Global land-sea ecosystem productivity estimation method based on SIF and light response

Abstract

The invention provides a global land-sea ecosystem productivity estimation method based on SIF and light response, and the method comprises the steps: 1, carrying out the data fusion of satellite remote sensing image data and meteorological data, and carrying out the consistency verification; 2, on each pixel point, inputting the input remote sensing data into a mechanical light response (MLR) theoretical formula to carry out total primary productivity GPP estimation, and calculating carbon dioxide partial pressure in a chloroplast matrix, a carbon dioxide compensation point of chloroplast, an open proportion of a photosynthetic system reaction center, an energy ratio of plant heat dissipation to plant fluorescence emission, and an escape rate; step 3, acquiring the distribution of C3 plants and C4 plants by using the vegetation type distribution data; and respectively calculating the total primary productivity of the C3 plant and the C4 plant. According to the scheme, the required number of vegetation parameters is simplified in the inversion process, the parameter acquisition difficulty is greatly reduced while the inversion precision is ensured, and meanwhile, the result uncertainty caused by the parameters is greatly reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of productivity quantitative evaluation, and in particular relates to a method for estimating the productivity of global land and sea ecosystems based on SIF and light response. Background Art

[0002] In recent years, chlorophyll fluorescence has been proven to be useful for estimating total primary productivity. Based on this discovery, chlorophyll fluorescence inversion technology has been widely used in agriculture, forestry, and environmental monitoring, especially in the assessment of vegetation growth conditions. This technology is based on the recently proposed mechanistic light response theory and combines remote sensing data to construct a method for estimating total primary productivity of vegetation that is universal for marine and terrestrial ecosystems around the world.

[0003] Gross Primary Productivity (GPP) is an important indicator of vegetation photosynthesis rate. Real-time monitoring and accurate estimation of it are of great significance for clarifying the impact of climate change and human activities on ecosystems. Model methods are the main tools for calculating large-scale and global GPP. However, due to differences in model structure, parameters and input data, the total amount and spatiotemporal distribution of global GPP calculated by different models vary greatly. This has caused great uncertainty in large-scale GPP estimation and further analysis of ecosystems.

[0004] Sun-induced Chl Fluorescence (SIF) is a special fluorescence emitted by excited chlorophyll molecules. As an energy expenditure, it shares the energy provided by light and effective radiation with photochemical reactions and heat dissipation. More importantly, it is tightly coupled with photosynthesis in basic biochemical and biophysical processes, which indicates that SIF can be used as a way to accurately estimate gross primary productivity. In addition, the physiological basis of SIF-GPP coupling ensures that SIF can track changes in photosynthetic rate under different environmental conditions. Therefore, the possibility of using chlorophyll fluorescence to simply and accurately calculate GPP is currently receiving increasing attention.

[0005] Studies based on ground-based, airborne, and satellite data have shown that SIF has a good correlation with GPP at the leaf scale, canopy scale, and ecosystem scale. Existing studies often use the SIF-GPP model based on linear relationships when calculating global and regional GPP using SIF satellite remote sensing data. However, existing studies have shown that there are differences in the SIF-GPP relationship of different plants. When using SIF to estimate GPP on a global scale, the SIF-GPP estimation method represented by the linear model will have errors due to the extremely complex global vegetation distribution, and it is difficult to fully utilize the advantages of SIF in the mechanism perspective when estimating GPP.

[0006] From the perspective of plant photosynthetic system structure and photosynthesis mechanism, most common plants can be divided into C3 plants and C4 plants. Recently, researchers proposed the Mechanistic Light Response (MLR) model, which explicitly considers the key mechanism connecting chlorophyll fluorescence emission with photosynthesis in C3 and C4 plants. While inheriting the advantages of enzyme kinetic models in terms of high theoreticality, high interpretability, and high versatility, this theory simplifies some of the difficult-to-obtain vegetation parameters required for enzyme kinetic models to estimate GPP by revealing the relationship between SIF and electron transfer rate, providing a new way to estimate GPP. This theory has received widespread attention since its proposal, and recent studies have shown that its estimated results are in good agreement with observed values. The MLR model has gradually demonstrated its potential in global GPP estimation, but there is no global GPP estimation model based on MLR theory and remote sensing data, and there is still a large gap in the global application of MLR theory. Summary of the invention

[0007] Purpose of the invention: The technical problem to be solved by the present invention is to provide a method for estimating the productivity of global land and sea ecosystems based on SIF (sun-induced Chl fluorescence, SIF) and light response in view of the deficiencies of the prior art, comprising the following steps: Step 1, preprocess the input data; Step 2, calculate key parameters; Step 3, calculate the total primary productivity.

[0008] Step 1 includes: inputting remote sensing data, including the following categories: temperature data, relative humidity data, fluorescence data, atmospheric carbon dioxide concentration data, vegetation type distribution data, and vegetation physiological data; Perform data fusion on the input remote sensing data (generally Kriging interpolation can be used). For areas with missing data after fusion, Kriging interpolation is used to complete them. The formula is: , Among them, x0 is the current position that needs to be interpolated; is the interpolation result at x0; n is the number of known pixel points used for interpolation, and a pixel point is a pixel point in a remote sensing image; λ i is the kriging weight used for weighting; Z(x i ) is the known i-th data point x i Observation values; Check the input data to see whether the spatial range, spatial resolution, temporal resolution, and data type of each type of input data are consistent. If yes, it means that the input data preprocessing has been completed; Otherwise, if the temporal resolution and spatial resolution are inconsistent, the spatial resolution and temporal resolution are downsampled according to the lowest value of all input data (the high-resolution data is divided into multiple blocks, each block corresponds to a pixel in the low-resolution grid. For each low-resolution pixel, the average value of all high-resolution pixels covered by it is calculated) to achieve uniform temporal and spatial resolution; If the data types are inconsistent, the input remote sensing data will be unified according to the type with the widest representation range (such as selecting decimal type from integer type and decimal type) to ensure compatibility.

[0009] In the case of inconsistent spatial ranges, the input data with the smallest spatial range is used as the simulation range, and the remaining data is clipped. For global simulations, the spatial range is usually fixed and this issue does not need to be considered.

[0010] Step 2 includes: at each pixel point, the input remote sensing data is brought into the mechanical light response (MLR) theoretical formula to estimate the gross primary productivity (GPP), and various parameters are calculated, including the partial pressure of carbon dioxide (CO2) in the chloroplast matrix, C c 、CO2 compensation point of chloroplasts , the open ratio of the photosynthetic system reaction center (The photosynthetic system of plants is divided into two parts, which are academically called photosynthetic system I and photosynthetic system II. The main function of photosynthetic system II is to use light energy to decompose water molecules into oxygen, protons and electrons. This process is called photolysis. Photosynthetic system I accepts electrons transferred from photosynthetic system II and uses light energy to increase the energy of these electrons to a higher level; the photosynthetic system reaction center here refers to the reaction center of photosynthetic system II), the energy ratio of plant heat dissipation and plant fluorescence emission k DF , and the ratio of the observed solar-induced chlorophyll fluorescence to the total emitted fluorescence, i.e., the escape velocity ε.

[0011] In step 2, the CO2 partial pressure in the chloroplast matrix is ​​set to C c The partial pressure of CO2 between cells i Same, C i The calculation formula is: , Where VPD is the saturated water vapor pressure difference, Ψ is the marginal water cost of plant carbon gain; is the partial pressure of carbon dioxide in the atmosphere.

[0012] In step 2, C can also be calculated by the following formula i : .

[0013] In step 2, the global atmospheric carbon dioxide concentration data is used to calculate : , in, is the carbon dioxide concentration, taken from atmospheric carbon dioxide concentration data; The CO2 compensation point of chloroplasts is calculated using the following formula: : , in, is the air temperature, obtained from remote sensing temperature data; Calculate the saturated water vapor pressure difference VPD by relative humidity and air temperature: , , in, is the saturated water vapor pressure, RH is the relative humidity, taken from remote sensing relative humidity data; exp is the natural exponential function; The open ratio is calculated by the following formula : , , Among them, SIF is the solar induced chlorophyll fluorescence data, which is obtained from remote sensing fluorescence data; m is a dimensionless parameter to be fitted, In μmol -1 m² s is a constant value of 1; T opt is the optimal growth temperature of plants; m opt Represents the optimal growth temperature of the plant opt The value of m when H d The optimal growth temperature of the plant is T opt The rate of decline of the above peak function; The optimal growth temperature of the plant is T opt The rate of rise of the following peak function; R is the universal gas constant.

[0014] In step 2, for remote sensing satellite products, the escape velocity ε is calculated using the following formula: , Among them, NIRv is the near-infrared reflectivity of vegetation; It is the photosynthetically active radiation component, and the data are taken from vegetation physiological data.

[0015] Step 3 includes: obtaining the distribution of C3 plants and C4 plants using vegetation type distribution data; For C3 plants, the GPP calculation formula is: , in, is the maximum photosynthetic rate of the photosynthetic system (referring to photosynthetic system II); k DF is the energy ratio of plant heat dissipation and plant fluorescence emission; For C4 plants, the calculation formula for GPP is: , Here, x is the proportion of the total electron transport in the plant that is allocated to the mesophyll tissue.

[0016] The present invention also provides an electronic device, comprising a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes the steps of the described method.

[0017] The present invention also provides a storage medium storing a computer program or instruction. When the computer program or instruction is run on a computer, the steps of the method described are executed.

[0018] This method can be applied in the following areas:

[0019] Global Ecosystem Monitoring: In the field of ecological protection, this technology is used to monitor the health status and growth dynamics of global ecosystems, provide a scientific basis for environmental management and protection, and support sustainable resource utilization.

[0020] Environmental monitoring and protection: Chlorophyll fluorescence inversion technology can be used to detect the response of vegetation to environmental changes, such as climate change, pollutant impacts, etc., thereby providing important data support for environmental protection and ecological restoration.

[0021] Global carbon cycle research: By estimating the total primary productivity of vegetation, this technology provides a new method for studying the changes and distribution of global carbon stocks, supporting the realization of carbon peak and carbon neutrality goals.

[0022] Ecosystem function research: In ecological research, understanding vegetation productivity and photosynthetic efficiency is crucial to studying ecosystem functions and dynamic changes. Chlorophyll fluorescence inversion technology provides an efficient means to obtain this information.

[0023] Beneficial effects: Compared with the SIF-GPP model that is commonly used in current inversion and is mainly based on linear relationships, the new scheme proposed in the present invention can more accurately achieve the inversion of global gross primary productivity. The versatility of this scheme has been significantly improved compared with previous schemes, and it can be directly applied to various types of vegetation. Unlike traditional enzyme kinetic models that require a large number of difficult-to-obtain vegetation physiological parameters during the inversion process, this scheme greatly simplifies the required number of vegetation parameters during the inversion process, while ensuring the inversion accuracy, greatly reducing the difficulty of parameter acquisition, and also greatly reducing the uncertainty of the results caused by the parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of the method of the present invention.

[0025] Figure 2 This is a schematic diagram of the land surface GPP results simulated by the BEPS model.

[0026] Figure 3 This is a schematic diagram of the land surface GPP results simulated by the NUIST-SIFRM method.

[0027] Figure 4 It is a schematic diagram of the distribution of Pearson correlation coefficients between the GPP estimated by NUIST-SIFRM and the GPP estimated by the model in each continent and ocean.

[0028] Figure 5 This is a schematic diagram of the verification results of the GPP estimated by NUIST-SIFRM using FLUXNET-GPP. DETAILED DESCRIPTION

[0029] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more clear.

[0030] The embodiment of the present invention provides a method for estimating the productivity of global land and sea ecosystems based on SIF and light response, comprising the following steps: Step 1, preprocess the input data; The input remote sensing data required for the model method constructed by this method include temperature data, relative humidity data, fluorescence data, atmospheric carbon dioxide concentration data, vegetation type distribution data, vegetation physiological data (including vegetation near-infrared reflectance NIRv and light and effective radiation components fPAR). Generally speaking, sea and land data are often published separately. Therefore, the present invention first performs data fusion on the sea and land input data, and uses Kriging interpolation to complete the missing areas after fusion. Kriging interpolation is a mature and commonly used spatial interpolation method, and the formula is: , Among them, x0 is the current position that needs to be interpolated; is the interpolation result at x0; n is the number of known data points used for interpolation; λ i is the kriging weight used for weighting; Z(x i ) is a known data point x i observation values; to ensure that the model can be executed correctly, the input data is checked to see whether the spatial range, spatial resolution, temporal resolution, and data type of each type of input data are consistent. After ensuring the consistency of the data, the input data preprocessing is considered to have been completed.

[0031] The verification test data used in the present invention include: (1) Global land surface temperature data from 2003 to 2023. The ERA5 reanalysis land surface temperature data provided by the European Centre for Medium-Range Weather Forecasts will be used. (2) Global land surface relative humidity data from 2003 to 2023. It is planned to use the ERA5 reanalysis relative humidity data provided by the European Centre for Medium-Range Weather Forecasts. (3) Global sea surface temperature data from 2003 to 2023. The global sea surface temperature dataset provided by NOAA is proposed to be used; (4) Global land surface fluorescence data from 2003 to 2023, using the GOSIF product produced by the Global Ecology Group of the University of New Hampshire based on the OCO-2 satellite; (5) Global ocean surface fluorescence data from 2003 to 2023, using the ocean fluorescence line height data released by Aqua-MODIS; (6) Global atmospheric CO2 concentration data from 2003 to 2023. It is planned to use the global atmospheric CO2 concentration data provided by NOAA since 1982; (7) Land use type data, which is planned to use the MCD12Q1 IGBP product generated by the MODIS satellite sensor; (8) Plant physiological data: photosynthetically active radiation products and vegetation index products provided by the MODIS satellite are proposed to be used.

[0032] Step 2, calculate key parameters; Under the premise of ensuring data consistency, the present invention calculates various parameters based on the input remote sensing data at each pixel point and brings them into the MLR theoretical formula to estimate GPP. In the MLR theory, the estimation of GPP requires the use of a series of parameters, among which the parameters that need to be calculated using the input data include the CO2 partial pressure C in the chloroplast matrix. c (in μbar), CO2 compensation point of chloroplasts (Unit: μbar); Opening ratio of the reaction center of the photosynthetic system (referring to photosynthetic system II) ; The energy ratio k of plant heat dissipation and plant fluorescence emission DF ; The ratio of the observed solar-induced chlorophyll fluorescence to the total emitted fluorescence is the escape rate ε. The calculation method of each parameter is as follows: In actual calculation, C c can be assumed to be related to the intercellular CO2 partial pressure (C i ) are consistent, while C i It can be calculated by the following formula: , Where VPD is the saturated water vapor pressure difference (in hPa), is the partial pressure of carbon dioxide in the atmosphere (in μbar), which can be directly calculated from the global atmospheric CO2 concentration data: , in, is the CO2 concentration (in ppm). Ψ is the marginal water cost of plant carbon gain (in mol H2Omol -1 C), usually set to 900. can be described as a function of air temperature: , in, is the air temperature (in °C). VPD can be calculated from relative humidity and air temperature: , , in, is the saturated water vapor pressure (in hPa), and RH is the relative humidity (in%). For simulation results with a coarse spatial resolution, C i You can also use the following method to make a quick estimate: , The following semi-empirical formula can be used for estimation: , , Among them, m is a dimensionless parameter to be fitted, In μmol -1 m² s is a constant value of 1; T air is the temperature (in K); T opt is the optimal growth temperature of the plant (in K); m opt Representative in T opt The value of m when H d It is in T opt The rate of decline of the above peak function; It is in T opt The rate of rise of the following peak function; R is the universal gas constant.

[0033] For remote sensing satellite products, ε can be estimated by the following formula: , Among them, NIRv is the near-infrared reflectivity of vegetation; is the photosynthetically active radiation component. So far, all intermediate parameters have been calculated. The calculation relationship between input data and intermediate parameters is as follows: Figure 1 shown.

[0034] Step 3, calculate the total primary productivity; After the necessary parameters are calculated, for C3 plants, the GPP calculation formula is: , in, It is usually set to 0.83; k DF It is the energy ratio of plant heat dissipation and plant fluorescence emission, which is usually set to 19. For C4 plants, the calculation formula of GPP is: , Here, x is the proportion of the total electron transport in the plant allocated to the mesophyll tissue, which is usually set to 0.4.

[0035] Model result verification: In order to verify the simulation effect of the present invention, the site observation data set FLUXNET-GPP, the land surface model simulation data set BEPS-GPP and the global ocean GPP product shared by Oregon State University were selected as the verification data of the model. The land surface GPP results simulated by the BEPS model and the land surface GPP results simulated by the NUIST-SIFRM method are shown in Figure 2. Figure 2 and Figure 3 Shown (unit is (10 15 ) grams of carbon), the total primary productivity values ​​and trend simulation results of the two for each continent are basically consistent. Figure 4 The Pearson correlation coefficient distribution between the GPP estimated by NUIST-SIFRM and the GPP estimated by the model in each continent and ocean is shown. In various regions, especially in the land area of ​​the Northern Hemisphere, NUIST-SIFRM and existing GPP products show significant consistency. The verification results of the GPP estimated by NUIST-SIFRM using FLUXNET-GPP are shown in Figure 2. Figure 5 As shown, the vertical axis is the result of FLUXNET-GPP, and the horizontal axis is the GPP result estimated by NUIST-SIFRM. The units of both are grams of carbon per square meter per day. The global overall correlation coefficient of the two units is r=0.7, and the probability is set It is less than 0.001, which proves that the simulation results are highly consistent with the field observation results.

[0036] The present invention provides a method for estimating the productivity of global land and sea ecosystems based on SIF and light response. There are many methods and ways to implement the technical solution. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented by existing technologies.

Claims

1. A method for estimating global terrestrial and marine ecosystem productivity based on SIF and light response, characterized in that: The following steps are involved: Step 1, preprocess the input data; Step 2, calculate key parameters; Step 3, calculate the total primary productivity; Step 1 includes: inputting remote sensing data, including the following categories: temperature data, relative humidity data, fluorescence data, atmospheric carbon dioxide concentration data, vegetation type distribution data, and vegetation physiological data; Data fusion is performed on the input remote sensing data. For the missing areas after fusion, Kriging interpolation is used to fill them. The formula is: ， Among them, x0 is the current position that needs to be interpolated; is the interpolation result at x0; n is the number of known pixel points used for interpolation, and a pixel point is a pixel point in a remote sensing image; λ i is the kriging weight used for weighting; Z(x i ) is the known i-th data point x i Observation values; Check the input data to see whether the spatial range, spatial resolution, temporal resolution, and data type of each type of input data are consistent. If yes, it means that the input data preprocessing has been completed; Otherwise, if the temporal resolution and spatial resolution are inconsistent, the spatial resolution and temporal resolution are downsampled according to the lowest value of all input data to achieve uniform temporal and spatial resolutions; Step 2 includes: at each pixel point, the input remote sensing data is brought into the mechanical light response MLR theoretical formula to estimate the total primary productivity GPP, and various parameters are calculated, including the partial pressure of carbon dioxide CO2 in the chloroplast matrix C c 、CO2 compensation point of chloroplasts , the open ratio of the photosynthetic system reaction center , the energy ratio k of plant heat dissipation and plant fluorescence emission DF , and the ratio of the observed solar-induced chlorophyll fluorescence to the total emitted fluorescence, i.e., the escape velocity ε; Step 3 includes: obtaining the distribution of C3 plants and C4 plants using vegetation type distribution data; For C3 plants, the GPP calculation formula is: ， in, is the maximum photosynthetic rate of the photosynthetic system; k DF is the energy ratio of plant heat dissipation and plant fluorescence emission; SIF is the solar induced chlorophyll fluorescence data; For C4 plants, the GPP calculation formula is: ， Here, x is the proportion of the total electron transport in the plant that is allocated to the mesophyll tissue.

2. The method according to claim 1, characterized in that In step 2, the CO2 partial pressure in the chloroplast matrix is ​​set to C c The partial pressure of CO2 between cells i Same, C i The calculation formula is: ， Where VPD is the saturated water vapor pressure difference, Ψ is the marginal water cost of plant carbon gain; is the partial pressure of carbon dioxide in the atmosphere.

3. The method according to claim 1, characterized in that In step 2, C is calculated by the following formula i : 。 4. The method according to claim 2 or 3, characterized in that: In step 2, the global atmospheric carbon dioxide concentration data is used to calculate : ， in, is the carbon dioxide concentration; The CO2 compensation point of chloroplasts is calculated using the following formula: : ， in, is the air temperature, obtained from remote sensing temperature data; Calculate the saturated water vapor pressure difference VPD by relative humidity and air temperature: ， ， in, is the saturated water vapor pressure, RH is the relative humidity; exp is the natural exponential function; The open ratio is calculated by the following formula : ， ， Among them, m is a dimensionless parameter to be fitted, In μmol -1 m² is a constant value in s; T opt is the optimal growth temperature of plants; m opt Represents the optimal growth temperature of the plant opt The value of m when H d The optimal growth temperature of the plant is T opt The rate of decline of the above peak function; The optimal growth temperature of the plant is T opt The rate of rise of the following peak function; R is the universal gas constant.

5. The method according to claim 4, characterized in that In step 2, for remote sensing satellite products, the escape velocity ε is calculated using the following formula: ， Among them, NIRv is the near-infrared reflectivity of vegetation; is the photosynthetically active radiation component.

6. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores program codes, and when the program codes are executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 5.

7. A storage medium, characterized in that: A computer program or instruction is stored, and when the computer program or instruction is run on a computer, the steps of the method according to any one of claims 1 to 5 are executed.

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