Method and system for trace gas flux observation

Abstract

This application discloses a method and system for observing trace gas fluxes, relating to the field of environmental remote sensing technology. The method includes simultaneously acquiring three-dimensional wind speed spatial distribution data and trace gas concentration spatial distribution data within the observation area, determining spatiotemporally matched wind speed-concentration data, extracting vertical wind speed fluctuation data and gas concentration fluctuation data from these data, determining the turbulent vertical transport rate jointly determined by the vertical wind speed fluctuations and gas concentration fluctuations, and using this as the target trace gas vertical turbulent flux at each spatial location; based on the target trace gas vertical turbulent flux at each spatial location, generating a two-dimensional distribution map of the target trace gas vertical turbulent flux within the observation area. This application can solve the problem of missing spatial information in the single-point method of related technologies, enabling direct area-scale imaging of trace gas turbulent fluxes and intuitively displaying the spatial characteristics of the flux.

Description

Technical Field

[0001] This application relates to the field of environmental remote sensing technology, and in particular to a method and system for observing trace gas fluxes. Background Technology

[0002] Trace gases refer to gases present in extremely small amounts in the atmosphere or other mixtures. In climate change and ecosystem research, it is necessary to accurately measure the exchange flux of trace gases between the Earth's surface and the atmosphere. However, existing technologies for observing trace gas fluxes suffer from insufficient spatial representativeness and the inability to directly obtain two-dimensional flux distribution maps, thus failing to meet the need for spatial distribution information of fluxes over complex underlying surfaces.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] This application provides a method and system for observing trace gas fluxes, which can solve the problem of missing spatial information in the single-point method of related technologies, realize direct surface-scale imaging of trace gas turbulent fluxes, and intuitively display the spatial characteristics of fluxes.

[0005] To solve the above-mentioned technical problems, this application provides the following technical solution: This application provides a method for observing trace gas fluxes, including: Simultaneously acquire three-dimensional wind speed spatial distribution data and target trace gas concentration spatial distribution data within the observation area; Based on the three-dimensional wind speed spatial distribution data and the concentration spatial distribution data, the spatiotemporally matched wind speed-concentration data are determined, and the vertical wind speed fluctuation data and the gas concentration fluctuation data of the target trace gas are extracted. Based on the vertical wind speed fluctuation data and the gas concentration fluctuation data, the turbulent vertical transport rate determined by the vertical wind speed fluctuation and the gas concentration fluctuation is determined as the target trace gas vertical turbulent flow rate at each spatial location. Based on the vertical turbulent flux of the target trace gas at each spatial location, a two-dimensional distribution map of the vertical turbulent flux of the target trace gas within the observation area is generated.

[0006] Another aspect of this application provides a trace gas flux observation system, including a lidar, a data acquisition controller, and a data processor; The data acquisition controller is connected to the lidar and the data processor respectively, and is configured to control the switching sequence of the lidar's scanning mode and laser transceiver mode, and send the three-dimensional wind speed spatial distribution data and target trace gas concentration spatial distribution data of the synchronously acquired observation area to the data processor. The data processor is configured to implement the steps of the trace gas flux observation method described above when executing a computer program.

[0007] The advantages of the technical solution provided in this application lie in the spatiotemporal alignment of synchronously acquired three-dimensional wind speed data and trace gas concentration data, avoiding data misalignment issues that may arise due to differences in measurement methods. This ensures that the physical quantities used in subsequent calculations originate from the same spatial location and time reference. By extracting pulsations, the fluctuation components caused by turbulent motion are accurately separated, eliminating the interference of average flow on flux calculations. The turbulent vertical transport volume derived from this accurately reflects the intensity of gas exchange between the surface and the atmosphere. The resulting two-dimensional flux distribution map achieves direct surface-scale imaging of turbulent fluxes, enabling direct observation of the spatial pattern of fluxes on non-uniform underlying surfaces. It can intuitively demonstrate the spatial heterogeneity of fluxes and identify source-sink hotspots, solving the fundamental problem of missing spatial information in traditional single-point methods. This solution is applicable not only to flat and uniform underlying surfaces but also to flux studies in non-uniform scenarios such as sea-land interfaces, forest-grassland ecotones, and urban boundary layers. Furthermore, this application also provides a corresponding implementation system for trace gas flux observation methods, further enhancing the practicality of the method. This system offers corresponding advantages.

[0008] The technical features mentioned above, those to be mentioned below, and those shown individually in the accompanying drawings can be arbitrarily combined, as long as the combined technical features are not contradictory. All feasible combinations of features are the technical content explicitly described in this application. Any one of the multiple sub-features contained in the same statement can be applied independently, without necessarily being applied together with other sub-features.

[0009] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this application. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1A schematic diagram of the hardware framework applicable to trace gas flux observation methods in related technologies; Figure 2 A schematic diagram of the hardware framework applicable to the trace gas flux observation method provided in this application; Figure 3 A flowchart illustrating a trace gas flux observation method provided in this application; Figure 4 A flowchart illustrating another trace gas flux observation method provided in this application; Figure 5 A structural frame diagram of an exemplary embodiment of the trace gas flux observation device provided in this application; Figure 6 This is a structural framework diagram of an exemplary embodiment of the trace gas flux observation system provided in this application. Detailed Implementation

[0012] To enable those skilled in the art to better understand the technical solutions of this application, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. The terms "first," "second," "third," "fourth," etc., used in the specification and the aforementioned drawings are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. The term "exemplary" means "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as superior to or better than other embodiments.

[0013] In climate change research, accurately measuring trace gas exchange fluxes between ecosystems and the atmosphere is crucial. Related techniques typically employ the eddy covariance method as a flux observation tool. This method belongs to a single-point, high-frequency measurement mode. By deploying an ultrasonic anemometer and a fast-response gas analyzer at a fixed observation point, the covariance of vertical wind speed fluctuations and gas concentration fluctuations is calculated to obtain the vertical turbulent flux at that point. Taking the measurement of carbon dioxide in carbon cycle assessment as an example, such as... Figure 1 As shown, an ultrasonic anemometer 1 and a fast-response CO2 analyzer 2 are installed on the observation tower. A point 3 is selected on the underlying surface for high-frequency sampling. The vertical turbulent flux at this point is calculated by the data acquisition device 4, but it cannot provide spatial distribution information of the flux.

[0014] However, this method only represents the net flux within a dynamically changing and difficult-to-define flux contribution area upwind, failing to provide information on the spatial distribution of flux in a two-dimensional plane. When applied to spatially heterogeneous underlying surface environments, such as land-sea interfaces, forest edges, farmland transition zones, or urban boundary layers, the spatial representativeness of single-point measurements is insufficient, failing to reveal the spatial distribution structure of flux and severely limiting its application in non-uniform underlying surface scenarios.

[0015] To acquire spatial flux information, one related technique involves establishing a dense network of flux towers to obtain multi-point spatial data. However, this method has extremely high equipment deployment costs, making it difficult to achieve large-scale spatial coverage. Another related technique is the large-aperture scintillation method, which provides path integral results with low spatial resolution, making it impossible to form spatial imaging of flux. Yet another related technique is the airborne eddy covariance measurement method, which, while capable of acquiring spatial data, cannot achieve long-term continuous fixed-point observations, failing to meet the continuity requirements of flux monitoring. Furthermore, related techniques combine scanning Doppler wind lidar with fixed-point differential absorption lidar to estimate flux by combining wind field scanning data with gas concentration measurements. This method suffers from large spatial matching errors due to the asynchronous acquisition of wind field and concentration data and different sampling sources in the space. Gas concentration measurements are typically in the form of path integrals, failing to obtain distance-resolved concentration fields, making accurate matching with wind field data difficult. The data processing relies on strong assumptions such as the Taylor frozen turbulence hypothesis, resulting in significant errors under complex flow field conditions.

[0016] This reveals that related technologies suffer from inherent spatial information deficiencies in single-point measurement modes and insufficient spatiotemporal synchronization in multi-device collaborative observations. Specifically, in independent device collaborative schemes, differences in the detection principles, sampling volumes, and time and spatial coordinate systems of each instrument make it difficult to accurately match wind speed and concentration data, introducing significant collaborative errors and severely impacting the accuracy of flux inversion. Furthermore, independent gas detection radars typically employ fixed pointing or slow scanning modes, making it difficult to synchronously obtain high-resolution spatial concentration fields. This mismatch in spatiotemporal resolution with the rapidly scanned wind field fails to meet the data consistency requirements for calculating spatial covariance. In addition, multi-device deployment schemes lead to high system complexity and operational difficulties.

[0017] In view of this, in order to solve the problems of insufficient spatial representativeness and inability to directly obtain two-dimensional flux distribution maps by using the single-point eddy covariance method in related technologies, as well as the problems of low spatial resolution or high cost of flux remote sensing technology, this application aims to achieve high spatiotemporal synchronous observation of wind field and concentration field on a single device and directly calculate two-dimensional flux distribution based on spatial distribution data. This application simultaneously acquires the three-dimensional wind speed vector field and trace gas concentration field on the observation area. By determining the net transport of trace gas in the vertical direction caused by turbulent motion, the vertical turbulent flux of trace gas at each spatial location point is obtained. The two-dimensional flux distribution map on the observation area, i.e., the scanning plane, is directly derived, realizing the leap from point measurement to area observation of trace gases. It can perform high spatiotemporal resolution and direct two-dimensional planar remote sensing of the turbulent flux of trace gases such as atmospheric carbon dioxide (CO2).

[0018] The specific application environment architecture or hardware architecture upon which the trace gas flux observation method depends is described here. The following section will combine... Figure 2 Examples of possible application scenarios related to the technical solutions of this application are provided, which may include the following: This embodiment provides an integrated trace gas flux observation system, such as Figure 2As shown, the system includes a scanning coherent differential absorption lidar 5 with rapid mode switching. For example, this lidar integrates coherent Doppler wind measurement and differential absorption, using coherent detection for high-precision radial wind speed measurement and differential absorption for precise measurement of specific gas column concentrations or range-resolved concentrations. Thus, it simultaneously performs wind and gas measurement functions. Mode switching corresponds to switching between these two functions, with rapid switching reaching pulse or pulse train levels. The system also includes a data acquisition controller 6 and a data processor 7. The data acquisition controller 6 controls the lidar's scanning mode (such as PPI (Plan Position Indicator Scan)), the timing of laser emission and reception mode switching, and simultaneously acquires raw data, which includes the three-dimensional spatial distribution data of wind speed and the spatial distribution data of the measured trace gas concentrations within the measurement area. Data processor 7 synchronously acquires three-dimensional wind speed spatial distribution data and target trace gas concentration spatial distribution data within the observation area. After obtaining these two types of data, it performs spatiotemporal alignment on the three-dimensional wind speed spatial distribution data and concentration spatial distribution data to obtain spatiotemporally matched wind speed-concentration data, and extracts vertical wind speed fluctuation data and target trace gas concentration fluctuation data from them. Next, based on the vertical wind speed fluctuation data and gas concentration fluctuation data, it determines the turbulent vertical transport rate jointly determined by the vertical wind speed fluctuation and gas concentration fluctuation, which serves as the target trace gas vertical turbulent flux at each spatial location. Finally, based on the target trace gas vertical turbulent flux at each spatial location, it generates a two-dimensional flux distribution map of the target trace gas vertical turbulent flux within the observation area. This two-dimensional distribution map can intuitively display the spatial variation law of flux on the horizontal plane and identify different source and sink regions.

[0019] As can be seen from the above, this embodiment ensures the co-location, coaxiality, and synchronization of wind and gas data through the rapid mode switching capability of the lidar 5, achieving high-precision spatial covariance calculation. The precise timing control of the data acquisition controller 6 ensures that spatial coverage and resolution are balanced while satisfying the turbulence sampling theorem. The data processor 7 converts raw data into flux images, enabling direct planar imaging of trace gas turbulence fluxes and providing a direct visualization of the flux's spatial characteristics.

[0020] It should be noted that the above application scenarios are only shown to facilitate understanding of the ideas and principles of this application, and the implementation methods of this application are not limited in any way. On the contrary, the implementation methods of this application can be applied to any applicable scenario. After introducing the technical solution of this application, various non-limiting implementation methods of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] Please see first. Figure 3According to the trace gas flux observation method provided in this application, it can be implemented as a computer program product, installed and run on a backend server such as those used for climate change research and carbon cycle assessment, to perform trace gas flux observation processing and directly output a two-dimensional flux distribution map. In some embodiments of the method, the method includes the following steps: S301: Simultaneously acquire three-dimensional wind speed spatial distribution data and target trace gas concentration spatial distribution data within the observation area.

[0022] The observation area refers to a specific atmospheric space and corresponding underlying surface area pre-defined based on the needs of trace gas flux observation and the detection performance of lidar, and which can be completely covered by the lidar scanning detection range for conducting surface-scale observations of vertical turbulent fluxes of trace gases. It can be an observation cone and serves as the geographical and spatial boundary for simultaneous detection of three-dimensional wind speed and target trace gas concentrations, flux calculation, and generation of two-dimensional distribution maps. The observation area is the target range for lidar scanning, determining the basis for setting scanning parameters (elevation angle, rotation speed, pulse sequence). It is the geographical reference range defined by a unified spatiotemporal coordinate system and spatiotemporal grid, ensuring that the matching and interpolation reconstruction of wind speed-concentration data are completed within fixed boundaries. It is also the display range for the two-dimensional flux distribution map; the final generated flux distribution image is overlaid with the geographic information image of this area to achieve a precise correspondence between flux data and underlying surface features. Three-dimensional wind speed spatial distribution data refers to the data set consisting of wind speed components (such as U, V, and W components, representing horizontal, vertical, and transverse wind speeds, respectively) at different spatial locations within the observation area. This data comprehensively reflects the spatial characteristics of the wind field in the observation area and describes its spatial distribution. The target trace gas is the gas for which flux observation is required, including but not limited to carbon dioxide, water vapor, or methane, and other atmospheric trace components whose flux needs to be monitored. Its concentration spatial distribution data refers to the data set consisting of the gas concentration values ​​at various spatial locations within the observation area, reflecting the gas's spatial distribution within the observation area. This step involves conducting observations in the designated observation area, simultaneously collecting three-dimensional wind speed spatial distribution data and target trace gas concentration spatial distribution data within this area. This ensures the synchronization of the two types of data in both observation time and space, laying the foundation for subsequent flux calculations.

[0023] S302: Based on the three-dimensional wind speed spatial distribution data and concentration spatial distribution data, determine the spatiotemporally matched wind speed-concentration data, and extract the vertical wind speed fluctuation data and the gas concentration fluctuation data of the target trace gas.

[0024] Spatiotemporal matching refers to aligning wind speed and concentration data acquired at different times and spatial locations to the same spatiotemporal reference, ensuring that the wind speed and concentration used for subsequent calculations originate from the same spatial volume element and the same point in time or a sufficiently close time period. Vertical wind speed fluctuation data refers to the fluctuation data of vertical wind speed deviating from its average state; it is the fluctuation of instantaneous vertical wind speed relative to its average value, reflecting turbulent motion in vertical wind speed. Gas concentration fluctuation data refers to the fluctuation data of target trace gas concentration deviating from its average state; it is the fluctuation of instantaneous gas concentration relative to its average value, representing the dynamic change characteristics of gas concentration under turbulent motion. The purpose of extracting these fluctuations is to separate the fluctuation components caused by turbulent motion. This step performs spatiotemporal alignment processing on the three-dimensional wind speed spatial distribution data and the target trace gas concentration spatial distribution data acquired synchronously in S301 to obtain spatiotemporally matched wind speed-concentration data; from this data, vertical wind speed fluctuation data and target trace gas concentration fluctuation data are extracted respectively.

[0025] S303: Based on vertical wind speed fluctuation data and gas concentration fluctuation data, determine the turbulent vertical transport rate jointly determined by vertical wind speed fluctuation and gas concentration fluctuation, and use it as the target trace gas vertical turbulent flow rate at each spatial location.

[0026] Among them, turbulent vertical transport, which is the amount of trace gas transported by the vertical motion of atmospheric turbulence, characterizes the net vertical transport of target trace gas caused by turbulent motion through a unit horizontal area per unit time. Its magnitude reflects the intensity of gas exchange between the Earth's surface and the atmosphere. Target trace gas vertical turbulent flux is the amount of target trace gas transported vertically by turbulence per unit time and unit area, and is an indicator for measuring the intensity of trace gas exchange between the ecosystem and the atmosphere. This step, based on the vertical wind speed fluctuation data and gas concentration fluctuation data extracted from S302, calculates the turbulent vertical transport, which is jointly determined by vertical wind speed fluctuation and gas concentration fluctuation, using the relevant principles of turbulent transport. This turbulent vertical transport is then used as the target trace gas vertical turbulent flux corresponding to each spatial location within the observation area.

[0027] S304: Generate a two-dimensional distribution map of the vertical turbulence of the target trace gas within the observation area based on the vertical turbulence of the target trace gas at each spatial location.

[0028] The two-dimensional distribution map is a graphic representation of the magnitude and distribution pattern of vertical turbulent fluxes of target trace gases at various locations within the observation area. This map visually illustrates the spatial variation of fluxes on a two-dimensional plane and identifies different source and sink regions. This step summarizes the vertical turbulent flux data of target trace gases at all spatial locations within the observation area and, through data visualization processing, generates a two-dimensional distribution map of vertical turbulent fluxes of target trace gases that intuitively reflects the spatial distribution of fluxes within the observation area.

[0029] In the technical solution provided in this application embodiment, the synchronously acquired three-dimensional wind speed data and trace gas concentration data are spatiotemporally aligned to avoid data misalignment problems that may be caused by differences in measurement methods. This ensures that the physical quantities used in subsequent calculations come from the same spatial location and the same time reference. By extracting pulsation quantities, the fluctuation components caused by turbulent motion are accurately separated, eliminating the interference of average flow on flux calculation. The turbulent vertical transport volume derived from this can truly reflect the intensity of gas exchange between the surface and the atmosphere. The resulting two-dimensional flux distribution map realizes direct surface-scale imaging of turbulent flux, enabling direct observation of the spatial pattern of flux on non-uniform underlying surfaces. It can intuitively display the spatial heterogeneity of flux and identify source-sink hotspots, solving the fundamental problem of missing spatial information in traditional single-point methods. It can be used not only for flat and uniform underlying surfaces but also for flux research in non-uniform scenarios such as sea-land interfaces, forest-grassland ecotones, and urban boundary layers.

[0030] In the above embodiments, there are no limitations on how to simultaneously acquire the three-dimensional wind speed spatial distribution data and the target trace gas concentration spatial distribution data within the observation area. This embodiment provides an implementation method for acquiring wind speed and concentration data from the same source, simultaneously, and coaxially, which may include the following: During the scanning of the observation area by the lidar, the echo signals corresponding to the laser pulses for wind measurement mode and the laser pulses for gas measurement mode, which are alternately emitted by the lidar at the pulse level or pulse train level time scale, are received. The three-dimensional wind speed spatial distribution data is obtained by solving the echo signal of the laser pulse for wind measurement mode, and the concentration spatial distribution data is obtained by solving the echo signal of the laser pulse for gas measurement mode.

[0031] Lidar, integrating wind and gas measurement functions, is an active remote sensing device that uses the echo signal of laser pulses to detect atmospheric physical quantities. It enables long-distance, high-resolution observation of physical quantities such as wind speed and gas concentration. The pulse-level / pulse-train-level timescale refers to the time interval between the laser pulses emitted by the lidar. The pulse-level timescale means that mode switching and transmission are completed within the duration and interval of a single laser pulse, while the pulse-train-level timescale means that mode switching occurs after a series of consecutive pulses has completed. This alternating transmission method ensures close temporal interleaving of wind field and concentration data, achieving mode switching at the millisecond level or even shorter. The laser pulse in wind measurement mode refers to the laser pulse used by the lidar to detect atmospheric wind speed; its wavelength and emission parameters are adapted to the coherent Doppler principle of wind speed detection. The laser pulse in gas measurement mode refers to the laser pulse used by the lidar to detect trace gas concentrations in a target; its wavelength is adapted to the differential absorption characteristics of the target gas. The echo signal is the signal reflected back to the lidar after the laser pulse emitted by the lidar interacts with particles and gas molecules in the atmosphere; it contains information on physical quantities such as atmospheric wind speed and gas concentration.

[0032] In this embodiment, during the scanning of the observation area by the lidar, the echo signals corresponding to the laser pulses used for wind measurement mode and the laser pulses used for gas measurement mode, which are emitted alternately at pulse-level or pulse-train-level time scales, are received. Based on the echo signals of the laser pulses used for wind measurement mode, radial wind speed can be calculated, for example, using the Doppler frequency shift principle. Then, the three-dimensional wind field can be inverted through scanning geometry to obtain three-dimensional wind speed spatial distribution data. Based on the echo signals of the laser pulses used for gas measurement mode, for example, the intensity difference between the laser pulse echo signals located on and outside the gas absorption line can be used, for example, using the differential absorption principle, to invert the distance-resolved gas concentration, thereby obtaining concentration spatial distribution data.

[0033] As can be seen from the above, the technical problem that this embodiment can solve is how to synchronously acquire the original data of wind field and concentration field that are close in time and space. By adopting the alternating transmission method of pulse level or pulse train level, the acquisition of the two types of data is closely intertwined in time, which provides a data foundation for subsequent high-precision spatiotemporal matching, thereby improving the accuracy of flux inversion.

[0034] Based on the above embodiments, this embodiment further defines the determination of spatiotemporally matched wind speed-concentration data based on the three-dimensional wind speed spatial distribution data and the concentration spatial distribution data, and the extraction of vertical wind speed fluctuation data and gas concentration fluctuation data of the target trace gas. This may include the following: Three-dimensional wind speed spatial distribution data and concentration spatial distribution data are matched in a unified spatiotemporal coordinate system to form a spatiotemporally matched wind speed-concentration data field; vertical wind speed fluctuations and gas concentration fluctuations of the target trace gas are extracted from each spatial location point in the wind speed-concentration data field.

[0035] In this context, a unified spatiotemporal coordinate system refers to a coordinate system that includes the same time and spatial dimensions. The time dimension may include a unified sampling time and time interval, while the spatial dimension may include a unified spatial coordinate origin, coordinate scale, and spatial grid division. A wind speed-concentration data field refers to a dataset composed of wind speed and concentration values ​​at various spatial locations and time points under a unified spatiotemporal coordinate system; it represents the integrated spatiotemporal presentation of wind speed and concentration data. A spatial location point refers to a discrete location point obtained after spatially dividing the observation area under a unified spatiotemporal coordinate system; it is the basic spatial unit for flux calculation. Vertical wind speed fluctuation is the specific numerical value of vertical wind speed fluctuation data, characterizing the degree to which the vertical wind speed at a single spatial location point deviates from the average value. Gas concentration fluctuation is the specific numerical value of gas concentration fluctuation data, characterizing the degree to which the target trace gas concentration at a single spatial location point deviates from the average value.

[0036] In this embodiment, based on the spatial range of the observation area and the sampling parameters of the lidar, a unified spatiotemporal coordinate system covering the entire observation area is established, and the time sampling interval and spatial grid division rules are determined. The three-dimensional wind speed spatial distribution data and concentration spatial distribution data are transformed and mapped according to the unified spatiotemporal coordinate system, ensuring that each spatial location point and each time point simultaneously has corresponding wind speed and concentration values, forming a spatiotemporally matched wind speed-concentration data field. For each spatial location point in the wind speed-concentration data field, the vertical wind speed fluctuation is extracted from the wind speed data at that location point, and the gas concentration fluctuation of the target trace gas is extracted from the concentration data at that point. The purpose of extracting the fluctuation is to separate the rapid fluctuation component caused by turbulence from the original time series, eliminating the influence of average flow and trend terms.

[0037] As can be seen from the above, this embodiment can solve the problem of temporal and spatial misalignment of wind speed and concentration data caused by alternating measurements, and how to extract the turbulent fluctuation component for flux calculation from the raw data. By establishing a unified spatiotemporal coordinate system and forming a data field, it is ensured that the physical quantities used in subsequent calculations originate from the same spatial volume element; by extracting the fluctuation quantity, the calculation object is focused on the exchange process caused by turbulence, thereby ensuring the physical meaning and accuracy of flux calculation.

[0038] Based on the above embodiments, during the spatiotemporal matching and alignment process, wind speed data and concentration data can be aligned to the same spatiotemporal grid. Each grid point corresponds to a specific spatial volume element and time, and simultaneously contains the three-dimensional wind speed information and concentration information of that point. An exemplary process for spatiotemporal matching and pulsation quantity extraction may include the following: Acquire the first acquisition time information and first spatial location information corresponding to each alternately measured three-dimensional wind speed spatial distribution data, and the second acquisition time information and second spatial location information corresponding to each alternately measured concentration spatial distribution data; based on the first acquisition time information, first spatial location information, second acquisition time information, and second spatial location information, map the three-dimensional wind speed spatial distribution data and concentration data at the same spatial location onto a preset unified spatiotemporal grid; for each grid point on the unified spatiotemporal grid, use one or more adjacent three-dimensional wind speed spatial distribution data and their corresponding acquisition time information and spatial location information to determine the wind speed value of the corresponding grid point through an interpolation algorithm, and use one or more adjacent concentration spatial distribution data and their corresponding acquisition time information and spatial location information to determine the concentration value of the corresponding grid point through an interpolation algorithm, forming a wind speed-concentration data cube; perform detrending processing on the wind speed time series data and concentration time series data at each grid point to separate the average component and the fluctuation component; obtain the vertical wind speed fluctuation amount and the concentration fluctuation amount of the target trace gas from the fluctuation component.

[0039] The acquisition time information records the moment each data point was acquired, while the spatial location information records the spatial orientation and distance of each data point. The first acquisition time / spatial location information refers to the sampling time (first acquisition time information) and spatial location (first spatial location information) of each data point when the lidar acquires three-dimensional wind speed spatial distribution data. The second acquisition time / spatial location information refers to the sampling time (second acquisition time information) and spatial location (second spatial location information) of each data point when the lidar acquires concentration spatial distribution data. A unified spatiotemporal grid refers to the grid structure obtained by regularly dividing the observation area into regular spatial and temporal sections under a unified spatiotemporal coordinate system. Each grid point is the basic storage and computation unit for spatiotemporal data. The interpolation algorithm is an algorithm that uses the numerical values ​​of discrete data points to estimate the values ​​of unknown points between adjacent discrete points through mathematical methods, enabling the continuous reconstruction of discrete data. The wind speed-concentration data cube refers to a three-dimensional dataset composed of wind speed and concentration values ​​of each grid point in the unified spatiotemporal grid, which can completely reflect the spatiotemporal continuous distribution characteristics of wind speed and concentration in a three-dimensional spatiotemporal dimension (two-dimensional space and one-dimensional time), and can fully reflect the spatiotemporal continuous distribution characteristics of wind speed and concentration. Wind speed / concentration time series data refers to a dataset where wind speed or concentration values ​​at different time points are arranged chronologically at a single grid point within a unified spatiotemporal grid. Detrending processing refers to the process of eliminating overall upward or downward trends in time series data using mathematical methods, such as high-pass filtering and moving averages, which can separate the average component and the fluctuation component of the data. The average component refers to the component in the time series data that reflects the overall average state and is the basic background value of the data. The fluctuation component refers to the part of the time series data that deviates from the average component and is a direct manifestation of atmospheric turbulence.

[0040] In this embodiment, during the alternating acquisition of three-dimensional wind speed and concentration data by the lidar, the spatial distribution data of each three-dimensional wind speed and the corresponding time and spatial location information of each spatial distribution data of concentration are simultaneously recorded. Based on the recorded spatiotemporal information, the spatial distribution data of three-dimensional wind speed and concentration at the same spatial location are mapped onto a preset unified spatiotemporal grid, realizing the initial spatiotemporal correspondence of discrete data. For each grid point on the unified spatiotemporal grid, using one or more adjacent three-dimensional wind speed spatial distribution data and their corresponding acquisition time and spatial location information, the wind speed value of the corresponding grid point is determined by interpolation algorithms such as inverse distance weighted interpolation and linear interpolation. Similarly, using one or more adjacent concentration spatial distribution data and their corresponding acquisition time and spatial location information, the concentration value of the corresponding grid point is determined by interpolation algorithms such as inverse distance weighted interpolation and linear interpolation. Thus, the values ​​on the regular grid points are estimated based on the discrete original data points. The wind speed and concentration values ​​of all grid points together form a wind speed-concentration data cube. Detrending processing was performed on the wind speed and concentration time series data at each grid point using methods such as high-pass filtering, moving average, or Reynolds decomposition. This removed low-frequency variations and trend terms from the time series while retaining high-frequency turbulent fluctuation components, thereby separating the mean component and the fluctuation component. Finally, the vertical wind speed fluctuation and the concentration fluctuation of the target trace gas at each grid point were extracted from the fluctuation component to obtain refined vertical wind speed fluctuation data and gas concentration fluctuation data.

[0041] As can be seen from the above, this embodiment can solve the problems of spatial and temporal discreteness of alternatingly sampled wind speed and concentration data, which cannot directly form a continuous data field, and the original data containing trend terms that interfere with the extraction of turbulent fluctuations. By introducing spatiotemporal labels and interpolation reconstruction technology, the wind field and concentration field are accurately aligned in time and space. Through detrending processing, it is ensured that the extracted fluctuation quantities truly reflect turbulent activity, realizing the continuous reconstruction of discrete data and the accurate separation of fluctuation data, providing high-quality data input for high-precision flux calculation.

[0042] Based on the above embodiments, this embodiment further addresses the technical problems that flux data at each grid point are discrete values, failing to intuitively reflect the continuous flux distribution in the observation area, and that flux data is disconnected from geographic information, making it difficult to combine with underlying surface feature analysis. It aims to achieve continuous visualization of flux data and fusion display of geographic information, and may include the following: Spatial interpolation is performed on the vertical turbulent flux of the target trace gas at each grid point in the wind speed-concentration data cube to obtain a continuous flux distribution field covering the observation area. The continuous flux distribution field is then visualized to generate a two-dimensional flux distribution image, which is then overlaid with the geographic information image of the observation area.

[0043] Spatial interpolation processing refers to using discrete grid point flux data to estimate flux values ​​at all locations within the observation area through interpolation algorithms. Since the flux values ​​at each grid point are discrete, spatial interpolation can estimate continuously changing flux values ​​between grid points, forming a complete flux field and realizing the transformation of discrete data into a continuous distribution field. A continuous flux distribution field refers to a continuous dataset of vertical turbulent flux distributions of target trace gases covering the entire observation area after spatial interpolation processing, reflecting the continuous spatial variation characteristics of fluxes within the observation area. Visualization refers to the processing method of converting numerical flux data into intuitive graphics and images, such as pseudo-color rendering and contour plotting, facilitating the intuitive identification of spatial distribution patterns of fluxes. A two-dimensional flux distribution image refers to a planar image obtained after visualizing the continuous flux distribution field, where the magnitude of flux values ​​can be represented by colors, contour lines, etc. Geographic information refers to information including the topography, satellite imagery, landforms, land use types, and administrative boundaries of the observation area. Image overlay, for example, can fuse two-dimensional flux distribution images with geographic information images, so that flux distribution data and geographic information are presented in the same image. This allows for intuitive analysis of the correspondence between flux distribution and underlying surface features, such as identifying the source and sink contributions of different surfaces like forests, farmland, or water bodies.

[0044] As can be seen from the above, the two-dimensional flux distribution image generated by spatial interpolation and visualization processing in this embodiment makes the flux values ​​intuitively distinguishable, and the observation results more intuitive and easy to understand. It facilitates the rapid identification of high flux value areas (source areas) and low flux value areas (sink areas) within the observation area. The overlay with geographic information images realizes the fusion display of flux data and underlying surface geographic features, which can directly associate flux distribution with geographic factors such as topography and land use type. This is beneficial for analyzing the causes of flux spatial distribution and the gas exchange between the ecosystem and the atmosphere.

[0045] Based on the above embodiments, this application also provides a standardized and quantifiable flux calculation method to achieve accurate calculation of vertical turbulent flux at various spatial locations, which may include the following: For each spatial location point in the wind speed-concentration data field, calculate the average value of the product of the vertical wind speed fluctuation and the target trace gas concentration fluctuation at each spatial location point during the sampling period; and use the product of the average value and the air density as the target trace gas vertical turbulence at the corresponding spatial location point.

[0046] The sampling period refers to the time interval during which the lidar continuously scans the observation area. It is the time range for calculating the average value of the fluctuations and must meet the sampling requirements for atmospheric turbulence statistics. The average value refers to the arithmetic mean of the product of the vertical wind speed fluctuation and the gas concentration fluctuation at a single spatial location point within the sampling period; it is a statistical measure characterizing the turbulence covariance. Air density refers to the mass density of the atmosphere within the observation area, and its value can be calculated based on meteorological parameters such as temperature, air pressure, and humidity. Using the wind speed-concentration data field as the basis for calculation, the following calculation steps are performed for each spatial location point in the data field: calculate the product of the vertical wind speed fluctuation and the target trace gas concentration fluctuation at that spatial location point; statistically analyze the above product value within the set sampling period to calculate its average value. This calculation process essentially involves statistically analyzing the degree of coordinated change between vertical wind speed fluctuations and concentration fluctuations: when upward movement is accompanied by high concentration and downward movement by low concentration, the product is positive and the average value is large, indicating net upward gas transport; conversely, when upward movement is accompanied by low concentration and downward movement by high concentration, the product is negative and the absolute value is large, indicating net downward gas transport. This average value is then multiplied by the air density corresponding to the observation area. Multiplying by the air density converts the concentration fluctuations in the sense of volume mixing ratio into mass flux, giving the result a definite physical unit (e.g., milligrams per square meter per second). The resulting product is used as the target trace gas vertical turbulent flux corresponding to that spatial location point, i.e., the turbulent vertical transport determined jointly by vertical wind speed fluctuations and gas concentration fluctuations.

[0047] As can be seen from the above, this embodiment directly obtains the net vertical transport caused by turbulence by statistically averaging the product of vertical wind speed fluctuations and concentration fluctuations over time. This calculation method is completely consistent with the physical nature of turbulent transport, ensuring the physical authenticity and quantitative accuracy of the flux results.

[0048] Finally, to make the implementation of the present invention clearer to those skilled in the art, the present invention also provides a method for observing carbon dioxide flux, such as... Figure 4 As shown, it may include the following: A1: Set the scanning parameters and control the integrated lidar to perform PPI scanning at a predetermined elevation angle.

[0049] Coherent wind measurement and differential absorption gas measurement functions are integrated into a fast-scanning lidar. At each scanning azimuth / range gate, the lidar alternately emits laser pulses at high repetition rate for wind measurement and gas concentration measurement, such as wavelength λ1_on and wavelength λ1_off.

[0050] A2: Record the echo signal of each laser pulse.

[0051] Through signal processing, radial wind speed is demodulated from the coherent channel, and CO2 concentration profile is calculated from the direct differential absorption channel. Using precise spatiotemporal labels, the alternately measured wind speed and concentration data are interpolated and reconstructed onto a unified spatiotemporal grid, forming synchronized, spatiotemporally matched three-dimensional wind speed field (U, V, W) and CO2 concentration field (C) data cubes. Here, U, V, and W components represent horizontal, vertical, and transverse wind speeds, respectively, and C represents the concentration field.

[0052] A3: Pulsating quantity separation.

[0053] For the time series data at each spatial grid point, taking the spatial grid point (i, j) in the i-th row and j-th column as an example, the average quantity (U) is separated out using high-pass filtering or moving average. i，j V i，j W i，j C i，j ) and pulsation quantity (u' i，j ,v' i，j w' i，j ,c' i，j U i，j V represents the horizontal east-west average wind speed at grid point (i, j). i，j W represents the horizontal north-south average wind speed at grid point (i, j). i，j Let C represent the vertical average wind speed at grid point (i, j). i，j Let u' represent the average CO2 concentration at grid point (i, j). i，j v' represents the horizontal east-west wind speed fluctuation at grid point (i, j). i，j w' represents the horizontal north-south wind speed fluctuation at grid point (i, j). i，j c' represents the vertical wind speed fluctuation at grid point (i, j). i，j This represents the CO2 concentration fluctuation at grid point (i, j).

[0054] A4: Calculation of spatial eddy covariance.

[0055] For each grid point on the scanning plane, the relation F is used. c_i，j =ρ a cov(w' i，j ,c' i，j Calculate the vertical wind speed fluctuation w' at grid point (i, j). i，j With CO2 concentration fluctuations c' i，j Covariance ρ during the sampling period a Let F be the air density, cov represent the covariance operation, and F be the air density. c_i，jLet represent the covariance result at grid point (i, j). By summing the covariances of all grid points, the covariance of the entire plane is obtained, and thus the vertical turbulent flux of CO2 in the space represented by that grid point can be directly obtained.

[0056] A5: Generation and output of two-dimensional flux graphs.

[0057] F of all grid points c_i，j Spatial interpolation and visualization are performed to generate a two-dimensional CO2 flux distribution image over the scanned area, and corresponding geographic information is superimposed.

[0058] As can be seen from the above, this embodiment achieves the best balance between turbulence statistics requirements and spatial coverage by designing PPI scanning parameters (such as elevation angle, rotation speed, and pulse sequence). The lidar achieves synchronous observation of wind field and concentration field through high-speed mode switching. Based on the synchronous spatial data field (rather than single-point time series), the covariance is calculated to directly obtain the two-dimensional flux distribution map. For alternating sampling data, the wind speed and concentration data cube with strict spatiotemporal matching is generated through interpolation algorithm to provide high-quality input for subsequent calculations.

[0059] It should be noted that there is no strict order of execution for the steps in this application. As long as they conform to a logical order, these steps can be executed simultaneously or in a certain preset order. Figures 3-4 This is just an illustrative example and does not mean that this is the only possible execution order.

[0060] This application also provides a corresponding apparatus for the trace gas flux observation method, further enhancing the practicality of the method. The apparatus can be described from both a functional module perspective and a hardware perspective. The trace gas flux observation apparatus provided in this application is described below. This apparatus is used to implement the trace gas flux observation method provided in this application. In this embodiment, the trace gas flux observation apparatus may include or be divided into one or more program modules. These one or more program modules are stored in a storage medium and executed by one or more processors to complete the trace gas flux observation method disclosed in Embodiment 1. The program module referred to in this embodiment refers to a series of computer program instruction segments capable of performing specific functions, which are more suitable than the program itself for describing the execution process of the trace gas flux observation apparatus in the storage medium. The following description will specifically introduce the functions of each program module in this embodiment. The trace gas flux observation apparatus described below can be referred to in correspondence with the trace gas flux observation method described above.

[0061] From the perspective of functional modules, see Figure 5 , Figure 5 This is a structural diagram of the trace gas flux observation device provided in this embodiment under one specific implementation. The device may include: The data synchronization module 501 is used to synchronously acquire three-dimensional wind speed spatial distribution data and target trace gas concentration spatial distribution data within the observation area. The data reconstruction module 502 is used to determine the spatiotemporally matched wind speed-concentration data based on the three-dimensional wind speed spatial distribution data and the concentration spatial distribution data. The pulse quantity separation module 503 is used to extract vertical wind speed pulse data and gas concentration pulse data of the target trace gas; The spatial eddy covariance calculation module 504 is used to determine the turbulent vertical transport amount jointly determined by the vertical wind speed fluctuation data and the gas concentration fluctuation data, which is used as the target trace gas vertical turbulent flow amount at each spatial location. The two-dimensional flux map generation module 505 is used to generate a two-dimensional distribution map of the vertical turbulent flux of the target trace gas within the observation area based on the vertical turbulent flux of the target trace gas at each spatial location.

[0062] For example, in some embodiments of this example, the data synchronization module 501 described above can also be used for: During the scanning of the observation area by the lidar, the echo signals corresponding to the laser pulses for wind measurement mode and the laser pulses for gas measurement mode, which are alternately emitted by the lidar at the pulse level or pulse train level time scale, are received. The three-dimensional wind speed spatial distribution data is obtained by solving the echo signal of the laser pulse for wind measurement mode, and the concentration spatial distribution data is obtained by solving the echo signal of the laser pulse for gas measurement mode.

[0063] For example, in some other embodiments of this embodiment, the data reconstruction module 502 can also be used to: match the three-dimensional wind speed spatial distribution data and the concentration spatial distribution data in a unified spatiotemporal coordinate system to form a spatiotemporally matched wind speed-concentration data field; correspondingly, the pulsation quantity separation module 503 can also be used to: extract the vertical wind speed pulsation quantity and the gas concentration pulsation quantity of the target trace gas from each spatial location point of the wind speed-concentration data field.

[0064] As an exemplary implementation of the above embodiments, the data reconstruction module 502 may further be used for: Acquire the first acquisition time information and the first spatial location information corresponding to each three-dimensional wind speed spatial distribution data measured alternately, as well as the second acquisition time information and the second spatial location information corresponding to each concentration spatial distribution data measured alternately; Based on the first acquisition time information, the first spatial location information, the second acquisition time information, and the second spatial location information, the three-dimensional wind speed spatial distribution data and concentration data at the same spatial location are mapped onto a preset unified spatiotemporal grid. For each grid point on a unified spatiotemporal grid, the wind speed value of the corresponding grid point is determined by interpolation algorithm using one or more neighboring three-dimensional wind speed spatial distribution data and their corresponding acquisition time and spatial location information. Similarly, the concentration value of the corresponding grid point is determined by interpolation algorithm using one or more neighboring concentration spatial distribution data and their corresponding acquisition time and spatial location information, thus forming a wind speed-concentration data cube.

[0065] As another exemplary implementation of the above embodiments, the pulsation quantity separation module 503 can be further used for: Detrending processing was performed on the wind speed time series data and concentration time series data at each grid point to separate the mean component and the fluctuation component. The vertical wind speed fluctuation and the concentration fluctuation of the target trace gas are obtained from the fluctuation component.

[0066] For example, in some other embodiments of this embodiment, the two-dimensional flux map generation module 505 can also be used to: perform spatial interpolation processing on the vertical turbulent flux of the target trace gas at each grid point in the wind speed-concentration data cube to obtain a continuous flux distribution field covering the observation area. The continuous flux distribution field is visualized to generate a two-dimensional flux distribution image, which is then overlaid with a geographic information image of the observation area.

[0067] For example, in some other embodiments of this example, the spatial eddy covariance calculation module 504 described above can also be used for: For each spatial location point in the wind speed-concentration data field, calculate the average value of the product of the vertical wind speed fluctuation and the target trace gas concentration fluctuation at each spatial location point during the sampling period; The product of the average value and the air density is used as the target trace gas vertical turbulent flux at the corresponding spatial location point.

[0068] The trace gas flux observation device mentioned above is described from the perspective of functional modules. Furthermore, this application also provides an electronic device, described from a hardware perspective. This electronic device includes a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any of the trace gas flux observation method embodiments described above.

[0069] It is understood that if the trace gas flux observation method in the above embodiments is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the related technology, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes, but is not limited to, various media capable of storing program code, such as: USB flash drive, mobile hard disk, read-only memory (ROM), random access memory (RAM), electrically erasable programmable ROM, register, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, removable disk, CD-ROM, magnetic disk, or optical disk. Based on this, this application also provides a non-volatile storage medium storing a computer program, which, when executed by a processor, performs the steps of the trace gas flux observation method as described in any of the above embodiments.

[0070] It is understood that if the trace gas flux observation method in the above embodiments is implemented as a software functional unit and sold or used as an independent product, the computer software product may not need to be stored in a physical storage medium. For example, it can be directly transmitted to a computer or other device with information processing capabilities via a wired or wireless network to execute all or part of the steps of the methods in the various embodiments of this application. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the related technology, or all or part of the technical solution, can be embodied in the form of a software product. Based on this, this application also provides a computer program product storing a computer program, which, when executed by a processor, performs the steps of the trace gas flux observation method as described in any of the above embodiments.

[0071] Finally, this application also provides a trace gas flux observation system, see [link to relevant documentation]. Figure 6The system may include a lidar 5, a data acquisition controller 6, and a data processor 7. The data acquisition controller 6 is connected to both the lidar 5 and the data processor 7, and is configured to control the switching timing of the lidar's scanning mode and laser transceiver mode (laser transmission and reception mode). It also transmits the synchronously acquired three-dimensional wind speed spatial distribution data and target trace gas concentration spatial distribution data within the observation area to the data processor 7, ensuring the synchronization and timing accuracy of the data acquisition. The data processor 7, configured to execute a computer program to implement the steps of the trace gas flux observation method described in any of the above embodiments, can be a high-performance server or workstation. It processes, inverts, and visualizes the data, ultimately outputting a two-dimensional flux distribution map.

[0072] Based on the above embodiments, the functions of the data acquisition controller and the scanning method of the lidar are further defined. The data acquisition controller 6 sends a scanning control command carrying scanning parameters and scanning mode type to the lidar 5. The lidar scans the observation area according to the scanning parameters and target scanning mode, and at each scanning azimuth / range gate, it alternately emits laser pulses for wind measurement and gas concentration measurement at a high repetition rate.

[0073] In this embodiment, scanning parameters refer to the technical parameters of the lidar for scanning observation, such as elevation angle (the vertical angle at which the lidar emits laser light), rotation speed (the speed at which the lidar rotates horizontally), and pulse sequence (the order, interval, and number of laser pulses emitted). Scanning mode types include PPI scanning, RHI (distance-height display) scanning, or custom sector scanning. Planar position display scanning refers to a scanning method where the lidar performs horizontal rotation scanning at a fixed elevation angle to obtain observation data on a conical surface within the observation area. This method is used to obtain conical surface data on a horizontal plane and is suitable for generating horizontal two-dimensional flux maps. Distance-height display scanning refers to a scanning method where the lidar adjusts its elevation angle at a fixed azimuth angle to obtain observation data on a vertical profile within the observation area. This method is used to obtain data on a vertical profile and is suitable for studying the vertical structure of flux. Custom sector scanning allows for flexible selection of the observation area and setting of the lidar's scanning azimuth and elevation angle ranges, scanning only a specified sector area according to actual needs. The scan control command is a control signal sent by the data acquisition controller to the lidar, containing scan parameters and scan mode type. The lidar can adjust its working state according to this command. The target scan mode is the scan mode specified by the data acquisition controller. The scan azimuth angle refers to the angle of horizontal rotation of the lidar, representing the horizontal emission direction of the laser pulse. The range gate refers to the range interval divided by the lidar based on the propagation time of the echo signal; each range gate corresponds to a specific range within the observation area. High repetition rate refers to the high repetition frequency of the laser pulses emitted by the lidar, that is, a large number of laser pulses emitted per unit time, enabling high time resolution observation.

[0074] As can be seen from the above, the scanning active remote sensing in this embodiment can flexibly adjust the scanning strategy according to the needs, monitor flux dynamics on a spatial scale of hundreds of meters and a time scale of minutes, and has high spatiotemporal resolution, which is unmatched by flux tower networks and scintillation meters.

[0075] Based on the above embodiments, the working mode of the lidar can be further defined. The lidar is an integrated device with the function of switching between wind measurement mode and gas measurement mode. The lidar is configured to switch between wind measurement mode and gas measurement mode on a pulse-level or pulse train-level time scale. At each scanning azimuth angle or range gate position, whenever a first wavelength laser pulse for wind measurement mode is emitted, a second wavelength laser pulse and a third wavelength laser pulse for gas measurement mode are emitted successively.

[0076] In this embodiment, coherent wind measurement and differential absorption gas measurement functions are integrated into a single high-speed scanning lidar. High-speed mode switching enables simultaneous observation of the wind field and concentration field, ensuring the homogeneity of the two data sources. Alternatively, a high-speed scanning coherent wind measurement lidar and one (or more) differential absorption lidars with rapid pointing capabilities can form a tightly coordinated network. When using independent devices for collaborative observation, this multi-radar network collaborative scheme increases the difficulty of matching wind speed and concentration data due to differences in instrument principles, sampling volume, time reference, and spatial coordinate systems. To avoid introducing significant collaborative errors that severely affect the accuracy of flux inversion, advanced data assimilation algorithms and precise space-time calibration can be used to approximate the observation target of a single device, but this makes the system more complex.

[0077] During the scanning of the observation area by the lidar, the integrated lidar is controlled on a pulse-level or pulse train-level time scale. After sending the first wavelength laser pulse for wind measurement mode, which is adapted to the coherent Doppler wind measurement principle and has high coherence, it can accurately detect atmospheric wind speed. Then, a second and third wavelength laser pulse are sent for gas measurement mode. The second wavelength laser pulse is located on the absorption line of the target trace gas, and this pulse is largely absorbed by the target gas, used to detect the concentration characteristics of the target gas. The third wavelength laser pulse is located outside the absorption line of the target trace gas, and this pulse is hardly absorbed by the target gas, serving as a reference pulse to eliminate interference from atmospheric extinction, instrument errors, etc. In practical applications, two wavelengths can be set, that is, the first and third wavelength laser pulses are laser pulses of the same wavelength. The second and third wavelength laser pulses absorb gases with different absorption rates. Since the first and third wavelength laser pulses are outside the absorption line, the loss is small, allowing for direct wind measurement. In other words, after transmitting the first wavelength laser pulse for wind measurement mode, the integrated control lidar immediately transmits a second wavelength laser pulse, while simultaneously re-emitting the first wavelength laser pulse. The first and second wavelength laser pulses are then used for gas measurement. The absorption line refers to the wavelength range in which trace gas molecules of the target exhibit strong absorption of laser light of a specific wavelength; this is the basis of the differential absorption gas measurement principle. Outside the absorption line refers to the wavelength range far from the absorption line of the target trace gas, within which laser light is almost not absorbed by the target gas. After transmitting the second and third wavelength laser pulses, the next first wavelength laser pulse for wind measurement mode is transmitted, and so on, with high repetition rate alternating between the first, second, and third wavelength laser pulses.

[0078] As can be seen from the above, this embodiment achieves direct surface-scale imaging of turbulent fluxes, intuitively demonstrating the spatial heterogeneity of fluxes and identifying source-sink hotspots. Furthermore, due to the integrated design of the lidar, wind speed and concentration data possess perfect spatiotemporal synchronization and coaxiality, greatly reducing coordination errors and resulting in high observation accuracy. This makes the flux values ​​calculated based on spatial covariance more realistic and reliable. Although a single device is complex, a single system can achieve the observation function equivalent to a regional flux tower network and reveal more spatial processes, demonstrating high cost-effectiveness from the perspective of scientific information acquisition efficiency.

[0079] The foregoing has provided a detailed description of a trace gas flux observation method and system provided in this application. The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. Whether the units and algorithm steps of the various examples described in the disclosed embodiments are implemented in electronic hardware or computer software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, and such implementations should not be considered beyond the scope of this application. Several improvements and modifications can be made to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for observing trace gas fluxes, characterized in that, include: Simultaneously acquire three-dimensional wind speed spatial distribution data and target trace gas concentration spatial distribution data within the observation area; Based on the three-dimensional wind speed spatial distribution data and the concentration spatial distribution data, the spatiotemporally matched wind speed-concentration data are determined, and the vertical wind speed fluctuation data and the gas concentration fluctuation data of the target trace gas are extracted. Based on the vertical wind speed fluctuation data and the gas concentration fluctuation data, the turbulent vertical transport rate determined by the vertical wind speed fluctuation and the gas concentration fluctuation is determined as the target trace gas vertical turbulent flow rate at each spatial location. Based on the vertical turbulent flux of the target trace gas at each spatial location, a two-dimensional distribution map of the vertical turbulent flux of the target trace gas within the observation area is generated.

2. The trace gas flux observation method according to claim 1, characterized in that, Simultaneously acquire three-dimensional wind speed spatial distribution data and target trace gas concentration spatial distribution data within the observation area, including: During the scanning of the observation area by the lidar, the echo signals corresponding to the laser pulses for wind measurement mode and the laser pulses for gas measurement mode, which are alternately emitted by the lidar at the pulse level or pulse train level time scale, are received. The three-dimensional wind speed spatial distribution data is obtained by solving the echo signal of the laser pulse for wind measurement mode, and the concentration spatial distribution data is obtained by solving the echo signal of the laser pulse for gas measurement mode.

3. The trace gas flux observation method according to claim 1, characterized in that, Based on the three-dimensional wind speed spatial distribution data and the concentration spatial distribution data, spatiotemporally matched wind speed-concentration data are determined, and vertical wind speed fluctuation data and gas concentration fluctuation data of the target trace gas are extracted, including: The three-dimensional wind speed spatial distribution data and concentration spatial distribution data are matched in a unified spatiotemporal coordinate system to form a spatiotemporally matched wind speed-concentration data field. The vertical wind speed fluctuation and the gas concentration fluctuation of the target trace gas are extracted from each spatial location point in the wind speed-concentration data field.

4. The trace gas flux observation method according to claim 3, characterized in that, Based on the three-dimensional wind speed spatial distribution data and the concentration spatial distribution data, spatiotemporally matched wind speed-concentration data are determined, and vertical wind speed fluctuation data and gas concentration fluctuation data of the target trace gas are extracted, including: Acquire the first acquisition time information and the first spatial location information corresponding to each three-dimensional wind speed spatial distribution data measured alternately, as well as the second acquisition time information and the second spatial location information corresponding to each concentration spatial distribution data measured alternately; Based on the first acquisition time information, the first spatial location information, the second acquisition time information, and the second spatial location information, the three-dimensional wind speed spatial distribution data and concentration data at the same spatial location are mapped onto a preset unified spatiotemporal grid; For each grid point on the unified spatiotemporal grid, the wind speed value of the corresponding grid point is determined by interpolation algorithm using one or more adjacent three-dimensional wind speed spatial distribution data and their corresponding acquisition time information and spatial location information. The concentration value of the corresponding grid point is determined by interpolation algorithm using one or more adjacent concentration spatial distribution data and their corresponding acquisition time information and spatial location information, thus forming a wind speed-concentration data cube. Detrending processing was performed on the wind speed time series data and concentration time series data at each grid point to separate the mean component and the fluctuation component. The vertical wind speed fluctuation and the concentration fluctuation of the target trace gas are obtained from the fluctuation component.

5. The trace gas flux observation method according to claim 4, characterized in that, Generate a two-dimensional distribution map of the vertical turbulent flux of the target trace gas within the observation area, including: Spatial interpolation is performed on the vertical turbulent flux of the target trace gas at each grid point in the wind speed-concentration data cube to obtain a continuous flux distribution field covering the observation area. The continuous flux distribution field is visualized to generate a two-dimensional flux distribution image, and the two-dimensional flux distribution image is overlaid with the geographic information image of the observation area.

6. The method for observing trace gas flux according to any one of claims 1 to 5, characterized in that, The wind speed-concentration data is a wind speed-concentration data field. Based on the vertical wind speed fluctuation data and the gas concentration fluctuation data, the turbulent vertical transport rate, which is jointly determined by the vertical wind speed fluctuation and the gas concentration fluctuation, is determined, including: For each spatial location point in the wind speed-concentration data field, calculate the average value of the product of the vertical wind speed fluctuation and the target trace gas concentration fluctuation at each spatial location point during the sampling period; The product of the average value and the air density is used as the target trace gas vertical turbulent flux at the corresponding spatial location point.

7. A trace gas flux observation system, characterized in that, Includes lidar, data acquisition controller, and data processor; The data acquisition controller is connected to the lidar and the data processor respectively, and is configured to control the switching sequence of the lidar's scanning mode and laser transceiver mode, and send the three-dimensional wind speed spatial distribution data and target trace gas concentration spatial distribution data of the synchronously acquired observation area to the data processor. The data processor is configured to implement the steps of the trace gas flux observation method as described in any one of claims 1 to 6 when executing a computer program.

8. The trace gas flux observation system according to claim 7, characterized in that, The data acquisition controller sends a scanning control command carrying scanning parameters and scanning mode type to the lidar. The lidar scans the observation area according to the scanning parameters and target scanning mode, and emits laser pulses at high repetition rate at each scanning azimuth angle or range gate for wind measurement and gas concentration measurement.

9. The trace gas flux observation system according to claim 7, characterized in that, The lidar is an integrated device with the function of switching between wind measurement mode and air measurement mode; The lidar is configured to switch between wind measurement mode and gas measurement mode on a pulse-level or pulse train-level time scale. At each scanning azimuth angle or range gate position, whenever a first wavelength laser pulse for wind measurement mode is emitted, a second wavelength laser pulse and a third wavelength laser pulse for gas measurement mode are emitted in succession. The second wavelength laser pulse is located on the absorption line of the target trace gas, while the third wavelength laser pulse is located outside the absorption line of the target trace gas.

10. The trace gas flux observation system according to claim 9, characterized in that, The third wavelength laser pulse uses the same wavelength value as the first wavelength laser pulse.

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