CN109063330B - Ground surface temperature downscaling method considering influence of soil moisture - Google Patents
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Abstract
The invention provides a ground surface temperature downscaling method considering the influence of soil moisture, and belongs to the technical field of ground surface temperature downscaling methods. The method includes constructing an input data set; respectively calculating vegetation coverage of the high-space and low-space resolution image pixels; calculating the surface temperature of four limit end members with low spatial resolution, and calculating the critical temperature between sufficient soil moisture in a root region/insufficient surface soil moisture and insufficient root region/insufficient surface soil moisture; calculating the temperature of soil and vegetation components of each pixel with low spatial resolution; and calculating the high spatial resolution earth surface temperature. The unreasonable property that the soil moisture in a research window is unchanged and the limitation that the window is required to be smaller in a common statistical regression earth surface temperature downscaling method are effectively solved, the influence of the soil moisture on the earth surface temperature is determined pixel by pixel based on the fact that high-low resolution remote sensing data have equal component temperatures, the spatial downscaling of the earth surface temperature is realized, and the precision of the spatial downscaling is improved.
Description
Technical Field
The invention belongs to the technical field of ground surface temperature downscaling methods, and particularly relates to a ground surface temperature downscaling method considering the influence of soil moisture.
Background
The earth surface temperature is a key parameter of regional and global scale earth surface physical processes, can provide space-time change information of earth surface energy balance state, and has wide application in research fields such as numerical prediction, global circulation mode, regional climate mode and the like. The thermal infrared remote sensing is the only means for acquiring the regional or global ground surface temperature with high aging efficiency, and the sensors commonly used for acquiring thermal infrared remote sensing images at present can be roughly divided into two types according to the difference of resolution, one type is high-space and low-time resolution, and the other type is low-space and high-time resolution. For this purpose, a spatial downscaling method of the surface temperature is proposed.
The existing ground surface temperature downscaling method mainly comprises a semi-empirical method, an approximate physical model method and a combined downscaling method. The semi-empirical method realizes the conversion from the low-spatial-resolution ground surface temperature to the high-spatial-resolution ground surface temperature by establishing a statistical relationship model of parameters such as the ground surface temperature and the vegetation index/vegetation coverage and assuming that the soil moisture of each pixel in a research area is consistent. The method is simple and easy to use, and the size reduction of the surface temperature can be realized only through the information of vegetation index/vegetation coverage and the like. However, in natural environment, the soil of the underlying surface and the plants have different abilities of intercepting solar radiation energy, the temperature change conditions of the soil and the vegetation in the mixed pixels and the soil moisture of each pixel are also different, and the soil moisture in the research area is difficult to be ensured to be consistent. In the existing semi-empirical model, the difference of soil moisture is not taken into account, so that the estimation result of the ground surface temperature downscaling model has uncertainty, and therefore, a ground surface temperature downscaling method taking the influence of the soil moisture into account is urgently needed.
In view of this, the invention is particularly proposed.
Disclosure of Invention
The invention aims to provide a method for measuring the land surface temperature drop by considering the influence of soil moisture; the surface temperature downscaling method provided by the invention takes the difference of the soil moisture of different pixels into consideration, and has higher accuracy.
According to one aspect of the present invention, there is provided a surface temperature downscaling method taking into account the effects of soil moisture, the method comprising the steps of:
(A) Determining input data required by the method, and constructing an input data set;
(B) Respectively calculating vegetation coverage of the high-space and low-space resolution image pixels;
(C) Calculating the surface temperature of four limit end members of low spatial resolution dry bare soil, dry vegetation, wet bare soil and wet vegetation, and calculating the critical temperature between sufficient soil moisture in a root zone/insufficient soil moisture in a surface layer and insufficient soil moisture in the root zone/no soil moisture in the surface layer;
(D) According to the earth surface temperature and the critical temperature of the four limit end members, calculating the soil and vegetation component temperature of each pixel element with low spatial resolution under the conditions of sufficient soil moisture/insufficient surface soil moisture of the root area and insufficient soil moisture/no soil moisture of the surface layer of the root area;
(E) And determining the temperature of the soil and vegetation of each pixel with high spatial resolution between the four extreme end members according to the temperature of the soil and vegetation component of each pixel with low spatial resolution, and calculating the surface temperature with high spatial resolution.
As a further preferable technical solution, in the step (a), the input data includes meteorological data and remote sensing data;
preferably, the meteorological data comprises atmospheric temperature, relative humidity, atmospheric pressure, wind speed, incident solar short-wave radiation and down long-wave radiation;
preferably, the remote sensing data includes reflectance, normalized vegetation index, leaf area index and surface temperature.
As a more preferable embodiment, in the step (B), the vegetation coverage is calculated by the following formula and is expressed as F v ：
Wherein, F v Is the vegetation coverage of each pixel, NDVI is the vegetation index of each pixel, NDVI min And NDVI max The minimum NDVI of bare soil and the maximum NDVI of fully planted cover, respectively.
As a further preferable embodiment, the step (C) includes:
(C1) Calculating the surface temperature of four limit end members of dry bare soil, dry vegetation, wet bare soil and wet vegetation with low spatial resolution according to the input data set;
(C2) And calculating the critical temperature between sufficient soil moisture/insufficient surface soil moisture in the root area and insufficient water moisture/no surface soil moisture in the root area by using the surface temperature of the four limit end members according to the input data set.
In a more preferred embodiment, in the step (C1), the surface temperature of the dry bare soil limit end member of the low-resolution remote sensing image is calculated as T using the following formula sd ：
And/or calculating the surface temperature of the low-resolution remote sensing image dry vegetation limit end member as T by using the following formula vd ：
And/or calculating the surface temperature of the limit end member of the wet bare soil of the low-resolution remote sensing image, which is recorded as T, by using the following formula sw ：
And/or calculating the surface temperature of the limit end member of the low-resolution remote sensing image humid vegetation, which is recorded as T, by using the following formula vw ：
Wherein, T sd 、T vd 、T sw And T vw Respectively are the temperature of the dry bare soil end member and the temperature of the dry vegetation end memberTemperature, temperature of the wet bare soil end members and temperature of the wet vegetation end members; rho is air density (kg/m) 3 )；C p Is a constant specific heat under pressure (J/(m.K)); γ is the wet and dry bulb constant (kPa/. Degree.C.); Δ is the slope of saturated vapor pressure difference versus temperature (kPa/. Degree. C.); VPD is water gas pressure deficiency (kPa); t is a unit of a The near-surface air temperature (K); r is vw And r vd Impedance (s/m) of vegetation canopy with sufficient water supply and dry vegetation canopy; r is av And r as Aerodynamic impedance (s/m) of vegetation and soil upper layers, respectively; r is n,s And R n,v Net radiation for soil components and vegetation components, respectively; g s Is the soil heat flux.
In a further preferred embodiment, in the step (C2), the critical temperature between sufficient soil moisture in the root zone/insufficient surface soil moisture and insufficient water moisture in the root zone/insufficient surface soil moisture is calculated by the following formula and is expressed as T * coarse ：
Wherein, F v,coarse Representing low spatial resolution vegetation coverage; t is sd And T vw Respectively the temperature of the dry bare soil end member and the temperature of the wet vegetation end member; t is * coarse Representing the critical surface temperature of each pixel at low spatial resolution.
As a further preferable technical scheme, in the step (D), when the root zone soil moisture is sufficient/the surface layer soil moisture is deficient, the surface temperature of the low spatial resolution remote sensing image mixed pixel is lower than or equal to the critical surface temperature, i.e. T R,coarse ≤T c * oarse Calculating the vegetation component temperature of each pixel with low spatial resolution as T v,coarse ：
T v,coarse ＝T vw
The temperature of the soil component of each pixel with low spatial resolution is calculated by the following formula and is recorded as T s,coarse ：
Wherein, T vw Is the temperature of the end member of the moist vegetation; t is a unit of R,coarse The surface temperature of the image mixed pixel is remotely sensed by low spatial resolution; f v,coarse Representing low spatial resolution vegetation coverage.
As a further preferable technical scheme, in the step (D), when the soil water in the root zone is deficient/the soil water is not present on the surface layer, the surface temperature of the low spatial resolution remote sensing image mixed pixel is higher than the critical surface temperature, that is, the surface temperature is higher than the critical surface temperature
The temperature of the soil component of each pixel with low spatial resolution is calculated by the following formula and is recorded as T s,coarse ：
T s,coarse ＝T sd
Calculating the vegetation component temperature of each pixel element with low spatial resolution as T v,coarse ：
Wherein, T sd Drying the bare soil end member temperature; t is R,coarse The surface temperature of the image mixed pixel is remotely sensed by low spatial resolution; f v,coarse Representing low spatial resolution vegetation coverage.
As a further preferable embodiment, the step (E) comprises: the earth surface temperature, the soil component temperature and the vegetation component temperature of the four extreme end elements are assumed to have scale invariance, namely the pixel component temperature of high spatial resolution is equal to the pixel component temperature of low resolution; calculating the surface temperature with high spatial resolution;
preferably, the high spatial resolution surface temperature, denoted T, is calculated using the following equation R,fine ：
Wherein, F v,fine Vegetation coverage for each pixel with high spatial resolution; t is v,coarse Vegetation component temperature for each pixel of low spatial resolution; t is s,coarse The temperature of the soil component of each pixel element with low spatial resolution.
According to another aspect of the invention, the invention provides an application of the land surface temperature downscaling method in the fields of high-resolution evapotranspiration, soil moisture inversion and drought monitoring.
The invention provides a ground surface temperature downscaling method considering the influence of soil moisture, which can effectively solve the irrationality of assuming that the soil moisture in a research window is unchanged and the limitation that a required window is smaller in a common ground surface temperature downscaling method.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to examples, but it will be understood by those skilled in the art that the following examples are only illustrative of the present invention and should not be construed as limiting the scope of the present invention. The examples, in which specific conditions are not specified, were carried out according to conventional conditions or conditions recommended by the manufacturer. The reagents or instruments used are not indicated by the manufacturer, and are conventional products available commercially.
According to one aspect of the present invention, there is provided a surface temperature downscaling method taking into account the effects of soil moisture, the method comprising the steps of:
(A) Determining input data required by the method, and constructing an input data set;
(B) Respectively calculating vegetation coverage of the high-space and low-space resolution image pixels;
(C) Calculating the surface temperature of four limit end members of low spatial resolution dry bare soil, dry vegetation, wet bare soil and wet vegetation, and calculating the critical temperature between sufficient soil moisture in a root zone/insufficient soil moisture in a surface layer and insufficient soil moisture in the root zone/no soil moisture in the surface layer;
(D) According to the earth surface temperature and the critical temperature of the four limit end members, calculating the soil and vegetation component temperature of each pixel element with low spatial resolution under the conditions of sufficient soil moisture/insufficient surface soil moisture of the root area and insufficient soil moisture/no soil moisture of the surface layer of the root area;
(E) And determining the temperature of the soil and vegetation of each pixel with high spatial resolution between four limit end members according to the temperature of the soil and vegetation components of each pixel with low spatial resolution, and calculating the surface temperature with high spatial resolution.
The land surface temperature downscaling method mainly develops a land surface temperature downscaling method considering soil moisture influence based on an end member information model, four limit end members are calculated based on the end member information model, the influence of the soil moisture on the land surface temperature is determined pixel by pixel, the temperatures of a low-resolution mixed pixel soil component and a vegetation component are respectively obtained, and the spatial downscaling of the land surface temperature is realized by determining the temperatures of soil and vegetation with high spatial resolution and each pixel positioned between the four limit end members and combining with known vegetation index (coverage) data with high spatial resolution.
The ground surface temperature downscaling method can effectively solve the unreasonable problem that the soil moisture in a research window is not changed and the limitation that the window is required to be smaller in the common ground surface temperature downscaling method, has equal component temperatures based on high-low resolution remote sensing data, determines the influence of the soil moisture on the ground surface temperature pixel by pixel, achieves the spatial downscaling of the ground surface temperature, and improves the precision of the spatial downscaling of the ground surface temperature.
As a further preferable technical solution, in the step (a), the input data includes meteorological data and remote sensing data.
In the step (A), the input data includes meteorological data and remote sensing data.
It should be noted that, in the step (a), the input data includes meteorological data and remote sensing data required in the calculation process, and the present invention does not specifically limit specific meteorological data and remote sensing data.
As a further preferable technical solution, the meteorological data includes atmospheric temperature, relative humidity, atmospheric pressure, wind speed, incident solar short-wave radiation and down long-wave radiation;
as a further preferred technical solution, the remote sensing data includes reflectivity, normalized vegetation index, leaf area index and surface temperature.
It should be noted that the preprocessing process for remote sensing data from different sensors and products includes: (1) Projection conversion, which is to convert the remote sensing data under two different projection systems into the same projection system; (2) Image space registration, namely performing registration processing on the remote sensing image based on space coordinates and feature points; (3) And (4) space cutting, wherein two groups of remote sensing data are cut based on the boundary data of the research area. And carrying out spatial aggregation on remote sensing inversion data such as the vegetation index with high spatial resolution, the earth surface reflectivity, the vegetation coverage and the like by utilizing arithmetic mean so as to obtain corresponding data with low spatial resolution.
As a more preferable embodiment, in the step (B), the vegetation coverage is calculated by the following formula and is represented as F v ：
Wherein, F v Is the vegetation coverage of each pixel, NDVI is the vegetation index of each pixel, NDVI min And NDVI max The minimum NDVI for bare soil and the maximum NDVI for full vegetation coverage were set at 0.2 and 0.86, respectively.
In a preferred embodiment of the invention, the vegetation coverage F of each picture element is obtained by the NDVI vegetation index of each picture element v 。
As a further preferable embodiment, the step (E) includes: the earth surface temperature, the soil component temperature and the vegetation component temperature of the four extreme end elements are assumed to have scale invariance, namely the pixel component temperature of high spatial resolution is equal to the pixel component temperature of low resolution; a high spatial resolution surface temperature is calculated.
As a further preferred embodiment, the high spatial resolution surface temperature, denoted as T, is calculated using the following equation R,fine ：
Wherein, F v,fine Vegetation coverage for each pixel with high spatial resolution; t is v,coarse Vegetation component temperature for each pixel of low spatial resolution; t is a unit of s,coarse The temperature of the soil component of each pixel with low spatial resolution.
The invention realizes the spatial downscaling of the earth surface temperature by assuming that the earth surface temperature, the soil temperature and the vegetation temperature of the four limit end members have scale invariance and combining the known vegetation index (coverage) data with high spatial resolution.
The technical solution of the present invention will be further described with reference to the following examples.
Example 1
A land surface temperature downscaling method considering soil moisture influence comprises the following steps:
(A) Determining input data required by the method, and constructing an input data set;
the (A) comprises: the construction of the input data set based on the end-member information model specifically comprises meteorological data (such as atmospheric temperature, relative humidity, atmospheric pressure, incident solar short-wave radiation and downlink long-wave radiation) and remote sensing data (such as LAI data, reflectivity, normalized vegetation index and earth surface temperature of MODIS).
The remote sensing data and products from different sensors are preprocessed, and the process comprises the following steps: (1) The projection conversion is used for converting the remote sensing data under two different projection systems into the same projection system; (2) Carrying out image space registration, and carrying out registration processing on the remote sensing image based on the space coordinate and the characteristic point; (3) And (4) space cutting, wherein the two groups of remote sensing data are cut based on the boundary data of the research area.
When a low spatial resolution characteristic space is constructed, the data such as the vegetation index, the earth surface reflectivity, the vegetation coverage and the like with high spatial resolution are subjected to spatial aggregation by using arithmetic mean, so that the corresponding data with low spatial resolution are obtained.
(B) Respectively calculating vegetation coverage of the high-space and low-space resolution image pixels;
the vegetation coverage was calculated as F using the formula v ：
Wherein, F v Is the vegetation coverage of each pixel, NDVI is the vegetation index of each pixel, NDVI min And NDVI max The minimum NDVI for bare soil and the maximum NDVI for full vegetation coverage were set at 0.2 and 0.86, respectively.
(C) Calculating the surface temperature of four limit end members of low spatial resolution dry bare soil, dry vegetation, wet bare soil and wet vegetation, and calculating the critical temperature between sufficient soil moisture in a root zone/insufficient soil moisture in a surface layer and insufficient soil moisture in the root zone/no soil moisture in the surface layer;
the step (C) comprises:
(C1) Respectively defining four limit end members of dry bare soil, dry vegetation, wet bare soil and wet vegetation, and calculating the surface temperature of the four limit end members of the dry bare soil, the dry vegetation, the wet bare soil and the wet vegetation with low spatial resolution according to an input data set;
the dry bare soil limit end member is defined as the surface layer relative to the soil moisture is 0;
the dry vegetation limit end member is defined as that the relative soil moisture of the surface layer and the root zone is 0;
the wet bare soil limit end member is defined as that the surface soil water content reaches saturation, and the relative soil water content is 1; the wet vegetation limit end member is defined as the saturation of the soil water content in the surface layer and the root area, and the relative soil water content is 1.
Calculating the surface temperature of the limit end member of the dry bare soil of the low-resolution remote sensing image by using the following formula and recording the surface temperature as T sd ：
Calculating the surface temperature of the low-resolution remote sensing image dry vegetation limit end member by using the following formula, and recording the surface temperature as T vd ：
The surface temperature of the limit end member of the wet bare soil of the low-resolution remote sensing image is calculated by the following formula and is recorded as T sw ：
The surface temperature of the low-resolution remote sensing image humid vegetation limit end member is calculated by the following formula and is recorded as T vw ：
Wherein, T sd 、T vd 、T sw And T vw Respectively the temperature of a dry bare soil end member, the temperature of a dry vegetation end member, the temperature of a wet bare soil end member and the temperature of a wet vegetation end member; ρ is the air density (kg/m) 3 )；C p Is the specific heat at constant pressure (J/(m.K)); γ is the dry-wet bulb constant (kPa/. Degree. C.); Δ is the slope of saturated vapor pressure difference versus temperature (kPa/. Degree. C.); VPD is water gas pressure deficiency (kPa); t is a unit of a The near-surface air temperature (K); r is vw And r vd Impedance (s/m) of vegetation canopy with sufficient water supply and dry vegetation canopy; r is av And r as Aerodynamic impedance (s/m) of vegetation and soil upper layers, respectively; r is n,s And R n,v Net radiation for soil components and vegetation components, respectively; g s Is the soil heat flux.
(C2) And calculating the critical temperature between sufficient soil moisture/insufficient surface soil moisture in the root area and insufficient water moisture/no surface soil moisture in the root area by using the surface temperature of the four limit end members according to the input data set.
Calculating the critical temperature between sufficient soil moisture in root zone/insufficient surface soil moisture and insufficient root zone/insufficient surface soil moisture by using the following formula, and recording as T * coarse ：
Wherein, F v,coarse Representing low spatial resolution vegetation coverage; t is sd And T vw Respectively the temperature of the dry bare soil end member and the temperature of the wet vegetation end member; t is * coarse Representing the critical surface temperature of each pixel at low spatial resolution.
(D) According to the earth surface temperature and the critical temperature of the four limit end members, calculating the soil and vegetation component temperature of each pixel element with low spatial resolution under the conditions of sufficient soil moisture/insufficient surface soil moisture of the root area and insufficient soil moisture/no soil moisture of the surface layer of the root area;
(1) when the soil moisture in the root zone is sufficient/the surface soil moisture is deficient, the surface temperature of the low-spatial-resolution remote sensing image mixed pixel is lower than or equal to the critical surface temperature, namely
The vegetation component temperature of each pixel of low spatial resolution is calculated using the formula T v,coarse ：
T v,coarse ＝T vw (7)
The temperature of the soil component of each pixel with low spatial resolution is calculated by the following formula and is recorded as T s,coarse ：
Wherein, T vw Is the temperature of the end member of the moist vegetation; t is a unit of R,coarse The surface temperature of the image mixed pixel is remotely sensed by low spatial resolution; f v,coarse Representing low spatial resolution vegetation coverage.
(2) When the soil moisture in the root area is deficient/the surface layer has no soil moisture, the surface temperature of the low-spatial-resolution remote sensing image mixed pixel is higher than the critical surface temperature, namely
The soil component temperature of each pixel element with low spatial resolution is calculated by the following formula and is recorded as T s,coarse ：
T s,coarse ＝T sd (9)
Calculating the vegetation component temperature of each pixel element with low spatial resolution as T v,coarse ：
Wherein, T sd Drying the bare soil end member temperature; t is a unit of R,coarse The surface temperature of the image mixed pixel is remotely sensed by low spatial resolution; f v,coarse Representing low spatial resolution vegetation coverage.
(E) And determining the temperature of the soil and vegetation of each pixel with high spatial resolution between the four extreme end members according to the temperature of the soil and vegetation component of each pixel with low spatial resolution, and calculating the surface temperature with high spatial resolution.
The step (E) includes:
and assuming that the earth surface temperature, the soil temperature and the vegetation temperature of the four limit end members have unchanged scales, the temperature of each pixel component with high spatial resolution is equal to that of each pixel component with low resolution.
The high spatial resolution surface temperature, denoted T, is calculated using the following equation R,fine ：
Wherein, F v,fine Vegetation coverage for each pixel with high spatial resolution; t is v,coarse Vegetation component temperature for each pixel of low spatial resolution; t is a unit of s,coarse The temperature of the soil component of each pixel with low spatial resolution.
The ground surface temperature downscaling method provided by the invention has the following characteristics: (1) The influence of soil moisture can be considered in the process of reducing the scale of the surface temperature, so that the precision of the spatial dimension reduction of the surface temperature is improved; (2) For high-resolution images, the spatial downscaling of the earth surface temperature can be realized only by providing vegetation index (coverage) data.
It should be understood that those not described in detail in the present specification are prior art to the present invention and are well known to those skilled in the art.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions recorded in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and these modifications or substitutions do not depart from the spirit of the corresponding technical solutions of the embodiments of the present invention.
Claims (5)
1. A method of land surface temperature downscaling to account for soil moisture effects, the method comprising the steps of:
(A) Determining input data required by the method, and constructing an input data set;
(B) Calculating vegetation coverage of the high-space and low-space resolution image pixels respectively;
in the step (B), the vegetation coverage is calculated by using the following formula and is marked as F v ：
Wherein, F v Is the vegetation coverage of each pixel, NDVI is the vegetation index of each pixel, NDVI min And NDVI max Respectively the minimum NDVI of the bare soil and the maximum NDVI of the fully planted and covered soil;
(C) Calculating the surface temperature of four limit end members of low spatial resolution dry bare soil, dry vegetation, wet bare soil and wet vegetation, and calculating the critical temperature between sufficient soil moisture in a root zone/insufficient surface soil moisture and insufficient root zone/insufficient surface soil moisture;
the step (C) comprises:
(C1) Calculating the surface temperature of four limit end members of dry bare soil, dry vegetation, wet bare soil and wet vegetation with low spatial resolution according to the input data set;
(C2) According to the input data set, calculating the critical temperature between sufficient soil moisture in the root zone/insufficient surface soil moisture and insufficient root zone/insufficient surface soil moisture by using the surface temperature of the four limit end members;
in the step (C1), the surface temperature of the limit end member of the dry bare soil of the low-resolution remote sensing image is calculated by using the following formula and is recorded as T sd ：
And/or calculating the earth surface temperature of the low-resolution remote sensing image dry vegetation limit end member by using the following formula, and recording the earth surface temperature as T vd ：
And/or calculating the surface temperature of the limit end element of the wet bare soil of the low-resolution remote sensing image by using the following formula and recording the surface temperature as T sw ：
And/or calculating the earth surface temperature of the limit end member of the low-resolution remote sensing image humid vegetation by using the following formulaDegree, is denoted as T vw ：
Wherein, T sd 、T vd 、T sw And T vw Respectively the temperature of a dry bare soil end member, the temperature of a dry vegetation end member, the temperature of a wet bare soil end member and the temperature of a wet vegetation end member; ρ is the air density (kg/m) 3 )；C p Is a constant specific heat under pressure (J/(m.K)); γ is the wet and dry bulb constant (kPa/. Degree.C.); Δ is the slope of saturated water vapor pressure difference versus temperature (kPa/. Degree. C.); VPD is water gas pressure deficiency (kPa); t is a unit of a The near-surface air temperature (K); r is a radical of hydrogen vw And r vd Impedance (s/m) of vegetation canopy with sufficient water supply and dryness; r is av And r as Aerodynamic impedance (s/m) of vegetation and soil upper layers, respectively; r n,s And R n,v Net radiation for soil components and vegetation components, respectively; g s Is the soil heat flux;
in the step (C2), the critical temperature between sufficient soil moisture in the root zone/insufficient surface soil moisture and insufficient root zone/insufficient surface soil moisture is calculated by using the following formula and is recorded as T * coarse ：
Wherein, F v,coarse Representing low spatial resolution vegetation coverage; t is a unit of sd And T vw Respectively the temperature of the dry bare soil end member and the temperature of the wet vegetation end member; t is * coarse Representing the critical surface temperature of each pixel with low spatial resolution;
(D) According to the earth surface temperature and the critical temperature of the four limit end members, calculating the soil and vegetation component temperature of each pixel element with low spatial resolution under the conditions of sufficient soil moisture/insufficient surface soil moisture of the root area and insufficient soil moisture/no soil moisture of the surface layer of the root area;
in the step (D), the root zone soilWhen the soil moisture is sufficient/the surface soil moisture is deficient, the surface temperature of the low-spatial-resolution remote sensing image mixed pixel is lower than or equal to the critical surface temperature, namely
Calculating the vegetation component temperature of each pixel element with low spatial resolution as T v,coarse ：
T v,coarse ＝T vw
The soil component temperature of each pixel element with low spatial resolution is calculated by the following formula and is recorded as T s,coarse ：
Wherein, T vw Is the temperature of the end member of the moist vegetation; t is R,coarse The surface temperature of the low spatial resolution remote sensing image mixed pixel is obtained; f v,coarse Representing low spatial resolution vegetation coverage;
and/or in the step (D), when the soil moisture in the root area is deficient/the surface layer has no soil moisture, the surface temperature of the mixed pixel of the low-spatial-resolution remote sensing image is higher than the critical surface temperature, namely
The soil component temperature of each pixel element with low spatial resolution is calculated by the following formula and is recorded as T s,coarse ：
T s,coarse ＝T sd
Calculating the vegetation component temperature of each pixel element with low spatial resolution as T v,coarse ：
Wherein, T sd Drying the bare soil end member temperature; t is R,coarse Surface of low spatial resolution remote sensing image mixed pixel(ii) temperature; f v,coarse Representing low spatial resolution vegetation coverage;
(E) Determining the temperature of the soil and vegetation of each pixel with high spatial resolution between four limit end members according to the temperature of the soil and vegetation component of each pixel with low spatial resolution, and calculating the surface temperature of the high spatial resolution;
the step (E) comprises: the earth surface temperature, the soil component temperature and the vegetation component temperature of the four limit end members are assumed to have scale invariance, namely the pixel component temperature of high spatial resolution is equal to the pixel component temperature of low resolution; calculating the high spatial resolution earth surface temperature;
the high spatial resolution surface temperature, denoted T, is calculated using the following equation R,fine ：
Wherein, F v,fine Vegetation coverage for each pixel with high spatial resolution; t is v,coarse Vegetation component temperature for each pixel of low spatial resolution; t is s,coarse The temperature of the soil component of each pixel element with low spatial resolution.
2. The method of claim 1, wherein in step (a), the input data comprises meteorological data and remote sensing data.
3. The surface temperature downscaling method of claim 2, wherein the meteorological data includes atmospheric temperature, relative humidity, atmospheric pressure, wind speed, incident solar short wave radiation, and down long wave radiation.
4. The method of claim 2, wherein the remote sensing data comprises reflectance, normalized vegetation index, leaf area index, and surface temperature.
5. Use of the surface temperature downscaling method of any one of claims 1-4 in the fields of high resolution evapotranspiration, soil moisture inversion, and drought monitoring.
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