An ecological environment pollution emission source space-based monitoring system and method

Abstract

The present application provides a kind of ecological environment pollution discharge source space-based monitoring system and method, wherein according to practical application demand, ecological environment pollution discharge source is classified, and then the monitoring target, target characteristic and corresponding space-based monitoring means of each type of pollution discharge source are determined;Wherein the ecological environment pollution discharge source includes water pollution discharge source, atmospheric pollution discharge source, ecological pollution discharge source;For different monitoring targets and their corresponding monitoring means, data processing and application method are proposed, and corresponding parameter inversion submodule is formed;Establish the ecological environment pollution discharge source monitoring system, according to specific application scene and monitoring task, select the combination of different parameter inversion submodule, build space-based monitoring system;Through the research on the nature of pollution source target, the target type and monitoring elements that need to be concerned in space-based monitoring are clarified, and the monitoring means are proposed, which provides a reference for the overall design of ecological environment satellite engineering development and ground supporting system construction.

Description

Technical Field

[0001] This invention relates to a space-based monitoring system and method for ecological and environmental pollution emission sources, belonging to the field of remote sensing satellite technology, and can be applied to the overall design and data application of ecological and environmental monitoring remote sensing satellites. Background Technology

[0002] As my country's development enters a new historical period, new tasks such as the battle against pollution, environmental protection supervision and law enforcement, and ecological and environmental performance evaluation have placed more and higher demands on environmental protection work, especially environmental monitoring. Traditional environmental monitoring, which is mainly based on ground sampling, is difficult to meet the needs of modern environmental management characterized by comprehensiveness, full process, full elements, and full cycle. There is an urgent need to expand ground-based point environmental monitoring to spatial surface monitoring, expand timed static environmental monitoring to real-time dynamic monitoring, and expand local discrete environmental monitoring to continuous monitoring of the entire region, so as to build a "high-precision, comprehensive, and short-cycle" three-dimensional remote sensing monitoring capability for the ecological environment.

[0003] After decades of development, China's environmental remote sensing monitoring has made significant progress, achieving a historic leap from nothing to something, from technological experimentation to operational use, and successfully developing and launching multiple satellites, including the Environmental Series and the Gaofen Series. However, the traditional space-based environmental remote sensing monitoring model has two significant shortcomings: First, the traditional remote sensing model is generally passive, reactive, and management-driven, making it difficult to promptly and proactively detect environmental pollution and ecological damage, only achieving "post-event verification." Second, the proposal and development of satellites have been more driven by technology than by application, resulting in a situation of "numerous but unusable" systems. The new tasks of the new era have brought unprecedented opportunities and challenges to space-based ecological and environmental monitoring. Therefore, it is necessary to build a space-based monitoring system from the specific operational perspective of the ecological and environmental field, using remote sensing to trace pollution emission sources and achieve a shift from "post-event verification" to "proactive detection."

[0004] With the mission objective of proactively identifying pollution emission sources, it is necessary to identify and classify the main emission sources based on their target characteristics, and extract space-based remote sensing monitoring methods suitable for different targets. By processing and inverting the corresponding remote sensing data, it is possible to identify situations such as temperature anomalies and gas concentration anomalies, thereby locating and tracing pollution sources and effectively guiding environmental protection supervision and law enforcement. Summary of the Invention

[0005] In view of this, the present invention provides a space-based monitoring method for ecological and environmental pollution emission sources. It proposes monitoring methods targeting the characteristics of ecological and environmental pollution sources, clarifies the corresponding data processing methods, establishes an ecological and environmental pollution emission source monitoring system, and enables remote sensing satellites to monitor ecological and environmental pollution point sources. The method includes:

[0006] Step S1: Based on actual application needs, classify the sources of ecological and environmental pollution emissions, and then determine the monitoring targets, target characteristics, and corresponding space-based monitoring methods for each type of pollution emission source; wherein the sources of ecological and environmental pollution emissions include water pollution emission sources, air pollution emission sources, and ecological pollution emission sources;

[0007] Step S2: For different monitoring targets and their corresponding monitoring methods, propose data processing and application methods to form corresponding parameter inversion sub-modules;

[0008] Step S3: Establish an ecological and environmental pollution emission source monitoring system. Based on specific application scenarios and monitoring tasks, select different combinations of the parameter inversion sub-modules to construct a space-based monitoring system.

[0009] Specifically, the monitoring targets for water pollution emission sources in step S1 include abnormal distribution of water quality and abnormal distribution of water temperature; the monitoring targets for air pollution emission sources include abnormal high temperature locations, pollutant gas composition and distribution; and the monitoring targets for ecological pollution emission sources include human activities and vegetation characteristics.

[0010] For the aforementioned abnormal water quality distribution, the monitoring elements include water quality characteristic parameters, which include chlorophyll a concentration and suspended solids concentration.

[0011] For abnormal temperature distribution, the monitoring elements include surface or water temperature, which includes fire point temperature, water temperature, and building temperature.

[0012] Regarding the composition and distribution of the polluting gases, the monitoring elements include the composition and concentration of the polluting gases, including the composition and concentration information of gases such as nitrogen dioxide, sulfur dioxide, and ozone;

[0013] Regarding the aforementioned human activity, the monitoring elements include visible light geometry and texture information;

[0014] For the vegetation characteristics, the monitoring elements include vegetation characteristic parameters, including vegetation coverage area and vegetation growth status.

[0015] Specifically, step S2 includes: setting up a corresponding parameter inversion module according to the classification and the target characteristics, and processing and inverting satellite data in a specific form;

[0016] Among them, the water quality characteristic parameters include comparing remote sensing data in the visible light multispectral band with ground-measured data to establish an inversion model for chlorophyll a concentration and suspended matter, thereby obtaining the water quality parameters.

[0017] The characteristics of the surface or water surface temperature are obtained by inverting the observation data of the two long-wave infrared spectral bands using a split-window algorithm;

[0018] For the composition and concentration of the pollutant gas, the vertical column concentration of the pollutant gas was obtained by applying differential optical absorption spectroscopy to the ultraviolet hyperspectral observation data, removing the stratosphere influence, and then inverting the data.

[0019] For the visible light geometric and texture information, feature extraction algorithms are used to identify features in the visible light panchromatic remote sensing data, and the target category is determined after comparison with the remote sensing feature library.

[0020] For the vegetation characteristic parameters, the normalized vegetation index (NDVI) is obtained by processing the red and near-infrared spectral data of the visible light multispectral system. Further processing yields the vegetation cover and vegetation growth status.

[0021] Specifically, step S3 includes: in the process of establishing an ecological and environmental pollution emission source monitoring system, firstly, clarifying the monitoring task; based on the target characteristics obtained in step S1 and the data processing and application methods obtained in step S2, setting up sub-modules corresponding to each type of monitoring target; according to the characteristics of the monitoring task, decomposing the monitoring task into different monitoring targets, selecting their corresponding sub-modules, and combining them to realize the monitoring task of that type.

[0022] Specifically, the chlorophyll a concentration inversion model employs a three-segment method, based on the formula...

[0023] Chla=A·[R(λ1) -1 -R(λ2) -1 ]·R(λ3)+B

[0024] Where λ1 is the spectral band located near the chlorophyll a absorption peak, λ2 is selected near the λ1 spectral band, and the absorption coefficients of suspended matter and yellow substances at λ1 and λ2 are approximately equal, and λ3 is selected from the spectral band where pure water absorption is dominant; R(λ1), R(λ2), and R(λ3) are the reflectances of the λ1, λ2, and λ3 spectral bands, respectively; A and B are the fitting coefficients of the linear model;

[0025] The suspended matter inversion model adopts the three-band method, according to the formula...

[0026] TSM=A·[R(λ2)+R(λ3)] / R(λ1)+B

[0027] Where R(λ1), R(λ2), and R(λ3) are the reflectances of the spectral bands λ1, λ2, and λ3, respectively. A and B are the fitting coefficients of the linear model;

[0028] The temperature inversion is achieved through long-wave infrared remote sensing. Data from two thermal infrared spectral bands configured on the remote sensing satellite payload are used to form a split-window channel, and the temperature is obtained using the following split-window algorithm:

[0029]

[0030] In the formula, T s For the inverted surface temperature; T i T j Let ε be the luminance temperature of the two split-window channels, ε=(ε i +ε j ) / 2, Δε=ε i -ε J , ε i and ε j denoted as , where is the surface emissivity of the split-window channel; A1, A2, A3, B1, B2, B3, C, and D are the regression coefficients of the algorithm.

[0031] This invention also proposes a space-based monitoring system for ecological and environmental pollution emission sources, comprising:

[0032] The ecological and environmental pollution emission source classification and target characteristic identification module is used to classify ecological and environmental pollution emission sources according to actual application needs, and then determine the monitoring targets, target characteristics and corresponding space-based monitoring methods for each type of pollution emission source; wherein the ecological and environmental pollution emission sources include water pollution emission sources, air pollution emission sources and ecological pollution emission sources;

[0033] The data processing and inversion module is used to propose data processing and application methods for different monitoring targets and their corresponding monitoring methods, forming corresponding parameter inversion sub-modules;

[0034] The module for establishing a space-based monitoring system for ecological and environmental pollution emission sources is used to establish such a system. Based on specific application scenarios and monitoring tasks, different combinations of the parameter inversion sub-modules are selected to construct the space-based monitoring system.

[0035] The beneficial effects of this invention compared to the prior art are:

[0036] (1) Clarify the targets and characteristics of space-based monitoring of ecological and environmental pollution emission sources. The space-based monitoring method for ecological and environmental pollution emission sources adopted in this invention clarifies the types of targets and monitoring elements that need to be focused on in space-based monitoring through the study of the nature of pollution source targets, and proposes monitoring methods, providing a reference for the key directions of ecological and environmental satellite engineering development and ground supporting system construction.

[0037] (2) Establishing data processing and inversion models: Based on the classification and characteristic identification of pollution source targets, corresponding data processing and inversion sub-modules were established, and inversion models with different parameters were given, laying the foundation for comprehensive application for specific pollution source monitoring tasks.

[0038] (3) A space-based monitoring system directly oriented towards the supervision and law enforcement applications of ecological and environmental departments has been constructed. There are many remote sensing satellite monitoring methods, including but not limited to visible light, ultraviolet, infrared, and spectral data. However, there is a certain degree of disconnect between the technical means and the end-user applications. The space-based monitoring system constructed in this invention is oriented towards the specific tasks and application scenarios of ecological and environmental supervision and law enforcement, such as tracing the source of pollution in industrial parks and investigating straw burning hotspots. It provides targeted comprehensive monitoring methods and directly supports end-user use. Attached Figure Description

[0039] Figure 1 This is a schematic diagram illustrating the decomposition principle of the classification of ecological and environmental pollution emission sources and their corresponding monitoring elements and methods according to the present invention.

[0040] Figure 2 This is a block diagram of the system components of the present invention. Detailed Implementation

[0041] The following is in conjunction with the appendix Figure 1-2 The present invention will be described in detail below with reference to specific embodiments.

[0042] This invention provides a space-based monitoring method for ecological and environmental pollution emission sources, wherein the decomposition principle of ecological and environmental pollution emission source classification and its corresponding monitoring elements and monitoring methods is as follows: Figure 1 As shown, the method includes:

[0043] Step S1: Based on actual application needs, classify the sources of ecological and environmental pollution emissions, and then clarify the monitoring targets, target characteristics, and corresponding space-based monitoring methods for each type of pollution emission source;

[0044] Water pollution source monitoring mainly includes the following aspects: ① Black and odorous water bodies, algal blooms, aquatic plants, etc. The target characteristic is abnormal water quality. By monitoring the distribution of abnormal water quality, the pollution source can be located and the pollution level can be assessed. The corresponding space-based monitoring method is visible light multispectral remote sensing; ② Thermal pollution of water bodies, thermal discharge from factories, thermal discharge from nuclear power plants, etc. The target characteristic is abnormal temperature. By monitoring the distribution of abnormal water temperature, the pollution source can be located. The corresponding space-based monitoring method is long-wave infrared remote sensing.

[0045] Air pollution source monitoring mainly includes the following aspects: ① Air pollution sources such as straw burning fire points and chimneys, whose target characteristic is temperature anomalies. Pollution sources can be located by monitoring high temperature anomaly points. The corresponding space-based monitoring method is mid-wave infrared combined with long-wave infrared remote sensing; ② Pollutant gases, whose target characteristic is the presence of specific pollutant gas components and their concentration exceeding the standard. Pollution sources can be located and pollution levels can be assessed by monitoring their distribution. The corresponding space-based monitoring method is ultraviolet hyperspectral remote sensing.

[0046] Ecological pollution source monitoring mainly includes the following aspects: ① Ecological red lines, whose target characteristics are human activities characterized by visible light geometric and textural features. Ecological pollution behavior can be determined by monitoring human activities within the ecological red lines. The corresponding space-based monitoring method is visible light panchromatic remote sensing; ② Vegetation growth and coverage, whose target characteristics are vegetation features. The scope of ecological pollution can be determined by monitoring vegetation features. The corresponding space-based monitoring method is visible light multispectral remote sensing.

[0047] Step S2: For monitoring targets and methods of different pollution emission sources, set up corresponding parameter inversion submodules to process and invert specific forms of satellite data;

[0048] (1) Water quality characteristic parameter inversion submodule

[0049] Remote sensing monitoring of water quality characteristics mainly relies on the optical properties of water bodies to establish quantitative estimation models for relevant parameters, typically utilizing the visible light multispectral band. The main influencing factors of water body optical properties include phytoplankton (chlorophyll a) and suspended matter. Based on ground-based measured data, chlorophyll a concentration retrieval models and suspended matter retrieval models are established.

[0050] The chlorophyll a concentration inversion model uses a three-segment method:

[0051] Chla=A·[R(λ1) -1 -R(λ2) -1 ]·R(λ3)+B

[0052] In the formula, λ1 is the spectral band located near the chlorophyll a absorption peak, λ2 is selected near the λ1 spectral band, and the absorption coefficients of suspended matter and yellow substances are approximately equal at λ1 and λ2, and λ3 is selected in the spectral band where pure water absorption is dominant. R(λ1), R(λ2), and R(λ3) are the reflectances of the λ1, λ2, and λ3 spectral bands, respectively. A and B are the fitting coefficients of the linear model.

[0053] The suspended matter inversion model uses the three-band method:

[0054] TSM=A·[R(λ2)+R(λ3)] / R(λ1)+B

[0055] In the formula, R(λ1), R(λ2), and R(λ3) are the reflectances of the spectral bands λ1, λ2, and λ3, respectively. A and B are the fitting coefficients of the linear model.

[0056] (2) Temperature Inversion Submodule

[0057] Temperature inversion is achieved through long-wave infrared remote sensing. Specifically, data from two thermal infrared bands configured on the remote sensing satellite payload are used to form a split-window channel, and the temperature is inverted using the following split-window algorithm:

[0058]

[0059] In the formula, T s For the inverted surface temperature; T i T j Let ε be the luminance temperature of the two split-window channels, ε=(ε i +ε j ) / 2, Δε=ε i -ε J , ε i and ε j denoted as , where A1, A2, A3, B1, B2, B3, C, and D are regression coefficients from the algorithm. Surface emissivity can be estimated using the ASTER GED global surface emissivity product database.

[0060] (3) Fire detection submodule

[0061] Fire detection is achieved through a combination of mid-wave infrared and long-wave infrared remote sensing. Specifically, it is determined using an absolute detection criterion, a difference judgment criterion, and a background judgment criterion. In the following formula, T... MWIR The brightness temperature at the mid-wavelength spectrum, T LWIR This refers to the brightness temperature in the long-wavelength spectral range.

[0062] Absolute detection criterion: T MWIR >360K (330K at night);

[0063] Difference judgment criteria: T MWIR -T LWIR >25K (10K at night);

[0064] Background judgment criteria: T MWIR >mean(T MWIR )+3std(T MWIR )&T MWIR -T LWIR >median(T MWIR -T LWIR )+3std(T MWIR -T LWIR ).

[0065] If all three criteria are met, the ignition point can be isolated.

[0066] (4) Pollutant gas concentration inversion submodule

[0067] Remote sensing inversion of vertical column concentrations of pollutants such as NO2 and SO2 is performed using the differential optical absorption spectroscopy (DOAS) method, which mainly includes the following steps: spectral fitting in the ultraviolet band to obtain oblique column concentrations; stratospheric estimation to remove the influence of stratospheric concentrations; calculation of tropospheric atmospheric quality factors to convert tropospheric oblique column concentrations into tropospheric vertical column concentrations, and finally obtaining the pollutant concentration inversion results.

[0068] (5) Visible light geometric and texture feature discrimination submodule

[0069] The visible light geometric and texture feature discrimination module uses high-resolution visible light panchromatic remote sensing imagery that has undergone radiometric and geometric correction. Geometric feature extraction employs the length-width extraction algorithm (LWEA) to extract the length and width of pixel groups connected by similar spectra. Texture feature extraction utilizes the Local Binary Pattern (LBP) algorithm, which first calculates the binary relationship between each pixel in the image and its local neighborhood points in grayscale, and then weights the binary relationships to form LBP codes. The extracted features are then compared with a target feature library to achieve precise identification of human activities such as buildings and vehicles.

[0070] (6) Vegetation characteristic parameter inversion submodule

[0071] Vegetation characteristic parameters mainly include vegetation cover and vegetation growth status. The Normalized Difference Vegetation Index (NDVI) is calculated using visible multispectral data configured in the remote sensing satellite payload.

[0072]

[0073] In the formula, R NIR R represents the near-infrared spectral reflectance. red This represents the reflectance in the red band.

[0074] Based on the NDVI calculation results, vegetation cover is divided into very low cover (NDVI<0.15), low cover (0.15≤NDVI<0.30), medium-low cover (0.30≤NDVI<0.45), medium cover (0.45≤NDVI<0.60), and high cover (NDVI≥0.60).

[0075] The trend of NDVI change is calculated by the following formula:

[0076]

[0077] In the formula, n represents the total number of years in the assessment period, i represents the year number, and NDVI i This represents the maximum value of NDVI in year i.

[0078] Based on the slope calculation results, the vegetation growth status is divided into significant degradation (slope < -0.01), slight degradation (-0.01 ≤ slope < -0.005), basically unchanged (-0.005 ≤ slope < 0.005), slight improvement (0.005 ≤ slope < 0.01), and significant improvement (slope ≥ 0.01).

[0079] Based on the calculation results of NDVI and slope given above, the ecological pollution situation can be assessed.

[0080] (7) Land-water separation submodule

[0081] The division of water bodies, land, and building areas is determined by a combination of three indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Building Index (NDBI).

[0082] Normalized Difference Water Index (NDWI) is calculated using the green and near-infrared bands:

[0083]

[0084] The Normalized Building Index (NDBI) is calculated using near-infrared and short-wave infrared light.

[0085]

[0086] A pixel is classified as a water body when its NDVI is less than 0, NDWI is greater than 0, and NDBI is less than 0; a pixel is classified as a building area when its NDVI is less than 0.2 and NDBI is greater than 0.

[0087] Step S3: Based on specific application requirements, select different sub-modules in the data processing and inversion module and combine them to achieve the task of monitoring ecological and environmental pollution emission sources for specific scenarios.

[0088] Taking pollution sources that are frequently the focus of environmental protection departments' inspections and enforcement as an example, a systematic description is provided.

[0089] (1) Monitoring of wastewater discharge from factories and enterprises

[0090]

[0091]

[0092] (2) Monitoring of straw burning hotspots

[0093] System Name Straw burning fire point space-based monitoring system Monitoring tasks Straw burning ignition point monitoring Monitoring elements Abnormal fire temperature and geometric texture features at the straw pile Monitoring methods Mid-wave infrared, long-wave infrared, visible panchromatic Calling submodules Fire detection submodule, visible light geometric texture feature discrimination submodule

[0094] (3) Monitoring of thermal wastewater from nuclear power plants

[0095]

[0096] (4) Monitoring of urban black and odorous water bodies

[0097] System Name Urban Black and Odorous Water Body Space-Based Monitoring System Monitoring tasks Urban black and odorous water body monitoring Monitoring elements Abnormal water quality parameters Monitoring methods Visible light multispectral Calling submodules Water quality characteristic parameter inversion submodule, land-water separation submodule

[0098] (5) Monitoring of illegal development within ecological protection red lines

[0099]

[0100] This invention also proposes a space-based monitoring system for ecological and environmental pollution emission sources, the system block diagram of which is shown below. Figure 2 As shown, it includes:

[0101] The ecological and environmental pollution emission source classification and target characteristic identification module is used to classify ecological and environmental pollution emission sources according to actual application needs, and then determine the monitoring targets, target characteristics and corresponding space-based monitoring methods for each type of pollution emission source; wherein the ecological and environmental pollution emission sources include water pollution emission sources, air pollution emission sources and ecological pollution emission sources;

[0102] The data processing and inversion module is used to propose data processing and application methods for different monitoring targets and their corresponding monitoring methods, forming corresponding parameter inversion sub-modules;

[0103] The module for establishing a space-based monitoring system for ecological and environmental pollution emission sources is used to establish such a system. Based on specific application scenarios and monitoring tasks, different combinations of the parameter inversion sub-modules are selected to construct the space-based monitoring system.

[0104] This system is similar to the technical solution in the aforementioned method embodiments, so it will not be described in detail again.

[0105] In summary, the method of this invention can well meet the requirements of the overall design and data application of ecological environment monitoring remote sensing satellites, and can be widely extended to other remote sensing methods such as UAVs and mobile monitoring vehicles, demonstrating strong practicality and versatility. The above are merely preferred embodiments of this invention and are not intended to limit the scope of protection of this invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

[0106] It will be apparent to those skilled in the art that the embodiments of the present invention are not limited to the details of the exemplary embodiments described above, and that the embodiments of the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the embodiments of the present invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the embodiments of the present invention is defined by the appended claims rather than the foregoing description. Therefore, all variations falling within the meaning and scope of equivalents of the claims are intended to be encompassed within the embodiments of the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units, modules, or devices recited in the system, apparatus, or terminal claims may also be implemented by the same unit, module, or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention and are not intended to limit them. Although the embodiments of the present invention have been described in detail with reference to the above preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the embodiments of the present invention should not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A space-based monitoring method for sources of ecological and environmental pollution emissions, characterized in that, include: Step S1: Based on actual application needs, classify the sources of ecological and environmental pollution emissions, and then determine the monitoring targets, target characteristics, and corresponding space-based monitoring methods for each type of pollution emission source; wherein the sources of ecological and environmental pollution emissions include water pollution emission sources, air pollution emission sources, and ecological pollution emission sources; Step S2: For different monitoring targets and their corresponding monitoring methods, propose data processing and application methods to form corresponding parameter inversion sub-modules; Step S3: Establish an ecological and environmental pollution emission source monitoring system. Based on specific application scenarios and monitoring tasks, select different combinations of the parameter inversion sub-modules to construct a space-based monitoring system. The monitoring targets for water pollution emission sources in step S1 include abnormal distribution of water quality and abnormal distribution of water temperature; the monitoring targets for air pollution emission sources include abnormal high temperature locations, pollutant gas composition and distribution; and the monitoring targets for ecological pollution emission sources include human activities and vegetation characteristics. For the aforementioned abnormal water quality distribution, the monitoring elements include water quality characteristic parameters, which include chlorophyll a concentration and suspended solids concentration. For abnormal temperature distribution, the monitoring elements include surface or water temperature, which includes fire point temperature, water temperature, and building temperature. Regarding the composition and distribution of the polluting gases, the monitoring elements include the composition and concentration of the polluting gases, including information on the composition and concentration of nitrogen dioxide, sulfur dioxide, and ozone gas; Regarding the aforementioned human activity, the monitoring elements include visible light geometry and texture information; For the vegetation characteristics, the monitoring elements include vegetation characteristic parameters, including vegetation coverage area and vegetation growth status; Step S2 includes: setting up a corresponding parameter inversion module according to the classification and the target characteristics, and processing and inverting satellite data of a specific form; Among them, the water quality characteristic parameters include comparing remote sensing data in the visible light multispectral band with ground-measured data to establish an inversion model for chlorophyll a concentration and suspended matter, thereby obtaining the water quality parameters. The characteristics of the surface or water surface temperature are obtained by inverting the observation data of the two long-wave infrared spectral bands using a split-window algorithm; For the composition and concentration of the pollutant gas, the vertical column concentration of the pollutant gas was obtained by applying differential optical absorption spectroscopy to the ultraviolet hyperspectral observation data, removing the stratosphere influence, and then inverting the data. For the visible light geometric and texture information, feature extraction algorithms are used to identify features in the visible light panchromatic remote sensing data, and the target category is determined after comparison with the remote sensing feature library. For the vegetation characteristic parameters, the normalized vegetation index (NDVI) is obtained by processing the red and near-infrared spectral data of the visible light multispectral system. Further processing yields the vegetation cover and vegetation growth status.

2. The space-based monitoring method for ecological and environmental pollution emission sources as described in claim 1, characterized in that, Step S3 includes: in the process of establishing an ecological and environmental pollution emission source monitoring system, firstly, the monitoring task is defined; based on the target characteristics obtained in step S1 and the data processing and application methods obtained in step S2, sub-modules corresponding to each type of monitoring target are set; based on the characteristics of the monitoring task, the monitoring task is decomposed into different monitoring targets, and their corresponding sub-modules are selected and combined to realize the monitoring task of that type.

3. The space-based monitoring method for ecological and environmental pollution emission sources as described in claim 2, characterized in that, The chlorophyll a concentration inversion model uses the three-segment method, based on the formula... in This is the spectral band located near the chlorophyll a absorption peak. exist Select near the spectral band, and and The absorption coefficients of suspended matter and yellow substances are approximately equal. Select the spectral band where pure water absorption is dominant; , , They are respectively , , Reflectance of the spectral band; A and B are the fitting coefficients of the linear model; The suspended matter inversion model adopts the three-band method, according to the formula... in, , , They are respectively , , Reflectance of the spectral band; A and B are the fitting coefficients of the linear model; The temperature inversion is achieved through long-wave infrared remote sensing. Data from two thermal infrared spectral bands configured on the remote sensing satellite payload are used to form a split-window channel, and the temperature is obtained using the following split-window algorithm: In the formula, This represents the inverted surface temperature; , The brightness temperature of the two split-window channels. , , and denoted as , where is the surface emissivity of the split-window channel; A1, A2, A3, B1, B2, B3, C, and D are the regression coefficients of the algorithm.

4. A space-based monitoring system for ecological and environmental pollution emission sources, employing the space-based monitoring method for ecological and environmental pollution emission sources as described in claim 1, characterized in that, include: The ecological and environmental pollution emission source classification and target characteristic identification module is used to classify ecological and environmental pollution emission sources according to actual application needs, and then determine the monitoring targets, target characteristics and corresponding space-based monitoring methods for each type of pollution emission source; wherein the ecological and environmental pollution emission sources include water pollution emission sources, air pollution emission sources and ecological pollution emission sources; The data processing and inversion module is used to propose data processing and application methods for different monitoring targets and their corresponding monitoring methods, forming corresponding parameter inversion sub-modules; The module for establishing a space-based monitoring system for ecological and environmental pollution emission sources is used to establish such a system. Depending on the specific application scenario and monitoring task, different combinations of the parameter inversion sub-modules are selected to construct the space-based monitoring system.

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