sensing data is a difficult problem in the field of quantitative remote sensing. At present, earth surface temperature inversion is carried out by using satellite remote sensing data, and inversion is mainly carried out by using thermal infrared remote sensing data. The spatial resolution of the thermal infrared remote sensing data is high and can reach kilometer level or even hundred meter level. However, thermal infrared remote sensing cannot penetrate through cloud layers, so that only the surface temperature under the clear sky condition can be acquired. Under the condition that the earth surface is covered by cloud, passive microwave remote sensing data is an effective method for acquiring the temperature of the earth surface under the cloud due to the advantage that the passive microwave remote sensing data can penetrate through the cloud layer to acquire the radiation of the earth surface under the cloud.
The existing surface temperature inversion method has the following problems: the existing passive microwave earth surface temperature algorithm simply ignores the influence of cloud layers, and does not quantify the influence of the cloud layers on passive microwave radiation, so that the accuracy of the inversion of the earth surface temperature under the cloud is insufficient. Since passive microwave data pixel dimensions are on the order of 10km, ground site data represents point data near the site. Therefore, the traditional method of directly comparing the remotely acquired surface temperature with the measured surface station temperature causes a large error in the precision evaluation.
Therefore, the existing passive microwave data cloud subsurface temperature inversion and verification technology has defects and needs to be improved.
Disclosure of Invention
The invention provides an FY-3D passive microwave data cloud-based surface temperature inversion and verification method aiming at the problems of low precision and difficulty in verification existing in current cloud-based surface temperature inversion, and the estimation and verification precision of the cloud-based surface temperature is improved.
In order to achieve the purpose, the invention provides the following scheme:
an FY-3D passive microwave data cloud subsurface surface temperature inversion and verification method comprises the following steps:
s1, acquiring FY-3D passive microwave data based on an MWRI sensor carried on an FY-3D satellite, and preprocessing the data to extract the dual-channel brightness temperature of 18.7GHz and 23.8GHz vertical polarization channels;
s2, acquiring ERA5 atmospheric profile data, and performing data processing to extract the content of atmospheric water vapor and liquid water;
s3, estimating the surface temperature under the cloud condition by using the two-channel brightness temperature of the 18.7GHz and 23.8GHz vertical polarization channels and combining the corresponding atmospheric water vapor and liquid water content data and adopting a two-channel physical algorithm;
and S4, verifying and correcting the surface temperature estimated in the step S3 by utilizing the surface temperature data under the measured cloud of the station.
Further, in step S1, the acquiring, based on the MWRI sensor mounted on the FY-3D satellite, the FY-3D passive microwave data, and performing data preprocessing to extract the dual-channel brightness temperatures of the 18.7GHz and 23.8GHz vertical polarization channels specifically includes:
s101, converting the count value in the microwave brightness temperature product of the MWRI sensor into a microwave brightness temperature, wherein the formula is expressed as follows:
TB＝gain×(DN-offset) (1)
in the formula, TBThe temperature is lightened by microwave; DN is a count value; gain and offset are gain and offset, respectively; gain of 18.7GHz vertical polarization channel for microwave bright temperature productsAnd offsets of 0.01 and 0, respectively, and the gain and offset of the 23.8GHz vertically polarized channel are 0.01 and 0, respectively;
and S102, carrying out image splicing, resampling and reprojection on the FY-3D microwave bright temperature product by using a remote sensing image processing tool ENVI and an attached programming tool IDL thereof to obtain the microwave bright temperature product with 10km spatial resolution projected by the global scale longitude and latitude.
Further, in step S102, resampling is implemented by using a bilinear interpolation method; the reprojection utilizes the ESD to convert the projection plane coordinates to geographic coordinates.
Further, in step S2, the acquiring ERA5 atmospheric profile data, and performing data processing to extract the content of atmospheric water vapor and liquid water specifically includes:
s201, downloading ERA5 atmosphere profile data with the spatial resolution of 0.25 degrees, and extracting atmospheric relative humidity, potential height, air temperature and ozone concentration parameters under various atmospheric pressures in the ERA5 atmosphere profile data;
s202, according to the actual elevation of the ground, performing elevation interpolation calculation on the atmospheric relative humidity, the potential height and the air humidity to obtain the atmospheric relative humidity, the potential height and the air temperature at the actual elevation of the ground;
s203, inputting the atmospheric relative humidity, the potential height and the air temperature at the actual elevation of the ground into an atmospheric radiation transmission model MODTRAN, and performing calculation to obtain the total atmospheric water vapor and liquid water content per hour each day;
s204, rasterizing the total atmospheric water vapor and the liquid water content to obtain an atmospheric water vapor image and a liquid water content distribution image of 0.25-degree spatial resolution of global scale longitude and latitude projection every hour;
s205, according to the acquisition time of the FY-3D passive microwave data, time interpolation is carried out on the total atmospheric water vapor and liquid water content in each hour, and the atmospheric water vapor and liquid water content in each day of the transit of the FY-3D satellite is obtained.
Further, in step S201, the plurality of atmospheric pressures are 1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 925, 950, 975 and 1000hPa, respectively.
Further, in step S3, the method for estimating the surface temperature under the cloud condition by using the two-channel brightness temperature of the 18.7GHz and 23.8GHz vertical polarization channels and combining the corresponding data of the atmospheric water vapor and the liquid water content and using the two-channel physical algorithm specifically includes:
the expression of the two-channel physical algorithm is as follows:
in the formula, TsThe inverted FY-3D passive microwave surface temperature is obtained; t isB18VAnd TB23VMicrowave brightness temperatures of the vertical polarization channels of 18.7GHz and 23.8GHz respectively; PWV is atmospheric water vapor; CLW is the atmospheric liquid water content; c. a, gamma23、γ18、η23And η18The fitting coefficients are obtained by least square fitting based on simulation data, and the values of the coefficients are respectively: 1.2628, 0.1087, γ23＝0.6262，γ18＝1.2765，η23＝0.1683，η18＝0.2355。
Further, in the step S4, the verifying and correcting the surface temperature estimated in the step S3 by using the surface temperature data under the station measured cloud includes:
s401, calculating the earth surface temperature by using the ground uplink and downlink long-wave radiation acquired by the ground actual measurement station through the following formula:
in the formula, TsSurface temperature measured for the site; f↑Surface up-going long wave radiation, F, measured for site sensors↓For down-going long-wave radiation, epsilon, received by the site sensorbThe emissivity is the wide band emissivity of the earth surface, and the sigma is the Spanderson-Boltzmann constant, and the value is 5.67 multiplied by 10-8W·m-2·K-4；
S402, screening the spatial uniformity of the station temperature through MODIS thermal infrared earth surface temperature with the spatial resolution of 1km and ground elevation model DEM data;
s403, performing time matching on the hourly ground surface temperature measured by the station and the FY-3D passive microwave lighting temperature data, and obtaining the actually measured ground surface temperature of the station with the same FY-3D passive microwave lighting temperature time through linear interpolation;
s404, screening the cloud condition by using a short wave uplink and downlink radiation cloud detection algorithm actually measured by the station, and evaluating the precision of the estimated earth surface temperature by using a direct comparison method.
Further, in step S402, the step of screening the spatial uniformity of the station temperature through the MODIS thermal infrared surface temperature and ground elevation model DEM data with higher spatial resolution specifically includes:
and counting standard deviations of the thermal infrared earth surface temperature and the ground elevation within a 10km range by taking the station as a center, and selecting the station data with the temperature standard deviation smaller than 2K and the elevation standard deviation smaller than 50m as effective data to ensure the spatial representativeness of the station data.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: according to the method for inverting and verifying the surface temperature under the FY-3D passive microwave data cloud, firstly, the influence of atmospheric water vapor and liquid water content in the cloud on passive microwave radiation is quantified, and high-precision estimation of the surface temperature under the cloud is realized; secondly, the measured data of the ground station is controlled by using the temperature standard deviation and the elevation standard deviation, the spatial representativeness of the station data is ensured, and the precision comparison verification of the ground point data and the passive microwave 10km remote sensing data is realized.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a method for inverting and verifying the subsurface temperature of the FY-3D passive microwave data cloud according to the present invention;
FIG. 2 is a schematic view of the global diurnal surface temperature of 2016, 7, 15 days in 2016 for passive microwave data inversion according to an embodiment of the present invention;
FIG. 3a is a scatter diagram comparing passive microwave ground surface temperature with ground station measured ground surface temperature under the condition of cloud in the daytime;
FIG. 3b is a scatter plot of passive microwave surface temperature versus measured surface temperature at a ground site during nighttime cloud conditions.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide an underground surface temperature inversion and verification method for FY-3D passive microwave data, which can improve the estimation and verification precision of the underground surface temperature.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the method for inverting and verifying the subsurface temperature of the FY-3D passive microwave data cloud provided by the invention comprises the following steps:
s1, acquiring FY-3D passive microwave data based on an MWRI sensor carried on an FY-3D satellite, and preprocessing the data to extract the dual-channel brightness temperature of 18.7GHz and 23.8GHz vertical polarization channels;
s2, acquiring ERA5 atmospheric profile data, and performing data processing to extract the content of atmospheric water vapor and liquid water;
s3, estimating the surface temperature under the cloud condition by using the two-channel brightness temperature of the 18.7GHz and 23.8GHz vertical polarization channels and combining the corresponding atmospheric water vapor and liquid water content data and adopting a two-channel physical algorithm;
and S4, verifying and correcting the surface temperature estimated in the step S3 by utilizing the surface temperature data under the measured cloud of the station.
In step S1, the method includes acquiring FY-3D passive microwave data based on an MWRI sensor mounted on an FY-3D satellite, and performing data preprocessing to extract dual-channel brightness temperatures of 18.7GHz and 23.8GHz vertical polarization channels, and specifically includes:
s101, converting the count value in the microwave brightness temperature product of the MWRI sensor into a microwave brightness temperature, wherein the formula is expressed as follows:
TB＝gain×(DN-offset) (1)
in the formula, TBThe temperature is microwave brightness; DN is a count value; gain and offset are gain and offset, respectively; for the microwave bright temperature product, the gain and the offset of the 18.7GHz vertical polarization channel are respectively 0.01 and 0, and the gain and the offset of the 23.8GHz vertical polarization channel are respectively 0.01 and 0;
s102, image splicing, resampling and re-projection processing are carried out on the FY-3D microwave bright temperature product by using a remote sensing image processing tool ENVI and an attached programming tool IDL, the resampling is realized by using a bilinear interpolation method in consideration of the calculation precision and time, the projection plane coordinate is converted into a geographic coordinate by using ESD, and finally the microwave bright temperature product with the 10km spatial resolution projected by the global scale longitude and latitude is obtained.
In step S2, the method of acquiring ERA5 atmospheric profile data and performing data processing to extract atmospheric water vapor and liquid water content includes:
s201, downloading ERA5 atmosphere profile data with the spatial resolution of 0.25 degrees, and extracting parameters such as atmosphere relative humidity, potential height, air temperature and ozone concentration and the like of which the atmospheric pressure is 1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 925, 950, 975 and 1000hPa in the ERA5 atmosphere profile data;
s202, performing elevation interpolation calculation on the atmospheric relative humidity, the potential height and the air humidity according to the actual elevation of the ground to obtain the atmospheric relative humidity, the potential height and the air temperature at the actual elevation of the ground;
s203, inputting the atmospheric relative humidity, the potential height and the air temperature at the actual elevation of the ground into an atmospheric radiation transmission model MODTRAN, and performing calculation to obtain the total atmospheric water vapor and liquid water content per hour each day;
s204, rasterizing the total atmospheric water vapor and the liquid water content to obtain an atmospheric water vapor image and a liquid water content distribution image of which the global scale longitude and latitude projection is 0.25 DEG per hour each day;
s205, according to the acquisition time of the FY-3D passive microwave data, time interpolation is carried out on the total atmospheric water vapor and liquid water content in each hour, and the atmospheric water vapor and liquid water content in each day of the transit of the FY-3D satellite is obtained.
In the step S3, the method for estimating the surface temperature in the cloud environment by using the two-channel light temperature of the 18.7GHz and 23.8GHz vertical polarization channels and combining the corresponding atmospheric water vapor and liquid water content data and using the two-channel physical algorithm specifically includes:
the expression of the two-channel physical algorithm is as follows:
in the formula, TsThe inverted FY-3D passive microwave surface temperature is obtained; t isB18VAnd TB23VMicrowave brightness temperatures of the vertical polarization channels of 18.7GHz and 23.8GHz respectively; PWV is atmospheric water vapor; CLW is the atmospheric liquid water content; c. a, gamma23、γ18、η23And η18The fitting coefficients are obtained by least square fitting based on simulation data, and the values of the coefficients are respectively as follows: 1.2628, 0.1087, γ23＝0.6262，γ18＝1.2765，η23＝0.1683，η18＝0.2355。
FIG. 2 shows the global diurnal earth surface temperature at 2016, 7, 15 days of passive microwave data inversion; as can be seen from the graph of FIG. 2, the FY-3D passive microwave data inversion of the earth surface temperature is not influenced by cloud layers, so that complete coverage on the global scale is realized, and the integral distribution and change trend of the global earth surface temperature is well reflected.
In the step S4, the verifying and correcting the surface temperature estimated in the step S3 by using the data of the surface temperature under the measured cloud of the station specifically includes:
s401, calculating the surface temperature by using the ground uplink and downlink long wave radiation acquired by the ground actual measurement station through the following formula:
in the formula, TsSurface temperature measured for the site; f↑Surface up-going long wave radiation, F, measured for site sensors↓For down-going long-wave radiation, epsilon, received by the site sensorbThe emissivity is the wide band emissivity of the earth surface, and the sigma is the Spanderson-Boltzmann constant, and the value is 5.67 multiplied by 10-8W·m-2·K-4；
S402, screening the spatial uniformity of the station temperature through MODIS thermal infrared earth surface temperature with the spatial resolution of 1km and ground elevation model DEM data; specifically, standard deviations of thermal infrared earth surface temperature and ground elevation within a 10km range with a station as a center are counted, and station data with the temperature standard deviation smaller than 2K and the elevation standard deviation smaller than 50m are selected as effective data to ensure spatial representativeness of the station data;
s403, performing time matching on the hourly ground surface temperature measured by the station and the FY-3D passive microwave lighting temperature data, and obtaining the actually measured ground surface temperature of the station with the same FY-3D passive microwave lighting temperature time through linear interpolation;
s404, screening the cloud condition by using a short wave uplink and downlink radiation cloud detection algorithm actually measured by the station, and evaluating the precision of the estimated earth surface temperature by using a direct comparison method.
FIGS. 3a and 3b are scattergrams comparing passive microwave ground surface temperature with ground surface temperature actually measured by a ground station under conditions of cloud in the daytime and at night in 2016, 7, 15 and 2016, respectively; as can be seen in fig. 3a and 3b, the FY-3D passive microwave data combined with the two-channel physics algorithm provides a good estimate of the surface temperature. Compared with the actually measured temperature of the ground station, the accuracy of the FY-3D passive microwave earth surface temperature under the cloud condition is about RMSE (RMSE) 3.6K, and higher accuracy is achieved.
According to the method for inverting and verifying the surface temperature under the FY-3D passive microwave data cloud, the influence of atmospheric water vapor and the content of liquid water in the cloud on passive microwave radiation is quantified, and high-precision estimation of the surface temperature under the cloud is realized; further, the measured data of the ground station is controlled by using the temperature standard deviation and the elevation standard deviation, the spatial representativeness of the station data is ensured, and the precision comparison verification of the ground point data and the passive microwave 10km remote sensing data is realized.
The principle and the embodiment of the present invention are explained by applying specific examples, and the above description of the embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (8)
1. An FY-3D passive microwave data cloud subsurface surface temperature inversion and verification method is characterized by comprising the following steps:
s1, acquiring FY-3D passive microwave data based on an MWRI sensor carried on an FY-3D satellite, and preprocessing the data to extract the dual-channel brightness temperature of 18.7GHz and 23.8GHz vertical polarization channels;
s2, acquiring ERA5 atmospheric profile data, and performing data processing to extract the content of atmospheric water vapor and liquid water;
s3, estimating the surface temperature under the cloud condition by using the two-channel brightness temperature of the 18.7GHz and 23.8GHz vertical polarization channels and combining the corresponding atmospheric water vapor and liquid water content data and adopting a two-channel physical algorithm;
and S4, verifying and correcting the surface temperature estimated in the step S3 by utilizing the surface temperature data under the measured cloud of the station.
2. The FY-3D passive microwave data cloud-based surface temperature inversion and verification method according to claim 1, wherein in the step S1, based on an MWRI sensor mounted on an FY-3D satellite, FY-3D passive microwave data is acquired, and data preprocessing is performed to extract dual-channel brightness temperatures of 18.7GHz and 23.8GHz vertical polarization channels, which specifically comprises:
s101, converting the count value in the microwave brightness temperature product of the MWRI sensor into a microwave brightness temperature, wherein the formula is expressed as follows:
TB＝gain×(DN-offset) (1)
in the formula, TBThe temperature is microwave brightness; DN is a count value; gain and offset are gain and offset, respectively; for the microwave bright temperature product, the gain and the offset of the 18.7GHz vertical polarization channel are respectively 0.01 and 0, and the gain and the offset of the 23.8GHz vertical polarization channel are respectively 0.01 and 0;
and S102, carrying out image splicing, resampling and reprojection on the FY-3D microwave bright temperature product by using a remote sensing image processing tool ENVI and an attached programming tool IDL thereof to obtain the microwave bright temperature product with 10km spatial resolution projected by the global scale longitude and latitude.
3. The FY-3D passive microwave data cloud subsurface temperature inversion and verification method according to claim 2, wherein in the step S102, resampling is realized by using a bilinear interpolation method; the reprojection utilizes the ESD to convert the projection plane coordinates to geographic coordinates.
4. The FY-3D passive microwave data cloud-based surface temperature inversion and verification method according to claim 1, wherein in the step S2, ERA5 atmospheric profile data are obtained, and data processing is performed to extract atmospheric water vapor and liquid water content, specifically comprising:
s201, downloading ERA5 atmosphere profile data with the spatial resolution of 0.25 degrees, and extracting atmospheric relative humidity, potential height, air temperature and ozone concentration parameters under various atmospheric pressures in the ERA5 atmosphere profile data;
s202, according to the actual elevation of the ground, performing elevation interpolation calculation on the atmospheric relative humidity, the potential height and the air humidity to obtain the atmospheric relative humidity, the potential height and the air temperature at the actual elevation of the ground;
s203, inputting the atmospheric relative humidity, the potential height and the air temperature at the actual elevation of the ground into an atmospheric radiation transmission model MODTRAN, and performing calculation to obtain the total atmospheric water vapor and liquid water content per hour each day;
s204, rasterizing the total atmospheric water vapor and the liquid water content to obtain an atmospheric water vapor image and a liquid water content distribution image of 0.25-degree spatial resolution of global scale longitude and latitude projection every hour;
s205, according to the acquisition time of the FY-3D passive microwave data, time interpolation is carried out on the total atmospheric water vapor and liquid water content in each hour, and the atmospheric water vapor and liquid water content in each day of the transit of the FY-3D satellite is obtained.
5. The FY-3D passive microwave data cloud-based surface temperature inversion and verification method according to claim 4, wherein in the step S201, the plurality of atmospheric pressures are 1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 925, 950, 975 and 1000hPa respectively.
6. The FY-3D passive microwave data inversion and verification method for surface temperature under cloud conditions according to claim 1, wherein in the step S3, the surface temperature under cloud conditions is estimated by using a two-channel physical algorithm by using two-channel brightness temperatures of 18.7GHz and 23.8GHz vertical polarization channels in combination with corresponding data of atmospheric water vapor and liquid water content, and specifically comprises:
the expression of the two-channel physical algorithm is as follows:
in the formula, TsIs reversedPerforming FY-3D passive microwave ground surface temperature; t isB18VAnd TB23VMicrowave brightness temperatures of the vertical polarization channels of 18.7GHz and 23.8GHz respectively; PWV is atmospheric water vapor; CLW is the atmospheric liquid water content; c. a, gamma23、γ18、η23And η18The fitting coefficients are obtained by least square fitting based on simulation data, and the values of the coefficients are respectively as follows: 1.2628, 0.1087, γ23＝0.6262，γ18＝1.2765，η23＝0.1683，η18＝0.2355。
7. The FY-3D passive microwave data cloud-based surface temperature inversion and verification method according to claim 1, wherein in the step S4, the verification and correction of the surface temperature estimated in the step S3 are performed by using the station measured cloud-based surface temperature data, and specifically include:
s401, calculating the earth surface temperature by using the ground uplink and downlink long-wave radiation acquired by the ground actual measurement station through the following formula:
in the formula, TsSurface temperature measured for the site; f↑Surface up-going long wave radiation, F, measured for site sensors↓For down-going long-wave radiation, epsilon, received by the site sensorbThe emissivity is the wide band emissivity of the earth surface, and the sigma is the Spanderson-Boltzmann constant, and the value is 5.67 multiplied by 10-8W·m-2·K-4；
S402, screening the spatial uniformity of the station temperature through MODIS thermal infrared earth surface temperature with the spatial resolution of 1km and ground elevation model DEM data;
s403, performing time matching on the hourly ground surface temperature measured by the station and the FY-3D passive microwave lighting temperature data, and obtaining the actually measured ground surface temperature of the station with the same FY-3D passive microwave lighting temperature time through linear interpolation;
s404, screening the cloud condition by using a short wave uplink and downlink radiation cloud detection algorithm actually measured by the station, and evaluating the precision of the estimated earth surface temperature by using a direct comparison method.
8. The FY-3D passive microwave data cloud-based surface temperature inversion and verification method according to claim 7, wherein in the step S402, spatial uniformity of the station temperature is screened through MODIS thermal infrared surface temperature and ground elevation model DEM data with higher spatial resolution, and the method specifically comprises the following steps:
and counting standard deviations of the thermal infrared earth surface temperature and the ground elevation within a 10km range by taking the station as a center, and selecting the station data with the temperature standard deviation smaller than 2K and the elevation standard deviation smaller than 50m as effective data to ensure the spatial representativeness of the station data.
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| CN116358709B (en) * | 2023-06-02 | 2023-08-29 | 中国科学院空天信息创新研究院 | Surface temperature inversion method based on passive microwave multiband temperature model |
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