CN112464980A - Method for inverting earth surface temperature by fusing thermal infrared and passive microwave remote sensing data - Google Patents

Method for inverting earth surface temperature by fusing thermal infrared and passive microwave remote sensing data Download PDF

Info

Publication number
CN112464980A
CN112464980A CN202011154269.7A CN202011154269A CN112464980A CN 112464980 A CN112464980 A CN 112464980A CN 202011154269 A CN202011154269 A CN 202011154269A CN 112464980 A CN112464980 A CN 112464980A
Authority
CN
China
Prior art keywords
surface temperature
amsr
modis
pixel
cloud
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202011154269.7A
Other languages
Chinese (zh)
Inventor
段四波
李召良
黄成�
冷佩
高懋芳
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Institute of Agricultural Resources and Regional Planning of CAAS
Original Assignee
Institute of Agricultural Resources and Regional Planning of CAAS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Institute of Agricultural Resources and Regional Planning of CAAS filed Critical Institute of Agricultural Resources and Regional Planning of CAAS
Priority to CN202011154269.7A priority Critical patent/CN112464980A/en
Publication of CN112464980A publication Critical patent/CN112464980A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J5/007—Radiation pyrometry, e.g. infrared or optical thermometry for earth observation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J5/80—Calibration
    • G01J5/802—Calibration by correcting for emissivity
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J5/80—Calibration
    • G01J5/804—Calibration using atmospheric correction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K11/00—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00
    • G01K11/006—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00 using measurement of the effect of a material on microwaves or longer electromagnetic waves, e.g. measuring temperature via microwaves emitted by the object

Abstract

The invention discloses a method for inverting earth surface temperature by fusing thermal infrared and passive microwave remote sensing data, which comprises the following steps: step 1: downloading MODIS data and performing data preprocessing; step 2: downloading AMSR-E data and carrying out data preprocessing; and step 3: inverting the surface temperature under the clear sky condition by using MODIS thermal infrared data; and 4, step 4: inverting the surface temperature under the cloud condition by using AMSR-E passive microwave data; and 5: and (4) fusing the MODIS surface temperature under the clear sky condition and the AMSR-E surface temperature under the cloud condition. The invention fully utilizes the respective advantages of thermal infrared remote sensing and passive microwave remote sensing, establishes a fusion method of the thermal infrared earth surface temperature under the clear sky condition and the passive microwave earth surface temperature under the cloud condition, and realizes the all-weather remote sensing monitoring of the earth surface temperature with high spatial resolution.

Description

Method for inverting earth surface temperature by fusing thermal infrared and passive microwave remote sensing data
Technical Field
The invention relates to the technical field of quantitative remote sensing, in particular to a method for inverting earth surface temperature by fusing thermal infrared and passive microwave remote sensing data.
Background
The earth surface temperature is an important indicator of the energy balance of the earth's surface and is an important characteristic physical quantity that characterizes the process changes of the earth's surface. It is a difficult problem how to quantitatively invert the surface temperature from the remotely acquired radiation information. The existing method for performing earth surface temperature inversion by using satellite remote sensing data mainly comprises two methods, namely thermal infrared remote sensing inversion and passive microwave remote sensing inversion, which have respective advantages and disadvantages. 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. The passive microwave remote sensing data has low spatial resolution, generally ten kilometers to dozens of kilometers, but can penetrate through a cloud layer to obtain the surface temperature under the cloud condition.
The existing surface temperature remote sensing inversion method has the following problems: the thermal infrared data cannot acquire the surface temperature under the cloud condition, the surface temperature inverted by the passive microwave data is the surface temperature, the spatial resolution is low, and the all-weather remote sensing monitoring of the surface temperature with high spatial resolution cannot be realized by the thermal infrared data and the surface temperature.
Accordingly, the prior art is deficient and needs improvement.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for inverting the earth surface temperature by fusing thermal infrared and passive microwave remote sensing data aiming at the defects of the prior art.
The technical scheme of the invention is as follows:
a method for inverting the earth surface temperature by fusing thermal infrared and passive microwave remote sensing data comprises the following steps:
step 1: downloading MODIS data and performing data preprocessing;
step 2: downloading AMSR-E data and carrying out data preprocessing;
and step 3: inverting the surface temperature under the clear sky condition by using MODIS thermal infrared data;
and 4, step 4: inverting the surface temperature under the cloud condition by using AMSR-E passive microwave data;
and 5: fusing the MODIS surface temperature under the clear sky condition with the AMSR-E surface temperature under the cloud condition;
when the surface temperature is fused, the method needs to be divided into three conditions according to the cloud condition of the AMSR-E pixel, namely, the method is completely clear and completely cloudy and partially cloudy:
(1) if the AMSR-E pixel is a completely clear sky pixel, namely the MODIS pixels corresponding to the AMSR-E pixel are all clear sky pixels, directly taking the surface temperature of the MODIS pixels as the fused surface temperature;
(2) if the AMSR-E pixel is a completely cloud pixel, namely all MODIS pixels corresponding to the AMSR-E pixel are cloud pixels, the earth surface temperature estimated by the AMSR-E data needs to be subjected to spatial downscaling to the MODIS pixel scale to serve as the final earth surface temperature;
carrying out spatial downscaling on the surface temperature of the AMSR-E by using a ground digital elevation model DEM:
Figure BDA0002742165430000021
in the formula (I), the compound is shown in the specification,
Figure BDA0002742165430000022
the skin temperature of the ith pixel after the AMSR-E pixel is downscaled to MODIS scale under the cloud condition;
Figure BDA0002742165430000023
is the skin temperature of the AMSR-E pixel; hiIs the surface elevation of the ith pixel on the MODIS scale; hmIs the average earth surface elevation within the AMSR-E pixel; TLR is temperature direct reduction rate, which means the speed of temperature reduction along with the increase of altitude, and the value is 6.5K/km;
Figure BDA0002742165430000024
namely the final surface temperature on the MODIS pixel scale under the cloud condition;
(3) if the AMSR-E pixel is a part of cloud pixels, namely, a part of MODIS pixels corresponding to the AMSR-E pixel is a part of cloud pixels and the other part of MODIS pixels is a clear sky pixel, the surface temperature of the cloud part in the AMSR-E pixel and the surface temperature of the clear sky part need to be separated; the surface temperature of the AMSR-E pixel can be expressed as the area weighted sum of the surface temperature of the cloud part and the surface temperature of the clear sky part in the pixel:
Figure BDA0002742165430000025
in the formula (I), the compound is shown in the specification,
Figure BDA0002742165430000026
the surface temperature of the AMSR-E pixel is obtained;
Figure BDA0002742165430000027
the surface temperature of a cloud part in the AMSR-E pixel is obtained;
Figure BDA0002742165430000028
the surface temperature of the clear air part in the AMSR-E pixel is obtained; f. ofcThe ratio of the cloud part in the AMSR-E pixel can be calculated from the cloud mask product of the MODIS pixel corresponding to the AMSR-E pixel;
the conversion relation between the skin temperature and the surface temperature is obtained by performing linear fitting on the AMSR-E surface temperature and the MODIS surface temperature under the clear sky condition:
Figure BDA0002742165430000031
in the formula (I), the compound is shown in the specification,
Figure BDA0002742165430000032
is the surface layer temperature under the condition of clear sky,
Figure BDA0002742165430000033
the skin temperature under clear sky condition, m and n are conversion coefficients; table with conversion coefficient inverted by AMSR-E under clear sky conditionFitting the layer temperature and the MODIS inverted skin temperature to obtain; the conversion coefficients of day and night are fitted respectively, and the coefficients are respectively: daytime m is 0.89, n is 34.5; at night, m is 0.76, n is 70.5;
calculating the surface temperature of the cloud part in the AMSR-E pixel according to the formula (8):
Figure BDA0002742165430000034
in the formula (I), the compound is shown in the specification,
Figure BDA0002742165430000035
is the surface temperature of the inversion of the AMSR-E,
Figure BDA0002742165430000036
the surface temperature of a cloud part in the AMSR-E pixel,
Figure BDA0002742165430000037
the surface temperature of the clear sky part in the AMSR-E pixel is obtained by calculating the MODIS surface temperature under the clear sky condition after space aggregation by using a formula (7), fcThe method is obtained by calculating the proportion of clear sky pixels in an MODIS cloud mask product;
after the surface temperature of a cloud part in the AMSR-E pixel is obtained, obtaining the surface temperature of the MODIS pixel scale under the cloud condition by using the same steps as under the complete cloud condition; and (4) combining the surface temperature of the clear sky pixel and the cloud pixel on the MODIS pixel scale to obtain the final surface temperature on the MODIS pixel scale.
In the method, in the step (1), an on-satellite radiance product of an MODIS sensor carried on an Aqua satellite is downloaded; the data preprocessing comprises the following steps:
(1) converting the count value in the on-satellite radiance product into on-satellite radiance:
L＝gain×(DN-offset) (5)
in the formula, L is the brightness of the starry upper spoke; DN is a count value; gain and offset are gain and offset; for the MODIS on-satellite radiance product, the gain and offset for the 31 th channel are 8.40022e-4 and 1577.3397, respectively, and the gain and offset for the 32 th channel are 7.296976e-4 and 1658.2213, respectively;
(2) according to the Planck equation, the brightness of the on-board spoke is converted into the brightness temperature of the on-board spoke:
Figure BDA0002742165430000038
wherein, TbIs the brightness temperature on the satellite, lambda is the equivalent wavelength, C1And C2Is a radiation constant, and takes the value of C1＝1.191×108W/(μm-4sr m2)，C2＝1.439×104μm K；
(3) And (3) carrying out image splicing, resampling, Reprojection and the like on the MODIS product by using an MODIS Reprojection tool MRT (MODIS reproduction tool) to obtain the MODIS product of the research area.
In the method, in the step (2), on-satellite bright-temperature products of AMSR-E sensors carried on Aqua satellites are downloaded; the data preprocessing comprises the following steps: extracting vertically polarized on-satellite brightness temperature data with 18.7GHz, 36.5GHz and 89.0GHz3 frequencies, and converting the count value into on-satellite brightness temperature; and cutting the on-satellite brightness temperature data to obtain the on-satellite brightness temperature of the research area.
According to the method, the counting value is converted into the on-satellite brightness temperature, and for AMSR-E on-satellite brightness temperature products, the conversion coefficient of all frequencies is 0.1.
In the step (3), based on the onboard brightness temperature data of the two adjacent channels 31 and 32 of the MODIS, the MODIS surface temperature under the clear sky is inverted by using a split window algorithm:
Figure BDA0002742165430000041
in the formula (I), the compound is shown in the specification,
Figure BDA0002742165430000042
is the inverted MODIS surface temperature; e ═ e (e)31+ε32) /2 is MODISAverage of the emissivity of the 31 and 32 channels; Δ ε ═ ε31-ε32Is the difference of the radiation ratio of the MODIS 31 st and 32 nd channels; the emissivity of the 31 th and 32 th channels is available from the MODIS emissivity products; t isb31And Tb32On-satellite brightness temperatures for MODIS channels 31 and 32, respectively; a is0-a5As a fitting coefficient, it can be obtained by least square fitting based on simulation data.
The method of, a0-a5The values are respectively as follows: a is0＝-0.91，a1＝1.01，a2＝0.47，a3＝0.50，a4＝41.96，a5＝20.98。
In the step (4), based on the on-satellite brightness temperature data of the AMSR-E3 frequencies, the AMSR-E surface temperature under the cloud condition is inverted by using a multi-channel algorithm:
Figure BDA0002742165430000043
in the formula (I), the compound is shown in the specification,
Figure BDA0002742165430000044
for inverted AMSR-E surface temperature, Tb,19V，Tb,37V，Tb,89VBrightness temperature on the satellite of the vertically polarized channels 18.7GHz, 36.5GHz and 89.0GHz of AMSR-E, respectively, b0-b4As a fitting coefficient, it can be obtained by least square fitting based on simulation data.
Said method, b0-b4The values are respectively as follows: b0＝21.40，b1＝0.936，b2＝0.701，b3＝1.694，b4＝0.0125。
The invention fully utilizes the respective advantages of thermal infrared remote sensing and passive microwave remote sensing, establishes a fusion method of the thermal infrared earth surface temperature under the clear sky condition and the passive microwave earth surface temperature under the cloud condition, and realizes the all-weather remote sensing monitoring of the earth surface temperature with high spatial resolution. As can be seen from fig. 2, the proportion of valid data of the thermal infrared surface temperature in the research area is less than 30% due to the influence of cloud cover, and the spatial variation of the surface temperature in the research area cannot be sufficiently represented. The fused earth surface temperature fully makes up the disadvantage of the thermal infrared earth surface temperature, realizes the complete coverage of the research area, and well displays the integral earth surface temperature space variation trend of the research area.
Drawings
FIG. 1 is a fusion flow chart of surface temperature of thermal infrared and passive microwave remote sensing;
FIG. 2 is a graph comparing the fusion of thermal infrared surface temperature (A) and surface temperature (B);
Detailed Description
The present invention will be described in detail with reference to specific examples.
Step 1: downloading MODIS data and preprocessing the data
First, an on-satellite radiance product of a MODIS (mode Resolution Imaging spectrometer) sensor mounted on an Aqua satellite is downloaded. Data download website: https:// search. The data preprocessing comprises the following steps:
(1) converting the count value in the on-satellite radiance product into on-satellite radiance:
L＝gain×(DN-offset) (1)
in the formula, L is the brightness of the starry upper spoke; DN is a count value; gain and offset are gain and offset. For the MODIS on-satellite radiance product, the gains and offsets for channels 31 and 32 are 8.40022e-4 and 1577.3397 (channel 31), 7.296976e-4 and 1658.2213 (channel 32), respectively.
(2) According to the Planck equation, the brightness of the on-board spoke is converted into the brightness temperature of the on-board spoke:
Figure BDA0002742165430000051
wherein, TbIs the brightness temperature on the satellite, lambda is the equivalent wavelength, C1And C2Is a radiation constant, and takes the value of C1＝1.191×108W/(μm-4sr m2)，C2＝1.439×104μm K。
(3) And (3) carrying out image splicing, resampling, Reprojection and the like on the MODIS product by using an MODIS Reprojection tool MRT (MODIS reproduction tool) to obtain the MODIS product of the research area.
Step 2: downloading AMSR-E data and performing data preprocessing
And downloading an on-satellite bright-warm product of an AMSR-E (Advanced Microwave Scanning Radiometer-EOS) sensor carried on an Aqua satellite. Data download website: https:// nsidc. org/data/amsre/. The data preprocessing comprises the following steps: on-satellite brightness temperature data of 3 frequencies (18.7GHz, 36.5GHz, and 89.0GHz) vertically polarized were extracted, and the count value was converted into an on-satellite brightness temperature. For the AMSR-E star bright temperature product, the conversion coefficient for all frequencies is 0.1. And cutting the on-satellite brightness temperature data to obtain the on-satellite brightness temperature of the research area.
And step 3: inversion of surface temperature under clear sky by using MODIS thermal infrared data
On-satellite brightness temperature data based on two adjacent channels 31 and 32 of MODIS are used for inverting the MODIS surface temperature under the clear sky condition by using a split window algorithm:
Figure BDA0002742165430000061
in the formula (I), the compound is shown in the specification,
Figure BDA0002742165430000062
for inverted MODIS surface temperature, ε ═ ε31+ε32) The/2 is the average value of the specific radiance of the 31 st channel and the 32 nd channel of the MODIS; Δ ε ═ ε31-ε32Is the difference between the radiation ratios of the 31 st and 32 nd channels of MODIS. The emissivity of the 31 th and 32 th channels is available from the MODIS emissivity products. T isb31And Tb32On-satellite brightness temperatures for MODIS channels 31 and 32, respectively; a is0-a5The fitting coefficient can be obtained by least square fitting based on simulation data, and the values are respectively as follows: a is0＝-0.91，a1＝1.01，a2＝0.47，a3＝0.50，a4＝41.96，a5＝20.98。
And 4, step 4: inversion of surface temperature under cloud condition by utilizing AMSR-E passive microwave data
Based on the on-satellite brightness temperature data of AMSR-E3 frequencies, the AMSR-E surface temperature under the cloud condition is inverted by using a multi-channel algorithm:
Figure BDA0002742165430000063
in the formula (I), the compound is shown in the specification,
Figure BDA0002742165430000064
for inverted AMSR-E surface temperature, Tb,19V，Tb,37V，Tb,89VBrightness temperature on the satellite of the vertically polarized channels 18.7GHz, 36.5GHz and 89.0GHz of AMSR-E, respectively, b0-b4The fitting coefficient can be obtained by least square fitting based on simulation data, and the values are respectively as follows: b0＝21.40，b1＝0.936，b2＝0.701，b3＝1.694，b4＝0.0125。
And 5: fusion of MODIS surface temperature under clear sky condition and AMSR-E surface temperature under cloud condition
The surface temperature can be divided into a skin temperature and a surface temperature according to the difference of the detection depth. The wavelength of the thermal infrared remote sensing wave band is shorter, so the surface temperature inverted by the thermal infrared data is the skin temperature. The passive microwave remote sensing wave band has longer wavelength and can penetrate through soil with a certain depth, so the surface temperature inverted by the passive microwave data is the surface temperature. The spatial resolution of the MODIS thermal infrared data is 1km, while the spatial resolution of the AMSR-E passive microwave data is 25 km. In order to fuse the MODIS skin temperature under the clear sky condition with the AMSR-E skin temperature under the cloud condition, the AMSR-E inverted skin temperature needs to be converted into the skin temperature, and the AMSR-E data needs to be downscaled to the spatial resolution matched with the MODIS data. The method has the advantages that the MODIS thermal infrared sensor and the AMSR-E passive microwave sensor are simultaneously carried on an Aqua satellite and can synchronously observe the ground, and the MODIS skin temperature under the clear sky condition and the AMSR-E surface layer temperature under the cloud condition are fused, so that the all-weather ground surface temperature with high spatial resolution is obtained.
When the surface temperature is fused, the three conditions are divided into three conditions according to the cloud condition of the AMSR-E pixel, namely completely clear air, completely cloud and partially cloud.
(1) And if the AMSR-E pixel is a completely clear sky pixel, namely all MODIS pixels corresponding to the AMSR-E pixel are clear sky pixels, directly taking the surface temperature of the MODIS pixels as the fused surface temperature.
(2) And if the AMSR-E pixel is a completely cloud pixel, namely all MODIS pixels corresponding to the AMSR-E pixel are cloud pixels, performing spatial downscaling on the earth surface temperature estimated by the AMSR-E data to the MODIS pixel scale to serve as the final earth surface temperature.
Under the condition of clouds, the surface temperature of different depths is uniform, and the MODIS skin temperature and the AMSR-E surface temperature are approximately equal. Therefore, under the condition that the AMSR-E pixel is completely covered by the cloud layer, the conversion from the surface temperature of the AMSR-E layer to the surface temperature of the skin is not needed, and the spatial downscaling is only needed to be carried out on the surface temperature of the AMSR-E layer, so that the AMSR-E pixel is matched with the MODIS pixel. In the presence of clouds, the surface temperature of the AMSR-E is primarily affected by the surface elevation. Performing spatial downscaling of the AMSR-E surface temperature by using a ground digital Elevation model DEM (digital Elevation model):
Figure BDA0002742165430000071
in the formula (I), the compound is shown in the specification,
Figure BDA0002742165430000081
the skin temperature of the ith pixel after the AMSR-E pixel is downscaled to MODIS scale under the cloud condition;
Figure BDA0002742165430000082
is the skin temperature of the AMSR-E pixel; hiIs the surface elevation of the ith pixel on the MODIS scale; hmIs the average earth surface elevation within the AMSR-E pixel; TLR (Lapse rate of temperature) is the rate of temperature decrease, which means the increase of altitudeThe temperature decrease speed is generally 6.5K/km.
Figure BDA0002742165430000083
Namely the final surface temperature on the MODIS pixel scale under the cloud condition.
(3) If the AMSR-E pixel is a part of cloud pixels, namely, a part of MODIS pixels corresponding to the AMSR-E pixel is cloud pixels, and the other part of MODIS pixels is clear sky pixels, the surface temperature of the cloud part in the AMSR-E pixel and the surface temperature of the clear sky part need to be separated. The surface temperature of the AMSR-E pixel can be expressed as the area weighted sum of the surface temperature of the cloud part and the surface temperature of the clear sky part in the pixel:
Figure BDA0002742165430000084
in the formula (I), the compound is shown in the specification,
Figure BDA0002742165430000085
the surface temperature of the AMSR-E pixel is obtained;
Figure BDA0002742165430000086
the surface temperature of a cloud part in the AMSR-E pixel is obtained;
Figure BDA0002742165430000087
the surface temperature of the clear air part in the AMSR-E pixel is obtained; f. ofcThe ratio of the cloud part in the AMSR-E pixel can be obtained by calculating the value of the cloud part from the cloud mask product of the MODIS pixel corresponding to the AMSR-E pixel.
Under the clear sky condition, the surface temperature of different depths has certain gradient, and the difference between epidermis temperature and surface layer temperature can not neglect. The conversion relation between the skin temperature and the surface temperature is obtained by performing linear fitting on the AMSR-E surface temperature and the MODIS surface temperature under the clear sky condition:
Figure BDA0002742165430000088
in the formula (I), the compound is shown in the specification,
Figure BDA0002742165430000089
is the surface layer temperature under the condition of clear sky,
Figure BDA00027421654300000810
the skin temperature in clear sky, m and n are conversion coefficients. The conversion coefficient is obtained by fitting the surface temperature inverted by AMSR-E and the surface temperature inverted by MODIS under clear sky. The conversion coefficients of day and night are fitted respectively, and the coefficients are respectively: m is 0.89, n is 34.5 (day); m is 0.76 and n is 70.5 (night).
Calculating the surface temperature of the cloud part in the AMSR-E pixel according to the formula (8):
Figure BDA00027421654300000811
in the formula (I), the compound is shown in the specification,
Figure BDA00027421654300000812
is the surface temperature of the inversion of the AMSR-E,
Figure BDA00027421654300000813
the surface temperature of a cloud part in the AMSR-E pixel,
Figure BDA0002742165430000091
the surface temperature of the clear sky part in the AMSR-E pixel can be obtained by calculating the MODIS surface temperature under the clear sky condition after space aggregation by using a formula (7), fcThe method is obtained by calculating the proportion of clear sky pixels in the MODIS cloud mask product.
After the surface temperature of the cloud part in the AMSR-E pixel is obtained, the surface temperature of the MODIS pixel scale under the cloud condition can be obtained by the same steps as under the complete cloud condition. And combining the surface temperature of the clear sky pixel and the cloud pixel on the MODIS pixel scale to obtain the final surface temperature on the MODIS pixel scale.
And combining the three conditions of completely clear sky pixel, completely cloud pixel and partially cloud pixel to obtain the all-weather surface temperature on the MODIS pixel scale.
FIG. 2 is a graph showing the fusion of thermal infrared surface temperature and surface temperature. A is the thermal infrared earth surface temperature before fusion, and B is the fusion result of the thermal infrared and passive microwave remote sensing earth surface temperature. As can be seen from fig. 2, the proportion of valid data of the thermal infrared surface temperature in the research area is less than 30% due to the influence of cloud cover, and the spatial variation of the surface temperature in the research area cannot be sufficiently represented. The fused earth surface temperature fully makes up the disadvantage of the thermal infrared earth surface temperature, realizes the complete coverage of the research area, and well displays the integral earth surface temperature space variation trend of the research area.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.

Claims (8)

1. A method for inverting the earth surface temperature by fusing thermal infrared and passive microwave remote sensing data is characterized by comprising the following steps:
step 1: downloading MODIS data and performing data preprocessing;
step 2: downloading AMSR-E data and carrying out data preprocessing;
and step 3: inverting the surface temperature under the clear sky condition by using MODIS thermal infrared data;
and 4, step 4: inverting the surface temperature under the cloud condition by using AMSR-E passive microwave data;
and 5: fusing the MODIS surface temperature under the clear sky condition with the AMSR-E surface temperature under the cloud condition;
when the surface temperature is fused, the method needs to be divided into three conditions according to the cloud condition of the AMSR-E pixel, namely, the method is completely clear and completely cloudy and partially cloudy:
(1) if the AMSR-E pixel is a completely clear sky pixel, namely the MODIS pixels corresponding to the AMSR-E pixel are all clear sky pixels, directly taking the surface temperature of the MODIS pixels as the fused surface temperature;
(2) if the AMSR-E pixel is a completely cloud pixel, namely all MODIS pixels corresponding to the AMSR-E pixel are cloud pixels, the earth surface temperature estimated by the AMSR-E data needs to be subjected to spatial downscaling to the MODIS pixel scale to serve as the final earth surface temperature;
carrying out spatial downscaling on the surface temperature of the AMSR-E by using a ground digital elevation model DEM:
Figure FDA0002742165420000011
in the formula (I), the compound is shown in the specification,
Figure FDA0002742165420000012
the skin temperature of the ith pixel after the AMSR-E pixel is downscaled to MODIS scale under the cloud condition;
Figure FDA0002742165420000013
is the skin temperature of the AMSR-E pixel; hiIs the surface elevation of the ith pixel on the MODIS scale; hmIs the average earth surface elevation within the AMSR-E pixel; TLR is temperature direct reduction rate, which means the speed of temperature reduction along with the increase of altitude, and the value is 6.5K/km;
Figure FDA0002742165420000014
namely the final surface temperature on the MODIS pixel scale under the cloud condition;
(3) if the AMSR-E pixel is a part of cloud pixels, namely, a part of MODIS pixels corresponding to the AMSR-E pixel is a part of cloud pixels and the other part of MODIS pixels is a clear sky pixel, the surface temperature of the cloud part in the AMSR-E pixel and the surface temperature of the clear sky part need to be separated; the surface temperature of the AMSR-E pixel can be expressed as the area weighted sum of the surface temperature of the cloud part and the surface temperature of the clear sky part in the pixel:
Figure FDA0002742165420000021
in the formula (I), the compound is shown in the specification,
Figure FDA0002742165420000022
the surface temperature of the AMSR-E pixel is obtained;
Figure FDA0002742165420000023
the surface temperature of a cloud part in the AMSR-E pixel is obtained;
Figure FDA0002742165420000024
the surface temperature of the clear air part in the AMSR-E pixel is obtained; f. ofcThe ratio of the cloud part in the AMSR-E pixel can be calculated from the cloud mask product of the MODIS pixel corresponding to the AMSR-E pixel;
the conversion relation between the skin temperature and the surface temperature is obtained by performing linear fitting on the AMSR-E surface temperature and the MODIS surface temperature under the clear sky condition:
Figure FDA0002742165420000025
in the formula (I), the compound is shown in the specification,
Figure FDA0002742165420000026
is the surface layer temperature under the condition of clear sky,
Figure FDA0002742165420000027
the skin temperature under clear sky condition, m and n are conversion coefficients; the conversion coefficient is obtained by fitting the surface temperature inverted by AMSR-E and the surface temperature inverted by MODIS under clear sky; the conversion coefficients of day and night are fitted respectively, and the coefficients are respectively: daytime m is 0.89, n is 34.5; at night, m is 0.76, n is 70.5;
calculating the surface temperature of the cloud part in the AMSR-E pixel according to the formula (8):
Figure FDA0002742165420000028
in the formula (I), the compound is shown in the specification,
Figure FDA0002742165420000029
is the surface temperature of the inversion of the AMSR-E,
Figure FDA00027421654200000210
the surface temperature of a cloud part in the AMSR-E pixel,
Figure FDA00027421654200000211
the surface temperature of the clear sky part in the AMSR-E pixel is obtained by calculating the MODIS surface temperature under the clear sky condition after space aggregation by using a formula (7), fcThe method is obtained by calculating the proportion of clear sky pixels in an MODIS cloud mask product;
after the surface temperature of a cloud part in the AMSR-E pixel is obtained, obtaining the surface temperature of the MODIS pixel scale under the cloud condition by using the same steps as under the complete cloud condition; and (4) combining the surface temperature of the clear sky pixel and the cloud pixel on the MODIS pixel scale to obtain the final surface temperature on the MODIS pixel scale.
2. The method according to claim 1, wherein in step (1), the on-board radiance product of MODIS sensors onboard the Aqua satellite is downloaded; the data preprocessing comprises the following steps:
(1) converting the count value in the on-satellite radiance product into on-satellite radiance:
L＝gain×(DN-offset) (5)
in the formula, L is the brightness of the starry upper spoke; DN is a count value; gain and offset are gain and offset; for the MODIS on-satellite radiance product, the gain and offset for the 31 th channel are 8.40022e-4 and 1577.3397, respectively, and the gain and offset for the 32 th channel are 7.296976e-4 and 1658.2213, respectively;
(2) according to the Planck equation, the brightness of the on-board spoke is converted into the brightness temperature of the on-board spoke:
Figure FDA0002742165420000031
wherein, TbIs the brightness temperature on the satellite, lambda is the equivalent wavelength, C1And C2Is a radiation constant, and takes the value of C1＝1.191×108W/(μm-4sr m2)，C2＝1.439×104μm K；
(3) And (3) carrying out image splicing, resampling, Reprojection and the like on the MODIS product by using an MODIS Reprojection tool MRT (MODIS reproduction tool) to obtain the MODIS product of the research area.
3. The method of claim 1, wherein in step (2), on-board light and warm products of AMSR-E sensors onboard Aqua satellites are downloaded; the data preprocessing comprises the following steps: extracting vertically polarized on-satellite brightness temperature data with 18.7GHz, 36.5GHz and 89.0GHz3 frequencies, and converting the count value into on-satellite brightness temperature; and cutting the on-satellite brightness temperature data to obtain the on-satellite brightness temperature of the research area.
4. The method of claim 3, wherein the count value is converted to on-board light temperature, and wherein the conversion factor for all frequencies is 0.1 for an AMSR-E on-board light temperature product.
5. The method according to claim 1, wherein in the step (3), based on the on-satellite brightness temperature data of the two adjacent channels 31 and 32 of MODIS, the MODIS surface temperature under the clear sky is inverted by using a split window algorithm:
Figure FDA0002742165420000032
in the formula (I), the compound is shown in the specification,
Figure FDA0002742165420000033
is the inverted MODIS surface temperature; e ═ e (e)31+ε32) /2 MODIS 31 st and 32 nd channel ratio(iv) average value of the refractive index; Δ ε ═ ε31-ε32Is the difference of the radiation ratio of the MODIS 31 st and 32 nd channels; the emissivity of the 31 th and 32 th channels is available from the MODIS emissivity products; t isb31And Tb32On-satellite brightness temperatures for MODIS channels 31 and 32, respectively; a is0-a5As a fitting coefficient, it can be obtained by least square fitting based on simulation data.
6. The method of claim 5, wherein a is0-a5The values are respectively as follows: a is0＝-0.91，a1＝1.01，a2＝0.47，a3＝0.50，a4＝41.96，a5＝20.98。
7. The method according to claim 1, wherein in the step (4), based on the on-satellite brightness temperature data of the AMSR-E3 frequencies, the AMSR-E surface temperature under the cloud condition is inverted by using a multi-channel algorithm:
Figure FDA0002742165420000041
in the formula (I), the compound is shown in the specification,
Figure FDA0002742165420000042
for inverted AMSR-E surface temperature, Tb,19V，Tb,37V，Tb,89VBrightness temperature on the satellite of the vertically polarized channels 18.7GHz, 36.5GHz and 89.0GHz of AMSR-E, respectively, b0-b4As a fitting coefficient, it can be obtained by least square fitting based on simulation data.
8. The method of claim 7, wherein b is0-b4The values are respectively as follows: b0＝21.40，b1＝0.936，b2＝0.701，b3＝1.694，b4＝0.0125。
CN202011154269.7A 2020-10-26 2020-10-26 Method for inverting earth surface temperature by fusing thermal infrared and passive microwave remote sensing data Pending CN112464980A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011154269.7A CN112464980A (en) 2020-10-26 2020-10-26 Method for inverting earth surface temperature by fusing thermal infrared and passive microwave remote sensing data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011154269.7A CN112464980A (en) 2020-10-26 2020-10-26 Method for inverting earth surface temperature by fusing thermal infrared and passive microwave remote sensing data

Publications (1)

Publication Number Publication Date
CN112464980A true CN112464980A (en) 2021-03-09

Family

ID=74835213

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011154269.7A Pending CN112464980A (en) 2020-10-26 2020-10-26 Method for inverting earth surface temperature by fusing thermal infrared and passive microwave remote sensing data

Country Status (1)

Country Link
CN (1) CN112464980A (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113158570A (en) * 2021-04-26 2021-07-23 电子科技大学 All-weather surface temperature near-real-time inversion method fusing multi-source satellite remote sensing
CN113643409A (en) * 2021-08-24 2021-11-12 中国农业大学 Method and device for representing vegetation production rate and storage medium
CN114218756A (en) * 2021-11-24 2022-03-22 中国农业科学院农业资源与农业区划研究所 Subsurface surface temperature reconstruction method based on surface temperature annual change model

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101738620A (en) * 2008-11-19 2010-06-16 中国农业科学院农业资源与农业区划研究所 Method by utilizing passive microwave remote sensing data AMSR-E (Advanced Microwave Scanning Radiometer-EOS ) to invert surface temperature
CN105137506A (en) * 2015-10-09 2015-12-09 中国农业科学院农业资源与农业区划研究所 Method for estimating ground surface temperature daily range based on MSG2-SEVIRI data
CN109446739A (en) * 2018-12-20 2019-03-08 中国农业科学院农业资源与农业区划研究所 A kind of surface temperature Multi-channel hot infrared remote sensing inversion method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101738620A (en) * 2008-11-19 2010-06-16 中国农业科学院农业资源与农业区划研究所 Method by utilizing passive microwave remote sensing data AMSR-E (Advanced Microwave Scanning Radiometer-EOS ) to invert surface temperature
CN105137506A (en) * 2015-10-09 2015-12-09 中国农业科学院农业资源与农业区划研究所 Method for estimating ground surface temperature daily range based on MSG2-SEVIRI data
CN105137506B (en) * 2015-10-09 2017-05-10 中国农业科学院农业资源与农业区划研究所 Method for estimating ground surface temperature daily range based on MSG2-SEVIRI data
CN109446739A (en) * 2018-12-20 2019-03-08 中国农业科学院农业资源与农业区划研究所 A kind of surface temperature Multi-channel hot infrared remote sensing inversion method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
SI-BO DUAN ET AL.: "A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data", 《REMOTE SENSING OF ENVIRONMENT》 *
徐瑞松: "《资源环境遥感探测》", 31 January 2012, 中国科学技术出版社 *
段四波: "高空间分辨率全天候地表温度反演方法研究", 《中国博士学位论文全文数据库基础科学辑》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113158570A (en) * 2021-04-26 2021-07-23 电子科技大学 All-weather surface temperature near-real-time inversion method fusing multi-source satellite remote sensing
CN113643409A (en) * 2021-08-24 2021-11-12 中国农业大学 Method and device for representing vegetation production rate and storage medium
CN114218756A (en) * 2021-11-24 2022-03-22 中国农业科学院农业资源与农业区划研究所 Subsurface surface temperature reconstruction method based on surface temperature annual change model

Similar Documents

Publication Publication Date Title
CN112464980A (en) Method for inverting earth surface temperature by fusing thermal infrared and passive microwave remote sensing data
CN109581372B (en) Ecological environment remote sensing monitoring method
Hulley et al. Generating consistent land surface temperature and emissivity products between ASTER and MODIS data for earth science research
Srivastava et al. Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data
CN102183237B (en) Device and method for measuring two-waveband cloud height of foundation
Sobrino et al. Land surface emissivity retrieval from different VNIR and TIR sensors
CN109883957B (en) MODIS image-based apparent reflectivity model construction method, system and calibration method
Ninomiya Rock type mapping with indices defined for multispectral thermal infrared ASTER data: case studies
Jiang et al. Development of split-window algorithm for land surface temperature estimation from the VIRR/FY-3A measurements
Negi et al. Retrieval of snow grain size and albedo of western Himalayan snow cover using satellite data
CN113447137B (en) Surface temperature inversion method for unmanned aerial vehicle broadband thermal imager
Gao et al. Cross-calibration of GF-1 PMS sensor with Landsat 8 OLI and Terra MODIS
CN105092575A (en) Method and apparatus for evaluating sand duststorm intensity
Suzuki et al. Spatial distribution of thermal properties on debris-covered glaciers in the Himalayas derived from ASTER data
CN110009584B (en) Multispectral remote sensing image atmospheric correction system and method based on reference spectrum matching
Anul Haq et al. Surface temperature estimation of Gangotri Glacier using thermal remote sensing
Tedesco et al. Atmospheric correction of AMSR-E brightness temperatures for dry snow cover mapping
Markelin et al. Validation of the radiometric processing chain of the Leica ADS40 airborne photogrammetric sensor
Yeom et al. Radiometric characteristics of KOMPSAT-3 multispectral images using the spectra of well-known surface tarps
Solberg et al. Multi-sensor and time-series approaches for monitoring of snow parameters
Oyoshi Hourly LST monitoring with Japanese geostationary satellite MTSAT-1R over the Asia-Pacific region
Velázquez Blázquez et al. Unfiltering of the EarthCARE Broadband Radiometer (BBR) observations: the BM–RAD product
Arai Vicarious calibration of the solar reflection channels of radiometers onboard satellites through the field campaigns with measurements of refractive index and size distribution of aerosols
Hang et al. Temperature and emissivity separation from thermal airborne hyperspectral imager (TASI) data
Zhong et al. A retrieval method for land surface temperatures based on UAV broadband thermal infrared images via the three-dimensional look-up table

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20210309

RJ01 Rejection of invention patent application after publication