Urban surface heat storage estimation method based on stationary satellite

Abstract

The invention discloses an urban surface heat storage estimation method based on a stationary satellite, and the method comprises the following steps: constructing a data set based on various satellite remote sensing data; estimating urban surface thermal inertia based on the data set in combination with a surface temperature change rule; based on the hourly surface temperature and the estimated surface thermal inertia, hourly urban scale surface thermal storage is obtained. According to the urban surface heat storage estimation method based on the stationary satellite, hour-by-hour large-scale urban area surface heat storage can be obtained, and the defects that the ground observation range is limited, and the numerical simulation error is large are overcome.

Description

Technical Field

[0001] The invention relates to a method for estimating urban surface heat storage based on a geostationary satellite, and belongs to the technical field of remote sensing. Background Art

[0002] Compared with the natural underlying surface, a large part of the urban underlying surface is impermeable, with strong heat conduction capacity, and the area of ​​the urban three-dimensional structure is about twice the plane area, which increases the effective absorption area and increases the magnitude of urban heat storage. For example, Mexico City's heat storage can account for up to 58% of the net radiation. Urban heat storage is the main cause of nighttime urban heat islands. As global warming intensifies, appropriate urban heat island mitigation and adaptation measures are essential to human life and health. The premise for proposing measures to mitigate the heat environment based on heat storage is to obtain reliable heat storage data that can reflect the daily changes in heat storage.

[0003] Remote sensing estimation of natural surface heat storage generally establishes an empirical relationship between it and surface net radiation, remote sensing surface parameters (leaf area index LAI, normalized difference vegetation index NDVI, vegetation coverage, etc.). Due to the small amount of vegetation in cities, the above method is not applicable to cities.

[0004] Although there are urban heat storage models in the prior art, the more famous ones are the OHM (Objective Hysteresis Model) model and the ESTM (Element Surface Temperature Method) model. The OHM model establishes an empirical relationship between heat storage and net radiation, takes into account the hysteresis between the two, and simulates the daily changes in heat storage by changing the net radiation over time. It has been widely used in single-point study areas. Although the prior art gives the coefficients corresponding to different surface types in the OHM model, the coefficients of different cities vary greatly, which all represent the empiricism of the OHM model. The ESTM model is developed based on the heat conduction equation and requires the surface temperature of the inner and outer layers of the building. Although it has high accuracy, remote sensing can only obtain the outer surface temperature, and cannot obtain the inner surface temperature, so the model cannot be directly applied to remote sensing.

[0005] Therefore, it is necessary to conduct more in-depth research on how to use remote sensing technology to obtain hourly urban surface heat storage in order to solve the above problems. Summary of the invention

[0006] In order to overcome the above problems, an in-depth study was conducted and a method for estimating urban surface heat storage based on geostationary satellites was proposed, which includes the following steps: Step 1: Based on a variety of satellite remote sensing data, construct a data set including surface temperature data, surface albedo data and surface downlink shortwave data; Step 2: Based on the data set and combined with the surface temperature variation pattern, estimate the urban surface thermal inertia; Step 3: Based on the hourly surface temperature and surface thermal inertia, obtain the hourly urban-scale surface heat storage.

[0007] In a preferred embodiment, in step 1, the multiple satellite remote sensing data include surface temperature data, surface albedo data and surface downlink shortwave data.

[0008] In a preferred embodiment, clear sky data from satellite remote sensing data is selected to create a data set.

[0009] In a preferred embodiment, step 2 includes the following sub-steps: Sub-step 21, obtaining the relationship between the surface temperature and time; Sub-step 22: setting the objective function, obtaining the optimal thermal inertia through an optimization method, and using the optimal thermal inertia as the estimated urban surface thermal inertia.

[0010] In a preferred embodiment, in sub-step 21, the relationship between the surface temperature and time is obtained based on the one-dimensional heat conduction formula of the surface and its boundary conditions.

[0011] In a preferred embodiment, the one-dimensional heat conduction formula of the ground surface is expressed as: ; in, represents the air density, is the specific heat capacity of air, Indicates depth, Indicates time, Indicates depth Location The surface temperature at that moment, It represents the thermal conductivity of air; The boundary conditions are: ; ; ; in, represents the surface shortwave radiation, represents the downward longwave radiation of the atmosphere, represents the upward longwave radiation from the surface, is the surface sensible heat flux, represents the latent heat flux, , represents the simplification coefficient, represents the blue sky albedo of the surface, represents the solar constant, represents the atmospheric transmittance, represents the solar zenith angle, represents the solar declination angle, represents the local latitude, Represents the frequency of the Earth's rotation.

[0012] In a preferred embodiment, the relationship between the surface temperature and time is obtained, which is expressed as: ; in, ; ; ; ; in, is the intermediate variable, represents the optimal thermal inertia, For configurable parameters, , is the n-th order Fourier coefficient.

[0013] In a preferred embodiment, the objective function in sub-step 22 is set to: ;

[0014] in, ; for Surface temperature data at the moment.

[0015] The present invention also provides an electronic device, comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform any of the above methods.

[0016] The present invention also provides a computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute any of the above-mentioned methods.

[0017] The beneficial effects of the present invention include: (1) Effectively utilize the hourly surface temperature changes of geostationary satellites to fully reflect the thermal inertia properties of the surface; (2) It solves the deficiency of existing methods that only consider a limited time period and improves the accuracy of surface thermal inertia estimation; (3) The hourly and large-scale surface heat storage in urban areas can be obtained, which makes up for the shortcomings of limited ground observation range and large numerical simulation errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic flow chart of a method for estimating urban surface heat storage based on a geostationary satellite according to a preferred embodiment of the present invention; Figure 2 It is a comparison chart of the estimated thermal storage scatter point value and fitting curve of Example 1 and the observed results of Experimental Example 1. DETAILED DESCRIPTION

[0019] The present invention will be further described in detail below through the accompanying drawings and embodiments. Through these descriptions, the characteristics and advantages of the present invention will become more clear and distinct.

[0020] The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise noted.

[0021] According to the present invention, a method for estimating urban surface heat storage based on a geostationary satellite comprises the following steps: Step 1: Based on a variety of satellite remote sensing data, construct a data set including surface temperature data, surface albedo data and surface downlink shortwave data; Step 2: Based on the data set and combined with the surface temperature variation pattern, estimate the urban surface thermal inertia; Step 3: Based on the hourly surface temperature and estimated surface thermal inertia, obtain the hourly urban-scale surface heat storage.

[0022] In step one, the multiple satellite remote sensing data include surface temperature data, surface albedo data and surface downlink shortwave data.

[0023] In the present invention, the specific source of the satellite remote sensing data is not limited, and those skilled in the art can freely select it according to actual needs. Preferably, the satellite remote sensing data includes Himawari-8 satellite remote sensing data, GLASS satellite remote sensing data, and CARE satellite remote sensing data.

[0024] Among them, Himawari-8 satellite remote sensing data is hourly complete regional surface temperature data, with a spatial resolution of 0.02° and a temporal resolution of hourly, covering the East Asian region; GLASS satellite remote sensing data is surface albedo data with a spatial resolution of 1 km, a temporal resolution of every 8 days, and a global coverage. The CARE satellite remote sensing data is surface downlink shortwave data with a spatial resolution of 0.05° and a temporal resolution of 10 minutes, covering the Asia-Pacific region.

[0025] Furthermore, different satellite remote sensing data are unified in spatial resolution and position projection and fused into the data set, that is, the data set contains time, position and corresponding surface temperature data, surface albedo data and surface downlink shortwave data.

[0026] Preferably, clear sky data from satellite remote sensing data is selected to produce a data set, wherein the clear sky data refers to data corresponding to a sunny day for 24 hours a day.

[0027] According to experience, the surface temperature change on a sunny day can reflect the true thermal inertia property of the surface. The surface temperature on a rainy day will bring a large error to the thermal inertia estimation. Therefore, in the present invention, the data set only contains cleared data.

[0028] Preferably, the data with a median value of 7 in the quality control data of the Himawari-8 satellite remote sensing data is used as the clear sky data.

[0029] Step 2 includes the following sub-steps: Sub-step 21, obtaining the relationship between the surface temperature and time; Sub-step 22: setting the objective function, obtaining the optimal thermal inertia through an optimization method, and using the optimal thermal inertia as the estimated urban surface thermal inertia.

[0030] In sub-step 21, based on the one-dimensional heat conduction formula of the ground surface and its boundary conditions, the relationship between the surface temperature and time is obtained.

[0031] The one-dimensional heat conduction formula of the ground surface is expressed as: ; in, represents the air density, is the specific heat capacity of air, Indicates depth, Indicates time, Indicates depth Location The surface temperature at that moment, Represents the thermal conductivity of air.

[0032] The boundary conditions are: ; ; ;

[0033] in, represents the surface shortwave radiation, represents the downward longwave radiation of the atmosphere, represents the upward longwave radiation from the surface, is the surface sensible heat flux, represents the latent heat flux, , represents the simplification coefficient, represents the blue sky albedo of the surface, represents the solar constant, represents the atmospheric transmittance, represents the solar zenith angle, represents the solar declination angle, represents the local latitude, Represents the frequency of the Earth's rotation.

[0034] According to the present invention, the surface shortwave radiation Obtained from surface downlink shortwave data.

[0035] In a preferred embodiment, the blue sky albedo is obtained based on the white sky reflectivity and the black sky reflectivity. The white sky albedo is the reflection of the ground surface to the diffuse scattered light of the atmosphere, the black sky albedo is the reflection of the ground surface to the direct sunlight, and the blue sky albedo is the reflection of the ground surface to the direct sunlight and the diffuse scattered light of the atmosphere, which is the weighted sum of the black and white sky albedo, and the weight is related to the sky scattering ratio factor. The relationship between the black sky, white sky and blue sky albedo is expressed by the following formula: in, represents the blue sky albedo of the surface, represents the black sky albedo, Represents the white sky albedo.

[0036] According to the present invention, , , , are all functions of the surface temperature, so they can be simplified to , by obtaining the simplified coefficient , The relationship between surface temperature and time can be obtained.

[0037] Preferably, the solar constant The value is set to 1361W / m 2 , the Earth's rotation frequency is a constant, and its value is 2π / (24*3600).

[0038] Furthermore, the one-dimensional heat conduction formula of the ground surface and its boundary conditions are combined to obtain the relationship between the surface temperature and time, which is expressed as: ; in, ; ; ; ;

[0039] in, is the intermediate variable, represents the optimal thermal inertia, For configurable parameters, , is the nth-order Fourier coefficient. We have shown through a large number of experiments that in urban areas That’s it. When n>2, the difference in thermal inertia results is very small.

[0040] The objective function in sub-step 22 is set as:

[0041] in,

[0042] for Time-based surface temperature data, such as Himawari-8 hourly surface temperature data.

[0043] The optimal thermal inertia can be obtained by using an optimization method, preferably a nonlinear least squares optimization method such as the Dogleg method. , taking the optimal thermal inertia as the estimated urban surface thermal inertia.

[0044] According to the present invention, by adopting the above method, it is possible to obtain an estimate of thermal inertia at each moment based on the data in the database. Compared with the method in the prior art, the accuracy of the obtained urban surface thermal inertia data is significantly improved.

[0045] In step 3, the hourly urban-scale surface heat storage It is expressed as:

[0046] in, represents the integration variable.

[0047] According to the present invention, the hourly surface heat storage can be obtained by utilizing thermal inertia in combination with the temporal variation pattern of the surface temperature.

[0048] The method of the present invention can use urban flux tower observation data to verify the hourly surface heat storage results obtained.

[0049] In areas with urban flux towers, the estimated hourly surface heat storage values ​​can be verified using observational data from urban flux towers.

[0050] Net radiation flux observed from urban flux towers , sensible heat flux , latent heat flux , the observed surface heat storage is obtained by the residual method , expressed as:

[0051] By comparing and verifying the observed surface heat storage with the estimated surface heat storage, it can be found that the estimated surface heat storage has a higher accuracy.

[0052] Various embodiments of the method described above in the present invention can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs, which may be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general programmable processor, which may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0053] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in the present disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document is not limited here. Example

[0054] Example 1 Estimating the surface heat storage for a city includes the following steps: Step 1: Based on a variety of satellite remote sensing data, construct a data set including surface temperature data, surface albedo data and surface downlink shortwave data; Step 2: Based on the data set and combined with the surface temperature variation pattern, estimate the urban surface thermal inertia; Step 3: Based on the hourly surface temperature and estimated surface thermal inertia, obtain the hourly urban-scale surface heat storage.

[0055] In step 1, the multiple satellite remote sensing data include Himawari-8 satellite remote sensing data, GLASS satellite remote sensing data, and CARE satellite remote sensing data, and clear sky data from the satellite remote sensing data is selected to produce a data set.

[0056] Step 2 includes the following sub-steps: Sub-step 21, obtaining the relationship between the surface temperature and time; Sub-step 22: setting the objective function, obtaining the optimal thermal inertia through an optimization method, and using the optimal thermal inertia as the estimated urban surface thermal inertia.

[0057] In sub-step 21, based on the one-dimensional heat conduction formula of the ground surface and its boundary conditions, the relationship between the surface temperature and time is obtained.

[0058] The one-dimensional heat conduction formula of the ground surface is expressed as:

[0059] The boundary conditions are:

[0060]

[0061]

[0062] Solar constant The value is set to 1361W / m 2 , the Earth's rotation frequency is a constant, and its value is 2π / (24*3600).

[0063] The relationship between the surface temperature and time is obtained, which is expressed as: ; in, ; ; ; ;

[0064] The objective function in sub-step 22 is set as: ;

[0065] in, ;

[0066] The Dogleg method, a nonlinear least squares optimization method, is used to solve the problem.

[0067] In step 3, the hourly urban-scale surface heat storage is expressed as: .

[0068] Experimental example

[0069] Experimental Example 1 The hourly surface heat storage results obtained were verified using the city's flux tower observation data.

[0070] Net radiation flux observed from urban flux towers , sensible heat flux , latent heat flux , the observed surface heat storage is obtained by the residual method , expressed as:

[0071] The observed surface heat storage was compared with the estimated surface heat storage and the results are as follows: Figure 2 As shown in the figure, the estimated surface heat storage in Example 1 and the observed surface heat storage have a R 2 Up to 0.92, RMSE is 39.04W / m 2 , MAE is 31.28W / m 2 , proving that the method in Example 1 has high accuracy and high stability.

[0072] The present invention has been described above in conjunction with preferred embodiments, but these embodiments are only exemplary and serve only as an illustration. On this basis, the present invention may be subjected to a variety of substitutions and improvements, all of which fall within the scope of protection of the present invention.

Claims

1. A method for estimating urban surface heat storage based on geostationary satellites, characterized in that: The following steps are involved: Step 1: Based on a variety of satellite remote sensing data, construct a data set including surface temperature data, surface albedo data and surface downlink shortwave data; Step 2: Based on the data set and combined with the surface temperature variation pattern, estimate the urban surface thermal inertia; Step 3: Based on the hourly surface temperature and surface thermal inertia, obtain the hourly urban-scale surface heat storage.

2. The method for estimating urban surface heat storage based on geostationary satellite according to claim 1, characterized in that: Clear sky data from satellite remote sensing data is selected to create a dataset.

3. The method for estimating urban surface heat storage based on geostationary satellite according to claim 1, characterized in that: Step 2 includes the following sub-steps: Sub-step 21, obtaining the relationship between the surface temperature and time; Sub-step 22: setting the objective function, obtaining the optimal thermal inertia through an optimization method, and using the optimal thermal inertia as the estimated urban surface thermal inertia.

4. The method for estimating urban surface heat storage based on geostationary satellite according to claim 3 is characterized in that: In sub-step 21, based on the one-dimensional heat conduction formula of the ground surface and its boundary conditions, the relationship between the surface temperature and time is obtained.

5. The method for estimating urban surface heat storage based on geostationary satellite according to claim 4 is characterized in that: The one-dimensional heat conduction formula of the ground surface is expressed as: ； in, represents the air density, is the specific heat capacity of air, Indicates depth, Indicates time, Indicates depth Location The surface temperature at the time, It represents the thermal conductivity of air; The boundary conditions are: ； ； ； in, represents the surface shortwave radiation, represents the downward longwave radiation of the atmosphere, represents the upward longwave radiation from the surface, is the surface sensible heat flux, represents the latent heat flux, , represents the simplification coefficient, represents the blue sky albedo of the surface, represents the solar constant, represents the atmospheric transmittance, represents the solar zenith angle, represents the solar declination angle, represents the local latitude, Represents the frequency of the Earth's rotation.

6. The method for estimating urban surface heat storage based on geostationary satellite according to claim 5, characterized in that: Obtain the relationship between surface temperature and time , expressed as: ； in, ； ； ； ； in, is the intermediate variable, represents the optimal thermal inertia, For configurable parameters, , is the n-th order Fourier coefficient.

7. The method for estimating urban surface heat storage based on geostationary satellite according to claim 6, characterized in that: The objective function in sub-step 22 is set as: ； in, ; for Surface temperature data at the moment.

8. The method for estimating urban surface heat storage based on geostationary satellite according to claim 7, characterized in that: In step 3, the hourly urban-scale surface heat storage It is expressed as: ； in, represents the integration variable.

9. An electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-8.

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