A method for monitoring carbon sources and sinks in terrestrial ecosystems based on satellite remote sensing

Abstract

The present invention discloses a method for monitoring terrestrial ecosystem carbon sources and sinks based on satellite remote sensing, comprising the following steps: 1) preparing satellite remote sensing data; 2) preparing ground observation data; 3) calculating vegetation cover based on the Normalized Difference Vegetation Index; 4) calculating the carbon density of aboveground and belowground biomass; 5) optimizing and calibrating key algorithm parameters; 6) calculating the autotrophic respiration rate of vegetation; 7) calculating the heterotrophic respiration rate of soil; and 8) characterizing the carbon source and sink of the vegetation ecosystem by net ecosystem carbon exchange. This method addresses the shortcomings of existing estimation methods, such as their inability to monitor over a large area, high cost, and inapplicability to areas with high spatial heterogeneity. By using satellite remote sensing data and ground observation data of a target pixel as input, this method calculates the ecosystem carbon source and sink, representing the carbon exchange between the vegetation ecosystem and the atmosphere in the target pixel. This method offers the advantages of wide monitoring range, high accuracy, and high feasibility.

Description

Technical Field

[0001] The present invention relates to a method for monitoring carbon sources and sinks in an ecosystem, and in particular to a method for monitoring carbon sources and sinks in a terrestrial ecosystem based on satellite remote sensing. Background Art

[0002] Carbon sequestration in terrestrial ecosystems is one of the key pathways to achieving the strategic goals of "carbon peak and carbon neutrality." However, due to my country's complex topography, diverse ecosystems, and vastly different natural conditions, it remains difficult to accurately assess or monitor the carbon sequestration capabilities of different regions or ecosystems through large-scale ground-based observational experiments. This is especially true in sparsely populated areas with inconvenient transportation and harsh environments, where the carbon sequestration capacity of these ecosystems is poorly understood.

[0003] Traditional methods primarily rely on field measurements, using photosynthetic meters (such as the Li-6400 Leaf Photosynthesis-Transpiration Measurement System) to measure plant photosynthesis and calculate vegetation productivity. Eddy covariance meters are used to build flux observation towers to measure ecosystem carbon exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RECO). While this approach offers advantages in accuracy, its disadvantages include limited coverage, high costs, short coverage periods, and the inability to measure changes in terrestrial ecosystem carbon sources and sinks at regional, county, provincial, or national scales. Another approach involves model calculations, which use ground-based observations (including temperature, wind speed, air pressure, vapor pressure deficit, and soil moisture) to establish statistical or physical relationships with various ecosystem state variables (such as GPP, RECO, and NEE) to simulate ecosystem carbon cycling processes. However, these methods currently fail to utilize vegetation productivity and aboveground biomass information from satellite remote sensing, resulting in poor accuracy and limited application in areas with scarce ground-based observations and high spatial heterogeneity. Summary of the Invention

[0004] In order to address the deficiencies of the above technologies, the present invention provides a method for monitoring carbon sources and sinks in terrestrial ecosystems based on satellite remote sensing.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for monitoring carbon sources and sinks in terrestrial ecosystems based on satellite remote sensing, the monitoring method includes the following steps:

[0006] 1) Preparation of satellite remote sensing data;

[0007] 2) Preparation of ground observation data;

[0008] 3) Based on the Normalized Difference Vegetation Index NDVI , calculate vegetation coverage fv ;

[0009] 4) Calculate the aboveground biomass carbon density and underground biomass carbon density of vegetation: Determine the vegetation functional type at the grid pixel in the corresponding period based on the land use change data LUCC, and use the aboveground biomass carbon density reference data of typical vegetation types to calculate the aboveground biomass carbon density of vegetation. And step 3) calculate the vegetation coverage fv Calculate the aboveground biomass carbon density of vegetation at grid pixels , and then the carbon density of vegetation aboveground biomass Calculate the carbon density of vegetation belowground biomass ;

[0010] 5) Optimization and calibration of key algorithm parameters: The key parameter for optimization and calibration is the temperature sensitivity parameter Q 10 , including temperature sensitivity parameters of vegetation carbon respiration , temperature sensitivity parameters of soil carbon respiration ;

[0011] 6) Calculation of vegetation autotrophic respiration rate:

[0012] The temperature sensitivity parameters of soil carbon respiration were obtained by optimization calibration. Then, calculate the aboveground respiration of vegetation R agb , underground breathing R ugb ;

[0013] Combined with gross primary productivity GPP , calculate the vegetation maintenance respiration rate R m , growth respiration rate R g , vegetation autotrophic respiration rate R a and net primary productivity NPP ;

[0014] 7) Calculation of soil heterotrophic respiration rate:

[0015] The temperature sensitivity parameters of vegetation carbon respiration were obtained in the optimization calibration Then, soil heterotrophic respiration rate was calculated R h ;

[0016] 8) Carbon sources and sinks of vegetation ecosystems are determined by net ecosystem carbon exchange NEE Characterized by autotrophic respiration of vegetation R a , soil heterotrophic respiration rate R h Combined gross primary productivity GPP Calculated.

[0017] Preferably, in step 1), the satellite remote sensing data includes: chlorophyll fluorescence data from satellite remote sensing SIF or gross primary productivity of vegetation GPP , aboveground biomass carbon density inverted by microwave satellite remote sensing C agb , Normalized Difference Vegetation Index NDVI , vegetation functional types, land use changes LUCC , land surface soil temperature .

[0018] Preferably, in step 2), the ground observation data includes: ground temperature T a , soil temperature T h , the ratio coefficient of aboveground biocarbon to belowground biocarbon of typical vegetation types, and soil carbon density.

[0019] As a preference, in step 3), the vegetation coverage fv The calculation of is shown in formula 11:

[0020] (11)

[0021] in, n The value is 2. and for NDVI The maximum and minimum values ​​of .

[0022] As a preference, in step 4), the carbon density of aboveground biomass of vegetation , vegetation belowground biomass carbon density The calculation is shown in formula 9-10:

[0023] (9)

[0024] (10)

[0025] in, fv represents the remote sensing vegetation cover, is the reference data of aboveground biomass carbon density of typical vegetation types. Indicates typical vegetation types The vegetation coverage corresponding to the reference data; b represents the conversion factor, which is calculated using forest sampling measurement data.

[0026] Preferably, in step 5), the temperature sensitivity parameter Q 10 The optimization calibration methods are:

[0027] Optimize and calibrate the temperature sensitivity parameters of vegetation carbon respiration for typical vegetation types based on soil respiration observation data from typical field sites and respiration rates measured in the laboratory using soil sampling data. , temperature sensitivity parameters of soil carbon respiration ;

[0028] Then, the soil heterotrophic respiration rate was calculated using the vegetation underground biomass carbon density and soil carbon density data of typical vegetation samples. R h , and calculate the respiration of vegetation underground R ugb , verify the simulation effect of key parameters.

[0029] As a preference, in step 6), the above-ground part of the vegetation respires R agb , underground breathing R ugb Temperature sensitivity parameters of soil carbon respiration , ground temperature T a , land surface soil temperature , aboveground biomass carbon density inverted by microwave satellite remote sensing C agb Calculated, as shown in Formula 7-8:

[0030] (7)

[0031] (8)

[0032] in, is the basic respiration rate of plant carbon at 10℃, As a temperature sensitivity parameter of soil carbon respiration, it represents the ground air temperature Or the multiple by which vegetation respiration rate increases for every 10°C increase in vegetation temperature.

[0033] As a preference, in step 6), the total primary productivity of vegetation is combined GPP , calculate the vegetation maintenance respiration rate R m , growth respiration rate R g , vegetation autotrophic respiration rate R a and net primary productivity NPP , the calculation process is shown in Formula 3-6:

[0034] Net primary productivity of vegetation NPP Gross primary productivity of vegetation GPP and vegetation autotrophic respiration rate Ra calculate:

[0035] (3)

[0036] Vegetation autotrophic respiration rate R a Vegetation-maintained respiration rate R m and growth respiration rate R g The sum is:

[0037] (4)

[0038] Vegetation maintenance respiration rate R m Respiration by the above-ground parts of vegetation R agb and the underground part breathes R ugb Calculation yields:

[0039] (5)

[0040] Growth respiration rate R g Gross primary productivity of vegetation GPP and vegetation maintenance respiration rate R m Estimate:

[0041] (6).

[0042] As a preference, in step 5) and step 7), soil heterotrophic respiration rate R h The calculation is shown in formula 2, which is based on the temperature sensitivity parameter of vegetation carbon respiration , land surface soil temperature , soil carbon density of typical vegetation types combined with vegetation cover fv Calculated:

[0043] (2)

[0044] in, is the soil content per unit area in the vegetation ecosystem (g C m -2 ), determined by field sampling; is the basic respiration rate of soil carbon at 10℃, As a temperature sensitivity parameter of soil carbon respiration, expressed as the land surface soil temperature T soil The multiple by which soil respiration rate increases with every 10°C increase.

[0045] Preferably, in step 8), the net ecosystem carbon exchange NEE The calculation is shown in Formula 1:

[0046] (1)

[0047] Among them, the total primary productivity of vegetation GPP Obtained through field photosynthesis observations or chlorophyll fluorescence based on satellite remote sensing SIF Data inversion; ecosystem respiration rate R eco is the ecosystem respiration rate R eco is the vegetation autotrophic respiration rate R a and soil heterotrophic respiration rate R h sum.

[0048] The present invention discloses a method for monitoring carbon sources and sinks of terrestrial ecosystems based on satellite remote sensing, which overcomes the shortcomings of existing estimation methods such as the inability to monitor over a large area, high cost, and inapplicability to areas with high spatial heterogeneity. By using satellite remote sensing data and ground observation data of remote sensing target pixels as input, the method calculates the ecosystem carbon sources and sinks of carbon exchange between vegetation ecosystems and the atmosphere in the target pixels. The method has the advantages of wide applicable monitoring range, high accuracy, and high feasibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Schematic diagram of the method of the present invention.

[0050] Figure 2 This is a comparison chart of total primary productivity and ecosystem carbon density observations based on satellite remote sensing inversion in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] The present invention proposes a method for monitoring carbon sources and sinks in terrestrial vegetation ecosystems, which uses the total primary productivity of vegetation inverted by satellite remote sensing of solar-induced chlorophyll fluorescence to measure the total primary productivity of vegetation. GPP , aboveground biomass carbon density inverted by microwave satellite remote sensing C agb , land surface soil temperature inverted by satellite remote sensing T soil , vegetation index derived from satellite remote sensing NDVI , land use change LUCC As input, combined with ground meteorological observations and field survey data, in any vegetation functional type PFTCarry out vegetation ecosystem carbon cycle inversion from land pixels to regional scales to achieve net ecosystem carbon exchange NEE Dynamic monitoring.

[0053] The principles of the carbon source and sink monitoring method are as follows:

[0054] The carbon source and sink of vegetation ecosystem at any pixel or spatial grid on land is determined by the net ecosystem carbon exchange NEE Characterized in grams of carbon per square meter per day (g C m -2 d -1 ), which can be determined by the ecosystem respiration rate R eco and gross primary productivity of vegetation GPP Estimate:

[0055] (1)

[0056] Among them, the total primary productivity of vegetation GPP It can be obtained by conducting field photosynthesis observations or by chlorophyll fluorescence based on satellite remote sensing. SIF Data inversion; ecosystem respiration rate R eco is the ecosystem respiration rate R eco is the vegetation autotrophic respiration rate R a and soil heterotrophic respiration rate R h sum.

[0057] Soil heterotrophic respiration rate R h It is determined by the rate at which soil carbon is decomposed by microorganisms and is calculated using the simplified soil respiration formula:

[0058] (2)

[0059] in, is the soil content per unit area in the vegetation ecosystem (g C m -2 ), which can be measured by field sampling; is the basic respiration rate of soil carbon at 10℃, is the temperature sensitivity parameter of soil carbon respiration, expressed as the land surface soil temperature T soil The multiple by which soil respiration rate increases with every 10°C increase. and Both parameters can be measured in the laboratory from field soil samples or calibrated using field carbon flux observation data.

[0060] Net primary productivity of vegetation NPP Gross primary productivity of vegetation GPP and vegetation autotrophic respiration rate R a calculate:

[0061] (3)

[0062] Vegetation autotrophic respiration rate R a Vegetation-maintained respiration rate R m and growth respiration rate R g The sum is:

[0063] (4)

[0064] Vegetation maintenance respiration rate R m Respiration by the above-ground parts of vegetation Ragb and the underground part breathes Rugb Calculation yields:

[0065] (5)

[0066] Growth respiration rate R g Gross primary productivity of vegetation GPP and vegetation maintenance respiration rate R m Estimate:

[0067] (6)

[0068] Among them, the vegetation autotrophic respiration rate R a Determined by the decomposition rate of the aboveground biomass carbon density and the belowground biomass carbon density of vegetation:

[0069] (7)

[0070] (8)

[0071] in, is the basic respiration rate of plant carbon at 10℃, is the temperature sensitivity parameter of soil carbon respiration, indicating the ground air temperature Or the multiple by which vegetation respiration rate increases for every 10°C increase in vegetation temperature; is the carbon density of aboveground biomass of vegetation, is the carbon density of vegetation belowground biomass.

[0072] Carbon density of aboveground biomass of vegetation The dry matter carbon content per unit area can be obtained from field forest surveys or grassland surveys, or by multiplying the aboveground biomass of the ecosystem based on satellite remote sensing inversion by a conversion coefficient. For example, the conversion coefficient of my country's major dominant tree species is approximately 0.5. See the "Guidelines for Measuring Carbon Stocks in Forest Ecosystems" (LY / T 2988-2018).

[0073] Vegetation belowground biomass carbon density It can be obtained from field surveys or through the carbon density of vegetation aboveground biomass. The conversion factor b is calculated by multiplying it by the conversion factor b, as shown in formula (9). The conversion factor b is calculated using a large amount of forest tree sampling measurement data.

[0074] Parameters of different vegetation functional types , Different, correspondingly, vegetation aboveground / belowground biomass carbon density ( , ) are different. The carbon density of vegetation aboveground biomass It is usually also affected by vegetation coverage. The carbon density obtained from field surveys is usually estimated under high coverage. Therefore, in practical applications, the carbon density of vegetation aboveground biomass is The reference data of aboveground biomass carbon density of typical vegetation types from formula (10) Multiply by coverage to get the conversion.

[0075] (9)

[0076] (10)

[0077] in, Indicates typical vegetation types Vegetation coverage corresponding to the reference data.

[0078] Vegetation coverage Normalized Difference Vegetation Index (NDVI) based on remote sensing calculate:

[0079] (11)

[0080] in, n Usually takes the value 2, and for NDVI The maximum and minimum values ​​of .

[0081] Based on the above algorithm principles, such as Figure 1As shown, the method for monitoring carbon sources and sinks of terrestrial ecosystems based on satellite remote sensing disclosed in the present invention comprises the following steps:

[0082] 1) Preparation of satellite remote sensing data: including chlorophyll fluorescence from satellite remote sensing SIF or gross primary productivity of vegetation GPP , aboveground biomass carbon density inverted by microwave satellite remote sensing C agb , Normalized Difference Vegetation Index NDVI , land use change LUCC , vegetation functional type, land surface soil temperature (i.e. remotely sensed surface temperature) and other data.

[0083] 2) Preparation of ground observation data: including ground temperature T a , soil temperature T h , the ratio coefficient of above-ground biocarbon to below-ground biocarbon of typical vegetation types, soil carbon density and other data.

[0084] 3) Based on the Normalized Difference Vegetation Index NDVI , calculate vegetation coverage fv , see formula (11).

[0085] 4) Calculate the aboveground and belowground biomass carbon densities of vegetation. The biomass carbon density in this step is calculated at grid pixels based on a large amount of field survey data, and is used for optimization and calibration of key parameters in the subsequent step 5).

[0086] Specifically: Land use change data LUCC The function of the method is to determine the vegetation functional type on the remote sensing pixel and the transformation of vegetation functional type with land use change, such as forest land to farmland. The present invention first uses land use change data to determine the vegetation functional type on the remote sensing pixel and the transformation of vegetation functional type with land use change, such as forest land to farmland. LUCC Determine the vegetation functional type at the grid pixel, and then use the reference data of aboveground biocarbon density of typical vegetation types Calculate vegetation coverage using step 3 fv Calculate the aboveground biomass carbon density of vegetation at grid pixels , and then by Calculate the carbon density of vegetation belowground biomass , see formula (9-10).

[0087] 5) Optimization and calibration of key algorithm parameters:

[0088] Temperature sensitivity parameter Q 10 It is an important parameter affecting biomass carbon density, including the temperature sensitivity of vegetation carbon respiration and the temperature sensitivity of soil carbon respiration.

[0089] The present invention optimizes and calibrates the temperature sensitivity parameters of vegetation carbon or soil carbon respiration of typical vegetation types based on soil respiration observation data from typical field sites and respiration rates measured in the laboratory using soil sampling data. The temperature sensitivity parameters of vegetation carbon respiration are recorded as , the temperature sensitivity parameter of soil carbon respiration is recorded as .

[0090] Subsequently, the soil heterotrophic respiration rate was calculated using the vegetation underground biomass carbon density and soil carbon density data of typical vegetation samples using formula (2): R h , the underground respiration of vegetation is calculated by formula (8) R ugb , verify the simulation effect of key parameters.

[0091] 6) Calculation of vegetation autotrophic respiration rate:

[0092] The temperature sensitivity parameters of soil carbon respiration were obtained by optimization calibration. Then, the ground temperature T a , land surface soil temperature , aboveground biomass carbon density inverted by microwave satellite remote sensing C agb , calculate aboveground respiration R agb , underground breathing R ugb , see formula (7-9);

[0093] Combined with gross primary productivity GPP , simulate and calculate vegetation maintenance respiration rate R m , growth respiration rate R g , vegetation autotrophic respiration rate R a and net primary productivity NPP , see formula (3-6).

[0094] 7) Calculation of soil heterotrophic respiration rate:

[0095] The temperature sensitivity parameters of vegetation carbon respiration were obtained in the optimization calibration Then, the surface soil temperature , soil carbon density of typical vegetation types combined with vegetation cover fv Simulated calculation of soil heterotrophic respiration rate R h , see formula (2);

[0096] 8) Carbon sources and sinks of vegetation ecosystems are determined by net ecosystem carbon exchangeNEE Characterized by autotrophic respiration of vegetation R a , soil heterotrophic respiration rate R h Calculating ecosystem respiration rate R eco , combined with total primary productivity GPP Calculated net ecosystem carbon exchange NEE , see formula (1).

[0097] It can be seen from this that the present invention addresses the shortcomings of existing estimation methods, such as the inability to monitor on a large scale, high cost, and inapplicability to areas with high spatial heterogeneity. It uses satellite remote sensing data of remote sensing target pixels (including vegetation productivity from solar-induced chlorophyll fluorescence remote sensing, aboveground biomass, normalized index, vegetation type, etc. from microwave satellite remote sensing) and ground observation data (including meteorological observations, aboveground / underground biomass determination, soil carbon density determination, etc.) as input to simulate the ecosystem carbon cycle process of the target pixel and calculate the net ecosystem carbon exchange amount. It is a new method that can realize the monitoring of changes in terrestrial ecosystem carbon sources and sinks at regional scales or large-scale scales such as counties, cities, and provinces, and has the advantages of a wide applicable monitoring range, high accuracy, and high feasibility.

[0098] The method for monitoring carbon sources and sinks in terrestrial ecosystems based on satellite remote sensing disclosed in the present invention will be further described below with reference to specific application examples.

[0099] In this example, three forest ecosystems, namely the evergreen broad-leaved forest at Dinghushan Station in Guangdong Province, the evergreen coniferous forest at Qianyanzhou Station in Jiangxi Province, and the mixed coniferous and broad-leaved forest at Changbaishan Station in Jilin Province, were selected as research objects. The aboveground biomass carbon density ( C agb ), underground biocarbon density ( C ugb ) and soil organic carbon density ( C soil ) and other measured data, the measured data are shown in Table 1.

[0100] The total primary productivity measured by the flux tower for 36 months from January 2003 to December 2005 ( GPP ), ecosystem respiration ( R eco ), net ecosystem carbon exchange ( NEE ) and other carbon flux observation data as well as surface air temperature ( Ta ), surface soil temperature ( T soil ) and other meteorological data as input to calibrate key parameters: temperature sensitivity parameter of vegetation carbon respiration ( Q 10,veg) and temperature sensitivity parameters of soil carbon respiration ( Q 10,soil ).

[0101] Furthermore, at each site scale, chlorophyll fluorescence remote sensing ( SIF ) driven vegetation carbon cycle process model (method of the present invention), simulating total primary productivity GPP, Ecosystem breathing R eco , net ecosystem carbon exchange NEE and net primary productivity NPP , Vegetation Autotrophic Respiration R a , soil heterotrophic respiration R h , and update the vegetation carbon pool annually ( C agb 、 C ugb ) and soil carbon pools ( C soil ).

[0102] Table 2 gives some measured data and simulation results of Dinghushan Station; Figure 2 The comparison between the measured data and the simulation results of this method is given. In the figure, the 1st to 3rd columns are respectively: Dinghushan Station, Qianyanzhou Station, Changbaishan Station. The 1st to 4th rows are respectively: Total Primary Productivity ( GPP ), ecosystem respiration ( R eco ), carbon flux simulation (net primary productivity NPP, vegetation autotrophic respiration R a , soil heterotrophic respiration R h ), Net Ecosystem Carbon Exchange ( NEE ).Depend on Figure 2 The comparison results show that GPP 、 R eco The overall simulation accuracy is high, with correlation coefficient R>0.95 and root mean square error RMSE<1.5 gC m -2 d -1 The accuracy of net ecosystem carbon exchange (NEE) was slightly lower, with R > 0.82 and RMSE < 2.63 gC m -2 d -1 .

[0103] Table 1. Site information and related model parameter settings for the application example of the present invention

[0104]

[0105] Table 2. Some measured data and simulation results (Dinghushan Station)

[0106]

[0107] The above embodiments are not limitations of the present invention, and the present invention is not limited to the above examples. Any changes, modifications, additions or substitutions made by technicians in this technical field within the scope of the technical solution of the present invention also fall within the scope of protection of the present invention.

Claims

1. A method for monitoring carbon sources and sinks in terrestrial ecosystems based on satellite remote sensing, characterized by: The monitoring method includes the following steps: 1) Preparation of satellite remote sensing data, including: gross primary productivity (GPP) of vegetation inverted by chlorophyll fluorescence satellite remote sensing, aboveground biomass carbon density (C) inverted by microwave satellite remote sensing agb , Normalized Difference Vegetation Index (NDVI), Land Use Change (LUCC), Vegetation Functional Type, Land Surface Soil Temperature ; 2) Preparation of ground observation data, including: ground temperature T a , land surface soil temperature , the ratio coefficient of aboveground biocarbon to belowground biocarbon of typical vegetation types, and soil carbon density; 3) Calculate vegetation coverage fv based on the normalized difference vegetation index NDVI; 4) Calculate the aboveground biomass carbon density and belowground biomass carbon density of vegetation: Determine the vegetation functional type at the grid pixel by land use change LUCC, and use the aboveground biomass carbon density reference data of typical vegetation types to calculate the aboveground biomass carbon density of vegetation. And step 3) calculate the vegetation coverage fv and calculate the aboveground biomass carbon density of the grid pixel , and then the carbon density of vegetation aboveground biomass Calculate the carbon density of vegetation belowground biomass ; 5) Optimization and calibration of key algorithm parameters: The key parameter for optimization and calibration is the temperature sensitivity parameter Q 10 , including temperature sensitivity parameters of vegetation carbon respiration , temperature sensitivity parameters of soil carbon respiration ; Temperature sensitivity parameter Q 10 The optimization calibration methods are: Optimize and calibrate the temperature sensitivity parameters of vegetation carbon respiration for typical vegetation types based on soil respiration observation data from typical field sites and respiration rates measured in the laboratory using soil sampling data. , temperature sensitivity parameters of soil carbon respiration ; Reuse of vegetation belowground biomass carbon density of typical vegetation samples , soil carbon density data information, calculate soil heterotrophic respiration rate R h , and calculate the underground respiration R ugb , verify the simulation effect of key parameters; 6) Calculation of vegetation autotrophic respiration rate: The temperature sensitivity parameters of vegetation carbon respiration were obtained in the optimization calibration Then, calculate the aboveground respiration R of vegetation agb 、Underground breathing R ugb ; Combined with the gross primary productivity GPP, the vegetation maintenance respiration rate R is calculated m , growth respiration rate R g , vegetation autotrophic respiration rate R a and net primary productivity, NPP; The calculation process is shown in Formula 3-8: The net primary productivity (NPP) of vegetation is composed of the gross primary productivity (GPP) of vegetation and the autotrophic respiration rate (R). a calculate: （3） Vegetation autotrophic respiration rate R a Vegetation-maintained respiration rate R m and growth respiration rate R g The sum is: （4） Vegetation maintenance respiration rate R m Respiration by the above-ground parts of vegetation agb and underground part breathing R ugb Calculation yields: （5） Growth respiration rate R g The vegetation gross primary productivity GPP and vegetation maintenance respiration rate R m Estimate: （6） Above-ground respiration of vegetation agb 、Underground breathing R ugb The calculation is shown in Formula 7-8: （7） （8） in, is the basic respiration rate of plant carbon at 10℃, As a temperature sensitivity parameter of vegetation carbon respiration, it represents the ground air temperature Or the multiple by which vegetation respiration rate increases for every 10°C increase in vegetation temperature; 7) Calculation of soil heterotrophic respiration rate: The temperature sensitivity parameters of soil carbon respiration were obtained by optimization calibration. Then, the soil heterotrophic respiration rate R was calculated. h ; Soil heterotrophic respiration rate R h See formula 2 for calculation: （2） in, It is the soil content per unit area in a vegetation ecosystem, measured by field sampling; is the basic respiration rate of soil carbon at 10℃, As the temperature sensitivity parameter of soil carbon respiration, it is expressed as the land surface soil temperature T soil The multiple of soil respiration rate that increases with every 10°C increase; 8) The carbon source and sink of vegetation ecosystems are characterized by net ecosystem carbon exchange (NEE), which is represented by vegetation autotrophic respiration (R). a , soil heterotrophic respiration rate R h Calculated in combination with gross primary productivity GPP; The calculation of net ecosystem carbon exchange (NEE) is shown in Formula 1: （1） Among them, the gross primary productivity (GPP) of vegetation is obtained by conducting field photosynthesis observations or inverting chlorophyll fluorescence (SIF) data based on satellite remote sensing; the ecosystem respiration rate (R) eco is the vegetation autotrophic respiration rate R a and soil heterotrophic respiration rate R h sum.

2. The method for monitoring carbon sources and sinks of terrestrial ecosystems based on satellite remote sensing according to claim 1, characterized in that: In step 3), the calculation of vegetation coverage fv is shown in formula 11: （11） Among them, n is 2, and are the maximum and minimum values ​​of NDVI.

3. The method for monitoring carbon sources and sinks of terrestrial ecosystems based on satellite remote sensing according to claim 2, characterized in that: In step 4), the carbon density of aboveground biomass of vegetation , vegetation belowground biomass carbon density The calculation is shown in formula 9-10: （9） （10） in, is the reference data of aboveground biomass carbon density of typical vegetation types; fv represents vegetation coverage; Indicates typical vegetation types The vegetation coverage corresponding to the reference data; b represents the conversion factor, which is calculated using forest sampling measurement data.

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