Urban surface heat island intensity evaluation method

Abstract

The invention provides an urban surface heat island intensity evaluation method, which comprises the following steps of: acquiring a satellite multi-band data image of a target region in a research period, dividing a water body and an urban built-up region on the basis, and selecting part of the water body as a reference station region; extracting seasonal and tendency factors according to the long-term observed surface temperature data of the reference base station area, correcting the surface temperature data of the reference base station area to obtain corrected temperature, and comparing the corrected temperature with the surface temperature of the urban built-up area to obtain the surface heat island intensity of the urban built-up area. Therefore, the fluctuation trend of seasons to the temperature of the reference station area is quantified, the spatial distribution of the urban thermal environment and the thermal risk is clearer and more visual and is convenient to position, and evaluation errors caused by seasons and background climate changes can be reduced.

Description

Technical Field

[0001] The invention relates to the field of environmental research, and in particular to a method for evaluating the intensity of urban surface heat islands. Background Art

[0002] The urban heat island effect is one of the most obvious climate change effects brought about by urbanization. In addition to exacerbating public health risks, the urban heat island effect also indirectly leads to adverse effects such as increased energy consumption and increased air pollution, which poses a major challenge to urban sustainability. With the further expansion / densification of cities and the coupled effects of global warming, the above adverse effects will escalate, resulting in more complex and difficult to manage compound and cascading risks. The intensity of urban surface heat island is one of the most widely used key indicators in current multi-scale research on heat islands due to its simple calculation and comparability. Accurate quantification of the intensity of urban surface heat islands is crucial for the process understanding and solutions of thermal environment changes.

[0003] The quantification of urban surface heat island intensity depends on the definition of reference areas. The existing methods for defining reference areas are inconsistent and are affected by factors such as the complexity of internal land cover and seasonal fluctuations. These factors reduce the universality and accuracy of urban heat island intensity quantification, leading to cognitive biases in the assessment of urban heat island effects, or even misperceptions, which brings difficulties to subsequent policy formulation and adaptation and mitigation measures. Summary of the invention

[0004] The present invention provides a method for evaluating the intensity of urban surface heat islands, which improves the problems of insufficient universality and accuracy in the quantification of urban heat island intensity in the prior art, and helps to improve the universality and accuracy of the quantification of urban heat island intensity.

[0005] To achieve the above object, the present invention provides a method for evaluating the intensity of urban surface heat island, comprising the following steps:

[0006] S1. Obtain satellite multi-band data images during the research period of the target area to obtain surface temperature data;

[0007] S2. Divide the satellite multi-band data image into at least a water body and an urban built-up area, wherein the surface temperature of the urban built-up area is an assessment temperature;

[0008] S3. Select part of the water body as the reference base station area; the temperature of the water body is relatively stable compared to the land, which is conducive to improving the accuracy of the assessment method.

[0009] S4. According to the surface temperature data of the reference base station area during the study period, seasonal factors and trend factors are extracted, and the surface temperature data of the reference base station area is corrected based on the seasonal factors and trend factors to obtain a reference temperature; extracting the seasonal and trend factors and correcting the surface temperature based on them can quantify the seasonal fluctuation trend of the temperature of the reference base station area and reduce the additional impact of macro-climate change, which is helpful to reduce the assessment errors caused by seasonal or background climate change.

[0010] S5. The difference between the evaluation temperature and the reference temperature is the surface heat island intensity.

[0011] As an optional technical solution, step S2 includes:

[0012] S201, selecting training samples based on the satellite multi-band data image;

[0013] S202, constructing a random forest model using the training samples;

[0014] S203, applying the random training model to perform land cover supervised classification on the satellite multi-band data image to obtain land cover data;

[0015] S204, dividing the satellite multi-band data image into construction land, water body, agricultural and forestry land and other land based on the land cover data;

[0016] S205, extracting the construction land pixels from the land cover data based on an urban clustering algorithm;

[0017] S206, using the extracted construction land pixels to identify the urban built-up area. By identifying the urban built-up area, on the one hand, the sampling location of the evaluation temperature can be clarified, and on the other hand, the influence range of the urban heat island can be delineated according to the scope of the urban built-up area, and the water bodies within the influence range can be excluded from the reference base station area, thereby improving the reliability of the reference temperature.

[0018] As an optional technical solution, step S3 includes:

[0019] S301, selecting the water body as the reference base station area;

[0020] S302, verifying the stability of the reference base station area by combining different remote sensing data sources;

[0021] S303, combining variance analysis and statistical characteristics to test the temperature fluctuation of the reference base station area and the difference with other covers to test its robustness, and adjust the reference base station area according to the test results. Through cross-validation of different data sources and identification of statistical methods, a part of the alternative water bodies with higher stability can be selected as the reference base station area, which is conducive to improving the accuracy of heat island assessment.

[0022] As an optional technical solution, the screening conditions of step S301 include:

[0023] Condition 1: The water body is relatively still and has an area greater than or equal to 0.01 square kilometers to ensure thermal stability due to its high heat capacity and scale effect;

[0024] Condition 2: The water body is located within the administrative division of the target area and within the urban built-up area heat island footprint to comply with the urban planning scope and reduce the impact of urbanization on the water body;

[0025] Condition 3: The elevation of the water body is within the range of ±50 meters of the elevation of the urban built-up area to reduce the impact of the elevation;

[0026] Condition 4: The scope shall not change by more than 10% within the five years to ensure its spatial stability and planning availability;

[0027] The heat island footprint is the continuous spatial range from the urban built-up area to its fifth buffer zone of equal area.

[0028] As an optional technical solution, the step S302 includes: when the reference base station area set is lower than the hard reference value, detecting, eliminating and optimizing abnormal values ​​and outliers;

[0029] The reference base station area set being lower than the hard reference value at least includes: the variance analysis p value is less than 0.05 and the standard deviation is less than other coverages; the Spearman rank correlation coefficient is greater than 0.7 and / or the significance p value is less than 0.05.

[0030] As an optional technical solution, step S4 includes:

[0031] S401, performing noise processing on the surface temperature inside the reference base station area;

[0032] S402, based on historical remote sensing data, recording the long-term temperature data of the reference base station area covering different seasons to form a temperature time series;

[0033] S403, using time series decomposition to extract seasonal components and trend components in the temperature time series; wherein the trend term component can reflect the long-term linear trend of climate change in the target area, and / or potential acceleration or deceleration effects, which helps to express the relatively complex change process of the climate in the target area.

[0034] S404, constructing a periodic temperature model of a reference water body based on the extracted seasonal component and the trend component, wherein the model is used to describe temperature changes throughout the year;

[0035] S405, calculating the temperature correction factor to be applied in each time period based on the current seasonal temperature value predicted by the model;

[0036] S406. Add the observed temperature of the water body during the study period to the temperature correction factor to obtain the reference temperature.

[0037] As an optional technical solution, the model is:

[0038]

[0039] Among them, a*t 2 +b*t is the trend component, is the seasonal component, i is the random component, LST t_base is the reference temperature in period t, a is the quadratic term coefficient, b is the linear term coefficient, c is the seasonal amplitude coefficient, P is the period, d is the phase shift, and t is the subset of the study period. The above expression of the trend term component introduces the quadratic term coefficient a and the linear term coefficient b, which respectively reflect the potential acceleration or deceleration effect of climate change in the target area and the long-term linear trend of climate change, making the model more flexible and able to express relatively complex change processes.

[0040] As an optional technical solution, the temperature correction factor is calculated as follows:

[0041] C LST =LST t_base -LST t_real

[0042] C LST is the correction factor for time period t, LST t_base is the reference temperature at time t, LST t_real The reference base station area usually includes multiple water bodies, and the correction factor can intuitively reflect the correction amount for the temperature of a single water body, which is convenient for researchers to understand the operation of the evaluation method and formulate response strategies.

[0043] As an optional technical solution, step S401 includes:

[0044] S4011, combining the local mean and standard deviation of the reference base station area, and performing noise identification and screening according to a dynamic noise threshold;

[0045] S4012, applying a Gaussian filtering smoothing algorithm to smooth the local area data of the reference base station area to reduce the impact of random noise;

[0046] S4013. Calculate the temperature difference between each temperature point and the neighboring data. Points with unusually large temperature differences are marked as noise candidates.

[0047] As an optional technical solution, step S5 is:

[0048] SUHII=LST Object -LST t_base =LST Object -LST t_real +C LST

[0049] Among them, SUHII is the heat island intensity, LST is Object is the observed surface temperature of the built-up area of ​​the city.

[0050] One or more technical solutions provided by the present invention have at least the following technical effects or advantages:

[0051] The urban surface heat island intensity assessment method of the present invention obtains satellite multi-band data images of the target area during the study period, divides the water body and the urban built-up area on this basis, selects part of the water body as the reference base station area, and extracts seasonal and trend factors based on the long-term observed surface temperature data of the reference base station area, and corrects the surface temperature data of the reference base station area to obtain the corrected temperature, which is then compared with the surface temperature of the urban built-up area to obtain the surface heat island intensity of the urban built-up area, thereby quantifying the seasonal fluctuation trend of the temperature of the reference base station area, making the spatial distribution of the urban thermal environment and heat risk clearer, more intuitive and easier to locate, which helps to reduce the assessment error caused by seasonal changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of the present invention, and do not constitute a limitation on the embodiments of the present invention;

[0053] Figure 1 It is a flow chart of the urban surface heat island intensity assessment method of the present invention;

[0054] Figure 2is an example image of the surface temperature of the case area obtained based on the inversion of satellite multi-band data images in an embodiment of the present invention;

[0055] Figure 3 is a schematic diagram of the land cover status of the target area in an embodiment of the present invention;

[0056] Figure 4 is a reference base station area recognition image in an embodiment of the present invention;

[0057] Figure 5 It is a schematic diagram of the surface temperature change of the reference base station area and other land covers in the embodiment of the present invention;

[0058] Figure 6 is a graph of reference temperature in an embodiment of the present invention;

[0059] Figure 7 It is an example image of the surface heat island intensity results of the target area based on the reference base station area. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] Embodiment 1

[0062] This embodiment provides a method for evaluating the intensity of urban surface heat island, including the following steps:

[0063] S1. Obtain satellite multi-band data images during the research period of the target area to obtain surface temperature data;

[0064] S2. Divide the satellite multi-band data image into at least a water body and an urban built-up area, wherein the surface temperature of the urban built-up area is an assessment temperature;

[0065] S3. Select part of the water body as the reference base station area; the temperature of the water body is relatively stable compared to the land, which is conducive to improving the accuracy of the assessment method.

[0066] S4, extracting seasonal factors according to the surface temperature data of the reference base station area during the study period, and correcting the surface temperature data of the reference base station area based on the seasonal factors to obtain a reference temperature;

[0067] S5. The difference between the evaluation temperature and the reference temperature is the surface heat island intensity.

[0068] In step S4, seasonal and trend factors are introduced to correct the surface temperature data of the reference base station area, and a cross-seasonal correction mechanism is established, so that the evaluation method of the present invention can dynamically adapt to the natural periodic changes in water temperature to more accurately evaluate the intensity of the heat island. By extracting the periodic and trend components, the seasonal impact on the fluctuation trend of the reference temperature and the background climate can be quantified. This helps to reduce the evaluation errors caused by seasonal changes and macro trends.

[0069] In different periods, water temperature will change due to various factors. The periodic correction mechanism can ensure the accuracy and consistency of the assessment in different periods by adjusting the model parameters in real time. This helps to obtain comparable heat island intensity assessment results in different time periods.

[0070] The periodic correction mechanism can provide accurate information about the seasonal changes in water temperature, which helps optimize resource allocation. For example, during the high summer temperature period, it may be necessary to increase the monitoring and mitigation measures for urban heat islands; while during the low winter temperature period, these measures can be reduced accordingly. Through periodic correction, relevant strategies can be formulated and implemented more scientifically.

[0071] Optionally, step S1 includes:

[0072] S101, obtaining satellite multi-band data images of the target area during the study period, including digital signal values;

[0073] S102. Preprocess satellite multi-band data images, including mosaic correction, cropping, cloud and shadow masking, and image registration.

[0074] S103, converting the digital signal value into radiant brightness;

[0075] S104, correcting the radiation brightness data in combination with atmospheric parameters, thereby eliminating errors caused by scattering, absorption and reflection of surface radiation by the atmosphere;

[0076] S105. Apply the radiation transfer equation to calculate the surface temperature and calculate the actual temperature value through the Planck function.

[0077] Embodiment 2

[0078] Based on the first embodiment, step S2 includes:

[0079] S201, selecting training samples based on the satellite multi-band data image;

[0080] S202, constructing a random forest model using the training samples;

[0081] S203, applying the random training model to perform land cover supervised classification on the satellite multi-band data image to obtain land cover data;

[0082] S204, dividing the satellite multi-band data image into construction land, water body, agricultural and forestry land and other land based on the land cover data;

[0083] S205, extracting the construction land pixel from the land cover data;

[0084] S206, using the extracted construction land pixels to identify the urban built-up area. By identifying the urban built-up area, on the one hand, the sampling location of the evaluation temperature can be clarified, and on the other hand, the influence range of the urban heat island can be delineated according to the scope of the urban built-up area, and the water bodies within the influence range can be excluded from the reference base station area, thereby improving the reliability of the reference temperature.

[0085] The land cover classification standards can be based on the following table:

[0086]

[0087] As an optional technical solution, step S3 includes:

[0088] S301, selecting the water body as the reference base station area;

[0089] S302, verifying the stability of the reference base station area by combining different remote sensing data sources;

[0090] S303, combining variance analysis and statistical characteristics to test the temperature fluctuation of the reference base station area and the difference with other covers to test its robustness, and adjust the reference base station area according to the test results. Through cross-validation of different data sources and identification of statistical methods, a part of the alternative water bodies with higher stability can be selected as the reference base station area, which is conducive to improving the accuracy of heat island assessment.

[0091] Among them, the above remote sensing data sources can be MODIS surface temperature data, ECOSTRESS surface temperature data, drone-mounted thermal infrared images, etc.

[0092] As an optional technical solution, the screening conditions of step S301 include:

[0093] Condition 1: The water body is relatively still and has an area greater than or equal to 0.01 square kilometers to ensure thermal stability due to its high heat capacity and scale effect;

[0094] Condition 2: The water body is located within the administrative division of the target area and within the urban built-up area heat island footprint to comply with the urban planning scope and reduce the impact of urbanization on the water body;

[0095] Condition 3: The elevation of the water body is within the range of ±50 meters of the elevation of the urban built-up area to reduce the impact of the elevation;

[0096] Condition 4: The scope shall not change by more than 10% within the five years to ensure its spatial stability and planning availability;

[0097] The heat island footprint is the continuous spatial range from the urban built-up area to its fifth buffer zone of equal area.

[0098] As an optional technical solution, the step S302 includes: when the reference base station area set is lower than the hard reference value, detecting, eliminating and optimizing abnormal values ​​and outliers;

[0099] Among them, the reference base station area set is lower than the hard reference value, which at least includes: the contrast analysis p value is less than 0.05 and the standard deviation is less than other land covers; the Spearman rank correlation coefficient is greater than 0.7 and the significance p value is less than 0.05.

[0100] As an optional technical solution, step S4 includes:

[0101] S401, performing noise processing on the surface temperature inside the reference base station area;

[0102] S402, based on historical remote sensing data, recording the long-term temperature data of the reference base station area covering different seasons to form a temperature time series;

[0103] S403, using time series decomposition to extract seasonal components and trend components in the temperature time series; wherein the trend term component can reflect the long-term linear trend of climate change in the target area, and / or potential acceleration or deceleration effects, which helps to express the relatively complex change process of the climate in the target area.

[0104] S404, constructing a periodic temperature model of a reference water body based on the extracted seasonal component and the trend component, wherein the model is used to describe temperature changes throughout the year;

[0105] S405, calculating the temperature correction factor to be applied in each time period based on the current seasonal temperature value predicted by the model;

[0106] S406. Add the observed temperature of the water body during the study period to the temperature correction factor to obtain the reference temperature.

[0107] Optionally, step S401 includes:

[0108] S4011, combining the local mean and standard deviation to identify and screen noise according to the dynamic noise threshold;

[0109] S4012, applying a Gaussian filter smoothing algorithm to smooth the base area data to reduce the impact of random noise; wherein the expression of the Gaussian filter smoothing algorithm is:

[0110]

[0111] Wherein, K(x, y) is the weight of the Gaussian kernel (x, y) at the position; k is half the size of the kernel. In this embodiment, a 3*3 kernel is used, i.e., k=1; I(i+x, j+y) is the pixel value at the corresponding position in the original image.

[0112] S4013. Calculate the temperature difference between each temperature point and the neighboring data. Points with unusually large temperature differences are marked as noise candidates to improve detection accuracy.

[0113] As an optional technical solution, the model is:

[0114]

[0115] Among them, a*t 2 +b*t is the trend component, is the seasonal component, i is the random component, LST t_base is the reference temperature in period t, a is the quadratic term coefficient, b is the linear term coefficient, c is the seasonal amplitude coefficient, P is the period, d is the phase shift, and t is the subset of the study period. The above expression of the trend term component introduces the quadratic term coefficient a and the linear term coefficient b, which respectively reflect the potential acceleration or deceleration effect of climate change in the target area and the long-term linear trend of climate change, making the model more flexible and able to express relatively complex change processes.

[0116] As an optional technical solution, the temperature correction factor is calculated as follows:

[0117] C LST =LST t_base -LST t_real

[0118] C LST is the correction factor for time period t, LST t_base is the reference temperature at time t, LST t_realThe reference base station area usually includes multiple water bodies, and the correction factor can intuitively reflect the correction amount for the temperature of a single water body, which is convenient for researchers to understand the operation of the evaluation method and formulate response strategies.

[0119] As an optional technical solution, after step S401 and before step S402, the following steps are further included:

[0120] S4011, combining the local mean and standard deviation of the reference base station area, and performing noise identification and screening according to a dynamic noise threshold;

[0121] S4012, applying a Gaussian filtering smoothing algorithm to smooth the local area data of the reference base station area to reduce the impact of random noise;

[0122] S4013. Calculate the temperature difference between each temperature point and the neighboring data. Points with unusually large temperature differences are marked as noise candidates.

[0123] As an optional technical solution, step S5 is:

[0124] SUHII=LST Object -LST t_base =LST Object -LST t_real +C LST

[0125] Among them, SUHII is the heat island intensity, LST is Object is the observed surface temperature of the built-up area of ​​the city.

[0126] In addition, in actual operation, the urban thermal environment classification standards can be negotiated by the city. The thermal environment conditions of each assessment unit are compared according to the standards, and the optimization design is carried out accordingly. The reference suggestions are as follows: For the assessment units facing extremely strong heat islands, red is marked, and the governance priority is increased. Professionals such as planners and designers adopt one or more cooling strategies for compound cooling based on local climate simulation. After the measures are implemented, the surface heat island intensity assessment is continuously and regularly repeated to monitor the changes in the thermal environment and the effectiveness of the governance measures.

[0127] Embodiment 3

[0128] This example takes a city as an example, and uses the urban surface heat island intensity assessment method described in Example 2 to carry out urban heat island intensity assessment. First, select the Landsat remote sensing image dataset (Landsat-8 and Landsat-9) since 2017, and control the high-quality images with cloud cover below 20% as the original data, a total of 81 images. For the original data set, carry out mosaic correction, cropping, cloud and shadow masking, image registration and other preprocessing in turn, and apply the radiation transfer equation to solve the surface temperature. Figure 2 The surface temperature data of the case area obtained based on the inversion of satellite multi-band data images for part of the time are displayed.

[0129] like Figure 3 As shown, combined with the construction land scope in the land cover data in 2023, the urban built-up area is identified through the urban clustering algorithm, where the scanning grid is 100 meters and the clustering distance is 200 meters.

[0130] Based on multi-source data such as land cover data, administrative division data, and elevation data, the reference base station area was screened. Figure 4 A total of 19 eligible water areas were selected in the case area to form a reference base station area set.

[0131] like Figure 5 As shown in the figure, some time data were selected as verification samples, and the surface temperature conditions of different covers and reference base station areas in the target area were statistically analyzed as shown in the following table. Variance analysis based on the mean (Avg) showed that there were significant differences between different covers (p<0.05). Among them, the internal temperature standard deviation (Std) of the reference base station area was significantly smaller than that of other cover types, that is, it was more uniform inside and the influence of outliers was reduced. The time series changes also showed that the surface temperature changes in the reference station area under different climate backgrounds were smaller and more robust than those in other covers.

[0132]

[0133] The reliability of the reference base station area compared with other covers is further verified by combining the downscaled MODIS surface temperature data as shown in the following table. The results show that there is a significant difference between the surface temperature of the reference station area and other covers, and there is a smaller standard deviation. This verifies the reliability and stability of the reference base station area. In addition, there is a high correlation between the MODIS surface temperature time series of the reference base station area and Landsat, with a Spearman correlation coefficient of 0.88 and a significant p value of less than 0.05. That is, the reference base station area has a strong applicability to different surface temperature data.

[0134]

[0135] like Figure 6As shown in the figure, based on 81 surface temperature images covering different seasons from 2017 to 2024, a temperature time series is formed. On the basis of smoothing the water body anomalies inside the reference base station area, a sinusoidal curve mathematical model describing the temperature changes throughout the year is formed, that is, the periodic temperature model of the reference water body mentioned above, which quantifies the fluctuation trend of the seasonal impact on the reference temperature.

[0136] The correction factor is calculated based on the difference between the model and the actual temperature, which is used to calculate the intensity of the regional urban heat island. Examples of calculation results for some dates are as follows: Figure 7 shown.

[0137] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0138] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for evaluating the intensity of urban surface heat island, characterized in that: The following steps are involved: S1. Obtain satellite multi-band data images during the research period of the target area to obtain surface temperature data; S2. Divide the satellite multi-band data image into at least a water body and an urban built-up area, wherein the surface temperature of the urban built-up area is an assessment temperature; S3, selecting part of the water body as the reference base station area; S4. Extract seasonal and trend factors according to the surface temperature data of the reference base station area during the study period, and correct the surface temperature data of the reference base station area based on the trend factors and the seasonal factors to obtain a reference temperature; S5. The difference between the evaluation temperature and the reference temperature is the surface heat island intensity.

2. The urban surface heat island intensity assessment method according to claim 1, characterized in that: The step S2 comprises: S201, selecting training samples based on the satellite multi-band data image; S202, constructing a random forest model using the training samples; S203, applying the random training model to perform land cover supervised classification on the satellite multi-band data image to obtain land cover data; S204, dividing the satellite multi-band data image into construction land, water body, agricultural and forestry land and other land based on the land cover data; S205, extracting the construction land pixel from the land cover data; S206: Use the extracted construction land pixels and a city clustering algorithm to identify the urban built-up area.

3. The method for evaluating the intensity of urban surface heat island according to claim 1, characterized in that: The step S3 comprises: S301, selecting the water body as the reference base station area; S302, verifying the stability of the reference base station area by combining different remote sensing data sources; S303, combining variance analysis and statistical characteristics to examine the temperature fluctuation of the reference base station area and the difference with other coverage to test its robustness, and adjust the reference base station area according to the test results.

4. The method for evaluating the intensity of urban surface heat island according to claim 3, characterized in that: The screening conditions of step S301 include: Condition 1: The water body is relatively still and its area is greater than or equal to 0.01 square kilometers; Condition 2: The water body is located within the administrative area of ​​the target area and outside the urban built-up area heat island footprint; Condition 3: The elevation of the water body is within the range of ±50 meters of the elevation of the urban built-up area; Condition 4: The scope change within the five years does not exceed 10%; The heat island footprint is the continuous spatial range from the urban built-up area to its fifth buffer zone of equal area.

5. The urban surface heat island intensity assessment method according to claim 3, characterized in that: The step S302 includes: when the reference base station area set is lower than the hard reference value, detecting, eliminating and optimizing abnormal values ​​and outliers; Among them, the reference base station area set is lower than the hard reference value, which at least includes: the variance analysis p value is less than 0.05 and the standard deviation is less than other coverages; the Spearman rank correlation coefficient is greater than 0.7 and the significance p value is less than 0.

05.

6. The method for evaluating the intensity of urban surface heat island according to claim 1, characterized in that: The step S4 comprises: S401, performing noise processing on the surface temperature inside the reference base station area; S402, based on historical remote sensing data, recording the long-term temperature data of the reference base station area covering different seasons to form a temperature time series; S403, extracting seasonal components and trend components in the temperature time series by using time series decomposition; S404, constructing a periodic temperature model of a reference water body based on the extracted seasonal component and the trend component, wherein the periodic temperature model is used to describe temperature changes throughout the year; S405, calculating the temperature correction factor to be applied in each time period based on the current seasonal temperature value predicted by the periodic temperature model; S406. Add the observed temperature of the water body during the study period to the temperature correction factor to obtain the reference temperature.

7. The method for evaluating the intensity of urban surface heat island according to claim 6, characterized in that: The periodic temperature model is: Among them, a*t 2 +b*t is the trend component, is the seasonal component, i is the random component, LST t_base is the reference temperature in period t, a is the quadratic term coefficient, b is the linear term coefficient, c is the seasonal amplitude coefficient, P is the period, d is the phase shift, and t is a subset of the study period.

8. The method for evaluating the intensity of urban surface heat island according to claim 6, characterized in that: The temperature correction factor is calculated as follows: C LST =LST t_base -LST t_real C LST is the correction factor for time period t, LST t_base is the reference temperature at time t, LST t_real is the observed temperature of the water body.

9. The method for evaluating the intensity of urban surface heat island according to claim 6, characterized in that: The step S401 includes: S4011, combining the local mean and standard deviation of the reference base station area, and performing noise identification and screening according to a dynamic noise threshold; S4012, applying a Gaussian filtering smoothing algorithm to smooth the local area data of the reference base station area to reduce the impact of random noise; S4013. Calculate the temperature difference between each temperature point and the neighboring data. Points with unusually large temperature differences are marked as noise candidates.

10. The method for evaluating the intensity of urban surface heat island according to claim 8, characterized in that: The step S5 is: Among them, SUHII is the heat island intensity, LST is Object is the observed surface temperature of the built-up area of ​​the city.

Images