A remote sensing method for estimating total primary productivity of vegetation considering soil moisture stress

Abstract

This invention discloses a remote sensing method for estimating total primary productivity of vegetation considering soil moisture stress, belonging to the field of remote sensing estimation technology for carbon cycle in terrestrial ecosystems. The method includes: acquiring and uniformly preprocessing multi-source data such as leaf area index, meteorological, soil, and remote sensing data; calculating the photosynthetically active radiation absorbed by sun-exposed and shaded leaves based on a two-leaf light energy utilization model, and constructing stress factors such as temperature, saturated vapor pressure difference, and carbon dioxide concentration; calculating the relative abundance or scarcity of available soil moisture for plants using soil moisture content and soil properties, constructing a soil moisture sensitivity coefficient by combining the ratio of actual evapotranspiration to potential evapotranspiration, and establishing a three-segment soil moisture stress function to characterize the photosynthetic inhibition effect under water deficit, adequate, and supersaturated conditions. This invention improves the accuracy and adaptability of remote sensing estimation of vegetation productivity under different water conditions by simultaneously characterizing the dual limiting effects of soil moisture deficit and supersaturation.

Description

Technical Field

[0001] This invention belongs to the field of remote sensing estimation technology of carbon cycle in terrestrial ecosystems, specifically involving a remote sensing estimation method for total primary productivity of vegetation that takes into account soil moisture stress. Background Technology

[0002] Gross primary productivity (GMP) is one of the most crucial fluxes in the carbon cycle of terrestrial ecosystems. Its accurate estimation is crucial for assessing global carbon source-sink patterns, understanding climate change feedback mechanisms, and serving ecological management and agricultural production. With the development of satellite remote sensing technology, GMP retrieval methods based on remote sensing data have become important tools for large-scale estimation. Among these, methods represented by light energy use efficiency (LEEP) models are widely used due to their simple structure, readily available parameters, and ease of coupling with multi-source remote sensing data. These models typically characterize vegetation carbon assimilation capacity based on the product of photosynthetically active radiation absorption (PARA) and light energy use efficiency (LEEP), playing a fundamental role in regional and even global carbon cycle research. However, against the backdrop of global climate change, extreme events such as drought, high temperatures, and abnormal precipitation are frequent, leading to significant spatiotemporal fluctuations in terrestrial ecosystem productivity. Soil moisture, as a key factor connecting climate and vegetation physiological processes, has been proven to play a dominant role in regulating GMP changes. Therefore, reasonably characterizing the impact of soil moisture on photosynthesis within a remote sensing estimation framework has become a critical scientific issue for improving model accuracy and reliability.

[0003] In existing remote sensing technologies for estimating total primary productivity of vegetation, light energy use efficiency (SEP) models are widely used due to their simple structure and readily available parameters. However, their characterization of environmental factors remains significantly inadequate. Current mainstream models typically use temperature and atmospheric moisture deficit as the main regulating variables, simplifying the influence of soil moisture to an indirect effect or ignoring it entirely. This leads to decreased model accuracy in areas with significant moisture variations. Studies have shown that soil moisture plays an independent and crucial regulatory role in vegetation photosynthesis, and relying solely on atmospheric indicators is insufficient to reflect the true moisture situation, resulting in systematic biases under drought or complex moisture conditions. This deficiency necessitates the introduction of empirical corrections or multi-source data in practical applications, increasing computational complexity and data costs, and reducing the efficiency and stability of the method.

[0004] To address the aforementioned issues, some existing technologies attempt to introduce soil moisture variables to improve model performance. For example, the technical solution described in patent number CN120579351A estimates available soil water by constructing a water balance model and establishes a water stress function to constrain photosynthesis, thereby improving the accuracy of water and carbon flux estimation. However, this type of method mainly focuses on the inhibitory effect of soil moisture deficit on photosynthesis, still based on the assumption of unidirectional regulation, that is, assuming that water no longer has a limiting effect after reaching a certain level, ignoring the negative effects under high water content conditions. Furthermore, this method relies on multi-model coupling and parameter calibration, resulting in a complex computational process, sensitivity to input data, and low efficiency and high implementation costs in large-scale remote sensing applications.

[0005] In addition, another type of technology, such as the one described in patent number CN121504282A, analyzes the response mechanism of vegetation to drought by comprehensively considering light energy utilization efficiency, carbon use efficiency, and water use efficiency, revealing the path of water stress from an ecological process perspective. However, this method is mainly used for response analysis and mechanism research, lacking a parameterized expression that can be directly embedded into remote sensing estimation models, making it difficult to apply to the rapid inversion of productivity. Furthermore, its calculation relies on multivariate statistical analysis, resulting in a complex processing flow, which is not conducive to continuous monitoring and large-scale application.

[0006] In summary, existing technologies have significant shortcomings in expressing the mechanisms of soil moisture action, particularly failing to uniformly characterize the effects of the entire process from water deficit to supersaturation, leading to systematic errors in the models under different moisture conditions. This invention introduces a regulatory mechanism that simultaneously represents the limitations of soil moisture deficit and supersaturation into the two-leaf light energy utilization efficiency model, directly coupling the effects of the entire soil moisture process to the light energy utilization efficiency parameter. This effectively improves estimation accuracy without increasing model complexity, thus overcoming the problems of insufficient accuracy and limited efficiency in existing technologies. Summary of the Invention

[0007] The purpose of this invention is to provide a remote sensing estimation method for total primary productivity of vegetation that takes into account soil moisture stress, which solves the technical problems of insufficient expression of soil moisture action mechanism in existing light energy use efficiency models, especially the lack of characterization of productivity inhibition effect under water supersaturation conditions.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A remote sensing method for estimating total primary productivity of vegetation considering soil moisture stress includes the following steps: Step S1: Obtain vegetation leaf area index, air temperature, dew point temperature, downwave radiation, atmospheric carbon dioxide concentration, land cover type, soil moisture content, actual evapotranspiration, potential evapotranspiration, soil sand content, soil clay content, soil organic carbon content, soil gravel fineness, and soil thickness, and perform unified spatiotemporal resolution preprocessing on all data. Step S2: Based on the two-leaf light energy utilization model, using the vegetation leaf area index, land cover type, and downward shortwave radiation, calculate the photosynthetically active radiation absorbed by sun-facing leaves and the photosynthetically active radiation absorbed by shade-facing leaves, and determine the maximum light energy utilization rate of sun-facing leaves and the maximum light energy utilization rate of shade-facing leaves according to the land cover type. Step S3: Calculate the saturated water vapor pressure difference using air temperature and dew point temperature, and construct the temperature stress factor, saturated water vapor pressure difference stress factor, and carbon dioxide concentration stress factor, respectively. Step S4: Calculate the relative abundance or scarcity of soil water available to plants using soil moisture content, soil sand content, soil clay content, soil organic carbon content, soil gravel fineness, soil thickness, actual evapotranspiration and potential evapotranspiration. Construct a soil water sensitivity coefficient using the ratio of actual evapotranspiration to potential evapotranspiration, and then establish a three-segment soil water stress function. Specifically, when the relative abundance or deficiency of soil moisture available to plants is less than the first threshold, the value of the three-stage soil moisture stress function decreases as the relative abundance or deficiency of soil moisture available to plants decreases to characterize water deficit stress; when the relative abundance or deficiency of soil moisture available to plants is greater than the second threshold, the value of the three-stage soil moisture stress function decreases as the relative abundance or deficiency of soil moisture available to plants increases to characterize water supersaturation stress; when the relative abundance or deficiency of soil moisture available to plants is between the first threshold and the second threshold, the value of the three-stage soil moisture stress function is one. Step S5: Using the three-segment soil moisture stress function as a multiplicative stress factor, the smaller of the temperature stress factor and the saturated vapor pressure difference stress factor is multiplied by the carbon dioxide concentration stress factor, the maximum light energy utilization rate of sun-sun leaves, and the photosynthetically active radiation absorbed by sun-sun leaves to obtain the total primary productivity of sun-sun leaf vegetation; similarly, the total primary productivity of shade-sun leaf vegetation is calculated, and the total primary productivity of sun-sun leaf vegetation and the total primary productivity of shade-sun leaf vegetation are summed to obtain the total primary productivity of vegetation.

[0009] Furthermore, the spatiotemporal resolution unification preprocessing includes: All data were resampled to the same spatial resolution using the nearest neighbor interpolation method and unified to the same time scale; The calculation of saturated vapor pressure difference using air temperature and dew point temperature is specifically performed according to the following formula:

[0010]

[0011]

[0012] in, The saturated vapor pressure, For air temperature, It is a natural constant. Relative humidity, Dew point temperature, This represents the saturated water vapor pressure difference.

[0013] Furthermore, the photosynthetically active radiation absorbed by the sun-bearing leaves Photosynthetically active radiation absorbed by shaded leaves Calculate according to the following formulas respectively:

[0014]

[0015] In the formula, For direct radiation, photosynthetically active radiation, The average blade tilt angle, The zenith angle of the sun. This is diffuse radiation, which is photosynthetically active radiation. This refers to the photosynthetically active radiation scattered below the canopy. The leaf area index is the vegetation leaf area index. This is multiple scattered radiation within the canopy. The leaf area index of sun-exposed leaves. This is the leaf area index for shaded leaves.

[0016] Furthermore, the leaf area index of the sun-loving leaves and shade leaf area index Calculated using the following formula:

[0017]

[0018] in, The zenith angle of the sun. The aggregation index, The leaf area index is the vegetation leaf area index. It is an exponential function.

[0019] Furthermore, the temperature stress factor and saturated water vapor pressure difference stress factor Calculate using the following formulas respectively:

[0020]

[0021] in, For air temperature, This is the lowest temperature for photosynthesis. This is the highest temperature for photosynthesis. The optimal temperature for photosynthesis, For half-saturation parameters, This represents the saturated water vapor pressure difference.

[0022] Furthermore, the carbon dioxide concentration stress factor Calculate using the following formula:

[0023]

[0024] in, This refers to the carbon dioxide concentration in the intercellular spaces of the leaf cells. This represents the carbon dioxide compensation point when there is no dark respiration. The concentration of carbon dioxide in the environment. This is the ratio of intercellular carbon dioxide concentration to atmospheric carbon dioxide concentration; It is obtained from the following formula:

[0025]

[0026] in, For saturated water vapor pressure difference, To characterize the saturated water vapor pressure difference and The parameters of the relationship between them The Michaelis constant for Rubisco enzymes. The viscous resistance constant of water; Calculated using the following formula:

[0027]

[0028]

[0029] in, For temperature Michaelis constant for the carboxylation reaction Oxygen partial pressure constant For temperature Michaelis constant for oxygenation reaction, For air temperature, is the molar gas constant.

[0030] Furthermore, the plant can utilize soil moisture relative abundance or scarcity index Calculate using the following formula:

[0031] in, Soil moisture content, This represents the soil moisture content corresponding to the permanent wilting point. The maximum effective water holding capacity of the soil; the maximum effective water holding capacity of the soil Soil moisture content corresponding to the permanent wilting point It is calculated based on soil sand content, soil clay content, soil organic carbon content, soil gravel fineness, and soil thickness.

[0032] Furthermore, the three-segment soil moisture stress function The specific expression is:

[0033] in, The first threshold, The second threshold, The soil moisture sensitivity coefficient in the water deficit section. The soil moisture sensitivity coefficient is given for the supersaturated soil section.

[0034] Furthermore, the soil moisture sensitivity coefficient in the water deficit section Soil moisture sensitivity coefficient in the supersaturated section Calculate using the following formula:

[0035]

[0036]

[0037] in, It is a constant 0. It is a constant 0. It is a constant of 0.733. For actual evaporation, For potential evaporation.

[0038] Furthermore, the determination of the maximum light energy utilization rate of sun-facing leaves and the maximum light energy utilization rate of shaded leaves based on land cover type is specifically as follows: the maximum light energy utilization rate of sun-facing leaves and the maximum light energy utilization rate of shaded leaves are determined by referring to a preset land cover type-maximum light energy utilization rate lookup table.

[0039] Furthermore, the direct radiation photosynthetically effective radiation Scattered radiation and photosynthetically active radiation Scattered radiation below the canopy and photosynthetically active radiation and multiple scattered radiation within the canopy Calculated using the following formula:

[0040]

[0041]

[0042]

[0043]

[0044]

[0045] in, For downlink shortwave radiation, The zenith angle of the sun. This represents the solar radiation flux ratio at the top of the atmosphere. The proportion of scattered radiation. Total photosynthetically active radiation, This is the scattering of partially photosynthetically active radiation. For direct partial photosynthetic effective radiation, This is diffuse radiation, which is photosynthetically active radiation. The aggregation index, The leaf area index is the vegetation leaf area index. Total photosynthetically active radiation above the canopy, These are the leaf angle distribution parameters.

[0046] Furthermore, the maximum effective water holding capacity of the soil Soil moisture content corresponding to the permanent wilting point Calculate using the following formulas respectively:

[0047]

[0048] in, The coarseness of the soil gravel, For soil thickness, This is the preset maximum root depth constant for vegetation. Field holding capacity The permanent wilting coefficient; the field water holding capacity. and permanent wilting coefficient Determine according to the following formula:

[0049]

[0050]

[0051]

[0052] in, The soil sand content, The clay content of the soil, This refers to the soil organic carbon content.

[0053] Furthermore, the first threshold The second threshold is 0.6. It is 0.9.

[0054] In addition, this invention also discloses a remote sensing estimation system for total primary productivity of vegetation considering soil moisture stress, used in the remote sensing estimation method for total primary productivity of vegetation considering soil moisture stress as described above, comprising: The data acquisition and preprocessing module is used to acquire vegetation leaf area index, air temperature, dew point temperature, downwave radiation, atmospheric carbon dioxide concentration, land cover type, soil moisture content, actual evapotranspiration, potential evapotranspiration, soil sand content, soil clay content, soil organic carbon content, soil gravel fineness and soil thickness, and perform unified spatiotemporal resolution preprocessing. The photosynthetically active radiation calculation module is used to calculate the photosynthetically active radiation absorbed by sun-facing leaves and the photosynthetically active radiation absorbed by shaded leaves based on the two-leaf light energy utilization model. The environmental stress factor construction module is used to calculate the saturated water vapor pressure difference and construct the temperature stress factor, the saturated water vapor pressure difference stress factor, and the carbon dioxide concentration stress factor. The soil moisture stress construction module is used to calculate the relative abundance or scarcity of soil moisture available to plants, construct the soil moisture sensitivity coefficient, and establish a three-segment soil moisture stress function. The total primary productivity estimation module is used to calculate and sum the total primary productivity of sun-sun and shade-sun vegetation by using the three-segment soil moisture stress function as a multiplicative stress factor, and output the total primary productivity of vegetation.

[0055] Compared with the prior art, the present invention has the following beneficial effects: This invention introduces a method to characterize the relative abundance or scarcity of soil moisture available to plants and constructs a three-segment soil moisture stress function. This function can simultaneously characterize the response of vegetation productivity to water changes under soil moisture deficit, suitability, and supersaturation conditions, overcoming the shortcomings of existing methods that mainly focus on atmospheric hydrothermal stress and struggle to depict the soil moisture limitation effect under supersaturation conditions. Furthermore, this invention directly couples the soil moisture stress function into the vegetation productivity remote sensing estimation model, thereby enhancing the model's adaptability to the intensity of water constraint under different environmental conditions and improving the accuracy, stability, and applicability of estimating total primary vegetation productivity. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0057] Figure 1 This is the overall flowchart of the present invention.

[0058] Figure 2 is a verification diagram of the results for all sites in the embodiment of the present invention.

[0059] Figure 3 shows the accuracy comparison results of each station in Embodiment 1 of the present invention. Detailed Implementation

[0060] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the embodiments of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0061] The following is in conjunction with the appendix Figures 1-3 The embodiments of the present invention will be described in detail below.

[0062] Example 1: A remote sensing method for estimating total primary productivity of vegetation considering soil moisture stress, comprising the following steps: Step S1: Obtain vegetation leaf area index, air temperature, dew point temperature, downwave radiation, atmospheric carbon dioxide concentration, land cover type, soil moisture content, actual evapotranspiration, potential evapotranspiration, soil sand content, soil clay content, soil organic carbon content, soil gravel fineness, and soil thickness, and perform spatiotemporal resolution unified preprocessing on all data.

[0063] Step S2: Based on the two-leaf light energy utilization model, using the vegetation leaf area index, land cover type, and downward shortwave radiation, calculate the photosynthetically active radiation absorbed by sun-facing leaves and the photosynthetically active radiation absorbed by shaded leaves, and determine the maximum light energy utilization rate of sun-facing leaves and the maximum light energy utilization rate of shaded leaves according to the land cover type.

[0064] Step S3: Calculate the saturated water vapor pressure difference using air temperature and dew point temperature, and construct the temperature stress factor, saturated water vapor pressure difference stress factor, and carbon dioxide concentration stress factor, respectively.

[0065] Step S4: Using soil moisture content, soil sand content, soil clay content, soil organic carbon content, soil gravel fineness, soil thickness, actual evapotranspiration, and potential evapotranspiration, calculate the relative abundance or deficiency of soil water available to plants. Construct a soil water sensitivity coefficient using the ratio of actual evapotranspiration to potential evapotranspiration, and then establish a three-stage soil water stress function. Specifically, when the relative abundance or deficiency of soil water available to plants is less than a first threshold, the value of the three-stage soil water stress function decreases as the relative abundance or deficiency of soil water available to plants decreases, representing water deficit stress; when the relative abundance or deficiency of soil water available to plants is greater than a second threshold, the value of the three-stage soil water stress function decreases as the relative abundance or deficiency of soil water available to plants increases, representing water supersaturation stress; when the relative abundance or deficiency of soil water available to plants is between the first and second thresholds, the value of the three-stage soil water stress function is one.

[0066] Step S5: Using the three-segment soil moisture stress function as a multiplicative stress factor, the smaller of the temperature stress factor and the saturated vapor pressure difference stress factor is multiplied by the carbon dioxide concentration stress factor, the maximum light energy utilization rate of sun-sun leaves, and the photosynthetically active radiation absorbed by sun-sun leaves to obtain the total primary productivity of sun-sun leaf vegetation; similarly, the total primary productivity of shade-sun leaf vegetation is calculated, and the total primary productivity of sun-sun leaf vegetation and the total primary productivity of shade-sun leaf vegetation are summed to obtain the total primary productivity of vegetation.

[0067] Furthermore, the formula for calculating the total primary productivity of the vegetation is as follows:

[0068]

[0069]

[0070] in, Total primary productivity of vegetation, in units of ; Total primary productivity of sun-loving foliage vegetation, in units of ; Total primary productivity of shade-leaved vegetation, in units of ; The maximum light energy utilization rate of sun-facing leaves, determined by land cover type, is expressed in units of... ; The maximum light energy utilization rate of shaded leaves, determined by land cover type, is expressed in units of... ; Photosynthetically active radiation absorbed by sun-loving leaves, in units of ; Photosynthetically active radiation absorbed by shaded leaves, in units of ; The carbon dioxide concentration stress factor is dimensionless. The temperature stress factor is dimensionless. The saturated water vapor pressure difference stress factor is dimensionless. This is a three-segment soil moisture stress function, dimensionless.

[0071] Furthermore, in step S1, the spatiotemporal resolution unification preprocessing includes: resampling all data to the same spatial resolution using the nearest neighbor interpolation method, and unifying them to the same time scale.

[0072] Furthermore, in step S3, the saturated water vapor pressure difference is calculated as follows:

[0073]

[0074]

[0075] in, This is the saturated vapor pressure, in units of... ; Air temperature, unit: ; It is a natural constant; Relative humidity, dimensionless; Dew point temperature, unit: ; This is the saturated water vapor pressure difference, in units of... .

[0076] Furthermore, in step S2, the photosynthetically active radiation absorbed by the sun-bearing leaf... Photosynthetically active radiation absorbed by shaded leaves Calculate according to the following formulas respectively:

[0077]

[0078] in, Photosynthetically active radiation is measured in units of direct radiation. ; The average blade tilt angle is expressed in units of 1 / 200°. In this embodiment, it is set to ; The solar zenith angle, in units of 1000°. Calculated based on latitude and longitude; Photosynthetically active radiation is diffused radiation, measured in units of . ; Photosynthetically active radiation (PAR) is the diffuse radiation below the canopy, measured in units of [missing information]. ; , which is the leaf area index of vegetation, dimensionless; This refers to multiple scattered radiation within the canopy, measured in units of... ; The leaf area index of sun-exposed leaves is dimensionless. The leaf area index for shaded leaves is dimensionless.

[0079] Furthermore, the leaf area index of the sun-loving leaves and shade leaf area index Calculated using the following formula:

[0080]

[0081] in, The solar zenith angle, in units of 1000°. ; The aggregation index is dimensionless and is set to 0.8 in this embodiment; , which is the leaf area index of vegetation, dimensionless; It is an exponential function.

[0082] Furthermore, in step S3, the temperature stress factor and saturated water vapor pressure difference stress factor Calculate using the following formulas respectively:

[0083]

[0084] in, Air temperature, unit: ; The minimum temperature for photosynthesis, in units of In this embodiment, it is set to ; The highest temperature for photosynthesis, measured in units of In this embodiment, it is set to ; The optimal temperature for photosynthesis, in units of In this embodiment, it is set to ; This is a half-saturation parameter, in units of In this embodiment, it is set to ; This is the saturated water vapor pressure difference, in units of... .

[0085] Furthermore, the carbon dioxide concentration stress factor Calculate using the following formula:

[0086]

[0087] in, The carbon dioxide concentration in the intercellular spaces of the leaf is dimensionless (volume fraction). The carbon dioxide compensation point is the point without dark respiration, and is dimensionless (volume fraction). The concentration of carbon dioxide in the environment is dimensionless (volume fraction). This is the ratio of intercellular carbon dioxide concentration to atmospheric carbon dioxide concentration, and is dimensionless.

[0088] Furthermore, the aforementioned It is obtained from the following formula:

[0089]

[0090] in, This is the saturated water vapor pressure difference, in units of... ; To characterize the saturated water vapor pressure difference and The parameters relating them are dimensionless. The Michaelis constant for Rubisco enzymes, in units of 1000 mJ. ; Let be the viscous resistance constant of water, which is dimensionless.

[0091] Furthermore, the aforementioned Calculated using the following formula:

[0092]

[0093]

[0094] in, For temperature The Michaelis constant for the lower carboxylation reaction, in units of ; Oxygen partial pressure constant, in units of . ; For temperature The Michaelis constant for the oxygenation reaction, in units of . ; Air temperature, unit: ; Let be the molar gas constant, and take the value of . ; It is a natural constant.

[0095] Furthermore, in step S4, the plant can utilize the relative abundance or scarcity index of soil moisture. Calculate using the following formula:

[0096] in, Soil moisture content, unit: ; This represents the soil moisture content corresponding to the permanent wilting point, in units of... ; The maximum effective water holding capacity of the soil, in units of .

[0097] Furthermore, the three-segment soil moisture stress function The specific expression is:

[0098] in, It is a dimensionless indicator of the relative abundance or scarcity of soil moisture available to plants. The first threshold is dimensionless and is set to 0.6 in this embodiment; The second threshold is dimensionless and is set to 0.9 in this embodiment; The soil moisture sensitivity coefficient for the water deficit section is dimensionless. The soil moisture sensitivity coefficient for the supersaturated soil section is dimensionless.

[0099] Furthermore, the soil moisture sensitivity coefficient in the water deficit section Soil moisture sensitivity coefficient in the supersaturated section Calculate using the following formula:

[0100]

[0101]

[0102] in, is the soil moisture sensitivity coefficient, dimensionless; It is a constant, and its value is 0; The first threshold is dimensionless. The second threshold is dimensionless. It is a constant, and its value is 0; It is a constant, with a value of 0.733; This refers to actual evaporation, in units of ; Potential evapotranspiration, in units of .

[0103] Furthermore, the maximum effective water holding capacity of the soil Soil moisture content corresponding to the permanent wilting point Calculate using the following formulas respectively:

[0104]

[0105] in, Field holding capacity, unit: ; This is the permanent wilting coefficient, in units of... ; The fineness of soil gravel is dimensionless. Soil thickness, in units of ; This is the preset maximum root depth constant for vegetation, in units of... .

[0106] Furthermore, the field water holding capacity and permanent wilting coefficient Determine according to the following formula:

[0107]

[0108]

[0109]

[0110] in, The content of soil sand is dimensionless. The soil clay content is dimensionless. Soil organic carbon content, dimensionless.

[0111] Furthermore, in step S2, the direct radiation photosynthetically effective radiation Scattered radiation and photosynthetically active radiation Scattered radiation below the canopy and photosynthetically active radiation and multiple scattered radiation within the canopy Calculated using the following formula:

[0112]

[0113]

[0114]

[0115]

[0116]

[0117] in, This refers to downlink shortwave radiation, in units of ; The solar zenith angle, in units of 1000°. ; The solar radiation flux at the top of the atmosphere is dimensionless. The proportion of scattered radiation is dimensionless. Total photosynthetically active radiation, in units of ; The scattered portion of photosynthetically active radiation, in units of ; Directly photosynthetically active radiation, in units of ; Photosynthetically active radiation is diffused radiation, measured in units of . ; The aggregation index is dimensionless. , which is the leaf area index of vegetation, dimensionless; Total photosynthetically active radiation above the canopy, in units of ; is the leaf angular distribution parameter, which is dimensionless.

[0118] Furthermore, in step S1, determining the maximum light energy utilization rate of sun-facing leaves and the maximum light energy utilization rate of shaded leaves based on land cover type specifically involves: determining the maximum light energy utilization rate of sun-facing leaves by referring to a preset land cover type-maximum light energy utilization rate lookup table. Maximum light energy utilization rate of shaded leaves .

[0119] In practical implementation, this embodiment is mainly based on the two-leaf light energy utilization model, and on this basis, a soil moisture stress mechanism is introduced. The overall process is as follows: Figure 1 As shown.

[0120] First, multi-source data such as vegetation leaf area index, air temperature, dew point temperature, downward shortwave radiation, carbon dioxide concentration, land cover, soil moisture content and soil properties are acquired, and the temporal and spatial resolutions of data from different sources are uniformly processed.

[0121] In its implementation, this model uses data including the GLASS leaf area index product, monthly-scale air temperature 2 meters above the surface, dew point temperature, and downwave radiation data provided by the European Centre for Medium-Range Weather Forecasts' fifth-generation atmospheric reanalysis dataset, carbon dioxide concentration products provided by the Earth System Research Laboratory of the National Oceanic and Atmospheric Administration (NOAA), land cover products provided by NASA, soil moisture, actual evapotranspiration, and potential evapotranspiration data from TerraClimate developed by the University of California, and soil attribute data from the World Soil Database constructed by the Food and Agriculture Organization of the United Nations and the International Institute for Applied Systems Research in Vienna, including soil sand content, soil clay content, soil organic carbon content, and soil gravel fineness, combined with soil thickness data provided by Sun Yat-sen University.

[0122] To address the differences in the spatiotemporal scales of various data, the nearest neighbor interpolation method was uniformly used to resample to the same spatial resolution and to the same time scale before inputting the data into the model. Then, the saturated water vapor pressure difference data was calculated using air temperature and dew point temperature and used for model operation.

[0123] In the model calculation process, the photosynthetically active radiation absorbed by the sun-facing leaves and the corresponding light energy utilization rate are first calculated under the two-leaf light energy utilization rate model framework. The light energy utilization efficiency is then adjusted by combining environmental factors such as temperature, saturated water vapor pressure difference and carbon dioxide concentration.

[0124] Based on this, we further calculated the soil moisture index available to plants and constructed a three-segment soil moisture stress function corresponding to three states: soil moisture deficit, suitable and supersaturated. At the same time, we combined the ratio of actual evapotranspiration to potential evapotranspiration to quantify the soil moisture sensitivity coefficient. Finally, we directly coupled the soil moisture constraint into the estimation process of total primary productivity of vegetation to realize the remote sensing estimation of total primary productivity of vegetation.

[0125] This embodiment proposes a remote sensing method for estimating total primary productivity of vegetation considering soil moisture stress, and its calculation expression is as follows:

[0126]

[0127]

[0128] in, Total primary productivity of vegetation; and The total primary productivity of vegetation canopy leaves (i.e., sun-exposed leaves) and shade-exposed leaves (i.e., shade-exposed leaves) are respectively represented. and These represent the maximum light energy utilization of sun-exposed and shaded foliage, respectively, determined by vegetation type (data source: land cover products provided by NASA). and The photosynthetically active radiation (PAR) absorbed by sun-exposed and shade-exposed leaves are respectively, and can be calculated using the following formula:

[0129]

[0130] in, , and The photosynthetically active radiation that vegetation can utilize from direct radiation, diffuse radiation, and diffuse radiation from beneath the canopy, respectively. Multiple scattered radiation within the canopy can be calculated using the following formula:

[0131]

[0132]

[0133]

[0134]

[0135]

[0136] in, This is downwave radiation (data source: European Centre for Medium-Range Weather Forecasts, 5th Generation Atmospheric Reanalysis Dataset); This is the solar zenith angle, which can be directly calculated from latitude and longitude. This is the average leaf tilt angle, typically set to 60° in the canopy. The aggregation index is set to 0.8. and These correspond to the leaf area index of vegetation for sun-exposed and shade-exposed leaves, respectively. The leaf area index (data source: GLASS leaf area index product) can be calculated using the following formula:

[0137]

[0138] also, , , They represent the atmosphere. Factors regulating the light energy utilization efficiency of vegetation leaves: concentration (data source: carbon dioxide concentration product provided by the Earth System Research Laboratory of the National Oceanic and Atmospheric Administration, USA), saturated water vapor pressure difference, and air temperature changes (data source: fifth-generation atmospheric reanalysis dataset from the European Centre for Medium-Range Weather Forecasts). The following formula can be used for calculation:

[0139]

[0140] in, and These represent the intercellular spaces of the leaf and the environment, respectively. Concentration (constant); When there is no dark breathing Compensation point (constant); Intercellular Concentration and Atmosphere The concentration ratio can be calculated using the following formula:

[0141]

[0142] in, Characterizing the saturated water vapor pressure difference ( )and The relationship between them; This represents the viscous resistance (constant) of water. The Michaelis constant (constant) representing Rubisco enzymes can be calculated using the following formula:

[0143]

[0144]

[0145] in, and They are respectively Michaelis constants (constants) for carboxylation and oxygenation reactions at specific temperatures; This is the partial pressure of oxygen (constant); This represents the molar gas constant, which is generally taken as a value of .

[0146] Saturated vapor pressure difference ( It can be determined by air temperature. With dew point temperature (Data source: European Centre for Medium-Range Weather Forecasts, 5th Generation Atmospheric Reanalysis Dataset) The calculation formula is as follows:

[0147]

[0148]

[0149] at the same time, and The following formula can be used for calculation:

[0150]

[0151] in, , and These are the minimum, maximum, and optimal temperatures for vegetation photosynthesis, set at 0°C, 40°C, and 20.33°C, respectively. express The half-saturation parameter in the constraint formula is set to 1.35 kPa.

[0152] Traditional methods often overlook the constraints of supersaturated soil moisture on plant respiration, potentially underestimating the stress effect of soil moisture. This embodiment quantifies the ratio of actual available soil water to the maximum effective water that the soil can store for plants. This study examines the stress effect of soil moisture on total primary productivity of vegetation. It is divided into three segments, each corresponding to a water deficit state. ), water saturation state ( ), water supersaturation state ( In this study and The values ​​are set to 0.6 and 0.9 respectively, and the specific calculation formulas are as follows:

[0153]

[0154] in The soil moisture sensitivity coefficient can be obtained by measuring actual evapotranspiration. ) and potential evapotranspiration ( The ratio (data source: TerraClimate actual evapotranspiration and potential evapotranspiration data) is quantified and calculated as follows:

[0155]

[0156]

[0157] in, It is characterized by the relative abundance or scarcity of soil water that can be absorbed and utilized by plants, and is quantified by the ratio of the actual available soil water to the maximum effective water that the soil can store for plants. Soil moisture content (mm) (data source: TerraClimate soil moisture data); constant , and The values ​​are 0, 0, and 0.733 respectively; among which... and The calculation method is as follows:

[0158]

[0159] in, It refers to the coarseness of soil gravel (data source: soil gravel coarseness data released by the Food and Agriculture Organization of the United Nations and the International Institute for Applied Systems Research in Vienna); The vertical distance required for the soil surface to reach the bedrock (soil thickness) (data source: soil thickness data released by Sun Yat-sen University); , The formulas for calculating field holding capacity and permanent wilting point, respectively, are as follows:

[0160]

[0161]

[0162]

[0163] in, , and These are soil sand content, soil clay content, and soil organic carbon content (data sourced from the World Soil Database, constructed by the Food and Agriculture Organization of the United Nations and the International Institute for Applied Systems Research in Vienna, showing soil sand content, soil clay content, and soil organic carbon content).

[0164] Vegetation leaf area index, downward shortwave radiation, saturated vapor pressure difference, air temperature, actual evapotranspiration, soil moisture content, soil clay content, soil sand content, land cover, atmospheric carbon dioxide concentration, potential evapotranspiration, soil gravel fineness, soil thickness → maximum light energy utilization of sun- and shade-spotted leaves, photosynthetically active radiation absorbed by sun- and shade-spotted leaves → atmospheric water and heat limitation, intensity of atmospheric drought stress → soil moisture saturation state, relative abundance or scarcity of soil water that can be absorbed by vegetation → total primary productivity of vegetation → total primary productivity of vegetation considering soil moisture stress Figure 1 Technical Flowchart In this embodiment, the model performance is verified by comparing the simulation accuracy of the original model and the improved model. The model simulation accuracy verification is mainly carried out around the nine flux observation stations released by the China Terrestrial Ecosystem Flux Observation and Research Network, namely Dinghushan Station, Changbaishan Station, Xishuangbanna Station, Yucheng Station, Haibei Shrubland Station, Haibei Wetland Station, Dangxiong Station, Qianyanzhou Station and Inner Mongolia Station.

[0165] First, download the GLASS leaf area index product, monthly-scale air temperature 2 meters above the surface, dew point temperature, and downwave radiation data from the European Centre for Medium-Range Weather Forecasts (ECMWF) 5th Generation Atmospheric Reanalysis dataset, carbon dioxide concentration products from the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory, land cover products from NASA, soil moisture, actual evapotranspiration, and potential evapotranspiration data from TerraClimate developed by the University of California, and soil attribute data from the World Soil Database constructed by the Food and Agriculture Organization of the United Nations and the International Institute for Applied Systems Research in Vienna, including soil sand content, soil clay content, soil organic carbon content, and soil gravel fineness, combined with soil thickness data provided by Sun Yat-sen University. Also, download the total primary productivity (TPF) data from the original model (EC-LUE). ) products.

[0166] To address the differences in spatiotemporal scales among various data types, a nearest neighbor interpolation method was uniformly used to resample to the same spatial resolution before inputting the data into the model, and the data was standardized to a monthly scale. Then, saturated vapor pressure difference data was calculated using air temperature and dew point temperature for model operation. Next, corresponding data was extracted based on the latitude and longitude of the stations, and the total primary productivity of vegetation simulated by the improved model (Revised version) was calculated according to the above process. ).

[0167] Subsequently, the original model (EC-LUE) and the improved model ( )of Simulated values ​​versus observations at flux stations The simulation results were compared separately to verify accuracy. Finally, the results show a significant improvement in overall simulation accuracy, with the original model (EC-LUE) achieving a higher accuracy. It is 0.55. for The revised version of the model It is 0.60. for ( Figure 2 At the site level, the vast majority of sites... All stations showed improvement, with only the simulation accuracy at Dangxiong Station, Haibei Wetland Station, and Haibei Shrub Station showing a slight decrease, while the accuracy at all stations improved. All are showing a downward trend. Figure 3 This case demonstrates that the modified model has a significant improvement in accuracy.

[0168] Example 2: This example discloses a remote sensing estimation system for total primary productivity of vegetation considering soil moisture stress, used in the remote sensing estimation method for total primary productivity of vegetation considering soil moisture stress as described above, including: The data acquisition and preprocessing module is used to acquire vegetation leaf area index, air temperature, dew point temperature, downwave radiation, atmospheric carbon dioxide concentration, land cover type, soil moisture content, actual evapotranspiration, potential evapotranspiration, soil sand content, soil clay content, soil organic carbon content, soil gravel fineness and soil thickness, and perform unified spatiotemporal resolution preprocessing. The photosynthetically active radiation calculation module is used to calculate the photosynthetically active radiation absorbed by sun-facing leaves and the photosynthetically active radiation absorbed by shaded leaves based on the two-leaf light energy utilization model. The environmental stress factor construction module is used to calculate the saturated water vapor pressure difference and construct the temperature stress factor, the saturated water vapor pressure difference stress factor, and the carbon dioxide concentration stress factor. The soil moisture stress construction module is used to calculate the relative abundance or scarcity of soil moisture available to plants, construct the soil moisture sensitivity coefficient, and establish a three-segment soil moisture stress function. The total primary productivity estimation module is used to calculate and sum the total primary productivity of sun-sun and shade-sun vegetation by using the three-segment soil moisture stress function as a multiplicative stress factor, and output the total primary productivity of vegetation.

[0169] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0170] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. It should be noted that any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A remote sensing method for estimating total primary productivity of vegetation considering soil moisture stress, characterized in that, Includes the following steps: Step S1: Obtain vegetation leaf area index, air temperature, dew point temperature, downwave radiation, atmospheric carbon dioxide concentration, land cover type, soil moisture content, actual evapotranspiration, potential evapotranspiration, soil sand content, soil clay content, soil organic carbon content, soil gravel fineness, and soil thickness, and perform unified spatiotemporal resolution preprocessing on all data. Step S2: Based on the two-leaf light energy utilization model, using the vegetation leaf area index, land cover type, and downward shortwave radiation, calculate the photosynthetically active radiation absorbed by sun-facing leaves and the photosynthetically active radiation absorbed by shade-facing leaves, and determine the maximum light energy utilization rate of sun-facing leaves and the maximum light energy utilization rate of shade-facing leaves according to the land cover type. Step S3: Calculate the saturated water vapor pressure difference using air temperature and dew point temperature, and construct the temperature stress factor, saturated water vapor pressure difference stress factor, and carbon dioxide concentration stress factor, respectively. Step S4: Calculate the relative abundance or scarcity of soil water available to plants using soil moisture content, soil sand content, soil clay content, soil organic carbon content, soil gravel fineness, soil thickness, actual evapotranspiration and potential evapotranspiration. Construct a soil water sensitivity coefficient using the ratio of actual evapotranspiration to potential evapotranspiration, and then establish a three-segment soil water stress function. Specifically, when the relative abundance or deficiency of soil water available to plants is less than the first threshold, the value of the three-stage soil water stress function decreases as the relative abundance or deficiency of soil water available to plants decreases to characterize water deficit stress; when the relative abundance or deficiency of soil water available to plants is greater than the second threshold, the value of the three-stage soil water stress function decreases as the relative abundance or deficiency of soil water available to plants increases to characterize water supersaturation stress. When the relative abundance or scarcity of soil moisture available to plants is between the first and second thresholds, the value of the three-segment soil moisture stress function is one. Step S5: Using the three-segment soil moisture stress function as a multiplicative stress factor, the smaller of the temperature stress factor and the saturated vapor pressure difference stress factor is multiplied by the carbon dioxide concentration stress factor, the maximum light energy utilization rate of sun-sun leaves, and the photosynthetically active radiation absorbed by sun-sun leaves to obtain the total primary productivity of sun-sun leaf vegetation; similarly, the total primary productivity of shade-sun leaf vegetation is calculated, and the total primary productivity of sun-sun leaf vegetation and the total primary productivity of shade-sun leaf vegetation are summed to obtain the total primary productivity of vegetation.

2. The remote sensing estimation method for total primary productivity of vegetation considering soil moisture stress according to claim 1, characterized in that, The spatiotemporal resolution unification preprocessing includes: All data were resampled to the same spatial resolution using the nearest neighbor interpolation method and unified to the same time scale; The calculation of saturated vapor pressure difference using air temperature and dew point temperature is specifically performed according to the following formula: in, The saturated vapor pressure, For air temperature, It is a natural constant. Relative humidity, Dew point temperature, This represents the saturated water vapor pressure difference.

3. The remote sensing estimation method for total primary productivity of vegetation considering soil moisture stress according to claim 1, characterized in that, The photosynthetically active radiation absorbed by the sun-loving leaves Photosynthetically active radiation absorbed by shaded leaves Calculate according to the following formulas respectively: In the formula, For direct radiation, photosynthetically active radiation, The average blade tilt angle, The zenith angle of the sun. This is diffuse radiation, which is photosynthetically active radiation. This refers to the photosynthetically active radiation scattered below the canopy. The leaf area index is the vegetation leaf area index. This is multiple scattered radiation within the canopy. The leaf area index of sun-exposed leaves. This is the leaf area index for shaded leaves.

4. The remote sensing estimation method for total primary productivity of vegetation considering soil moisture stress according to claim 3, characterized in that, The leaf area index of sun-loving leaves and shade leaf area index Calculated using the following formula: in, The zenith angle of the sun. The aggregation index, The leaf area index is the vegetation leaf area index. It is an exponential function.

5. The remote sensing estimation method for total primary productivity of vegetation considering soil moisture stress according to claim 1, characterized in that, The temperature stress factor and saturated water vapor pressure difference stress factor Calculate using the following formulas respectively: in, For air temperature, This is the lowest temperature for photosynthesis. This is the highest temperature for photosynthesis. The optimal temperature for photosynthesis, For half-saturation parameters, This represents the saturated water vapor pressure difference.

6. The remote sensing estimation method for total primary productivity of vegetation considering soil moisture stress according to claim 1, characterized in that, The carbon dioxide concentration stress factor Calculate using the following formula: in, This refers to the carbon dioxide concentration in the intercellular spaces of the leaf cells. This represents the carbon dioxide compensation point when there is no dark respiration. The concentration of carbon dioxide in the environment. This is the ratio of intercellular carbon dioxide concentration to atmospheric carbon dioxide concentration; It is obtained from the following formula: in, For saturated water vapor pressure difference, To characterize the saturated water vapor pressure difference and The parameters of the relationship between them The Michaelis constant for Rubisco enzymes. The viscous resistance constant of water; Calculated using the following formula: in, For temperature Michaelis constant for the carboxylation reaction Oxygen partial pressure constant For temperature Michaelis constant for oxygenation reaction, For air temperature, is the molar gas constant.

7. The remote sensing estimation method for total primary productivity of vegetation considering soil moisture stress according to claim 1, characterized in that, The plant can utilize the relative abundance or scarcity of soil moisture indicators Calculate using the following formula: in, Soil moisture content, This represents the soil moisture content corresponding to the permanent wilting point. The maximum effective water holding capacity of the soil; the maximum effective water holding capacity of the soil Soil moisture content corresponding to the permanent wilting point It is calculated based on soil sand content, soil clay content, soil organic carbon content, soil gravel fineness, and soil thickness.

8. The remote sensing estimation method for total primary productivity of vegetation considering soil moisture stress according to claim 7, characterized in that, The three-segment soil moisture stress function The specific expression is: in, The first threshold, The second threshold, The soil moisture sensitivity coefficient in the water deficit section. The soil moisture sensitivity coefficient is given for the supersaturated soil section.

9. The remote sensing estimation method for total primary productivity of vegetation considering soil moisture stress according to claim 8, characterized in that, The soil moisture sensitivity coefficient in the water deficit section Soil moisture sensitivity coefficient in the supersaturated section Calculate using the following formula: in, constant , constant , constant , For actual evaporation, For potential evaporation.

10. The remote sensing estimation method for total primary productivity of vegetation considering soil moisture stress according to claim 1, characterized in that, The determination of the maximum light energy utilization rate of sun-facing leaves and the maximum light energy utilization rate of shaded leaves based on land cover type is specifically as follows: the maximum light energy utilization rate of sun-facing leaves and the maximum light energy utilization rate of shaded leaves are determined by referring to a preset land cover type-maximum light energy utilization rate lookup table.

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