A method and apparatus for simultaneously calculating the three cardinal points of vegetation photosynthetic temperature.

Abstract

This invention relates to a method and apparatus for simultaneously calculating the three cardinal points of vegetation photosynthetic temperature. The method includes: S10: acquiring relevant data of the study area, including high temporal resolution vegetation photosynthetic data and environmental temperature data; S20: identifying the growing season of vegetation based on the vegetation photosynthetic data; S30: based on the growing season results extracted in S20, constructing grouped temperature-photosynthetic (GPP) paired data within each growing season; S40: using the WE function to perform nonlinear fitting on the temperature-photosynthetic (GPP) paired data, and obtaining the three cardinal points of vegetation photosynthetic temperature after fitting. The method proposed in this invention can simultaneously estimate the three cardinal points of temperature, has a stronger mechanistic basis, and is applicable to multiple scales such as station scale and grid scale, and can be extended to global vegetation cardinal point estimation.

Description

Technical Field

[0001] This invention relates to the field of ecological and environmental information technology, and in particular to a method and apparatus for simultaneously calculating the three cardinal points of vegetation photosynthetic temperature. Background Technology

[0002] Photosynthesis in terrestrial ecosystems is a core process in the global carbon cycle, and the photosynthetic efficiency of vegetation is highly sensitive to temperature changes. Studies have shown that the relationship between vegetation photosynthesis and temperature typically exhibits a unimodal response curve, with key characteristics determined by the three cardinal points of temperature (minimum temperature). Optimal temperature With maximum temperature These three parameters collectively define the thermal adaptation range of vegetation photosynthesis, serving as the foundation for assessing the degree of high and low temperature stress on vegetation, predicting ecosystem productivity, and constructing vegetation physiological models.

[0003] In terrestrial ecosystem models and vegetation productivity simulations, the cardinal temperatures are key physiological parameters for constructing the photosynthetic temperature response function, and their values ​​directly determine the model's accuracy in simulating photosynthetic efficiency and extreme temperature stress. However, current methods for determining the cardinal temperatures are quite crude; almost all models use fixed, pre-set empirical values ​​for the cardinal temperatures, ignoring the physiological adaptation differences formed over long-term evolution in different climate zones and vegetation types, and failing to consider the spatial heterogeneity of the cardinal temperatures across different regions. This limits the simulation accuracy of models at regional scales. Although some studies have recognized this problem and begun to parametrically derive the spatial heterogeneity of the cardinal temperatures, current research mainly focuses on... Estimation methods, such as quadratic function fitting or group averaging, are used to determine the peak position of the photosynthesis-temperature response curve. However, these methods struggle to simultaneously deduce physiologically significant parameters. and Therefore, in existing vegetation productivity simulations, even with the introduction of more accurate methods... The parameters still generally rely on fixed empirical thresholds. and The value of ...

[0004] In summary, current research on the estimation of the three cardinal points of temperature still has gaps, especially regarding the lack of research on... and The lack of a reasonable estimation method for the three cardinal points of temperature (CPT) in a synchronous and coordinated manner makes it difficult for existing technologies to accurately characterize the true physiological responses of vegetation under the background of climate change. Especially in the current context of frequent extreme weather events, this technological gap severely restricts the accuracy of ecosystem carbon budget assessment. Therefore, there is an urgent need to propose a method that can synchronously and accurately estimate the three cardinal points of temperature for vegetation photosynthesis, enabling multi-scale estimation of CPT from site to global levels, in order to fill the technological gap in this field. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a method for simultaneously calculating the three cardinal points of vegetation photosynthetic temperature. By fusing high temporal resolution remote sensing vegetation indices with temperature data (or flux station measured data), and after data quality control and screening, the Wang-Engel (WE) function is used for fitting to achieve simultaneous solution of the three cardinal points of temperature. This method can ultimately generate spatially continuous vegetation temperature cardinal point distribution data from station to global scales, overcoming the limitations of traditional methods in terms of simultaneous estimation of the three cardinal points, estimation mechanism, and spatial scalability.

[0006] More specifically, according to one aspect of the invention, a method for simultaneously calculating the three cardinal points of vegetation photosynthetic temperature includes:

[0007] S10: Acquire relevant data for the study area, including high temporal resolution vegetation photosynthesis data, i.e., total primary productivity (GPP) data, and ambient temperature data.

[0008] S20: Identifying the growing season of vegetation based on Gross Primary Productivity (GPP) data;

[0009] S30: Based on the growing season results extracted from S20, grouped temperature-GPP paired data are constructed within each growing season according to the following steps:

[0010] S31: Pair GPP data with corresponding ambient temperature data and remove records with daily average temperatures below -5°C to reduce the impact of GPP noise at low temperatures;

[0011] S32: Divide the GPP value into multiple equal-width temperature chambers based on the average temperature, and remove GPP outlier points in each chamber;

[0012] S33: Select the top N largest GPP values ​​in each temperature chamber, calculate the average of these values, and use this average as the average temperature-GPP value corresponding to that temperature chamber, thus obtaining temperature-GPP paired data; the largest GPP value in the obtained temperature-GPP paired data is called the GPP peak value, and the temperature corresponding to the GPP peak value is the initial optimal temperature; and

[0013] S34: Normalize the GPP values ​​in the temperature-GPP paired data by dividing them by the GPP peak value;

[0014] S40: The WE function is used to perform nonlinear fitting on the normalized temperature-GPP paired data obtained in S34 to obtain the three cardinal points of temperature for vegetation photosynthesis. The WE function is shown in the following formula:

[0015]

[0016]

[0017] in , and These represent the three cardinal temperatures of vegetation photosynthesis fitted by the function, i.e., the minimum temperatures. Optimal temperature With maximum temperature T represents the ambient temperature for vegetation growth, and β is the curvature factor.

[0018] According to an embodiment of the present invention, in S10, the high temporal resolution is an hourly scale or a daily scale.

[0019] According to an embodiment of the present invention, in S32, the plurality of values ​​are 40-60; for remote sensing data, the outlier identification method can identify outliers exceeding the average value ± 2 times the standard deviation, and for station observation data, the outlier identification method can identify outliers exceeding the average value ± 3 times the standard deviation.

[0020] According to an embodiment of the present invention, in S40, the nonlinear fitting uses a sequential least squares programming algorithm with the objective of minimizing the sum of squared residuals, setting the maximum number of iterations to 10,000 and the convergence tolerance to 1 × 10⁻⁶. -6 .

[0021] According to an embodiment of the present invention, in S40, the optimal temperature The initial value is the initial optimal temperature described in S33, during the fitting process. The range of values ​​is subject to multiple constraints, including a range not exceeding 0~40℃, not exceeding ±3℃ of the initial optimal temperature, and not exceeding the range from the lowest to the highest daily average temperature in the temperature record; the intersection of these ranges, i.e., the maximum value of the lower limit to the minimum value of the upper limit, is taken as the optimal temperature. The final range of values.

[0022] According to an embodiment of the present invention, the minimum temperature The initial value is the lowest daily average temperature, during the fitting process The value range is subject to multiple constraints, including a range not exceeding -20 to 25°C, and a range not exceeding (minimum daily average temperature -5)°C to the initial optimal temperature range. The intersection of these ranges, i.e., the maximum value of the lower limit to the minimum value of the upper limit, is used as... The final range of values.

[0023] According to an embodiment of the present invention, the maximum temperature The initial value is the highest daily average temperature, during the fitting process The range of values ​​is subject to multiple constraints, including a range not exceeding 5~45℃ and a range not exceeding the initial optimal temperature ~ (maximum daily average temperature + 5)℃. The intersection of these ranges, i.e., the maximum value of the lower limit to the minimum value of the upper limit, is used as... The final range of values.

[0024] According to an embodiment of the present invention, the curvature factor The initial value is 1, and the value range is 0.5 to 5.

[0025] According to an embodiment of the present invention, the method for synchronously calculating the three cardinal points of vegetation photosynthetic temperature further includes S50, determining the temperature corresponding to a WE function value of 0.05 as the effective temperature threshold, and defining them as the adjusted minimum temperature. and the adjusted maximum temperature As the low temperature threshold and high temperature threshold, the fitted result is... , , These three points serve as the three cardinal points for photosynthetic temperature at the current location.

[0026] According to another aspect of the present invention, an apparatus for synchronously calculating the three cardinal points of vegetation photosynthetic temperature is provided, comprising:

[0027] The data acquisition module is used to acquire relevant data for the study area, including high temporal resolution vegetation photosynthesis data, i.e., total primary productivity (GPP) data, and ambient temperature data.

[0028] The vegetation growing season identification module is used to identify the growing season of vegetation based on total primary productivity (GPP) data.

[0029] The grouped temperature-GPP paired data construction module is used to construct grouped temperature-GPP paired data for each growing season based on the growing season results extracted from S20. It includes the following sub-modules:

[0030] The temperature-GPP data preliminary matching submodule is used to match GPP data with corresponding ambient temperature data and remove records with daily average temperatures below -5°C to reduce the impact of GPP noise at low temperatures.

[0031] The equal-width temperature chamber partitioning submodule is used to divide the GPP value into multiple equal-width temperature chambers based on the average temperature, and remove GPP outlier points within each chamber.

[0032] The GPP average calculation submodule is used to select the top N largest GPP values ​​in each temperature chamber, calculate the average of these values, and use this average as the average temperature-corresponding GPP value for that temperature chamber, thus obtaining temperature-GPP paired data; and

[0033] The normalization processing submodule is used to normalize the GPP values ​​in the temperature-GPP paired data by dividing them by the GPP peak value.

[0034] The nonlinear fitting module is used to perform nonlinear fitting on the normalized temperature-GPP paired data obtained by using the WE function to obtain the three cardinal points of temperature for vegetation photosynthesis. The WE function is shown in the following equation:

[0035]

[0036]

[0037] in , and These represent the three cardinal temperatures of vegetation photosynthesis fitted by the function, i.e., the minimum temperatures. Optimal temperature With maximum temperature T represents the ambient temperature for vegetation growth, and β is the curvature factor.

[0038] According to an embodiment of the present invention, the device further includes an adjustment module, used to determine the temperature corresponding to a WE function value of 0.05 as an effective temperature threshold, and define it as the adjusted minimum temperature. and the adjusted maximum temperature As the low temperature threshold and high temperature threshold, the fitted result is... , , These three points serve as the three cardinal points for photosynthetic temperature at the current location.

[0039] Compared with existing technologies, this method achieves simultaneous fitting of the three cardinal points of photosynthetic temperature, changing the fact that existing technologies can only estimate in isolation. This method addresses the limitations of traditional methods by introducing the WE function, which has a clear physiological mechanism, for three-cardinal temperature fitting. Compared to the quadratic function commonly used in existing techniques, this significantly improves the mechanistic nature of the fitting results. The method proposes several techniques, including vegetation growth stage identification, multi-level screening of temperature chamber data, and boundary constraints for three-cardinal fitting, effectively enhancing the stability and success rate of nonlinear fitting. Furthermore, this method proposes a "5% threshold" correction method, innovatively using the 5% peak response point as the judgment point for the highest and lowest temperatures, effectively solving the convergence instability and boundary "tailing" problems in nonlinear fitting. Attached Figure Description

[0040] Figure 1 A flowchart illustrating a method for simultaneously calculating the three cardinal points of vegetation photosynthetic temperature according to an embodiment of the present invention;

[0041] Figure 2 A figure showing the fitting results of the method for synchronously calculating the three cardinal points of vegetation photosynthetic temperature according to an embodiment of the present invention;

[0042] Figure 3 a~3g are spatial distribution maps of global vegetation temperature cardinal points processed by the method for synchronously calculating the three cardinal points of vegetation photosynthetic temperature according to an embodiment of the present invention.

[0043] Figure 4 a~4f are graphs showing the results of temperature triads of Asian vegetation in different growing seasons, processed by the method for synchronously calculating the triads of vegetation photosynthetic temperature according to an embodiment of the present invention; and

[0044] Figure 5 This invention relates to a device for simultaneously calculating the three cardinal points of vegetation photosynthetic temperature according to an embodiment of the present invention. Detailed Implementation

[0045] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only used to explain the present invention and are not intended to limit the present invention.

[0046] Figure 1 The flowchart of the method for synchronously calculating the three cardinal points of vegetation photosynthesis temperature according to an embodiment of the present invention is shown in the figure. The embodiment of the present invention is based on high temporal resolution remote sensing or flux station data and uses a nonlinear function to synchronously estimate the three cardinal points of vegetation photosynthesis temperature. The specific steps are as follows.

[0047] S10 Data Preparation

[0048] This method is based on high temporal resolution (hourly and daily scales) photosynthetic data (GPP) and ambient temperature data. To ensure sufficient data volume and robustness of results, daily-scale data with a time span of five years (approximately 1825 days) or more are recommended. This method can be used at both site and regional scales. At the site scale, flux station observation data (e.g., GPP and temperature observations) are recommended, while at the regional scale, remote sensing GPP data and ERA5-land raster-scale temperature data are recommended.

[0049] S20 Growing Season Identification

[0050] Before identifying the three cardinal points of temperature, it is necessary to first identify the vegetation's growing season to eliminate interference from non-growing season data. Growing season identification can be based on daily-scale GPP time series. The Savitzky-Golay filtering method is used to smooth and normalize the GPP curve, followed by seasonal decomposition of the time series to extract the seasonal fluctuations of GPP each year. A growing season is defined as the period between two consecutive GPP minimum values, with each segment lasting at least 90 days. Pixels with bimodal GPP curves within a year (such as farmland with two crops a year) are considered as having two independent growing seasons, and two growing seasons are extracted separately. If a pixel shows three or more GPP peaks, the GPP data is considered to contain intra-year noise and is treated as a single growing season.

[0051] S40 Temperature-GPP Pairing Data Construction

[0052] Based on the growing season results extracted from S30, grouped temperature GPP paired data were constructed within each growing season according to the following steps:

[0053] 1) Pair daily GPP data with daily average temperature and remove records with daily average temperature below 5°C to reduce the impact of GPP noise at low temperatures.

[0054] 2) Divide the GPP values ​​into N equal-width temperature bins based on the daily average temperature. The number of bins depends on the amount of data, generally 40-60 is sufficient (e.g., remote sensing data can be divided into 40 bins, while station-scale observation data can be divided into 60 bins due to sufficient data volume). Within each bin, remove GPP outliers. Outlier identification methods can be based on values ​​exceeding the mean ± 2 standard deviations (remote sensing data) or the mean ± 3 standard deviations (station observation data).

[0055] 3) Select the top N largest GPP values ​​in each temperature chamber (20 for remote sensing data and 40 for station observations), calculate the average of these values, and use this average as the GPP value corresponding to the temperature (average temperature) of that temperature chamber, which is called a "temperature-GPP" pairing data.

[0056] 4) The temperature-GPP paired data obtained in the above steps represent the potential maximum GPP level at different temperatures. The largest GPP value is called the "GPP peak", and the temperature corresponding to the GPP peak is determined as the "initial optimal temperature". All GPP values ​​in the temperature-GPP paired data are normalized by dividing by the GPP peak (scaled to between 0 and 1).

[0057] S40 Nonlinear Fitting

[0058] The WE function is used to perform nonlinear fitting on the normalized "temperature-GPP" paired data obtained in S40 to obtain the temperature response curve and the three temperature base points (°C).

[0059] Research has found that the functions used in existing fitting methods are too simplistic. For example, while quadratic functions have peak values, they can effectively estimate photosynthesis. However, because its bilateral symmetry violates the true physiological mechanism of vegetation photosynthesis, it cannot reflect the heterogeneity of vegetation photosynthesis's response to low and high temperatures, thus making it impossible to scientifically estimate... and This implementation scheme fits the three cardinal points of temperature based on the Wang-Engel (WE) function. This function can be used to describe the response of crop relative growth rate to temperature, and to characterize the nonlinear relationship between air temperature and GPP. Its formula is:

[0060]

[0061]

[0062] in , and These represent the three cardinal temperatures of vegetation photosynthesis fitted by the function, i.e., the minimum temperatures. Optimal temperature With maximum temperature T represents the ambient temperature for vegetation growth, and β is the curvature factor.

[0063] More specifically, the fitting can use the "SLSQP" (Sequential Least Squares Programming) algorithm from the `scipy.optimize.minimize` module in Python, with the objective of minimizing the sum of squared residuals. The maximum number of iterations is set to 10,000, and the convergence tolerance is 1 × 10⁻⁶. The initial guesses and parameter ranges used for fitting are shown in Table 1. Four WE function parameters are obtained through fitting: the minimum temperature value (…). ), optimal temperature value ( ), maximum temperature value ( ), and curvature factor β. These four actual fitted values ​​are used to construct the temperature response function.

[0064] in, The initial value is the aforementioned "initial optimal temperature". During the fitting process, The value range is subject to multiple constraints, including an absolute range not exceeding 0-40℃, a range not exceeding ±3℃ of the "initial optimal temperature," and a range from the lowest to the highest daily average temperature in temperature records (such as annual temperature records). The intersection of these ranges, i.e., the maximum value of the lower limit to the minimum value of the upper limit, is used as the fitted value. The final range of values.

[0065] Minimum temperature fit value ( ), maximum temperature fitting value ( Similarly, it needs to follow similar value range restrictions, the specific range of which is shown in Table 1 below.

[0066] Table 1: Initial values ​​and range of fitting parameters for the WE function

[0067]

[0068] Furthermore, since the derivative of the WE function approaches zero near the minimum and maximum temperatures, a tailing effect exists, and the iterative optimization process may lead to deviations in parameter estimation near the boundary. Therefore, this method can further include step S50, determining the temperature corresponding to a WE function value of 0.05 as the effective temperature threshold, and defining it as the adjusted minimum temperature (…). ) and maximum temperature ( The values ​​represent the low and high temperatures at which GPP drops to 5% of its peak, serving as the low-temperature and high-temperature thresholds. Through the above adjustments, potential deviations can be effectively eliminated.

[0069] Therefore, the result obtained after fitting , , It is defined as the three cardinal points of photosynthetic temperature at the current location. Figure 2 The figure shows the fitting results of the method for synchronously calculating the three cardinal points of vegetation photosynthetic temperature according to an embodiment of the present invention. The gray scatter points represent the original data of daily-scale GPP and daily mean temperature from 2001 to 2019. After grouping and filtering the temperature data, the blue hollow circles represent the filtered data, the dark blue dots represent the "temperature-GPP" paired data, and the dark blue line is the curve formed by these paired data. The WE function is used to perform nonlinear fitting on the "temperature-GPP" paired data, and the magenta unimodal curve is the fitted WE temperature response function. The three cardinal points of temperature can be calculated based on the temperature response function, where the blue triangle, green pentagram, and red rhombus represent the fitted minimum temperature (…). ), optimal temperature ( ) and maximum temperature ( The light blue inverted triangle and the pink square represent the minimum temperature after adjustment to the 5% maximum GPP threshold (green dashed line). ) and maximum temperature ( ).

[0070] Figure 5 This is a schematic diagram of a device for simultaneously calculating the three cardinal points of vegetation photosynthetic temperature according to an embodiment of the present invention. Figure 5 As shown, the device includes: a data acquisition module 210 for acquiring relevant data of the study area, including high temporal resolution vegetation photosynthesis data, i.e., total primary productivity (GPP) data, and ambient temperature data; a vegetation growing season identification module 220 for identifying the vegetation growing season based on the GPP data; a grouped temperature-GPP paired data construction module 230 for constructing grouped temperature-GPP paired data for each growing season based on the growing season results extracted in S20; a nonlinear fitting module 240 for performing nonlinear fitting on the normalized temperature-GPP paired data using the WE function to obtain the three cardinal points of vegetation photosynthesis temperature; and an adjustment module 250 for determining the temperature corresponding to a WE function value of 0.05 as the effective temperature threshold, which is defined as the adjusted minimum temperature. and the adjusted maximum temperature As the low temperature threshold and high temperature threshold, the fitted result is... , , These three points serve as the three cardinal points for photosynthetic temperature at the current location.

[0071] The following example illustrates the application of this method by providing a case study of temperature tri-point estimation at flux stations and a global grid scale (0.05° resolution):

[0072] At the site scale, flux station records from FLUXNET 2015, spanning from 1992 to 2014, were used. The GPP variable was the total primary productivity reference value (GPP_DT_VUT_REF) calculated based on the daytime differentiation method, and the temperature variable was instantaneous air temperature (TA_F). For each site, the growing season was identified according to S20, and WE function fitting and temperature cardinal points were calculated based on S30, S40, and S50. The estimated temperature cardinal points for each site were obtained after calculation.

[0073] At the global raster scale, this method was used to extract the three cardinal points of temperature based on FluxSatGPP remote sensing data (2001-2019, daily scale, global 0.05° resolution) and ERA5Land daily average temperature data (2001-2019, daily scale, global 0.1° resolution, resampled to 0.05° resolution). The specific steps were as follows: For each raster, growth period identification was performed according to S20, and WE function fitting and temperature cardinal point calculation were performed based on S30, S40, and S50. The same method was used to calculate the cardinal points for each raster, resulting in a spatial distribution map of the photosynthetic temperature cardinal points at a global resolution of 0.05°.

[0074] The estimation results are as follows Figure 3 As shown in a~3g. Figure 3 a, 3b, and 3c represent the minimum temperatures required for photosynthesis in an ecosystem. ), optimal temperature ( ) and maximum temperature ( The spatial distribution of the data is shown in the figure. The histogram in the lower left corner of the figure shows the proportion of pixels in different temperature ranges. The circles in the figure represent the results calculated from the FLUXNET2015 flux data. Figure 3 d represents the latitudinal distribution of the calculated temperature triads. The scatter plots represent the temperature triads of stations based on FLUXNET2015. The blue, green, and red dots represent the minimum temperatures, respectively. ), optimal temperature ( ) and maximum temperature ( The gray dashed line represents 0 °C. Figure 3Figures e, 3f, and 3g compare the differences in temperature cardinal points calculated based on FLUXNET2015 GPP and FluxSat GPP data, respectively. The gray dashed line represents the 1:1 line. The results show that the spatial distribution of global vegetation temperature and photosynthesis cardinal points generally decreases from low latitudes to high latitudes. Significant consistency exists between the temperature cardinal points calculated from the stations and remote sensing data, indicating that the accuracy of temperature cardinal points estimated based on remote sensing GPP data is reliable.

[0075] For regions with multiple growing seasons, such as farmland with a double cropping system, the growing seasons are first identified based on FluxSat GPP, and then the GPP of pixels with two growing seasons is fitted to the temperature data respectively. Figure 4 a, 4b, 4c, 4d, 4e, and 4f show the three cardinal points of vegetation photosynthetic temperature in different growing seasons in Asia. , and Spatial differences exist. Taking the North China Plain as an example, the cultivated land in this region is mainly cultivated using a wheat-corn rotation, with winter wheat in the first season and summer corn in the second. Since winter wheat mainly grows from spring to early summer, while summer corn is sown in late summer and grows during the high-temperature season, the heat conditions during the corn growing season are generally higher than those for wheat. Therefore, the three cardinal points of photosynthesis for corn are also correspondingly higher, such as the minimum photosynthetic temperature of summer corn (…). Figure 4 d, approximately 10~20℃) significantly higher than winter wheat ( Figure 4 a, approximately 0°C).

[0076] The method proposed in this invention can simultaneously estimate the three cardinal points of temperature and is applicable to multiple scales, including station-level and grid-level calculations, with the potential to be extended to global vegetation cardinal point estimation. In contrast, existing technologies can only estimate in isolation. The minimum and maximum temperatures cannot be estimated. This invention is a fundamental improvement over the prior art. It is the first to achieve simultaneous estimation of the three cardinal points of temperature, and the estimation method is more mechanistic.

[0077] The above description is a specific implementation of the embodiments of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for simultaneously calculating the three cardinal points of vegetation photosynthetic temperature, characterized in that, include: S10: Acquire relevant data for the study area, including high temporal resolution vegetation photosynthesis data, i.e., total primary productivity (GPP) data, and ambient temperature data. S20: Identifying the growing season of vegetation based on Gross Primary Productivity (GPP) data; S30: Based on the growing season results extracted from S20, grouped temperature-GPP paired data are constructed within each growing season according to the following steps: S31: Pair GPP data with corresponding ambient temperature data and remove records with daily average temperatures below -5°C to reduce the impact of GPP noise at low temperatures; S32: Divide the GPP value into multiple equal-width temperature chambers based on the average temperature, and remove GPP outlier points in each chamber; S33: Select the top N largest GPP values ​​in each temperature chamber, calculate the average of these values, and use this average as the average temperature-corresponding GPP value for that temperature chamber, thus obtaining temperature-GPP paired data. In the obtained temperature-GPP paired data, the largest GPP value is called the GPP peak value, and the temperature corresponding to the GPP peak value is the initial optimal temperature; and S34: Normalize the GPP values ​​in the temperature-GPP paired data by dividing them by the GPP peak value; S40: The WE function is used to perform nonlinear fitting on the normalized temperature-GPP paired data obtained in S34 to obtain the three cardinal points of temperature for vegetation photosynthesis. The WE function is shown in the following formula: in , and These represent the three cardinal temperatures of vegetation photosynthesis fitted by the function, i.e., the minimum temperatures. Optimal temperature With maximum temperature T represents the ambient temperature for vegetation growth, and β is the curvature factor.

2. The method for synchronously calculating the three cardinal points of vegetation photosynthetic temperature according to claim 1, characterized in that, In S10, the high temporal resolution is either hourly or daily.

3. The method for synchronously calculating the three cardinal points of vegetation photosynthetic temperature according to claim 1, characterized in that, In S32, the plurality of values ​​are 40-60; for remote sensing data, the outlier identification method can identify outliers exceeding the average value by ±2 times the standard deviation, and for station observation data, the outlier identification method can identify outliers exceeding the average value by ±3 times the standard deviation.

4. The method for synchronously calculating the three cardinal points of vegetation photosynthetic temperature according to claim 1, characterized in that, In S40, the nonlinear fitting uses a sequential least squares programming algorithm, with the objective of minimizing the sum of squared residuals. The maximum number of iterations is set to 10,000, and the convergence tolerance is 1 × 10⁻⁶. -6 .

5. The method for synchronously calculating the three cardinal points of vegetation photosynthetic temperature according to claim 1, characterized in that, In S40, the optimal temperature The initial value is the initial optimal temperature described in S33, during the fitting process. The range of values ​​is subject to multiple constraints, including a range not exceeding 0~40℃, not exceeding ±3℃ of the initial optimal temperature, and not exceeding the range from the lowest to the highest daily average temperature in the temperature record; the intersection of these ranges, i.e., the maximum value of the lower limit to the minimum value of the upper limit, is taken as the optimal temperature. The final range of values.

6. The method for synchronously calculating the three cardinal points of vegetation photosynthetic temperature according to claim 5, characterized in that, Minimum temperature The initial value is the lowest daily average temperature, during the fitting process The value range is subject to multiple constraints, including a range not exceeding -20 to 25°C, and a range not exceeding (minimum daily average temperature -5)°C to the initial optimal temperature range. The intersection of these ranges, i.e., the maximum value of the lower limit to the minimum value of the upper limit, is used as... The final range of values.

7. The method for synchronously calculating the three cardinal points of vegetation photosynthetic temperature according to claim 6, characterized in that, Maximum temperature The initial value is the highest daily average temperature, during the fitting process The range of values ​​is subject to multiple constraints, including a range not exceeding 5~45℃ and a range not exceeding the initial optimal temperature ~ (maximum daily average temperature + 5)℃. The intersection of these ranges, i.e., the maximum value of the lower limit to the minimum value of the upper limit, is used as... The final range of values.

8. The method for synchronously calculating the three cardinal points of vegetation photosynthetic temperature according to claim 7, characterized in that, curvature factor The initial value is 1, and the value range is 0.5 to 5.

9. The method for synchronously calculating the three cardinal points of vegetation photosynthetic temperature according to claim 1, characterized in that, It also includes S50, which defines the temperature corresponding to a WE function value of 0.05 as the effective temperature threshold, and defines it as the adjusted minimum temperature. and the adjusted maximum temperature As the low temperature threshold and high temperature threshold, the fitted result is... , , These three points serve as the three cardinal points for photosynthetic temperature at the current location.

10. A device for synchronously calculating the three cardinal points of vegetation photosynthetic temperature, characterized in that, include: The data acquisition module is used to acquire relevant data for the study area, including high temporal resolution vegetation photosynthesis data, i.e., total primary productivity (GPP) data, and ambient temperature data. The vegetation growing season identification module is used to identify the growing season of vegetation based on total primary productivity (GPP) data. The grouped temperature-GPP paired data construction module is used to construct grouped temperature-GPP paired data for each growing season based on the growing season results extracted from S20. It includes the following sub-modules: The temperature-GPP data preliminary matching submodule is used to match GPP data with corresponding ambient temperature data and remove records with daily average temperatures below -5°C to reduce the impact of GPP noise at low temperatures. The equal-width temperature chamber partitioning submodule is used to divide the GPP value into multiple equal-width temperature chambers based on the average temperature, and remove GPP outlier points within each chamber. The GPP average value calculation submodule is used to select the top N largest GPP values ​​in each temperature chamber, calculate the average value of these values, and use the average value as the GPP value corresponding to the average temperature of the temperature chamber to obtain temperature-GPP pairing data. as well as The normalization processing submodule is used to normalize the GPP values ​​in the temperature-GPP paired data by dividing them by the GPP peak value. The nonlinear fitting module is used to perform nonlinear fitting on the normalized temperature-GPP paired data obtained by using the WE function to obtain the three cardinal points of temperature for vegetation photosynthesis. The WE function is shown in the following equation: in , and These represent the three cardinal temperatures of vegetation photosynthesis fitted by the function, i.e., the minimum temperatures. Optimal temperature With maximum temperature T represents the ambient temperature for vegetation growth, and β is the curvature factor.

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