A method for quantitatively evaluating ecological environment quality by integrating multi-source remote sensing indexes

Abstract

The present application relates to the technical field of ecological environment quality evaluation, and particularly relates to a method for quantitatively evaluating ecological environment quality by integrating multi-source remote sensing indexes, comprising the following steps: S1: collecting remote sensing data such as Landsat, SRTMDEM, MOD11A2, GlobeLand30 and FLDAS, and performing pretreatment; S2: extracting human pressure index, biological abundance index, vegetation cover index, land surface temperature and ground relative humidity from the multi-source remote sensing data set obtained in step S1; S3: using the regression Kriging method to downscale the ground relative humidity data to match the resolution of other data; and S4: after standardizing all indexes, using principal component analysis to construct MCEEI to reflect the comprehensive quality of the ecological environment; the present application can quickly quantify the surface ecological environment quality condition from multi-source remote sensing data by integrating five indexes, and can solve the problem that the traditional remote sensing evaluation method for ecological environment quality cannot effectively balance the regional difference in large-scale research.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ecological environment quality evaluation, and particularly relates to a method for quantitatively evaluating ecological environment quality by using comprehensive multi-source remote sensing indexes. BACKGROUND

[0002] Early ecological environment quality evaluation was performed by using plants as indexes, for example, benthic and planktonic plants were used as evaluation indexes to classify the eutrophication of river bifurcations, river inlets and coastal zones. In addition, in 1969, the United States enacted the National Environmental Policy Act to evaluate the impact of individual environmental pollutants on the ecological environment. Subsequently, Sweden, Canada and other countries introduced environmental evaluation systems in their legal environmental protection framework. Because the assessment of regional ecological environment quality is widely needed when making protection and restoration decisions, the use of ecological indexes has become more frequent in the past three decades. These ecological indexes are mostly based on the summary of selected sites to determine regional trends, and therefore, they cannot be directly applied to larger regions and cannot predict the global consequences of human activities.

[0003] In recent years, remote sensing technology has been proven to be an effective means for quickly obtaining regional ecological environment spatio-temporal changes because it can record and process various spatio-temporal data over a large area. However, early applications were usually focused on a single ecological factor or one aspect of the ecological environment, such as normalized difference vegetation index (NDVI) or land surface temperature (LST), which was not systematic and comprehensive enough for ecological environment quality evaluation. Subsequently, some remote sensing ecological quality evaluation indexes were proposed and effectively applied to the ecological environment quality evaluation of urban or urban agglomeration areas, including remote sensing based ecological index (RSEI), comprehensive evaluation index (CEI) and comprehensive ecological evaluation index (CEEI). These remote sensing ecological quality evaluation methods can quickly and effectively analyze the ecological environment quality of a region and accurately describe the regional ecological environment health status.

[0004] However, the existing ecological quality index depends largely on remote sensing satellite images for calculation. In large-scale, cloudy and rainy areas, it is difficult to obtain full-range, time-consistent or even seasonal-consistent remote sensing images (due to frequent and continuous rainfall and other meteorological factors, historical images are rarely available in some areas), which directly affects the remote sensing quantitative inversion of ecological environment quality. In addition, if the collection time of adjacent images is different (for example, one is from summer and the other is from winter), the key land surface parameters such as NDVI or LST extracted will have significant differences at the edges of the images, which will have a significant impact on the inversion results of the ecological environment quality, making it unsuitable for remote sensing quantitative evaluation of ecological environment quality in large regional scale.

[0005] In view of the above problems, it is necessary to develop a new method for remote sensing quantitative evaluation of ecological environment quality based on comprehensive multi-source data sets, which is used for remote sensing quantitative evaluation of ecological environment quality in large-scale research. SUMMARY

[0006] The purpose of the present application is to provide a method for quantitative evaluation of ecological environment quality based on comprehensive multi-source remote sensing indicators, which quickly quantifies the ecological environment quality of the land surface from multi-source remote sensing data by integrating five indicators, and solves the problem that the traditional remote sensing evaluation method of ecological environment quality cannot effectively balance the regional differences in large-scale research.

[0007] To achieve the above technical purposes and effects, the present application is realized by the following technical solutions:

[0008] A method for quantitative evaluation of ecological environment quality based on comprehensive multi-source remote sensing indicators, comprising the following steps:

[0009] S1: Collect Landsat, SRTMDEM, MOD11A2, GlobeLand30 and FLDAS remote sensing data, and perform preprocessing.

[0010] S2: Extract human pressure index, biological abundance index, vegetation cover index, land surface temperature and ground relative humidity from the multi-source remote sensing data set obtained by preprocessing in step S1.

[0011] S3: Downscale the ground relative humidity data using the regression kriging method to match the resolution of other data.

[0012] S4: After standardizing all indicators, use principal component analysis to construct MCEEI to reflect the comprehensive quality of the ecological environment.

[0013] Further, step S1 specifically includes: after collecting multi-source remote sensing data, image preprocessing is performed according to the actual situation of the study area, including but not limited to geometric correction, atmospheric correction, image stitching and cropping.

[0014] Further, step S2 specifically comprises the following sub-steps:

[0015] S2.1: Extracting anthropogenic pressure index (API) from the pre-processed multi-source remote sensing data set, which is composed of impervious surface cover (ISC) and soil and built-up coverage (SBC). ISC refers to the coverage of impervious surface in unit area, which quantifies the percentage of impervious surface in the landscape in unit area. The calculation formula is as follows:

[0016]

[0017]

[0018] In the formula, A i is the size of the set unit area, a ij is the area of the set unit pixel as impervious surface, and SBC is the soil and built-up coverage in the watershed, with the calculation formula as follows:

[0019]

[0020]

[0021] Wherein, WC r is the water area coverage, VC r is the vegetation coverage, A is the area, A w is the area of the regional water body.

[0022] S2.2: Extracting biological richness index (BRI) from the pre-processed multi-source remote sensing data set. BRI is calculated based on land use product data (GlobeLand30), with the specific method as follows:

[0023]

[0024] In the formula, A BRI is the normalization coefficient of BRI (value is 692.09602), A is the area, A f is the forest area, A g is the grassland area, A w is the wetland area, A p is the cultivated land area, A c is the construction land area, and A u is the unused area.

[0025] S2.3: Vegetation coverage index (VCI) extraction from the pre-processed multi-source remote sensing data set, the specific method is as follows:

[0026]

[0027] Wherein, A VCI is the normalized coefficient of VCI (value is 601.110997) A is the area of the region, A f is the forest area, A g is the grassland area, A p is the cultivated land area, A c is the construction land area, A u is the unused area.

[0028] S2.4: Landsurface temperature (LST) extraction from the pre-processed landsurface temperature product data set (MOD11A2), and the conversion of landsurface temperature in Celsius is carried out by the following formula:

[0029]

[0030] Wherein, T b is the pixel value of temperature product data, and LST is the landsurface temperature in ℃. Since there are many null values in the landsurface temperature product data, the universal kriging interpolation model is used to fill in the null values, and the calculation formula is as follows:

[0031]

[0032] In the formula, Z UK (x) is the kriging estimate at x, λ i is the weight of the i th value, x i is the i th observation value.

[0033] S2.5: Selecting specific humidity (SH) from the pre-processed FLDAS data set as the basic data, and extracting surface relative humidity (SRH), the specific method is as follows:

[0034]

[0035]

[0036] Wherein, w is the mass mixing ratio of water vapor and dry air (dimensionless), w sIt is the mass ratio of water vapor to dry air under equilibrium conditions (dimensionless), p is atmospheric pressure (Pa), and q is specific humidity (kg·kg⁻¹). -1 T is the Kelvin temperature (K), T0 is the reference temperature (usually 273.15 K), and H is the elevation (m).

[0037] Furthermore, step S3 specifically includes: using the regression kriging (RK) method to spatially downscale the sample to obtain a spatial resolution consistent with API, BRI, VCI, and LST, as calculated by the following formula:

[0038]

[0039] Where, Z RK (x) is a deduction The predicted value of SRH, for The fitting trend of the position. This is the residual value at that location.

[0040] Furthermore, step S4 specifically includes the following sub-steps:

[0041] S4.1: Standardize the extracted API, BRI, VCI, LST, and SRH data. Higher BRI, VCI, and SRH values ​​indicate better ecological environment quality, while higher API and LST values ​​indicate worse ecological environment quality. To facilitate calculation and ensure all indicators have a consistent direction—that is, higher values ​​indicate better ecological environment quality—standardize all indicators as follows:

[0042] Indicator standardization (BRI, VCI, and SRH).

[0043]

[0044] Indicator standardization (API and LST).

[0045]

[0046] Among them, L i It is l i Standardized value, l max and l min These represent the maximum and minimum values ​​of each indicator (API, BRI, VCI, LST, SRH). The standardized values ​​of API, BRI, VCI, LST, and SRH are between 0 and 1.

[0047] S4.2: The extracted API, BRI, VCI, LST, SRH image maps are superimposed to construct the improved comprehensive ecological quality evaluation index MCEEI function:

[0048]

[0049] In the formula, LDI is a comprehensive land degradation index, and f is an integrated function of API, BRI, VCI, LST and SRH.

[0050] S4.3: MCEEI function solution, the principal component analysis (PCA) method is used to solve the MCEEI function, and the original multi-dimensional remote sensing data set is compressed through principal component analysis, wherein the first component obtained by principal component analysis, i.e. the first principal component (PC1) contains most of the information in the original data set, and MCEEI is constructed by using the linear combination of PC1:

[0051]

[0052] In the formula, PC1 is the first principal component obtained by principal component analysis, PC1 min and PC1 max are the minimum and maximum values of PC1 respectively. The value of MCEEI is between 0 and 1, and the larger the value, the better the ecological environment quality in the study area.

[0053] The beneficial effects of the present application are:

[0054] The present application integrates multiple remote sensing data sources such as Landsat, SRTMDEM, MOD11A2, GlobeLand30 and FLDAS, and realizes comprehensive monitoring of different levels of ecological environment. Landsat provides 30m spatial resolution multispectral information of the earth's surface, SRTMDEM provides terrain information, MOD11A2 provides land surface temperature data for large-scale research, and GlobeLand30 and FLDAS provide detailed land use and land surface humidity information in a large area. The fusion of such multi-source data makes the evaluation index not only rich and complementary, but also can comprehensively reflect the ecological environment from multiple angles, improving the comprehensiveness and accuracy of the evaluation.

[0055] In the development of large-scale ecological environment quality assessment, there are two major challenges. Firstly, due to the influence of different regional topographic conditions and climate differences (partly cloudy and rainy), a single data source cannot effectively obtain complete multi-dimensional observation data. Secondly, the existing wetness (Wet) index used in ecological quality evaluation has significant regional and altitude differences, and the global wetness information is unbalanced, which is not conducive to the monitoring of ecological quality in large-scale research. The present application effectively obtains complete five key ecological quality evaluation indicators by synthesizing multi-source remote sensing data. The surface relative humidity (SRH) can objectively evaluate the humidity environment in different regions in large-scale research, and can also consider the influence of water environment factors (BRI) to a certain extent without the need for masking and removing water bodies. In addition, the pan-Kriging method is used to fill in the missing values and downscale the land surface temperature (LST), and the regression Kriging method is used to downscale the ground relative humidity (SRH) data to match the spatial resolution of other data. The regression Kriging method combines regression models with spatial interpolation, which can effectively utilize the trend of meteorological data while maintaining the spatial details of the data. Therefore, in large-scale, cloudy and rainy areas, the present application can effectively solve the problem of inconsistent data acquisition and improve the accuracy of remote sensing inversion results.

[0056] The present application extracts and standardizes human pressure index, biological abundance index, vegetation cover index, land surface temperature and ground relative humidity from five key ecological dimensions. Through principal component analysis (PCA) method, an improved comprehensive ecological quality evaluation index (MCEEI) is constructed, which not only simplifies the complex calculation process of multiple indicators, but also effectively captures the contribution of each indicator in the comprehensive ecological quality. PCA extracts the first principal component (PC1) with the maximum amount of information through dimension reduction technology, thereby ensuring the scientific integration and comprehensive trade-off of multiple indicators in the evaluation process.

[0057] With the high temporal and spatial resolution and global coverage of remote sensing data, the present application can track the dynamic changes of ecological environment quality in real time. Especially in areas where human activities are dense and climate change is sensitive, the rapid response capability of the present application provides important support for early warning and management decision of ecological environment. Through standardization and index direction adjustment, the results are intuitive and easy to understand, helping managers quickly identify potential ecological degradation areas and develop appropriate protection measures.

[0058] Of course, implementing any product of the present application does not necessarily require all the advantages described above. BRIEF DESCRIPTION OF DRAWINGS

[0059] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a schematic diagram of the overall process of the present invention;

[0061] Figure 2 Schematic diagram of surface temperature data; (a) LST data after MOD11A2 unit conversion; (b) LST data after using pan-kriging interpolation;

[0062] Figure 3 This is a schematic diagram of surface humidity according to the present invention; (a) SH data in the FLDAS dataset; (b) SRH data calculated by the SRH conversion formula; (c) SRH after RK prediction interpolation;

[0063] Figure 4 (a) is a schematic diagram of true-color display of OLI image, and a comparison diagram of RSEI (b) and MCEEI (c); (d) is a correlation analysis; (e) is an RSEI index diagram;

[0064] Figure 5 This is a diagram showing the comparison results of EI data released by government departments in 2020 with RSEI, CEEI, and MCEEI in different cities and regions.

[0065] Figure 6 A schematic diagram illustrating the spatiotemporal characteristics of the surface ecological environment quality in the Pearl River Basin from 2000 to 2020;

[0066] Figure 7 The images are schematic diagrams of Google Earth images of the surface ecological environment of the Pearl River Basin; (a) the distribution of surface EEQ levels in the Pearl River Basin in 2020, (b) artificial surface extension of mountains and forests obtained by Google Earth Pro (23°25'17”N, 113°08'58”E, December 21, 2017), (c) woodland obtained by Google Earth Pro (23°26'02”N, 108°27'59”E, December 05, 2019), (d) urban center area obtained by Google Earth Pro (23°25'17”N, 113°08'58”E, January 26, 2019). Detailed Implementation

[0067] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of the present application.

[0068] Embodiment 1

[0069] The method for quantitatively evaluating ecological environment quality by comprehensively using multi-source remote sensing indexes in the embodiment is a method for quantitatively evaluating ecological environment quality by comprehensively using multi-source remote sensing indexes, and a construction process of a modified comprehensive ecological evaluation index (MCEEI) is as shown in Figure 1 The method mainly includes the following steps: multi-source remote sensing data acquisition and preprocessing, anthropogenic pressures index (API) extraction, biological richness index (BRI) extraction, vegetation coverage index (VCI) extraction, land surface temperature (LST) extraction, surface relative humidity (SRH) extraction, and integration of the modified comprehensive ecological evaluation index (MCEEI).

[0070] 1) Multi-source remote sensing data acquisition: after collecting multi-source remote sensing data, image preprocessing is performed according to the actual situation of the research area, mainly including geometric correction, atmospheric correction, image stitching, cutting, and the like.

[0071] 2) Anthropogenic pressures index (API) extraction from the preprocessed multi-source remote sensing data set. The index is composed of impervious surface cover (ISC) and soil and built-up coverage (SBC). The impervious surface cover (ISC) refers to the coverage content of the impervious surface in a unit area, which quantifies the percentage of the impervious surface in the landscape in a unit area. The calculation formula is as follows:

[0072]

[0073]

[0074] In the formula, A i is the size of the set unit area, a ijSBC is the soil and building coverage in the basin, and the calculation formula is as follows:

[0075]

[0076]

[0077] wherein, WC r is the water area coverage, VC r is the vegetation coverage, A is the area of the region, A w is the area of the water body in the region.

[0078] 3) Extract the biological richness index (BRI) from the pretreated multi-source remote sensing data set, which is calculated based on the land use product data (GlobeLand30), and the specific method is as follows:

[0079]

[0080] wherein, A BRI is the normalization coefficient of BRI (the value is 692.09602), A is the area of the region, A f is the forest area, A g is the grassland area, A w is the wetland area, A p is the cultivated land area, A c is the construction land area, and A u is the unused area.

[0081] 4) Extract the vegetation coverage index (VCI) from the pretreated multi-source remote sensing data set, and the specific method is as follows:

[0082]

[0083] wherein, A VCI is the normalization coefficient of VCI (the value is 601.110997), A is the area of the region, A f is the forest area, A g is the grassland area, A p is the cultivated land area, A c is the construction land area, and A u is the unused area.

[0084] 5) Extract land surface temperature (LST) from the preprocessed land surface temperature product dataset (MOD11A2) and convert the land surface temperature to Celsius using the following formula:

[0085]

[0086] Among them, T b Here are the pixel values ​​for the temperature product data; LST is the surface temperature in °C. Because there are many missing values ​​in the surface temperature product data, a universal kriging interpolation model is used to fill in the missing values. The calculation formula is as follows:

[0087]

[0088] In the formula, Z UK (x) is the Kriging estimate at x, λ i x is the weight of the i-th value. i Let be the i-th observation.

[0089] 6) Select specific humidity (SH) from the preprocessed FLDAS dataset as the base data, and extract surface relative humidity (SHR). The specific method is as follows:

[0090]

[0091]

[0092] Where w is the mass mixing ratio of water vapor and dry air (dimensionless), w s It is the mass ratio of water vapor to dry air under equilibrium conditions (dimensionless), p is atmospheric pressure (Pa), and q is specific humidity (kg·kg⁻¹). -1 T is the Kelvin temperature (K), T0 is the reference temperature (usually 273.15 K), and H is the elevation (m).

[0093] 7) Since SRH is calculated using the FLDAS dataset, which has a spatial resolution of 0.1°, regression kriging (RK) is used to spatially downscale it, resulting in a resolution consistent with API, BRI, VCI, and LST. The calculation formula is as follows:

[0094]

[0095] Where, Z RK (x) is a deduction the predicted value of SRH, is the fitted trend of the location, is the residual value of the location.

[0096] 8) Integration of MCEEI, the specific method is as follows:

[0097] The above extracted API, BRI, VCI, LST, SRH data are standardized, because the larger the BRI, VCI and SRH values, the better the ecological environment quality, and the larger the API and LST values, the worse the ecological environment quality. In order to facilitate calculation, the values of all indicators have a unified direction, that is, the larger the value, the better the ecological environment quality. Therefore, all indicators are standardized, the method is as follows:

[0098] Index standardization (BRI, VCI and SRH),

[0099]

[0100] Index standardization (API and LST),

[0101]

[0102] Where, L i is the standardized value, l i and l max and l min respectively represent the maximum and minimum values of each index (API, BRI, VCI, LST, SRH). The standardized values of API, BRI, VCI, LST, SRH data are between 0 and 1.

[0103] The above extracted API, BRI, VCI, LST, SRH image maps are superimposed to construct the improved comprehensive ecological quality evaluation index MCEEI function:

[0104]

[0105] In the formula, LDI is the comprehensive land degradation index, and f is the integration function of API, BRI, VCI, LST and SRH five indexes.

[0106] MCEEI function solution, the principal component analysis (principal component analysis, PCA) method is used to solve the MCEEI function, and the original multi-dimensional remote sensing data set is compressed through principal component analysis. The first component obtained by principal component analysis, that is, the first principal component (PC1) contains most of the information in the original data set, and MCEEI is constructed by using the linear combination of PC1:

[0107]

[0108] In the formula, PC1 is the first principal component after principal component analysis, PC1 min and PC1 max are the minimum and maximum values of PC1, respectively. The value of MCEEI is between 0 and 1, and the greater the value, the better the ecological environment quality in the study area.

[0109] In the embodiment, the constructed MCEEI is not limited to being applicable to the SRTMDEM, MOD11A2, GlobeLand30, and FLDAS data sets, and can also be applicable to other remote sensing data sets that have approximate ground surface environmental parameter information, and is not limited to the method described in the embodiment.

[0110] In the embodiment, the API, BRI, and VCI three indexes can be replaced by indexes having similar functions.

[0111] In the embodiment, the MCEEI can be used to divide the ecological environment quality into different grades, and according to the MCEEI value, the grades can be divided into five grades (poor, relatively poor, medium, good, and excellent) with a step of 0.2, and other grade division methods can also be used.

[0112] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details, and the application is not limited to the specific embodiments described. Obviously, according to the content of the specification, many modifications and changes can be made. The specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and use the application. The application is limited only by the claims and their entire scope and equivalents.

Claims

1. A method for quantitatively evaluating ecological environment quality by integrating multi-source remote sensing indicators, characterized in that, The method comprises the following steps: S1: Collect Landsat, SRTM DEM, MOD11A2, GlobeLand30 and FLDAS remote sensing data, and perform preprocessing; S2: Extract the human pressure index, biological abundance index, vegetation cover index, ground surface temperature and ground relative humidity from the multi-source remote sensing data set obtained after step S1 preprocessing; S3: The ground relative humidity data is reduced in scale by using the regression kriging method to match the resolution of other data; S4: After standardizing all indexes, the MCEEI is constructed by using principal component analysis to reflect the comprehensive quality of the ecological environment; The step S3 specifically comprises: using the regression kriging method to reduce the spatial scale, obtaining the API, BRI, VCI, LST spatial resolution consistent, the calculation formula is as follows: ； wherein, in the formula, Z RK (x) is the estimated the predicted value of the SRH at the position, is the fitted trend at the position, is the residual value at the position; The step S4 specifically comprises the following sub-steps: S4.1: The API, BRI, VCI, LST, SRH data extracted above are standardized; S4.2: The API, BRI, VCI, LST, SRH image maps extracted above are superimposed to construct the improved comprehensive ecological quality evaluation index MCEEI function: ； In the formula, LDI is the comprehensive land degradation index, and f is the integration function of API, BRI, VCI, LST and SRH five indexes; S4.3: MCEEI function solution, the MCEEI function is solved by using the principal component analysis method, and the original multi-dimensional remote sensing data set is compressed through the principal component analysis, wherein the first component obtained by the principal component analysis, that is, the first principal component contains most of the information in the original data set, and the MCEEI is constructed by using the linear combination of the first principal component: ； In the formula, PC1 is the first principal component after principal component analysis, PC1 min and PC1 max are the minimum and maximum values of PC1, respectively; the value of MCEEI is between 0 and 1, and the greater the value, the better the ecological environment quality in the study area.

2. The method for quantitatively evaluating ecological environment quality by integrated multi-source remote sensing indicators according to claim 1, characterized in that: Step S1 specifically comprises: after collecting the multi-source remote sensing data, the image preprocessing is performed according to the actual situation of the research area, including but not limited to geometric correction, atmospheric correction, image splicing and cutting.

3. The method of claim 1, wherein the method comprises the following steps: 1) determining the weight of each index; 2) calculating the comprehensive index value of each region; 3) determining the ecological environment quality of each region according to the comprehensive index value. Step S2 specifically comprises the following sub-steps: S2.1: The human pressure index API is extracted from the preprocessed multi-source remote sensing data set, which is composed of impervious surface coverage and soil and building ground coverage. The impervious surface coverage refers to the coverage content of the impervious surface in unit area, which quantifies the area percentage of the impervious surface in the unit area. The calculation formula is as follows: ； ； In the formula, A i is the set size of unit area, a ij is the set area of unit pixel that is impervious surface, and SBC is the soil and building coverage in the basin, and the calculation formula is as follows: ； ； Wherein, WC r is the water body area coverage, VC r is the vegetation coverage, A is the area of the region, A w is the area of the water body in the region; S2.2: The biological abundance index BRI is extracted from the preprocessed multi-source remote sensing data set, which is calculated based on the land use product data, and the specific method is as follows: ； wherein A BRI is a normalization coefficient for BRI, A is area of region, A f is area of forest land, A g is area of grassland, A w is area of wetland, A p is area of farmland, A c is area of construction land, A u is area of unused land; S2.3: The vegetation cover index VCI is extracted from the preprocessed multi-source remote sensing data set, as follows: ； wherein, A VCI is the normalized coefficient of VCI, A is the area of the region, A f is the area of forest land, A g is the area of grassland, A p is the area of arable land, A c is the area of construction land, A u is the area of unused land; S2.4: The ground surface temperature is extracted from the preprocessed ground surface temperature product data set MOD11A2, and the conversion of the ground surface temperature from Celsius degree is carried out by the following formula: ； where T b is the pixel value of the temperature product data, and LST is the land surface temperature in °C; due to many missing values in the land surface temperature product data, the missing values were filled using the universal Kriging interpolation model, and the calculation formula is as follows: ； where Z UK (x) is the kriging estimate at x, λ i is the weight of the ith value, x i is the ith observation. S2.5: The ground relative humidity is extracted from the preprocessed FLDAS data set by selecting the ground specific humidity as the basic data, and the specific method is as follows: ； ； where w is the mass mixing ratio of water vapor to dry air, w s is the mass mixing ratio of water vapor to dry air in equilibrium, p is the atmospheric pressure, q is the specific humidity, T is the temperature in Kelvin, To is the reference temperature, and H is the elevation.

4. The method of claim 1, wherein the method comprises the following steps: 1) determining the weight of each index; 2) calculating the comprehensive index value of each region; 3) determining the ecological environment quality of each region according to the comprehensive index value. Because the greater the BRI, VCI and SRH values indicate the better the ecological environment quality, and the greater the API and LST values indicate the worse the ecological environment quality; in order to facilitate the calculation and make all the values of the indicators have a unified direction, i.e., the greater the value indicates the better the ecological environment quality; therefore, all the indicators are standardized, the method is as follows: Standardization of indicators BRI, VCI and SRH, ； Standardization of indicators (API and LST), ； wherein L i is the standardized value, l i and l max and l min respectively represent the maximum and minimum values of the indices API, BRI, VCI, LST, SRH; the standardized values of API, BRI, VCI, LST, SRH are between 0 and 1.

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