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

Remote sensing earth surface temperature upscaling method and system

Technical Field

The invention relates to the technical field of satellite remote sensing, in particular to a remote sensing earth surface temperature upscaling method and system.

Background

The surface temperature is an important index for representing the energy balance and the climate change of the land surface, is a key parameter of regional and global scale surface physical processes, and has wide application requirements in a plurality of research fields such as weather, hydrology, geology, ecology, urban environment, disaster monitoring and the like. Currently, in the development of global variation studies, it is critical how to obtain a second scale (i.e., with lower resolution) surface temperature using a first scale (i.e., with higher resolution) surface temperature. In the existing ground surface temperature upscaling method, the average value of the ground surface temperature of the first scale is mostly adopted to replace the ground surface temperature of the second scale after upscaling, and the method does not consider that the ground surface temperature is a physical quantity irrelevant to unit area, so that the direct calculation of the average value inevitably brings large errors.

Disclosure of Invention

Technical problem to be solved

The technical problem to be solved by the invention is as follows: how to reduce the error generated when the surface temperature is upscaled.

(II) technical scheme

In order to solve the technical problem, the invention provides a remote sensing earth surface temperature upscaling method, which comprises the following steps:

s1: acquiring a first scale remote sensing image of a scale to be increased;

s2: calculating a first normalized difference vegetation index NDVI corresponding to each pixel in the first-scale remote sensing image_highPerforming pixel aggregation on the first-scale remote sensing image according to the multiple of the upscaling requirement to obtain a second normalized difference vegetation index NDVI corresponding to each pixel in the upscaled second-scale remote sensing image_low；

S3: establishing a nonlinear model between the normalized difference vegetation index and the earth surface estimated temperature;

s4: enabling a first normalized difference vegetation index NDVI corresponding to each pixel in the first-scale remote sensing image_highRespectively substituting the obtained data into the nonlinear model to obtain a first earth surface estimated temperature corresponding to each pixel in the first scale remote sensing image, and respectively substituting a second normalized difference vegetation index NDVI corresponding to each pixel in the second scale remote sensing image_lowRespectively substituting the non-linear model to obtain a second surface estimated temperature corresponding to each pixel in the second-scale remote sensing image;

s5: and calculating the earth surface temperature corresponding to each pixel in the second scale remote sensing image by using the first earth surface estimated temperature corresponding to each pixel in the first scale remote sensing image, the second earth surface estimated temperature corresponding to each pixel in the second scale remote sensing image and the earth surface temperature corresponding to each pixel in the first scale remote sensing image in an error correction mode.

Preferably, step S2 specifically includes the following steps:

s21: calculating a first normalized difference vegetation index NDVI corresponding to each pixel in the first-scale remote sensing image_high；

S22: assuming that the scale of the first-scale remote sensing image is G1, the scale G2 of the larger-size remote sensing image is shown as the following formula,

G2＝N*G1

wherein N is a multiple of the required upscaling scale;

s23: each pixel on the larger-size remote sensing image respectively corresponds to N pixel on the first-scale remote sensing image, and the first normalized difference vegetation index NDVI corresponding to the N pixel_highThe average value of the pixels is used as a second normalized difference vegetation index NDVI corresponding to the pixels on the larger-size remote sensing image_low。

Preferably, step S3 specifically includes the following steps:

s31: constructing the nonlinear model, wherein the expression of the nonlinear model is as follows,

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

wherein T (NDVI) is the surface estimated temperature, a₀And a₁Are each constant, NDVI is the normalized differential vegetation index, NDVI_maxNDVI, the maximum value of the normalized differential vegetation index_minIs the minimum of the normalized differential vegetation index;

s32: enabling a first normalized difference vegetation index NDVI corresponding to each pixel in the first-scale remote sensing image_highAnd substituting the earth surface temperature corresponding to each pixel in the first-scale remote sensing image into the nonlinear model to calculate a of the nonlinear model₀And a₁；

S33: a to be calculated₀And a₁Substituting into the nonlinear model to establish a nonlinear model between the normalized differential vegetation index and the surface estimated temperature.

Preferably, step S5 specifically includes the following steps:

s51: setting the second surface estimated temperature corresponding to each pixel in the second-scale remote sensing image as

Each pixel on the larger-size remote sensing image respectively corresponds to N x N pixels on the first-scale remote sensing image, and the average value of the estimated temperatures of the first earth surface corresponding to the N x N pixels is obtained

S52: calculating the average value of the first surface estimated temperature corresponding to the N x N pixels

And the second surface estimated temperatureDifference Δ T between_low；

S53: obtaining the average value of the earth surface temperature corresponding to the N pixel elements

Calculating the earth surface temperature T corresponding to each pixel in the second-scale remote sensing image by the following formula_low，

<math> <mrow> <msub> <mi>T</mi> <mi>low</mi> </msub> <mo>=</mo> <msubsup> <mover> <mi>T</mi> <mo>&OverBar;</mo> </mover> <mi>high</mi> <mrow> <mi>N</mi> <mo>*</mo> <mi>N</mi> </mrow> </msubsup> <mo>+</mo> <msub> <mi>&Delta;T</mi> <mi>low</mi> </msub> </mrow> </math>

Wherein,

the invention also discloses a remote sensing earth surface temperature upscaling system, which comprises:

the acquisition module is used for acquiring a first scale remote sensing image of a scale to be increased;

a vegetation index calculation module for calculating a first normalized difference vegetation index NDVI corresponding to each pixel in the first scale remote sensing image_highPerforming pixel aggregation on the first-scale remote sensing image according to the multiple of the upscaling requirement to obtain a second normalized difference vegetation index NDVI corresponding to each pixel in the upscaled second-scale remote sensing image_low；

The model establishing module is used for establishing a nonlinear model between the normalized difference vegetation index and the earth surface estimated temperature;

an estimated temperature calculation module used for calculating the first normalized difference vegetation index NDVI corresponding to each pixel in the first scale remote sensing image_highRespectively substituting the nonlinear models to obtain the first scaleEstimating the temperature of a first earth surface corresponding to each pixel in the remote sensing image, and comparing a second normalized difference vegetation index NDVI corresponding to each pixel in the second-scale remote sensing image_lowRespectively substituting the non-linear model to obtain a second surface estimated temperature corresponding to each pixel in the second-scale remote sensing image;

and the error correction module is used for calculating the surface temperature corresponding to each pixel in the second-scale remote sensing image by utilizing the first surface estimated temperature corresponding to each pixel in the first-scale remote sensing image, the second surface estimated temperature corresponding to each pixel in the second-scale remote sensing image and the surface temperature corresponding to each pixel in the first-scale remote sensing image in an error correction mode.

(III) advantageous effects

According to the method, the normalized difference vegetation index sensitive to the earth surface temperature is calculated by fully utilizing the first scale remote sensing image, the nonlinear model between the normalized difference vegetation index and the earth surface temperature is established, the earth surface estimated temperature difference of the normalized difference vegetation index under two scales is utilized, the error correction of the scale-up earth surface temperature is realized, and the error generated when the earth surface temperature is subjected to scale-up is reduced.

Drawings

FIG. 1 is a flow chart of a method for remote sensing surface temperature upscaling according to one embodiment of the invention.

Detailed Description

The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.

FIG. 1 is a flow chart of a method for remote sensing surface temperature upscaling according to one embodiment of the present invention; referring to fig. 1, the method of the present embodiment includes the steps of:

s1: acquiring a first scale remote sensing image of a scale to be increased;

s2: calculating a first normalized difference vegetation index NDVI corresponding to each pixel in the first-scale remote sensing image_highPerforming pixel aggregation on the first-scale remote sensing image according to the multiple of the upscaling requirement to obtain a second normalized difference vegetation index NDVI corresponding to each pixel in the upscaled second-scale remote sensing image_low；

S3: establishing a nonlinear model between the normalized difference vegetation index and the earth surface estimated temperature;

s4: enabling a first normalized difference vegetation index NDVI corresponding to each pixel in the first-scale remote sensing image_highRespectively substituting the obtained data into the nonlinear model to obtain a first earth surface estimated temperature corresponding to each pixel in the first scale remote sensing image, and respectively substituting a second normalized difference vegetation index NDVI corresponding to each pixel in the second scale remote sensing image_lowRespectively substituting the non-linear model to obtain a second surface estimated temperature corresponding to each pixel in the second-scale remote sensing image;

s5: and calculating the earth surface temperature corresponding to each pixel in the second scale remote sensing image by using the first earth surface estimated temperature corresponding to each pixel in the first scale remote sensing image, the second earth surface estimated temperature corresponding to each pixel in the second scale remote sensing image and the earth surface temperature corresponding to each pixel in the first scale remote sensing image in an error correction mode.

Preferably, step S2 specifically includes the following steps:

s21: calculating a first normalized difference vegetation index NDVI corresponding to each pixel in the first-scale remote sensing image_high；

S22: assuming that the scale of the first-scale remote sensing image is G1, the scale G2 of the larger-size remote sensing image is shown as the following formula,

G2＝N*G1

wherein N is a multiple of the required upscaling scale; meaning that any one pixel contains N x N pixels of G1 scale inside it at the G2 scale. Pixel set acquisition NDVI_lowIs prepared by setting N × N window size and NDVI the window under G1 scale_highSequentially moving the data, setting the moving step size as one window size, and finally carrying out NDVI on the corresponding G1 scales in each window_highThe average value of the pixels is used as NDVI under the G2 scale_lowAnd (4) pixel value.

S23: each pixel on the larger-size remote sensing image respectively corresponds to N pixel on the first-scale remote sensing image, and the first normalized difference vegetation index NDVI corresponding to the N pixel_highThe average value of the pixels is used as a second normalized difference vegetation index NDVI corresponding to the pixels on the larger-size remote sensing image_low。

Preferably, step S3 specifically includes the following steps:

s31: constructing the nonlinear model, wherein the expression of the nonlinear model is as follows,

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

wherein T (NDVI) is the surface estimated temperature, a₀And a₁Respectively, constant, NDVI is normalized differential vegetation index, NDVI_maxNDVI, the maximum value of the normalized differential vegetation index_minIs the minimum of the normalized differential vegetation index;

s32: enabling a first normalized difference vegetation index NDVI corresponding to each pixel in the first-scale remote sensing image_highAnd substituting the earth surface temperature corresponding to each pixel in the first-scale remote sensing image into the nonlinear model to calculate a of the nonlinear model₀And a₁；

S33: a to be calculated₀And a₁Substituting into the nonlinear model to establish a nonlinear model between the normalized differential vegetation index and the surface estimated temperature.

Preferably, step S5 specifically includes the following steps:

s51: setting the second surface estimated temperature corresponding to each pixel in the second-scale remote sensing image as

Each pixel on the larger-size remote sensing image respectively corresponds to N x N pixels on the first-scale remote sensing image, and the average value of the estimated temperatures of the first earth surface corresponding to the N x N pixels is obtained

S52: calculating the average value of the first surface estimated temperature corresponding to the N x N pixels

And the second surface estimated temperature

Difference Δ T between_low；

S53: obtaining the average value of the earth surface temperature corresponding to the N pixel elements

Calculating the earth surface temperature T corresponding to each pixel in the second-scale remote sensing image by the following formula_low，

<math> <mrow> <msub> <mi>T</mi> <mi>low</mi> </msub> <mo>=</mo> <msubsup> <mover> <mi>T</mi> <mo>&OverBar;</mo> </mover> <mi>high</mi> <mrow> <mi>N</mi> <mo>*</mo> <mi>N</mi> </mrow> </msubsup> <mo>+</mo> <msub> <mi>&Delta;T</mi> <mi>low</mi> </msub> </mrow> </math>

Wherein,

the invention also discloses a remote sensing earth surface temperature upscaling system, which comprises:

the acquisition module is used for acquiring a first scale remote sensing image of a scale to be increased;

a vegetation index calculation module for calculating a first normalized difference vegetation index NDVI corresponding to each pixel in the first scale remote sensing image_highPerforming pixel aggregation on the first-scale remote sensing image according to the multiple of the upscaling requirement to obtain a second normalization corresponding to each pixel in the upscaled second-scale remote sensing imageDifferential vegetation index NDVI_low；

The model establishing module is used for establishing a nonlinear model between the normalized difference vegetation index and the earth surface estimated temperature;

an estimated temperature calculation module used for calculating the first normalized difference vegetation index NDVI corresponding to each pixel in the first scale remote sensing image_highRespectively substituting the obtained data into the nonlinear model to obtain a first earth surface estimated temperature corresponding to each pixel in the first scale remote sensing image, and respectively substituting a second normalized difference vegetation index NDVI corresponding to each pixel in the second scale remote sensing image_lowRespectively substituting the non-linear model to obtain a second surface estimated temperature corresponding to each pixel in the second-scale remote sensing image;

and the error correction module is used for calculating the surface temperature corresponding to each pixel in the second-scale remote sensing image by utilizing the first surface estimated temperature corresponding to each pixel in the first-scale remote sensing image, the second surface estimated temperature corresponding to each pixel in the second-scale remote sensing image and the surface temperature corresponding to each pixel in the first-scale remote sensing image in an error correction mode.

The above embodiments are only for illustrating the invention and are not to be construed as limiting the invention, and those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention, therefore, all equivalent technical solutions also belong to the scope of the invention, and the scope of the invention is defined by the claims.

Claims (5)

1. A remote sensing earth surface temperature upscaling method is characterized by comprising the following steps:

s1: acquiring a first scale remote sensing image of a scale to be increased;

s2: calculating a first normalized difference vegetation index NDVI corresponding to each pixel in the first-scale remote sensing image_highPerforming pixel aggregation on the first-scale remote sensing image according to the multiple of the upscaling requirement to obtain a second normalized difference vegetation index NDVI corresponding to each pixel in the upscaled second-scale remote sensing image_low；

S3: establishing a nonlinear model between the normalized difference vegetation index and the earth surface estimated temperature;

s4: enabling a first normalized difference vegetation index NDVI corresponding to each pixel in the first-scale remote sensing image_highRespectively substituting the obtained data into the nonlinear model to obtain a first earth surface estimated temperature corresponding to each pixel in the first scale remote sensing image, and respectively substituting a second normalized difference vegetation index NDVI corresponding to each pixel in the second scale remote sensing image_lowRespectively substituting the non-linear model to obtain a second surface estimated temperature corresponding to each pixel in the second-scale remote sensing image;

s5: and calculating the earth surface temperature corresponding to each pixel in the second scale remote sensing image by using the first earth surface estimated temperature corresponding to each pixel in the first scale remote sensing image, the second earth surface estimated temperature corresponding to each pixel in the second scale remote sensing image and the earth surface temperature corresponding to each pixel in the first scale remote sensing image in an error correction mode.

2. The method according to claim 1, wherein step S2 specifically comprises the steps of:

s21: calculating a first normalized difference vegetation index NDVI corresponding to each pixel in the first-scale remote sensing image_high；

S22: assuming that the scale of the first-scale remote sensing image is G1, the scale G2 of the larger-size remote sensing image is shown as the following formula,

G2＝N*G1

wherein N is a multiple of the required upscaling scale;

s23: each pixel on the larger-size remote sensing image respectively corresponds to N pixel on the first-scale remote sensing image, and the first normalized difference vegetation index NDVI corresponding to the N pixel_highThe average value of the pixels is used as a second normalized difference vegetation index NDVI corresponding to the pixels on the larger-size remote sensing image_low。

3. The method according to claim 1, wherein step S3 specifically comprises the steps of:

s31: constructing the nonlinear model, wherein the expression of the nonlinear model is as follows,

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

wherein T (NDVI) is the surface estimated temperature, a₀And a₁Respectively, constant, NDVI is normalized differential vegetation index, NDVI_maxNDVI, the maximum value of the normalized differential vegetation index_minIs the minimum of the normalized differential vegetation index;

s32: enabling a first normalized difference vegetation index NDVI corresponding to each pixel in the first-scale remote sensing image_highAnd substituting the earth surface temperature corresponding to each pixel in the first-scale remote sensing image into the nonlinear model to calculate a of the nonlinear model₀And a₁；

S33: a to be calculated₀And a₁Substituting the non-lineAnd in the nature model, a nonlinear model between the normalized difference vegetation index and the earth surface estimated temperature is established.

4. The method according to claim 1, wherein step S5 specifically comprises the steps of:

s51: setting the second surface estimated temperature corresponding to each pixel in the second-scale remote sensing image asEach pixel on the larger-size remote sensing image respectively corresponds to N x N pixels on the first-scale remote sensing image, and the average value of the estimated temperatures of the first earth surface corresponding to the N x N pixels is obtained

S52: calculating the average value of the first surface estimated temperature corresponding to the N x N pixels

And the second surface estimated temperature

Difference Δ T between_low；

S53: obtaining the average value of the earth surface temperature corresponding to the N pixel elements

Calculating the earth surface temperature T corresponding to each pixel in the second-scale remote sensing image by the following formula_low，

<math> <mrow> <msub> <mi>T</mi> <mi>low</mi> </msub> <mo>=</mo> <msubsup> <mover> <mi>T</mi> <mo>&OverBar;</mo> </mover> <mi>high</mi> <mrow> <mi>N</mi> <mo>*</mo> <mi>N</mi> </mrow> </msubsup> <mo>+</mo> <msub> <mi>&Delta;T</mi> <mi>low</mi> </msub> </mrow> </math>

Wherein,

5. a remote sensing surface temperature upscaling system, characterized in that the system comprises:

the acquisition module is used for acquiring a first scale remote sensing image of a scale to be increased;

a vegetation index calculation module for calculating a first normalized difference vegetation index NDVI corresponding to each pixel in the first scale remote sensing image_highPerforming pixel aggregation on the first-scale remote sensing image according to the multiple of the upscaling requirement to obtain a second normalized difference vegetation index NDVI corresponding to each pixel in the upscaled second-scale remote sensing image_low；

The model establishing module is used for establishing a nonlinear model between the normalized difference vegetation index and the earth surface estimated temperature;

an estimated temperature calculation module used for calculating the first normalized difference vegetation index NDVI corresponding to each pixel in the first scale remote sensing image_highRespectively substituting the obtained data into the nonlinear model to obtain a first earth surface estimated temperature corresponding to each pixel in the first scale remote sensing image, and respectively substituting a second normalized difference vegetation index NDVI corresponding to each pixel in the second scale remote sensing image_lowRespectively substituting the non-linear model to obtain a second surface estimated temperature corresponding to each pixel in the second-scale remote sensing image;

and the error correction module is used for calculating the surface temperature corresponding to each pixel in the second-scale remote sensing image by utilizing the first surface estimated temperature corresponding to each pixel in the first-scale remote sensing image, the second surface estimated temperature corresponding to each pixel in the second-scale remote sensing image and the surface temperature corresponding to each pixel in the first-scale remote sensing image in an error correction mode.

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