CN110319938B - High-spatial-resolution earth surface temperature generation method - Google Patents
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Abstract
The invention relates to a high-spatial-resolution earth surface temperature generation method, and belongs to the technical field of digital image processing. Different from the existing high-spatial-resolution earth surface temperature generation method, the method considers the difference between different sensors, combines the earth surface coverage information with high spatial resolution and the advantage of the earth surface temperature regression model with low spatial resolution, and directly establishes the earth surface temperature regression model with high spatial resolution, thereby solving the problems that the thermal infrared remote sensor has generally low spatial resolution and cannot provide refined earth surface temperature.
Description
Technical Field
The invention relates to a high-spatial-resolution earth surface temperature generation method, and belongs to the technical field of digital image processing.
Background
The surface temperature is an important parameter for environmental monitoring, such as energy balance and radiation transmission model modeling, urban heat island effect monitoring, land surface carbon recovery, and the like. Besides, the system can also be used for fire point monitoring, water body thermal pollution monitoring, capacity monitoring of steel plants, climate change monitoring and the like.
However, sensors with thermal infrared remote sensing do not have high spatial resolution, and it is therefore difficult to obtain fine high spatial resolution surface temperatures. In practical applications, many environmental monitoring applications require small-scale surface temperature inversion, such as small-area straw burning monitoring, water body thermal pollution monitoring, factory productivity monitoring, and the like. Therefore, the research on the ground surface temperature generation method technology with high spatial resolution has important significance for improving the monitoring capability of the thermal infrared remote sensor.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the problems that the current thermal infrared remote sensor has lower spatial resolution, and the remote sensor with high spatial resolution does not have a thermal infrared spectrum section, the invention provides a high-spatial resolution earth surface temperature generation method, which can fuse the advantages of different remote sensors to directly obtain an earth surface temperature value with high spatial resolution, so that the refinement problem of the earth surface temperature can be solved by the method under the condition of saving the performance improvement cost of the thermal infrared remote sensor; and then, predicting the high-resolution earth surface temperature by using the regression model and the high-resolution earth surface coverage information, and adding a residual error of the low-resolution earth surface temperature regression model in the prediction process so as to finally obtain the high-resolution earth surface temperature.
The technical scheme of the invention is as follows:
a high spatial resolution earth surface temperature generation method comprises the following steps:
(1) acquiring a high-resolution remote sensing image and a low-resolution remote sensing image of a target ground, wherein the target ground is divided into N pixel-level regions;
the high-resolution remote sensing image is a remote sensing image of a multispectral spectral band;
the low-resolution remote sensing image comprises a thermal infrared spectrum remote sensing image and a multispectral spectrum remote sensing image;
(2) performing spectral registration on the high-resolution remote sensing image and the low-resolution remote sensing image acquired in the step (1) to obtain a registered high-resolution remote sensing image and a registered low-resolution remote sensing image, wherein the registered low-resolution remote sensing image comprises a registered thermal infrared spectral remote sensing image and a registered multispectral spectral remote sensing image;
(3) calculating high-resolution earth surface coverage information according to the registered high-resolution remote sensing image obtained in the step (2), and calculating low-resolution earth surface coverage information according to the registered low-resolution remote sensing image obtained in the step (2);
(4) calculating the low-resolution earth surface temperature with refined data level according to the registered high-resolution remote sensing image obtained in the step (2) and the registered thermal infrared spectrum remote sensing image obtained in the step (2);
(5) establishing a low-grade earth surface temperature regression model according to the low-grade earth surface coverage information obtained in the step (3) and the low-grade earth surface temperature obtained in the step (4);
(6) establishing a high-resolution earth surface temperature regression model according to the low-resolution earth surface temperature regression model established in the step (5) and the high-resolution earth surface coverage information obtained in the step (3);
(7) obtaining a surface temperature value of the target land according to the high-resolution surface temperature regression model obtained in the step (6), wherein the obtained surface temperature value of the target land comprises a plurality of pixel-level temperature values, and the temperature value of each pixel-level area on the target land is obtained;
(8) comparing the temperature value of each pixel level area obtained in the step (7) with a set threshold, indicating that the temperature of the pixel level area on the target ground is abnormal if the temperature value of the pixel level area is higher than the set threshold according to the comparison result, guiding a temperature high temperature abnormal measure of the pixel level area on the target ground according to the abnormal result, and indicating that the temperature of the pixel level area on the target ground is normal if the temperature value of the pixel level area is not higher than the set threshold.
In the step (1), the multispectral spectral band comprises a red band, a green band, a near-infrared band and a short-wave infrared band;
in the step (2), the spectral registration comprises registration between a low-resolution remote sensing image and a high-resolution remote sensing image, and also comprises registration between a thermal infrared spectral remote sensing image and a multispectral spectral remote sensing image in the low-resolution remote sensing image;
in the step (3), the land cover information refers to a ground feature type spectral index, and comprises a normalized vegetation index NDVI, a normalized water body index NDWI and a normalized building index NDBI;
in the step (4), the method for calculating the low-resolution earth surface temperature of the data-level refinement comprises the following steps:
firstly, obtaining a ground surface ratio radiation value according to the registered high-resolution remote sensing image;
secondly, calculating a brightness temperature value T of each pixel level area on the target ground according to the registered low-resolution remote sensing image;
thirdly, obtaining a low-resolution earth surface temperature value with data level refinement by adopting a single-channel earth surface temperature inversion method according to the earth surface ratio radiation value obtained in the first step and the brightness temperature value T of each pixel level area obtained in the second step;
in the step (5), the low-resolution surface temperature regression model is
LSTl＝f(NDVIl,NDWIl,NDBIl)
Wherein, LSTlFor low apparent surface temperatures, NDVIlNormalizing the value of the vegetation index for low-resolution surface temperature, NDWIlNormalizing the value of the water body index, NDBI, for low-score surface temperaturelNormalizing the value of the building index for low-score surface temperature; f is a function between the surface coverage information and the surface temperature;
in the step (6), the high-resolution surface temperature regression model is as follows:
LSTh′＝f(NDVIh,NDWIh,NDBIh)+Δ
Δ＝LSTh-LSTl
wherein, LSTh' is a predicted value of high-resolution surface temperature, NDVIhNormalizing the value of the vegetation index for high-resolution surface temperature, NDWIhNormalizing the value of the water body index, NDBI, for high-resolution surface temperaturehNormalizing the value of the building index for the high-resolution surface temperature; f is a function between the surface coverage information and the surface temperature; delta is the regression error, namely the error between the regression models of the high-molecular earth surface temperature and the low-molecular earth surface temperature, wherein LSThTo high surface temperature, LSTlThe earth surface temperature is calculated according to a low-resolution earth surface temperature regression model;
and the calculation of the earth surface coverage information:
in the establishment of the surface temperature regression model, the surface coverage information needs to be calculated, when the multispectral spectral band information with low resolution is input, the surface coverage information with low resolution is obtained, and when the multispectral spectral band information with high resolution is input, the surface coverage information with high resolution is obtained. Considering that the main types of terrain are vegetation, water and built-up areas, one way that can be achieved is as follows: calculating the spectral indexes such as NDVI, NDWI and NDBI, and the like as follows:
NDVI is the normalized vegetation index, defined as:
NDVI＝(NIR-R)/(NIR+R)
wherein, NIR is the reflection value of a near infrared band, and R is the reflection value of a red band;
NDWI is the normalized water body index, can effectively highlight water body information, weakens the luminance value of other ground objects in the characteristic image simultaneously, and NDWI defines as:
NDWI＝(G-NIR)/(G+NIR)
wherein G is the reflection value of a green wave band, and NIR is the reflection value of a near infrared wave band;
NDBI is a normalized building index, and can be used for automatic extraction of a built-up area, and the NDBI index is defined as:
NDBI＝(SWIR-NIR)/(SWIR+NIR)
wherein SWIR is the reflection value of a short wave infrared band, and NIR is the reflection value of a near infrared band;
the method comprises the following specific steps of data-level refined low-resolution earth surface temperature calculation:
1) inputting a high-resolution multispectral remote sensing image and a low-resolution thermal infrared spectrum remote sensing image, and performing spectrum registration;
2) calculating a refined earth surface emissivity value according to the registered high-resolution multispectral remote sensing image, wherein the earth surface emissivity value is calculated by a plurality of methods, one of the methods is given below, and an NDVI threshold method is given as follows:
whereinsi、viThe specific radiance of the pure bare soil and the pure vegetation in the ith wave band respectively, and the NDVIs and the NDVIv are the NDVI values of the pure bare soil and the pure vegetation respectively, and are respectively 0.2 and 0.5. CiIncluding the emissivity increment in the pixel due to the cavity effect caused by multiple scattering, the formula is calculated as (6):
Ci＝(1-si)(1-fv)·F·vi
wherein F is a shape factor, the value range is 0-1, and is generally 0.55;
3) and (3) calculating the brightness temperature according to the thermal infrared spectrum after registration, wherein the calculation formula is as follows:
Tsensor＝K2/ln(1+K1/Lλ)
wherein Tsensor is bright temperature LλIn order to convert the radiation intensity value of the gray value, K1 and K2 are coefficients and can be obtained by calculation according to the wave band setting of the satellite;
4) calculating the earth surface temperature by adopting a single-channel earth surface temperature inversion method according to the earth surface emissivity and the brightness temperature value;
the calculation of the earth surface temperature Ts requires determining the emissivity, the atmospheric transmittance tau and the atmospheric average acting temperature Ta, different remote sensors, different imaging time and imaging place, and different parameter values, wherein the specific values can be inquired (http:// atmcorr. gsfc. NASA. gov) through a website published by NASA, and the imaging time and the image center longitude and latitude are input to obtain corresponding parameters
Ts＝[a(1-C-D)+(b(1-C-D))+C+D]Tsensor-DTa]/C
C＝τ
D＝(1-τ)[1+(1-)τ]
Establishing a low-score surface temperature regression model:
the low-resolution surface temperature regression model is a function relationship between the surface coverage information and the surface temperature, and is represented by the following formula:
LSTl＝f(NDVIl,NDWIl,NDBIl)
wherein, LSTlFor low apparent surface temperatures, NDVIl，NDWIlAnd NDBIlThe values of the low-score earth normalized vegetation index, the normalized water body index and the normalized building index, respectively, f is a function between earth surface coverage information and earth surface temperature.
Prediction of a high-resolution surface temperature regression model:
by utilizing the low-grade earth surface temperature regression model and the high-grade earth surface coverage information, the high-grade earth surface temperature can be predicted, and the residual error of the low-grade regression model is added in the prediction process, as shown in the following formula:
LSTh′＝f(NDVIh,NDWIh,NDBIh)+Δ
Δ＝LSTh-LSTl
wherein, LSTh' is a predicted value of high-resolution surface temperature, NDVIh，NDWIhAnd NDBIhThe values of the high-resolution earth normalized vegetation index, the normalized water body index and the normalized building index, respectively, and f is a function between earth surface coverage information and earth surface temperature. Delta is the regression error, i.e. the error between the high-score earth surface temperature and the low-score regression model, wherein LSThTo high surface temperature, LSTlThe earth surface temperature is calculated according to a low-score regression model;
calculation of high-resolution surface temperature
After obtaining the high-resolution earth surface temperature regression model, calculating to obtain high-resolution earth surface coverage information NDVIh，NDWIhAnd NDBIhAnd then, the relational expression can be applied, and the value of the high-molecular surface temperature is calculated.
The invention has the advantages that:
(1) the method can be used for combining the advantages of different remote sensors to directly obtain the earth surface temperature value with high spatial resolution, and can solve the problem of surface temperature refinement under the condition of saving the performance improvement cost of the thermal infrared remote sensor.
(2) The method has the advantages that the regression model of the earth surface temperature is established through the low-resolution earth surface temperature refined in the data level, the residual error of the low-spatial-resolution regression model is added into the regression model, the prediction errors caused by different remote sensors and different resolutions are fully considered, and therefore the generated earth surface temperature result with the high spatial resolution is high in precision.
(3) The method is suitable for various thermal infrared remote sensing satellite earth surface temperature inversion systems, and the algorithm architecture is basically unchanged.
(4) A high-spatial-resolution earth surface temperature generation method is provided based on different sensor characteristics. The method utilizes a regression model and high-spatial-resolution earth surface coverage information to predict and obtain the earth surface temperature with high spatial resolution, and in the prediction process, in order to further improve the prediction precision, the residual error of the low-spatial-resolution regression model is added into the regression model. Different from the existing high-spatial-resolution earth surface temperature generation method, the method considers the difference between different sensors, combines the earth surface coverage information with high spatial resolution and the advantage of the earth surface temperature regression model with low spatial resolution, and directly establishes the earth surface temperature regression model with high spatial resolution, thereby solving the problems that the thermal infrared remote sensor has generally low spatial resolution and cannot provide refined earth surface temperature.
Drawings
FIG. 1 is a multi-spectral range LANDSAT8(30 meters) of a low-resolution input image according to the present invention;
FIG. 2 is a multi-spectral band WORLDVIEW3(1.2 meters) of a high-resolution input image according to the present invention;
FIG. 3 is a plot of the surface coverage information (NDVI) of a low-resolution imagel-NDWIl-NDBIl)；
FIG. 4 is a table of surface coverage information (NDVI) of a high resolution imageh-NDWIh-NDBIh)；
FIG. 5 is a high spatial resolution surface temperature results plot of the present invention.
Detailed Description
Taking the LANDSAT8 satellite as one of the international major thermal infrared remote sensors as an example, the refinement process of the inversion of the earth surface temperature of the satellite is carried out. The LANDSAT8 satellite thermal infrared spectral band resolution is 100 meters (resampling is 30 meters), and for temperature anomaly of small-scale targets, fine detection cannot be performed due to the low thermal infrared spectral band resolution. However, the spatial resolution of the international main high-resolution remote sensor, such as WORLDVIEW3, has reached 1.2 meters, but WORLDVIEW3 does not have a thermal infrared spectrum, and then the high-resolution surface temperature generation method of the present invention is adopted to calculate the surface temperature value with the resolution of 1.2 meters.
Firstly, finding the same region, registering the LANDSAT8 multispectral image, the thermal infrared image and the WORLDVIEW3 multispectral image at the similar time as much as possible, and registering the images before algorithm processing, wherein generally, the registration of the spectral segments is already carried out between different spectral segments of the same remote sensor, and the registration of the images between different remote sensors is mainly carried out, and can be completed by adopting software registration or some known registration methods; next, the surface coverage information, i.e., NDVI, of the LANDSAT8 images is calculated separatelyl，NDWIlAnd NDBIlIndex value, NDVI of WORLDVIEW3 imageh，NDWIhAnd NDBIhAn index value; then, according to a single-channel earth surface temperature inversion method, calculating the data-level refined earth surface temperature of the LANDSAT8 image, and accordingly establishing a low-resolution earth surface temperature regression model; and finally, predicting a high-resolution earth surface temperature regression model according to the low-resolution earth surface temperature regression model and the earth surface coverage information of the WORLDVIEW3 image, and applying the model formula to calculate the earth surface temperature with the spatial resolution of 1.2 meters.
The exemplary embodiments of the present invention are described with reference to the LANDSAT8 and WORLDVIEW3 images, but the scope of the present invention is not limited to the LANDSAT8 and WORLDVIEW3 images.
Examples
1-5, a high spatial resolution surface temperature generation method, comprising the steps of:
(1) acquiring a high-resolution remote sensing image and a low-resolution remote sensing image of a target place, and assuming that the high-resolution remote sensing image is N pixels, the corresponding low-resolution remote sensing image is N pixels, and N is greater than N;
the high-resolution remote sensing image is a remote sensing image of a multispectral spectral band;
the low-resolution remote sensing image comprises a thermal infrared spectrum remote sensing image and a multispectral spectrum remote sensing image;
(2) performing spectral registration on the high-resolution remote sensing image and the low-resolution remote sensing image acquired in the step (1) to obtain a registered high-resolution remote sensing image and a registered low-resolution remote sensing image, wherein the registered low-resolution remote sensing image comprises a registered thermal infrared spectral remote sensing image and a registered multispectral spectral remote sensing image;
(3) calculating high-resolution earth surface coverage information according to the registered high-resolution remote sensing image obtained in the step (2), and calculating low-resolution earth surface coverage information according to the registered low-resolution remote sensing image obtained in the step (2);
(4) performing data-level thinning processing on the low-resolution earth surface coverage information obtained in the step (3), namely performing linear interpolation to obtain N x N pixels;
(4) calculating the low-resolution earth surface temperature with refined data level according to the registered high-resolution remote sensing image obtained in the step (2) and the registered thermal infrared spectrum remote sensing image obtained in the step (2), wherein the resolution is N x N;
(5) establishing a low-grade earth surface temperature regression model according to the refined low-grade earth surface coverage information obtained in the step (3) and the refined low-grade earth surface temperature obtained in the step (4);
(6) establishing a high-resolution earth surface temperature regression model according to the low-resolution earth surface temperature regression model established in the step (5) and the high-resolution earth surface coverage information obtained in the step (3);
(7) obtaining a surface temperature value of the target ground according to the high-resolution surface temperature regression model obtained in the step (6), wherein the resolution is N x N pixels, and the temperature value of each pixel level area on the target ground is obtained;
(8) comparing the temperature value of each pixel level area obtained in the step (7) with a set threshold, indicating that the temperature of the pixel level area on the target ground is abnormal if the temperature value of the pixel level area is higher than the set threshold according to the comparison result, guiding a temperature high temperature abnormal measure of the pixel level area on the target ground according to the abnormal result, and indicating that the temperature of the pixel level area on the target ground is normal if the temperature value of the pixel level area is not higher than the set threshold.
In the step (1), the multispectral spectral band comprises a red band, a green band, a near-infrared band and a short-wave infrared band;
in the step (2), the spectral registration comprises registration between a low-resolution remote sensing image and a high-resolution remote sensing image, and also comprises registration between a thermal infrared spectral remote sensing image and a multispectral spectral remote sensing image in the low-resolution remote sensing image;
in the step (3), the land cover information refers to a ground feature type spectral index, and comprises a normalized vegetation index NDVI, a normalized water body index NDWI and a normalized building index NDBI;
in the step (4), the method for calculating the low-resolution earth surface temperature of the data-level refinement comprises the following steps:
firstly, obtaining a ground surface ratio radiation value according to the registered high-resolution remote sensing image;
secondly, calculating a brightness temperature value T of each pixel level area on the target ground according to the registered low-resolution remote sensing image;
and thirdly, carrying out data level refinement on the brightness temperature value T, namely linearly interpolating to the resolution of the high-resolution multispectral remote sensing image.
Fourthly, obtaining a low-resolution earth surface temperature value with data level refinement by adopting a single-channel earth surface temperature inversion method according to the earth surface ratio radiation value obtained in the first step and the brightness temperature value T of each pixel level area obtained in the second step;
in the step (5), the low-resolution surface temperature regression model is
LSTl′＝f(NDVIl,NDWIl,NDBIl)
Wherein, LSTl' refined is low apparent temperature, NDVIlFor refined low-resolution surface temperature normalized vegetation index value, NDWIlFor refined low-resolution surface temperature normalized water body index values, NDBIlNormalizing the value of the building index for the refined low-resolution surface temperature; f is a function between the surface coverage information and the surface temperature, the f function being:
LSTl′＝b(1)×NDVIl+b(2)×NDWIl+b(3)×NDBIl+b(4)
b (1), b (2), b (3) and b (4) are coefficients.
In the step (6), the high-resolution surface temperature regression model is as follows:
LSTh＝f(NDVIh,NDWIh,NDBIh)+Δ
Δ＝LSTl′-LSTl
LSTl＝f(NDVIl,NDWIl,NDBIl)
wherein, LSThFor the prediction of high-resolution surface temperature, NDVIhNormalizing the value of the vegetation index for high-resolution surface temperature, NDWIhNormalizing the value of the water body index, NDBI, for high-resolution surface temperaturehNormalizing the value of the building index for the high-resolution surface temperature; f is a function between the surface coverage information and the surface temperature; delta is the regression error, namely the error between the regression models of the high-molecular earth surface temperature and the low-molecular earth surface temperature, wherein LSTl' for refined Low-divided surface temperature, LSTlThe earth surface temperature is calculated according to a low-molecular earth surface temperature regression model.
The invention is not described in detail and is within the knowledge of a person skilled in the art.
Claims (6)
1. A high spatial resolution earth surface temperature generation method is characterized by comprising the following steps:
(1) acquiring a high-resolution remote sensing image and a low-resolution remote sensing image of a target ground, wherein the target ground is divided into N pixel-level regions;
the high-resolution remote sensing image is a remote sensing image of a multispectral spectral band;
the low-resolution remote sensing image comprises a thermal infrared spectrum remote sensing image and a multispectral spectrum remote sensing image;
(2) performing spectral registration on the high-resolution remote sensing image and the low-resolution remote sensing image acquired in the step (1) to obtain a registered high-resolution remote sensing image and a registered low-resolution remote sensing image, wherein the registered low-resolution remote sensing image comprises a registered thermal infrared spectral remote sensing image and a registered multispectral spectral remote sensing image;
(3) calculating high-resolution earth surface coverage information according to the registered high-resolution remote sensing image obtained in the step (2), and calculating low-resolution earth surface coverage information according to the registered low-resolution remote sensing image obtained in the step (2);
(4) calculating the low-resolution earth surface temperature with refined data level according to the registered high-resolution remote sensing image obtained in the step (2) and the registered thermal infrared spectrum remote sensing image obtained in the step (2);
(5) establishing a low-grade earth surface temperature regression model according to the low-grade earth surface coverage information obtained in the step (3) and the low-grade earth surface temperature obtained in the step (4);
(6) establishing a high-resolution earth surface temperature regression model according to the low-resolution earth surface temperature regression model established in the step (5) and the high-resolution earth surface coverage information obtained in the step (3);
(7) obtaining an earth surface temperature value of the target land according to the high-resolution earth surface temperature regression model obtained in the step (6), wherein the obtained earth surface temperature value of the target land comprises a plurality of pixel-level temperature values;
(8) comparing the temperature value of each pixel level area obtained in the step (7) with a set threshold, and according to the comparison result, if the temperature value of the pixel level area is higher than the set threshold, indicating that the temperature of the pixel level area on the target ground is abnormal, and if the temperature value of the pixel level area is not higher than the set threshold, indicating that the temperature of the pixel level area on the target ground is normal;
in the step (3), the land cover information refers to a ground feature type spectral index, and comprises a normalized vegetation index NDVI, a normalized water body index NDWI and a normalized building index NDBI;
in the step (4), the method for calculating the low-resolution earth surface temperature of the data-level refinement comprises the following steps:
firstly, obtaining a ground surface ratio radiation value according to the registered high-resolution remote sensing image;
secondly, calculating a brightness temperature value T of each pixel level area on the target ground according to the registered low-resolution remote sensing image;
thirdly, obtaining a low-resolution earth surface temperature value with data level refinement by adopting a single-channel earth surface temperature inversion method according to the earth surface ratio radiation value obtained in the first step and the brightness temperature value T of each pixel level area obtained in the second step;
in the step (5), the low-resolution surface temperature regression model is
LSTl＝f(NDVIl,NDWIl,NDBIl)
Wherein, LSTlFor low apparent surface temperatures, NDVIlNormalizing the value of the vegetation index for low-resolution surface temperature, NDWIlNormalizing the value of the water body index, NDBI, for low-score surface temperaturelNormalizing the value of the building index for low-score surface temperature; f is a function between the surface coverage information and the surface temperature;
in the step (6), the high-resolution surface temperature regression model is as follows:
LST′h＝f(NDVIh,NDWIh,NDBIh)+Δ
Δ＝LSTh-LSTl
wherein, LST'hFor the prediction of high-resolution surface temperature, NDVIhNormalizing the value of the vegetation index for high-resolution surface temperature, NDWIhNormalizing the value of the water body index, NDBI, for high-resolution surface temperaturehNormalizing the value of the building index for the high-resolution surface temperature; f is a function between the surface coverage information and the surface temperature; delta is the regression error, i.e. the error between the regression models of high and low-resolution surface temperatures, where LSThTo high surface temperature, LSTlThe earth surface temperature is calculated according to a low-molecular earth surface temperature regression model.
2. The high spatial resolution surface temperature generation method of claim 1, wherein: in the step (1), the multispectral spectral band comprises a red band, a green band, a near-infrared band and a short-wave infrared band.
3. The high spatial resolution surface temperature generation method of claim 1, wherein: in the step (2), the spectral registration comprises registration between the low-resolution remote sensing image and the high-resolution remote sensing image, and further comprises registration between the thermal infrared spectral remote sensing image and the multispectral spectral remote sensing image in the low-resolution remote sensing image.
4. The high spatial resolution surface temperature generation method of claim 1, wherein: the determination method of the earth surface coverage information comprises the following steps:
in the establishment of the surface temperature regression model, the surface coverage information needs to be calculated, when low-resolution multispectral spectral band information is input, the low-resolution surface coverage information is obtained, when high-resolution multispectral spectral band information is input, the high-resolution surface coverage information is obtained, and the surface coverage information comprises NDVI, NDWI and NDBI spectral indexes.
5. The high spatial resolution surface temperature generation method of claim 4, wherein: the NDVI normalized vegetation index is:
NDVI＝(NIR-R)/(NIR+R)
wherein, NIR is the reflection value of a near infrared band, and R is the reflection value of a red band.
6. The high spatial resolution surface temperature generation method of claim 4, wherein:
the NDWI normalized water body index is:
NDWI＝(G-NIR)/(G+NIR)
wherein G is the reflection value of a green wave band, and NIR is the reflection value of a near infrared wave band;
the NDBI normalized building index is:
NDBI＝(SWIR-NIR)/(SWIR+NIR)
wherein SWIR is the reflection value of the short wave infrared band, and NIR is the reflection value of the near infrared band.
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