CN114722350B - A method for inversion and verification of sub-cloud surface temperature using FY-3D passive microwave data - Google Patents

Abstract

The invention provides an inversion and verification method of surface temperature under FY-3D passive microwave data cloud, which comprises the following steps: acquiring FY-3D passive microwave data, and carrying out data preprocessing to extract the two-channel bright temperatures of 18.7GHz and 23.8GHz vertical polarization channels; obtaining ERA5 atmospheric profile data, and performing data processing to extract the content of atmospheric water vapor and liquid water; based on the two-channel bright temperature of the 18.7GHz and 23.8GHz vertical polarization channels, the surface temperature under the cloud condition is estimated by adopting a two-channel physical algorithm in combination with corresponding atmospheric water vapor and liquid water content data; and verifying and correcting the estimated surface temperature by using the site actually measured subsurface surface temperature data. 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 subsurface ground surface temperature under the cloud is improved, and the precision comparison verification of the subsurface ground surface point data and the FY-3D passive microwave data subsurface ground surface temperature is realized.

Description

A method for inversion and verification of sub-cloud surface temperature using FY-3D passive microwave data

Technical Field

The invention relates to the field of quantitative remote sensing technology, and in particular to a method for inverting and verifying surface temperature under a cloud using FY-3D passive microwave data.

Background Art

The surface temperature is a very important characteristic physical quantity that characterizes the changes in surface processes. It is an indispensable parameter for studying the exchange of matter and energy between the surface and the atmosphere, climate change, etc. It involves many basic disciplines and major application fields. How to quantitatively invert the surface temperature from the radiation information obtained from remote sensing data is a difficult problem in the field of quantitative remote sensing. At present, the inversion of surface temperature using satellite remote sensing data mainly uses thermal infrared remote sensing data. The spatial resolution of thermal infrared remote sensing data is relatively high, reaching the kilometer level or even the hundred-meter level. However, thermal infrared remote sensing cannot penetrate clouds, so it can only obtain the surface temperature under clear sky conditions. When the surface is covered with clouds, passive microwave remote sensing data is an effective method to obtain the surface temperature under the clouds because it can penetrate the clouds to obtain the surface radiation under the clouds.

The existing surface temperature inversion methods have the following problems: The existing passive microwave surface temperature algorithm simply ignores the influence of clouds and does not quantify the influence of clouds on passive microwave radiation, resulting in insufficient accuracy of surface temperature inversion under clouds. Since the pixel scale of passive microwave data is at the 10km level, and the ground station data represents point data near the station, the traditional method of directly comparing the surface temperature obtained by remote sensing with the measured temperature of the ground station will lead to large errors in accuracy assessment.

Therefore, the existing passive microwave data subcloud surface temperature inversion and verification technology has defects and needs to be improved.

Summary of the invention

Aiming at the problems of low precision and difficult verification in the current inversion of surface temperature under clouds, the present invention provides a method for inverting and verifying surface temperature under clouds using FY-3D passive microwave data, so as to improve the estimation and verification accuracy of surface temperature under clouds.

To achieve the above object, the present invention provides the following solutions:

A method for inverting and verifying the surface temperature under the cloud using FY-3D passive microwave data includes the following steps:

S1, based on the MWRI sensor carried on the FY-3D satellite, obtains FY-3D passive microwave data and performs data preprocessing to extract the dual-channel brightness temperature of the 18.7 GHz and 23.8 GHz vertically polarized channels;

S2, obtain ERA5 atmospheric profile data and process the data to extract atmospheric water vapor and liquid water content;

S3, using the dual-channel brightness temperature of the 18.7 GHz and 23.8 GHz vertically polarized channels, combined with the corresponding atmospheric water vapor and liquid water content data, a dual-channel physical algorithm is used to estimate the surface temperature in cloudy conditions;

S4, using the actual measured surface temperature data under the cloud at the site to verify and correct the surface temperature estimated in step S3.

Furthermore, in step S1, based on the MWRI sensor carried on the FY-3D satellite, FY-3D passive microwave data is obtained, and data preprocessing is performed to extract the dual-channel brightness temperature of the 18.7 GHz and 23.8 GHz vertical polarization channels, specifically including:

S101, convert the count value in the microwave brightness temperature product of the MWRI sensor into microwave brightness temperature. The formula is as follows:

T _B = gain × (DN-offset) (1)

Wherein, _TB is microwave brightness temperature; DN is count value; gain and offset are gain and offset respectively; for microwave brightness temperature products, the gain and offset of 18.7 GHz vertical polarization channel are 0.01 and 0 respectively, and the gain and offset of 23.8 GHz vertical polarization channel are 0.01 and 0 respectively;

S102: Use the remote sensing image processing tool ENVI and its accompanying programming tool IDL to perform image stitching, resampling and reprojection on the FY-3D microwave brightness temperature product to obtain a microwave brightness temperature product with a spatial resolution of 10 km in global latitude and longitude projection.

Furthermore, in step S102, resampling is implemented using bilinear interpolation; and reprojection uses ESD to convert the projection plane coordinates into geographic coordinates.

Furthermore, in step S2, ERA5 atmospheric profile data is obtained, and data processing is performed to extract atmospheric water vapor and liquid water content, specifically including:

S201, download ERA5 atmospheric profile data with a spatial resolution of 0.25°, and extract atmospheric relative humidity, geopotential height, air temperature and ozone concentration parameters under various atmospheric pressures from ERA5 atmospheric profile data;

S202, performing elevation interpolation calculation on the atmospheric relative humidity, potential height and air humidity according to the actual elevation of the ground, to obtain the atmospheric relative humidity, potential height and air temperature at the actual elevation of the ground;

S203, inputting the atmospheric relative humidity, potential height and air temperature at the actual ground elevation into the atmospheric radiation transfer model MODTRAN, and performing calculations to obtain the total atmospheric water vapor and liquid water content every day and every hour;

S204, rasterizing the total water vapor and liquid water content in the atmosphere to obtain an atmospheric water vapor image and a liquid water content distribution image with a spatial resolution of 0.25° in the global-scale latitude and longitude projection every day and every hour;

S205, performing time interpolation on the total atmospheric water vapor and liquid water content per hour according to the acquisition time of the FY-3D passive microwave data, and obtaining the atmospheric water vapor and liquid water content during the daily FY-3D satellite transit.

Furthermore, in step S201, the various 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 1000 hPa.

Furthermore, in step S3, the dual-channel brightness temperature of the 18.7 GHz and 23.8 GHz vertical polarization channels is used, combined with the corresponding atmospheric water vapor and liquid water content data, and a dual-channel physical algorithm is used to estimate the surface temperature under cloudy conditions, specifically including:

Dual-channel physical algorithm expression:

where _Ts is the inverted FY-3D passive microwave surface temperature; _TB18V and _TB23V are the microwave brightness temperatures of the 18.7 GHz and 23.8 GHz vertical polarization channels, respectively; PWV is atmospheric water vapor; CLW is the atmospheric liquid water content; c, a, _γ23 , _γ18 , _η23 and _η18 are fitting coefficients, which are obtained by least squares fitting based on the simulated data, and the values of the coefficients are: c=1.2628, a=0.1087, _γ23 =0.6262, _γ18 =1.2765, _η23 =0.1683, _η18 =0.2355.

Furthermore, in step S4, the surface temperature estimated in step S3 is verified and corrected using the ground surface temperature data measured at the site, specifically including:

S401, using the ground uplink and downlink longwave radiation obtained by the ground measurement station, calculate the surface temperature by the following formula:

Where _Ts is the surface temperature measured at the site; F ^↑ is the upward long-wave radiation measured by the site sensor, F ^↓ is the downward long-wave radiation received by the site sensor, _εb is the broadband emissivity of the surface, and σ is the Stepan-Boltzmann constant, which is 5.67× ^10-8 W·m ^-2 ·K ^-4 .

S402, the spatial uniformity of the station temperature is screened using MODIS thermal infrared surface temperature and ground elevation model DEM data with a spatial resolution of 1 km;

S403, time matching the hourly surface temperature measured at the site with the FY-3D passive microwave brightness temperature data, and obtaining the site measured surface temperature with the same FY-3D passive microwave brightness temperature time by linear interpolation;

S404, using the shortwave uplink and downlink radiation cloud detection algorithm measured at the site to screen for cloud conditions, and using the direct comparison method to evaluate the accuracy of the estimated surface temperature.

Furthermore, in step S402, the spatial uniformity of the station temperature is screened by using MODIS thermal infrared surface temperature and ground elevation model DEM data with high spatial resolution, specifically including:

The standard deviation of thermal infrared surface temperature and ground elevation within a 10km range centered on the station is calculated, and the station data with a temperature standard deviation less than 2K and an elevation standard deviation less than 50m are selected as valid data to ensure the spatial representativeness of the station data.

According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the FY-3D passive microwave data cloud surface temperature inversion and verification method provided by the present invention, first, quantifies the influence of atmospheric water vapor and liquid water content in clouds on passive microwave radiation, and realizes high-precision estimation of surface temperature under clouds; second, uses temperature standard deviation and elevation standard deviation to control the measured data of ground stations, ensures the spatial representativeness of station data, and realizes precision comparison and verification of ground point data and passive microwave 10km remote sensing data.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

FIG1 is a schematic diagram of a flow chart of a method for inverting and verifying the surface temperature under a cloud using FY-3D passive microwave data according to the present invention;

FIG2 is a schematic diagram of the global daytime surface temperature on July 15, 2016 inverted from FY-3D passive microwave data according to an embodiment of the present invention;

Figure 3a is a scatter plot comparing the passive microwave surface temperature and the measured surface temperature at the ground station under cloudy conditions during the day;

Figure 3b is a scatter plot comparing the passive microwave surface temperature and the measured surface temperature at ground stations under cloudy conditions at night.

DETAILED DESCRIPTION

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

The purpose of the present invention is to provide a method for inverting and verifying the surface temperature under the cloud using FY-3D passive microwave data, which can improve the estimation and verification accuracy of the surface temperature under the cloud.

In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

As shown in FIG1 , the FY-3D passive microwave data cloud surface temperature inversion and verification method provided by the present invention comprises the following steps:

S1, based on the MWRI sensor carried on the FY-3D satellite, obtains FY-3D passive microwave data and performs data preprocessing to extract the dual-channel brightness temperature of the 18.7 GHz and 23.8 GHz vertically polarized channels;

S2, obtain ERA5 atmospheric profile data and process the data to extract atmospheric water vapor and liquid water content;

S3, using the dual-channel brightness temperature of the 18.7 GHz and 23.8 GHz vertically polarized channels, combined with the corresponding atmospheric water vapor and liquid water content data, a dual-channel physical algorithm is used to estimate the surface temperature in cloudy conditions;

S4, using the actual measured surface temperature data under the cloud at the site to verify and correct the surface temperature estimated in step S3.

Wherein, in the step S1, based on the MWRI sensor carried on the FY-3D satellite, FY-3D passive microwave data is obtained, and data preprocessing is performed to extract the dual-channel brightness temperature of the 18.7 GHz and 23.8 GHz vertical polarization channels, specifically including:

S101, convert the count value in the microwave brightness temperature product of the MWRI sensor into microwave brightness temperature. The formula is as follows:

T _B = gain × (DN-offset) (1)

Wherein, _TB is microwave brightness temperature; DN is count value; gain and offset are gain and offset respectively; for microwave brightness temperature products, the gain and offset of 18.7 GHz vertical polarization channel are 0.01 and 0 respectively, and the gain and offset of 23.8 GHz vertical polarization channel are 0.01 and 0 respectively;

S102, using the remote sensing image processing tool ENVI and its accompanying programming tool IDL to perform image stitching, resampling and reprojection processing on the FY-3D microwave brightness temperature product. Considering the calculation accuracy and time, the resampling is implemented using bilinear interpolation, and the projection plane coordinates are converted into geographic coordinates using ESD, ultimately obtaining a microwave brightness temperature product with a spatial resolution of 10 km in global-scale latitude and longitude projection.

In step S2, ERA5 atmospheric profile data is obtained, and data processing is performed to extract atmospheric water vapor and liquid water content, specifically including:

S201, download ERA5 atmospheric profile data with a spatial resolution of 0.25°, and extract atmospheric relative humidity, potential height, air temperature, ozone concentration and other parameters at atmospheric pressures of 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 1000 hPa respectively;

S202, performing elevation interpolation calculation on the atmospheric relative humidity, potential height and air humidity according to the actual elevation of the ground, to obtain the atmospheric relative humidity, potential height and air temperature at the actual elevation of the ground;

S203, inputting the atmospheric relative humidity, potential height and air temperature at the actual ground elevation into the atmospheric radiation transfer model MODTRAN, and performing calculations to obtain the total atmospheric water vapor and liquid water content every day and every hour;

S204, rasterizing the total water vapor and liquid water content in the atmosphere to obtain an atmospheric water vapor image and a liquid water content distribution image with a spatial resolution of 0.25° in the global-scale latitude and longitude projection every day and every hour;

S205, performing time interpolation on the total atmospheric water vapor and liquid water content per hour according to the acquisition time of the FY-3D passive microwave data, and obtaining the atmospheric water vapor and liquid water content during the daily FY-3D satellite transit.

In step S3, the dual-channel brightness temperature of the 18.7 GHz and 23.8 GHz vertical polarization channels is used, combined with the corresponding atmospheric water vapor and liquid water content data, and a dual-channel physical algorithm is used to estimate the surface temperature in the case of clouds, specifically including:

Dual-channel physical algorithm expression:

where _Ts is the inverted FY-3D passive microwave surface temperature; _TB18V and _TB23V are the microwave brightness temperatures of the 18.7 GHz and 23.8 GHz vertical polarization channels, respectively; PWV is atmospheric water vapor; CLW is the atmospheric liquid water content; c, a, _γ23 , _γ18 , _η23 and _η18 are fitting coefficients, which are obtained by least squares fitting based on the simulated data, and the values of the coefficients are: c=1.2628, a=0.1087, _γ23 =0.6262, _γ18 =1.2765, _η23 =0.1683, _η18 =0.2355.

Figure 2 shows the global daytime surface temperature on July 15, 2016, inverted from FY-3D passive microwave data. As can be seen from Figure 2, the surface temperature inverted from FY-3D passive microwave data is not affected by clouds, achieving relatively complete coverage on a global scale, and well reflecting the overall distribution and changing trend of the global surface temperature.

In step S4, the surface temperature estimated in step S3 is verified and corrected using the actual surface temperature data under the cloud measured at the site, specifically including:

S401, using the ground uplink and downlink longwave radiation obtained by the ground measurement station, calculate the surface temperature by the following formula:

Where _Ts is the surface temperature measured at the site; F ^↑ is the upward long-wave radiation measured by the site sensor, F ^↓ is the downward long-wave radiation received by the site sensor, _εb is the broadband emissivity of the surface, and σ is the Stepan-Boltzmann constant, which is 5.67× ^10-8 W·m ^-2 ·K ^-4 .

S402, screening the spatial uniformity of the station temperature through MODIS thermal infrared surface temperature and ground elevation model DEM data with a spatial resolution of 1 km; specifically, statistically analyzing the standard deviation of thermal infrared surface temperature and ground elevation within a 10 km range centered on the station, and selecting station data with a temperature standard deviation less than 2K and an elevation standard deviation less than 50m as valid data to ensure the spatial representativeness of the station data;

S403, time matching the hourly surface temperature measured at the site with the FY-3D passive microwave brightness temperature data, and obtaining the site measured surface temperature with the same FY-3D passive microwave brightness temperature time by linear interpolation;

S404, using the shortwave uplink and downlink radiation cloud detection algorithm measured at the site to screen for cloud conditions, and using the direct comparison method to evaluate the accuracy of the estimated surface temperature.

Figures 3a and 3b are scatter plots comparing passive microwave surface temperature and ground station measured surface temperature in cloudy conditions during the day and night of July 15, 2016. It can be seen from Figures 3a and 3b that FY-3D passive microwave data combined with the dual-channel physical algorithm can estimate the surface temperature well. Compared with the measured temperature at the ground station, the accuracy of FY-3D passive microwave surface temperature in cloudy conditions is about RMSE = 3.6K, which has achieved a relatively high accuracy.

The FY-3D passive microwave data subcloud surface temperature inversion and verification method provided by the present invention quantifies the influence of atmospheric water vapor and liquid water content in clouds on passive microwave radiation, thereby realizing high-precision estimation of subcloud surface temperature. Furthermore, the temperature standard deviation and elevation standard deviation are used to control the measured data of ground stations to ensure the spatial representativeness of the station data, thereby realizing precision comparison and verification between ground point data and passive microwave 10km remote sensing data.

This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims (5)

1. The method for inversion and verification of the surface temperature under the FY-3D passive microwave data cloud is characterized by comprising the following steps:

S1, acquiring FY-3D passive microwave data based on MWRI sensors mounted on an FY-3D satellite, and carrying out data preprocessing to extract the two-channel bright temperatures 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 in cloud, wherein the method specifically comprises the following steps:

s201, downloading ERA5 atmospheric profile data with the spatial resolution of 0.25 degrees, and extracting atmospheric relative humidity, high potential, air temperature and ozone concentration parameters under various atmospheric pressures in the ERA5 atmospheric profile data;

S202, according to the actual elevation of the ground, carrying out high Cheng Chazhi calculation on the relative humidity, the potential height and the air humidity of the atmosphere to obtain the relative humidity, the potential height and the air temperature of the atmosphere 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 MODTRA, and performing operation calculation to obtain the total atmospheric water vapor and the liquid water content in the cloud every hour every day;

S204, rasterizing the total atmospheric water vapor and the liquid water content in the cloud to obtain an atmospheric water vapor image with 0.25-degree spatial resolution projected by global scale longitude and latitude every hour and a liquid water content distribution image in the cloud;

S205, performing time interpolation on the total atmospheric water vapor and the liquid water content in the cloud in each hour according to the acquisition time of FY-3D passive microwave data to obtain the atmospheric water vapor and the liquid water content in the cloud when the FY-3D satellite passes the border every day;

S3, estimating the surface temperature under the cloud condition by using a two-channel physical algorithm by utilizing the two-channel bright temperature of the 18.7GHz and 23.8GHz vertical polarization channels and combining the corresponding atmospheric water vapor and liquid water content data in the cloud, wherein the method specifically comprises the following steps:

The two-channel physical algorithm expression:

wherein T _s is the inverted FY-3D passive microwave surface temperature; t _B18V and T _B23V are respectively the microwave brightness temperatures of 18.7GHz and 23.8GHz vertical polarization channels; PWV is atmospheric water vapor; CLW is the liquid water content in the cloud of the atmosphere; c. a, gamma ₂₃、γ₁₈、η₂₃ and eta ₁₈ are fitting coefficients, the values of the coefficients are respectively as follows based on simulation data by least square fitting: c=1.2628, a= 0.1087, γ ₂₃＝0.6262,γ₁₈＝1.2765,η₂₃＝0.1683,η₁₈ = 0.2355;

s4, verifying and correcting the ground surface temperature estimated in the step S3 by using the data of the ground surface temperature under the cloud actually measured by the station, and specifically comprising the following steps:

s401, calculating the surface temperature by using the ground uplink and downlink long wave radiation acquired by the ground actual measurement site through the following formula:

Wherein T _s is the surface temperature measured by the station; f ^↑ is the surface uplink long-wave radiation measured by the station sensor, F ^↓ is the downlink long-wave radiation received by the station sensor, epsilon _b is the broadband emissivity of the surface, sigma is the Spanish-Boltzmann constant, and the value is 5.67 multiplied by 10 ^-8W·m^-2·K^-4;

s402, screening the space uniformity of the site temperature through MODIS thermal infrared surface temperature with the space resolution of 1km and DEM data of a ground elevation model;

S403, performing time matching on the earth surface temperature measured by the station and FY-3D passive microwave bright temperature data, and obtaining the actual earth surface temperature of the station with consistent FY-3D passive microwave bright temperature time through linear interpolation;

S404, screening cloud conditions by using a short wave uplink and downlink radiation cloud detection algorithm actually measured by a station, and evaluating the accuracy of the estimated surface temperature by adopting a direct comparison method.

2. The method for inversion and verification of subsurface temperature of FY-3D passive microwave data cloud according to claim 1, wherein in the step S1, based on MWRI sensors mounted on FY-3D satellites, FY-3D passive microwave data is obtained, and data preprocessing is performed to extract two-channel bright temperatures of 18.7GHz and 23.8GHz vertical polarization channels, specifically comprising:

s101, converting a count value in a microwave bright temperature product of the MWRI sensor into the microwave bright temperature, wherein the formula is expressed as follows:

T_B＝gain×(DN-offset) (1)

Wherein T _B is the microwave bright temperature; DN is a count value; gain and offset are gain and offset, respectively; for a 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, performing image splicing, resampling and reprojection processing on the FY-3D microwave bright temperature product by using a remote sensing image processing tool ENVI and an attached programming tool IDL to obtain a microwave bright temperature product with 10km spatial resolution of global scale longitude and latitude projection.

3. The FY-3D passive microwave data cloud subsurface temperature inversion and verification method according to claim 2, wherein in step S102, resampling is achieved by using a bilinear interpolation method; reprojection converts projection plane coordinates into geographic coordinates using ESD.

4. The method for inversion and verification of subsurface temperature of FY-3D passive microwave data cloud according to claim 1, wherein 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.

5. The FY-3D passive microwave data subsurface temperature inversion and verification method according to claim 1, wherein in step S402, spatial uniformity of site temperature is screened by MODIS thermal infrared surface temperature and ground elevation model DEM data with higher spatial resolution, specifically comprising:

And (3) counting standard deviation of the internal thermal infrared surface temperature and the ground elevation in a range of 10km with the site as a center, and selecting site data with the temperature standard deviation smaller than 2K and the elevation standard deviation smaller than 50m as effective data to ensure the space representativeness of the site data.