Method and device for obtaining scaled leaf water use efficiency maps

Abstract

The present invention provides a method and device for obtaining a scaled leaf water use efficiency map, relating to the field of ecological technology. The method for obtaining the leaf water use efficiency map includes: obtaining environmental and ecological data related to plant leaf water use efficiency in a target region during a preset time period; obtaining a carbon isotope fractionation landscape map for the target region during the preset time period based on the environmental and ecological data; and obtaining a leaf water use efficiency map for the target region during the preset time period using a leaf water use efficiency calculation equation based on the carbon isotope fractionation landscape map. The present invention can derive a regional, large-scale carbon-water coupling indicator from in-situ leaf-scale water use efficiency, thereby providing a physiologically significant, large-scale evaluation indicator for carbon-water cycling and coupling research in terrestrial ecosystems.

Description

Technical Field

[0001] The present invention relates to the field of ecological technology, and in particular to a method and device for acquiring a scaled leaf water use efficiency map. Background Art

[0002] Intrinsic Water-Use Efficiency (iWUE) is a key metric for assessing carbon-water coupling in terrestrial ecosystems. It can reveal how plants or ecosystems perform in carbon-water cycling and their responses to climate change. Depending on the scale of research, iWUE is subdivided into multiple levels: leaf, plant, community, and ecosystem.

[0003] Of these scales, the leaf and ecosystem scales are currently the most intensively studied. Leaf-scale studies offer a deep physiological understanding of the responses of vegetation water-carbon coupling to environmental change, providing fundamental data for ecosystem carbon and water cycling mechanisms. However, data at this scale are difficult to directly apply to regional or even global estimates, limiting their application in large-scale studies. In contrast, ecosystem-scale studies can provide information on the status and dynamics of large-scale carbon and water cycles, which is crucial for understanding global carbon cycling and climate change. However, ecosystem-scale estimates are often difficult to interpret directly from a vegetation physiological perspective, lacking direct physiological evidence.

[0004] Therefore, how to derive the water use efficiency from the in situ leaf scale to the regional large-scale carbon-water coupling index, so as to provide large-scale evaluation indicators with physiological significance for the study of carbon-water cycle and coupling in terrestrial ecosystems, is a technical problem that needs to be solved urgently. Summary of the Invention

[0005] In response to the above-mentioned problems existing in the prior art, the present invention provides a method and device for obtaining a scaled leaf water use efficiency map, which realizes the derivation from leaf-scale water use efficiency to large-scale carbon-water coupling indicators, so as to provide indicators with physiological significance for the study of carbon and water cycles in terrestrial ecosystems.

[0006] The present invention provides a method for obtaining a scaled leaf water use efficiency atlas, comprising the following steps.

[0007] Acquire environmental ecological data related to plant leaf water use efficiency in a target area during a preset time period; acquire a carbon isotope fractionation value landscape map of the target area during the preset time period based on the environmental ecological data; and obtain a leaf water use efficiency map of the target area during the preset time period using a leaf water use efficiency calculation equation based on the carbon isotope fractionation value landscape map.

[0008] According to a method for obtaining an upscaled leaf water use efficiency map provided by the present invention, the carbon isotope fractionation value landscape map of the target area in the preset time period is obtained based on the environmental ecological data, including: using the regional boundary vector data of the target area, based on a first preset spatial resolution, dividing the geographical coverage of the target area into multiple grid units; based on the C3 plant coverage data of the target area in the preset time period, determining C3 plant grid pixels from the multiple grid units; according to the geographical coordinates of the C3 plant grid pixels and the environmental ecological data, using a carbon isotope landscape map prediction model, obtaining the C3 plant leaf carbon isotope fractionation value corresponding to each of the C3 plant grid pixels; wherein the carbon isotope landscape map prediction model is a trained regression machine learning model; and according to the C3 plant leaf carbon isotope fractionation values ​​corresponding to all the C3 plant grid pixels, obtaining the carbon isotope fractionation value landscape map.

[0009] According to a method for obtaining an upscaled leaf water use efficiency map provided by the present invention, the environmental ecological data include multispectral observation data, atmospheric carbon dioxide concentration data, and solar-induced chlorophyll fluorescence data; the method obtains the C3 plant leaf carbon isotope fractionation value corresponding to each C3 plant grid pixel based on the geographic coordinates of the C3 plant grid pixel and the environmental ecological data, using the carbon isotope landscape map prediction model, including: obtaining the C3 plant leaf carbon isotope fractionation value corresponding to each C3 plant grid pixel based on the geographic coordinates of the C3 plant grid pixel and the multispectral observation data, the atmospheric carbon dioxide concentration data, and the solar-induced chlorophyll fluorescence data, using the carbon isotope landscape map prediction model.

[0010] According to a method for obtaining an upscaled leaf water use efficiency map provided by the present invention, the carbon isotope landscape map prediction model is obtained by training in the following manner: using a training data set, an initial regression machine learning model is trained to obtain the carbon isotope landscape map prediction model; wherein, the training data set includes sample data and labels of the sample data; the sample data includes environmental ecological data samples related to plant leaf water use efficiency in the target area during the preset time period, and the geographic coordinates of the C3 plant grid pixel; the label of the sample data is the C3 plant leaf carbon isotope fractionation value corresponding to the C3 plant grid pixel.

[0011] According to a method for obtaining an upscaled leaf water use efficiency map provided by the present invention, the leaf water use efficiency calculation equation includes a first parameter unrelated to geographic location, a second parameter related to geographic location, and carbon isotope fractionation values ​​of C3 plant leaves; the leaf water use efficiency map of the target area in the preset time period is obtained by using the leaf water use efficiency calculation equation based on the carbon isotope fractionation value landscape map, comprising: rasterizing the second parameter according to regional boundary vector data of the target area and a second preset spatial resolution to obtain second parameter values ​​corresponding to target grid pixels; wherein the target grid pixels correspond to grid pixels in the carbon isotope fractionation value landscape map; obtaining the leaf water use efficiency corresponding to each target grid pixel according to the leaf water use efficiency calculation equation based on the first parameter, the second parameter value corresponding to each target grid pixel, and the C3 plant leaf carbon isotope fractionation value corresponding to each target grid pixel; and obtaining the leaf water use efficiency map according to the leaf water use efficiencies corresponding to all target grid pixels.

[0012] According to a method for obtaining an upscaled leaf water use efficiency map provided by the present invention, the second parameter includes a carbon dioxide compensation point when mitochondrial respiration is not considered, and the carbon dioxide compensation point is obtained by a carbon dioxide compensation point calculation formula; the second parameter of the preset time period is grid pixelated according to the regional boundary vector data of the target area and the second preset spatial resolution to obtain the second parameter value corresponding to each target grid pixel, including: obtaining the average temperature grid data of the target area; using a bilinear interpolation method to resample the average temperature grid data to obtain temperature data corresponding to each target grid pixel; and using the carbon dioxide compensation point calculation formula according to the temperature data corresponding to each target grid pixel to obtain the carbon dioxide compensation point parameter corresponding to each target grid pixel.

[0013] The present invention also provides a device for obtaining a scaled leaf water use efficiency map, comprising the following modules:

[0014] The first acquisition module is used to obtain environmental ecological data related to plant leaf water use efficiency in the target area during a preset time period; the second acquisition module is used to obtain a carbon isotope fractionation value landscape map of the target area during the preset time period based on the environmental ecological data; and the third acquisition module is used to obtain a leaf water use efficiency map of the target area during the preset time period based on the carbon isotope fractionation value landscape map and a leaf water use efficiency calculation equation.

[0015] The present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for obtaining the upscaled leaf water use efficiency map as described in any one of the above methods is implemented.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for obtaining the upscaled leaf water use efficiency map as described in any of the above.

[0017] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method for obtaining the upscaled leaf water use efficiency map as described in any one of the above.

[0018] The present invention provides a method and device for obtaining a scaled leaf water use efficiency (WUE) map. This method utilizes environmental and ecological data closely related to plant leaf WUE within a preset time period in a target region to obtain a carbon isotope fractionation landscape map. This landscape map accurately depicts the spatial distribution characteristics of carbon isotope fractionation in plant leaves within the target region. Based on the WUE landscape map, a leaf WUE calculation equation is applied to obtain a WUE map. The WUE landscape map serves as an intermediate bridge, connecting microscopic leaf WUE data with macroscopic, large-scale WUE data. This allows the resulting WUE map to not only intuitively display the spatial distribution pattern of leaf WUE in the target region, but also, based on a physiologically meaningful WUE calculation equation, yields a highly scientific and reliable result, providing a reference for in-depth research on the carbon-water cycle in plants or ecosystems and their response to climate change. This enables the derivation of regional, large-scale carbon-water coupling indicators from in situ leaf-scale WUE, providing a physiologically meaningful, large-scale evaluation indicator for the study of carbon-water cycling and coupling in terrestrial ecosystems. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 It is a flow chart of the method for obtaining the scaled leaf water use efficiency map provided by the present invention.

[0021] Figure 2It is a flow chart of the method for obtaining the carbon isotope fractionation value landscape map provided by the present invention.

[0022] Figure 3 It is a flow chart of a method for obtaining a leaf water use efficiency map based on a carbon isotope fractionation value landscape map provided by the present invention.

[0023] Figure 4 It is a schematic diagram of the carbon isotope fractionation value landscape provided by the present invention.

[0024] Figure 5 It is a schematic diagram of the first parameter value of the leaf water use efficiency calculation equation provided by the present invention.

[0025] Figure 6 It is a schematic diagram of the carbon dioxide concentration parameter value provided by the present invention.

[0026] Figure 7 It is a schematic diagram of the carbon dioxide compensation point parameter value provided by the present invention.

[0027] Figure 8 It is a schematic diagram of the leaf water use efficiency map provided by the present invention.

[0028] Figure 9 It is a structural schematic diagram of a device for acquiring a scaled leaf water use efficiency map provided by the present invention.

[0029] Figure 10 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0030] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0031] The following combination Figures 1-8 The method for obtaining the upscaled leaf water use efficiency map of the present invention is described.

[0032] Figure 1 Schematic diagram of the process of obtaining the scaled leaf water use efficiency map provided by the present invention, such as Figure 1 As shown, the method includes the following:

[0033] Step 101: Acquire environmental ecological data related to plant leaf water use efficiency in a target area during a preset time period.

[0034] The target region is the geographic area where leaf water use efficiency (LWUE) is to be studied. Based on LWUE in that region, the status and dynamics of carbon and water cycles at that scale can be studied. For example, the target region could be a nature reserve or grassland ecosystem.

[0035] During the specific implementation process, the preset time period may be a year (e.g., 2024), a month, or a quarter.

[0036] Environmental and ecological data related to plant leaf water use efficiency (WUE) refer to environmental factors and ecological parameters that can directly or indirectly influence plant leaf WUE. These factors and parameters reflect the environmental conditions in which plants operate, influencing physiological processes such as photosynthesis, transpiration, and carbon isotope fractionation, which in turn influence leaf WUE. These data play a crucial role in estimating plant leaf WUE.

[0037] Environmental ecological data may include but is not limited to the following factors or parameters.

[0038] MODIS (Moderate Resolution Imaging Spectroradiometer) remote sensing spectral reflectance products. These spectral reflectance data can reflect the coverage and growth status of surface vegetation and are important basic data for estimating plant leaf water use efficiency.

[0039] Atmospheric CO2 concentration (Ca) data. Atmospheric CO2 concentration directly affects plant photosynthesis rates, thereby affecting leaf water use efficiency. During implementation, atmospheric CO2 concentration can be obtained from climate monitoring and research institutions.

[0040] Solar-induced chlorophyll fluorescence data (e.g., the Global Gridded Solar-Induced Chlorophyll Fluorescence dataset (GOSIF)) are considered a direct probe of plant photosynthesis and can reflect plant photosynthetic activity. GOSIF data provide an important reference for estimating leaf water use efficiency.

[0041] Leaf sample point elevation data (ELE). Altitude is one of the important factors affecting plant growth and physiological processes. It affects leaf water use efficiency by affecting environmental conditions such as temperature and light.

[0042] MODIS annual land cover data (MCD12C1) can reflect the type and distribution of surface vegetation and provide a basis for estimating leaf water use efficiency under different vegetation types.

[0043] Step 102: Obtain a carbon isotope fractionation value landscape map of the target area in a preset time period based on the environmental ecological data.

[0044] The carbon isotope fractionation landscape map is a data visualization expression based on geographic spatial information, which shows the carbon isotope fractionation values ​​of plant leaves in a specific area (Δ 13 C) spatial distribution patterns. Leaf carbon isotope fractionation landscape maps have broad applications in ecology, geography, and environmental science. They can not only be used to study plant responses to environmental changes but also provide a scientific basis for assessing ecosystem carbon sequestration functions and formulating carbon reduction strategies.

[0045] In the specific implementation process, by integrating remote sensing technology, geographic information system (GIS) and ground observation data, the carbon isotope fractionation values ​​of plant leaves can be presented on the map in a continuous or classified manner, thereby revealing the spatial variation of plant physiological responses and carbon and water use efficiency under different environmental conditions.

[0046] During the specific implementation process, a carbon isotope fractionation value landscape map of the target area in a preset time period can be obtained in a variety of ways based on environmental ecological data, which is not limited to the description of this specification.

[0047] For an embodiment of obtaining the carbon isotope fractionation value landscape map of the target area in the preset time period based on environmental ecological data, see Figure 2 The relevant description in will not be repeated here.

[0048] Step 103: Based on the carbon isotope fractionation value landscape map, using the leaf water use efficiency calculation equation, obtain a leaf water use efficiency map of the target area in a preset time period.

[0049] Leaf Water Use Efficiency Calculation Equation The equation used to calculate leaf water use efficiency.

[0050] For a detailed description of the leaf water use efficiency map obtained for the target area in a preset time period based on the carbon isotope fractionation value landscape map and the leaf water use efficiency calculation equation, see Figure 3 The relevant content in will not be repeated here.

[0051] Figure 2 Schematic diagram of the process of obtaining the carbon isotope fractionation value landscape map provided by the present invention, such as Figure 2 As shown, the method includes the following:

[0052] Step 201: Using the region boundary vector data of the target region, based on a first preset spatial resolution, divide the geographical coverage of the target region into a plurality of grid cells.

[0053] Regional boundary vector data is a data type used to accurately describe the shape and location of geographic area boundaries. It's typically stored as a vector file (e.g., a regional boundary vector file). It uses a series of coordinate points (geometric elements such as points, lines, and surfaces) to outline the boundaries of the target area. It's highly accurate and editable, accurately reflecting subtle changes in geographic boundaries. For example, for a nature reserve, the regional boundary vector data for that reserve would record the latitude and longitude coordinates of key points on the reserve's boundary in detail. These coordinate points are then connected into lines and then into surfaces, accurately defining the reserve's geographic scope.

[0054] The preset spatial resolution (including both the first and second preset spatial resolutions in this specification) refers to the pre-set angular scale used to divide a geographic area into a series of uniform grid cells during geospatial data processing and analysis. For example, a preset spatial resolution of 0.05° in latitude and longitude means that each grid cell in the target area has a width and height of 0.05° in the geographic coordinate system.

[0055] In the specific implementation process, programming languages ​​can be used to implement the division of raster cells. For example, using the R language raster package, a blank raster template is created based on the regional boundary vector data of the target area, thereby obtaining the raster cells of the target area. The specific implementation process is as follows:

[0056] Load the raster package in the R language environment. Define a raster extent within the package based on the target area defined by the boundary vector data. The raster package can obtain the target area's boundary information by reading boundary vector files (such as Shapefile format) to define the extent. Pass a preset spatial resolution (such as 0.05°) as a parameter to the relevant raster package functions to set the raster resolution, instructing the package to divide the target area into multiple grid cells with a side length of 0.05°. After defining the raster extent and resolution, use the raster() function in the raster package to create a blank raster template. This template is a raster data structure composed of multiple raster cells, each with a width and height of 0.05° in the geographic coordinate system. The cells in the template are used as the raster cells of the target area.

[0057] Step 202: Based on the C3 plant coverage data of the target area in a preset time period, determine the C3 plant grid pixel from the plurality of grid units.

[0058] Leaf water use efficiency refers to the carbon fixed per unit water consumption of plants.13 C) can be used to reliably estimate water use efficiency at the physiologically significant leaf scale. Therefore, based on the C3 plant cover data for the target area during a preset time period, cells whose corresponding C3 plant cover data exceeds a preset threshold can be selected from multiple grid cells as C3 plant cells.

[0059] In the specific implementation process, all grid cells in the target area can be masked according to the MODIS land cover data MCD12C1 to remove areas other than vegetation; and areas with a C4 plant proportion exceeding 50% can be masked according to the C3 / C4 plant cover ratio data to reduce the interference of C4 plants, and then the C3 plant grid pixels can be obtained.

[0060] Step 203 : Based on the geographic coordinates and environmental ecological data of the C3 plant grid pixels, the carbon isotope landscape atlas prediction model is used to obtain the C3 plant leaf carbon isotope fractionation value corresponding to each C3 plant grid pixel.

[0061] The geographic coordinates of the C3 plant grid pixel may include a combination of one or more of longitude, latitude, and altitude.

[0062] The carbon isotope landscape prediction model is a trained regression machine learning model. In practice, the carbon isotope landscape prediction model can be constructed based on a variety of machine learning models (e.g., random forests, decision trees, etc.), and is not limited to the description in this specification.

[0063] The carbon isotope landscape prediction model takes as input the geographic coordinates of a C3 plant raster pixel and its corresponding environmental and ecological data, and outputs the carbon isotope fractionation value of the C3 plant leaf corresponding to that C3 plant raster pixel. In practice, the environmental and ecological data can be cropped and spatially resampled based on a first preset spatial resolution to obtain the environmental and ecological data for each C3 plant raster pixel.

[0064] In some embodiments, the environmental ecological data includes multispectral observation data, atmospheric carbon dioxide concentration data, and solar-induced chlorophyll fluorescence data. A carbon isotope landscape prediction model can be used to obtain the C3 plant leaf carbon isotope fractionation value corresponding to each C3 plant grid pixel based on the geographic coordinates of the C3 plant grid pixel and its corresponding multispectral observation data, atmospheric carbon dioxide concentration data, and solar-induced chlorophyll fluorescence data.

[0065] During the specific implementation process, the carbon isotope landscape prediction model can be trained in the following way: using the training data set, training the initial regression machine learning model to obtain the carbon isotope landscape prediction model.

[0066] Among them, the training data set includes sample data and labels of sample data; the sample data includes environmental ecological data samples related to plant leaf water use efficiency in the target area during a preset time period, as well as the geographic coordinates of C3 plant grid pixels; the labels of the sample data are the carbon isotope fractionation values ​​of C3 plant leaves corresponding to the C3 plant grid pixels.

[0067] The environmental ecological data sample is the same as the environmental ecological data used when predicting using the carbon isotope landscape map prediction model. For more detailed description, please refer to the relevant content in step 101, which will not be repeated here.

[0068] During the specific implementation process, environmental ecological data samples can be obtained through the following methods.

[0069] Multispectral observation data: Seven MODIS remote sensing spectral reflectance products (bands B1-B7) are obtained from the satellite platform. These surface reflectance bands have been atmospherically corrected, and pixels with poor quality have been removed based on the quality assurance band (QA).

[0070] Atmospheric CO2 concentration data: Atmospheric CO2 concentration (Ca) data are from the National Oceanic and Atmospheric Administration.

[0071] Solar-induced chlorophyll fluorescence data: The Global Gridded Solar-Induced Chlorophyll Fluorescence dataset (GOSIF) can be used as a direct probe of plant photosynthesis.

[0072] For remote sensing data, screening is performed based on quality assurance bands to ensure data quality.

[0073] For the atmospheric data and geographic data obtained above, after necessary unit conversion and format adjustment, the data needs to be filtered to select the final prediction variables.

[0074] Because not all predictive variables are conducive to the construction of the carbon isotope fractionation value landscape, it is necessary to evaluate the importance of each data. Variable screening is used to achieve the minimum number of variables to represent the optimal model results, thereby reducing redundant variables, which helps to reduce model running time and improve modeling accuracy. Specifically, the following variable screening methods can be used:

[0075] Multicollinearity test: You can use the vifstep function in the R language "usdm" package to perform a multicollinearity test. When the VIF (variance inflation factor) of a predictor variable is greater than 10, it indicates that the variable is collinear and is filtered out. The formula is as follows:

[0076] (1)

[0077] Among them, B1-B7 are the reflectance variables of the seven spectral bands observed by MODIS, GOSIF is the solar-induced chlorophyll fluorescence variable, Ca is the atmospheric carbon dioxide concentration, Lat, Long and ELE are the latitude, longitude and altitude of the leaf sample, respectively.

[0078] Recursive Feature Elimination (RFE): This method uses the "Caret" package's recursive feature elimination method to select the optimal number of predictors. RFE builds a model based on recursively deleted features and the remaining features, and identifies which variables and how many combinations of variables contribute most to the modeling results based on model accuracy. The formula used is as follows:

[0079] (2)

[0080] After multicollinearity test and RFE filtering, the following predictor variables were finally retained: B3, B5, B7, GOSIF, δ 13 Ca, Lat, Long, and ELE. These predictor variables are closely related to plant structure and function and can represent plant physiological and ecological information.

[0081] In some embodiments, the label of the sample data (the carbon isotope fractionation value of the C3 plant leaves corresponding to each C3 plant grid pixel) can be obtained in the following manner.

[0082] Through field research, C3 plant leaf samples were collected from multiple natural plots within the target area, corresponding to multiple C3 plant grid pixels. The leaf samples were oven-dried at 60°C for 48 hours and stored. The dried leaf samples were crushed and sieved using a grinder (e.g., Retsch MM200). The sieved samples were placed in a tin cup and sealed tightly. The stable carbon isotope composition of the leaf organic matter was determined using a combination of a Costech elemental analyzer and an Elementar Isoprime. The sampling time, latitude and longitude, altitude, and plant type were also recorded.

[0083] During the specific implementation process, the environmental ecological data samples can be cropped and spatial resolution resampled based on the first preset spatial resolution to obtain the environmental ecological data samples of each C3 plant grid pixel.

[0084] In some embodiments, the carbon isotope composition of C3 plant leaves can also be obtained through open source databases.

[0085] The carbon isotope composition of C3 plant leaf samples from multiple natural plots in the target area was obtained using the above method. The leaf carbon isotope composition and atmospheric carbon isotope composition can then be used to Calculated carbon isotope fractionation values ​​of C3 plant leaves :

[0086] (3)

[0087] When the preset time period is long, for each C3 plant grid pixel, the average carbon isotope fractionation value of the C3 plant leaves within the preset time period can be used as the final C3 plant leaf carbon isotope fractionation value corresponding to the C3 plant grid pixel.

[0088] To train the initial regression machine learning model, it is necessary to establish a loss function (such as the mean squared error loss function) to use the loss function to determine the difference between the model prediction value and the sample label; adjust the model parameters based on the preset optimization algorithm to reduce the difference between the model prediction value and the sample label until the loss function converges.

[0089] In some embodiments, a carbon isotope landscape prediction model is constructed based on a random forest algorithm:

[0090] (4)

[0091] In order to optimize the random forest model, its key parameters need to be repeatedly optimized. For example, two key parameters, ntree (range 100-1000) and mtry (range 1-10), were selected for the random forest algorithm, and the algorithm was set to undergo 100 iterations. In the model evaluation phase, three evaluation schemes were adopted: training set (70%) - test set (30%), ten-fold cross-validation, and 100 random resampling. Four statistical criteria, correlation coefficient (R), Nash-Sutcliffe efficiency, root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the carbon isotope landscape prediction model constructed based on random forest. The results showed that the model had good performance in predicting the carbon isotope fractionation value (Δ 13 C) aspects of performance.

[0092] Step 204 : Obtain a carbon isotope fractionation value landscape map based on the carbon isotope fractionation values ​​of C3 plant leaves corresponding to all C3 plant grid pixels.

[0093] In a specific implementation process, the carbon isotope fractionation values ​​of C3 plant leaves corresponding to all the C3 plant grid pixels can be integrated to obtain the carbon isotope fractionation value landscape map. For example, the carbon isotope fractionation value of C3 plant leaves corresponding to each discrete C3 plant grid pixel can be spatially interpolated according to its geographic coordinates through the spatial analysis function of the geographic information system or related data processing software, thereby generating a continuous carbon isotope fractionation value landscape map (such as a landscape map) that covers the entire target area and is characterized by the carbon isotope fractionation values ​​of C3 plant leaves. Figure 4shown).

[0094] Figure 3 It is a flow chart of a method for obtaining a leaf water use efficiency map based on a carbon isotope fractionation value landscape map provided by the present invention.

[0095] This method is based on the carbon isotope fractionation value landscape map and uses the leaf water use efficiency calculation equation to obtain the leaf water use efficiency map of the target area in a preset time period.

[0096] Leaf water use efficiency is defined as the ratio of photosynthetic rate (A) to stomatal conductance (gs), and can be calculated based on the leaf water use efficiency calculation equation. In specific implementations, a variety of leaf water use efficiency calculation equations can be used, not limited to the descriptions in this specification. For example only, the leaf water use efficiency calculation equation is as follows:

[0097] (5)

[0098] in:

[0099] a is 4.4‰, indicating the fractionation of atmospheric carbon dioxide during the diffusion process in leaf stomata;

[0100] a m is 1.8‰, indicating isotopic fractionation related to the dissolution and diffusion of carbon dioxide in the mesophyll;

[0101] b is 29‰, indicating fractionation during carboxylation;

[0102] k is 1.6, which represents the ratio of the diffusivity of water vapor and carbon dioxide;

[0103] f ' is 11‰, indicating isotope fractionation during photorespiration (f ' =fαb / αf, where αb=1+b and αf=1+f, f=11‰); f '=[0.011×(1+0.029)] / (1+0.011)=0.011319≈0.011=11‰;

[0104] gs / gm is 0.79, gs represents stomatal conductance (mol / m2 / s), gm represents mesophyll conductance (mol / m2 / s);

[0105] is 2.5‰, indicating the isotope correction of leaf biomass to take into account the effects of non-photosynthesis;

[0106] Ca is the atmospheric carbon dioxide concentration, which varies with time and has a unique value in the annual space;

[0107] Γ represents the carbon dioxide compensation point (μmol / mol2) when mitochondrial respiration is not considered: Calculated according to the following carbon dioxide compensation point calculation formula:

[0108] (6)

[0109] Where T represents the leaf temperature (°C), and it can be assumed that the leaf temperature is equal to the air temperature.

[0110] The leaf water use efficiency calculation equation includes the first parameter that is independent of geographical location: a, a m , b, k, f ', gs / gm, the second parameter related to geographic location: .

[0111] like Figure 3 As shown, the following steps are used to calculate the leaf water use efficiency equation and obtain the leaf water use efficiency map of the target area in the preset time period:

[0112] Step 301: Pixelate the second parameter based on the target region's regional boundary vector data and a second preset spatial resolution to obtain a second parameter value corresponding to a target grid pixel; wherein the target grid pixel corresponds to a grid unit in a carbon isotope fractionation value landscape map.

[0113] In a specific implementation process, the second parameter can be pixelated in the following manner:

[0114] The second spatial resolution is determined according to the size of the grid cells in the carbon isotope fractionation value landscape map. For example, the second spatial resolution is the same as the resolution of the grid cells in the carbon isotope fractionation value landscape map or is proportionally enlarged.

[0115] Divide the geographic coverage of the target area into multiple target raster pixels based on the region boundary vector data and a second preset spatial resolution. For example, you can use the raster() function in the raster package to create a blank raster template based on the region boundary vector data and the second spatial resolution, where each raster cell in the raster template corresponds to a target raster pixel.

[0116] The second parameter value corresponding to each target grid pixel is set according to the geographic spatial distribution characteristics of the second parameter.

[0117] For atmospheric carbon dioxide concentration, we can obtain CO2 concentration observation data for a preset time period (for example, 2005) from the atmospheric background station closest to the target area; assume that the CO2 concentration in the same time layer is uniform throughout the entire study area; use the init() function of the raster package to fill the CO2 concentration observation data into the global grid according to the preset time period, and obtain the following: Figure 6 The spatiotemporal dynamic changes of CO2 concentration are shown.

[0118] The carbon dioxide compensation point Γ when mitochondrial respiration is not considered , the average temperature grid data of the target area can be obtained; the bilinear interpolation method is used to resample the average temperature grid data to obtain the temperature data corresponding to each target grid pixel; based on the temperature data corresponding to each target grid pixel, the carbon dioxide compensation point calculation formula is used to obtain the carbon dioxide compensation point parameters corresponding to each target grid pixel.

[0119] For example, the average temperature grid data of a preset time period can be obtained from an open source climate dataset (e.g., WorldClim v2.1); the temperature grid data can be resampled to a second preset spatial resolution using bilinear interpolation; the resampled temperature grid data can be substituted into the carbon dioxide compensation point calculation formula shown in formula (6) using the calc() function of the raster package, and the above nonlinear formula operation can be performed on the temperature grid at each moment; finally, the calc() function returns a new raster object, which contains the value calculated according to the formula. Figure 7 Γ shown The spatiotemporal dynamic changes in the value of a variable.

[0120] Step 302: Based on the first parameter, the second parameter value corresponding to each target grid pixel, and the C3 plant leaf carbon isotope fractionation value corresponding to each target grid pixel, the leaf water use efficiency calculation equation is used to obtain the leaf water use efficiency corresponding to each target grid pixel.

[0121] For the first parameter, such as Figure 5 As shown, the first parameter value of each target grid cell can be set to a constant value.

[0122] For example, you can use the R language raster package to create a blank raster template, and use the setValues() function to set the first parameter (a, a m , b, k, f', gs / gm) are assigned global constant values. All grid cells maintain the same value in all time layers, for example, b = 29‰.

[0123] In the specific implementation process, for each target grid pixel, the first parameter, the second parameter value corresponding to the target grid pixel, and the carbon isotope fractionation value of C3 plant leaves (obtained from the grid unit corresponding to the carbon isotope fractionation value landscape map) are substituted into the leaf water use efficiency calculation equation shown in formula (5) to obtain the leaf water use efficiency corresponding to each target grid pixel.

[0124] 303. According to the leaf water use efficiency corresponding to all target grid pixels, a leaf water use efficiency map is obtained.

[0125] In the specific implementation process, the leaf water use efficiency corresponding to all target grid pixels can be filled into the leaf water use efficiency map of the target area; by marking different leaf water use efficiency values ​​with different colors, the following can be obtained: Figure 8 Leaf water use efficiency maps for the target regions shown.

[0126] The following describes the device for obtaining the scaled leaf water use efficiency map provided by the present invention. The device for obtaining the scaled leaf water use efficiency map described below and the method for obtaining the scaled leaf water use efficiency map described above can refer to each other.

[0127] Figure 9 Schematic diagram of the structure of the device for obtaining the scaled leaf water use efficiency map provided by the present invention. Figure 9 As shown, the device 900 includes the following modules.

[0128] The first acquisition module 910 is configured to acquire environmental ecological data related to water use efficiency of plant leaves in a target area within a preset time period.

[0129] The second acquisition module 920 is configured to acquire a carbon isotope fractionation value landscape map of the target area in the preset time period based on the environmental ecological data.

[0130] The third acquisition module 930 is configured to obtain a leaf water use efficiency map of the target area in the preset time period based on the carbon isotope fractionation value landscape map and using a leaf water use efficiency calculation equation.

[0131] Figure 10 An example of a physical structure diagram of an electronic device is shown below. Figure 10As shown, the electronic device may include: a processor 1010, a communications interface 1020, a memory 1030, and a communications bus 1040, wherein the processor 1010, the communications interface 1020, and the memory 1030 communicate with each other via the communications bus 1040. The processor 1010 may invoke logic instructions in the memory 1030 to execute a method for obtaining an upscaled leaf water use efficiency map, the method comprising: obtaining environmental ecological data related to plant leaf water use efficiency in a target area during a preset time period; obtaining a carbon isotope fractionation value landscape map of the target area during the preset time period based on the environmental ecological data; and obtaining a leaf water use efficiency map of the target area during the preset time period using a leaf water use efficiency calculation equation based on the carbon isotope fractionation value landscape map.

[0132] Furthermore, the logic instructions in the aforementioned memory 1030 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion 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 includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0133] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for obtaining the up-scaled leaf water use efficiency map provided by the above methods, which includes: obtaining environmental ecological data related to plant leaf water use efficiency in the target area during a preset time period; obtaining a carbon isotope fractionation value landscape map of the target area during the preset time period based on the environmental ecological data; and obtaining a leaf water use efficiency calculation equation based on the carbon isotope fractionation value landscape map to obtain a leaf water use efficiency map of the target area during the preset time period.

[0134] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for obtaining the upscaled leaf water use efficiency map provided by the above-mentioned methods, the method comprising: obtaining environmental ecological data related to plant leaf water use efficiency in a target area during a preset time period; obtaining a carbon isotope fractionation value landscape map of the target area during the preset time period based on the environmental ecological data; and obtaining a leaf water use efficiency calculation equation based on the carbon isotope fractionation value landscape map to obtain a leaf water use efficiency map of the target area during the preset time period.

[0135] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0136] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for obtaining a scaled leaf water use efficiency map, characterized in that: include: Acquiring environmental ecological data related to plant leaf water use efficiency in a target area during a preset time period; wherein the environmental ecological data includes multispectral observation data, atmospheric carbon dioxide concentration data, and solar-induced chlorophyll fluorescence data; Obtaining a carbon isotope fractionation value landscape map of the target area during the preset time period based on the environmental ecological data; Based on the carbon isotope fractionation value landscape map, using the leaf water use efficiency calculation equation, obtaining the leaf water use efficiency map of the target area in the preset time period; Wherein, obtaining the carbon isotope fractionation value landscape map of the target area in the preset time period based on the environmental ecological data includes: Using the region boundary vector data of the target region, based on a first preset spatial resolution, the geographical coverage of the target region is divided into a plurality of grid cells; Determining a C3 plant grid pixel from the plurality of grid cells based on the C3 plant coverage data of the target area in the preset time period; According to the geographic coordinates of the C3 plant grid pixel and the environmental ecological data, a carbon isotope landscape prediction model is used to obtain the C3 plant leaf carbon isotope fractionation value corresponding to each C3 plant grid pixel; wherein the carbon isotope landscape prediction model is a trained regression machine learning model; The carbon isotope fractionation value landscape map is obtained according to the carbon isotope fractionation values ​​of the C3 plant leaves corresponding to all the C3 plant grid pixels.

2. The method for obtaining a scaled leaf water use efficiency map according to claim 1, characterized in that: The method of obtaining the C3 plant leaf carbon isotope fractionation value corresponding to each C3 plant grid pixel according to the geographic coordinates of the C3 plant grid pixel and the environmental ecological data using the carbon isotope landscape atlas prediction model comprises: According to the geographic coordinates of the C3 plant grid pixels and the multispectral observation data, the atmospheric carbon dioxide concentration data, and the solar-induced chlorophyll fluorescence data, the carbon isotope landscape atlas prediction model is used to obtain the C3 plant leaf carbon isotope fractionation value corresponding to each C3 plant grid pixel.

3. The method for obtaining the scaled leaf water use efficiency map according to claim 1 or 2, characterized in that: The carbon isotope landscape prediction model is obtained by training in the following way: Using the training data set, training an initial regression machine learning model to obtain the carbon isotope landscape prediction model; In which, the training data set includes sample data and labels of the sample data; the sample data includes environmental ecological data samples related to plant leaf water use efficiency in the target area during the preset time period, and the geographic coordinates of the C3 plant grid pixels; the labels of the sample data are the C3 plant leaf carbon isotope fractionation values ​​corresponding to the C3 plant grid pixels.

4. The method for obtaining a scaled leaf water use efficiency map according to claim 1, wherein: The leaf water use efficiency calculation equation includes a first parameter that is unrelated to the geographical location, a second parameter that is related to the geographical location, and a carbon isotope fractionation value of the C3 plant leaf; The method of obtaining the leaf water use efficiency map of the target area in the preset time period based on the carbon isotope fractionation value landscape map and using a leaf water use efficiency calculation equation includes: Pixelating the second parameter according to the target region's regional boundary vector data and a second preset spatial resolution to obtain a second parameter value corresponding to each target grid pixel; wherein the target grid pixel corresponds to a grid unit in the carbon isotope fractionation value landscape map; According to the first parameter, the second parameter value corresponding to each target grid pixel, and the C3 plant leaf carbon isotope fractionation value corresponding to each target grid pixel, the leaf water use efficiency calculation equation is used to obtain the leaf water use efficiency corresponding to each target grid pixel; The leaf water use efficiency map is obtained according to the leaf water use efficiency corresponding to all target grid pixels.

5. The method for obtaining the scaled leaf water use efficiency map according to claim 4, characterized in that: The second parameter includes a carbon dioxide compensation point when mitochondrial respiration is not considered, and the carbon dioxide compensation point is obtained by a carbon dioxide compensation point calculation formula; The step of performing grid pixelization on the second parameter of the preset time period according to the region boundary vector data of the target region and the second preset spatial resolution to obtain a second parameter value corresponding to each target grid pixel includes: Obtaining average temperature grid data of the target area; Resampling the average temperature grid data using a bilinear interpolation method to obtain temperature data corresponding to each target grid pixel; According to the temperature data corresponding to each target grid pixel, the carbon dioxide compensation point parameter corresponding to each target grid pixel is obtained using the carbon dioxide compensation point calculation formula.

6. A device for obtaining a scaled leaf water use efficiency map, characterized in that: include: A first acquisition module is configured to acquire environmental ecological data related to plant leaf water use efficiency in a target area during a preset time period; wherein the environmental ecological data includes multispectral observation data, atmospheric carbon dioxide concentration data, and solar-induced chlorophyll fluorescence data; A second acquisition module is configured to acquire a carbon isotope fractionation value landscape map of the target area in the preset time period based on the environmental ecological data; A third acquisition module is configured to obtain a leaf water use efficiency map of the target area in the preset time period based on the carbon isotope fractionation value landscape map and using a leaf water use efficiency calculation equation; Wherein, obtaining the carbon isotope fractionation value landscape map of the target area in the preset time period based on the environmental ecological data includes: Using the region boundary vector data of the target region, based on a first preset spatial resolution, the geographical coverage of the target region is divided into a plurality of grid cells; Determining a C3 plant grid pixel from the plurality of grid cells based on the C3 plant coverage data of the target area in the preset time period; According to the geographic coordinates of the C3 plant grid pixel and the environmental ecological data, a carbon isotope landscape prediction model is used to obtain the C3 plant leaf carbon isotope fractionation value corresponding to each C3 plant grid pixel; wherein the carbon isotope landscape prediction model is a trained regression machine learning model; The carbon isotope fractionation value landscape map is obtained according to the carbon isotope fractionation values ​​of the C3 plant leaves corresponding to all the C3 plant grid pixels.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for obtaining the upscaled leaf water use efficiency map according to any one of claims 1 to 5 is implemented.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for obtaining the upscaled leaf water use efficiency map according to any one of claims 1 to 5 is implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for obtaining the upscaled leaf water use efficiency map according to any one of claims 1 to 5 is implemented.

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