CN102661811A - Remote sensing earth surface temperature up-scaling method and system - Google Patents

Abstract

The invention discloses a remote sensing earth surface temperature up-scaling method and a system, and relates to the technical field of satellite remote sensing. According to the invention, a first scale remote sensing image is fully used for calculating a normalized difference vegetation index sensitive to the earth surface temperature, by building a non-linear model between the normalized difference vegetation index and the earth surface temperature and using earth surface evaluated temperature difference of the normalized difference vegetation index under two scales, error correction to up-scaled earth surface temperature is achieved, and errors caused during up-scaling of the earth surface temperature are reduced.

Description

The remote sensing surface temperature rises two time scales approach and system

Technical field

The present invention relates to the satellite remote sensing technology field, particularly a kind of remote sensing surface temperature rises two time scales approach and system.

Background technology

Surface temperature is the important indicator that characterizes top energy equilibrium and climate change; Be zone and a key parameter of global yardstick face of land physical process, in numerous research fields such as meteorology, the hydrology, geology, ecology, urban environment and disaster monitoring demand that all is widely used.Current, in carrying out the global change research due process, how utilizing the surface temperature of first yardstick (promptly having higher resolution) to obtain second yardstick (promptly having lower resolution) surface temperature becomes key.Rise the second yardstick surface temperature after the mean value that adopts the first yardstick surface temperatures in the two time scales approach replaces rising yardstick more in existing surface temperature; This method is owing to reckon without the physical quantity that surface temperature is and unit area is irrelevant, and direct calculating mean value will inevitably bring than mistake.

Summary of the invention

The technical matters that (one) will solve

The technical matters that the present invention will solve is: how to reduce the error that is produced when surface temperature carries out rising yardstick.

(2) technical scheme

For solving the problems of the technologies described above, the invention provides a kind of remote sensing surface temperature and rise two time scales approach, said method comprising the steps of:

S1: obtain the first yardstick remote sensing images of waiting to rise yardstick;

S2: calculate the first corresponding normalization difference vegetation index NDVI of each pixel in the said first yardstick remote sensing images _High, the multiple that rises yardstick as required carries out the pixel polymerization to the said first yardstick remote sensing images, to obtain to rise the second normalization difference vegetation index NDVI of each pixel correspondence in the second yardstick remote sensing images behind the yardstick _Low

S3: set up the normalization difference vegetation index and the face of land and estimate the nonlinear model between the temperature;

S4: with the first corresponding normalization difference vegetation index NDVI of each pixel in the said first yardstick remote sensing images _HighThe said nonlinear model of substitution is estimated temperature to obtain first face of land that each pixel is corresponding in the said first yardstick remote sensing images respectively, and with the second corresponding normalization difference vegetation index NDVI of each pixel in the said second yardstick remote sensing images _LowThe said nonlinear model of substitution is estimated temperature to obtain second face of land that each pixel is corresponding in the said second yardstick remote sensing images respectively;

S5: utilize first face of land that each pixel is corresponding in the said first yardstick remote sensing images to estimate that the surface temperature that second face of land of each pixel correspondence in temperature, the said second yardstick remote sensing images estimates that each pixel is corresponding in temperature and the said first yardstick remote sensing images adopts the mode of error correction to calculate the corresponding surface temperature of each pixel in the said second yardstick remote sensing images.

Preferably, step S2 specifically may further comprise the steps:

S21: calculate the first corresponding normalization difference vegetation index NDVI of each pixel in the said first yardstick remote sensing images _High

S22: the yardstick of establishing the said first yardstick remote sensing images is G1, the yardstick G2 of then said large-size remote sensing images shown in following formula,

G2＝N*G1

Wherein, N is the said multiple that needs to rise yardstick;

S23: N*N pixel and the first normalization difference vegetation index NDVI that said N*N pixel is corresponding on the respectively corresponding said first yardstick remote sensing images of each pixel on the said large-size remote sensing images _HighPixel mean value as the second corresponding normalization difference vegetation index NDVI of the pixel on the said large-size remote sensing images _Low

Preferably, step S3 specifically may further comprise the steps:

S31: make up said nonlinear model, the expression formula of said nonlinear model is following,

$T\left(\mathrm{NDVI}\right)=a_0+a_1\left[{1-\left(\frac{{\mathrm{NDVI}}_{\max }-\mathrm{NDVI}}{{\mathrm{NDVI}}_{\max }-{\mathrm{NDVI}}_{\min }}\right)}^{0.625}\right]$

Wherein, T (NDVI) estimates temperature, a for the face of land ₀And a ₁Be respectively constant, NDVI is a normalization difference vegetation index, NDVI _MaxBe the maximal value of normalization difference vegetation index, NDVI _MinMinimum value for normalization difference vegetation index;

S32: with the first corresponding normalization difference vegetation index NDVI of each pixel in the said first yardstick remote sensing images _HighIn the corresponding said nonlinear model of surface temperature substitution of each pixel in the said first yardstick remote sensing images, to calculate a of said nonlinear model ₀And a ₁

S33: with a that calculates ₀And a ₁In the said nonlinear model of substitution, estimate the nonlinear model between the temperature to set up the normalization difference vegetation index and the face of land.

Preferably, step S5 specifically may further comprise the steps:

S51: establish second face of land that each pixel is corresponding in the said second yardstick remote sensing images and estimate that temperature for N*N pixel on the respectively corresponding said first yardstick remote sensing images of each pixel on

said large-size remote sensing images, obtains the mean value

that temperature is estimated on the first corresponding face of land of said N*N pixel

S52: calculate the mean value that temperature is estimated on the first corresponding face of land of said N*N pixel

Estimate temperature with said second face of land Between difference DELTA T _Low

S53: the mean value that obtains the corresponding surface temperature of said N*N pixel

Calculate the corresponding surface temperature T of each pixel in the said second yardstick remote sensing images through formula _Low,

$T_{\mathrm{low}}={\stackrel{\‾}{T}}_{\mathrm{high}}^{N*N}+{\mathrm{\ΔT}}_{\mathrm{low}}$

Wherein,

The invention also discloses a kind of remote sensing surface temperature and rise the yardstick system, said system comprises:

Acquisition module is used to obtain the first yardstick remote sensing images of waiting to rise yardstick;

The vegetation index computing module is used for calculating the first corresponding normalization difference vegetation index NDVI of each pixel of the said first yardstick remote sensing images _High, the multiple that rises yardstick as required carries out the pixel polymerization to the said first yardstick remote sensing images, to obtain to rise the second normalization difference vegetation index NDVI of each pixel correspondence in the second yardstick remote sensing images behind the yardstick _Low

Model building module is used to set up normalization difference vegetation index and the nonlinear model between the temperature is estimated on the face of land;

Estimate the temperature computation module, be used for the first corresponding normalization difference vegetation index NDVI of each pixel of the said first yardstick remote sensing images _HighThe said nonlinear model of substitution is estimated temperature to obtain first face of land that each pixel is corresponding in the said first yardstick remote sensing images respectively, and with the second corresponding normalization difference vegetation index NDVI of each pixel in the said second yardstick remote sensing images _LowThe said nonlinear model of substitution is estimated temperature to obtain second face of land that each pixel is corresponding in the said second yardstick remote sensing images respectively;

Error correction module, second face of land that is used for utilizing the first corresponding face of land of each pixel of the said first yardstick remote sensing images to estimate that each pixel is corresponding in temperature, the said second yardstick remote sensing images estimates that the surface temperature that each pixel is corresponding in temperature and the said first yardstick remote sensing images adopts the mode of error correction to calculate the corresponding surface temperature of each pixel in the said second yardstick remote sensing images.

(3) beneficial effect

The present invention calculates the normalization difference vegetation index responsive with surface temperature through making full use of the first yardstick remote sensing images; Through setting up the nonlinear model between normalization difference vegetation index and the surface temperature; Utilize the face of land of normalization difference vegetation index under two kinds of yardsticks to estimate temperature contrast; Realization has reduced the error that is produced when surface temperature carries out rising yardstick to rising the error correction of yardstick surface temperature.

Description of drawings

Fig. 1 is the process flow diagram that rises two time scales approach according to the remote sensing surface temperature of one embodiment of the present invention.

Embodiment

Below in conjunction with accompanying drawing and embodiment, specific embodiments of the invention describes in further detail.Following examples are used to explain the present invention, but are not used for limiting scope of the present invention.

Fig. 1 is the process flow diagram that rises two time scales approach according to the remote sensing surface temperature of one embodiment of the present invention; With reference to Fig. 1, the method for this embodiment may further comprise the steps:

S1: obtain the first yardstick remote sensing images of waiting to rise yardstick;

S2: calculate the first corresponding normalization difference vegetation index NDVI of each pixel in the said first yardstick remote sensing images _High, the multiple that rises yardstick as required carries out the pixel polymerization to the said first yardstick remote sensing images, to obtain to rise the second normalization difference vegetation index NDVI of each pixel correspondence in the second yardstick remote sensing images behind the yardstick _Low

S3: set up the normalization difference vegetation index and the face of land and estimate the nonlinear model between the temperature;

S4: with the first corresponding normalization difference vegetation index NDVI of each pixel in the said first yardstick remote sensing images _HighThe said nonlinear model of substitution is estimated temperature to obtain first face of land that each pixel is corresponding in the said first yardstick remote sensing images respectively, and with the second corresponding normalization difference vegetation index NDVI of each pixel in the said second yardstick remote sensing images _LowThe said nonlinear model of substitution is estimated temperature to obtain second face of land that each pixel is corresponding in the said second yardstick remote sensing images respectively;

S5: utilize first face of land that each pixel is corresponding in the said first yardstick remote sensing images to estimate that the surface temperature that second face of land of each pixel correspondence in temperature, the said second yardstick remote sensing images estimates that each pixel is corresponding in temperature and the said first yardstick remote sensing images adopts the mode of error correction to calculate the corresponding surface temperature of each pixel in the said second yardstick remote sensing images.

Preferably, step S2 specifically may further comprise the steps:

S21: calculate the first corresponding normalization difference vegetation index NDVI of each pixel in the said first yardstick remote sensing images _High

S22: the yardstick of establishing the said first yardstick remote sensing images is G1, the yardstick G2 of then said large-size remote sensing images shown in following formula,

G2＝N*G1

Wherein, N is the said multiple that needs to rise yardstick; Mean that under the G2 yardstick any pixel inside comprises the pixel of N*N G1 yardstick.NDVI is obtained in the pixel set _LowBe through the N*N size windows is set, and with window NDVI under the G1 yardstick _HighOrder moves on the data, and it is a window size that moving step length is set, the most at last NDVI under the interior corresponding G1 yardstick of each window _HighPixel mean value is as NDVI under the G2 yardstick _LowThe pixel value.

S23: N*N pixel and the first normalization difference vegetation index NDVI that said N*N pixel is corresponding on the respectively corresponding said first yardstick remote sensing images of each pixel on the said large-size remote sensing images _HighPixel mean value as the second corresponding normalization difference vegetation index NDVI of the pixel on the said large-size remote sensing images _Low

Preferably, step S3 specifically may further comprise the steps:

S31: make up said nonlinear model, the expression formula of said nonlinear model is following,

$T\left(\mathrm{NDVI}\right)=a_0+a_1\left[{1-\left(\frac{{\mathrm{NDVI}}_{\max }-\mathrm{NDVI}}{{\mathrm{NDVI}}_{\max }-{\mathrm{NDVI}}_{\min }}\right)}^{0.625}\right]$

Wherein, T (NDVI) estimates temperature, a for the face of land ₀And a ₁Be respectively constant, NDVI is a normalization difference vegetation index, NDVI _MaxBe the maximal value of normalization difference vegetation index, NDVI _MinMinimum value for normalization difference vegetation index;

S32: with the first corresponding normalization difference vegetation index NDVI of each pixel in the said first yardstick remote sensing images _HighIn the corresponding said nonlinear model of surface temperature substitution of each pixel in the said first yardstick remote sensing images, to calculate a of said nonlinear model ₀And a ₁

S33: with a that calculates ₀And a ₁In the said nonlinear model of substitution, estimate the nonlinear model between the temperature to set up the normalization difference vegetation index and the face of land.

Preferably, step S5 specifically may further comprise the steps:

S51: establish second face of land that each pixel is corresponding in the said second yardstick remote sensing images and estimate that temperature for N*N pixel on the respectively corresponding said first yardstick remote sensing images of each pixel on

said large-size remote sensing images, obtains the mean value

that temperature is estimated on the first corresponding face of land of said N*N pixel

S52: calculate the mean value that temperature is estimated on the first corresponding face of land of said N*N pixel

Estimate temperature with said second face of land

Between difference DELTA T _Low

S53: the mean value that obtains the corresponding surface temperature of said N*N pixel

Calculate the corresponding surface temperature T of each pixel in the said second yardstick remote sensing images through formula _Low,

$T_{\mathrm{low}}={\stackrel{\‾}{T}}_{\mathrm{high}}^{N*N}+{\mathrm{\ΔT}}_{\mathrm{low}}$

Wherein,

The invention also discloses a kind of remote sensing surface temperature and rise the yardstick system, said system comprises:

Acquisition module is used to obtain the first yardstick remote sensing images of waiting to rise yardstick;

The vegetation index computing module is used for calculating the first corresponding normalization difference vegetation index NDVI of each pixel of the said first yardstick remote sensing images _High, the multiple that rises yardstick as required carries out the pixel polymerization to the said first yardstick remote sensing images, to obtain to rise the second normalization difference vegetation index NDVI of each pixel correspondence in the second yardstick remote sensing images behind the yardstick _Low

Model building module is used to set up normalization difference vegetation index and the nonlinear model between the temperature is estimated on the face of land;

Estimate the temperature computation module, be used for the first corresponding normalization difference vegetation index NDVI of each pixel of the said first yardstick remote sensing images _HighThe said nonlinear model of substitution is estimated temperature to obtain first face of land that each pixel is corresponding in the said first yardstick remote sensing images respectively, and with the second corresponding normalization difference vegetation index NDVI of each pixel in the said second yardstick remote sensing images _LowThe said nonlinear model of substitution is estimated temperature to obtain second face of land that each pixel is corresponding in the said second yardstick remote sensing images respectively;

Error correction module, second face of land that is used for utilizing the first corresponding face of land of each pixel of the said first yardstick remote sensing images to estimate that each pixel is corresponding in temperature, the said second yardstick remote sensing images estimates that the surface temperature that each pixel is corresponding in temperature and the said first yardstick remote sensing images adopts the mode of error correction to calculate the corresponding surface temperature of each pixel in the said second yardstick remote sensing images.

Above embodiment only is used to explain the present invention; And be not limitation of the present invention; The those of ordinary skill in relevant technologies field under the situation that does not break away from the spirit and scope of the present invention, can also be made various variations and modification; Therefore all technical schemes that are equal to also belong to category of the present invention, and scope of patent protection of the present invention should be defined by the claims.

Claims (5)

1. a remote sensing surface temperature rises two time scales approach, it is characterized in that, said method comprising the steps of:

S1: obtain the first yardstick remote sensing images of waiting to rise yardstick;

S2: calculate the first corresponding normalization difference vegetation index NDVI of each pixel in the said first yardstick remote sensing images _High, the multiple that rises yardstick as required carries out the pixel polymerization to the said first yardstick remote sensing images, to obtain to rise the second normalization difference vegetation index NDVI of each pixel correspondence in the second yardstick remote sensing images behind the yardstick _Low

S3: set up the normalization difference vegetation index and the face of land and estimate the nonlinear model between the temperature;

S4: with the first corresponding normalization difference vegetation index NDVI of each pixel in the said first yardstick remote sensing images _HighThe said nonlinear model of substitution is estimated temperature to obtain first face of land that each pixel is corresponding in the said first yardstick remote sensing images respectively, and with the second corresponding normalization difference vegetation index NDVI of each pixel in the said second yardstick remote sensing images _LowThe said nonlinear model of substitution is estimated temperature to obtain second face of land that each pixel is corresponding in the said second yardstick remote sensing images respectively;

S5: utilize first face of land that each pixel is corresponding in the said first yardstick remote sensing images to estimate that the surface temperature that second face of land of each pixel correspondence in temperature, the said second yardstick remote sensing images estimates that each pixel is corresponding in temperature and the said first yardstick remote sensing images adopts the mode of error correction to calculate the corresponding surface temperature of each pixel in the said second yardstick remote sensing images.

2. the method for claim 1 is characterized in that, step S2 specifically may further comprise the steps:

S21: calculate the first corresponding normalization difference vegetation index NDVI of each pixel in the said first yardstick remote sensing images _High

S22: the yardstick of establishing the said first yardstick remote sensing images is G1, the yardstick G2 of then said large-size remote sensing images shown in following formula,

G2＝N*G1

Wherein, N is the said multiple that needs to rise yardstick;

S23: N*N pixel and the first normalization difference vegetation index NDVI that said N*N pixel is corresponding on the respectively corresponding said first yardstick remote sensing images of each pixel on the said large-size remote sensing images _HighPixel mean value as the second corresponding normalization difference vegetation index NDVI of the pixel on the said large-size remote sensing images _Low

3. the method for claim 1 is characterized in that, step S3 specifically may further comprise the steps:

S31: make up said nonlinear model, the expression formula of said nonlinear model is following,

$T\left(\mathrm{NDVI}\right)=a_0+a_1\left[{1-\left(\frac{{\mathrm{NDVI}}_{\max }-\mathrm{NDVI}}{{\mathrm{NDVI}}_{\max }-{\mathrm{NDVI}}_{\min }}\right)}^{0.625}\right]$

Wherein, T (NDVI) estimates temperature, a for the face of land ₀And a ₁Be respectively constant, NDVI is a normalization difference vegetation index, NDVI _MaxBe the maximal value of normalization difference vegetation index, NDVI _MinMinimum value for normalization difference vegetation index;

S32: with the first corresponding normalization difference vegetation index NDVI of each pixel in the said first yardstick remote sensing images _HighIn the corresponding said nonlinear model of surface temperature substitution of each pixel in the said first yardstick remote sensing images, to calculate a of said nonlinear model ₀And a ₁

S33: with a that calculates ₀And a ₁In the said nonlinear model of substitution, estimate the nonlinear model between the temperature to set up the normalization difference vegetation index and the face of land.

4. the method for claim 1 is characterized in that, step S5 specifically may further comprise the steps:

S51: establish second face of land that each pixel is corresponding in the said second yardstick remote sensing images and estimate that temperature for N*N pixel on the respectively corresponding said first yardstick remote sensing images of each pixel on said large-size remote sensing images, obtains the mean value

that temperature is estimated on the first corresponding face of land of said N*N pixel

S52: calculate the mean value that temperature is estimated on the first corresponding face of land of said N*N pixel

Estimate temperature with said second face of land

Between difference DELTA T _Low

S53: the mean value that obtains the corresponding surface temperature of said N*N pixel

Calculate the corresponding surface temperature T of each pixel in the said second yardstick remote sensing images through formula _Low,

$T_{\mathrm{low}}={\stackrel{\‾}{T}}_{\mathrm{high}}^{N*N}+{\mathrm{\ΔT}}_{\mathrm{low}}$

Wherein,

5. a remote sensing surface temperature rises the yardstick system, it is characterized in that, said system comprises:

Acquisition module is used to obtain the first yardstick remote sensing images of waiting to rise yardstick;

The vegetation index computing module is used for calculating the first corresponding normalization difference vegetation index NDVI of each pixel of the said first yardstick remote sensing images _High, the multiple that rises yardstick as required carries out the pixel polymerization to the said first yardstick remote sensing images, to obtain to rise the second normalization difference vegetation index NDVI of each pixel correspondence in the second yardstick remote sensing images behind the yardstick _Low

Model building module is used to set up normalization difference vegetation index and the nonlinear model between the temperature is estimated on the face of land;

Estimate the temperature computation module, be used for the first corresponding normalization difference vegetation index NDVI of each pixel of the said first yardstick remote sensing images _HighThe said nonlinear model of substitution is estimated temperature to obtain first face of land that each pixel is corresponding in the said first yardstick remote sensing images respectively, and with the second corresponding normalization difference vegetation index NDVI of each pixel in the said second yardstick remote sensing images _LowThe said nonlinear model of substitution is estimated temperature to obtain second face of land that each pixel is corresponding in the said second yardstick remote sensing images respectively;

Error correction module, second face of land that is used for utilizing the first corresponding face of land of each pixel of the said first yardstick remote sensing images to estimate that each pixel is corresponding in temperature, the said second yardstick remote sensing images estimates that the surface temperature that each pixel is corresponding in temperature and the said first yardstick remote sensing images adopts the mode of error correction to calculate the corresponding surface temperature of each pixel in the said second yardstick remote sensing images.

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