Carbon sink metering method and device based on remote sensing and flux observation

Abstract

The invention discloses a carbon sink metering method and device based on remote sensing and flux observation, relates to the technical field of remote sensing and ecology, and mainly aims to be suitable for low-cost, long-time-sequence, efficient and accurate estimation of multi-temporal-spatial-scale carbon sinks. According to the main technical scheme, the method comprises the steps of obtaining multi-source data in a research area, and constructing a carbon sink inversion optimization model according to a light energy utilization rate theory; according to the carbon sink inversion optimization model and the multi-source data, regional vegetation carbon sink inversion in the measurement time scale is carried out, and a regional carbon sink remote sensing estimation value is obtained; and comparing and analyzing the carbon sink remote sensing estimated value in the preset flux source area range with the actually measured carbon sink value of the carbon flux station in the preset flux source area range to verify the precision of the carbon sink remote sensing estimated value, thereby completing carbon sink metering.

Description

Technical Field

[0001] The present invention relates to the technical fields of remote sensing and ecology, and particularly to a carbon sink measurement method and device based on remote sensing and flux observation. Background Art

[0002] As an important greenhouse gas, the increase in the concentration of CO2 intensifies the greenhouse effect and further leads to climate warming. This phenomenon has a profound impact on the dynamic processes of the ecosystem and the carbon cycle mechanism. To alleviate this problem, carbon sink operations have become a necessary means. A carbon sink is to absorb carbon dioxide in the atmosphere through measures such as afforestation and vegetation restoration to reduce the concentration of greenhouse gases in the atmosphere.

[0003] Currently, remote sensing-based carbon sink estimation methods include carbon sink estimation based on geochemical process models, land surface physical process models, and biological process models. Among them, the biological process model takes vegetation as the core and simulates the carbon cycle process of forest ecosystems, covering biogeographical models, dynamic vegetation models, and light use efficiency models.

[0004] However, there are some deficiencies in existing remote sensing carbon sink measurement methods: in terms of data use, medium and low-resolution data are often directly used; in terms of model applicability, the discrimination accuracy of vegetation types is not high, making it difficult to meet the high-precision inversion requirements of carbon sinks in different ecosystems; in terms of accuracy verification, existing studies mostly use the measured data of flux stations and inversion results for point-to-point verification. However, since the flux tower measures the carbon sink of the local underlying surface, the mismatch in spatial scale leads to deviations in verification accuracy and lacks scientificity. Therefore, it is necessary to improve the existing methods to improve the accuracy and reliability of carbon sink measurement. 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, and the main purpose is to be able to estimate the carbon sink at multiple spatio-temporal scales with low cost, long time series, high efficiency and accuracy.

[0006] To solve the above technical problems, the present invention proposes the following solutions:

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

[0008] Obtain multi-source data in the study area, where the multi-source data includes cloud-free medium and high-resolution remote sensing images that have been quantitatively processed, temperature products and photosynthetically active radiation products that have been preprocessed, optimized vegetation classification data obtained by reclassifying and cropping preliminary vegetation classification data, and measurement data of a carbon flux station within the measurement time scale that has been preprocessed;

[0009] Construct an optimized model for carbon sink inversion based on the light energy utilization efficiency theory;

[0010] Perform regional vegetation carbon sink inversion within the measurement time scale according to the optimized carbon sink inversion model and the multi-source data, and obtain the remotely sensed estimation value of the regional carbon sink;

[0011] Compare and analyze the remotely sensed estimation value of the carbon sink within the preset flux source area with the measured carbon sink value of the carbon flux station within the preset flux source area to verify the accuracy of the remotely sensed estimation value of the carbon sink, thereby completing carbon sink measurement.

[0012] In a second aspect, the present invention provides a carbon sink measurement device based on remote sensing and flux observation, and the device includes:

[0013] A data acquisition unit for acquiring multi-source data within the study area, where the multi-source data includes cloud-free medium and high-resolution remotely sensed images subjected to quantification processing, temperature products and photosynthetically active radiation products subjected to preprocessing, optimized vegetation classification data obtained by reclassifying and cropping preliminary vegetation classification data, and measurement data of a carbon flux station within the measurement time scale subjected to preprocessing;

[0014] A model construction unit for constructing an optimized model for carbon sink inversion based on the light energy utilization efficiency theory;

[0015] A carbon sink inversion unit for performing regional vegetation carbon sink inversion within the measurement time scale according to the optimized carbon sink inversion model constructed by the model construction unit and the multi-source data acquired by the data acquisition unit, and obtaining the remotely sensed estimation value of the regional carbon sink;

[0016] A result verification unit for comparing and analyzing the remotely sensed estimation value of the carbon sink 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 remotely sensed estimation value of the carbon sink, thereby completing carbon sink measurement.

[0017] To achieve the above object, according to a third aspect of the present invention, there is provided a storage medium, and the storage medium includes a stored program, wherein when the program runs, it controls the device where the storage medium is located to execute the carbon sink measurement method based on remote sensing and flux observation in the first aspect above.

[0018] To achieve the above object, according to a fourth aspect of the present invention, there is provided a processor, and the processor is used to run a program, wherein when the program runs, it executes the carbon sink measurement method based on remote sensing and flux observation in the first aspect above.

[0019] With the above technical solutions, a carbon sink measurement method and device based on remote sensing and flux observation provided by the present invention obtain multi-source data in the study area. The multi-source data includes: the quantitative processing results of medium and high-resolution remote sensing images under cloudless conditions, the preprocessed air temperature and photosynthetically active radiation products, the optimized vegetation classification data obtained through reclassification and cropping, and the measurement data of the preprocessed carbon flux station within the measurement time scale. Compared with medium and low-resolution data, these medium and high-resolution remote sensing images provide a more solid foundation for constructing an optimized carbon sink inversion model. On this basis, the optimized vegetation classification data obtained by more refined subdivision and cropping of the preliminary vegetation classification data significantly improves the accuracy of vegetation classification, thus better meeting the requirements of high-precision inversion of carbon sinks in different ecosystems. Next, an optimized carbon sink inversion model can be constructed according to the light use efficiency theory. Subsequently, the regional vegetation carbon sink can be inverted within the measurement time scale based on the carbon sink inversion model and multi-source data to obtain the remote sensing estimation value of the regional carbon sink. Finally, in order to verify the accuracy of these remote sensing estimation values of carbon sinks, the remote sensing estimation values of carbon sinks within the preset flux source area can be compared and analyzed with the measured carbon sink values of the carbon flux station within the same range. This verification method is no longer a simple point-to-point comparison, but a comprehensive analysis based on the remote sensing inversion results and measured data within the preset flux source area. The reason for this is that by comparing remote sensing data with ground measured data within the same flux source area, it can be ensured that the verification process takes into account the factors of spatial consistency and ecosystem dynamic changes, thus significantly improving the verification accuracy and completing the measurement work of the regional carbon sink.

[0020] The above description is only an overview of the technical solutions of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention are specifically exemplified below. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0022] Figure 1 Shows a flowchart of a carbon sink measurement method based on remote sensing and flux observation provided by an embodiment of the present invention;

[0023] Figure 2 Shows a flowchart of another carbon sink measurement method based on remote sensing and flux observation provided by an embodiment of the present invention;

[0024] Figure 3 The block diagram of a carbon sink measurement device based on remote sensing and flux observation provided by an embodiment of the present invention is shown;

[0025] Figure 4 The block diagram of another carbon sink measurement device based on remote sensing and flux observation provided by an embodiment of the present invention is shown;

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

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

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

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

[0030] Figure 7(B) shows the contribution diagram of the flux source area of a certain park provided by an embodiment of the present invention. Detailed implementation manners

[0031] Hereinafter, the exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be completely conveyed to those skilled in the art.

[0032] In view of the problems mentioned in the above background art, through a large number of creative activities, the inventor has 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 in the study area, including cloud-free high-resolution remote sensing images, air temperature products, photosynthetically active radiation (PAR) products, preliminary vegetation classification data, and measurement data of carbon flux stations within the measurement time scale. In this process, first, detailed preprocessing is performed on the acquired data: for cloud-free high-resolution remote sensing images, quantitative processing is carried out to ensure the quality and accuracy of the data; necessary preprocessing is performed on air temperature products and photosynthetically active radiation (PAR) products to eliminate noise and adjust them to a unified time scale; at the same time, the preliminary vegetation classification data is reclassified and cropped to optimize the vegetation classification accuracy to meet the needs of different ecosystems. These preprocessing steps lay a solid foundation for the subsequent construction of a carbon sink inversion optimization model. Then, a carbon sink inversion optimization model is constructed based on the light use efficiency theory, and subsequently, the regional vegetation carbon sink can be inverted within the measurement time scale according to the carbon sink inversion optimization model and multi-source data to obtain the remote sensing estimation value of the regional carbon sink.

[0033] Finally, the remote sensing estimation value of the carbon sink within the preset flux source area is compared and analyzed with the measured carbon sink value of the carbon flux station in the same area. This process not only verifies the accuracy of the remote sensing inversion results, but also by comparing the remote sensing estimation value with the measured data within the same preset flux source area, considering factors such as spatial consistency and ecosystem dynamic changes, greatly improves the verification accuracy. This method transcends the traditional point-to-point verification mode, ensures wider regional data consistency, and realizes efficient and accurate measurement of carbon sinks of different vegetation types.

[0034] Next, in combination with Figure 1 , a carbon sink measurement method based on remote sensing and flux observation proposed in an embodiment of the present invention will be described. The specific implementation steps are as Figure 1 shown, including:

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

[0036] In this step, for the acquired cloud-free high-resolution remote sensing images of the study area, quantitative processing can be performed, including radiometric calibration, atmospheric correction, etc., to obtain the quantitatively processed cloud-free high-resolution remote sensing images.

[0037] For the acquired air temperature products and photosynthetically active radiation products of the study area, format conversion, projection conversion, spatial interpolation, cropping, etc. can be completed to obtain the preprocessed air temperature products and photosynthetically active radiation products.

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

[0039] In addition, for the measurement data of the carbon flux station within the measurement time scale, unit conversion, format conversion and other processing can also be carried out to obtain the measurement data of the carbon flux station within the measurement time scale after preprocessing. Among them, the measurement data includes longitude and latitude, air temperature, photosynthetically active radiation, net ecosystem productivity, wind speed, wind direction, friction velocity, Obukhov length, cross-wind variance and boundary layer height.

[0040] In this method, the measurement time scale refers to the data acquisition frequency. For example, if measurements are taken every half hour, the measurement time scale is half an hour. This setting ensures that all acquired data (including high-resolution remote sensing images without clouds, air temperature products, photosynthetically active radiation (PAR) products, preliminary vegetation classification data, and data from carbon flux stations) are processed and analyzed within a unified time framework.

[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 measured data and remote sensing data are under the same ecosystem conditions.

[0042] 102. Construct an optimized model for carbon sink inversion based on the light use efficiency theory.

[0043] 103. Conduct regional vegetation carbon sink inversion within the measurement time scale according to the optimized model for carbon sink inversion and multi-source data to obtain the remote sensing estimation value of the regional carbon sink.

[0044] In step 102, an optimized model for carbon sink inversion can be constructed based on the light use efficiency theory. Subsequently, in step 103, regional vegetation carbon sink inversion can be carried out according to the optimized model for carbon sink inversion and multi-source data within the measurement time scale to obtain the remote sensing estimation value of the regional carbon sink.

[0045] It should be noted that when conducting regional vegetation carbon sink inversion, the time scale of the parameter data used in the optimized model for carbon sink inversion should be consistent with the time scale of vegetation carbon sink inversion. Ensuring the matching of the two time scales is crucial for accurately reflecting the dynamics of vegetation carbon sink. This can ensure that the model input parameters (such as photosynthetically active radiation, temperature stress index, etc.) and vegetation carbon sink estimation are carried out within the same time framework, 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 optimized model for carbon sink inversion are usually raster data files in a geographic information system (GIS). Each raster cell represents a geographical location and contains the remote sensing estimation value of the carbon sink at that location.

[0047] It should be noted that when the carbon sink inversion optimization model processes data, it usually performs operations with these grids as the basic units. The model needs to analyze and calculate the vegetation characteristics, environmental factors, etc. within each grid to obtain the remotely sensed carbon sink estimation value corresponding to that grid. Therefore, inversion results for multiple grids will be generated, and finally, the remotely sensed carbon sink estimation value for the entire region is obtained based on the remotely sensed carbon sink estimation values corresponding to all the grids in this region.

[0048] In addition, in order to study the dynamic changes of vegetation carbon sinks, the same vegetation type can be observed and inverted multiple times at different time points. Each observation will obtain an inversion result based on grids. By analyzing these grid data in the time series, the dynamic characteristics such as seasonal changes and interannual changes of vegetation carbon sinks can be understood.

[0049] 104. Compare and analyze the remotely sensed carbon sink estimation value within the preset flux source area with the measured carbon sink value of the carbon flux station within the preset flux source area to verify the accuracy of the remotely sensed carbon sink estimation value, thereby completing carbon sink measurement.

[0050] Since there may be certain differences between the remotely sensed carbon sink estimation value and the measured carbon sink value in terms of time and space scales, fine data matching and synchronization operations are required:

[0051] On the time scale, accurately correspond the measurement time periods of the remotely sensed carbon sink estimation value and the measured carbon sink value to ensure that both reflect the vegetation carbon sink situation in the same period.

[0052] That is to say, in this step, the flux source area range can be determined based on the daily and seasonal change laws of key climate elements such as precipitation and temperature in the meteorological data, and then according to the geographical boundary of the flux source area, the remotely sensed carbon sink estimation value belonging to the preset flux source area range is extracted from the remotely sensed carbon sink estimation value of the study area. Specifically, the vector boundary of the flux source area can be superimposed with the grid data of carbon sink estimation to ensure the accuracy of data extraction.

[0053] If it is found that the comparison result meets the set threshold when the remotely sensed carbon sink estimation value is compared with the actual carbon sink value, it can be determined that the remotely sensed carbon sink estimation value has high accuracy and can be used for subsequent carbon sink measurement and related research; conversely, if the accuracy requirement is not met, it is necessary to trace and check the previous carbon sink 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, a carbon sink measurement method based on remote sensing and flux observation provided by the present invention obtains multi-source data in the study area. The multi-source data includes: the quantitative processing results of medium and high-resolution remote sensing images under cloudless conditions, the preprocessed air temperature and photosynthetically active radiation products, the optimized vegetation classification data obtained through reclassification and cropping, and the measurement data of the preprocessed carbon flux station within the measurement time scale. Compared with medium and low-resolution data, these medium and high-resolution remote sensing images provide a more solid foundation for constructing an optimized carbon sink inversion model. On this basis, the optimized vegetation classification data obtained by further refining and cropping the preliminary vegetation classification data significantly improves the accuracy of vegetation classification, thus better meeting the requirements of high-precision inversion of carbon sinks in different ecosystems. Next, an optimized carbon sink inversion model can be constructed according to the light use efficiency theory. Subsequently, the regional vegetation carbon sink can be inverted within the measurement time scale based on the carbon sink inversion optimization model and multi-source data to obtain the remote sensing estimation value of the regional carbon sink. Finally, in order to verify the accuracy of these remote sensing estimation values of carbon sinks, the remote sensing estimation values of carbon sinks within the preset flux source area can be compared and analyzed with the measured carbon sink values of the carbon flux station within the same range. This verification method is no longer a simple point-to-point comparison, but a comprehensive analysis based on the remote sensing inversion results and measured data within the preset flux source area. The reason for this is that by comparing remote sensing data with ground measured data within the same flux source area, it can be ensured that the verification process takes into account the factors of spatial consistency and ecosystem dynamic changes, thus significantly improving the accuracy of verification and completing the measurement of the regional carbon sink.

[0055] Further, as a refinement and extension of the embodiment shown in Figure 1 , the embodiment of the present invention also provides another carbon sink measurement method based on remote sensing and flux observation, as shown in Figure 2 . The specific steps are as follows:

[0056] 201. Obtain multi-source data in 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 effects and solve the same technical problems, so it will not be repeated here.

[0058] 202. Construct an optimized carbon sink inversion model according to the light use efficiency theory.

[0059] 203. Invert the regional vegetation carbon sink within the measurement time scale based on the carbon sink inversion optimization model and multi-source data to obtain the remote sensing estimation value of the regional carbon sink.

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

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

[0062] (Equation 1)

[0063] Where NEP′ is the optimized net primary ecosystem productivity, PAR′ is the optimized photosynthetically active radiation, LUE max ′ is the maximum light use efficiency corresponding to different vegetation types, T s ′ is the optimized air temperature stress index, FPAR is the fraction of photosynthetically active radiation absorbed, W s is the water stress index, and Reco is the ecosystem respiration.

[0064] In step 203, the fine vegetation classification can be obtained based on the optimized vegetation classification data in the multi-source data. According to the fine vegetation classification and combined with the measured maximum light use efficiency, the corresponding maximum light use efficiency is assigned to different vegetation types in the classification. At the same time, the current air temperature product data is optimized according to the air temperature data in the measurement data of the carbon flux station in the multi-source data within the measurement time scale, and the optimized air temperature stress index is obtained.

[0065] And the current photosynthetically active radiation can be optimized according to the photosynthetically active radiation data in the measurement data of the carbon flux station in the multi-source data within the measurement time scale to obtain the optimized photosynthetically active radiation. At the same time, the fraction of photosynthetically active radiation absorbed, the water stress index, and the ecosystem respiration are determined.

[0066] After determining the above data, for each spatial grid in the region, the corresponding optimized air temperature stress index, optimized photosynthetically active radiation, maximum light use efficiency, fraction of photosynthetically active radiation absorbed, water stress index, and ecosystem respiration can be input into the carbon sink inversion optimization model to obtain the corresponding remote sensing estimation value of the carbon sink. Then, the remote sensing estimation value of the regional carbon sink is obtained according to the remote sensing estimation value of the carbon sink corresponding to each spatial grid. That is, each grid has the corresponding optimized air temperature stress index, optimized photosynthetically active radiation, maximum light use efficiency, fraction of photosynthetically active radiation absorbed, water stress index, and ecosystem respiration.

[0067] Specifically, when optimizing the current air temperature product data according to the air 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 air temperature stress index, the following steps can be adopted:

[0068] First: Process the air temperature data in the measurement data of the carbon flux station within the measurement time scale into the average air temperature of the carbon flux station within the carbon sink monitoring time scale:

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

[0070]

[0071] Wherein, T T is the average temperature of the carbon flux station at the monitoring time scale, is the temperature data within the measurement time scale of the carbon flux station, and n is the number of temperature data of the flux station within the monitoring time scale.

[0072] The monitoring time scale refers to the time interval for statistical calculation of the data within the measurement time scale. For example, if the monitoring time scale is one month and the measurement time scale is half an hour, then within the time range of one month, the data obtained every half an hour can be summarized and calculated. This setting ensures that within a long monitoring period, the change 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] Wherein, T E is the average temperature of the temperature products at the monitoring time scale, is the temperature product, and n is the number of temperature products within the monitoring time scale.

[0076] It should be noted that the monitoring time scale of the temperature products should be consistent with the temperature monitoring time scale of the carbon flux station, but the measurement time scale of the temperature products can be different from that of the carbon flux station. Specifically, although they match in the time interval for data summary and analysis, there can be differences in the actual data collection frequency.

[0077] For example, if the carbon flux station records temperature data every half an hour, while the temperature products may provide data at different frequencies (such as every hour or every day). In this case, despite the different measurement frequencies, through appropriate interpolation or aggregation methods, it can be ensured that effective comparison and analysis are carried out on the same monitoring time scale.

[0078] Second: According to the average temperature of the carbon flux station and the average temperature of the temperature products, combined with the linear regression method, obtain the temperature calibration formula:

[0079] T T = a0T E + b0

[0080] (Formula Four)

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

[0082] Third: According to the temperature calibration formula, obtain the calibrated average temperature based on the original average temperature:

[0083] T′ = a0T + b0

[0084] (Formula Five)

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

[0086] Fourth: In the obtained optimized vegetation classification data, obtain the optimal temperature for vegetation growth, the maximum temperature for vegetation growth, and the minimum temperature for vegetation growth corresponding to each vegetation classification respectively.

[0087] Fifth: Calculate the optimized temperature stress index corresponding to each spatial grid according to 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 classification respectively:

[0088]

[0089] In the formula, T′ is the calibrated average temperature, 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, according to the photosynthetically active radiation data in the measurement data of the carbon flux station in the multi-source data within the measurement time scale, optimizing the current photosynthetically active radiation to obtain the optimized photosynthetically active radiation can follow the following steps:

[0091] First: Process the photosynthetically active radiation data in the measurement data of the carbon flux station within the measurement time scale into the cumulative photosynthetically active radiation of the carbon sink monitoring time scale:

[0092] (1) For the cumulative photosynthetically active radiation:

[0093]

[0094] In the formula, PAR T is the cumulative photosynthetically active radiation (PAR) of the carbon flux station on the monitoring time scale, is the photosynthetically active radiation data of the carbon flux station per measurement time scale, and n is the number of photosynthetically active radiation data of the flux station within the monitoring time scale.

[0095] (2) Process the photosynthetically active radiation products in the multi-source data into the cumulative value of the photosynthetically active radiation products within the monitoring time scale:

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

[0097]

[0098] In the formula, PAR M is the cumulative value of the PAR product for the monitoring time scale, is the PAR product, and n is the number of PAR products within the monitoring time scale.

[0099] Second: According to the cumulative photosynthetically active radiation and the cumulative value of the photosynthetically active radiation product, combined with the linear regression method, the calibration formula for the photosynthetically active radiation is obtained:

[0100] PAR T = a1PAR M + b1

[0101] (Formula 9)

[0102] Third: According to the calibration formula of the photosynthetically active radiation, based on the original photosynthetically active radiation corresponding to each spatial grid, the optimized photosynthetically active radiation corresponding to each spatial grid is obtained:

[0103] PAR′ = a1PAR + b1

[0104] (Formula 10)

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

[0106] Finally, when obtaining the fine vegetation classification based on the optimized vegetation classification data in the multi-source data, and according to the fine vegetation classification, combining the measured maximum light use efficiency to assign the corresponding maximum light use efficiency to different vegetation types in the classification, the following steps can be followed:

[0107] First: The fine vegetation classification can be obtained through the following steps:

[0108] Based on the optimized vegetation classification data in the multi-source data, apply the multi-scale segmentation algorithm to segment the cloud-free high-resolution remote sensing images with quantitative processing in the multi-source data, and determine the optimal segmentation scale according to the optimal segmentation scale determination method; extract and produce vegetation classification samples, with the number of samples for each vegetation type not less than 20 and evenly distributed throughout the study area; based on the vegetation classification samples, apply supervised classification and other methods to complete the fine vegetation classification. One is to subdivide the vegetation types, and the other is to make a more refined and accurate division of the patches of each vegetation type to obtain the fine vegetation classification.

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

[0110] Among them, it can be referred to Figure 6 , Figure 6 which is the carbon sink inversion result of a certain park provided by an embodiment of the present invention.

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

[0112] 204. Calculate the preset flux source area range.

[0113] In this step, the wind speed and wind direction data in the carbon flux station measurement data in the multi-source data can be obtained, and the wind speed, wind direction and distribution frequency at the observation site location can be calculated. Then, combined with the friction velocity, Obukhov length, cross-wind variance, and boundary layer height data in the carbon flux station measurement data in the multi-source data, the source area range distribution with different contribution rates is calculated using the Kljun footprint model; taking the flux tower at the observation point as the origin (0,0) to establish a coordinate, the positive direction of the x-axis represents the upwind distance, and the calculation formula for the preset flux source area range is:

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

[0115] (Formula XI)

[0116] In the formula, z m is the effective observation point height, f is the conversion function of the carbon sink, that is, the footprint function, representing the contribution rate density of a certain point (x,y) on the surface to the observed value at z m , Q c represents the point source intensity of the surface carbon sink, F c is the flux value measured at z m , and R represents the underlying surface area contributing to the flux value.

[0117] 205. Compare and analyze the remote sensing estimation value of the carbon sink within the preset flux source area range with the measured carbon sink value of the carbon flux station within the preset flux source area range to verify the accuracy of the remote sensing estimation value of the carbon sink, thereby completing the carbon sink measurement.

[0118] Among them, the implementation manner 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] Among them, reference can be made to FIG. 7. FIG. 7(A) is the wind speed and wind direction rose diagram of a certain park provided by the embodiment of the present invention, and FIG. 7(B) is the contribution diagram of the flux source area of a certain park provided by the embodiment of the present invention.

[0120] Further, as an implementation of the above Figure 1 shown method, the embodiment of the present invention also provides a carbon sink measurement device based on remote sensing and flux observation for implementing the above Figure 1 shown method. The device embodiment corresponds to the foregoing method embodiment. For the convenience of reading, the details of the foregoing method embodiment will not be described one by one in this device embodiment, but it should be clear that the device in this embodiment can correspondingly implement all the contents of the foregoing method embodiment. As Figure 3 shown, the device includes:

[0121] A data acquisition unit 301, configured to acquire multi-source data in the study area, where the multi-source data includes cloud-free medium and high-resolution remote sensing images after quantification processing, temperature products and photosynthetically active radiation products after preprocessing, optimized vegetation classification data obtained by reclassifying and cropping the preliminary vegetation classification data, and measurement data of the carbon flux station within the measurement time scale after preprocessing;

[0122] A model construction unit 302, configured to construct an optimized carbon sink inversion model according to the light use efficiency theory;

[0123] A carbon sink inversion unit 303, configured to perform regional vegetation carbon sink inversion within the measurement time scale according to the optimized carbon sink inversion model constructed by the model construction unit and the multi-source data acquired by the data acquisition unit, and obtain a regional carbon sink remote sensing estimation value;

[0124] A result verification unit 304, configured to compare and analyze the carbon sink remote sensing estimation value 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 estimation value, thereby completing carbon sink measurement.

[0125] Further, as an implementation of the above Figure 2 shown method, the embodiment of the present invention also provides another carbon sink measurement device based on remote sensing and flux observation for implementing the above Figure 2The method shown is implemented. The device embodiment corresponds to the foregoing method embodiment. For the convenience of reading, the details in the foregoing method embodiment will not be repeated one by one in this device embodiment. However, it should be clear that the device in this embodiment can correspondingly implement all the content in the foregoing method embodiment. As Figure 4 shown, the device includes:

[0126] A data acquisition unit 301, configured to acquire multi-source data in the study area, where the multi-source data includes cloud-free medium and high-resolution remote sensing images after quantitative processing, temperature products and photosynthetically active radiation products after preprocessing, optimized vegetation classification data obtained by reclassifying and cropping preliminary vegetation classification data, and measurement data of a carbon flux station within the measurement time scale after preprocessing;

[0127] A model construction unit 302, configured to construct an optimized carbon sink inversion model according to the light use efficiency theory;

[0128] A carbon sink inversion unit 303, configured to perform regional vegetation carbon sink inversion within the measurement time scale according to the optimized carbon sink inversion model constructed by the model construction unit and the multi-source data acquired by the data acquisition unit, to obtain a regional carbon sink remote sensing estimation value;

[0129] A result verification unit 304, configured to compare and analyze the carbon sink remote sensing estimation value 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 estimation value, thereby completing carbon sink measurement.

[0130] In an optional implementation manner, the optimized carbon sink inversion model constructed by the model construction unit 302 is:

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

[0132] In the formula, NEP′ is the carbon sink remote sensing estimation value, PAR′ is the optimized photosynthetically active radiation, LUE max ′ is the maximum light use efficiency corresponding to different vegetation types, T s ′ is the optimized temperature stress index, FPAR is the photosynthetically active radiation absorption ratio, W s is the water stress index, and Reco is the ecosystem respiration.

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

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

[0135] The second data determination module 3032 is configured to optimize the current air temperature product data according to the air temperature data in the measurement data of the carbon flux station within the measurement time scale in the multi-source data to obtain an optimized air temperature stress index.

[0136] The third data determination module 3033 is configured to optimize the current photosynthetically active radiation according to the photosynthetically active radiation data in the measurement data of the carbon flux station within the measurement time scale in the multi-source data to obtain an optimized photosynthetically active radiation.

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

[0138] The carbon sink inversion module 3035 is configured to input, for each spatial grid in the region, the corresponding optimized air 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 absorption ratio determined by the fourth data determination module 3034, the water stress index, and the ecosystem respiration into the carbon sink inversion optimization model to obtain a corresponding remotely sensed carbon sink estimation value.

[0139] The regional carbon sink determination module 3036 is configured to obtain a remotely sensed regional carbon sink estimation value according to the remotely sensed carbon sink estimation values corresponding to each spatial grid obtained by the carbon sink inversion module 3035.

[0140] In an optional implementation manner, the second data determination module 3032 is specifically configured to:

[0141] Process the air temperature data in the measurement data of the carbon flux station within the measurement time scale into the average air temperature of the carbon flux station within the carbon sink monitoring time scale.

[0142] Process the air temperature product in the multi-source data into the average air temperature of the air temperature product within the monitoring time scale.

[0143] Obtain an air temperature calibration formula according to the average air temperature of the carbon flux station and the average air temperature of the air temperature product in combination with the linear regression method.

[0144] Obtain the calibrated average air temperature based on the original average air temperature according to the air temperature calibration formula.

[0145] In obtaining the optimized vegetation classification data, determine the optimal temperature for vegetation growth, the maximum temperature for vegetation growth, and the minimum temperature for vegetation growth corresponding to each vegetation classification respectively;

[0146] Calculate the optimized air temperature stress index corresponding to each spatial grid according to the calibrated average air 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 respectively.

[0147] In an optional implementation manner, the calculation formula of the optimized air temperature stress index in the second data determination module 3032 is:

[0148]

[0149] In the formula, T ′ is the calibrated average air temperature, 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.

[0150] In an optional implementation manner, the third data determination module 3033 is specifically configured to:

[0151] Process the photosynthetically active radiation data in the measurement data of the carbon flux station within the measurement time scale into the cumulative photosynthetically active radiation of the carbon sink monitoring time scale;

[0152] Process the photosynthetically active radiation products in the multi-source data into the cumulative value of the photosynthetically active radiation products within the monitoring time scale;

[0153] According to the cumulative photosynthetically active radiation and the cumulative value of the photosynthetically active radiation products, and in combination with the linear regression method, obtain the photosynthetically active radiation calibration formula:

[0154] According to the photosynthetically active radiation calibration formula, obtain the corresponding optimized photosynthetically active radiation based on the original photosynthetically active radiation corresponding to each spatial grid.

[0155] In an optional implementation manner, the calculation formula of the preset flux source area range is:

[0156]

[0157] In the formula, zm is the height of the effective observation point, f is the conversion function of the carbon sink, representing the contribution rate density of a certain point (x, y) on the surface to the zm observation value at the place, Qc represents the point source intensity of the surface carbon sink, Fc is at zm the flux value measured at the place, and R represents the underlying surface area contributing to the flux value.

[0158] Further, an embodiment of the present invention also provides a storage medium for storing a computer program, wherein when the computer program runs, it controls the device where the storage medium is located to execute the above-mentioned Figure 1-2 carbon sink measurement method based on remote sensing and flux observation described in

[0159] Further, an embodiment of the present invention also provides a processor for running a program, wherein when the program runs, it executes the above-mentioned Figure 1-2 carbon sink measurement method based on remote sensing and flux observation described in

[0160] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0161] It can be understood that the relevant features in the above methods and devices can be referred to each other. In addition, the "first", "second", etc. in the above embodiments are used to distinguish each embodiment, and do not represent the advantages and disadvantages of each embodiment.

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

[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 structure required to construct such systems is obvious from the above description. In addition, the present invention is not directed to any particular programming language. It should be understood that the content of the present invention described herein can be implemented using various programming languages, and the description of the specific language above is for disclosing the best mode of the present invention.

[0164] In addition, the memory may include non-permanent memory in a computer-readable medium, 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 storage chip.

[0165] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented 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 the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, as well as the combination of flows and / or blocks in the flowchart and / or block diagram. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0167] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0168] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

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

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

[0171] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, 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, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information accessible by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0172] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0173] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, system or computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0174] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A carbon sink measurement method based on remote sensing and flux observation, characterized in that: The method comprises: Acquire multi-source data in the study area, including quantitatively processed cloud-free medium- and high-resolution remote sensing images, pre-processed temperature products and photosynthetically active radiation products, optimized vegetation classification data after reclassification and clipping of preliminary vegetation classification data, and pre-processed carbon flux station measurement data within the measurement time scale; Construct a carbon sink inversion optimization model based on light energy utilization theory; Perform regional vegetation carbon sink inversion within the measurement time scale according to the carbon sink inversion optimization model and the multi-source data to obtain a regional carbon sink remote sensing estimation value; The remote sensing estimated value of carbon sink 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 remote sensing estimated value of carbon sink, thereby completing the carbon sink measurement.

2. The method according to claim 1, characterized in that: The carbon sink inversion optimization model is: NEP′=PAR′×FPAR×LUE max ′×T s ′×W s -Reco Where NEP′ is the carbon sink remote sensing estimation value, PAR′ is the optimized photosynthetically active radiation, LUE max ′ is the maximum light energy utilization rate corresponding to different vegetation types, T s ′ is the optimized temperature stress index, FPAR is the photosynthetically active radiation absorption ratio, W s is the water stress index and Reco is the ecosystem respiration.

3. The method according to claim 1, characterized in that The regional vegetation carbon sink inversion within the measurement time scale is performed according to the carbon sink inversion optimization model and the multi-source data to obtain the regional carbon sink remote sensing estimation value, including: Obtaining a fine vegetation classification based on the optimized vegetation classification data in the multi-source data, and assigning corresponding maximum light energy utilization rates to different vegetation types in the classification according to the fine vegetation classification and in combination with the measured maximum light energy utilization rates; Optimizing the current temperature product data according to the temperature data in the measurement data of the carbon flux station in the multi-source data within the measurement time scale to obtain an optimized temperature stress index; Optimizing the current photosynthetically active radiation according to the photosynthetically active radiation data in the measurement data within the measurement time scale of the carbon flux station in the multi-source data to obtain optimized photosynthetically active radiation; Determine photosynthetically active radiation absorption ratio, water stress index and ecosystem respiration; For each spatial grid in the region, the corresponding optimized temperature stress index, the optimized photosynthetically active radiation, the maximum light energy utilization rate, the photosynthetically active radiation absorption ratio, the water stress index and the ecosystem respiration are input into the carbon sink inversion optimization model to obtain the corresponding carbon sink remote sensing estimation value; The regional carbon sink remote sensing estimation value is obtained based on the carbon sink remote sensing estimation value corresponding to each spatial grid.

4. The method according to claim 3, characterized in that The current temperature product data is optimized according to the temperature data in the measurement data of the carbon flux station in the multi-source data within the measurement time scale to obtain an 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 multi-source data are processed into the average temperature of the temperature products within the monitoring time scale; According to the average temperature of the carbon flux station and the average temperature of the temperature product, a temperature calibration formula is obtained by combining a linear regression method; According to the temperature calibration formula, a calibrated average temperature is obtained based on the original average temperature; Obtain the optimal vegetation growth temperature, the maximum vegetation growth temperature, and the minimum vegetation growth temperature corresponding to each vegetation classification in the optimized vegetation classification data; The optimized temperature stress index corresponding to each spatial grid is calculated based on the calibrated average temperature, the optimum temperature for vegetation growth corresponding to each vegetation type, the maximum temperature for vegetation growth, and the minimum temperature for vegetation growth.

5. The method according to claim 4, characterized in that The calculation formula of the optimized temperature stress index is: Where T′ is the average temperature after calibration, Topt is the optimum temperature for vegetation growth, Tmax is the maximum temperature for vegetation growth, and Tmin is the minimum temperature for vegetation growth.

6. The method according to claim 3, characterized in that: Optimizing the current photosynthetically active radiation according to the photosynthetically active radiation data in the measurement data within the measurement time scale of the carbon flux station in the multi-source data to obtain the optimized photosynthetically active radiation, including: Process the photosynthetically active radiation data in the measurement data of the carbon flux station within the measurement time scale into the cumulative photosynthetically active radiation of the carbon sink monitoring time scale; The photosynthetically active radiation products in multi-source data are processed into the cumulative value of photosynthetically active radiation products within the monitoring time scale; According to the cumulative photosynthetically active radiation and the cumulative value of the photosynthetically active radiation product, combined with the linear regression method, the photosynthetically active radiation rate determination formula is obtained: According to the photosynthetically active radiation determination formula, the corresponding optimized photosynthetically active radiation is obtained based on the original photosynthetically active radiation corresponding to each spatial grid.

7. The method according to claim 1, characterized in that The calculation formula for the preset flux source area range is: In the formula, z m is the height of the effective observation point, f is the conversion function of the carbon sink, representing the conversion of a point (x, y) on the surface to z m The contribution rate density of the observation at Q c represents the point source intensity of the surface carbon sink, F c For m The flux value measured at the location, R represents the underlying surface area that contributes to the flux value.

8. A carbon sink measurement device based on remote sensing and flux observation, characterized in that: The device comprises: A data acquisition unit is used to acquire multi-source data in the study area, wherein the multi-source data include cloud-free medium- and 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 clipping of preliminary vegetation classification data, and pre-processed measurement data of carbon flux stations within the measurement time scale; A model building unit, used to build a carbon sink inversion optimization model based on light energy utilization theory; A carbon sink inversion unit, used to perform regional vegetation carbon sink inversion within the measurement time scale according to the carbon sink inversion optimization model constructed by the model construction unit and the multi-source data acquired by the data acquisition unit, to obtain a regional carbon sink remote sensing estimation value; The result verification unit is used to compare and analyze the carbon sink remote sensing estimation value 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 estimation value, thereby completing carbon sink measurement.

9. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the carbon sink measurement method based on remote sensing and flux observation as described in any one of claims 1 to 7.

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

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