Vegetation total primary productivity estimation method based on IVPM model

Abstract

The invention discloses a vegetation total primary productivity estimation method based on an IVPM model, and the method comprises the following steps: calculating a vapor pressure deficit parameter and a soil water content parameter, and calculating a joint stress factor; calculating a photosynthetically active radiation absorption proportion and photosynthetically active radiation, and calculating a final maximum light energy utilization rate; calculating surface temperature remote sensing data based on the vegetation total primary productivity observation data, and calculating a temperature stress factor based on the surface temperature remote sensing data; calculating a surface water index, and calculating a water stress factor based on the surface water index; determining a phenological stress factor, and calculating a final light energy utilization rate based on the joint stress factor, the maximum light energy utilization rate, the temperature stress factor, the moisture stress factor and the phenological stress factor; a vegetation overall primary productivity estimate is calculated based on the final light energy utilization rate, the photosynthetically active radiation absorption ratio, and the photosynthetically active radiation.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of ecological remote sensing and carbon cycle research, and particularly relates to a vegetation gross primary productivity estimation method based on an IVPM model. BACKGROUND

[0002] As a core index in global carbon cycle, vegetation GPP directly reflects the carbon sequestration capacity of an ecological system and is an important basis for carbon cycle model research. Accurate quantification of GPP is of great significance for in-depth understanding of the mechanism of carbon flux in a terrestrial ecological system. GPP models based on light energy utilization rate have become mainstream tools for large-scale carbon cycle simulation driven by remote sensing due to their clear physiological and ecological basis and process algorithm characteristics. Among them, the VPM model simplifies the structure of the LUE model through equivalent substitution of stress factors, has a lower application threshold among numerous LUE models, and can make full use of remote sensing data. However, from the application effect in existing research, the accuracy of the VPM model in many scenarios still needs to be improved. In the real and variable environment background, water, temperature, and phenology cannot effectively reflect the changes in the environment. For example, the model significantly underestimates the GPP in grassland and farmland ecological systems (relative error > 20%), and there is a large difference between the estimated GPP product and the eddy correlation flux observation value. Therefore, it is necessary to find more complex and adaptive environmental stress factors to increase the explanatory power of the model. SUMMARY

[0003] The application aims to solve the problems in the prior art and provides the following scheme: A vegetation gross primary productivity estimation method based on an IVPM model, comprising the following steps: Calculate a vapor pressure deficit parameter and a soil moisture parameter, and calculate a joint stress factor; Calculate a photosynthetically active radiation absorption ratio and a photosynthetically active radiation, and calculate a final maximum light energy utilization rate; Calculate LST remote sensing data based on GPP observation data, and calculate a temperature stress factor based on the LST remote sensing data; Calculate a surface water index, and calculate a water stress factor based on the surface water index; Determine a phenology stress factor, calculate a final light energy utilization rate based on the joint stress factor, the maximum light energy utilization rate, the temperature stress factor, the water stress factor, and the phenology stress factor; Calculate a vegetation gross primary productivity estimation value based on the final light energy utilization rate, the photosynthetically active radiation absorption ratio, and the photosynthetically active radiation.

[0004] Preferably, the vapor pressure deficit parameter is: wherein, VPD represents the vapor pressure deficit parameter, TA represents the temperature of the surface 2m, p represents the atmospheric pressure, q represents the specific humidity data every three hours; said soil water content parameter is: wherein, SWC represents the soil water content parameter, V 1 represents the soil water content at the depth of 0-7cm, α 1 represents V the weight proportion value of 1, V 2 represents the soil water content at the depth of 7-28cm, α 2 represents V the weight proportion value of 2, V 3 represents the soil water content at the depth of 28-100cm, α 3 represents V the weight proportion value of 3; said joint stress factor is: wherein, fVPD × SWC represents the joint stress factor, VPD × SWC ) max represents the maximum value of the product.

[0005] Preferably, said photosynthetically active radiation absorption ratio is: wherein, FAPARPAV represents the photosynthetically active radiation absorption ratio, EVI represents the enhanced vegetation index, ρnir represents the reflectance value of the near-infrared band of the corrected image grid, ρred represents the reflectance value of the red band of the corrected image grid, ρblue represents the reflectance value of the blue band of the corrected image grid; said final maximum light energy utilization rate is: wherein, εmax represents the final maximum light energy utilization rate, LUEmaxi represents the maximum light energy utilization rate, n represents the number of calculated maximum light energy utilization rates, GEEmax represents the observation valueGEE from small to large 98.5% to 99.5% of the area value, GEE representing the total ecosystem carbon exchange, PAR representing the photosynthetically active radiation.

[0006] Preferably, the land surface temperature remote sensing data is: wherein, GPPobs representing the total primary productivity of vegetation observation data; The method for calculating the temperature stress factor based on the LST remote sensing data comprises: Taking 10% of the minimum value of the total primary productivity of vegetation as a threshold, substituting it into the logarithmic relationship of the LST remote sensing data to obtain the minimum temperature Tmin ; Taking the LST remote sensing data at the maximum EVI of a selected number of time periods as the initial optimum temperature, and averaging it to obtain the optimum temperature Topt ; Obtaining the maximum temperature of the region to be estimated by looking up a table Tmax ; Based on the minimum temperature Tmin , the optimum temperature Topt and the maximum temperature Tmax , the temperature stress factor is calculated: wherein, Tscaler representing the temperature stress factor, T representing the land surface temperature data.

[0007] Preferably, the water stress factor is: wherein, Wscaler representing the water stress factor, LSWI representing the land surface moisture index, ρnir representing the reflectance value of the near-infrared band of the corrected image grid, ρswir representing the reflectance value of the short-wave infrared band.

[0008] Preferably, the final light energy utilization rate is: wherein, εg representing the final light energy utilization rate, εmax representing the final maximum light energy utilization rate, Tscaler representing the temperature stress factor, Wscaler representing the water stress factor, Pscalerrepresents a phenological stress factor, fVPD x SWC represents a combined stress factor.

[0009] Preferably, the total vegetation primary productivity estimated value is: wherein, GPP represents a total vegetation primary productivity estimated value, εg represents a final light energy utilization rate, FAPARPAV represents a photosynthetically active radiation absorption ratio, PAR represents a photosynthetically active radiation.

[0010] Compared with the prior art, the present application has the following beneficial effects: Through the estimation of the present application, the estimation accuracy of the total vegetation primary productivity of grassland vegetation can be greatly improved, which makes up for the problem of insufficient accuracy of the VPM model in the field of total vegetation primary productivity estimation. At the same time, the calculation data of the combined stress factor of the vapor pressure deficit parameter and the soil moisture content parameter are all from remote sensing data, and the calculation is simple, which greatly improves the use efficiency of the new model. In addition, the combined stress factor also shows good adaptability and stable estimation effect in regions with complex and diverse climate conditions. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the present application, the following briefly introduces the drawings needed to be used in the embodiments, and obviously, the drawings described in the following are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0012] GPP The present application provides a method flowchart. DETAILED DESCRIPTION

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application, and obviously, the described embodiments are only some of the embodiments of the present application, but not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0014] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0015] The present application provides a kind of total vegetation primary productivity (VPP) estimation method, which is used for estimating the total vegetation primary productivity of grassland vegetation. GPPAn improved version of the VPM model, IVPM, is proposed to improve estimation accuracy and reduce model parameter complexity. This model innovatively introduces a cooperative environmental stress factor. Figure 1 - GPP Among them, vapor pressure deficit ( VPD ) affects the efficiency of stomatal opening and closing in vegetation, thus affecting photosynthetic efficiency, soil moisture content ( SWC The combined effects of photosynthetic efficiency and water transport on vegetation root absorption can reflect the combined influence of these two processes on vegetation photosynthesis, and thus quantify the complex mechanisms underlying their interactions. VPD Estimate the impact, especially under complex and variable weather conditions, to improve the model's adaptability to the environment.

[0016] Example In this embodiment, as SWC As shown, a method for estimating total primary productivity of vegetation based on the IVPM model includes the following steps: S1. Calculate the vapor pressure deficit parameters and soil moisture content parameters, and calculate the combined stress factor.

[0017] In this embodiment, remote sensing data of surface temperature, atmospheric pressure, and specific humidity of the target area are obtained through Google Cloud Platform, and vapor pressure deficit parameters and soil moisture content parameters are calculated.

[0018] The vapor pressure deficit parameters are: in, GPP This indicates the vapor pressure deficit parameter. Figure 1 This represents the temperature at a depth of 2 meters above the Earth's surface. p Indicates atmospheric pressure. q This indicates the specific humidity data every three hours; VPD , p , q The data were synthesized into 8-day data sets, and VPD data with a time resolution of 8 days was calculated.

[0019] Soil moisture content parameters are: in, TA This indicates the soil moisture content parameter. V 1 indicates the soil moisture content at a depth of 0-7cm. α 1 represents V The weighting percentage of 1 V 2 indicates the soil moisture content at a depth of 7-28cm. α 2 indicates V The weighting percentage of 2, V 3 indicates the soil moisture content at a depth of 28-100cm. α 3 indicatesV The weighting percentage of 3; [The text abruptly ends here, likely due to an incomplete sentence or a formatting error.] α 1. α 2. α 3 is set to 0.07, 0.21, and 0.72.

[0020] Finally, multiply the two to obtain the joint parameter of the target region in 2009. Then, divide this parameter by the maximum value of the joint parameter and scale the value to the range of 0-1 to obtain the joint stress factor: in, TA × SWC Indicates the combined stress factor, ( fVPD × SWC ) VPD This represents the maximum value of the product.

[0021] S2. Calculate the photosynthetically active radiation absorption ratio and photosynthetically active radiation, and calculate the final maximum light energy utilization rate.

[0022] In this embodiment, the enhanced vegetation index is calculated using corrected band data obtained through the Google Cloud Platform. SWC : in, max This represents the reflectance value of the near-infrared band (841-876nm) of the corrected image raster. EVI This represents the reflectance value of the red band (620-670 nm) of the corrected image raster. ρnir This represents the reflectance value of the blue band (459-479 nm) of the corrected image raster; The photosynthetically active radiation absorption ratio is: in, ρred This indicates the proportion of photosynthetically active radiation absorbed.

[0023] Obtaining photosynthetically active radiation (RAEP) on Google Cloud Platform ρblue ) and the total ecosystem carbon exchange measured in the target area ( FAPARPAV ) data, for PAR Sort the data in ascending order and obtain the 98.5%-99.5% range. GEE and its corresponding GEE and GEE The data was used to calculate multiple maximum light energy utilization rates from the observed data: in, FAPARPAV Indicates the maximum light energy utilization rate. PAR Represents the observed valueLUEmaxi After arranging from small to large, 98.5% to 99.5% of the area values, GEEmax represents the photosynthetically active radiation.

[0024] Finally, the average of the plurality of maximum light energy utilization rates is calculated to obtain the final maximum light energy utilization rate: wherein, GEE represents the final maximum light energy utilization rate, n represents the number of calculated maximum light energy utilization rates.

[0025] S3. Calculate the land surface temperature remote sensing data based on the total vegetation primary productivity observation data, and calculate the temperature stress factor based on the land surface temperature remote sensing data.

[0026] In this embodiment, first, the land surface temperature (LST) remote sensing data in 2004, 2006 and 2008 is obtained, and the missing data of the land surface temperature is filled by the MICE method to obtain complete land surface temperature remote sensing data. Second, the land surface temperature remote sensing data and the vegetation total primary productivity observation data corresponding to the flux site are plotted into a scatter plot, and the Origin software is used for nonlinear fitting to obtain the land surface temperature remote sensing data as: wherein, PAR represents the land surface temperature remote sensing data, εmax represents the vegetation total primary productivity observation data.

[0027] Then, the method for calculating the temperature stress factor based on the land surface temperature remote sensing data comprises: Taking 10% of the minimum value of the vegetation total primary productivity as a threshold, the threshold is substituted into the logarithmic relationship of the land surface temperature remote sensing data to obtain the minimum temperature LST ; taking the land surface temperature remote sensing data at the maximum EVI in 2004, 2006 and 2008 as the initial optimum temperature, and averaging the initial optimum temperature to obtain the optimum temperature GPPobs ; the highest temperature Tmin of the region to be estimated is obtained by looking up the table; the minimum temperature Topt , the optimum temperature Tmax and the highest temperature Tmin are used to calculate the temperature stress factor: wherein, Topt represents the temperature stress factor, T represents the land surface temperature data.

[0028] S4. Calculate the land surface water index, and calculate the water stress factor based on the land surface water index.

[0029] In this embodiment, the surface moisture index is calculated based on the corrected band data. Tmax and water stress factors Tscaler : in, LSWI Indicates water stress factor. Wscaler This represents the surface moisture index. Wscaler This represents the reflectance value in the near-infrared band of the corrected image raster. LSWI This represents the reflectance value in the short-wave infrared band (2105-2155 nm).

[0030] S5. Determine the phenological stress factors, and calculate the final light energy utilization rate based on the combined stress factor, maximum light energy utilization rate, temperature stress factor, water stress factor, and phenological stress factor.

[0031] In this embodiment, to simplify the phenological stress factor, it can be empirically defined as follows: ρnir The value is set to 1: .

[0032] The final light energy utilization rate is: in, ρswir Indicates the final light energy utilization rate. Pscaler This represents the final maximum light energy utilization rate. εg Indicates the temperature stress factor. εmax Indicates water stress factor. Tscaler Indicates phenological stress factors, Wscaler × Pscaler This indicates a combined stress factor.

[0033] S6. Calculate the estimated value of total primary productivity of vegetation based on the final light energy utilization rate, the proportion of photosynthetically active radiation absorbed, and the photosynthetically active radiation.

[0034] In this embodiment, the estimated total primary productivity of vegetation is: in, fVPD This represents an estimate of total primary productivity of vegetation. SWC Indicates the final light energy utilization rate. GPP Indicates the proportion of photosynthetically active radiation absorbed. εg This indicates photosynthetically active radiation.

[0035] Received FAPARPAV Estimated value and PAR GPP GPP The comparison of observed values ​​is shown in Table 1: Table 1 In 2005, the MRE and RMSE of the IVPM model were reduced by 49% and 19% respectively relative to the VPM model; in 2007, the MRE and RMSE were reduced by 53% and 6% respectively relative to the VPM model; in 2009, the MRE and RMSE were reduced by 20% and 21% respectively relative to the VPM model; and in 2010, the MRE and RMSE were reduced by 27% and 24% respectively relative to the VPM model.

[0036] The above-described embodiments are merely intended to describe the preferred modes of the present application, and are not intended to limit the scope of the present application. Various modifications and improvements to the technical solutions of the present application made by those skilled in the art without departing from the design spirit of the present application shall fall within the scope of protection of the present application as defined by the claims.

Claims

1. A method for estimating total primary productivity of vegetation based on the IVPM model, characterized in that, Includes the following steps: Calculate vapor pressure deficit parameters and soil moisture content parameters, and calculate the combined stress factor; Calculate the photosynthetically active radiation absorption ratio and photosynthetically active radiation, and calculate the final maximum light energy utilization rate; Land surface temperature remote sensing data are calculated based on total primary productivity observation data of vegetation, and temperature stress factor is calculated based on the land surface temperature remote sensing data. Calculate the surface water index, and calculate the water stress factor based on the surface water index; Determine the phenological stress factors, and calculate the final light energy utilization rate based on the combined stress factor, the maximum light energy utilization rate, the temperature stress factor, the moisture stress factor, and the phenological stress factors; The estimated total primary productivity of vegetation is calculated based on the final light energy utilization rate, the photosynthetically active radiation absorption ratio, and the photosynthetically active radiation.

2. The method for estimating total primary productivity of vegetation based on the IVPM model according to claim 1, characterized in that, The vapor pressure deficit parameter is: in, VPD This indicates the vapor pressure deficit parameter. TA This represents the temperature at a depth of 2 meters above the Earth's surface. p Indicates atmospheric pressure. q This indicates humidity data every three hours; The soil moisture content parameter is as follows: in, SWC This indicates the soil moisture content parameter. V 1 indicates the soil moisture content at a depth of 0-7cm. α 1 represents V The weighting percentage of 1 V 2 indicates the soil moisture content at a depth of 7-28cm. α 2 indicates V The weighting percentage of 2, V 3 indicates the soil moisture content at a depth of 28-100cm. α 3 indicates V The weighting percentage of 3; The combined stress factor is: in, fVPD × SWC Indicates the combined stress factor, ( VPD × SWC ) max This represents the maximum value of the product.

3. The method for estimating total primary productivity of vegetation based on the IVPM model according to claim 1, characterized in that, The photosynthetically active radiation absorption ratio is: in, FAPARPAV Indicates the proportion of photosynthetically active radiation absorbed. EVI Indicates the enhanced vegetation index, ρnir This represents the reflectance value in the near-infrared band of the corrected image raster. ρred This represents the reflectance value of the red band in the corrected image raster. ρblue This represents the reflectance value of the blue band in the corrected image raster. The final maximum light energy utilization rate: in, εmax This represents the final maximum light energy utilization rate. LUEmaxi Indicates the maximum light energy utilization rate. n This indicates the number of maximum light energy utilization rates calculated. GEEmax Represents the observed value GEE The values ​​in the 98.5% to 99.5% range after arranging them from smallest to largest. GEE This represents the total ecosystem carbon exchange. PAR This indicates photosynthetically active radiation.

4. The method for estimating total primary productivity of vegetation based on the IVPM model according to claim 1, characterized in that, The surface temperature remote sensing data is as follows: in, LST Represents remote sensing data of land surface temperature. GPPobs This represents observational data on total primary productivity of vegetation; The method for calculating the temperature stress factor based on the aforementioned surface temperature remote sensing data includes: Using 10% of the minimum total primary productivity of vegetation as a threshold, and substituting it into the logarithmic relationship of the land surface temperature remote sensing data, the minimum temperature is obtained. Tmin ; Using the surface temperature remote sensing data at the maximum EVI value over a selected period of time as the initial optimum temperature, and calculating the average value, the optimum temperature is obtained. Topt ; The highest temperature of the area to be estimated is obtained by looking up a table. Tmax ; Based on the lowest temperature Tmin The optimal temperature Topt and the highest temperature Tmax Calculate the temperature stress factor: in, Tscaler Indicates the temperature stress factor. T This represents land surface temperature data.

5. The method for estimating total primary productivity of vegetation based on the IVPM model according to claim 1, characterized in that, The water stress factor is: in, Wscaler Indicates water stress factor. LSWI This represents the surface moisture index. ρnir This represents the reflectance value in the near-infrared band of the corrected image raster. ρswir This represents the reflectance value in the shortwave infrared band.

6. The method for estimating total primary productivity of vegetation based on the IVPM model according to claim 1, characterized in that, The final light energy utilization rate is: in, εg Indicates the final light energy utilization rate. εmax This represents the final maximum light energy utilization rate. Tscaler Indicates the temperature stress factor. Wscaler Indicates water stress factor. Pscaler Indicates phenological stress factors, fVPD × SWC This indicates a combined stress factor.

7. The method for estimating total primary productivity of vegetation based on the IVPM model according to claim 1, characterized in that, The estimated total primary productivity of the vegetation is: in, GPP This represents an estimate of total primary productivity of vegetation. εg Indicates the final light energy utilization rate. FAPARPAV Indicates the proportion of photosynthetically active radiation absorbed. PAR This indicates photosynthetically active radiation.

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