Method and system for constructing near-field measured data set of marine net primary productivity

Abstract

The invention belongs to the technical field of ocean water color and ocean ecology, and relates to a near-field actual measurement data set construction method and system for ocean net primary productivity, and the method comprises the steps: collecting profile data observed by a biogeochemical buoy BGC-Argo; s2, respectively performing data quality control processing on the Chl-a profile data and the bbp (700) profile data to obtain profile data after quality control; s3, based on the profile data after quality control, matching remote sensing parameters of a medium-resolution imaging spectrometer (MODIS); wherein the remote sensing parameters comprise photosynthetically active radiation (PAR) and a diffusion attenuation coefficient Kd (490) at a 490nm wave band; s4, on the basis of the profile data after quality control, PAR and Kd (490), utilizing a CbPM model to calculate marine net primary productivity NPP profile data; and S5, carrying out integration on the NPP profile data in a true light layer depth range to obtain NPP data of water column integration. The method can economically and efficiently obtain a large number of NPP samples actually measured in the near field, and makes up for the deficiency of existing actually measured data.

Description

Technical Field

[0001] The present invention belongs to the technical field of ocean water color and marine ecology, and specifically relates to a method and system for constructing a near-field measured data set of marine net primary productivity. Background Art

[0002] Marine net primary productivity (NPP) refers to the difference between the carbon fixed by phytoplankton photosynthesis and the carbon consumed by phytoplankton respiration. The amount of carbon fixed by the marine primary production process is equivalent to the amount of carbon fixed by terrestrial vegetation, which represents the carbon fixation basis in the biological pump process and provides energy sources for the life activities of marine organisms. Under the background of climate change, the response characteristics of NPP will directly affect the changes in the marine ecological environment, the development of fishery resources and the spatial and temporal distribution of the marine carbon fixation level.

[0003] since 14 Since the C tracer technology was proposed, the global ocean has accumulated nearly 10,000 field observation samples of marine NPP profiles, which provides a basic understanding of the distribution characteristics of global marine net primary productivity; however, the technical process is complex, the human participation is high, the marine environment is polluted, and the sampling efficiency is extremely low, which cannot achieve high-frequency and large-scale observations. At present, the amount of marine NPP field observation data is very scarce, the spatial coverage is limited, and the resolution of the observation profile is low, making it difficult to achieve large-scale and long-term analysis of NPP. At present, dozens of marine primary productivity remote sensing inversion models have been developed from the perspective of remote sensing, such as the marine primary productivity estimation model based on carbon biomass, which has a relatively complete theoretical process and is widely used, referred to as the CbPM model, which effectively realizes the large-scale and long-term inversion of NPP and significantly improves the understanding of marine net primary productivity; however, after comparative evaluation, it is found that the existing models have large differences in both the inversion of marine NPP values ​​and the inversion of its long-term trends; measured data are of great significance to the parameterization and evaluation and verification process of remote sensing models, and the lack of measured data seriously hinders the improvement of the accuracy of existing remote sensing models.

[0004] The biogeochemical float, referred to as the BGC-Argo float, can overcome the limitations of insufficient on-site observations and the fact that satellite ocean color remote sensing can only achieve surface observations. It is designed to measure six biogeochemical and optical parameters, including dissolved oxygen concentration, nitrate concentration, pH, chlorophyll concentration Chl-a, and particle backscatter coefficient b at 700nm. bp(700), irradiance or photosynthetically active radiation PAR. The BGC-Argo floats provide processes and time scales that cannot be observed through ship-based measurements. The amount of quality-controlled data accumulated by the BGC-Argo floats exceeds any other biogeochemical data set collected in the global ocean. To date, more than 120,000 Chl-a profiles, 120,000 b bp (700) profiles and 50,000 irradiance profiles, many of which were obtained in ocean areas where observations have been unavailable or sparse so far. The profiles observed by BGC-Argo can be further used to estimate NPP. Since BGC-Argo obtains data through in-situ observations, it can provide more accurate NPP estimates than satellite remote sensing, thereby effectively solving the challenge of insufficient measured NPP samples. Based on this, how to construct a high-precision near-site measured NPP dataset can provide a parameterization and verification evaluation data basis for the development of remote sensing models, and provide solutions to problems such as high uncertainty in existing NPP remote sensing products. Summary of the invention

[0005] Based on the above-mentioned shortcomings and deficiencies in the prior art, one of the objects of the present invention is to solve at least one or more of the above-mentioned problems in the prior art. In other words, one of the objects of the present invention is to provide a method and system for constructing a near-field measured data set of marine net primary productivity that meets one or more of the above-mentioned needs.

[0006] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions: A method for constructing a near-field measured data set of ocean net primary productivity comprises the following steps: S1. Collect profile data from biogeochemical buoy BGC-Argo observations; The profile data include the Chl-a profile data of chlorophyll concentration and the b profile data of particle backscattering coefficient at 700nm. bp (700) profile data; S2, respectively, for Chl-a profile data and b bp (700) The profile data is processed for data quality control to obtain the quality-controlled Chl-a profile data and b bp (700) profile data; S3, based on the Chl-a profile data after quality control and b bp (700) Profile data, matching remote sensing parameters of the Moderate Resolution Imaging Spectroradiometer MODIS; Among them, the remote sensing parameters include photosynthetically active radiation PAR and diffuse attenuation coefficient K at 490nm band d (490); S4, based on the Chl-a profile data after quality control, b bp (700) Profile data, photosynthetically active radiation PAR, diffuse attenuation coefficient K d (490) The carbon-based ocean net primary productivity model CbPM was used to calculate the ocean net primary productivity NPP profile data; S5. Integrate the NPP profile data in the true light layer depth range to obtain water column integrated NPP data.

[0007] As a preferred solution, in step S2, the process of data quality control processing includes the following steps: S21. Only profile data with data quality labels of 1, 2, 3 and 5 in the BGC-Argo data quality control are retained; S22, evaluating the number of observation layers in each profile data, and retaining only profile data with more than N observation layers; wherein N is an integer greater than 5; S23, screening the profile data within a target range and removing spike signals; S24, smoothing the profile data; S25, interpolate the profile data in the true light layer depth range to the target resolution, and obtain the quality-controlled Chl-a profile data and b bp (700) Profile data.

[0008] As a preferred solution, in step S23, for the screening of Chl-a profile data, the corresponding target range is 0 to 50 mg·m -3 ; For b bp (700) Screening of profile data, the corresponding target range is 0 to 0.1m -1 .

[0009] As a preferred solution, in step S23, the process of removing spike signals includes: The data of each observation layer in the profile data of the target range are subtracted from the median of all the observation layer data of the entire profile data, and then arranged from small to large, and the observation layer data that is less than 2 times the 10% quantile and greater than 2 times the 90% quantile are eliminated.

[0010] As a preferred solution, between step S24 and step S25, the section data satisfying any of the following conditions is eliminated: (1) Profile data with less than 5 observation layers; (2) Profile data with no observation layer data within a depth range of 5 m from top to bottom; (3) Profile data with more than 10 layers of observation data were eliminated.

[0011] As a preferred solution, in step S24, for the Chl-a profile data, non-photochemical quenching NPQ correction and calibration factor adjustment are first performed before smoothing.

[0012] As a preferred solution, in step S3, the matching process includes: The geographical location corresponding to the profile data to be matched is taken as the center, the satellite pixel covering the center is taken as the window, and the M×M pixel grid is taken as the matching window; where M is an odd number greater than 1; There must be at least K valid remote sensing parameters in the M×M grid and the data homogeneity test must be met, that is, the data is in the interval [mean-1.5×std, mean+1.5×std], where mean and std are the mean and standard deviation of all valid remote sensing parameters in the grid, respectively; K is an integer greater than 4; The remote sensing parameters that pass the above screening in the grid are averaged and used as the remote sensing parameters for matching the profile data to be matched.

[0013] As a preferred solution, in step S3, the matched remote sensing parameters are remote sensing data with a time resolution of daily average, a spatial resolution of 4 km, and a product level of L3.

[0014] As a preferred solution, in step S4, the calculation formula of the carbon-based ocean net primary productivity model CbPM is: ; in, is the net primary productivity of the ocean at depth z, for phytoplankton carbon; ; ; in, ; ; ; ; ; ; in, is the ratio of Chl-a to phytoplankton carbon at depth z, is the photosynthetically active radiation at depth z, is the photosynthetically active radiation at the sea surface, and fraction is used to convert Decomposed into a vector of coefficients in the 400, 412, 443, 490, 510, 555, 625, 670, and 700 nm bands; is the diffuse attenuation coefficient at band λ, according to K d (490) is calculated; daylength is the sunshine duration, , All are units, representing the chlorophyll content per day and per unit mass of carbon respectively.

[0015] The present invention also provides a near-field measured data set construction system for marine net primary productivity, which is applied to the near-field measured data set construction method as described in any of the above schemes, and the near-field measured data set construction system comprises: The acquisition module is used to collect the profile data observed by the biogeochemical buoy BGC-Argo; the profile data includes the Chl-a profile data of chlorophyll concentration and the b profile data of the particle backscattering coefficient at the 700nm band. bp (700) profile data; The quality control module is used to analyze the Chl-a profile data and b bp (700) The profile data is processed for data quality control to obtain the quality-controlled Chl-a profile data and b bp (700) profile data; Matching module, used to match the Chl-a profile data and b bp (700) profile data, matching the remote sensing parameters of the Moderate Resolution Imaging Spectroradiometer MODIS; the remote sensing parameters include photosynthetically active radiation PAR and diffuse attenuation coefficient K at 490nm band d (490); Calculation module, used to calculate the Chl-a profile data after quality control, bp (700) Profile data, photosynthetically active radiation PAR, diffuse attenuation coefficient K d (490) The carbon-based ocean net primary productivity model CbPM was used to calculate the ocean net primary productivity NPP profile data; The integration module is used to integrate the NPP profile data in the true light layer depth range to obtain the water column integrated NPP data.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) Due to the serious shortage of existing NPP measured samples in the ocean, the near-site measured data set construction method of the present invention is based on the profile data observed by BGC-Argo, the remote sensing parameters of the Moderate Resolution Imaging Spectroradiometer MODIS, and the improved CbPM model. It can economically and efficiently obtain a large number of near-site measured NPP samples to make up for the shortage of existing measured data; (2) The near-field measured NPP data obtained by the near-field measured data set construction method of the present invention can provide a basis for parameterization and verification evaluation for the construction of remote sensing large models, and further provide high-quality data support for marine ecological environment monitoring, marine fishery resource development, marine carbon source and sink accounting and evaluation, etc.; (3) Since BGC-Argo is a data observation program carried out on a global scale, the number of buoys deployed will be further increased in subsequent development, which means that the amount of BGC-Argo observation data will continue to accumulate and increase. Therefore, the near-field measured data set construction method of the present invention can continuously and massively obtain near-field measured NPP data. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a module composition diagram of a near-field measured data set construction system according to an embodiment of the present invention; Figure 2 It is a distribution diagram of the NPP inverted by the extreme gradient boosting tree XGBoost model trained by the data set obtained by the near-field measured data set construction method of the application case of the present invention and the measured NPP; Figure 3 This is the distribution diagram of NPP inverted by the existing traditional CbPM model and the measured NPP. DETAILED DESCRIPTION

[0018] In order to more clearly illustrate the embodiments of the present invention, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other accompanying drawings and other implementation methods can be obtained based on these accompanying drawings without creative work.

[0019] The amount of existing global publicly available ocean NPP profile field observation data is very scarce; the method for constructing a near-field measured data set of ocean net primary productivity in an embodiment of the present invention mainly realizes the construction of a near-field measured data set of ocean net primary productivity by combining BGC-Argo field observation profile data, remote sensing data monitored by the moderate resolution imaging spectrometer MODIS and an improved CbPM model.

[0020] Specifically, the method for constructing a near-field measured dataset of ocean net primary productivity includes the following steps: Step 1: Obtain the profile data observed by the biogeochemical buoy BGC-Argo, including the Chl-a profile data of chlorophyll concentration and the b profile data of the particle backscattering coefficient at 700nm. bp (700) Profile data; the data type is real-time R-mode data after preliminary automatic quality control by BGC-Argo.

[0021] Step 2: Chl-a profile data and b bp (700) The profile data is uniformly quality controlled, that is, data quality control processing is performed to obtain the quality-controlled Chl-a profile data and b bp (700) profile data to ensure the consistency of all data processing. Specifically, the quality control process required for both profile data includes the following steps: 1) Only the BGC-Argo profile data samples with specific quality control marks (data quality labels) are retained; Specifically, the embodiment of the present invention only retains the profile data with data quality labels of 1, 2, 3 and 5 in the BGC-Argo data quality control; 2) Eliminate data samples with too few observation layers in the profile data processed in step 1); Specifically, the number of observation layers in each profile data is evaluated, and only the profile data with more than N observation layers are retained; where N is an integer greater than 5, such as 10; 3) Screen the target range and remove spike signals from the profile data processed in step 2) according to the quality control standard; Specifically, for the screening of Chl-a profile data, the corresponding target range is 0 to 50 mg·m -3 ; For b bp (700) Screening of profile data, the corresponding target range is 0 to 0.1m -1 ; The specific process of removing the spike signal includes: Subtract the median of all observation layer data of the entire profile data from each observation layer data in the target range and arrange them in ascending order, and remove observation layer data that is less than 2 times the 10% quantile and greater than 2 times the 90% quantile; Among them, the Chl-a profile data needs additional corrections, including: a) non-photochemical quenching NPQ correction; this is because under the influence of strong light or limited nutrient levels, due to the physiological regulation mechanism of phytoplankton to protect itself from strong light damage, during the day, especially at noon, the energy of chlorophyll after being excited by photons is not released by emitting fluorescence, but lost in the form of heat energy, resulting in a decrease in the fluorescence signal. This phenomenon causes the Chl-a concentration above the mixed layer to be underestimated to a large extent; b) The data observed by the WET Labs ECO series Chl-a fluorometer was overestimated by 2 times during the factory calibration stage, so the Chl-a profile data was adjusted by a calibration factor of 1 / 2. The above additional corrections can be processed before smoothing the profile data; 4) Smoothing the profile data processed in step 3); the smoothing process may adopt an existing conventional smoothing algorithm, such as a 7-point sliding average method; After smoothing, profile data that meets any of the following conditions can be further eliminated according to actual application requirements: (I) Profile data with less than 5 observation layers; (II) Profile data with no observation layer data within a depth range of 5 m from top to bottom; (III) The profile data with more than 10 layers of observation layer data were eliminated; 5) Interpolate the profile data within the true light layer depth range (i.e., 0 to 200 m) to the target resolution, such as 1 m resolution, to obtain the quality-controlled Chl-a profile data and b bp (700) Profile data.

[0022] Step 3: Based on the Chl-a profile data after quality control and b bp (700) profile data, matched with the moderate resolution imaging spectroradiometer MODIS Aqua, obtained remote sensing parameters with daily average temporal resolution, 4km spatial resolution and L3 product level; among them, remote sensing parameters include photosynthetically active radiation PAR and diffuse attenuation coefficient K at 490nm band d (490); The specific strategy for the above matching is as follows: taking the geographical location corresponding to the profile data to be matched as the center and the satellite pixels covering the center as the window, an M×M pixel grid is taken as the matching window; wherein, M is an odd number greater than 1, usually 3; there must be at least K valid remote sensing parameters in the M×M grid, and the data homogeneity test must be satisfied, that is, the data is in the interval [mean-1.5×std, mean+1.5×std], mean and std are the mean and standard deviation of all valid remote sensing parameters in the grid, respectively; wherein, K is an integer greater than 4; the remote sensing parameters that pass the above screening in the grid are averaged as the remote sensing parameters for matching the profile data to be matched.

[0023] Step 4: Improve the CbPM model. The traditional CbPM model is based on the surface Chl-a and b observed by remote sensing. bp , PAR, K d (490) and the observed mixed layer depth and the reanalyzed nitrate jump layer depth are used as input to calculate the NPP profile data, wherein the mixed layer depth and the reanalyzed nitrate jump layer depth are used to calculate the phytoplankton carbon profile. In the model calculation, it is assumed that the parameters in the mixed layer are evenly distributed. The specific method can be referred to in the prior art. The embodiment of the present invention improves the CbPM model as follows: Chl-a and b used in the intermediate process of the CbPM model bpThe profile is directly based on the Chl-a and b observed by BGC-Argo. bp profile data, and does not use the assumption that the parameters in the mixed layer are constant; compared with the traditional CbPM model, the improved CbPM model reduces the inference of Chl-a and b from the surface values ​​observed by remote sensing. bp The uncertainty caused by profile data is eliminated and the additional error propagation caused by the mixed layer depth and the reanalyzed nitrate cline depth data in the calculation is avoided.

[0024] Step 5: Calculate the near-field measured data set of ocean NPP; this data set can include both depth-resolved NPP profile data and water column-integrated NPP data. bp (700) PAR and K of profile and remote sensing observations d (490) and the latitude, longitude and time information are brought into the improved CbPM model to calculate the NPP profile data. The specific calculation formula of the improved CbPM model is as follows: ; in, is the net primary productivity of the ocean at depth z, for phytoplankton carbon; ; ; in, ; ; ; ; ; ; in, is the ratio of Chl-a to phytoplankton carbon at depth z, is the photosynthetically active radiation at depth z, is the photosynthetically active radiation at the sea surface, and fraction is used to convert The vector composed of coefficients decomposed into 400, 412, 443, 490, 510, 555, 625, 670, and 700 nm bands is [0.0029, 0.0032, 0.0035, 0.0037, 0.0037, 0.0036, 0.0032, 0.0030, 0.0024]; is the diffuse attenuation coefficient at band λ, according to K d(490) is calculated. The specific calculation process belongs to the prior art and can be referred to the following literature: Austin, RW, and TJ Petzold,Spectral dependence of the diffuseattenuation coefficient of light in ocean waters,Optical Engineering,1986,25,473-479; is the ratio of the maximum potential Chl-a to phytoplankton carbon under given light conditions; is the factor affecting the decrease in phytoplankton growth rate due to nutrient and temperature limitations under a given light level, with a value range of 0-1; is the factor affecting the decrease in phytoplankton growth rate due to light limitation, with a value range of 0-1; is the ratio of Chl-a to phytoplankton carbon when the growth rate is equal to 0; is the maximum growth rate of phytoplankton observed in nature, and its value is ; daylength is the sunshine duration, , All are units, representing the chlorophyll content per day and per unit mass of carbon respectively.

[0025] Then, the NPP profile data are integrated in a trapezoidal manner in the true light layer depth range of 0 to 200 m to obtain the water column integrated NPP data, thereby constructing a near-field measured data set.

[0026] Based on the method for constructing a near-field measured data set of the net primary productivity of the ocean described above in the embodiment of the present invention, the embodiment of the present invention also provides a system for constructing a near-field measured data set of the net primary productivity of the ocean, such as Figure 1 As shown, it includes the following functional modules: acquisition module, quality control module, matching module, calculation module and integration module; Specifically, the acquisition module of the embodiment of the present invention is used to collect profile data observed by the biogeochemical buoy BGC-Argo; wherein the profile data includes Chl-a profile data of chlorophyll concentration and b profile data of particle backscattering coefficient at 700nm band. bp (700) profile data; the quality control module of the present invention is used to respectively analyze the Chl-a profile data and b bp (700) The profile data is processed for data quality control to obtain the quality-controlled Chl-a profile data and b bp (700) profile data; the matching module of the embodiment of the present invention is used to match the Chl-a profile data and bbp (700) profile data, matching the remote sensing parameters of the Moderate Resolution Imaging Spectroradiometer MODIS; the remote sensing parameters include photosynthetically active radiation PAR and diffuse attenuation coefficient K at 490nm band d (490); The calculation module of the embodiment of the present invention is used to calculate the Chl-a profile data after quality control, bp (700) Profile data, photosynthetically active radiation PAR, diffuse attenuation coefficient K d (490) The carbon-based ocean net primary productivity model CbPM is used to calculate the ocean net primary productivity NPP profile data; the integration module of this embodiment is used to integrate the NPP profile data in the true light layer depth range to obtain the water column integrated NPP data; the specific processing process of the above-mentioned functional modules can refer to the detailed description of the above-mentioned near-field measured data set construction method, which will not be repeated here.

[0027] The following further explains the method for constructing a near-field measured data set of marine net primary productivity according to an embodiment of the present invention through a specific application case, which specifically includes the following steps: (1) Download Chl-a and b of global BGC-Argo bp (700) R-mode profile data, which have been quality controlled by the BGC-Argo float; (2) Further calibrate the Chl-a profile data and b profile data of BGC-Argo bp (700) profile data. The specific process includes: 1) retaining only the data samples with quality control marks of 1, 2, 3, and 5 in BGC-Argo, which represent good data, possibly good data, possibly bad data, or data with changed values, respectively; 2) evaluating the number of observation layers in each profile data, retaining only the profile data with more than 10 observation layers and performing subsequent quality control processing; 3) removing the 0 to 50 mg m -3 Values ​​outside the range, for b bp (700) Profile data removed 0 to 0.1 m -1For spike signals, the data of each observation layer in the profile data are subtracted from the median of all the observation layer data of the entire profile data, and then arranged from small to large, and the observation layer data with less than 2 times the 10% percentile and greater than 2 times the 90% percentile are eliminated; 4) The Chl-a profile data observed between 08:00 and 16:00 during the daytime are NPQ corrected, that is, the Chl-a above the mixed layer is replaced by the value at the interface below the mixed layer; the Chl-a profile data observed by the global WET LabsECO series sensors are adjusted by a calibration factor of 1 / 2; 5) The profile data are smoothed by the 7-point sliding average method; 6) The profile data samples with any of the following characteristics are further eliminated: profile data with less than 5 observation layers after the above processing, profile data with no observation layer data within a depth range of 5m from top to bottom, and profile data with more than 10 observation layer data eliminated; 7) In the range of 0 to 200 m range, vertically interpolate the profile data to 1 m resolution; finally obtain the quality-controlled Chl-a profile data and b bp (700) profile data; (3) Centered on the BGC-Argo quality-controlled samples (i.e., quality-controlled profile data), matching the daily average of remote sensing MODIS Aqua, 4km L3 PAR, K d (490). The specific process is as follows: 1) Taking the geographical location of the sample point to be matched as the center and the satellite pixel covering the sample point as the window, a 3×3 pixel grid is taken as the matching window; 2) There must be at least 5 remote sensing valid values ​​in the 3×3 grid, and the mean and standard deviation of the valid values ​​are calculated, and samples outside mean-1.5×std to mean+1.5×std are eliminated; 3) The values ​​that pass the above screening process are averaged as the remote sensing matching result of the target sample; (4) The longitude, latitude and time information corresponding to the sample is used for day length, and the Chl-a profile data after quality control, b bp (700) Profile data, matching MODIS PAR and K d (490) Input the above improved CbPM model to calculate the ocean net primary productivity NPP profile data; (5) By integrating the ocean net primary productivity (NPP) profile data in the range of 0 to 200 m using the trapezoidal integration method, the water column integrated NPP data can be obtained.

[0028] The distribution of the near-site measured NPP profile samples obtained in this application case in the four seasons of spring, summer, autumn and winter includes: 1877 near-site measured NPP profile samples in spring, 2231 near-site measured NPP profile samples in summer, 1944 near-site measured NPP profile samples in autumn, and 1498 near-site measured NPP profile samples in winter.

[0029] In order to evaluate the application potential of the near-field measured NPP dataset obtained by this invention, Figure 2 It is a comparison between the NPP obtained by training the model XGBoost_CbPM based on the dataset of the present invention and the measured NPP. Figure 3 This is a comparison between the NPP obtained by the traditional CbPM model and the measured NPP. The results show that the model inversion values ​​of the present invention are evenly distributed on both sides of the 1:1 line with the measured values, which are more consistent and have a lower root mean square error; while the inversion values ​​of the traditional CbPM model are negatively correlated with the measured values, and the root mean square error is twice that of the present invention, indicating that the accuracy of the traditional CbPM model is significantly lower than that of the model trained by the data set of the present invention.

[0030] The above description is only a detailed description of the preferred embodiments and principles of the present invention. For ordinary technicians in this field, according to the ideas provided by the present invention, there will be changes in the specific implementation methods, and these changes should also be regarded as the protection scope of the present invention.

Claims

1. A method for constructing a near-field measured data set of marine net primary productivity, characterized in that: The following steps are involved: S1. Collect profile data from biogeochemical buoy BGC-Argo observations; The profile data include the Chl-a profile data of chlorophyll concentration and the b profile data of particle backscattering coefficient at 700nm. bp (700) profile data; S2, respectively, for Chl-a profile data and b bp (700) The profile data is processed for data quality control to obtain the quality-controlled Chl-a profile data and b bp (700) profile data; S3, based on the Chl-a profile data after quality control and b bp (700) Profile data, matching remote sensing parameters of the Moderate Resolution Imaging Spectroradiometer MODIS; Among them, the remote sensing parameters include photosynthetically active radiation PAR and diffuse attenuation coefficient K at 490nm band d (490); S4, based on the Chl-a profile data after quality control, b bp (700) Profile data, photosynthetically active radiation PAR, diffuse attenuation coefficient K d (490) The carbon-based ocean net primary productivity model CbPM was used to calculate the ocean net primary productivity NPP profile data; S5. Integrate the NPP profile data in the true light layer depth range to obtain water column integrated NPP data.

2. The method for constructing a near-field measured data set according to claim 1, characterized in that: In step S2, the process of data quality control processing includes the following steps: S21. Only profile data with data quality labels of 1, 2, 3 and 5 in the BGC-Argo data quality control are retained; S22, evaluating the number of observation layers in each profile data, and retaining only profile data with more than N observation layers; wherein N is an integer greater than 5; S23, screening the profile data within a target range and removing spike signals; S24, smoothing the profile data; S25, interpolate the profile data in the true light layer depth range to the target resolution, and obtain the quality-controlled Chl-a profile data and b bp (700) Profile data.

3. The method for constructing a near-field measured data set according to claim 2, characterized in that: In step S23, the target range for screening the Chl-a profile data is 0 to 50 mg·m -3 ; For b bp (700) Screening of profile data, the corresponding target range is 0 to 0.1m -1 .

4. The method for constructing a near-field measured data set according to claim 3, characterized in that: In step S23, the process of removing the spike signal includes: The data of each observation layer in the profile data of the target range are subtracted from the median of all the observation layer data of the entire profile data, and then arranged from small to large, and the observation layer data that is less than 2 times the 10% quantile and greater than 2 times the 90% quantile are eliminated.

5. The method for constructing a near-field measured data set according to claim 4, characterized in that: The step between step S24 and step S25 also includes eliminating the profile data that meets any of the following conditions: (1) Profile data with less than 5 observation layers; (2) Profile data with no observation layer data within a depth range of 5 m from top to bottom; (3) Profile data with more than 10 layers of observation data were eliminated.

6. The method for constructing a near-field measured data set according to claim 2, characterized in that: In the step S24, for the Chl-a profile data, non-photochemical quenching NPQ correction and calibration factor adjustment are first performed before smoothing.

7. The method for constructing a near-field measured data set according to any one of claims 1 to 6, characterized in that: In step S3, the matching process includes: The geographical location corresponding to the profile data to be matched is taken as the center, the satellite pixel covering the center is taken as the window, and the M×M pixel grid is taken as the matching window; where M is an odd number greater than 1; There must be at least K valid remote sensing parameters in the M×M grid and the data homogeneity test must be met, that is, the data is in the interval [mean-1.5×std, mean+1.5×std], where mean and std are the mean and standard deviation of all valid remote sensing parameters in the grid, respectively; K is an integer greater than 4; The remote sensing parameters that pass the above screening in the grid are averaged and used as the remote sensing parameters for matching the profile data to be matched.

8. The method for constructing a near-field measured data set according to claim 7, characterized in that: In step S3, the matched remote sensing parameters are remote sensing data with a time resolution of daily average, a spatial resolution of 4 km, and a product level of L3.

9. The method for constructing a near-field measured data set according to any one of claims 1 to 6, characterized in that: In step S4, the calculation formula of the carbon-based ocean net primary productivity model CbPM is: ； in, is the net primary productivity of the ocean at depth z, for phytoplankton carbon; ; ； in, ; ； ； ； ； ； in, is the ratio of Chl-a to phytoplankton carbon at depth z, is the photosynthetically active radiation at depth z, is the photosynthetically active radiation at the sea surface, and fraction is used to convert Decomposed into a vector of coefficients in the 400, 412, 443, 490, 510, 555, 625, 670, and 700 nm bands; is the diffuse attenuation coefficient at band λ, according to K d (490) is calculated; daylength is the sunshine duration, , All are units, representing the chlorophyll content per day and per unit mass of carbon respectively.

10. A system for constructing a near-field measured data set of marine net primary productivity, applied to the near-field measured data set construction method according to any one of claims 1 to 9, characterized in that: The near-field measured data set construction system comprises: The acquisition module is used to collect the profile data observed by the biogeochemical buoy BGC-Argo; the profile data includes the Chl-a profile data of chlorophyll concentration and the b profile data of the particle backscattering coefficient at the 700nm band. bp (700) profile data; The quality control module is used to analyze the Chl-a profile data and b bp (700) The profile data is processed for data quality control to obtain the quality-controlled Chl-a profile data and b bp (700) profile data; Matching module, used to match the Chl-a profile data and b bp (700) profile data, matching the remote sensing parameters of the Moderate Resolution Imaging Spectroradiometer MODIS; the remote sensing parameters include photosynthetically active radiation PAR and diffuse attenuation coefficient K at 490nm band d (490); Calculation module, used to calculate the Chl-a profile data after quality control, bp (700) Profile data, photosynthetically active radiation PAR, diffuse attenuation coefficient K d (490) The carbon-based ocean net primary productivity model CbPM was used to calculate the ocean net primary productivity NPP profile data; The integration module is used to integrate the NPP profile data in the true light layer depth range to obtain the water column integrated NPP data.

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