A carbon sink measurement method and device based on remote sensing and flux observation

Abstract

The application discloses a kind of carbon sink measurement method and device based on remote sensing and flux observation, it is related to remote sensing and ecological technical field, main purpose is in low cost, long time series, efficient and accurate estimation of carbon sink of multiple spatiotemporal scale can be applied.This application mainly technical scheme is: obtaining the multi-source data in research area, according to the light energy utilization rate theory constructs carbon sink inversion optimization model;According to carbon sink inversion optimization model and multi-source data, the regional vegetation carbon sink inversion in measurement time scale is carried out, and the regional carbon sink remote sensing estimated value is obtained;Carbon sink remote sensing estimated value in the preset flux source area range is compared and analyzed with the measured carbon sink value of carbon flux station in the preset flux source area range, to verify the accuracy of carbon sink remote sensing estimated value, to complete carbon sink measurement.

Description

Technical Field

[0001] This invention relates to the fields of remote sensing and ecology, and in particular to a carbon sink measurement method and device based on remote sensing and flux observation. Background Technology

[0002] As a significant greenhouse gas, the increasing concentration of CO2 exacerbates the greenhouse effect, further contributing to global warming. This phenomenon has profoundly impacted the dynamic processes of ecosystems and carbon cycle mechanisms. To mitigate this problem, carbon sequestration has become a necessary measure. Carbon sequestration involves absorbing carbon dioxide from the atmosphere through measures such as afforestation and vegetation restoration, thereby reducing the concentration of greenhouse gases in the atmosphere.

[0003] Currently, remote sensing-based carbon sink estimation methods include those based on geochemical process models, land surface physical process models, and biological process models. Among them, biological process models, with vegetation as the core, simulate the carbon cycle processes of forest ecosystems and encompass biogeographical models, dynamic vegetation models, and light energy use efficiency models.

[0004] However, existing remote sensing carbon sequestration methods have some shortcomings: in terms of data usage, they often directly use low-to-medium resolution data; regarding model applicability, the accuracy in distinguishing vegetation types is not high, making it difficult to meet the high-precision inversion requirements of carbon sequestration in different ecosystems; in terms of accuracy verification, existing studies mostly use flux station measured data and inversion results for point-to-point verification, but since flux towers measure carbon sequestration on a local underlying surface, the mismatch in spatial scale leads to deviations in verification accuracy and lacks scientific rigor. Therefore, it is necessary to improve existing methods to enhance the accuracy and reliability of carbon sequestration. Summary of the Invention

[0005] In view of the above problems, the present invention provides a carbon sink measurement method and device based on remote sensing and flux observation. The main purpose is to enable low-cost, long-term, efficient and accurate estimation of carbon sinks applicable to multiple spatiotemporal scales.

[0006] To solve the above-mentioned technical problems, the present invention proposes the following solution:

[0007] In a first aspect, the present invention provides a carbon sink measurement method based on remote sensing and flux observation, the method comprising:

[0008] Acquire multi-source data within the study area, including quantitatively processed cloudless medium-high resolution remote sensing images, preprocessed temperature and photosynthetically active radiation products, optimized vegetation classification data after reclassification and cropping of preliminary vegetation classification data, and preprocessed carbon flux station measurement data within the measurement time scale.

[0009] A carbon sink inversion optimization model was constructed based on the theory of light energy utilization.

[0010] Based on the carbon sink inversion optimization model and the multi-source data, the regional vegetation carbon sink within the measurement time scale is inverted to obtain the regional carbon sink remote sensing estimate.

[0011] The carbon sink remote sensing estimate within the preset flux source area is compared and analyzed with the measured carbon sink value of the carbon flux station within the preset flux source area to verify the accuracy of the carbon sink remote sensing estimate, thereby completing the carbon sink measurement.

[0012] Secondly, the present invention provides a carbon sink metering device based on remote sensing and flux observation, the device comprising:

[0013] The data acquisition unit is used to acquire multi-source data within the study area. The multi-source data includes cloudless medium-high resolution remote sensing images that have been quantitatively processed, pre-processed temperature products and photosynthetically active radiation products, optimized vegetation classification data after reclassification and cropping of the preliminary vegetation classification data, and pre-processed carbon flux station measurement data within the measurement time scale.

[0014] The model building unit is used to construct a carbon sink inversion optimization model based on the light energy utilization theory.

[0015] The carbon sink inversion unit is used to perform regional vegetation carbon sink inversion within the measurement time scale based on the carbon sink inversion optimization model constructed by the model building unit and the multi-source data acquired by the data acquisition unit, so as to obtain the regional carbon sink remote sensing estimate.

[0016] The result verification unit is used to compare and analyze the carbon sink remote sensing estimate within the preset flux source area obtained by the carbon sink inversion unit with the measured carbon sink value of the carbon flux station within the preset flux source area to verify the accuracy of the carbon sink remote sensing estimate, thereby completing the carbon sink measurement.

[0017] To achieve the above objectives, according to a third aspect of the present invention, a storage medium is provided, the storage medium including a stored program, wherein, when the program is executed, the device on which the storage medium is located executes the carbon sink measurement method based on remote sensing and flux observation of the first aspect.

[0018] To achieve the above objectives, according to a fourth aspect of the present invention, a processor is provided for running a program, wherein the program executes the carbon sink measurement method based on remote sensing and flux observation described in the first aspect.

[0019] By employing the above technical solution, this invention provides a carbon sink measurement method and device based on remote sensing and flux observation. It acquires multi-source data within the study area, including: quantitative processing results of medium-to-high resolution remote sensing images under cloudless conditions; pre-processed air temperature and photosynthetically active radiation products; optimized vegetation classification data after reclassification and cropping; and pre-processed carbon flux station measurement data within the measurement timescale. These medium-to-high resolution remote sensing images provide a more solid foundation for constructing an optimized carbon sink inversion model compared to low-to-medium resolution data. Based on this, the preliminary vegetation classification data is further refined and cropped to obtain optimized vegetation classification data, significantly improving the accuracy of vegetation classification and thus better meeting the needs of high-precision carbon sink inversion for different ecosystems. Next, a carbon sink inversion optimization model can be constructed based on light energy utilization theory. Subsequently, regional vegetation carbon sink inversion within the measurement timescale can be performed based on the optimized carbon sink inversion model and multi-source data to obtain a regional carbon sink remote sensing estimate. Finally, to verify the accuracy of these carbon sink remote sensing estimates, the estimated carbon sink values ​​within the pre-defined flux source area can be compared with the measured carbon sink values ​​from carbon flux stations within the same area. This verification method is no longer a simple point-to-point comparison, but a comprehensive analysis based on remote sensing inversion results and measured data within the pre-defined flux source area. This is done because comparing remote sensing data with ground-based measured data within the same flux source area ensures that the verification process considers spatial consistency and dynamic changes in the ecosystem, thereby significantly improving the accuracy of the verification and completing the regional carbon sink measurement.

[0020] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0021] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0022] Figure 1 This invention provides a flowchart of a carbon sink measurement method based on remote sensing and flux observation.

[0023] Figure 2 This invention provides a flowchart of another carbon sink measurement method based on remote sensing and flux observation.

[0024] Figure 3 This diagram illustrates a block diagram of a carbon sink metering device based on remote sensing and flux observation, provided by an embodiment of the present invention.

[0025] Figure 4 This invention provides a block diagram of another carbon sink metering device based on remote sensing and flux observation.

[0026] Figure 5(A) shows an example of the original maximum light energy utilization data of a park provided by an embodiment of the present invention;

[0027] Figure 5(B) shows an example of the optimized maximum light energy utilization data of a park provided by an embodiment of the present invention;

[0028] Figure 6 An example diagram of the carbon sink inversion results of a park provided by an embodiment of the present invention is shown;

[0029] Figure 7(A) shows a wind speed and direction rose diagram of a park provided in an embodiment of the present invention;

[0030] Figure 7(B) shows a flux source region contribution map of a park provided by an embodiment of the present invention. Detailed Implementation

[0031] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0032] To address the problems mentioned in the background section, the inventors, through extensive creative work, proposed a carbon sink measurement method based on remote sensing and flux observation. This method aims to improve the accuracy and reliability of carbon sink estimation by integrating multi-source data from the study area, including high-resolution remote sensing imagery in cloud-free conditions, temperature products, photosynthetically active radiation (PAR) products, preliminary vegetation classification data, and measurement data from carbon flux stations within the measurement timescale. This process begins with detailed preprocessing of the acquired data: high-resolution remote sensing imagery in cloud-free conditions undergoes quantitative processing to ensure data quality and accuracy; temperature and PAR products undergo necessary preprocessing to eliminate noise and adjust to a uniform timescale; and preliminary vegetation classification data is reclassified and cropped to optimize classification accuracy and adapt to the needs of different ecosystems. These preprocessing steps lay a solid foundation for the subsequent construction of an optimized carbon sink retrieval model. Next, a carbon sink inversion optimization model is constructed based on the light energy utilization theory. Subsequently, regional vegetation carbon sink inversion is performed within the measurement time scale based on the carbon sink inversion optimization model and multi-source data to obtain the regional carbon sink remote sensing estimate.

[0033] Finally, the remote sensing estimates of carbon sinks within the pre-defined flux source area were compared and analyzed with the measured carbon sink values ​​from carbon flux stations within the same area. This process not only verified the accuracy of the remote sensing inversion results but also significantly improved the accuracy of the verification by considering spatial consistency and dynamic changes in the ecosystem through comparison of remote sensing estimates and measured data within the same pre-defined flux source area. This method surpasses the traditional point-to-point verification model, ensuring broader regional data consistency and achieving efficient and accurate measurement of carbon sinks for different vegetation types.

[0034] Next, combine Figure 1 This paper describes a carbon sink measurement method based on remote sensing and flux observation proposed in an embodiment of the present invention. The specific execution steps are as follows: Figure 1 As shown, it includes:

[0035] 101. Obtain multi-source data within the study area.

[0036] In this step, the acquired cloudless medium-high resolution remote sensing images of the study area can be quantitatively processed, including radiometric calibration and atmospheric correction, to obtain quantitatively processed cloudless medium-high resolution remote sensing images.

[0037] The obtained temperature and photosynthetically active radiation products of the study area can be processed by format conversion, projection conversion, spatial interpolation, and cropping to obtain preprocessed temperature and photosynthetically active radiation products.

[0038] For the preliminary vegetation classification data in the study area, reclassification and cropping can be performed to obtain optimized vegetation classification data.

[0039] Furthermore, the measurement data from carbon flux stations within the measurement timescale can be processed through unit conversion and format conversion to obtain pre-processed measurement data for the carbon flux stations within the measurement timescale. This measurement data includes latitude and longitude, air temperature, photosynthetically active radiation, net ecosystem productivity, wind speed, wind direction, friction rate, Obukhov length, crosswind variance, and boundary layer height.

[0040] In this method, the measurement timescale refers to the frequency of data acquisition. For example, if a measurement is performed every half hour, then the measurement timescale is half an hour. This setting ensures that all acquired data (including high-resolution remote sensing imagery in cloudless conditions, temperature products, photosynthetically active radiation (PAR) products, preliminary vegetation classification data, and carbon flux station data) are processed and analyzed within a unified time frame.

[0041] It should be noted that the selected carbon flux station should be located on the same underlying surface type as the study area. This selection ensures that the ground-based measured data and the remote sensing data are under the same ecosystem conditions.

[0042] 102. Construct a carbon sink inversion optimization model based on the theory of light energy utilization.

[0043] 103. Based on the carbon sink inversion optimization model and multi-source data, regional vegetation carbon sink inversion is performed within the measurement time scale to obtain the regional carbon sink remote sensing estimate.

[0044] In step 102, a carbon sink inversion optimization model can be constructed based on the light energy utilization theory. Subsequently, in step 103, regional vegetation carbon sink inversion can be performed within the measurement time scale based on the carbon sink inversion optimization model and multi-source data to obtain the regional carbon sink remote sensing estimate.

[0045] It is important to note that when performing regional vegetation carbon sink inversion, the time scale of the parameter data used in the carbon sink inversion optimization model should be consistent with the time scale of the vegetation carbon sink inversion. Ensuring the matching of the two time scales is crucial for accurately reflecting the dynamics of vegetation carbon sink. This ensures that the model input parameters (such as photosynthetically active radiation, temperature stress index, etc.) and the vegetation carbon sink estimation are performed within the same time frame, thereby improving the accuracy and reliability of the carbon sink inversion results.

[0046] It should be noted that the carbon sink inversion results generated by the carbon sink inversion optimization model are usually raster data files in a geographic information system (GIS). Each raster cell represents a geographic location and contains the remote sensing estimate of the carbon sink at that location.

[0047] It should be noted that carbon sink retrieval optimization models typically use these grids as basic units for calculations when processing data. The model needs to analyze and calculate the vegetation characteristics and environmental factors within each grid to obtain the corresponding remote sensing estimate of carbon sink. Therefore, it will generate retrieval results for multiple grids. Finally, the remote sensing estimate of carbon sink for the entire region is obtained based on the remote sensing estimates of carbon sink corresponding to all grids in this region.

[0048] Furthermore, to study the dynamic changes in vegetation carbon sinks, multiple observations and inversions can be performed on the same vegetation type at different time points. Each observation yields a raster-based inversion result, and by analyzing these time-series raster data, the dynamic characteristics of vegetation carbon sinks, such as seasonal and interannual variations, can be understood.

[0049] 104. Compare and analyze the remote sensing estimates of carbon sinks within the preset flux source area with the measured carbon sink values ​​of carbon flux stations within the preset flux source area to verify the accuracy of the remote sensing estimates of carbon sinks, thereby completing the carbon sink measurement.

[0050] Because remote sensing estimates of carbon sequestration may differ from measured values ​​in terms of time and space, precise data matching and synchronization are necessary.

[0051] In terms of time scale, the remote sensing estimates of carbon sequestration are precisely correlated with the measured carbon sequestration values ​​over the same time period to ensure that they reflect the vegetation carbon sequestration situation during the same period.

[0052] In other words, this step involves determining the flux source region's extent based on the diurnal and seasonal variations of key climate elements such as precipitation and temperature in meteorological data. Then, according to the geographical boundaries of the flux source region, carbon sink remote sensing estimates belonging to the preset flux source region are extracted from the carbon sink remote sensing estimates of the study area. Specifically, the vector boundaries of the flux source region can be overlaid with the raster data of the carbon sink estimates to ensure the accuracy of data extraction.

[0053] If the carbon sequestration remote sensing estimate meets the set threshold when compared with the actual carbon sequestration value, it can be determined that the carbon sequestration remote sensing estimate has high accuracy and can be used for subsequent carbon sequestration measurement and related research. Conversely, if it does not meet the accuracy requirements, it is necessary to retrospectively investigate the previous carbon sequestration inversion optimization model, remote sensing data processing and other links to find the root cause of the error and correct it.

[0054] Based on the above Figure 1As can be seen from the implementation method, the carbon sink measurement method provided by this invention, based on remote sensing and flux observation, acquires multi-source data within the study area. This multi-source data includes: quantitative processing results of medium-to-high resolution remote sensing images under cloudless conditions, pre-processed air temperature and photosynthetically active radiation products, optimized vegetation classification data after reclassification and cropping, and pre-processed carbon flux station measurement data within the measurement timescale. Compared to medium-to-low resolution data, these medium-to-high resolution remote sensing images provide a more solid foundation for constructing an optimized carbon sink inversion model. Based on this, the preliminary vegetation classification data is further refined and cropped to obtain optimized vegetation classification data, significantly improving the accuracy of vegetation classification and thus better meeting the needs of high-precision carbon sink inversion for different ecosystems. Next, an optimized carbon sink inversion model can be constructed based on light energy utilization theory. Subsequently, regional vegetation carbon sink inversion within the measurement timescale can be performed based on the optimized carbon sink inversion model and multi-source data to obtain a regional carbon sink remote sensing estimate. Finally, to verify the accuracy of these carbon sink remote sensing estimates, the estimated carbon sink values ​​within the pre-defined flux source area can be compared with the measured carbon sink values ​​from carbon flux stations within the same area. This verification method is no longer a simple point-to-point comparison, but a comprehensive analysis based on remote sensing inversion results and measured data within the pre-defined flux source area. This is done because comparing remote sensing data with ground-based measured data within the same flux source area ensures that the verification process considers spatial consistency and dynamic changes in the ecosystem, thereby significantly improving the accuracy of the verification and completing the regional carbon sink measurement.

[0055] Furthermore, as a response to Figure 1 Further refinement and extension of the illustrated embodiments, this invention also provides another carbon sequestration measurement method based on remote sensing and flux observation, such as... Figure 2 As shown, the specific steps are as follows:

[0056] 201. Obtain multi-source data within the study area.

[0057] It should be noted that the implementation method of step 201 is the same as that of step 101, and can achieve the same technical effect and solve the same technical problem, so it will not be repeated here.

[0058] 202. Construct a carbon sink inversion optimization model based on the light energy utilization rate theory.

[0059] 203. Based on the carbon sink inversion optimization model and multi-source data, regional vegetation carbon sink inversion is performed within the measurement time scale to obtain the regional carbon sink remote sensing estimate.

[0060] Specifically, the carbon sink inversion optimization model is as follows:

[0061] NEP′=PAR′×FPAR×LUEmax ′×T s ′×W s -Reco

[0062] (Formula 1)

[0063] In the formula, NEP′ represents the optimized net primary ecosystem productivity, PAR′ represents the optimized photosynthetically active radiation, and LUE represents the optimized net primary ecosystem productivity. max ′ represents the maximum light energy utilization rate corresponding to different vegetation types, T s ′ represents the optimized temperature stress index, FPAR represents the photosynthetically active radiation absorptivity, and W s Reco represents the water stress index, while Reco represents ecosystem respiration.

[0064] In step 203, a fine vegetation classification can be obtained based on the optimized vegetation classification data in the multi-source data. Based on the fine vegetation classification and the measured maximum light energy utilization rate, a corresponding maximum light energy utilization rate is assigned to different vegetation types in the classification. At the same time, the current temperature product data is optimized based on the temperature data in the measurement data of the carbon flux station in the multi-source data within the measurement time scale to obtain the optimized temperature stress index.

[0065] Furthermore, the current photosynthetically active radiation (PADR) can be optimized based on the PDR data from the carbon flux station within the measurement timescale of the multi-source data, resulting in the optimized PDR; at the same time, the PDR absorptivity, water stress index, and ecosystem respiration can be determined.

[0066] After determining the above data, for each spatial grid in the region, the corresponding optimized temperature stress index, optimized photosynthetically active radiation, maximum light energy utilization rate, photosynthetically active radiation absorptivity, water stress index, and ecosystem respiration can be input into the carbon sink inversion optimization model to obtain the corresponding remote sensing estimate of carbon sink. Then, based on the carbon sink remote sensing estimate of each spatial grid, the regional carbon sink remote sensing estimate is obtained. That is, each grid has a corresponding optimized temperature stress index, optimized photosynthetically active radiation, maximum light energy utilization rate, photosynthetically active radiation absorptivity, water stress index, and ecosystem respiration.

[0067] Specifically, when optimizing the current temperature product data based on temperature data from carbon flux station measurements within the measurement timescale in multi-source data to obtain the optimized temperature stress index, the following steps can be taken:

[0068] First: Process the temperature data from the carbon flux station measurements over the measurement timescale to obtain the average temperature from the carbon flux station over the carbon sink monitoring timescale.

[0069] (1) For the average temperature of carbon flux stations within the monitoring time scale:

[0070]

[0071] In the formula, T T To monitor the average temperature of carbon flux stations over a time scale. The data represents the temperature data measured at the carbon flux station within the specified timescale, where n represents the number of temperature data points from the flux station within the specified timescale.

[0072] The monitoring timescale refers to the time interval for statistical calculations on data within the measurement timescale. For example, if the monitoring timescale is one month and the measurement timescale is half an hour, then data acquired every half hour can be summarized and calculated within the one-month timeframe. This setup ensures that, over a longer monitoring period, the changing trends and overall characteristics of high-frequency measurement data can be comprehensively analyzed.

[0073] (2) Process the temperature products in the multi-source data into the average temperature of the temperature products within the monitoring time scale:

[0074]

[0075] In the formula, T E To monitor the average temperature of temperature products over a time scale, Here, n represents the temperature product, and n represents the quantity of temperature products within the monitoring time scale.

[0076] It should be noted that the monitoring timescale of the temperature product should be consistent with that of the carbon flux station, but the measurement timescale of the temperature product can differ from that of the carbon flux station. Specifically, although the time intervals for data aggregation and analysis are matched, the actual frequency of data collection can differ.

[0077] For example, if a carbon flux station records temperature data every half hour, while temperature products may provide data at different frequencies (such as hourly or daily), then despite the different measurement frequencies, appropriate interpolation or aggregation methods can ensure effective comparison and analysis of both at the same monitoring timescale.

[0078] Second: Based on the average temperature of the carbon flux station and the average temperature of the temperature product, combined with the linear regression method, the temperature calibration formula is obtained:

[0079] T T =a0T E +b0

[0080] (Formula 4)

[0081] It should be noted that the values ​​of a0 and b0 can be customized according to actual needs, but they must satisfy the temperature calibration formula mentioned above.

[0082] Third: Based on the temperature calibration formula, the calibrated average temperature is obtained from the original average temperature:

[0083] T′=a0T+b0

[0084] (Formula 5)

[0085] In the formula, T′ is the average temperature after calibration, and T is the original average temperature.

[0086] Fourth: Obtain the optimal temperature, maximum temperature, and minimum temperature for vegetation growth for each vegetation category in the optimized vegetation classification data.

[0087] Fifth: Calculate the optimized temperature stress index for each spatial grid cell based on the calibrated average air temperature, the optimal temperature for vegetation growth, the maximum temperature for vegetation growth, and the minimum temperature for vegetation growth for each vegetation category.

[0088]

[0089] In the formula, T′ is the average air temperature after calibration, Topt is the optimal temperature for vegetation growth, Tmax is the maximum temperature for vegetation growth, and Tmin is the minimum temperature for vegetation growth.

[0090] Specifically, the current photosynthetically active radiation (PAR) is optimized based on the PRA data from carbon flux stations within the measurement timescale of multi-source data. The optimized PRA can be obtained by following these steps:

[0091] First: Process the photosynthetically active radiation (PAR) data from the carbon flux station's measurement timescale into cumulative PAR for the carbon sink monitoring timescale.

[0092] (1) Regarding cumulative photosynthetically active radiation:

[0093]

[0094] In the formula, PAR T To monitor the cumulative photosynthetically active radiation (PAR) at carbon flux stations over time scales, denoted as , where is the photosynthetically active radiation data for each measurement timescale of the carbon flux station, and n is the number of photosynthetically active radiation data points from the flux station within the monitoring timescale.

[0095] (2) The photosynthetically active radiation product in the multi-source data is processed into the cumulative value of photosynthetically active radiation product over the monitoring time scale:

[0096] Based on the latitude and longitude of the carbon flux station in the multi-source data, the cumulative value of photosynthetically active radiation product of the pixel where the carbon flux station is located is extracted:

[0097]

[0098] In the formula, PAR M To monitor the cumulative value of PAR products over a time scale, For PAR products, n represents the quantity of PAR products within the monitoring timescale.

[0099] Second: Based on the cumulative photosynthetically active radiation and the cumulative value of photosynthetically active radiation products, combined with the linear regression method, the formula for determining the photosynthetically active radiation rate is obtained:

[0100] PAR T =a1PAR M +b1

[0101] (Formula Nine)

[0102] Third: Based on the photosynthetically active radiance determination formula, the optimized photosynthetically active radiance for each spatial grid is obtained from the original photosynthetically active radiance corresponding to each spatial grid:

[0103] PAR′=a1PAR+b1

[0104] (Formula 10)

[0105] In the formula, PAR′ represents the photosynthetically active radiation corresponding to each spatial grid after calibration, and PAR represents the original photosynthetically active radiation corresponding to each spatial grid.

[0106] Finally, when obtaining a fine vegetation classification based on optimized vegetation classification data from multi-source data, and assigning corresponding maximum light energy utilization rates to different vegetation types in the classification based on the fine vegetation classification and the measured maximum light energy utilization rate, the following steps can be followed:

[0107] First: Fine classification of vegetation can be obtained through the following steps:

[0108] Based on optimized vegetation classification data from multi-source datasets, a multi-scale segmentation algorithm was applied to segment the quantitatively processed cloudless, high-resolution remote sensing images from the multi-source datasets. The optimal segmentation scale was determined according to the optimal segmentation scale determination method. Vegetation classification samples were extracted and prepared, with no fewer than 20 samples for each vegetation type, evenly distributed throughout the study area. Based on the vegetation classification samples, supervised classification and other methods were applied to complete the fine-grained vegetation classification. This involved two main steps: first, further subdividing the vegetation types; and second, further refining and accurately dividing the patches of each vegetation type, resulting in a fine-grained vegetation classification.

[0109] Second: Based on the fine classification of vegetation and combined with the measured maximum light energy utilization rate, a corresponding maximum light energy utilization rate is assigned to different vegetation types in the classification. The light energy utilization rate data before and after optimization can be referred to Figure 5. Figure 5(A) shows the original maximum light energy utilization rate data of a park provided in the embodiment of the present invention, and Figure 5(B) shows the optimized maximum light energy utilization rate data of a park provided in the embodiment of the present invention.

[0110] Among them, you can refer to Figure 6 , Figure 6 The carbon sink inversion results of a park provided in an embodiment of the present invention.

[0111] Finally, it should be explained that the spatial grid mentioned in this invention refers to multiple spatial grids obtained after dividing the geographical area into which the remote sensing estimate of carbon sink is to be calculated.

[0112] 204. Calculate the range of the preset flux source region.

[0113] In this step, wind speed and direction data from carbon flux station measurements in multi-source data can be obtained to calculate the wind speed, direction, and distribution frequency at the observation station location. Then, combined with friction rate, Obukhov length, crosswind variance, and boundary layer height data from the carbon flux station measurements in multi-source data, the Kljun footprint model is used to calculate the source region distribution for different contribution rates. A coordinate system is established with the flux tower at the observation point as the origin (0,0), and the positive x-axis represents the upwind distance. The preset formula for calculating the flux source region range is:

[0114] F c (0,0,z m )=∫ R Q c (x,y)f(x,y)dxdy

[0115] (Formula Eleven)

[0116] In the formula, z m The effective observation point height is given by f, which is the carbon sink conversion function, also known as the footprint function, representing the elevation of a point (x, y) on the surface relative to z. m Contribution density of observations at point Q c F represents the point source intensity of surface carbon sinks. c For in z m The flux value measured at the location, where R represents the underlying surface area that contributes to the flux value.

[0117] 205. Compare and analyze the remote sensing estimates of carbon sinks within the preset flux source area with the measured carbon sink values ​​of carbon flux stations within the preset flux source area to verify the accuracy of the remote sensing estimates of carbon sinks, thereby completing the carbon sink measurement.

[0118] The implementation method of step 205 is the same as that of step 104, and can achieve the same technical effect and solve the same technical problem, so it will not be repeated here.

[0119] Referring to Figure 7, Figure 7(A) is a wind speed and direction rose diagram of a park provided in an embodiment of the present invention, and Figure 7(B) is a flux source area contribution diagram of a park provided in an embodiment of the present invention.

[0120] Furthermore, as a response to the above Figure 1 In addition to the method shown, this embodiment of the invention also provides a carbon sequestration metering device based on remote sensing and flux observation, used for measuring the aforementioned carbon sequestration. Figure 1 The method shown is implemented accordingly. This device embodiment corresponds to the foregoing method embodiment. For ease of reading, this device embodiment will not repeat the details of the foregoing method embodiment, but it should be clear that the device in this embodiment can implement all the contents of the foregoing method embodiment. Figure 3 As shown, the device includes:

[0121] The data acquisition unit 301 is used to acquire multi-source data in the study area. The multi-source data includes cloudless medium-high resolution remote sensing images that have been quantitatively processed, pre-processed temperature products and photosynthetically active radiation products, optimized vegetation classification data after reclassification and cropping of the preliminary vegetation classification data, and pre-processed carbon flux station measurement data within the measurement time scale.

[0122] Model building unit 302 is used to build a carbon sink inversion optimization model based on the light energy utilization theory;

[0123] The carbon sink inversion unit 303 is used to perform regional vegetation carbon sink inversion within the measurement time scale based on the carbon sink inversion optimization model constructed by the model building unit and the multi-source data acquired by the data acquisition unit, so as to obtain the regional carbon sink remote sensing estimate.

[0124] The result verification unit 304 is used to compare and analyze the carbon sink remote sensing estimate within the preset flux source area obtained by the carbon sink inversion unit with the measured carbon sink value of the carbon flux station within the preset flux source area, so as to verify the accuracy of the carbon sink remote sensing estimate and thus complete the carbon sink measurement.

[0125] Furthermore, as a response to the above Figure 2 In addition to the method shown, this embodiment of the invention also provides another carbon sequestration metering device based on remote sensing and flux observation, used for measuring the aforementioned carbon sequestration. Figure 2The method shown is implemented accordingly. This device embodiment corresponds to the foregoing method embodiment. For ease of reading, this device embodiment will not repeat the details of the foregoing method embodiment, but it should be clear that the device in this embodiment can implement all the contents of the foregoing method embodiment. Figure 4 As shown, the device includes:

[0126] The data acquisition unit 301 is used to acquire multi-source data in the study area. The multi-source data includes cloudless medium-high resolution remote sensing images that have been quantitatively processed, pre-processed temperature products and photosynthetically active radiation products, optimized vegetation classification data after reclassification and cropping of the preliminary vegetation classification data, and pre-processed carbon flux station measurement data within the measurement time scale.

[0127] Model building unit 302 is used to build a carbon sink inversion optimization model based on the light energy utilization theory;

[0128] The carbon sink inversion unit 303 is used to perform regional vegetation carbon sink inversion within the measurement time scale based on the carbon sink inversion optimization model constructed by the model building unit and the multi-source data acquired by the data acquisition unit, so as to obtain the regional carbon sink remote sensing estimate.

[0129] The result verification unit 304 is used to compare and analyze the carbon sink remote sensing estimate within the preset flux source area obtained by the carbon sink inversion unit with the measured carbon sink value of the carbon flux station within the preset flux source area, so as to verify the accuracy of the carbon sink remote sensing estimate and thus complete the carbon sink measurement.

[0130] In one optional implementation, the carbon sink inversion optimization model constructed by model building unit 302 is as follows:

[0131] NEP′=PAR′×FPAR×LUE max ′×T s ′×W s -Reco

[0132] In the formula, NEP′ is the remote sensing estimate of carbon sink, PAR′ is the optimized photosynthetically active radiation, and LUE is... max ′ represents the maximum light energy utilization rate corresponding to different vegetation types, T s ′ represents the optimized temperature stress index, FPAR represents the photosynthetically active radiation absorptivity, and W s Reco represents the water stress index, while Reco represents ecosystem respiration.

[0133] In one optional implementation, the carbon sink inversion unit 303 includes:

[0134] The first data determination module 3031 is used to obtain a fine classification of vegetation based on the optimized vegetation classification data in the multi-source data, and to assign a corresponding maximum light energy utilization rate to different vegetation types in the classification based on the fine classification of vegetation and the measured maximum light energy utilization rate.

[0135] The second data determination module 3032 is used to optimize the current temperature product data based on the temperature data in the measurement data of the carbon flux station in the measurement time scale of the multi-source data, so as to obtain the optimized temperature stress index.

[0136] The third data determination module 3033 is used to optimize the current photosynthetically active radiation based on the photosynthetically active radiation data in the measurement data of the carbon flux station in the measurement time scale of the multi-source data, so as to obtain the optimized photosynthetically active radiation.

[0137] The fourth data determination module 3034 is used to determine the photosynthetically active radiation absorptivity, water stress index, and ecosystem respiration.

[0138] The carbon sink inversion module 3035 is used to input the optimized temperature stress index determined by the second data determination module 3032, the optimized photosynthetically active radiation determined by the third data determination module 3033, the maximum light energy utilization rate determined by the first data determination module 3031, the photosynthetically active radiation absorptivity determined by the fourth data determination module 3034, the water stress index, and the ecosystem respiration into the carbon sink inversion optimization model for each spatial grid in the region, so as to obtain the corresponding carbon sink remote sensing estimate.

[0139] The regional carbon sink determination module 3036 is used to obtain the regional carbon sink remote sensing estimate value based on the carbon sink remote sensing estimate value corresponding to each spatial grid obtained by the carbon sink inversion module 3035.

[0140] In one optional implementation, the second data determination module 3032 is specifically used for:

[0141] The temperature data in the measurement data of the carbon flux station within the measurement time scale is processed into the average temperature of the carbon flux station within the carbon sink monitoring time scale;

[0142] The temperature products in the multi-source data are processed into the average temperature of the temperature products within the monitoring time scale;

[0143] Based on the average temperature of the carbon flux station and the average temperature of the temperature product, and using a linear regression method, the temperature calibration formula is obtained.

[0144] Based on the temperature calibration formula, the calibrated average temperature is obtained from the original average temperature.

[0145] In the optimized vegetation classification data, the optimal temperature, maximum temperature, and minimum temperature for vegetation growth are obtained for each vegetation classification.

[0146] Based on the calibrated average temperature, the optimal temperature for vegetation growth, the maximum temperature for vegetation growth, and the minimum temperature for vegetation growth corresponding to each vegetation type, the optimized temperature stress index corresponding to each spatial grid is calculated.

[0147] In one optional implementation, the optimized temperature stress index calculation formula in the second data determination module 3032 is as follows:

[0148]

[0149] In the formula, T ′ represents the average air temperature after calibration, Topt represents the optimal temperature for vegetation growth, Tmax represents the maximum temperature for vegetation growth, and Tmin represents the minimum temperature for vegetation growth.

[0150] In one optional implementation, the third data determination module 3033 is specifically used for:

[0151] The photosynthetically active radiation data in the measurement data of the carbon flux station within the measurement time scale are processed into the cumulative photosynthetically active radiation in the carbon sink monitoring time scale.

[0152] The photosynthetically active radiation product in the multi-source data is processed into the cumulative value of photosynthetically active radiation product over the monitoring time scale.

[0153] Based on the cumulative photosynthetically active radiation and the cumulative value of photosynthetically active radiation products, and using a linear regression method, the formula for determining photosynthetically active radiation is obtained:

[0154] Based on the aforementioned photosynthetically active radiative determination formula, the corresponding optimized photosynthetically active radiative is obtained based on the original photosynthetically active radiative for each spatial grid.

[0155] In one optional implementation, the formula for calculating the preset flux source region range is:

[0156]

[0157] In the formula, zm For the effective observation point height, f The conversion function for carbon sinks represents the conversion of a point (x, y) on the surface to carbon sinks. zm Contribution density of the observed values Qc This represents the point source intensity of the surface carbon sink. Fc In order to be in zm The flux value measured at the location, where R represents the underlying surface area that contributes to the flux value.

[0158] Furthermore, embodiments of the present invention also provide a storage medium for storing a computer program, wherein the computer program, when running, controls the device where the storage medium is located to execute the above-described... Figure 1-2 The carbon sink measurement method based on remote sensing and flux observation described in the article.

[0159] Furthermore, embodiments of the present invention also provide a processor for running a program, wherein the program executes the above-described... Figure 1-2 The carbon sink measurement method based on remote sensing and flux observation described in the article.

[0160] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0161] It is understood that the relevant features in the above methods and apparatus can be referenced interchangeably. Furthermore, the terms "first," "second," etc., in the above embodiments are used to distinguish between embodiments and do not represent the superiority or inferiority of any particular embodiment.

[0162] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0163] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0164] In addition, the memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0165] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0166] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0167] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0168] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0169] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0170] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0171] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0172] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0173] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0174] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A carbon sequestration measurement method based on remote sensing and flux observation, characterized in that, The method includes: Acquire multi-source data within the study area, including quantitatively processed cloudless medium-high resolution remote sensing images, preprocessed temperature and photosynthetically active radiation products, optimized vegetation classification data after reclassification and cropping of preliminary vegetation classification data, and preprocessed carbon flux station measurement data within the measurement time scale. A carbon sink inversion optimization model was constructed based on the theory of light energy utilization. Based on the carbon sink inversion optimization model and the multi-source data, the regional vegetation carbon sink within the measurement time scale is inverted to obtain the regional carbon sink remote sensing estimate. The carbon sink remote sensing estimate within the preset flux source area is compared and analyzed with the measured carbon sink value of the carbon flux station within the preset flux source area to verify the accuracy of the carbon sink remote sensing estimate, thereby completing the carbon sink measurement. Based on the carbon sequestration inversion optimization model and the multi-source data, regional vegetation carbon sequestration within the measurement timescale is inverted to obtain remote sensing estimates of regional carbon sequestration, including: Based on the optimized vegetation classification data in the multi-source data, a fine vegetation classification is obtained, and based on the fine vegetation classification and the measured maximum light energy utilization rate, a corresponding maximum light energy utilization rate is assigned to different vegetation types in the classification. The current temperature product data is optimized based on the temperature data from the carbon flux station measurements within the measurement timescale in the multi-source data to obtain the optimized temperature stress index. Based on the photosynthetically active radiation data from the carbon flux station measurements within the measurement timescale of the multi-source data, the current photosynthetically active radiation is optimized to obtain the optimized photosynthetically active radiation. Determine the photosynthetically active radiation absorptivity, water stress index, and ecosystem respiration; For each spatial grid in the region, the corresponding optimized temperature stress index, optimized photosynthetically active radiation, maximum light energy utilization rate, photosynthetically active radiation absorptivity, water stress index, and ecosystem respiration are input into the carbon sink inversion optimization model to obtain the corresponding remote sensing estimate of carbon sink. The regional carbon sequestration remote sensing estimate is obtained based on the carbon sequestration remote sensing estimate corresponding to each spatial grid. Based on the temperature data from the carbon flux station measurements within the measurement timescale of the multi-source data, the current temperature product data is optimized to obtain the optimized temperature stress index, including: The temperature data in the measurement data of the carbon flux station within the measurement time scale is processed into the average temperature of the carbon flux station within the carbon sink monitoring time scale; The temperature products in the multi-source data are processed into the average temperature of the temperature products within the monitoring time scale; Based on the average temperature of the carbon flux station and the average temperature of the temperature product, and using a linear regression method, the temperature calibration formula is obtained. Based on the temperature calibration formula, the calibrated average temperature is obtained from the original average temperature. In the optimized vegetation classification data, the optimal temperature, maximum temperature, and minimum temperature for vegetation growth are obtained for each vegetation classification. The optimized temperature stress index for each spatial grid is calculated based on the calibrated average temperature, the optimal temperature for vegetation growth, the maximum temperature for vegetation growth, and the minimum temperature for vegetation growth for each vegetation type. Based on the photosynthetically active radiation (PAR) data from the carbon flux station measurements within the measurement timescale of the multi-source data, the current PAR is optimized to obtain the optimized PAR, including: The photosynthetically active radiation data in the measurement data of the carbon flux station within the measurement time scale are processed into the cumulative photosynthetically active radiation in the carbon sink monitoring time scale. The photosynthetically active radiation product in the multi-source data is processed into the cumulative value of photosynthetically active radiation product over the monitoring time scale. Based on the cumulative photosynthetically active radiation and the cumulative value of photosynthetically active radiation products, and using a linear regression method, the formula for determining photosynthetically active radiation is obtained: Based on the aforementioned photosynthetically active radiative determination formula, the corresponding optimized photosynthetically active radiative is obtained based on the original photosynthetically active radiative for each spatial grid.

2. The method according to claim 1, characterized in that, The carbon sink inversion optimization model is as follows: In the formula, This is a remote sensing estimate of carbon sequestration. For optimized photosynthetically active radiation, The maximum light energy utilization rate corresponding to different vegetation types. The optimized temperature stress index. The photosynthetically active radiation absorptivity ratio, This is the water stress index. Breathing for the ecosystem.

3. The method according to claim 1, characterized in that, The optimized temperature stress index is calculated using the following formula: In the formula, Tmax is the average air temperature after calibration, Tmax is the optimal temperature for vegetation growth, Tmax is the maximum temperature for vegetation growth, and Tmin is the minimum temperature for vegetation growth.

4. The method according to claim 1, characterized in that, The formula for calculating the range of the preset flux source region is: In the formula, For the effective observation point height, The conversion function for carbon sinks represents the conversion of a point (x, y) on the surface to carbon sinks. Contribution density of the observed values This represents the point source intensity of the surface carbon sink. In order to be in The flux value measured at the location, where R represents the underlying surface area that contributes to the flux value.

5. A carbon sequestration metering device based on remote sensing and flux observation, characterized in that, The device includes: The data acquisition unit is used to acquire multi-source data within the study area. The multi-source data includes cloudless medium-high resolution remote sensing images that have been quantitatively processed, pre-processed temperature products and photosynthetically active radiation products, optimized vegetation classification data after reclassification and cropping of the preliminary vegetation classification data, and pre-processed carbon flux station measurement data within the measurement time scale. The model building unit is used to construct a carbon sink inversion optimization model based on the light energy utilization theory. The carbon sink inversion unit is used to perform regional vegetation carbon sink inversion within the measurement time scale based on the carbon sink inversion optimization model constructed by the model building unit and the multi-source data acquired by the data acquisition unit, so as to obtain the regional carbon sink remote sensing estimate. The result verification unit is used to compare and analyze the carbon sink remote sensing estimate within the preset flux source area obtained by the carbon sink inversion unit with the measured carbon sink value of the carbon flux station within the preset flux source area, so as to verify the accuracy of the carbon sink remote sensing estimate and thus complete the carbon sink measurement. The carbon sink inversion unit includes: The first data determination module is used to obtain a fine classification of vegetation based on the optimized vegetation classification data in the multi-source data, and to assign a corresponding maximum light energy utilization rate to different vegetation types in the classification based on the fine classification of vegetation and the measured maximum light energy utilization rate. The second data determination module is used to optimize the current temperature product data based on the temperature data in the measurement data of the carbon flux station within the measurement time scale in the multi-source data, so as to obtain the optimized temperature stress index. The third data determination module is used to optimize the current photosynthetically active radiation based on the photosynthetically active radiation data in the carbon flux station measurement data within the measurement time scale in the multi-source data, and obtain the optimized photosynthetically active radiation. The fourth data determination module is used to determine the photosynthetically active radiation absorptivity, water stress index, and ecosystem respiration. The carbon sink inversion module is used to input the optimized temperature stress index determined by the second data determination module, the optimized photosynthetically active radiation determined by the third data determination module, the maximum light energy utilization rate determined by the first data determination module, the photosynthetically active radiation absorptivity determined by the fourth data determination module, the water stress index, and the ecosystem respiration into the carbon sink inversion optimization model for each spatial grid in the region, so as to obtain the corresponding carbon sink remote sensing estimate. The regional carbon sink determination module is used to obtain the regional carbon sink remote sensing estimate value based on the carbon sink remote sensing estimate value corresponding to each spatial grid obtained by the carbon sink inversion module. The second data determination module is specifically used for: The temperature data in the measurement data of the carbon flux station within the measurement time scale is processed into the average temperature of the carbon flux station within the carbon sink monitoring time scale; The temperature products in the multi-source data are processed into the average temperature of the temperature products within the monitoring time scale; Based on the average temperature of the carbon flux station and the average temperature of the temperature product, and using a linear regression method, the temperature calibration formula is obtained. Based on the temperature calibration formula, the calibrated average temperature is obtained from the original average temperature. In the optimized vegetation classification data, the optimal temperature, maximum temperature, and minimum temperature for vegetation growth are obtained for each vegetation classification. The optimized temperature stress index for each spatial grid is calculated based on the calibrated average temperature, the optimal temperature for vegetation growth, the maximum temperature for vegetation growth, and the minimum temperature for vegetation growth for each vegetation type. The third data determination module is specifically used for: The photosynthetically active radiation data in the measurement data of the carbon flux station within the measurement time scale are processed into the cumulative photosynthetically active radiation in the carbon sink monitoring time scale. The photosynthetically active radiation product in the multi-source data is processed into the cumulative value of photosynthetically active radiation product over the monitoring time scale. Based on the cumulative photosynthetically active radiation and the cumulative value of photosynthetically active radiation products, and using a linear regression method, the formula for determining photosynthetically active radiation is obtained: Based on the aforementioned photosynthetically active radiative determination formula, the corresponding optimized photosynthetically active radiative is obtained based on the original photosynthetically active radiative for each spatial grid.

6. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the storage medium to perform the carbon sink measurement method based on remote sensing and flux observation as described in any one of claims 1 to 4.

7. A processor, characterized in that, The processor is used to run a program, wherein the program executes the carbon sink measurement method based on remote sensing and flux observation as described in any one of claims 1 to 4.

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