CN111046120B - Extreme air temperature semantic inverse distance weight interpolation method - Google Patents
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Abstract
The invention discloses an extreme air temperature semantic reverse distance weight interpolation method in the technical field of geospatial data processing, which comprises the steps of preprocessing remote sensing earth surface temperature data, carrying out earth surface temperature inversion, constructing semantic hierarchies participating in earth surface land utilization classification, calculating earth surface semantic similarity, utilizing the semantic similarity to adjust factor weight, embedding a reverse distance spatial interpolation model, and finishing spatial interpolation estimation by taking the earth surface temperature data as an example to construct an earth surface temperature interpolation data set; the invention improves the traditional inverse distance interpolation method by fully utilizing the geographical hierarchical semantic similarity and adjusting the weight of the interpolation factor, does not need any auxiliary data except the extreme temperature data, is easy to realize, has high practical value, and can be used for the business operation of the relevant data such as earth surface temperature, vegetation coverage, soil humidity data and the like of continuous scenes, which are easy to cause data errors and loss and the like.
Description
Technical Field
The invention relates to the technical field of geospatial data processing, in particular to an extreme air temperature semantic inverse distance weight interpolation method.
Background
One of the meanings of spatial dependence is that it is possible to obtain a relatively accurate description of the earth's surface using some reasonably located samples. The best estimation for obtaining an area average condition is to perform sampling observation, such as exploring the change of the extreme temperature of the earth surface of an area, and hopefully ensuring that each position in the area has equal opportunity of being sampled, but in practical application, observation sampling points are sparse and non-randomly distributed, and if the positions of the sample points are systematically and smoothly changed, the corresponding meteorological model is mostly obtained by sampling by using an observation station at present. The spatial interpolation method is commonly used for converting discrete observation point data into a continuous curved surface, so that a better spatial distribution mode of measuring data elements is facilitated. The traditional numerical spatial interpolation method, such as an inverse distance weight interpolation method, a kriging interpolation method, a spline interpolation access and calculation, a trend surface method and the like, is widely applied in different fields. The most methods have some limitations in application, for example, the distance weighting method has the problems that the calculation result is influenced by the distance, and the method is not suitable for a large range. The kriging method variation function needs to be selected manually, the problem that the calculated amount of the variation function is increased greatly when the variation function is combined is solved, and the spline method is not suitable for sparse limited sampling points and is commonly used for high-density sampling point interpolation. The trend surface method depends more on the existing spatial distribution trend of the interpolation elements. Therefore, many scholars at home and abroad continuously explore and improve the spatial interpolation method. If the learners introduce the complexity of the terrain and the elevation factor into the inverse distance weight, the harmonic weight coefficient of the azimuth such as Lizhengquan is introduced into the distance weight interpolation method. Segment equality introduces natural proximity relations into distance weight interpolation, and another scholars introduces fuzzy trigonometric functions into distance weight interpolation. Some other people consider the time-space variation characteristics of the geographic factors, and introduce time sequence data to remove some numerical value fluctuations in time, such as space-time weighted kriging and space-time inverse distance weighted interpolation. The series of methods proposed by the scholars obtain remarkable academic influence, but most of the methods are based on a numerical interpolation method and do not introduce geospatial semantics.
The method is inspired by the gradient theory in the field of image processing, the gradient is the first order differential of the gray value, the change rate between adjacent pixels in the x and y directions is reflected, and the place with larger image gradient change rate is often the place where the ground surface coverage type changes, such as an image land and water boundary line. The existing research based on remote sensing image inversion, such as surface temperature inversion, is a model describing the relationship between remote sensing signals or remote sensing data and surface application to a certain extent, for example, the temperature of inversion of buildings at residential sites is greatly different from the temperature of inversion of forest lands, and the temperature difference between buildings at residential sites is small. Therefore, the geography description of the earth surface remote sensing pixel information can not be discussed without the geographic space semantics. At present, the existing scholars propose a semantic kriging method, so that good research effects are obtained, but the problems that the computation of introducing a mutation function into semantic similarity is complex and the like still exist, the influence of parameter value uncertainty is weakened by embedding geographic semantics into a Bayesian network to predict multivariate meteorological factors, and the knowledge of meteorological modeling is lacked. Based on the above, the invention designs an extreme air temperature semantic inverse distance weight interpolation method to solve the above problems.
Disclosure of Invention
The invention aims to provide an extreme air temperature semantic inverse distance weight interpolation method to solve the problem that the geographic semantics of an interpolated sample point is not considered in the practical application of the traditional spatial interpolation method in the background art.
In order to achieve the purpose, the invention provides the following technical scheme: an extreme air temperature semantic inverse distance weight interpolation method comprises the steps of introducing geographic semantic similarity into a formula of a traditional inverse distance weight interpolation method, adjusting a weight coefficient, and comprehensively considering geographic semantic difference between a sample point and an estimation point in an interpolation process, and comprises the following specific steps:
step one, surface temperature inversion
Based on an atmospheric correction method, performing surface temperature inversion by using Landsat 8TIRS data, preprocessing the data through radiometric calibration and atmospheric correction, and calculating basic parameters such as radiance, surface specific radiance, black body radiance and the like to obtain surface temperature;
step two, calculating the geographic semantic similarity
The used calculation method of the semantic similarity is a concept fuzzy equivalence matrix algorithm based on a geographic entity, and the calculation method of the semantic similarity analyzes influence factors of the geographic semantic similarity on the basis of a fuzzy equivalence relation by utilizing the attribute of the geographic entity semantic;
in the (G, M, I) form conceptual table, if two objects have the same attribute, the two objects are equivalent, if R is a fuzzy equivalent matrix on the set M, then Σ (M, R) can be used as a fuzzy granularity knowledge base, and R can be obtained by,
if two elements Q i ,Q j Epsilon, M, the similarity is as follows
Corresponding fuzzy relation matrix
Can satisfy reflexibility and symmetry, but in many cases, does not satisfy transmissibility
Just as a fuzzy similarity matrix. The fuzzy equivalence relation R can be obtained by means of a transitive closure algorithm, as shown in the formula
Step three, semantic reverse distance interpolation weight calculation
The semantic reverse distance interpolation method reconsiders the weight of interpolation, increases the weight of the same geographic semantics on the basis of distance, reduces the weight of different land utilization types on the basis of distance, and forms the semantic reverse distance interpolation method, and the formula is as follows
In the formula, T 0 The estimated value of the ith point to be estimated; t is a unit of i The measured value of the ith discrete point is obtained; d i The distance between the ith point to be estimated and the discrete point; x is the number of i The latitude of the point to be estimated; x is the latitude of the discrete point; y is i The longitude of the point to be estimated; y is the longitude of the discrete point; m is the number of actually measured sample points participating in interpolation;
wherein n is 2, k i The semantic similarity between the point i to be interpolated and the discrete point i is within the value range of 0 & lt kappa 1 Less than or equal to 1; when the land use type of the point i to be interpolated is equivalent to that of the discrete point i, k i 1; when the land use types of the point i to be interpolated and the discrete point i are not equivalent, 0 & lt kappa i ＜1。
Preferably, the step one first establishes an interpolation study area.
Preferably, in the first step, remote sensing data preprocessing is performed on the basis of the interpolation research area, then surface temperature inversion is performed, inversion accuracy verification is performed, and finally temperature data extraction is performed.
Preferably, in the second step, on the basis of the interpolation research area, a semantic hierarchical tree is first constructed, then the semantic type represented by each sample point in the interpolation research area is determined, the semantic similarity between the surface coverage of each pair of nodes in the semantics is evaluated, the geographic semantic similarity corresponding to each pair of sample points is calculated, and a semantic similarity matrix between each pair of sample points is constructed.
Preferably, in the third step, the interpolation of each interpolation point is modified by using the semantic similarity, and then the attribute value is calculated by using the semantic modified traditional spatial interpolation method, so as to complete the semantic inverse distance weight interpolation.
Preferably, the atmospheric correction method is implemented as follows:
thermal infrared radiation brightness value R received by sensor of remote sensing satellite λ Is to radiate luminance R from the atmosphere upwards ↑ Energy reaching the satellite sensor after the real radiation brightness of the ground penetrates the atmosphere and reflected energy R after the atmosphere radiates downwards to the ground ↓ Three parts, namely a thermal infrared radiation brightness value R received by a sensor of a remote sensing satellite λ The radiation transmission equation of
R λ ＝[φA(T z )+(1-φ)R ↓ ]δ+R ↑
Wherein phi represents the ground emissivity; t is z Representing the true surface temperature; a (T) z ) Represents the black body radiation brightness; delta is the transmittance of the atmosphere in the thermal infrared band;
wherein, the radiation brightness A (T) of the black body with the temperature T in the thermal infrared band z ) Is expressed as
For T z Function acquisition with Planck's formula
T z ＝K 2 /ln[K 1 /A(T z )+1]
For TM data, K 1 ＝607.76W/(m 2 *μm*sr)，K 2 1260.56K; for ETM + data, K 1 ＝666.09W/(m 2 *μm*sr)，K 2 1282.71K; k for TIRSBand10 data 1 ＝774.89W/(m 2 *μm*sr)，K 2 ＝1321.08K。
Compared with the prior art, the invention has the beneficial effects that: the invention improves the traditional inverse distance interpolation method by fully utilizing the geographical hierarchical semantic similarity and adjusting the weight of the interpolation factor, does not need any auxiliary data except the extreme temperature data, is easy to realize, has high practical value, and can be used for the business operation of the relevant data such as earth surface temperature, vegetation coverage, soil humidity data and the like of continuous scenes, which are easy to cause data errors and loss and the like.
The method comprises the following steps that 1, in consideration of sparseness and high cost of observation stations, surface temperature of continuous scenes, vegetation coverage, soil humidity data and the like, the problems of data error loss and the like are easy to exist, and reconstructed data can be made up by using semantic inverse distance weight interpolation.
2, considering the reasons of observation conditions, sensor errors and inversion errors, the existing surface temperature product data have outliers which contain some noises and have larger errors, and the interpolation precision is improved by combining semantic inverse distance weight interpolation.
And 3, when all the required pixels are null values, selecting the actually measured pixel value of the current adjacent position, and completing interpolation estimation by utilizing semantic reverse distance interpolation.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of an embodiment of the present invention.
FIG. 2 is a semantic description table of a terrestrial water system of GB/T20258.1-2007 part according to an embodiment of the present invention.
FIG. 3 is a partial geographic entity formalized conceptual table according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-3, the present invention provides a technical solution: an extreme air temperature semantic inverse distance weight interpolation method comprises the steps of introducing geographic semantic similarity into a formula of a traditional inverse distance weight interpolation method, and adjusting a weight coefficient, and is characterized in that the interpolation process comprehensively considers the geographic semantic difference between a sample point and an estimation point, and the method comprises the following specific steps:
step one, surface temperature inversion
Based on an atmospheric correction method, performing surface temperature inversion by using Landsat 8TIRS data, preprocessing the data through radiometric calibration and atmospheric correction, and calculating basic parameters such as radiance, surface specific radiance, black body radiance and the like to obtain surface temperature;
firstly establishing an interpolation research area, preprocessing remote sensing data on the basis of the interpolation research area, then performing surface temperature inversion, verifying inversion accuracy and finally extracting temperature data;
step two, calculating the geographic semantic similarity
The used calculation method of the semantic similarity is a concept fuzzy equivalent matrix algorithm based on a geographic ontology, and the calculation method of the semantic similarity analyzes the influence factors of the geographic semantic similarity on the basis of a fuzzy equivalent relationship by utilizing the ontology attribute of the geographic entity semantics;
on the basis of an interpolation research area, firstly constructing a semantic hierarchy tree, then determining the semantic type represented by each sample point of the interpolation research area, evaluating the semantic similarity between the earth surface coverage of each pair of nodes in the semantic, calculating the geographic semantic similarity corresponding to each pair of sample points, and constructing a semantic similarity matrix between the sample points;
in the (G, M, I) form conceptual table, if two objects have the same attribute, then the two objects are equivalent, and if R is a fuzzy equivalent matrix on the set M, then Σ (M, R) can be used as a fuzzy granularity knowledge base, and R can be obtained by:
if two elements Q i ,Q j E.g. M, the similarity is as follows
Corresponding fuzzy relation matrix
Can satisfy reflexibility and symmetry, but in most cases, transmissibility is not satisfied
Just as a fuzzy similarity matrix. The fuzzy equivalence relation R can be generally obtained by means of a transitive closure algorithm, as shown in the formula
Step three, semantic reverse distance interpolation weight calculation
Firstly, modifying the interpolation of each interpolation point by utilizing the semantic similarity, then, utilizing the semantic similarity to modify the traditional spatial interpolation method, calculating an attribute value and finishing the semantic inverse distance weight interpolation;
the semantic reverse distance interpolation method reconsiders the weight of interpolation, increases the weight of the same geographic semantics on the basis of distance, reduces the weight of different land utilization types on the basis of distance, and forms the semantic reverse distance interpolation method, and the formula is as follows
In the formula, T 0 The estimated value of the ith point to be estimated; t is i The measured value of the ith discrete point is obtained; d i For the point between the ith point to be evaluated and the discrete pointThe distance of (a); x is the number of i The latitude of the point to be estimated; x is the latitude of the discrete point; y is i Longitude of the point to be estimated; y is the longitude of the discrete point; m is the number of actually measured sample points participating in interpolation;
wherein n is 2, k i The semantic similarity between the point i to be interpolated and the discrete point i is within the value range of 0 & lt kappa 1 Less than or equal to 1; when the land use type of the point i to be interpolated is equivalent to that of the discrete point i, kappa i 1 is ═ 1; when the land use types of the point i to be interpolated and the discrete point i are not equivalent, kappa is more than 0 i ＜1。
One specific application of this embodiment is:
the first stage is as follows: pre-processing and inversion of surface temperature data
The empirical data used in the study are Landsat8TRIS data, and the imaging time is as follows: 10/02/40/7/2013, 9/02/52/8/2013, 11/02/37/2/2017, 11/02/50/1/2018, 17/02/49/4/2018, 19/02/37/4/2018, and several data for verified surface inversion accuracy. The remote sensing images have less cloud amount and better imaging quality, and are beneficial to the research of the inversion experiment of the LST. In the example, the LST of the research area is inverted by using Landsat8TRIS data on the basis of an atmospheric correction method, and the experiment is completed on ENVI 5.2.
The specific implementation of the atmospheric correction method is as follows:
thermal infrared radiation brightness value R received by sensor of remote sensing satellite λ Is the radiation of a luminance R from the atmosphere upwards ↑ Energy reaching the satellite sensor after the real radiation brightness of the ground penetrates the atmosphere and reflected energy R after the atmosphere radiates downwards to the ground ↓ Three parts, namely a thermal infrared radiation brightness value R received by a sensor of a remote sensing satellite λ The radiation transmission equation of
R λ ＝[φA(T z )+(1-φ)R ↓ ]δ+R ↑
Wherein phi represents the ground emissivity; t is a unit of z Representing the true surface temperature; a (T) z ) Represents the black body radiation brightness; delta is the transmittance of the atmosphere in the thermal infrared band;
wherein, the radiation brightness A (T) of the black body with the temperature T in the thermal infrared band z ) Is expressed as
For T z Function acquisition with Planck's formula
T z ＝K 2 /ln[K 1 /A(T z )+1]
For TM data, K 1 ＝607.76W/(m 2 *μm*sr)，K 2 1260.56K; for ETM + data, K 1 ＝666.09W/(m 2 *μm*sr)，K 2 1282.71K; k for TIRSBand10 data 1 ＝774.89W/(m 2 *μm*sr)，K 2 ＝1321.08K。
In conclusion, the Landsat 8TIRS LST inversion based on the atmospheric correction method requires two parameters, namely an atmospheric profile parameter and a surface emissivity. On the website provided by NASA, atmospheric profile parameters can be inquired by inputting the imaging time of the image and the latitude and longitude of the center point of the image, which is suitable for data with only one thermal infrared band such as Landsat TM/ETM +/TIRS.
And a second stage: geographic semantic similarity computation
Step 2-1, semantic description of geographic entity
Formalized geographic entity concepts include extension and connotation, both of which are generally considered equally important, but in the geographic domain, the semantic basis of an ontology often refers to the connotation of a concept, and the connotation of a concept is often determined by some important ontological attributes. The extension and connotation of the geographic concept are usually extracted from the dictionary of the professional field and the semantic description of the standard specification. Taking the semantic description of the attributes of the terrestrial water system as an example, the semantic description of the terrestrial water system is given as a standard part. FIG. 2 is a part of semantic descriptions of terrestrial water system elements described by the basic geographic information element classification and codes (GB/T13923-2006) and data dictionaries (GB/T20258.1-2007).
From the perspective of linguistic understanding, the text description of geographic concepts in different professional dictionaries has domain understanding difference, and the conceptualization of geographic ontology forms can reduce language background interference. Because national standards and specifications relate to a wide range of contents, part of elements are selected according to research needs and strict and integral principles, ontology attributes of geographic concepts are extracted, and a form conceptual table of the field is constructed, which is shown in figure 3.
In fig. 3, the connotations a to o sequentially express that the material property is water, the material property is stone, the material property is natural, the material property is artificial, the space property is long line, the space property is low-lying land, the space property is building, the space property is ground, the space property is underground, the time property is perennial, the time property is seasonal, the material property is fluidity, the function is transportation, the function is flood control, and the function is water storage.
Step 2-2, fuzzy equivalent matrix
Assuming U is a given domain, let U A :a→[0，1]Is U to [0, 1 ]]A mapping over a closed interval. If for any a ∈ U, there is a unique U A (a)∈[0，1]Then, in such a mapping relationship,
referred to as a fuzzy subset on the universe of discourse U
u A Referred to as fuzzy subsets
Membership function of u A (a) Called a-pair paste subset
Degree of membership.
Assuming that U and V are two common sets, the direct product of U and V, U × V, is one fuzzy subset over { a × b | a ∈ U, b ∈ V }
I.e. a fuzzy relation of U to V. When U and V are finite sets, their fuzzy relation
Can also be represented by a matrix, as shown in
Let R be the fuzzy relation on uxv if it satisfies the following property:
the method comprises the following steps:
r (a, a) ═ 1; for example, "equal to", "as high", etc. are all self-returning relationships, i.e., an object has a relationship with itself.
Symmetry:
r (a, b) ═ R (b, a); if any element a is related to b as R, then b is related to a as R, then R is called as symmetrical relation. For example, the real relationships of "couple", "country and the like.
Third, transmissibility:
∨[U(a,c)∧U(c,b)]u (a, b) is less than or equal to U; if any element a has a relationship R with c, and c also has a relationship R with b, then a is related to b by R, such R is said to be symmetric. For example, "longer than …", "shorter than" etc. are realistic.
If meeting the requirements of first and second, calling R to be a fuzzy similar relation on U; if meeting the first, second and third, then the R is a fuzzy equivalence relation on the U.
The ontology form of the geographic domain is defined as the pair K ═ U, a, U is a non-empty finite set called the ontology domain of the geographic domain, and a is a binary equivalence relation. An equivalence relation on U can be expressed as
For
The indistinguishable relationship produced by X is
Representing the highest resolution and degree of representation of the domain ontology.
According to the rough set correlation theory, the domain knowledge A is the class [ U ] in which each divided object U on U can not be located] A And (4) resolving. Therefore, a fuzzy equivalent matrix corresponding to A is introduced, formal concept analysis is carried out, and semantic similarity between two concepts is measured.
In the (G, M, I) form conceptual table, if two objects have the same attribute, the two objects are equivalent. If R is a fuzzy equivalence matrix on the set M, then ∑ M, R may serve as a fuzzy granularity knowledge base. R can be obtained by:
if two elements O i ,O j E.g. M, the similarity is as follows:
corresponding fuzzy relation matrix
Can satisfy reflexibility and symmetry, but in most cases, transmissibility is not satisfied
Just as a fuzzy similarity matrix. The fuzzy equivalence relation R can be generally obtained by means of a transitive closure algorithm, as shown in the formula
And a third stage: semantic inverse distance interpolation weight calculation
The semantic reverse distance interpolation method reconsiders the weight of interpolation, increases the weight of the same geographic semantics on the basis of distance, reduces the weight of different land utilization types on the basis of distance, and forms the semantic reverse distance interpolation method, and the formula is as follows
In the formula, T 0 The estimated value of the ith point to be estimated; t is i Is the measured value of the ith discrete point; d is a radical of i The distance between the ith point to be estimated and the discrete point; x is the number of i The latitude of the point to be estimated; x is the latitude of the discrete point; y is i Longitude of the point to be estimated; y is the longitude of the discrete point; m is the number of actually measured sample points participating in interpolation;
wherein n is 2, k i The semantic similarity between the point i to be interpolated and the discrete point i is within the value range of 0 & lt kappa 1 Less than or equal to 1; when the land use type of the point i to be interpolated is equivalent to that of the discrete point i, kappa i 1 is ═ 1; when the land use types of the point i to be interpolated and the discrete point i are not equivalent, 0 & lt kappa i ＜1。
In the description herein, references to the description of "one embodiment," "an example," "a specific example," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand the invention for and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (2)
1. An extreme air temperature semantic inverse distance weight interpolation method comprises the steps of introducing geographic semantic similarity into a formula of a traditional inverse distance weight interpolation method, adjusting a weight coefficient, and comprehensively considering geographic semantic difference between a sample point and an estimation point in an interpolation process, and is characterized by comprising the following specific steps of:
step one, surface temperature inversion
Based on an atmospheric correction method, performing surface temperature inversion by using Landsat 8TIRS data, preprocessing the data through radiometric calibration and atmospheric correction, obtaining the surface temperature by calculating basic parameters such as radiance, surface specific radiance, black body radiance and the like, establishing an interpolation research area, preprocessing remote sensing data on the basis of the interpolation research area, performing surface temperature inversion, verifying inversion accuracy, and extracting temperature data;
step two, calculating the geographic semantic similarity
The method for calculating the semantic similarity is a concept fuzzy equivalent matrix algorithm based on a geographic ontology, the method for calculating the semantic similarity utilizes the ontology attribute of geographic entity semantics, analyzes influence factors of the geographic semantic similarity on the basis of a fuzzy equivalent relationship, firstly constructs a semantic hierarchical tree on the basis of an interpolation research area, then determines the semantic type represented by each sample point of the interpolation research area, evaluates the semantic similarity between the surface coverage of each pair of nodes in the semantic, calculates the geographic semantic similarity corresponding to each pair of sample points, and constructs a semantic similarity matrix between the sample points;
in the (G, M, I) form conceptual table, if two objects have the same attribute, the two objects are equivalent, and if R is a fuzzy equivalent matrix on the set M, Σ (M, R) can be used as a fuzzy granularity knowledge base, and R can be obtained as follows
If two elements Q i ,Q j E.g. M, the similarity is as follows
Corresponding fuzzy relation matrix
Can satisfy reflexivity and symmetry, and can be used as fuzzy similarity matrix only, and the fuzzy equivalence relation R can be obtained by means of transitive closure algorithm, as shown in formula
Step three, semantic reverse distance interpolation weight calculation
Firstly, the interpolation of each interpolation point is modified by utilizing the semantic similarity, then the traditional spatial interpolation method is modified by utilizing the semantic similarity, the attribute value is calculated, the semantic reverse distance weight interpolation is completed, the weight of the interpolation is reconsidered by the semantic reverse distance interpolation method, the weight of the same geographic semantic meaning is increased on the basis of the distance, the weight of different land utilization types is reduced on the basis of the distance, the semantic reverse distance interpolation method is formed, and the formula is as follows
In the formula, T 0 The estimated value of the ith point to be estimated; t is i Is the measured value of the ith discrete point; d i The distance between the ith point to be estimated and the discrete point; x is the number of i The latitude of the point to be estimated; x is the latitude of the discrete point; y is i Longitude of the point to be estimated; y is the longitude of the discrete point; m is the number of actually measured sample points participating in interpolation;
wherein n is 2, k i The semantic similarity between the point i to be interpolated and the discrete point i is within the value range of 0 & lt kappa 1 Less than or equal to 1; when the land use type of the point i to be interpolated is equivalent to that of the discrete point i, kappa i 1 is ═ 1; when the land use types of the point i to be interpolated and the discrete point i are not equivalent, kappa is more than 0 i ＜1。
2. The extreme air temperature semantic inverse distance weight interpolation method according to claim 1, wherein the atmospheric correction method is implemented as follows:
thermal infrared radiation brightness value R received by sensor of remote sensing satellite λ Is the radiation of a luminance R from the atmosphere upwards ↑ Energy reaching the satellite sensor after the real radiation brightness of the ground penetrates the atmosphere and reflected energy R after the atmosphere radiates downwards to the ground ↓ Three parts, namely a thermal infrared radiation brightness value R received by a sensor of a remote sensing satellite λ The radiation transmission equation of
R λ ＝[φA(T z )+(1-φ)R ↓ ]δ+R ↑
Wherein phi represents the ground emissivity; t is z Representing the true surface temperature; a (T) z ) Represents the black body radiation brightness; delta is the transmittance of the atmosphere in the thermal infrared band;
wherein, the radiation brightness A (T) of the black body with the temperature T in the thermal infrared band z ) Is expressed as
For T z Function acquisition with Planck's formula
T z ＝K 2 /ln[K 1 /A(T z )+1]
For TM data, K 1 ＝607.76W/(m 2 *μm*sr)，K 2 1260.56K; for ETM + data, K 1 ＝666.09W/(m 2 *μm*sr)，K 2 1282.71K; k for TIRS Band10 data 1 ＝774.89W/(m 2 *μm*sr)，K 2 ＝1321.08K。
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