CN111460003B - Method for detecting coupling relation between land utilization and ground surface temperature based on city group scale - Google Patents
Method for detecting coupling relation between land utilization and ground surface temperature based on city group scale Download PDFInfo
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Abstract
The invention discloses a method for detecting the coupling relation between land utilization and ground surface temperature based on urban mass scale, which comprises the following steps: (1) Land utilization data of an area to be researched are obtained, and landscape pattern characteristics are analyzed; (2) Acquiring remote sensing image data of a region to be researched, and inverting the surface temperature to obtain the surface temperature; (3) Performing global Morgan index calculation and local Morgan index calculation based on the surface temperature, and then discussing the space concentration characteristics of the surface average temperature of the area to be researched; (4) And obtaining the average earth surface temperature in each grid unit and the utilization ratio of various lands in each grid unit, adopting a geographic weighted regression model, and analyzing the coupling relation and the spatial instability between the land utilization and the earth surface temperature. The method combines the landscape pattern index, the global Morlan index, the LISA cluster map and the GWR model to effectively disclose the local coupling relation between land utilization and ground surface temperature in different areas in the urban process, thereby realizing breakthrough in precision.
Description
Technical Field
The invention belongs to the research of coupling relation between land utilization and ground surface temperature, and particularly relates to a method for detecting coupling relation between land utilization and ground surface temperature based on urban mass scale.
Background
Related studies on land use/coverage changes have become the leading edge and focus of current global change research since the 90 s of the 20 th century. Land utilization/coverage changes can have an impact on regional climate and global energy balance. With the development of social economy, population is rapidly increased, the original land utilization pattern is changed in large-scale and rapid urban, and serious threat is caused to the ecological safety of the area. Therefore, the coupling relation between land utilization and surface temperature is researched, and theoretical reference can be provided for effectively revealing the health condition of urban groups in the area and guaranteeing the ecological safety of the area.
Due to the expansion of cities, suburb natural landscapes mainly comprising permeable surfaces and transpiration vegetation are gradually replaced by artificial landscapes mainly comprising impermeable surfaces, and the properties of the sublevel surfaces of the cities are changed, so that conditions are created for the formation of urban heat islands. Studies have shown that variations in land use type are a major factor in causing variations in surface temperature, with significant differences in surface temperature from land use type to land use type. Traditional administrative division statistical analysis can generate unreasonable fracture on surface temperature characteristics, has the phenomenon of scale mismatch, and cannot better reflect actual space pattern characteristics. Currently, global regression models (e.g., least squares ordinary linear regressions, OLS) have been used to explore the coupling relationship of land utilization types to surface temperatures. However, because of a certain space non-stationarity of the influence of land utilization change on the surface temperature, the requirement of research cannot be met by establishing a global regression fit model.
Disclosure of Invention
The invention aims to: the invention aims to provide a method for detecting the coupling relation between land utilization and ground surface temperature based on urban mass scale, which combines a landscape pattern index, a global Morlan index, a LISA cluster map and a GWR model to effectively reveal the local coupling relation between land utilization and ground surface temperature in different areas in the urban process, improve the accuracy of the analysis result of the coupling relation and provide references for future urban healthy development, reasonable regulation and control of urban heat island effect and regional ecological security.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the method for detecting the coupling relation between land utilization and surface temperature based on the city group scale comprises the following steps:
(1) Land utilization data of an area to be researched are obtained, various land utilization ratios and landscape indexes in the area to be researched are analyzed based on the land utilization data, and landscape pattern features are analyzed based on the landscape indexes;
(2) Acquiring remote sensing image data of an area to be researched, preprocessing the remote sensing image data, and inverting the surface temperature based on the preprocessed remote sensing image data to obtain the surface temperature;
(3) Performing global Morgan index calculation and local Morgan index calculation based on the surface temperature, then obtaining a spatial correlation local index cluster map, and discussing spatial concentration characteristics of the surface average temperature of the area to be researched based on the spatial correlation local index cluster map;
(4) And obtaining the average earth surface temperature in each grid unit and the utilization ratio of various lands in each grid unit, adopting a geographic weighted regression model, and analyzing the coupling relation and the spatial instability between the land utilization and the earth surface temperature.
Specifically, the step of analyzing the landscape architecture features in the step (1) is as follows:
and respectively calculating 7 landscape indexes of a maximum plaque area index, a plaque density, an average area, a plaque shape index, an average nearest distance, a plaque combination degree and a aggregation degree by taking the grid units with the set size as evaluation units, and analyzing the landscape fragmentation degree and connectivity of the area to be researched based on the calculated landscape indexes.
Specifically, the step (2) of performing the earth surface temperature inversion method based on the preprocessed remote sensing image data includes:
(201) Calculating a normalized difference vegetation index based on the preprocessed remote sensing image data, and then calculating the surface vegetation coverage;
(202) Estimating the emissivity of the natural surface pixels and the emissivity of the town pixels based on the surface vegetation coverage;
(203) Calculating a radiation brightness value based on the emissivity of the natural surface pixels and the emissivity of the town pixels;
(204) And calculating the surface temperature based on the amplitude brightness value.
Specifically, the step of step (3) includes:
(301) According to the surface temperature, carrying out statistics on average temperatures in grid units with set sizes, and calculating a global Morgan index and a local Morgan index of each grid unit based on a space autocorrelation tool;
(302) Performing autocorrelation analysis based on the calculation result of the global moland index;
(302) And carrying out spatial clustering on the calculation result of the local Morand index to obtain a spatial correlation local index cluster map, and analyzing according to the spatial correlation local index cluster map to obtain the surface temperature aggregation characteristic, wherein the surface temperature aggregation characteristic comprises high aggregation, low aggregation and insignificant aggregation.
Specifically, step (4) includes:
(401) Based on average surface temperature of each grid unit and proportion of different land utilization types, calculating by a geographic weighted regression analysis tool based on the grid units with set size as standards to obtain various land utilization type correlations;
(402) And analyzing different degrees of influence of each land utilization type on the surface temperature in space from the aspects of model fitting goodness and correlation coefficients.
Compared with the prior art, the invention has the beneficial effects that: the invention combines landscape pattern index, global Moran's I index, LISA cluster map and geographic weighted regression model GWR to analyze the relationship between the two. The landscape pattern index can reflect the crushing degree of the land type of the area to be researched and is used as the basis of analysis; the global Morand index and LISA cluster diagram are adopted to analyze the spatial correlation and aggregation of the surface temperature; the relation between the land type and the temperature can be simply analyzed through the LISA cluster map and the land utilization space distribution map; the GWR model can reflect the difference of the heating or cooling capacity in space, can deeply analyze the relationship between the land type and the temperature, effectively reveal the local coupling relationship between land utilization and the surface temperature of different areas in the urban process, improve the accuracy of the analysis result of the coupling relationship, and provide references for future urban healthy development, reasonable regulation and control of urban heat island effect and regional ecological security.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method of the present invention for detecting land use based on city group scale and surface temperature coupling relationship;
FIG. 2 is a spatial distribution diagram of the land use pattern in the investigation region;
FIG. 3 is a plot of surface temperature profile for an investigation region;
FIG. 4 is a LISA cluster map of the surface average temperature of the investigation region;
fig. 5 is a spatially diverse plot of land utilization ratio versus surface temperature fitted by a GWR model for a study area.
Detailed Description
The invention is further elucidated below in connection with the drawings and the detailed description. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
Referring to fig. 1, the method for detecting the coupling relationship between land utilization and surface temperature based on urban mass scale provided by the embodiment includes the following steps:
step 1, land utilization data of an area to be researched, for example, the land utilization data can be simple list data, such as cultivated land, downloaded from an administrative unit server where the area to be researched is located: the area is 200 square kilometers, a graph is formed according to the data, a direct downloaded space distribution diagram can be also adopted, as shown in figure 2, the calculation of the utilization ratio statistics and the landscape index of various lands is carried out, and the landscape pattern characteristics are analyzed. Landscape pattern features include landscape fragmentation and connectivity.
Specifically, the processing procedure of this step is as follows:
in step 101, a maximum plaque AREA index (LPI), plaque Density (PD), average AREA (area_mn), plaque SHAPE index (shape_mn), average nearest distance (enn_mn), plaque binding degree (junction), and aggregation degree (AI) 7 landscape index (meaning and ecology meaning are shown in table 1) is selected by using a grid unit of 2km×2km (of course, other set size) as an evaluation unit.
TABLE 1
And 102, calculating each landscape index by using Fragstats4.2 software, and analyzing the regional landscape fragmentation degree and connectivity based on the landscape index. Based on land use data of the region studied in the test example, the results of the calculated respective landscape indexes are shown in table 2. Based on the land utilization data of the researched area in the test example, the land utilization ratios of various types of the researched area are calculated, and based on the land utilization ratios of various types of the researched area, the land utilization ratios of various types of the grid units can be calculated.
TABLE 2
TABLE 3 Table 3
| Land use type | Duty cycle (%) |
| Cultivated land | 8.13 |
| Round land | 18.73 |
| Woodlands | 53.15 |
| Grassland | 3.76 |
| Construction land | 12.12 |
| Water area | 3.24 |
Note that: the unused land was 0.87%.
As shown in table 2, the 7 landscape index calculation results indicate that: the maximum plaque density of grasslands, the minimum index of LPI, AREA_MN and SHAPE_MN, show that the crushing degree is high but the SHAPE is more regular; the plaque density of the forest land and the water area is relatively smaller, but the calculation result of ENN_MN has larger difference, the nearest plaque distance of the water area is the largest, and the nearest plaque distance between the forest lands is the smallest, so that the water area is discrete distribution, the degree of fragmentation of the forest land is lower, and the water area is in sheet distribution. Meanwhile, from the aspects of physical Connectivity (COHESION) and polymerization degree index (AI), the woodland is 99.86% and 79.61%, and the grassland is only 36.07% and 15.45%, so that the method further shows that among 6 land utilization types, the landscaping fragmentation degree of the woodland is smaller and the polymerization degree is higher; the grassland distribution is relatively broken and the physical connectivity is low.
And 2, acquiring remote sensing image data of the area to be researched, for example, downloading the remote sensing image data from an administrative unit server where the area to be researched is located, performing data preprocessing of geometric correction, radiometric calibration, atmospheric correction and registration cutting on the remote sensing image data by using ENVI software, and performing earth surface temperature inversion by using a radiation transmission equation method to obtain earth surface temperature.
Specifically, the method for performing the earth surface temperature inversion method based on the remote sensing image data comprises the following steps:
step 201, calculating the surface vegetation coverage Fv according to the formula (1);
Fv＝(NDVI-NDVIs)/(NDVIv-NDVIs) (1)
in the formula, NDVI is a normalized difference vegetation index, and ndviv=0.70 and ndvis=0.05 are taken.
Step 202, estimating the emissivity of natural surfaces and town pixels by formulas (2) and (3);
ε surface ＝0.9625+0.0614Fv-0.0461Fv 2 (2)
ε building ＝0.9589+0.086Fv-0.0671Fv 2 (3)
wherein ε surface And epsilon building Respectively representing the emissivity of the natural surface pixels and the emissivity of the town pixels.
Step 203, calculating the temperature T according to formula (4)s-radiation brightness value B (T s )；
B(T s )＝[L λ -L↑-τ·(1-ε)L↓]/τ·ε (4)
Wherein, the atmospheric transmittance tau, the atmospheric uplink radiation L ∈and the downlink radiation L ∈can be input into imaging time and central longitude and latitude query calculation through a NASA functional network (http:// atm corr. Gsfc. NASA. Gov /), epsilon is the earth surface emissivity value, L λ The values are scaled for radiation in the thermal infrared band. The emissivity of the earth surface is the sum of the emissivity of the water body, the natural surface and the urban pixels, and is generally calculated according to the formula (2) and (3) by assigning the emissivity of the water body pixels to 0995. The radiation calibration value of the thermal infrared band is calculated according to the deviation parameter, the on-board radiation brightness value and the like in the header file of the image, and is calculated by using a Radiomotric Calibration tool in ENVI5.0 software.
Step 204, obtaining the surface temperature T by using the formula (5) s 。
T s ＝K 2 /ln(K 1 /B(T s )+1)-273.15 (5)
Wherein K is 1 And K 2 Respectively, are scaling constants, K in Landsat8 1 ＝480.89，K 2 ＝1201.14。
The temperature inversion was performed using the corresponding tool in ENVI5.0 software in the experimental example, and the results are shown in fig. 3.
And step 3, calculating a global Morgan index and a local Morgan index on the basis of the surface temperature inversion data, and discussing the space aggregation characteristic of the surface average temperature of the area based on the LISA cluster map.
Specifically, first, the average temperature within the mesh units of the set size is counted based on the surface temperature calculated in step 2, and the global Moran's I (Moran index) and the local Moran's I of each mesh unit are calculated based on the spatial autocorrelation tool.
The calculation formula of the global Morgan index is as follows:
the calculation formula of the local Morgan index is as follows:
wherein I represents the Morganella index, x i 、x j Represented as the vulnerability index means in the ith and jth grid cells,refers to the vulnerability average value, w, of all evaluation units ij Refers to a space weight matrix, s represents the sum of elements of the space weight matrix, and n is the number of grid cells.
Then, spatial clustering is carried out on the calculation result of the local Morgan index to obtain a spatial correlation local index (Local Indicators of Spatial Association, LISA) clustering chart, wherein the clustering chart presents the distribution condition of a clustering mode and mainly comprises 5 different spatial aggregation modes: high aggregation (H-H), high low aggregation (H-L), low high aggregation (L-H), low aggregation (L-L), and insignificant (No signaficant). The definition of the various LISA aggregation modes is shown in table 4.
TABLE 4 Table 4
And judging the correlation on the surface temperature space according to the global Morand index. The global Moran's I index of the surface temperature of the study area in the test is calculated to be 0.7809, the Z score is 188.9284 (at p <0.05, Z >1.96 or Z < -1.96, the study object has obvious spatial autocorrelation in space, and Moran's I index >0 is positive correlation, and conversely is negative correlation), which indicates that the surface temperature of the urban mass has obvious spatial autocorrelation and is obvious positive correlation.
From the various cluster pattern duty cycles and distribution in the LISA cluster map (as shown in fig. 4), it can be seen that: the urban mass region surface temperature exhibits significant spatial concentration and is dominated by high (H-H) concentration and low (L-L) concentration. A significant high concentration is exhibited in the eastern coastal zone, indicating that these areas are high value concentration areas; whereas in western mountainous areas, the surface temperature exhibits low concentrations, indicating that these areas are low value concentrations.
And 4, acquiring the average surface temperature and the proportion of different land utilization types in each grid unit, and analyzing the coupling relation and the spatial instability between the land utilization and the surface temperature by adopting a Geographic Weighted Regression (GWR) model.
Specifically, in this step, first, with the mesh units of a set size as a standard, based on the average surface temperature of each mesh unit and the proportion of different land utilization types, the relevance of various land utilization types is calculated by a geographic weighted regression analysis tool.
Then, the degree of influence of each land utilization type on the surface temperature in space is analyzed in terms of model fitting goodness and correlation coefficient (as shown in table 5). Based on the analysis result, a corresponding display diagram can be produced, as shown in fig. 5, and a corresponding analysis report can also be produced.
The invention focuses on analyzing the local coupling relation between 4 land utilization types of cultivated land, woodland, construction land and water area and the surface temperature, and fig. 5 shows the spatial difference of the relation between land utilization ratio and the surface temperature based on GWR model fitting.
As can be seen from the results graph: the cultivated land, the construction land and the water area have the warming effect, and the warming degree of the cultivated land and the construction land has large difference in space. The increase in unit area of cultivated land or construction land in the hilly areas of the mountain areas in the middle and west causes a much greater rise in temperature than in the southeast plain. It may be that the cultivated land or the construction land occupies the original garden area, not only the condition of the underlying surface of the area is changed, but also the growth and cooling capacity of the surrounding garden land and the forest land are affected by the environmental disturbance caused by the change of land utilization type. The water area should normally have a cooling effect, but the result reveals that the water area in the research area has a warming effect. The possible reasons are that the water area coverage proportion is smaller, the distribution is scattered, the artificial disturbance effect is strong, such as excavation drainage, but the areas of the surrounding construction land, cultivated land, garden land and the like of the water area are enlarged, and the surface temperature is not reduced and reversely increased due to the environmental disturbance.
TABLE 5
The forest land has a cooling effect, and as shown in table 5, the spatial instability of the forest land on the influence of the ground surface temperature is also remarkable. In fig. 5, (a), (B), (C) and (D) represent arable land, woodland, construction land and water area, respectively, and (a), (B) and (C) show the duty ratio, correlation coefficient and model fitting goodness, respectively. As can be seen from fig. 5, the cooling capacity of the forest land in the forest area is higher than that in the eastern coastal area, and the main reason may be that in the plain area, especially in the urban gathering area, the forest land has a smaller proportion and a higher degree of fragmentation, and the vegetation growth situation of the forest land is poor due to the urban environmental effect, so that the transpiration effect of the vegetation is weaker.
From the analysis of the above steps, it can be seen that: obvious difference exists between the surface temperatures of different land utilization types in the research area, the construction land temperature is highest, and the forest land temperature and the garden land temperature are lower; the surface temperature has obvious positive space autocorrelation and space aggregation, and is in a space distribution pattern of northwest low temperature and southeast high temperature. In eastern coastal areas, which are mainly construction land, cultivated land and garden land, the surface temperature shows high aggregation; whereas the western mountain areas, which are mainly woodland, are represented as low value aggregation areas. The farmland, the garden, the grasslands, the construction land and the water area have the warming effect, the woodland has the cooling effect, but the warming capacity and the cooling capacity of different space positions have larger difference; the instability of the surface temperature in space is closely related to the land utilization type and the surrounding environment thereof, the expansion of the construction land, the increase of the cultivated land or the garden land can change the underlying condition, and the environmental disturbance can influence the regulation and control capability of the original land utilization type on the temperature; the cooling effect of the forest land can be spatially different, and has great relation with the aggregation degree of the distribution of the forest land, the growth environment of the internal vegetation, the artificial influence and the like.
There are many methods for analyzing land utilization and surface temperature, and the method is to analyze the relationship between the land utilization and the surface temperature by combining a landscape pattern index, a global Moran's I index, a LISA cluster map and a geographic weighted regression model GWR. The landscape pattern index can reflect the crushing degree of the land type of the area to be researched and is used as the basis of analysis; the global Morand index and LISA cluster diagram are adopted to analyze the spatial correlation and aggregation of the surface temperature; the relationship between the land type and the temperature can be simply analyzed through the LISA cluster map and the land utilization space distribution map, for example, the surface temperature shows high aggregation in eastern coastal areas mainly including construction land, cultivated land and garden land; whereas the western mountain areas, which are mainly woodland, are represented as low value aggregation areas. "
The method has the advantages that the relationship between the land utilization and the ground surface temperature is achieved through many researches, such as a least squares (OLS) method, but the effect of heating or cooling of a certain land type can be obtained only through analysis, but the influence of land utilization change on the ground surface temperature is a certain space non-stationarity, the GWR model can reflect the difference of heating or cooling capacity in space, the relationship between the land type and the temperature can be deeply analyzed, the local coupling relationship between the land utilization and the ground surface temperature in different areas in the urban process can be effectively revealed, the accuracy of the analysis result of the coupling relationship is improved, and references are provided for future urban healthy development, urban heat island effect reasonable regulation and regional ecological safety guarantee.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (1)
1. The method for detecting the coupling relation between land utilization and surface temperature based on the city group scale is characterized by comprising the following steps:
(1) Land utilization data of an area to be researched are obtained, various land utilization ratios and landscape indexes in the area to be researched are analyzed based on the land utilization data, and landscape pattern features are analyzed based on the landscape indexes;
(2) Acquiring remote sensing image data of an area to be researched, preprocessing the remote sensing image data, and inverting the surface temperature based on the preprocessed remote sensing image data to obtain the surface temperature;
(3) Performing global Morgan index calculation and local Morgan index calculation based on the surface temperature, then obtaining a spatial correlation local index cluster map, and discussing spatial concentration characteristics of the surface average temperature of the area to be researched based on the spatial correlation local index cluster map;
(4) Obtaining average earth surface temperature in each grid unit and various land utilization ratios in each grid unit, adopting a geographic weighted regression model, and analyzing the coupling relation and the spatial instability between the land utilization of the area and the earth surface temperature;
the step (3) comprises:
(301) According to the surface temperature, carrying out statistics on average temperatures in grid units with set sizes, and calculating a global Morgan index and a local Morgan index of each grid unit based on a space autocorrelation tool;
(302) Performing autocorrelation analysis based on the calculation result of the global moland index;
(303) Carrying out spatial clustering on the calculation result of the local Morgan index to obtain a spatial correlation local index cluster map, and analyzing according to the spatial correlation local index cluster map to obtain the surface temperature aggregation characteristic, wherein the surface temperature aggregation characteristic comprises high aggregation, low aggregation and insignificant aggregation;
the step (4) comprises:
(401) Based on average surface temperature of each grid unit and proportion of different land utilization types, calculating by a geographic weighted regression analysis tool based on the grid units with set size as standards to obtain various land utilization type correlations;
(402) Analyzing different influence degrees of each land utilization type on the surface temperature in space from the aspects of model fitting goodness and correlation coefficients;
the calculation formula of the global Morgan index is as follows:
the calculation formula of the local Morgan index is as follows:
wherein I represents a global Morganella index, I U Represents the local Morgan index, x i 、x j Represented as the vulnerability index means in the ith and jth grid cells,refers to the vulnerability average value, w, of all evaluation units ij The method is characterized by comprising the steps of referring to a space weight matrix, wherein s represents the sum of elements of the space weight matrix, and n is the number of grid units;
judging the correlation on the surface temperature space according to the global Morgan index;
at p <0.05, Z >1.96 or Z < -1.96, the subject has a significant spatial autocorrelation in space, and I >0 is a positive correlation, whereas is a negative correlation, p and Z are a first saliency parameter and a second saliency parameter, respectively, p and Z representing a level of saliency;
the step of analyzing the landscape pattern features in the step (1) is as follows:
respectively calculating 7 landscape indexes of a maximum plaque area index, a plaque density, an average area, a plaque shape index, an average nearest distance, a plaque combination degree and a aggregation degree by taking a grid unit with a set size as an evaluation unit, and analyzing the landscape fragmentation degree and connectivity of the area to be researched based on the calculated landscape indexes;
the step (2) of performing a ground surface temperature inversion method based on the preprocessed remote sensing image data comprises the following steps:
(201) Calculating a normalized difference vegetation index based on the preprocessed remote sensing image data, and then calculating the surface vegetation coverage;
(202) Estimating the emissivity of the natural surface pixels and the emissivity of the town pixels based on the surface vegetation coverage;
(203) Calculating a radiation brightness value based on the emissivity of the natural surface pixels and the emissivity of the town pixels;
(204) Calculating the surface temperature based on the radiation brightness value;
specifically, the method for performing the earth surface temperature inversion method based on the remote sensing image data comprises the following steps:
step 201, calculating the surface vegetation coverage Fv according to the formula (1);
Fv＝(NDVI-NDVIs)/(NDVIv-NDVIs) (1)
wherein NDVI is a normalized difference vegetation index, and ndviv=0.70 and ndvis=0.05 are taken;
step 202, estimating the emissivity of natural surfaces and town pixels by formulas (2) and (3);
ε surface ＝0.9625+0.0614Fv-0.0461Fv 2 (2)
ε building ＝0.9589+0.086Fv-0.0671Fv 2 (3)
wherein ε surface And epsilon building Respectively representing the emissivity of the natural surface pixels and the emissivity of the town pixels;
step 203, calculating the radiation brightness value B (T) at the temperature Ts according to the formula (4) s )；
B(T s )＝[L λ -L ↑ -τ·(1-ε)L ↓ ]/τ·ε (4)
In the atmosphere transmissionRate τ and atmospheric upstream radiation L ↑ And downstream radiation L ↓ Can be input into imaging time and central longitude and latitude query calculation through NASA, epsilon is the earth's surface emissivity value, L λ Calibrating a value for radiation in a thermal infrared band;
the emissivity of the earth surface is obtained by adding the emissivity of the water body, the natural surface and the urban pixels, the emissivity of the water body pixels is assigned to 0.995, and the emissivity of the natural surface and the urban pixels is calculated according to the formulas (2) and (3); the radiation calibration value of the thermal infrared band is calculated according to the deviation parameter and the on-board radiation brightness value in the header file of the image and is obtained by using a Radiomotric Calibration tool in ENVI5.0 software;
step 204, obtaining the surface temperature T by using the formula (5) s ；
T s ＝K 2 /ln(K 1 /B(T s )+1)-273.15 (5)
Wherein K is 1 And K 2 Respectively, are scaling constants, K in Landsat8 1 ＝480.89，K 2 ＝1201.14。
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