Solar elevation angle-based remote sensing sensible heat flux correction method and device

Abstract

A solar elevation angle-based remote sensing sensible heat flux correction method and device, the method comprising: classifying land according to obtained land remote sensing image information; obtaining and based on meteorological data of the land, estimating the sensible heat flux of each region at a certain moment, and obtaining a first sensible heat flux difference, fitting a solar elevation angle function, and calculating a second sensible heat flux difference when the solar elevation angle reaches a theoretical maximum; and correcting the sensible heat flux of a high-rise building area according to the second sensible heat flux difference and the average sensible heat flux of a low-rise building area. The solar elevation angle-based remote sensing sensible heat flux correction method provided in the embodiment can effectively solve the problem of underestimation of sensible heat flux caused by the mismatch of spatial scales of meteorological data and remote sensing data, and can be applied to remote sensing-ground surface energy balance model modeling, thereby providing a new technical means for accurate quantitative assessment of urban thermal environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban thermal environment evaluation, and particularly relates to a remote sensing sensible heat flux correction method and device based on a solar elevation angle. BACKGROUND

[0002] Anthropogenic heat is an important source term in urban multi-scale energy budget, and plays an important role in urban thermal environment and urban climate. The main sources of anthropogenic heat include vehicle traffic, industrial production, building energy consumption and human metabolism, and its value is difficult to measure directly, but a variety of estimation methods have been proposed, mainly including energy inventory method, surface energy balance method and building energy consumption simulation method. Among them, the surface energy balance method considers that the imbalance of urban energy budget is caused by anthropogenic heat flux (AHF), so the residual term of the urban surface energy balance equation can be used as the anthropogenic heat flux. However, the use of this method to estimate anthropogenic heat flux needs to estimate other components in the equation first. The traditional method based on instrument measurement is high in cost and cannot be popularized to large-scale research areas, which greatly restricts the application of the energy balance equation method.

[0003] In recent years, remote sensing technology has been widely used in surface energy modeling. Based on high-resolution multispectral remote sensing imagery, biophysical parameters over a large surface area can be obtained, supporting the estimation of components in the surface energy balance equation. This method was initially applied to surface evapotranspiration measurement, such as the Surface Energy Balance Algorithm for Land (SEBAL) model and the Surface Energy Balance System (SEBS) model. This method has been applied to the field of urban thermal environment and has become one of the classic methods for estimating urban anthropogenic heat emissions. The basic assumption of the Remote Sensing-Surface Energy Balance (RS-SEB) model for estimating anthropogenic heat is that anthropogenic heat emissions only perturb sensible heat flux; therefore, the anthropogenic heat obtained is actually the increase in sensible heat flux caused by anthropogenic heat. However, the anthropogenic heat estimated based on this method in city centers is generally underestimated. This is mainly because RS-SEB expresses the instantaneous anthropogenic heat actually released into the atmosphere due to the lag effect of stored heat, which can be understood as the increase in sensible heat flux or temperature caused by anthropogenic heat. On the other hand, building shadows reduce the remotely sensed surface temperature and reflectivity, while coarse-resolution meteorological data still show higher temperatures and solar radiation in these areas, thus affecting the accurate estimation of sensible heat flux. This is especially true in areas with a high concentration of tall buildings, where the underestimation of sensible heat flux due to data spatial scale mismatch will seriously interfere with the results of anthropogenic heat estimation. In summary, the correction of sensible heat flux in densely populated areas of mid- to high-rise buildings is one of the key problems that current remote sensing-surface energy balance models urgently need to solve. However, there is still a lack of complex physical quantitative models or easy-to-use empirical methods to solve this problem. Summary of the Invention

[0004] This invention provides a method and apparatus for correcting remote sensing sensible heat flux based on solar altitude angle. By establishing a solar altitude angle function to correct remote sensing sensible heat flux, it can effectively solve the problem of underestimation of sensible heat flux caused by the spatial scale mismatch between meteorological data and remote sensing data. It can also be applied to remote sensing-surface energy balance modeling, providing a new technical means for accurate quantitative assessment of urban thermal environment and has high practical value.

[0005] Firstly, a remote sensing sensible heat flux correction method based on solar altitude angle is provided, comprising: acquiring land remote sensing image information, including the height of ground buildings; dividing the land into high-rise building areas and low-rise building areas based on the height of ground buildings; acquiring and estimating the sensible heat flux of each area at a certain moment based on meteorological data of the land; obtaining the first sensible heat flux difference between the high-rise building area and the low-rise building area at a certain moment based on the sensible heat flux, and establishing a relationship between the first sensible heat flux difference and the solar altitude angle function through the following formula:

[0006]

[0007] In the formula, R l-h It is the difference in the first sensible heat flux between high-rise and low-rise buildings, H lr and H hr These are the mean sensible heat fluxes of low-rise and high-rise building areas in the land remote sensing image, respectively. f(SA) is the solar altitude angle function, ε is the error term, and SA represents the normalized solar altitude angle.

[0008] The solar altitude angle function f(SA) is fitted using the least squares method, and the second sensible heat flux difference when the solar altitude angle reaches its theoretical maximum value is calculated using formula (1). The sensible heat flux of the high-rise building area is corrected based on the second sensible heat flux difference and the average sensible heat flux of the low-rise building area. The sensible heat flux of the high-rise building area is corrected using the following formula:

[0009]

[0010] In the formula, This represents the second sensible heat flux difference, i.e., the percentage difference of sample point d (at a specific time and region) under ideal direct sunlight conditions, where 1 represents the theoretical maximum value of the solar altitude angle. This is the corrected sensible heat flux of the high-rise building area, w i This is the inverse distance weight; n represents the n nearest low-rise building cells to the high-rise building cell.

[0011] In one possible implementation, the method further includes: if the sensible heat flux of the high-rise building area after correction is lower than that before correction, then the original value is maintained.

[0012] In one possible implementation, the sensible heat flux of each region at a given time is estimated using the following formula, based on meteorological data of the land:

[0013]

[0014] In the formula, H is the sensible heat flux, ρ is the air density, and C is the sensible heat flux. p For the specific heat of air at constant pressure, r a For aerodynamic impedance, T s For surface temperature, T a Temperature.

[0015] In one possible implementation, before acquiring and estimating the sensible heat flux of each region at a given time based on land meteorological data, the method further includes: acquiring land surface temperature data and air temperature and wind speed data for each region, and calculating the aerodynamic impedance using the following formula:

[0016]

[0017] In the formula, r a For aerodynamic impedance, Z u and Z t These are the heights at which wind speed and temperature are measured, Z. 0m and Z 0h These are the momentum transport roughness and heat transport roughness, respectively, where d is the zero-plane displacement height, and u is the heat transport roughness. z Let be the wind speed, and k be a constant.

[0018] In one possible implementation, land surface temperature data is obtained based on Landsat satellite data, and temperature and wind speed data for each region are obtained based on meteorological data; in Python software, temperature and wind speed are sampled to the same resolution as Landsat satellite data using the nearest neighbor sampling algorithm.

[0019] In one possible implementation, land is classified according to a random forest algorithm.

[0020] In one possible implementation, land remote sensing image information is acquired based on Landsat satellite; the land is divided into high-rise building areas and low-rise building areas based on the ArcGIS platform; the spectral and texture features of the land remote sensing images are calculated based on the ENVI platform; and the spectral and texture features are input into Python software for land classification.

[0021] This application provides a method and apparatus for correcting remote sensing sensible heat flux based on solar altitude angle. The method includes: classifying land according to acquired land remote sensing image information; acquiring and estimating the sensible heat flux of each region at a certain moment based on meteorological data of the land, obtaining a first sensible heat flux difference, fitting a solar altitude angle function, and calculating a second sensible heat flux difference when the solar altitude angle reaches its theoretical maximum value; and correcting the sensible heat flux of high-rise building areas based on the second sensible heat flux difference and the average sensible heat flux of low-rise building areas. The remote sensing sensible heat flux correction method based on solar altitude angle provided by this invention, which corrects remote sensing sensible heat flux based on establishing a solar altitude angle function, can effectively solve the problem of underestimation of sensible heat flux caused by the mismatch in spatial scale between meteorological data and remote sensing data, and can be applied to remote sensing-surface energy balance model modeling, providing a new technical means for accurate quantitative assessment of urban thermal environment. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of a remote sensing sensible heat flux correction method based on solar elevation angle provided in an embodiment of the present invention;

[0023] Figure 2 This is a schematic diagram of fitting a function of the change in solar altitude angle using the least squares method, provided by an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings.

[0025] In the description of the embodiments of the present invention, the words "exemplary," "for example," or "for instance" are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design that is described as "exemplary," "for example," or "for instance" in the embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of the words "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0026] In the description of the embodiments of this invention, the term "and / or" is merely a description of the association relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, B existing alone, and A and B existing simultaneously. Furthermore, unless otherwise stated, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple terminals refer to two or more terminals.

[0027] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0028] Meteorological data, such as temperature, humidity, wind speed, and air pressure, possess coarse-resolution characteristics at a spatial scale that cannot match the spatial details of surface physical properties, spatial structure, and surface temperature expressed by high-resolution remote sensing imagery. This leads to the widely concerned underestimation of RS-SEB in city centers. The decrease in surface temperature caused by the shadowing of mid- to high-rise buildings and the overestimation of temperature in shaded areas by meteorological data result in an underestimation of sensible heat flux in urban areas, thus interfering with the estimation of anthropogenic heat. Therefore, it is extremely important to find a convenient and effective way to correct the results of remote sensing estimation of sensible heat flux.

[0029] Therefore, this application proposes a remote sensing sensible heat flux correction method based on solar elevation angle, including:

[0030] Step S101: Obtain land remote sensing image information, which includes the height of ground buildings.

[0031] Specifically, in this scheme, remote sensing image information of the target land can be obtained from Landsat remote sensing satellite. The information includes vegetation, trees, water bodies and buildings on the ground, as well as the height information of the ground vegetation, trees and buildings. It can be understood that the higher the building is, the larger the area of ​​its shadow, which leads to a decrease in the surface temperature. The overestimation of the temperature in the shadow area by meteorological data will lead to a serious underestimation of the sensible heat flux in some areas of the city.

[0032] Step S102: Divide the land into high-rise building areas and low-rise building areas based on the height of the ground buildings.

[0033] Specifically, in this scheme, the area can be divided into high-rise building areas and low-rise building areas based on the ArcGIS platform. Sample points of multiple land cover types are selected based on visual interpretation, including low vegetation, forests, water bodies, bare soil, sandy land, roads, low-rise buildings, and mid / high-rise buildings. The spectral and texture features of the land remote sensing images are calculated on the ENVI (The Environment for Visualizing Images) platform, and the Landsat spectral and texture features are input into the Python software. Land use classification is performed based on the sample points using a random forest model.

[0034] Understandably, other algorithms can also be used to extract the underlying and upper-level buildings.

[0035] Step S103: Acquire and estimate the sensible heat flux of each region at a certain moment based on the meteorological data of the land.

[0036] Specifically, in this scheme, surface temperature data of the target land can be obtained from Landsat satellites, and the aerodynamic impedance r can be calculated based on meteorological data and land use data of the target land. a The following formula is used:

[0037]

[0038] In the formula, r a For aerodynamic impedance, Z u and Z t These are the heights at which wind speed and temperature are measured, Z. 0m and Z 0h These are the momentum transport roughness and heat transport roughness, respectively, where d is the zero-plane displacement height, and u is the heat transport roughness. z Let be the wind speed, and k be a constant.

[0039] Due to the coarseness of meteorological data, it is necessary to sample air temperature and wind speed in Python software to the same resolution as the Landsat satellite based on the nearest neighbor sampling algorithm in order to improve sampling accuracy.

[0040] In one embodiment, the meteorological data for the target land includes the air temperature and wind speed, which can be actually measured. In one example, wind speed and temperature can be measured at distances of 2m and 10m above the ground.

[0041] After calculating the aerodynamic impedance of the target land area, the sensible heat flux of the target land area is calculated using the following formula:

[0042]

[0043] In the formula, H is the sensible heat flux, ρ is the air density, and C is the sensible heat flux. p For the specific heat of air at constant pressure, r a For aerodynamic impedance, T s For surface temperature, T a Temperature.

[0044] It is understandable that the surface temperature, air temperature, and wind speed vary at different times (or time periods). Therefore, sampling and calculation can be performed separately for different times (or time periods) to obtain the aerodynamic impedance and sensible heat flux for multiple times (or time periods). For the same reason, multiple sampling and calculations can be performed for multiple regions to obtain the aerodynamic impedance and sensible heat flux for multiple regions.

[0045] Step S104: Based on the sensible heat flux, obtain the first sensible heat flux difference between the high-rise building area and the low-rise building area at a certain moment, and establish a relationship between the first sensible heat flux difference and the solar altitude angle function using the following formula:

[0046]

[0047] In the formula, R l-h It is the difference in the first sensible heat flux between high-rise and low-rise buildings, H lr and H hr , respectively, are the mean sensible heat fluxes of low-rise and high-rise building areas within the land remote sensing image, f(SA) is the solar altitude angle function, ε is the error term, and SA represents the normalized solar altitude angle.

[0048] Step S105: Fit the solar altitude angle function f(SA) based on the least squares method, and calculate the second sensible heat flux difference when the solar altitude angle reaches the theoretical maximum value using formula (1).

[0049] Specifically, in this scheme, the solar altitude angle can be fitted using the least squares method in R language, and the fitting result is as follows: Figure 2 As shown. Calculate the solar altitude angle function value f(1) when the solar altitude angle reaches its theoretical maximum value, and calculate R at this time. l-h The calculation result is denoted as

[0050] Step S106: Correct the sensible heat flux of the high-rise building area based on the second sensible heat flux difference and the average sensible heat flux of the low-rise building area, wherein the sensible heat flux of the high-rise building area is corrected using the following formula:

[0051]

[0052] In the formula, This represents the second sensible heat flux difference, i.e., the percentage difference of sample point d (at a specific time and region) under ideal direct sunlight conditions, where 1 represents the theoretical maximum value of the solar altitude angle. This is the corrected sensible heat flux of the high-rise building area, w i This is the inverse distance weight; n represents the n nearest low-rise building cells to the high-rise building cell.

[0053] Understandably, a pixel can also represent a region.

[0054] In one embodiment, if the sensible heat flux of the high-rise building area after correction is lower than that before correction, the original value is maintained. This indicates that the area is not shaded, and therefore sensible heat flux correction is unnecessary.

[0055] This application also proposes a remote sensing sensible heat flux correction device based on solar altitude angle, comprising: a data acquisition module for acquiring land remote sensing image information and meteorological data; and a data processing module for processing the data information acquired by the data processing module to obtain the remote sensing sensible heat flux correction result.

[0056] This application also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the steps of the remote sensing sensible heat flux correction method as described above.

[0057] This application also provides a computer program product that stores instructions that, when executed by a computer, cause the computer to perform any of the methods described above and in the corresponding descriptions.

[0058] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this application.

[0059] Furthermore, various aspects or features of the embodiments of this application can be implemented as methods, apparatus, or articles of manufacture using standard programming and / or engineering techniques. The term "article of manufacture" as used in this application encompasses a computer program accessible from any computer-readable device, carrier, or medium. For example, computer-readable media may include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks, or magnetic tapes), optical discs (e.g., compact discs (CDs), digital versatile discs (DVDs), etc.), smart cards, and flash memory devices (e.g., erasable programmable read-only memory (EPROMs), cards, sticks, or key drives, etc.). Additionally, the various storage media described herein may represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing, and / or carrying instructions and / or data.

[0060] In the above embodiments, when implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., high-density digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0061] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of this application.

[0062] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0063] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0064] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0065] If the aforementioned function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application embodiment, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or an access network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0066] The above description is merely a specific implementation of the embodiments of this application, but the protection scope of the embodiments of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the embodiments of this application should be covered within the protection scope of the embodiments of this application.

Claims

1. A method for correcting remotely sensed heat flux based on solar elevation angle, characterized in that, include: Acquire land remote sensing image information, the land remote sensing image information including the height of ground buildings; The land is divided into high-rise building areas and low-rise building areas based on the height of the ground buildings. Acquire and estimate the sensible heat flux of each region at a certain moment based on the meteorological data of the land. Based on the sensible heat flux, the first sensible heat flux difference between the high-rise building area and the low-rise building area at a certain moment is obtained, and the first sensible heat flux difference is linked to the solar altitude angle function using the following formula: （1） In the formula, It is the difference in the first sensible heat flux between high-rise and low-rise buildings. and These are the average sensible heat fluxes of low-rise and high-rise building areas within the land remote sensing image, respectively. It is a function of solar altitude angle. SA represents the normalized solar altitude angle, which is the error term. The solar altitude angle function was fitted using the least squares method. And calculate the difference in the second sensible heat flux when the solar altitude angle reaches the theoretical maximum value using formula (1); The sensible heat flux of the high-rise building area is corrected based on the second sensible heat flux difference and the average sensible heat flux of the low-rise building area, wherein the sensible heat flux of the high-rise building area is corrected using the following formula: In the formula, This represents the second sensible heat flux difference, i.e., the percentage difference at sample point d under ideal direct sunlight conditions, where 1 represents the theoretical maximum value of the solar altitude angle. This is the corrected sensible heat flux for the high-rise building area. This is the inverse distance weight; n represents the n nearest low-rise building cells to the high-rise building cell.

2. The method according to claim 1, characterized in that, The method further includes: if the sensible heat flux of the high-rise building area after correction is lower than that before correction, then the original value is maintained.

3. The method according to claim 1, characterized in that, The sensible heat flux of each region at a certain moment is estimated using the following formula, based on the meteorological data of the land: In the formula, For sensible heat flux, air density, The specific heat of air at constant pressure. For aerodynamic impedance, For surface temperature, Temperature.

4. The method according to claim 3, characterized in that, Before acquiring and estimating the sensible heat flux of each region at a certain moment based on the meteorological data of the land, the process further includes: Obtain the land surface temperature data, as well as the air temperature and wind speed data for each region, and calculate the aerodynamic impedance using the following formula: In the formula, For aerodynamic impedance, and These are the heights at which wind speed and temperature are measured, respectively. and These are the momentum transport roughness and the heat transport roughness, respectively, where d is the height of the zero-plane displacement. For wind speed, It is a constant.

5. The method according to claim 4, characterized in that, Land surface temperature data is obtained based on Landsat satellite data, and air temperature and wind speed data for each region are obtained based on meteorological data. In Python software, the air temperature and wind speed are sampled to the same resolution as the Landsat satellite using a nearest neighbor sampling algorithm.

6. The method according to claim 1, characterized in that, The land is classified according to the random forest algorithm.

7. The method according to claim 1, characterized in that, Land remote sensing image information was acquired based on Landsat satellite; the area was divided into high-rise building areas and low-rise building areas based on the ArcGIS platform. The spectral and textural features of the land remote sensing image were calculated based on the ENVI platform. The spectral and texture features are input into Python software for land classification.

8. A remote sensing sensible heat flux correction device based on solar altitude angle, characterized in that, The remote sensing sensible heat flux correction device is used to perform the method as described in any one of claims 1-7, including: The data acquisition module is used to acquire land remote sensing image information and meteorological data; The data processing module processes the data information collected by the data processing module to obtain the remote sensing sensible heat flux correction result.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the remote sensing sensible heat flux correction method as described in any one of claims 1-7.

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