CN109446693B - Time expansion method for thermal radiation directivity intensity of urban building scene - Google Patents

Time expansion method for thermal radiation directivity intensity of urban building scene Download PDF

Info

Publication number
CN109446693B
CN109446693B CN201811324082.XA CN201811324082A CN109446693B CN 109446693 B CN109446693 B CN 109446693B CN 201811324082 A CN201811324082 A CN 201811324082A CN 109446693 B CN109446693 B CN 109446693B
Authority
CN
China
Prior art keywords
temperature
calculating
wall
ground
angle
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201811324082.XA
Other languages
Chinese (zh)
Other versions
CN109446693A (en
Inventor
陈云浩
王丹丹
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Normal University
Original Assignee
Beijing Normal University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Normal University filed Critical Beijing Normal University
Priority to CN201811324082.XA priority Critical patent/CN109446693B/en
Publication of CN109446693A publication Critical patent/CN109446693A/en
Application granted granted Critical
Publication of CN109446693B publication Critical patent/CN109446693B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • Y02A30/60—Planning or developing urban green infrastructure

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Geometry (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • Theoretical Computer Science (AREA)
  • Civil Engineering (AREA)
  • Structural Engineering (AREA)
  • Computational Mathematics (AREA)
  • Architecture (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Photometry And Measurement Of Optical Pulse Characteristics (AREA)

Abstract

The invention provides a time expansion method of the heat radiation directivity intensity of an urban building scene, which comprises the steps of dividing the components of the urban building scene into a roof, an illumination ground, a shadow ground and walls in different directions; inputting a geometric structure of a building scene and an angle of a solar sensor; calculating the component proportion in the view field according to the geometric structure of the building scene and the angle of the solar sensor; defining wall temperature T w (ii) a Calculating the average brightness temperature of the wall in the view field; defining the illumination ground brightness temperature in the field of view as T ig Shadow ground light temperature is T sg The brightness temperature of the roof is T roof (ii) a Calculating the brightness temperature in the inclined direction; calculating the brightness temperature of the zenith direction; calculating the heterogeneity intensity in the bright temperature direction; parameterizing model coefficients; and calculating the heterogeneity intensity in the bright temperature direction at any moment. The method realizes accurate simulation of the brightness temperature directional intensity along with the time change. The model considers the specific attributes of a building scene, is suitable for remote sensing inversion, and can be applied to angle correction of temperature observation data in different time directions.

Description

Time expansion method for thermal radiation directivity intensity of urban building scene
Technical Field
The invention relates to a time expansion method for the thermal radiation directivity intensity of an urban building scene.
Background
Remote sensing is an effective means of obtaining surface temperature over a wide range. The method obtains the thermodynamic information of the urban earth surface by means of remote sensing earth surface temperature, and further discloses the spatial pattern, the time evolution and the driving mechanism of the urban thermal environment, and the method becomes the main field of quantitative remote sensing. However, the thermal infrared remote sensing can only acquire information of one or a few angles, the whole heat radiation of the whole 2 pi upper hemisphere of the urban underlying surface cannot be snooped, and the satellite thermal infrared remote sensing (except for a static satellite) can only acquire information of one or a few time sections, so that the continuous change condition of the surface temperature of the urban underlying surface in the time dimension is difficult to acquire. Most of the heat radiation simulation models constructed in recent years for the urban underlying surface have a larger distance from the real remote sensing inversion, and the distance is mainly derived from that the earth surface temperature changes rapidly in the time dimension and has a remarkable daily change characteristic. Taking the MODIS/LST data as an example, although in most regions of the world, there are 4 surface temperature observations per day, however, the 4 observations are different in observation time except for different observation angles. The difference in observation times has little effect on albedo inversion. However, the surface geometry and physical properties are not the only factors determining the surface temperature, and the external climatic weather background (various compels) also plays an important role.
Currently, models available to simulate temporal changes in urban thermal radiation directivity include the kernel-driven model proposed by Vinnikov et al (2012) and the parameterized model proposed by Lagouarde et al (2008). Ermida et al, (2018) evaluated the ability of these two models to simulate the change in the thermal radiation directivity of vegetation over time. In the Vinnikov model, the thermal radiation directionality is expressed as the sum of the effects of homogeneous composition, emissivity directionality, and composition temperature heterogeneity. The component temperature difference kernel in the Vinnikov model represents the component temperature difference through the solar zenith angle and simulates the change of the thermal radiation directivity along with time. However, since no modulation kernel is used in a parametric model like that proposed by Lagouarde et al. (2008), the Vinnikov model underestimates the thermal radiation directivity. In the parameterized model proposed by Lagouarde et al. (2008), the distribution of the radiation directivity intensity over the hemispherical space is determined by two variables, respectively the intensity Δ T of the heterogeneity in the direction of the hot spot HS And a shape factor characterizing the intensity of heterogeneity as a function of observed zenith angle. Wherein, Δ T HS Is a time-varying quantity. Ermida et al (2018) compares Δ T HS Describing Δ T as a function of solar zenith angle and atmospheric layer top solar radiation HS Daily and annual changes. However, this relationship is not applicable to urban building scenarios. Δ T in the architectural setting, according to Krayenhoff and Voogt (2016) HS Not only with the temperature difference between the components, but also with the building structure, and this relationship exhibits a piecewise law. At the same time, it is difficult to infer the physical meaning of the form factor, and therefore no clear relationship can be constructed between the form factor and the surface attributes. In essence, the parameterized model is an empirical model, and does not take into account the unique properties of the urban building scenario. While various strategies have emerged that attempt to time normalize the surface temperature for different observation times during the day (Duan et al, 2014), these methods have not taken into account angular effects. In one word, a near-field scale is established which takes into account both rapid changes in the time dimension of the surface temperature and directional changes in the remotely sensed surface temperatureThe urban underlying surface heat radiation directional model' is very urgent.
Disclosure of Invention
The invention aims to solve the technical problem of providing a thermal radiation directivity model of an urban underlying surface under a close-range scale, which not only considers the rapid change of the surface temperature in a time dimension, but also considers the directivity change of the remote sensing surface temperature.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
a time expanding method for the thermal radiation directivity intensity of an urban building scene comprises the following steps:
step 1: dividing the components of an urban building scene into a roof, an illumination ground, a shadow ground and walls in different directions;
step 2: inputting a building scene geometry including an average building height-to-space ratio (h/w) and a rooftop ratio (λ) and a sun sensor angle p ) (ii) a The solar sensor angle comprises the solar zenith angle (theta) s ) And the sensor observes the zenith angle (theta) v ) And the relative azimuth angle of the sensor and the sun
Figure BDA0001858224980000031
And step 3: calculating the component proportion in the view field according to the geometric structure of the building scene and the angle of the solar sensor;
and 4, step 4: defining wall temperature T w ；
And 5: calculating the average brightness temperature of the wall in the view field;
and 6: defining the illumination ground brightness temperature in the field of view as T ig Shadow ground light temperature is T sg The roof brightness temperature is T roof ；
And 7: calculating the average brightness temperature of an object in a view field when the sensor is obliquely observed, namely the brightness temperature in the oblique direction;
and 8: calculating the average brightness temperature of objects in a visual field when the sensor vertically and visually measures, namely the brightness temperature in the zenith direction;
and step 9: calculating the heterogeneity intensity in the bright temperature direction;
step 10: parameterizing model coefficients;
step 11: and calculating the heterogeneity intensity in the bright temperature direction at any moment.
The method combines the change of component temperature difference along with time with a heat radiation directivity geometric model by taking the geometric structure of a building scene, the observation angles of the sun and a sensor and the brightness temperature heterogeneity intensities at multiple angles at different moments as input variables and the brightness temperature heterogeneity intensities at other moments under a clear sky condition as output variables, thereby realizing the accurate simulation of the change of the brightness temperature directivity intensity along with time. The model is provided based on a physical process, considers the specific attributes of a building scene, is suitable for remote sensing inversion, and can be applied to angle correction of temperature observation data in different time directions.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below.
FIG. 1 shows a technical flow chart of a time expansion method for the thermal radiation directivity intensity of an urban building scene.
FIG. 2 is a comparison of model coefficients obtained by correcting a GUTA-T model using light temperature directional intensity simulation data for one year (including different months for different years) and model coefficients obtained by fitting the corrected GUTA-T model to model coefficients of an energy balance model TUF-3D simulation according to the method of the present invention.
FIG. 3 is a graph of brightness and temperature directional strength changes of different months and different moments in a day calculated by correcting a GUTA-T model by utilizing brightness and temperature directional strength simulation data of a year (including different months in different years) according to the method of the invention and inversely substituting model coefficients obtained by fitting into the GUTA-T model, and comparing the brightness and temperature directional strength changes with simulation results of a sensor observation simulation model SUM.
FIG. 4 shows the verification result of GUTA-T simulation of heterogeneity intensity in the light temperature direction in the day by using measured data.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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, the time expansion method for the thermal radiation directivity intensity of the urban building scene comprises the following steps:
A. and (4) dividing the components. The components of an urban building scene are divided into a roof, an illumination ground, a shadow ground and walls in different orientations.
B. Building scene geometry and solar sensor angles are input. The architectural scene geometry includes an average architectural height-to-space ratio (h/w) and a rooftop ratio (λ) p ) (ii) a The solar sensor angle comprises the solar zenith angle (theta) s ) And the sensor observes the zenith angle (theta) v ) And the relative azimuth angle of the sensor and the sun
Figure BDA0001858224980000055
C. The component proportions in the field of view are calculated. And calculating the component proportion in the field of view according to the input building scene geometry and the solar sensor angle. Wall proportion f wall The calculation method of (2) is as follows:
Figure BDA0001858224980000051
shadow ground scale f visible to the sensor vsg The following were used:
Figure BDA0001858224980000052
the proportion f of the illuminated floor that is visible to the sensor vig The following were used:
Figure BDA0001858224980000053
D. defining wall temperature T w . The stronger the illumination of the wall surface is, the higher the temperature is, and the parameterization is carried out on the surface temperature of the wall surface according to the included angle between the orientation of the wall surface and the sun. Assuming that the temperature of the wall body not directly radiated by the sun is T 0 All wall surface temperature variation ranges are T a Then define the wall temperature T by the formula (4) w Comprises the following steps:
Figure BDA0001858224980000054
wherein n represents a unit normal vector perpendicular to the long side illuminated wall; s represents a unit normal vector of the sun direction.
E. And calculating the average brightness temperature of the wall in the visual field.
Figure BDA0001858224980000061
Wherein the content of the first and second substances,
Figure BDA0001858224980000062
is the sensor and sun relative azimuth angle, ranging between 0and pi.
F. Define the bright temperature of other components in the field of view. Setting the brightness and temperature of the illuminated ground as T ig Shadow ground light temperature is T sg The brightness temperature of the roof is T roof 。
G. And calculating the average brightness temperature of the object in the visual field when the sensor is observed obliquely, namely the brightness temperature in the oblique direction.
The calculation formula of the brightness temperature in the inclined direction is as follows:
Figure BDA0001858224980000063
H. and calculating the average brightness temperature of the object in the visual field when the sensor vertically and visually measures, namely the brightness temperature in the zenith direction.
Will theta v =0 band inAnd (6) obtaining a ceiling direction brightness temperature calculation formula as follows:
Figure BDA0001858224980000064
I. and calculating the heterogeneity intensity in the bright temperature direction. The heterogeneity intensity in the bright temperature direction is equal to the direction bright temperature minus the zenith direction bright temperature. Combining the formulas (5) and (6) to obtain the (theta) under any geometric conditions of the sun-building-sensor s ,θ v ,
Figure BDA0001858224980000065
) Intensity of heterogeneity in bright temperature direction:
Figure BDA0001858224980000066
wherein, the first and the second end of the pipe are connected with each other,
f iso ＝T 0 -T ig (9)
f ori ＝T a (10)
f shw ＝T sg -T ig (11)
Figure BDA0001858224980000071
Figure BDA0001858224980000072
Figure BDA0001858224980000073
wherein the content of the first and second substances,
Figure BDA0001858224980000074
Figure BDA0001858224980000075
k iso ,k ori ,k shw the method is characterized in that the method is a known parameter obtained by inputting known observation angles, sun angles and building scene geometric structures and directly calculating through formulas (12) - (14). f. of iso ,f ori ,f shw The three unknown coefficients of the model are respectively equal to the temperature difference between the shadow wall and the illumination ground, the temperature difference between the walls in different directions and the temperature difference between the shadow ground and the illumination ground in numerical value; the observation and fitting needs to be carried out through bright temperature observation in multiple angles and directions. f. of iso k iso ,f ori k ori ,f shw k shw And respectively representing the contributions of the shadow wall, the wall orientation change and the shadow ground to the heterogeneity intensity in the bright temperature direction.
J. And (5) parameterizing model coefficients. The component temperature differences in the formulas (9) - (11) are further parameterized and expressed as the solar zenith angle theta s The function of (d) is as follows:
T 0 -T ig ＝a 1 cosθ s (17)
T a ＝a 2 sin2θ s (18)
T sg -T ig ＝a 3 cosθ s (19)
wherein, a 1 ,a 2 ,a 3 The coefficients are new coefficients of the model, respectively represent the sensitivity of the temperature difference between the shadow wall and the illumination ground, the temperature difference between the wall with different directions and the temperature difference between the shadow ground and the illumination ground to the solar zenith angle, and are related to material properties, building structures and weather conditions.
K. And calculating the heterogeneity intensity in the bright temperature direction at any moment. The calculation method is as follows:
Figure BDA0001858224980000076
inputting construction scene structure parameter lambda p H/w, sun angle and observation angle, k in the calculation formula (20) iso ,k ori ,k shw (ii) a Obtaining the coefficient a in the formula (20) by fitting the heterogeneity intensity of the brightness temperature directions in a plurality of observation directions at different moments 1 ,a 2 ,a 3 A step of,; and inputting the sun angle and the sensor observation angle at any moment, and calculating the heterogeneity intensity of the bright temperature direction in the observation direction.
Results analysis and superiority of the method:
(1) The GUTA-T model is corrected by using the brightness temperature direction heterogeneity intensity simulation data of one year (including different months in different years), and the comparison result of the model coefficients obtained by fitting and the model coefficients simulated by the energy balance model TUF-3D is shown in FIG. 2. FIGS. 2a, b, c show three coefficients a 1 ,a 2 ,a 3 And (5) comparison of fitting results.
(2) And correcting the GUTA-T model by utilizing the simulation data of the brightness and temperature directivity intensity of one year (including different months in different years), inversely substituting the model coefficient obtained by fitting into the GUTA-T model, calculating to obtain the brightness and temperature directivity intensity changes of different months and different moments in one day, and comparing the simulation result with the simulation result of the sensor observation simulation model SUM, such as the graph in FIG. 3. FIGS. 3 (a 1), (b 1), (c 1), (d 1) are graphs of the intensity of heterogeneity of the SUM model simulation over time in different observation directions. FIGS. 3 (a 2), (b 2), (c 2), (d 2) are graphs of the intensity of heterogeneity of the corresponding GUTA-T simulations as a function of time. FIGS. 3 (a 3), (b 3), (c 3), (d 3) are graphs of the average error of GUTA-T and SUM simulations per month.
(3) And (3) verifying the heterogeneity intensity of the GUTA-T simulated day-inside bright temperature direction by using the measured data, wherein the verification result is shown in figure 4.
(4) The test result shows that the model parameter fitting result is greatly influenced by the building density, the smaller the sky visible factor of the building scene is, the larger the model parameter fitting error is. But the fitting result can represent the influence of the building structure on the component temperature difference to a certain extent. GUTA-T can simulate the distribution rule of the brightness, temperature and heterogeneity intensity in different months and at different moments in a day. In (observe the zenith angle theta) v Observation azimuth angle
Figure BDA0001858224980000081
) Is composed ofThe absolute errors of the heterogeneous intensity simulations were 0.33,0.36,1.10and 0.64 deg. (40 deg., 130 deg.), (40 deg., 220 deg.), (60 deg., 180 deg.) and (60 deg., 270 deg.). And (3) verifying the heterogeneity intensity of the GUTA-T simulated bright temperature direction by using the measured data, wherein the root mean square error of the GUTA-T simulated bright temperature direction heterogeneity intensity is 2.02 ℃.
The above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (6)

1. A time expanding method for the thermal radiation directivity intensity of an urban building scene is characterized by comprising the following steps:
step 1: dividing the components of an urban building scene into a roof, an illumination ground, a shadow ground and walls in different directions;
step 2: inputting a building scene geometry comprising an average building height-to-separation ratio h/w and a rooftop occupancy ratio λ and a solar sensor angle p (ii) a The solar sensor angle comprises a solar zenith angle theta s And the sensor observes the zenith angle theta v And the relative azimuth angle of the sensor and the sun
Figure FDA0003926404200000011
And step 3: calculating the component proportion in the view field according to the geometric structure of the building scene and the angle of the solar sensor;
and 4, step 4: defining wall temperature T w ；T 0 Represents the temperature of the wall not directly irradiated by the sun; t is a unit of a Representing the variation amplitude of all wall surface temperatures;
and 5: calculating the average brightness temperature of the wall in the view field;
step 6: defining the illumination ground brightness temperature in the field of view as T ig Shadow ground light temperature is T sg The brightness temperature of the roof is T roof ；
And 7: calculating the average brightness temperature of an object in a view field when the sensor is obliquely observed, namely the brightness temperature in the oblique direction;
and 8: calculating the average brightness temperature of objects in a visual field when the sensor vertically and visually measures, namely the brightness temperature in the zenith direction;
and step 9: calculating the heterogeneity intensity in the bright temperature direction; the heterogeneity intensity calculation formula in the bright temperature direction is as follows:
Figure FDA0003926404200000012
wherein the content of the first and second substances,
f iso ＝T 0 -T ig (2)
f ori ＝T a (3)
f shw ＝T sg -T ig (4)
Figure FDA0003926404200000021
Figure FDA0003926404200000022
Figure FDA0003926404200000023
wherein the content of the first and second substances,
Figure FDA0003926404200000024
Figure FDA0003926404200000025
k iso ,k ori ,k shw the method is characterized in that the method is characterized by comprising the following steps of inputting known observation angles, solar angles and building scene geometric structures into three kernel functions of a model, and directly calculating the kernel functions through formulas (12) to (14) to obtain known parameters; f. of iso ,f ori ,f shw The three unknown model coefficients are respectively equal to the temperature difference between the shadow wall and the illumination ground, the temperature difference between the walls in different directions and the temperature difference between the shadow ground and the illumination ground in numerical value; f is obtained by observing and fitting bright temperature in multiple angles iso k iso ,f ori k ori ,f shw k shw Respectively representing the contributions of the shadow wall, the wall orientation change and the shadow ground to the heterogeneity strength of the bright temperature direction;
step 10: the model coefficient f in the formula (9) -11 in the step 9 is compared iso ,f ori ,f shw Further parameterised, expressed as the solar zenith angle θ s The function of (d) is as follows:
T 0 -T ig ＝a 1 cosθ s (10)
T a ＝a 2 sin2θ s (11)
T sg -T ig ＝a 3 cosθ s (12)
wherein, a 1 ,a 2 ,a 3 Is a new model coefficient which respectively represents the temperature difference T between the shadow wall and the illumination ground 0 -T ig Temperature difference T of wall body in different directions a Temperature difference T between shadow ground and illumination ground sg -T ig Sensitivity to solar zenith angles;
step 11: calculating the heterogeneity intensity of the bright temperature direction at any moment; the method for calculating the heterogeneity intensity in the bright temperature direction at any moment comprises the following steps:
Figure FDA0003926404200000031
input roof ratio λ p The average building height-to-space ratio h/w, the brightness and temperature observed values in different time directions, the sun angle and the observation angle are fitted with a formula (20), and a model coefficient a is obtained 1 ,a 2 ,a 3 And then inputting the sun angle and the sensor observation angle at any moment, and calculating the heterogeneity intensity of the bright temperature direction in the observation direction.
2. The method of claim 1, wherein: wall proportion f in the component proportions in step 3 wall The calculation method of (2) is as follows:
Figure FDA0003926404200000032
shadow ground ratio f vsg The following were used:
Figure FDA0003926404200000033
ratio f of illuminated floor vig The following were used:
Figure FDA0003926404200000034
3. the method of claim 1, wherein: step 4, the wall temperature T w Is defined as:
Figure FDA0003926404200000035
wherein n represents a unit normal vector perpendicular to the long side illumination wall; s represents a unit normal vector of the sun direction.
4. The method of claim 1, wherein: in the step 5, the average brightness temperature of the wall body is calculated by the following formula:
Figure FDA0003926404200000036
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003926404200000037
is the sensor and sun relative azimuth angle, ranging between 0and pi.
5. The method according to claim 1, wherein the calculation formula of the bright temperature in the inclined direction in step 7 is as follows:
Figure FDA0003926404200000041
6. the method according to claim 1, wherein the zenith direction light temperature calculation formula in step 8 is as follows:
Figure FDA0003926404200000042
CN201811324082.XA 2018-11-08 2018-11-08 Time expansion method for thermal radiation directivity intensity of urban building scene Active CN109446693B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811324082.XA CN109446693B (en) 2018-11-08 2018-11-08 Time expansion method for thermal radiation directivity intensity of urban building scene

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811324082.XA CN109446693B (en) 2018-11-08 2018-11-08 Time expansion method for thermal radiation directivity intensity of urban building scene

Publications (2)

Publication Number Publication Date
CN109446693A CN109446693A (en) 2019-03-08
CN109446693B true CN109446693B (en) 2022-12-23

Family

ID=65551892

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811324082.XA Active CN109446693B (en) 2018-11-08 2018-11-08 Time expansion method for thermal radiation directivity intensity of urban building scene

Country Status (1)

Country Link
CN (1) CN109446693B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112985607B (en) * 2021-01-25 2021-09-24 中国科学院空天信息创新研究院 Method for correcting heat radiation directivity of geostationary satellite earth surface uplink long-wave radiation product
CN114323308B (en) * 2022-01-06 2023-05-02 中国地质大学（北京） Correction method for remote sensing city ground surface temperature angle effect
CN114323291B (en) * 2022-01-06 2023-06-13 中国地质大学（北京） Calculation method for satellite observation city ground surface temperature angle effect

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007003308A (en) * 2005-06-22 2007-01-11 Pasuko:Kk Method of estimating ground temperature and program for it
CN102235911A (en) * 2010-04-20 2011-11-09 北京师范大学 Method for detecting thermal radiation directivity of urban goal

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007003308A (en) * 2005-06-22 2007-01-11 Pasuko:Kk Method of estimating ground temperature and program for it
CN102235911A (en) * 2010-04-20 2011-11-09 北京师范大学 Method for detecting thermal radiation directivity of urban goal

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
A geometric model to simulate thermal anisotropy over a sparse urban surface(GUTA-sparse);Dandan Wang 等;《Remote Sensing of Environment》;20180319;第209卷;第263-274页 *

Also Published As

Publication number Publication date
CN109446693A (en) 2019-03-08

Similar Documents

Publication Publication Date Title
Park et al. Impacts of tree and building shades on the urban heat island: Combining remote sensing, 3D digital city and spatial regression approaches
CN109446693B (en) Time expansion method for thermal radiation directivity intensity of urban building scene
Lagouarde et al. Modelling daytime thermal infrared directional anisotropy over Toulouse city centre
Lagouarde et al. Airborne experimental measurements of the angular variations in surface temperature over urban areas: case study of Marseille (France)
CN109115995B (en) Soil moisture information acquisition methods, device, computer equipment and storage medium
Liu et al. Contrasting the performance capabilities of urban radiation field between three microclimate simulation tools
CN109211960B (en) Method for calculating heat radiation directivity intensity of dense city building scene
CN108168710A (en) A kind of city tropical island effect appraisal procedure based on remote sensing technology
CN109186774B (en) Surface temperature information acquisition method, device, computer equipment and storage medium
CN101915914A (en) Lookup table based pixel-by-pixel atmospheric correction method of remote sensing images
Dochev et al. Calculating urban heat demands: An analysis of two modelling approaches and remote sensing for input data and validation
CN108009392B (en) Remote sensing reflectivity model construction and calibration application method for dense vegetation surface
CN103646175A (en) Computing method for spectral radiance of target
Subramaniam et al. A more accurate approach for calculating illuminance with daylight coefficients
Barbaro et al. Observational characterization of the downward atmospheric longwave radiation at the surface in the city of São Paulo
Du et al. Modeling directional brightness temperature of the winter wheat canopy at the ear stage
Li et al. STMRT: A simple tree canopy radiative transfer model for outdoor mean radiant temperature
CN104933302A (en) Method and system for realizing aerial remote sensing simulation of load of unmanned aerial vehicle
CN116187807A (en) Photovoltaic power generation amount evaluation method, device, computer equipment, medium and product
CN115546393A (en) Open GL-based infrared virtual scene radiance drawing method
Ma et al. Remote sensing parameterization of regional land surface heat fluxes over arid area in northwestern China
Escobar et al. Solar Energy Resource Assessment in Chile: Satellite estimation and ground station measurement
CN115808246A (en) Space normalization method for observation temperature of remote sensing thermal infrared sensor
Qualls et al. Directional radiometric temperature profiles within a grass canopy
Cremer et al. Surface mine signature modeling for passive polarimetric IR

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant