Carbon emission remote sensing monitoring system and monitoring method thereof

Abstract

The application discloses a carbon emission remote sensing monitoring system and a monitoring method thereof, relates to the technical field of carbon emission remote sensing monitoring, and comprises a carbon emission remote sensing satellite module, a regional cloud layer change prediction module and a cloud layer influence prevention module. The carbon emission remote sensing satellite module is connected with the regional cloud layer change prediction module, and the cloud layer influence prevention module is connected with a remote sensing data feedback module. The carbon emission remote sensing monitoring system and the monitoring method thereof are characterized in that the flight equipment is provided with a data acquisition sensor, so that the flight equipment and the satellite can collect data of the region at the same time. The satellite collects data of a region not covered by a cloud layer, and the flight equipment collects data of a region covered by a cloud layer. After the two kinds of data are processed, the carbon emission analysis output module calculates the carbon emission of the region. Since the data of the region covered by the cloud layer is collected, the result obtained by the carbon emission analysis output module is more accurate, and thus the interference of the cloud layer is completely eliminated.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission remote sensing monitoring, and in particular to a carbon emission remote sensing monitoring system and a monitoring method thereof. Background Art

[0002] Satellite remote sensing monitoring of carbon emissions is a method that uses sensors carried by satellites to remotely observe and analyze carbon dioxide and other greenhouse gases on the surface and in the atmosphere. It combines satellite observation data with other auxiliary information (such as ground observation station data, meteorological data, etc.) to analyze and estimate carbon emissions. By calculating the changes in carbon concentration in the atmosphere in a certain area and combining wind field data to infer carbon emissions, it uses concentration distribution maps and pattern recognition methods to identify high-emission areas (such as industrial areas, cities, fire areas, etc.), monitor the ability of vegetation and oceans to absorb CO2, and evaluate changes in carbon sinks.

[0003] In existing satellite remote sensing monitoring of carbon emissions, clouds are a major interference factor. Clouds can block spectral signals on the surface or in the atmosphere and have a significant impact on the radiation transmission process. For areas completely covered by clouds, even if they are corrected, the observation data of the cloud-covered areas often have large errors and can usually only be eliminated. However, this also leads to the loss of observation data in the cloud-covered areas, and inversion can only be performed based on observation data from surrounding areas. This places high demands on the calculation model and errors may still occur.

[0004] Therefore, in view of this, the existing structure and deficiencies are studied and improved, and a carbon emission remote sensing monitoring system and a monitoring method thereof are proposed. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present invention provides a carbon emission remote sensing monitoring system and a monitoring method thereof, which solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a carbon emission remote sensing monitoring system, comprising a carbon emission remote sensing satellite module, a regional cloud change prediction module, and a cloud impact prevention module, wherein the carbon emission remote sensing satellite module is connected to the regional cloud change prediction module, and the regional cloud change prediction module is connected to the cloud impact prevention module, the cloud impact prevention module is connected to a remote sensing data feedback module, and the remote sensing data feedback module is connected to a data fusion module, and the data fusion module is connected to a carbon emission analysis output module;

[0007] The carbon emission remote sensing satellite module includes a satellite positioning unit, a carbon emission monitoring area sequence unit, a monitoring time estimation unit and a time output unit. The satellite positioning unit is connected to the carbon emission monitoring area sequence unit, and the carbon emission monitoring area sequence unit is connected to the monitoring time estimation unit, and the monitoring time estimation unit is connected to the time output unit.

[0008] Furthermore, the satellite positioning unit is used to locate the position of the carbon emission remote sensing monitoring satellite and record its movement trajectory, the carbon emission monitoring area sequence unit is used to arrange the order of areas that need to be monitored according to the movement trajectory of the carbon emission remote sensing monitoring satellite, and the monitoring time estimation unit is used to estimate the time when the carbon emission remote sensing monitoring satellite arrives at each area for carbon emission monitoring.

[0009] Furthermore, the regional cloud change prediction module includes a data acquisition unit, a data processing unit, a cloud prediction model, a time input unit and a cloud change output unit. The data acquisition unit is connected to the data processing unit, and the data processing unit is connected to the cloud prediction model. The cloud prediction model is respectively connected to the time input unit and the cloud change output unit.

[0010] Furthermore, the data acquisition unit is used to obtain indicator data of cloud amount, cloud height, cloud phase, and optical thickness through geostationary satellites and polar-orbiting satellites, and also obtains temperature, humidity, wind speed and direction, cloud thickness, cloud height, and cloud scattering characteristics data through ground meteorological stations, cloud radars, and lidars. The data processing unit is used to eliminate outliers and noise in the data, unify the time and spatial resolution, integrate multi-source data into an initial field, and use data assimilation algorithms to improve data accuracy.

[0011] Furthermore, the cloud prediction model uses a multispectral threshold method or a machine learning method to identify clouds based on the processed data, and generates a cloud mask for all monitoring areas at the current time. The time input unit is used to input the time when the carbon emission remote sensing monitoring satellite arrives at each monitoring area, and the cloud prediction model uses an optical flow algorithm, machine learning, and an artificial intelligence prediction model to extrapolate the cloud change trend based on the input time.

[0012] Furthermore, the anti-cloud impact module includes a carbon emission monitoring base station unit, a preset monitoring point unit, a carbon emission monitoring machine flight unit, a sub-cloud monitoring unit and a monitoring machine data feedback unit. The carbon emission monitoring base station unit is connected to the preset monitoring point unit, and the preset monitoring point unit is connected to the carbon emission monitoring machine flight unit. The carbon emission monitoring machine flight unit is connected to the sub-cloud monitoring unit, and the sub-cloud monitoring unit is connected to the monitoring machine data feedback unit.

[0013] Furthermore, the carbon emission monitoring base station unit is set up in each monitoring area. After receiving the time when the carbon emission remote sensing monitoring satellite arrives at the monitoring area and the cloud information of the area, the carbon emission monitoring base station unit presets the collection point for the cloud-covered area in the area through the preset monitoring point unit.

[0014] Furthermore, the carbon emission monitoring machine flight unit carries the sub-cloud monitoring unit via a drone to the preset collection location, and the time when the sub-cloud monitoring unit collects carbon emission data is consistent with the time when the carbon emission remote sensing monitoring satellite collects carbon emission data.

[0015] Furthermore, the monitoring machine data feedback unit is connected to a data fusion module. The remote sensing data feedback module is used to feedback data collected by the carbon emission remote sensing monitoring satellite, which does not include areas covered by clouds, and the monitoring machine data feedback unit is used to feedback data on areas covered by clouds below the clouds.

[0016] A carbon emission remote sensing monitoring method is provided, which utilizes the aforementioned carbon emission remote sensing monitoring system. The carbon emission remote sensing monitoring method comprises the following steps:

[0017] Step 1: Based on the area where carbon emissions monitoring is required, the data acquisition unit obtains indicator data on cloud amount, cloud height, cloud phase, and optical thickness through geostationary satellites and polar-orbiting satellites. It also obtains temperature, humidity, wind speed and direction, cloud thickness, cloud height, and cloud scattering characteristics data through ground-based meteorological stations, cloud radars, and lidars. The data processing unit then removes outliers and noise from the data, unifies the temporal and spatial resolutions, integrates multi-source data into an initial field, and uses a data assimilation algorithm to improve data accuracy. The cloud prediction model then uses the multispectral threshold method or machine learning method based on the processed data to identify clouds and generate cloud masks for all monitoring areas at the current time.

[0018] Step 2: The carbon emission remote sensing monitoring satellite moves along a preset trajectory and collects carbon emission data each time it arrives at a monitoring location. The satellite positioning unit locates the current position of the carbon emission remote sensing monitoring satellite, and the monitoring time estimation unit estimates the time when the carbon emission remote sensing monitoring satellite arrives at the next area for carbon emission monitoring. Based on the time of arrival at the next area, the time input unit inputs the time when the carbon emission remote sensing monitoring satellite arrives at each monitoring area. The cloud prediction model uses the optical flow algorithm, machine learning, and artificial intelligence prediction model to extrapolate the cloud change trend based on the input time.

[0019] Step 3: After receiving the arrival time of the carbon emission remote sensing monitoring satellite and the cloud cover information for the monitoring area, the carbon emission monitoring base station unit uses the preset monitoring point unit to preset collection points in the cloud-covered area of ​​the area. The carbon emission monitoring aircraft flight unit then uses the drone to carry the cloud-covered monitoring unit to the preset collection points. The time for the cloud-covered monitoring unit to collect carbon emission data coincides with the time for the carbon emission remote sensing monitoring satellite to collect carbon emission data. The remote sensing data feedback module returns the data collected by the carbon emission remote sensing monitoring satellite, which does not include the cloud-covered area. The monitoring aircraft data feedback unit is used to return data for the cloud-covered area below the cloud. The two data sets are transmitted to the data fusion module for data processing. The processed data is input into the carbon emission analysis output module. The carbon emission analysis output module uses the input data to perform carbon emission analysis and calculations, calculate the changes in atmospheric carbon concentration in a certain area, and combine it with wind field data to estimate carbon emissions. It also uses concentration distribution maps and pattern recognition methods to identify high-emission areas such as industrial areas, cities, and fire areas, or monitor the ability of vegetation and oceans to absorb CO2 to assess changes in carbon sinks.

[0020] The present invention provides a carbon emission remote sensing monitoring system and a monitoring method thereof, which have the following beneficial effects:

[0021] The carbon emission remote sensing monitoring system and its monitoring method are based on the sorting of areas that need to be monitored. Before the satellite monitors the area, multi-source cloud data is collected to predict cloud changes in each area during satellite monitoring. For cloud-covered areas in each area, data collection points under the clouds are pre-set. A data collection sensor is carried on a flying device to collect data from the area at the same time as the satellite. The satellite collects data from areas not covered by clouds, while the flying device collects data from cloud-covered areas under the clouds. After the two types of data are processed, the carbon emissions of the area are inferred through a carbon emission analysis output module. Since the data of the cloud-covered areas are collected, the results obtained by the carbon emission analysis output module are more accurate, thereby completely eliminating cloud interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0023] Figure 2 This is a schematic diagram of the internal operation flow of the carbon emission remote sensing satellite module of the present invention;

[0024] Figure 3 This is a schematic diagram of the internal operation flow of the regional cloud change prediction module of the present invention;

[0025] Figure 4 This is a schematic diagram of the internal operation flow of the cloud layer impact prevention module of the present invention;

[0026] Figure 5 The monitoring machine data feedback unit output flowchart of the present application.

[0027] In the figure: 1, carbon emission remote sensing satellite module; 101, satellite positioning unit; 102, carbon emission monitoring area sequence unit; 103, monitoring time estimation unit; 104, time output unit; 2, area cloud change prediction module; 201, data acquisition unit; 202, data processing unit; 203, cloud prediction model; 204, time input unit; 205, cloud change output unit; 3, cloud influence prevention module; 301, carbon emission monitoring base station unit; 302, preset monitoring point unit; 303, carbon emission monitoring machine flight unit; 304, cloud monitoring unit; 305, monitoring machine data feedback unit; 4, remote sensing data feedback module; 5, data fusion module; 6, carbon emission analysis output module. DETAILED DESCRIPTION

[0028] The embodiments of the present application will be further described below in conjunction with the drawings and examples. The following examples are used to illustrate the present application, but cannot be used to limit the scope of the present application.

[0029] As Figure 1-Figure 5As shown, the present invention provides a technical solution: a carbon emission remote sensing monitoring system, including a carbon emission remote sensing satellite module 1, a regional cloud change prediction module 2 and an anti-cloud impact module 3, the carbon emission remote sensing satellite module 1 is connected to the regional cloud change prediction module 2, and the regional cloud change prediction module 2 is connected to the anti-cloud impact module 3, the anti-cloud impact module 3 is connected to the remote sensing data feedback module 4, and the remote sensing data feedback module 4 is connected to the data fusion module 5, the data fusion module 5 is connected to the carbon emission analysis output module 6, the carbon emission remote sensing satellite module 1 includes a satellite positioning unit 101, a carbon emission monitoring area sequence unit 102, a monitoring time estimation unit 103 and a time output unit 104, the satellite positioning unit 101 is connected to the carbon emission The monitoring area sequence unit 102 is placed, and the carbon emission monitoring area sequence unit 102 is connected to a monitoring time estimation unit 103, the monitoring time estimation unit 103 is connected to a time output unit 104, the satellite positioning unit 101 is used to locate the position of the carbon emission remote sensing monitoring satellite and record its movement trajectory, the carbon emission monitoring area sequence unit 102 is used to arrange the order of areas that need to be monitored according to the movement trajectory of the carbon emission remote sensing monitoring satellite, the monitoring time estimation unit 103 is used to estimate the time when the carbon emission remote sensing monitoring satellite arrives at each area for carbon emission monitoring, the regional cloud change prediction module 2 includes a data acquisition unit 201, a data processing unit 202, a cloud prediction model 203, a time input unit 204 and a cloud The data acquisition unit 201 is connected to the data processing unit 202, and the data processing unit 202 is connected to the cloud prediction model 203. The cloud prediction model 203 is respectively connected to the time input unit 204 and the cloud change output unit 205. The data acquisition unit 201 is used to obtain index data of cloud amount, cloud height, cloud phase, and optical thickness through geostationary satellites and polar orbiting satellites, and also obtains temperature, humidity, wind speed and direction, cloud thickness, cloud height, and cloud scattering characteristic data through ground meteorological stations, cloud radars, and lidars. The data processing unit 202 is used to eliminate outliers and noise in the data, unify the time and space resolution, integrate multi-source data into an initial field, and use data assimilation. The algorithm improves data accuracy. The cloud prediction model 203 uses a multispectral threshold method or a machine learning method to identify clouds based on the processed data and generates a cloud mask for all monitoring areas at the current time. The time input unit 204 is used to input the time when the carbon emission remote sensing monitoring satellite arrives at each monitoring area, and the cloud prediction model 203 uses an optical flow algorithm, machine learning, and an artificial intelligence prediction model based on the input time to extrapolate the cloud change trend. The anti-cloud impact module 3 includes a carbon emission monitoring base station unit 301, a preset monitoring point unit 302, a carbon emission monitoring machine flight unit 303, a cloud monitoring unit 304, and a monitoring machine data feedback unit 305. The carbon emission monitoring base station unit 301 is connected to the preset monitoring point unit 302.The preset monitoring point unit 302 is connected to the carbon emission monitoring machine flight unit 303, the carbon emission monitoring machine flight unit 303 is connected to the cloud monitoring unit 304, and the cloud monitoring unit 304 is connected to the monitoring machine data feedback unit 305. The carbon emission monitoring base station unit 301 is set in each monitoring area. After receiving the time when the carbon emission remote sensing monitoring satellite arrives at the monitoring area and the cloud information of the area, the carbon emission monitoring base station unit 301 presets the collection point for the cloud-covered area in the area through the preset monitoring point unit 302. The carbon emission monitoring machine flight unit 303 uses an unmanned aerial vehicle to carry the cloud monitoring unit 304 to the preset collection point, and the time when the cloud monitoring unit 304 collects carbon emission data is consistent with the time when the carbon emission remote sensing monitoring satellite collects carbon emission data. The monitoring machine data feedback unit 305 is connected to the data fusion module 5. The remote sensing data feedback module 4 is used to feedback the data collected by the carbon emission remote sensing monitoring satellite. The data does not include the cloud-covered area, and the monitoring machine data feedback unit 305 is used to feedback the data of the cloud-covered area below the cloud layer.

[0030] The specific operation is as follows: based on the area where carbon emissions monitoring is required, the data acquisition unit 201 obtains index data of cloud amount, cloud height, cloud phase, and optical thickness through geostationary satellites and polar-orbiting satellites, and also obtains temperature, humidity, wind speed and direction, cloud thickness, cloud height, and cloud scattering characteristics data through ground meteorological stations, cloud radars, and lidars. The data processing unit 202 is then used to remove outliers and noise in the data, unify the time and spatial resolution, integrate multi-source data into an initial field, and use data assimilation algorithms to improve data accuracy. The cloud prediction model 203 is then based on the processed data. The data is used to identify clouds using a multispectral threshold method or a machine learning method, and a cloud mask is generated for all monitoring areas at the current time. The satellite positioning unit 101 locates the current position of the carbon emission remote sensing monitoring satellite. The monitoring time estimation unit 103 estimates the time when the carbon emission remote sensing monitoring satellite arrives at the next area for carbon emission monitoring. Based on the time of arrival at the next area, the time input unit 204 inputs the time when the carbon emission remote sensing monitoring satellite arrives at each monitoring area. The cloud layer prediction model 203 uses an optical flow algorithm, machine learning, and an artificial intelligence prediction model based on the input time to extrapolate the cloud layer change trend. After the carbon emission monitoring base station unit 301 receives the arrival time of the carbon emission remote sensing monitoring satellite in the monitoring area and the cloud information in the area, it presets the collection point in the cloud-covered area in the area through the preset monitoring point unit 302, and then uses the carbon emission monitoring aircraft flight unit 303 to carry the cloud-covered monitoring unit 304 through the drone to go to the preset collection. The time when the cloud-covered monitoring unit 304 collects carbon emission data is consistent with the time when the carbon emission remote sensing monitoring satellite collects carbon emission data. The remote sensing data feedback module 4 feeds back the data collected by the carbon emission remote sensing monitoring satellite, which does not include the data collected by the cloud-covered area. The cloud-covered area, and the monitoring machine data feedback unit 305 is used to feedback data of the cloud-covered area below the cloud layer. The two data sets are transmitted to the data fusion module 5 for data processing. The processed data is input to the carbon emission analysis output module 6. The carbon emission analysis output module 6 performs carbon emission analysis and calculation based on the input data, calculates the change of carbon concentration in the atmosphere in a certain area, and calculates the carbon emissions in combination with wind field data, or uses concentration distribution maps and pattern recognition methods to determine high emission areas such as industrial areas, cities, and fire areas, or monitors the ability of vegetation and oceans to absorb CO2 to evaluate changes in carbon sinks;

[0031] Based on the above description, the present invention is based on the sorting of areas that need to be monitored. Before the satellite monitors the area, multi-source cloud data is collected to predict the cloud changes in each area during satellite monitoring. For the cloud-covered areas in each area, data collection points under the clouds are pre-set. The flying equipment is equipped with data collection sensors to collect data for the area at the same time as the satellite. The satellite collects data from areas not covered by clouds, while the flying equipment collects data from areas covered by clouds under the clouds. After the two types of data are processed, the carbon emissions of the area are inferred through the carbon emission analysis output module 6. Since the data of the cloud-covered areas are collected, the results obtained by the carbon emission analysis output module 6 are more accurate, thereby completely eliminating cloud interference.

[0032] A carbon emission remote sensing monitoring method is provided, which utilizes the aforementioned carbon emission remote sensing monitoring system. The carbon emission remote sensing monitoring method comprises the following steps:

[0033] Step 1: Based on the area where carbon emissions monitoring is required, the data acquisition unit 201 obtains indicator data on cloud cover, cloud height, cloud phase, and optical thickness through geostationary satellites and polar-orbiting satellites. It also obtains temperature, humidity, wind speed and direction, cloud thickness, cloud height, and cloud scattering characteristics data through ground meteorological stations, cloud radars, and lidars. The data processing unit 202 then removes outliers and noise from the data, unifies the temporal and spatial resolutions, integrates multi-source data into an initial field, and uses a data assimilation algorithm to improve data accuracy. The cloud prediction model 203 then uses a multispectral threshold method or a machine learning method based on the processed data to identify clouds and generate a cloud mask for all monitoring areas at the current time.

[0034] Step 2: The carbon emission remote sensing monitoring satellite moves along a preset trajectory and collects carbon emission data each time it arrives at a monitoring location. The satellite positioning unit 101 locates the current position of the carbon emission remote sensing monitoring satellite, and the monitoring time estimation unit 103 estimates the time when the carbon emission remote sensing monitoring satellite arrives at the next area for carbon emission monitoring. Based on the time of arrival at the next area, the time input unit 204 inputs the time when the carbon emission remote sensing monitoring satellite arrives at each monitoring area. The cloud prediction model 203 uses an optical flow algorithm, machine learning, and an artificial intelligence prediction model to extrapolate cloud change trends based on the input time.

[0035] Step 3: After the carbon emission monitoring base station unit 301 receives the arrival time of the carbon emission remote sensing monitoring satellite in the monitoring area and the cloud information in the area, it presets the collection point in the cloud-covered area in the area through the preset monitoring point unit 302, and then uses the carbon emission monitoring aircraft flight unit 303 to carry the cloud-covered monitoring unit 304 through the drone to go to the preset collection. The time when the cloud-covered monitoring unit 304 collects carbon emission data is consistent with the time when the carbon emission remote sensing monitoring satellite collects carbon emission data. The remote sensing data feedback module 4 feeds back the data collected by the carbon emission remote sensing monitoring satellite, which does not include The data feedback unit 305 of the monitoring machine is used to feedback the data of the cloud-covered area below the cloud layer. The two data sets are transmitted to the data fusion module 5 for data processing, and the processed data are input to the carbon emission analysis output module 6. The carbon emission analysis output module 6 performs carbon emission analysis and calculation based on the input data, calculates the change of carbon concentration in the atmosphere in a certain area, and calculates the carbon emissions in combination with the wind field data, or uses the concentration distribution map and pattern recognition method to judge high emission areas such as industrial areas, cities, and fire areas, or monitors the ability of vegetation and oceans to absorb CO2 to evaluate changes in carbon sinks.

[0036] The embodiments of the present invention are presented for purposes of illustration and description and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments are chosen and described in order to better illustrate the principles of the invention and its practical application and to enable those skilled in the art to understand the invention and design various embodiments with various modifications as suited for specific applications.

Claims

1. A carbon emission remote sensing monitoring system, comprising a carbon emission remote sensing satellite module (1), a regional cloud change prediction module (2) and a cloud impact prevention module (3), characterized in that: The carbon emission remote sensing satellite module (1) is connected to a regional cloud layer change prediction module (2), and the regional cloud layer change prediction module (2) is connected to a cloud layer impact prevention module (3), and the cloud layer impact prevention module (3) is connected to a remote sensing data feedback module (4), and the remote sensing data feedback module (4) is connected to a data fusion module (5), and the data fusion module (5) is connected to a carbon emission analysis output module (6); The carbon emission remote sensing satellite module (1) comprises a satellite positioning unit (101), a carbon emission monitoring area sequence unit (102), a monitoring time estimation unit (103) and a time output unit (104); the satellite positioning unit (101) is connected to the carbon emission monitoring area sequence unit (102), the carbon emission monitoring area sequence unit (102) is connected to the monitoring time estimation unit (103), and the monitoring time estimation unit (103) is connected to the time output unit (104); The anti-cloud layer impact module (3) comprises a carbon emission monitoring base station unit (301), a preset monitoring point unit (302), a carbon emission monitoring machine flight unit (303), a cloud layer monitoring unit (304) and a monitoring machine data feedback unit (305); the carbon emission monitoring base station unit (301) is connected to the preset monitoring point unit (302), and the preset monitoring point unit (302) is connected to the carbon emission monitoring machine flight unit (303); the carbon emission monitoring machine flight unit (303) is connected to the cloud layer monitoring unit (304), and the cloud layer monitoring unit (304) is connected to the monitoring machine data feedback unit (305); the carbon emission monitoring base station unit (301) is set in each monitoring area; after receiving the time when the carbon emission remote sensing monitoring satellite arrives at the monitoring area and the cloud layer information of the area, the carbon emission monitoring base station unit (301) presets a collection point for the cloud-covered area in the area through the preset monitoring point unit (302).

2. A carbon emission remote sensing monitoring system according to claim 1, characterized in that: The satellite positioning unit (101) is used to locate the position of the carbon emission remote sensing monitoring satellite and record its movement trajectory, the carbon emission monitoring area sequence unit (102) is used to arrange the order of areas to be monitored according to the movement trajectory of the carbon emission remote sensing monitoring satellite, and the monitoring time estimation unit (103) is used to estimate the time when the carbon emission remote sensing monitoring satellite arrives at each area to perform carbon emission monitoring.

3. A carbon emission remote sensing monitoring system according to claim 2, characterized in that: The regional cloud layer change prediction module (2) comprises a data acquisition unit (201), a data processing unit (202), a cloud layer prediction model (203), a time input unit (204) and a cloud layer change output unit (205); the data acquisition unit (201) is connected to the data processing unit (202), and the data processing unit (202) is connected to the cloud layer prediction model (203); and the cloud layer prediction model (203) is respectively connected to the time input unit (204) and the cloud layer change output unit (205).

4. A carbon emission remote sensing monitoring system according to claim 3, characterized in that: The data acquisition unit (201) is used to obtain index data of cloud amount, cloud height, cloud phase, and optical thickness through geostationary satellites and polar-orbiting satellites, and also obtains temperature, humidity, wind speed and direction, cloud thickness, cloud height, and cloud scattering characteristic data through ground meteorological stations, cloud radars, and laser radars. The data processing unit (202) is used to eliminate abnormal values ​​and noise in the data, unify the time and space resolutions, integrate multi-source data into an initial field, and use a data assimilation algorithm to improve data accuracy.

5. A carbon emission remote sensing monitoring system according to claim 4, characterized in that: The cloud layer prediction model (203) identifies the cloud layer using a multispectral threshold method or a machine learning method based on the processed data, and generates a cloud mask for all monitoring areas at the current time. The time input unit (204) is used to input the time when the carbon emission remote sensing monitoring satellite arrives at each monitoring area, and the cloud layer prediction model (203) uses an optical flow algorithm, machine learning, and an artificial intelligence prediction model based on the input time to extrapolate the cloud layer change trend.

6. A carbon emission remote sensing monitoring system according to claim 5, characterized in that: The carbon emission monitoring machine flight unit (303) carries the cloud-based monitoring unit (304) via an unmanned aerial vehicle to a preset collection location, and the time when the cloud-based monitoring unit (304) collects carbon emission data is consistent with the time when the carbon emission remote sensing monitoring satellite collects carbon emission data.

7. A carbon emission remote sensing monitoring system according to claim 6, characterized in that: The monitoring machine data feedback unit (305) is connected to a data fusion module (5), and the remote sensing data feedback module (4) is used to feed back data collected by the carbon emission remote sensing monitoring satellite, which does not include areas covered by clouds, while the monitoring machine data feedback unit (305) is used to feed back data of areas covered by clouds below the clouds.

8. A carbon emission remote sensing monitoring method, which uses the carbon emission remote sensing monitoring system according to claim 7, characterized in that: The carbon emission remote sensing monitoring method comprises the following steps: Step 1: Based on the area where carbon emissions monitoring is required, the data acquisition unit (201) acquires index data of cloud amount, cloud height, cloud phase, and optical thickness through geostationary satellites and polar-orbiting satellites, and also acquires temperature, humidity, wind speed and direction, cloud thickness, cloud height, and cloud scattering characteristic data through ground meteorological stations, cloud radars, and lidars. The data processing unit (202) is then used to remove outliers and noise from the data, unify the time and spatial resolution, integrate multi-source data into an initial field, and use a data assimilation algorithm to improve data accuracy. The cloud prediction model (203) then uses a multispectral threshold method or a machine learning method to identify clouds based on the processed data, and generates cloud masks at the current time for all monitoring areas. Step 2: The carbon emission remote sensing monitoring satellite moves along a preset moving trajectory and collects carbon emission data each time it arrives at a monitoring location, wherein the satellite positioning unit (101) locates the current position of the carbon emission remote sensing monitoring satellite, the monitoring time estimation unit (103) estimates the time when the carbon emission remote sensing monitoring satellite arrives at the next area for carbon emission monitoring, and based on the time of arrival at the next area, the time input unit (204) inputs the time when the carbon emission remote sensing monitoring satellite arrives at each monitoring area, and the cloud prediction model (203) uses an optical flow algorithm, machine learning, and an artificial intelligence prediction model to extrapolate the cloud change trend based on the input time; Step 3: After the carbon emission monitoring base station unit (301) receives the arrival time of the carbon emission remote sensing monitoring satellite in the monitoring area and the cloud information in the area, it presets the collection point in the cloud-covered area in the area through the preset monitoring point unit (302), and then uses the carbon emission monitoring aircraft flight unit (303) to carry the cloud-covered monitoring unit (304) through the unmanned aerial vehicle to go to the preset collection point, and the time when the cloud-covered monitoring unit (304) collects carbon emission data is consistent with the time when the carbon emission remote sensing monitoring satellite collects carbon emission data. The remote sensing data feedback module (4) feeds back the data collected by the carbon emission remote sensing monitoring satellite. The area covered by clouds is not included, and the monitoring machine data feedback unit (305) is used to feedback data of the area covered by clouds below the clouds. The two data sets are transmitted to the data fusion module (5) for data processing, and the processed data are input to the carbon emission analysis output module (6). The carbon emission analysis output module (6) performs carbon emission analysis and calculation based on the input data, calculates the change of carbon concentration in the atmosphere in a certain area, and calculates the carbon emission amount in combination with the wind field data, or uses the concentration distribution map and pattern recognition method to judge high emission areas such as industrial areas, cities, and fire areas, or monitors the ability of vegetation and oceans to absorb CO2 and evaluate the change of carbon sinks.

Images