A method for estimating total primary productivity of pantropical forest vegetation depending on leaf age by remote sensing

Abstract

The present application relates to the technical field of remote sensing, in particular to a method for estimating total primary productivity of pantropical forest vegetation depending on leaf age, which comprises obtaining a weather basic data set and a leaf age-based vegetation leaf area index data set, grouping current leaves according to leaf age, including new leaves, mature leaves and old leaves, obtaining the maximum light energy utilization rate of all leaves, and outputting the final productivity through a remote sensing estimation model of total primary productivity. The present application considers the regulation of leaf phenology on canopy photosynthesis, distinguishes the photosynthetic capacity difference between leaf age components, and constructs a remote sensing estimation method suitable for total primary productivity of pantropical forest vegetation, so as to improve the simulation accuracy of total primary productivity of pantropical forest vegetation, and help to accurately monitor the evolution trend of photosynthesis of pantropical forest under climate change.

Description

Technical Field

[0001] This invention relates to the field of remote sensing technology, and more specifically, to a remote sensing method for estimating the total primary productivity of pantropical forest vegetation based on leaf age. Background Technology

[0002] Gross primary productivity (GPP) represents the total amount of organic carbon fixed by vegetation through photosynthesis per unit time, and is a key carbon flux in the terrestrial carbon cycle. Pantropical forest ecosystems contribute approximately 34% of global terrestrial vegetation GPP, playing a crucial role in the global carbon cycle. The continued increase in greenhouse gases such as carbon dioxide further exacerbates global warming. Accurately estimating changes in pantropical forest GPP is beneficial for improving our understanding of the pantropical forest carbon cycle and provides a reference for formulating strategies to address climate change.

[0003] Due to the limited spatial representativeness of eddy covariance measurements, various ecosystem models combined with multi-source remote sensing information have been widely considered the scientific approach for monitoring vegetation gross primary productivity (GPP) over the past few decades. Light use efficiency (LUE) models can estimate vegetation GPP based on the canopy's maximum light use efficiency, absorbed photosynthetically active radiation, and various environmental stresses. With the rapid development of satellite remote sensing technology and the application of remote sensing data at multiple spatiotemporal scales, LUE models have gradually become an effective tool for estimating large-scale terrestrial vegetation productivity. Thanks to their simple model structure and ease of parameterization, LUE models are widely used for remote sensing estimation of vegetation GPP in temperature-sensitive and cold-climate regions. However, pantropical forests have unique phenological rhythms; their canopies remain evergreen throughout the year, with leaves constantly falling and regenerating, making it difficult for conventional remote sensing methods to accurately identify vegetation change information (mainly manifested as changes in leaf area). This also limits the accurate estimation of vegetation's photosynthetic carbon sequestration capacity, resulting in low accuracy in simulating the total primary productivity of pantropical forest vegetation. Summary of the Invention

[0004] The purpose of this invention is to provide a remote sensing estimation method for total primary productivity of pantropical forest vegetation that depends on leaf age, in order to solve the problems mentioned above in the prior art.

[0005] This invention is achieved through the following technical solution:

[0006] A remote sensing method for estimating the total primary productivity of pantropical forest vegetation based on leaf age includes:

[0007] Acquire a basic weather dataset and a vegetation leaf area index dataset based on leaf age. The basic weather dataset includes air temperature, dew point temperature, downwave radiation, and carbon dioxide concentration. Preprocess the basic weather dataset.

[0008] The current leaves are grouped according to their age, including new leaves, mature leaves and old leaves, and the maximum light energy utilization rate of sun-exposed and shade-exposed leaves is obtained for each type of leaf.

[0009] A remote sensing estimation model for total primary productivity of vegetation was established. Based on the weather baseline dataset, the vegetation leaf area index dataset of leaf age, and the maximum light energy utilization rate of sun-exposed and shade-exposed leaves, the final productivity was output through the remote sensing estimation model for total primary productivity of vegetation.

[0010] Preferably, the establishment of the remote sensing estimation model for total primary productivity of vegetation includes:

[0011]

[0012] In the formula, For productivity, This refers to the atmospheric carbon dioxide concentration. For saturated water vapor pressure difference, It is a moderating factor on the effect of air temperature changes on the light energy utilization rate of plant leaves. To maximize the light energy utilization rate of sun-loving leaves, Photosynthetic radiation is generated by sun-exposed leaves. The maximum light energy utilization rate for shaded leaves It is the photosynthetic effective radiation for shaded leaves.

[0013] Preferably, the atmospheric carbon dioxide concentration includes:

[0014]

[0015]

[0016] In the formula, and These represent the intercellular space of the leaf and the carbon dioxide concentration in the environment, respectively. This represents the carbon dioxide compensation point when there is no dark respiration. This is the ratio of intercellular carbon dioxide concentration to atmospheric carbon dioxide concentration.

[0017] Preferably, the ratio of the intercellular carbon dioxide concentration to the atmospheric carbon dioxide concentration includes:

[0018]

[0019]

[0020] In the formula, To characterize VPD and The relationship between them, where VPD is the vapor pressure difference. For the viscous resistance of water, Let represent the Michaelis constant of the Rubisco enzyme.

[0021] Preferably, the Michaelis constant of the Rubisco enzyme includes:

[0022]

[0023]

[0024]

[0025] In the formula, and They are respectively Michaelis constants for carboxylation and oxygenation reactions at temperature The current temperature. The partial pressure of oxygen. This represents the molar gas constant.

[0026] Preferably, the regulating factors for the effect of saturated water vapor pressure difference and air temperature changes on the light energy utilization rate of vegetation leaves include:

[0027]

[0028]

[0029] In the formula, , and These are the minimum, maximum, and optimal temperatures for vegetation photosynthesis. This refers to the half-saturation parameter in the VPD constraint formula.

[0030] Preferably, the maximum light energy utilization rate of sun-exposed leaves and the maximum light energy utilization rate of shaded leaves include:

[0031]

[0032]

[0033]

[0034]

[0035] In the formula, The maximum light energy utilization rate for sun-exposed or shade-exposed leaves. The maximum light energy utilization rate of new leaves, whether they are sun-exposed or shade-exposed. The maximum light energy utilization rate of mature leaves, whether in sunny or shady conditions. The maximum light energy utilization rate of older leaves, whether they are in sunlight or shade. , and These are the first dynamic parameters for new leaves, mature leaves, and old leaves (sun-facing or shade-facing leaves), respectively. , and The second dynamic parameter refers to the sun-exposed or shade-exposed leaves, representing new leaves, mature leaves, and old leaves, respectively. , and The third dynamic parameter refers to the sun-exposed or shade-exposed leaves of new leaves, mature leaves, and old leaves, respectively. , and Standardized for new leaves, mature leaves, and old leaves respectively. value.

[0036] Preferably, the photosynthetically active radiation of the sun-exposed leaves and the photosynthetically active radiation of the shade-exposed leaves include:

[0037]

[0038]

[0039] In the formula, , and These are direct radiation, diffuse radiation, and diffuse radiation from beneath the canopy, respectively. The zenith angle of the sun. The average blade tilt angle, This is multiple scattered radiation within the canopy. and The leaf area index of vegetation is denoted by sun-exposed leaves and shade-exposed leaves, respectively. This represents the leaf area index of vegetation.

[0040] Preferably, the leaf area index of the vegetation, including sun-exposed and shade-exposed leaves, includes:

[0041]

[0042]

[0043] In the formula, This is the aggregation index.

[0044] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0045] The method provided by this invention mainly includes acquiring a basic weather dataset and a vegetation leaf area index dataset based on leaf age. Current leaves are grouped according to leaf age, including new leaves, mature leaves, and old leaves. The maximum light energy utilization rate of sun-exposed and shaded leaves is obtained for all leaves. The final productivity is output through a remote sensing estimation model of total primary productivity (TPP). This invention considers the regulation of canopy photosynthesis by leaf phenology, distinguishes the differences in photosynthetic capacity among leaf age groups, and constructs a remote sensing estimation method suitable for TTP of pantropical forest vegetation. This improves the accuracy of TTP simulation in pantropical forests and helps to accurately monitor the evolution trend of photosynthesis in pantropical forests under climate change. Attached Figure Description

[0046] 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 on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0048] Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0050] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. The naming or numbering of steps in this application does not imply that the steps in the method flow must be executed in the chronological / logical order indicated by the naming or numbering. The execution order of named or numbered process steps can be changed according to the desired technical objective, as long as the same or similar technical effect is achieved.

[0051] Please refer to Figures 1-2This invention provides a remote sensing method for estimating total primary productivity (GPP) of pantropical forest vegetation based on leaf age. It primarily addresses the problems of existing technologies, including low simulation accuracy of GPP remote sensing estimation models in pantropical regions, and the greatest uncertainty in global-scale GPP estimation results in pantropical regions. Current GPP remote sensing estimation models neglect the correlation mechanism between canopy leaf age structure changes and photosynthesis, making it difficult to accurately quantify the seasonal variations in GPP in pantropical forests.

[0052] Specifically, including:

[0053] S101: Obtain a basic weather dataset and a vegetation leaf area index dataset based on leaf age. The basic weather dataset includes air temperature, dew point temperature, downwave radiation, and carbon dioxide concentration. Preprocess the basic weather dataset.

[0054] The data used in this scheme include monthly air temperature (Ta) and dew point temperature (Td) at 2 meters above the surface, as well as downward short-wave radiation (DSR) data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) Generation 5 Atmospheric Reanalysis dataset; carbon dioxide concentration products from the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory; land cover products from NASA; and leaf age-based pantropical forest vegetation leaf area index datasets.

[0055] Since the data differ in temporal and spatial resolution, a bilinear method was used to resample them to the same spatial resolution and time scale before inputting them into the model. Subsequently, saturated vapor pressure deficit (VPD) data were calculated using air temperature and dew point temperature and used for model execution.

[0056] S102: Group the current leaves according to their age, including new leaves, mature leaves and old leaves, and obtain the maximum light energy utilization rate of sun-exposed and shade-exposed leaves for all leaves.

[0057] S103: Establish a remote sensing estimation model for total primary productivity of vegetation. Based on the weather baseline dataset, the vegetation leaf area index dataset of leaf age, and the maximum light energy utilization rate of sun-exposed and shade-exposed leaves, the final productivity is output through the remote sensing estimation model for total primary productivity of vegetation.

[0058] In one exemplary embodiment of the present invention, the establishment of the remote sensing estimation model for total primary productivity of vegetation includes:

[0059]

[0060] In the formula, For productivity, This refers to the atmospheric carbon dioxide concentration. For saturated water vapor pressure difference, It is a moderating factor on the effect of air temperature changes on the light energy utilization rate of plant leaves. To maximize the light energy utilization rate of sun-loving leaves, Photosynthetic radiation is generated by sun-exposed leaves. The maximum light energy utilization rate for shaded leaves It is the photosynthetic effective radiation for shaded leaves.

[0061] Specifically, the atmospheric carbon dioxide concentration includes:

[0062]

[0063]

[0064] In the formula, and These represent the intercellular space of the leaf and the carbon dioxide concentration in the environment, respectively. This represents the carbon dioxide compensation point when there is no dark respiration. This is the ratio of intercellular carbon dioxide concentration to atmospheric carbon dioxide concentration.

[0065] Specifically, the ratio of intercellular carbon dioxide concentration to atmospheric carbon dioxide concentration is:

[0066]

[0067]

[0068] In the formula, To characterize VPD and The relationship between them, where VPD is the vapor pressure difference. For the viscous resistance of water, Let represent the Michaelis constant of the Rubisco enzyme.

[0069] Secondly, the Michaelis constant of the Rubisco enzyme includes:

[0070]

[0071]

[0072]

[0073] In the formula, and They are respectively Michaelis constants for carboxylation and oxygenation reactions at temperature The current temperature. The partial pressure of oxygen. This represents the molar gas constant, which is generally taken as a value of .

[0074] The regulating factors of the saturated water vapor pressure difference and air temperature changes on the light energy utilization rate of vegetation leaves include:

[0075]

[0076]

[0077] In the formula, , and These represent the minimum, maximum, and optimal temperatures for vegetation photosynthesis, set at 0°C, 40°C, and 20.33°C, respectively. This refers to the half-saturation parameter in the VPD constraint formula.

[0078] The maximum light energy utilization rate of sun-exposed leaves and the maximum light energy utilization rate of shaded leaves include:

[0079]

[0080]

[0081]

[0082]

[0083] In the formula, Indicates whether the leaf is in the sun or in the shade. The maximum light energy utilization rate for sun-exposed or shade-exposed leaves. The maximum light energy utilization rate of new leaves, whether they are sun-exposed or shade-exposed. The maximum light energy utilization rate of mature leaves, whether in sunny or shady conditions. The maximum light energy utilization rate of older leaves, whether they are in sunlight or shade. , and These are the first dynamic parameters for new leaves, mature leaves, and old leaves (sun-facing or shade-facing leaves), respectively. , and The second dynamic parameter refers to the sun-exposed or shade-exposed leaves, representing new leaves, mature leaves, and old leaves, respectively. , and The third dynamic parameter refers to the sun-exposed or shade-exposed leaves of new leaves, mature leaves, and old leaves, respectively. , and Standardized for new leaves, mature leaves, and old leaves respectively. value.

[0084] Among them, leaves aged 1-2 months are new leaves, leaves aged 3-6 months are mature leaves, and leaves aged more than 6 months are old leaves.

[0085] This invention uses vegetation GPP data observed at pantropical forest flux stations and employs the Markov chain-Monte Carlo method to obtain model parameters, the specific parameters of which are shown in Table 1. It is standardized Value, using the research period The mean and its standard deviation are calculated; to avoid potential differences among the three leaf age groups. To address the scenario where the result is zero, this invention references the maximum carboxylation rate of different leaf age components in pantropical forests and sets three constant ratios (0.27, 0.52, and 0.19) to determine the maximum light energy utilization rate. The minimum value of ) is used to ensure the feasibility of the method.

[0086] Table 1. Calculation parameters for maximum light energy utilization under different leaf age groups

[0087]

[0088] in, , , The first, second, and third dynamic parameters are for sun-exposed or shade-exposed leaves of different ages.

[0089] Secondly, the photosynthetically active radiation of sun-exposed leaves and the photosynthetically active radiation of shade-exposed leaves include:

[0090]

[0091]

[0092] In the formula, , and These are direct radiation, diffuse radiation, and diffuse radiation from beneath the canopy, respectively. The zenith angle of the sun. The average blade tilt angle, This is multiple scattered radiation within the canopy. and The leaf area index of vegetation with sun-exposed and shade-exposed leaves are respectively. This represents the leaf area index of vegetation.

[0093] The leaf area index of the vegetation, including sun-facing and shade-facing leaves, includes:

[0094]

[0095]

[0096] In the formula, This is the aggregation index.

[0097] In summary, compared with existing technologies, the beneficial effects of this invention are as follows: Traditional remote sensing models often rely on vegetation indices constructed from spectral information of remote sensing images to estimate vegetation GPP. However, the evergreen canopy characteristics of pantropical vegetation mean that vegetation index changes are not significant, leading to considerable uncertainty in model estimation results. Unlike most studies that focus on the regulation of vegetation photosynthesis by environmental variables, this invention proposes a remote sensing estimation method for pantropical forest vegetation GPP that couples plant traits (leaf age). By linking leaf growth changes and canopy photosynthesis with plant physiological characteristics, this method effectively improves the simulation accuracy of pantropical forest vegetation GPP, reduces the uncertainty of GPP remote sensing model estimation, and is beneficial to improving the ability to observe, simulate, and even predict parameters of carbon cycle processes in tropical and subtropical ecosystems. Meanwhile, unlike traditional light energy utilization models that mostly set light energy utilization as a constant related to vegetation type, this invention dynamically calculates the maximum light energy utilization and absorbed photosynthetically active radiation of different leaf age structures by considering the structural changes of canopy leaves of different ages. This further refines the commonly used two-leaf strategy to different leaf age components, effectively characterizing the seasonal changes in the photosynthetic capacity of the pantropical forest canopy.

[0098] This invention also provides a remote sensing system for estimating the total primary productivity of pantropical forest vegetation based on leaf age, used to perform the aforementioned remote sensing method for estimating the total primary productivity of pantropical forest vegetation based on leaf age, comprising:

[0099] The data processing module is configured to acquire a basic weather dataset and a vegetation leaf area index dataset based on leaf age. The basic weather dataset includes air temperature, dew point temperature, downwave radiation, and carbon dioxide concentration. The module also preprocesses the basic weather dataset. The module groups the current leaves according to their leaf age, including new leaves, mature leaves, and old leaves, and acquires the maximum light energy utilization rate of sun-exposed and shaded leaves for all leaves.

[0100] The estimation module is configured to establish a remote sensing estimation model for total primary productivity of vegetation. Based on the weather baseline dataset, the vegetation leaf area index dataset of leaf age, and the maximum light energy utilization rate of sun-exposed and shaded leaves, the model outputs the final productivity.

[0101] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0102] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. This computer software product, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0103] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A remote sensing method for estimating the total primary productivity of pantropical forest vegetation based on leaf age, characterized in that, include: Acquire a basic weather dataset and a vegetation leaf area index dataset based on leaf age. The basic weather dataset includes air temperature, downwave radiation, and carbon dioxide concentration. The basic weather dataset is then preprocessed. The current leaves are grouped according to their age, including new leaves, mature leaves and old leaves, and the maximum light energy utilization rate of sun-exposed and shade-exposed leaves is obtained for each type of leaf. A remote sensing estimation model for total primary productivity of vegetation was established. Based on the weather baseline dataset, the vegetation leaf area index dataset of leaf age, and the maximum light energy utilization rate of sun-exposed and shade-exposed leaves, the final productivity was output through the remote sensing estimation model for total primary productivity of vegetation. The establishment of the remote sensing estimation model for total primary productivity of vegetation includes: In the formula, For productivity, This refers to the atmospheric carbon dioxide concentration. For saturated water vapor pressure difference, It is a moderating factor on the effect of air temperature changes on the light energy utilization rate of plant leaves. To maximize the light energy utilization rate of sun-loving leaves, Photosynthetic radiation is generated by sun-exposed leaves. The maximum light energy utilization rate for shaded leaves Photosynthetic effective radiation for shaded leaves; The maximum light energy utilization rate of sun-exposed leaves and the maximum light energy utilization rate of shaded leaves include: In the formula, The maximum light energy utilization rate for sun-exposed or shade-exposed leaves. The maximum light energy utilization rate of new leaves, whether they are sun-exposed or shade-exposed. The maximum light energy utilization rate of mature leaves, whether in sunny or shady conditions. The maximum light energy utilization rate of older leaves, whether they are in sunlight or shade. , and These are the first dynamic parameters for new leaves, mature leaves, and old leaves (sun-facing or shade-facing leaves), respectively. , and The second dynamic parameter refers to the sun-exposed or shade-exposed leaves, representing new leaves, mature leaves, and old leaves, respectively. , and The third dynamic parameter refers to the sun-exposed or shade-exposed leaves of new leaves, mature leaves, and old leaves, respectively. , and Standardized for new leaves, mature leaves, and old leaves respectively. value.

2. The remote sensing estimation method for total primary productivity of pantropical forest vegetation based on leaf age according to claim 1, characterized in that, The atmospheric carbon dioxide concentration includes: In the formula, and These represent the intercellular space of the leaf and the carbon dioxide concentration in the environment, respectively. This represents the carbon dioxide compensation point when there is no dark respiration. This is the ratio of intercellular carbon dioxide concentration to atmospheric carbon dioxide concentration.

3. The remote sensing estimation method for total primary productivity of pantropical forest vegetation based on leaf age according to claim 2, characterized in that, The ratio of intercellular carbon dioxide concentration to atmospheric carbon dioxide concentration includes: In the formula, To characterize VPD and The relationship between them, where VPD is the vapor pressure difference. For the viscous resistance of water, Let represent the Michaelis constant of the Rubisco enzyme.

4. The remote sensing estimation method for total primary productivity of pantropical forest vegetation dependent on leaf age as described in claim 3, characterized in that, The Michaelis constants of the Rubisco enzyme include: In the formula, and They are respectively Michaelis constants for carboxylation and oxygenation reactions at temperature The current temperature. The partial pressure of oxygen. This represents the molar gas constant.

5. The remote sensing estimation method for total primary productivity of pantropical forest vegetation dependent on leaf age as described in claim 4, characterized in that, The regulating factors of the saturated water vapor pressure difference and air temperature changes on the light energy utilization rate of vegetation leaves include: In the formula, , and These are the minimum, maximum, and optimal temperatures for vegetation photosynthesis. This refers to the half-saturation parameter in the VPD constraint formula.

6. The remote sensing estimation method for total primary productivity of pantropical forest vegetation based on leaf age according to claim 5, characterized in that, The photosynthetically active radiation of sun-exposed leaves and the photosynthetically active radiation of shade-prone leaves include: In the formula, , and These are direct radiation, diffuse radiation, and diffuse radiation from beneath the canopy, respectively. The zenith angle of the sun. The average blade tilt angle, This is multiple scattered radiation within the canopy. and The leaf area index of vegetation with sun-exposed and shade-exposed leaves are respectively. This represents the leaf area index of vegetation.

7. The remote sensing estimation method for total primary productivity of pantropical forest vegetation dependent on leaf age as described in claim 6, characterized in that, The leaf area index of the vegetation, including sun-facing and shade-facing leaves, includes: In the formula, This is the aggregation index.

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