Canopy combustible moisture content prediction method combining remote sensing and meteorological data

Abstract

The invention belongs to the technical field of remote sensing, and relates to a method for predicting canopy combustible moisture content (CFMC) by combining remote sensing CFMC products and meteorological data. Specifically, key input parameters for different tree species in an MEDFATE model are identified through global two-stage sensitivity analysis, a cost function for measuring an error between CFMC simulated by an MEDFATE ecological physical process model and CFMC measured on site is constructed by using the parameters, species-based MEDFATE localization correction is realized, and the method has the advantages that the method is simple and convenient to operate, and the method is suitable for popularization and application. And then through a four-dimensional variation data assimilation technology, a remote sensing CFMC product is combined with a localized MEDFATE. Enhanced simulation of the CFMC is realized, and the time resolution of the remote sensing data and the spatial resolution of the MEDFATE model are improved while the CFMC simulation precision of the MEDFATE is improved. The method can achieve the effective prediction of the CFMC of the vegetation every day under the condition of obtaining accurate weather forecast data.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of remote sensing, and relates to a model for simulating (predicting) canopy fuel moisture content (CFMC) by combining remote sensing CFMC products and meteorological data-driven ecological process model MEDFATE. BACKGROUND

[0002] Wildfires are an important ecological process that affects the carbon and nutrient cycles of the atmosphere and biosphere. Wildfires naturally shape ecosystems by regulating vegetation growth, nutrient cycling, and biodiversity, but wildfires exacerbated by climate change also pose significant risks. Canopy fuel moisture content (CFMC), defined as the percentage of water content in the vegetation canopy relative to its dry mass, is a key factor influencing the probability of fire ignition, spread rate, and intensity. Vegetation with high CFMC is less likely to ignite and sustain combustion, while dry vegetation with low CFMC becomes highly flammable and contributes to rapidly spreading fires. Therefore, monitoring and predicting CFMC is crucial for effective wildfire management, timely early warning systems, and disaster mitigation strategies.

[0003] In recent years, optical and microwave remote sensing technologies have become indispensable tools for large-scale estimation of CFMC. Platforms such as Sentinel-2, Landsat, and MODIS are widely used to obtain vegetation indices related to CFMC. These sensors provide frequent global observations, supporting near-real-time tracking of vegetation moisture dynamics. Microwave sensors, whether active or passive, excel at continuous all-weather monitoring, unaffected by clouds and atmospheric interference. Despite their advantages, active microwave sensing faces challenges such as high cost, sensitivity to heterogeneous vegetation and complex terrain, while passive systems face the issue of low spatial resolution. In contrast, meteorological data can alleviate some limitations of remote sensing in predicting fuel moisture. Garcí a et al. combined meteorological data with remote sensing observations to improve previous empirical models, thereby improving predictions of surface vegetation cover in shrubs and grasslands.

[0004] However, Ruffault et al. pointed out that spatial CFMC estimation should not rely solely on drought indices from meteorological data due to significant variations in performance across different species, scales, and regions. In addition, process-based models such as MEDFATE have also been developed to simulate vegetation growth and CFMC dynamics using meteorological inputs. The MEDFATE model incorporates vegetation and soil characteristics, providing a comprehensive framework for ecosystem-level CFMC simulation. In addition to meteorological inputs, the model also considers soil water availability, root water uptake, and plant transpiration, providing detailed insights into vegetation responses to environmental changes.

[0005] Recent improvements to the MEDFATE model have been made by modifying the soil water balance model used to estimate pre-dawn leaf water potential, improving the accuracy of MEDFATE model simulations of daily CFMC for specific vegetation types. Despite these advances, there remain challenges in accurately predicting CFMC at the species level, particularly for tree species. Tree species typically have deeper root systems and more effective stomatal control, allowing them to better buffer short-term fluctuations in shallow soil water availability. As a result, tree species exhibit the smallest seasonal variation in CFMC, maintaining relatively stable canopy fuel moisture throughout the fire season compared to grasses, seedling shrubs, and regenerative shrubs. These characteristics continue to present challenges for accurate modeling and prediction of CFMC at the species level. SUMMARY

[0006] The present invention aims to provide a method for enhanced daily CFMC simulation by combining remote sensing CFMC products with localized MEDFATE eco-physiological process models through a four-dimensional variational data assimilation technique. The method employed by the present invention is outlined as follows: Firstly, key input parameters for different tree species within the MEDFATE model are determined through global sensitivity analysis. Furthermore, these parameters guide the creation of a cost function to quantify the difference between MEDFATE simulations and field-measured CFMC, enabling model localization calibration for specific species. Finally, the DEoptim global optimization algorithm is employed to integrate remote sensing CFMC product values into the MEDFATE model through a four-dimensional variational data assimilation technique.

[0007] The present invention provides a method for predicting canopy fuel moisture content combining remote sensing and meteorological data, comprising the following steps:

[0008] Step 1: Sensitivity analysis of parameters; obtain key input species parameters affecting the simulation of canopy fuel moisture content (CFMC) by the MEDFATE eco-physiological process model, then perform sensitivity analysis to screen the species parameter vector;

[0009] Step 2: Species-based localization correction; minimize the difference between simulated and field-measured CFMC values using the vegetation simulation ecological rules of the MEDFATE eco-physiological process model as constraints, and optimize the screened species parameter vector using an evolutionary algorithm. The optimized species parameter values are used as standard species parameter inputs for the MEDFATE eco-physiological process model;

[0010] Step 3: Four-dimensional variational data assimilation; use the standard input species parameters of the MEDFATE eco-physiological process model as state field variables for four-dimensional variational data assimilation, and improve the accuracy of CFMC simulation by the MEDFATE eco-physiological process model through data assimilation techniques.

[0011] Step 4: The optimized MEDFATE ecophysical process model is used to make CFMC predictions based on meteorological forecast data.

[0012] Step 1 is described in detail as follows:

[0013] First, key input species parameters affecting the MEDFATE ecophysical process model's simulation of CFMC are obtained, including meteorological data, soil, geographical and vegetation information. Sensitive vegetation parameters for the MEDFATE ecophysical process model's simulation of CFMC include vegetation allometric growth coefficient, vegetation anatomy parameters, radiation balance and water interception parameters, vegetation photosynthetic parameters, as well as related hydraulic and vegetation water storage parameters. The model output is set as the mean and variance of the simulated CFMC.

[0014] Then, sensitivity analysis was performed using the test method based on the mean and variance of the simulated CFMC to obtain the parameter vector of the screened species, including m sensitivity parameters.

[0015] The test method is the extended Fourier amplitude sensitivity test, which selects parameters whose total order sensitivity index shows a significant decrease or is greater than 0.1 as species parameter vectors.

[0016] The evolutionary algorithm is specifically the DEoptim global optimization algorithm, and the formula is as follows:

[0017]

[0018] in, Indicates species parameters as hour, The model in the Japanese simulation ; Indicates the first Japanese Measured value; express Upper bound of the feasible domain for species parameters in the ecological physical process model; express Lower bound of the feasible domain for species parameters in the ecological physical process model; Represents a species parameter vector. For the m-th sensitivity parameter, Indicates the first The default values ​​for each sensitivity parameter, Indicates the first Lower bound of the domain of a sensitivity parameter Indicates the first Upper bound of the domain of each sensitivity parameter; This represents the lower limit of the m-th sensitivity parameter. This represents the upper bound of the m-th sensitivity parameter; n represents... The amount of measured CFMC data used in the local correction of the eco-physical process model.

[0019] The four-dimensional variational data assimilation optimizes the accuracy of the MEDFATE eco-physical process model simulation of CFMC by constructing the following cost function:

[0020]

[0021] In the formula, is the cost function; represents the species parameter vector, represents the value of X in the last iteration, and is the background field error covariance matrix; represents the MEDFATE model, is the CFMC remote sensing observation value, which is only used in the background field correction and model verification; is the observation field error matrix.

[0022] The species parameter vector includes 8 sensitivity parameters, specifically, the maximum living fuel moisture content, the hydraulic vulnerability curve of the stem xylem, the leaf plus branch weight to leaf weight ratio of 6.35 mm branches, two holographic coefficients of leaf biomass, the osmotic potential of fully expanded leaves, the hydraulic vulnerability curve of leaves, and the permeable part in stem tissue.

[0023] The beneficial effects of the present application are as follows:

[0024] The present application provides a model for daily CFMC enhanced simulation by combining remote sensing CFMC products with the MEDFATE model through four-dimensional variational data assimilation technology. A cost function is first established to measure the error between the MEDFATE model simulation of CFMC and the measured CFMC, to realize the local correction of the species-based MEDFATE input species parameters, and then the remote sensing CFMC products are combined with the localized MEDFATE through the four-dimensional variational data assimilation technology. The enhanced simulation of CFMC is realized, which improves the accuracy of the MEDFATE simulated CFMC, and improves the temporal resolution of the remote sensing data and the spatial resolution of the MEDFATE model. This progress makes it possible to reliably predict vegetation CFMC, provided that accurate weather forecasts are obtained. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 Fig. 4 is a diagram in which the species parameters are sorted according to the high-order sensitivity index, wherein (a) reflects the overall impact of the input parameters on the model in terms of the mean value of the simulated LFMC, and (b) captures the variability of the model response to the input parameters in terms of the variance of the simulated LFMC.

[0026] Figure 2Two analysis plots for the observed field CFMC product (MODIS-based CFMC product), where (a) the line plot shows the change in R 2 and RMSE for the four parts of the CFMC product, which are divided according to ascending order of the inversion values, and (b) the scatter plot shows the correlation between the absolute error of the remote sensing CFMC value and the measured value and the remote sensing CFMC value.

[0027] Figure 3 The comparison diagram of simulated / estimated CFMC and field measured values, where (a) MEDFATE simulation using default Mediterranean species; (b) observed field remote sensing product accuracy; (c) background field simulation accuracy; (d) MEDFATE simulation accuracy after combining the remote sensing CFMC product using the four-dimensional variational data assimilation technique. DETAILED DESCRIPTION

[0028] In order to better understand the purpose, structure and function of the present application, the following will be further described in detail in combination with the drawings.

[0029] The present application provides a canopy fuel moisture content prediction method combining remote sensing and meteorological data. First, the key input parameters of the MEDFATE model for different tree species are identified through global two-stage sensitivity analysis. In addition, these parameters are used to construct a cost function to quantify the difference between the CFMC value simulated by MEDFATE and the field measured value, thereby realizing model localization based on tree species classification. Then, the remote sensing CFMC product value is integrated into the MEDFATE model by combining the DEoptim global optimization algorithm and the four-dimensional variational data assimilation technique.

[0030] The canopy fuel moisture content prediction method combining remote sensing and meteorological data provided by the present application specifically includes the following steps:

[0031] Step 1: Sensitivity analysis of parameters. First, the key input species parameters affecting the MEDFATE simulated CFMC are obtained, and then the parameters having significant influence on the MEDFATE simulated CFMC are subjected to global sensitivity analysis, thereby enhancing the feasibility and universality of the model and improving the calculation efficiency.

[0032] MEDFATE (version 3.2.0) is a soil-vegetation-atmosphere based process model that simulates daily CFMC for specific species, driven by meteorological data, integrating soil, geographical and vegetation information. The meteorological inputs include: daily minimum temperature (℃), daily maximum temperature (℃), daily minimum relative humidity (%), daily maximum relative humidity (%), daily precipitation (mm), daily global solar radiation (MJ / m²). The invention extracts and processes relevant meteorological data from the ERA5-LAND dataset, setting unnecessary climate parameters to the default values of MEDFATE. While the soil depth plays a key role in the formation of the root habitat of vegetation, the invention divides the soil into 4 layers, with depths of 0-10 cm, 10-20 cm, 20-60 cm and 60-100 cm. The clay content, sand content, organic matter content and rock fragment percentage data are obtained from the SoilGrids 2.0 system (250-meter resolution) and are down-sampled to a spatial resolution of 0.1°*0.1°. For fine root depth, the values of 20 cm and 100 cm are used for the locations where 50% (Z50) and 95% (Z95) of the roots are located, respectively. Other species parameter values are set to the default settings of MEDFATE, supplemented by the latest plot data from the third national forest resource inventory in Spain.

[0033] The sensitive vegetation parameters of MEDFATE in simulating CFMC include vegetation allometric coefficients, vegetation anatomical parameters, radiation balance and water interception parameters, vegetation photosynthesis parameters, and related hydrology and parameters representing water storage of vegetation. To ensure that only species parameter variables change, climate data remains consistent, the simulation step of the MEDFATE model is set to 1 year, and the output of the model is set to the mean and variance of the simulated CFMC to represent the influence of input parameters on the model. The parameters affecting the simulation of CFMC by MEDFATE are selected according to the MEDFATE guidebook, and the selected parameters are subjected to global sensitivity analysis using the extended Fourier amplitude sensitivity test method. The two groups of sensitivity analysis results of the mean and variance of CFMC are sorted according to the total order sensitivity index, see Figure 1The left figure shows the overall impact of input parameters on the simulated CFMC based on the mean, while the right figure reflects the fluctuation impact of input parameters on the simulated CFMC based on variance. Finally, parameters with a significant decrease in the overall sensitivity index or greater than 0.1 were selected as the model state field variables for four-dimensional variational data assimilation. Ultimately, eight species parameters were selected, including maximum live fuel water content (maxFMC), the hydraulic vulnerability curve of the stem xylem (VCsteam_d), the ratio of leaf-branch weight to leaf weight of a 6.35 mm branch (r635), two holographic coefficients of leaf biomass (b_fbt and c_fbt), used to calculate the coefficients of crown and diameter, the osmotic potential when the leaf is fully expanded (LeafPI0), the hydraulic vulnerability curve of the leaf (VCleaf_d), which characterizes the vulnerability or resistance of the plant stem xylem under water stress, and the permeable part in the stem tissue (StemAF).

[0034] The Extended Fourier Amplitude Sensitivity Test (EFAST) is... Others conducted Fourier amplitude sensitivity tests ( Based on ) and combined The proposed method is a global sensitivity analysis method based on variance decomposition. EFAST selects a suitable search curve and runs it in a multidimensional parameter space. It assigns a set of nonlinearly correlated integer frequencies to all input parameters of the model and introduces a function with common independent parameters to the selected parameters in the model, making the model a periodic function with independent parameters, thus reducing the multidimensional integral to a one-dimensional integral. By transforming the objective function into a Fourier series, the Fourier spectrum curves of each frequency are obtained, and the parameters can be calculated from the spectrum curves. The ratio of the resulting model output variance to the total variance is the sensitivity of this parameter.

[0035] Step 2: Species-based localization correction;

[0036] Plant growth is the result of the combined effects of multiple factors, including meteorology, soil, and geography. The MEDFATE model is primarily driven by meteorological data while also considering soil, geographical, and vegetation information to simulate the daily CFMC of a given species. The MEDFATE model cleverly transforms vegetation information into specific species parameters to characterize vegetation growth rate, morphology, and survival ability. The CFMC measurements used in this invention come from different sites, and the ecological environments corresponding to different sites often differ. Moreover, even the same vegetation will exhibit different growth performances at different sites, and these differences are reflected through different values ​​of species parameters. Therefore, localized adjustments to species are crucial for accurately simulating vegetation growth. Since measuring species parameters in different experimental areas is difficult and complex, this invention transforms the problem into an extremum problem using variational thinking to improve model feasibility. By constructing a cost function to describe the difference between simulated and measured CFMC values, and minimizing the difference between simulated and measured CFMC values ​​under dynamic constraints, the simulation of the background field is achieved. Species parameters were used as decision variables. The objective function was defined as the sum of the root mean square errors of the simulated CFMC values ​​from the MEDFATE model and the measured CFMC values ​​from the Globe LFMC 2.0 dataset. MEDFATE's vegetation simulation ecological rules were used as constraints, and the feasible region was defined as 50% to 200% of MEDFATE's default species parameters. The DEoptim global optimization algorithm (differential evolution algorithm) was used for background field simulation, and the optimized species parameter values ​​were used as the standard species parameter input values ​​for the MEDFATE model. The specific formula for the DEoptim algorithm is as follows:

[0037]

[0038] In the formula, Indicates species parameters as hour, The model in the Japanese simulation ; Indicates the first Japanese Actual measured values; express Upper bound of the feasible domain for species parameters; express Lower bound of the feasible region for model species parameters; This represents a vector of species parameters, specifically sensitivity parameters for eight species. Indicates the first Default values ​​for species parameters Indicates the first Lower bound of parameter domain Indicates the first Upper bound of the parameter domain; This represents the lower limit of the m-th sensitivity parameter. This represents the upper bound of the m-th sensitivity parameter; n represents... The amount of measured CFMC data used during model localization correction.

[0039] The MEDFATE model's R package provides default values ​​for some species parameters in the Mediterranean region, but using these default values ​​for CFMC simulations of the target vegetation does not yield very good results. (See...) Figure 3 In (a), R 2 =0.25, RMSE=27.41%, p<0.01. This paper constructs an optimization problem using the difference between simulated and measured CFMC values ​​as the cost function, and performs species-based background field simulations for the species *Quercus faginea* and *Quercus ilex*. By simulating vegetation parameters based on species, the obtained vegetation parameters are used as species parameter input values ​​for the MEDFATE model, improving the accuracy of simulated vegetation CFMC, as shown in Figure 3(c). 2 =0.53, RMSE=19.28%, p<0.01. The obtained simulation values ​​are also used as the background state for subsequent four-dimensional variational data assimilation.

[0040] Step 3: Four-dimensional variational data assimilation;

[0041] Four-dimensional variational data assimilation techniques are commonly used in meteorological models, where the assimilated variables are mostly meteorological parameters. However, the application of four-dimensional variational data assimilation techniques in this invention is quite different. This invention uses the input species parameters of the MEDFATE model as the state field variables for four-dimensional variational data assimilation, improving the accuracy of the MEDFATE model in simulating CFMC through data assimilation techniques. Four-dimensional variational data assimilation mainly consists of the background field, the background field error covariance matrix, the observation field, the observation field error covariance matrix, and the forecast model. The background field and observation field error covariance matrices define the relative weights of background information and observation information, as well as the relative information content projected onto the assimilated variables in each iteration. The cost function of four-dimensional variational data assimilation consists of two parts: a background term and an observation term.

[0042]

[0043] In the formula, The cost function; This represents the model state field vector, which in this embodiment is the species parameter vector, corresponding to 8 sensitivity parameters; This represents the background field state vector, and X represents the value of X in the previous iteration. The background field error covariance matrix; represents a prediction model, MEDFATE in the present application; is an observation vector, here CFMC remote sensing observation, which is only used in background field correction and model test; is an observation error matrix.

[0044] For the calculation of the observation field error covariance matrix, the observation field data is derived from the remote sensing CFMC product, and the remote sensing CFMC inversion value for a certain product, longitude, latitude and time is often unique, and the inversion error of the remote sensing product is usually the overall simulation accuracy, not the error of a single inversion value, so it is difficult to obtain the observation field error covariance matrix. The present application performs precision analysis on the observation field CFMC product, and arranges the observation field CFMC in ascending order and divides it into four parts (for convenience, the parts from low value to high value are named as first part to fourth part in turn), and calculation shows that the observation field CFMC remote sensing product has the highest precision in the first part, and the precision gradually decreases in the second, third and fourth parts, as shown in a of Figure 2 . At the same time, the present application also calculates the absolute value of the error between the observation field CFMC remote sensing product value and the CFMC measured value, as shown in b of Figure 2 , and finds that the error and the observation field CFMC product value have significant positive correlation, r=0.91, ubRMSE=13.49%, p<0.01. Therefore, in order to realize the error characteristics of the observation data represented by the observation field error covariance matrix, and to reflect the effect of the weighted observation data on the model state in the data assimilation process, and to overcome the difficulty of obtaining the observation field error covariance matrix, the present application simplifies the cost function of four-dimensional variational data assimilation, simplifies the observation field error covariance matrix into the observation field error matrix, and assumes that the non-diagonal elements of the observation field error matrix are all zero, and the diagonal elements are the product of the overall RMSE of the observation field CFMC product and the observation value, and through normalization processing, the influence of the dimension between the assimilated variable (CFMC) and the model state field variable is eliminated. Although the above simplification will reduce the input of correct information of the observation field and the background field, it greatly reduces the calculation amount and improves the universality of the model, and solves the need for a large amount of historical data and experience information for calculating the background field covariance matrix and the difficulty of obtaining the observation field error covariance matrix. Figure 3 b is the precision of the observation field CFMC product of the four-dimensional variational data assimilation (R 2 =0.36, RMSE=37.16%, p<0.01). The simulation value is taken as the background state value of the background field, the CFMC product value is taken as the observation field data, and the MEDFATE is taken as the prediction model, and the four-dimensional variational data assimilation technology is used to perform enhanced simulation of CFMC of five sampling points in Spain (as shown in d of Figure 3 ), which significantly improves the simulation precision of the MEDFATE simulated vegetation CFMC (R2 = 0.72, RMSE = 12.58%, p < 0.01).

[0045] It is to be understood that the application is not limited to particular embodiments described, as such may vary within the spirit and scope of the application. What is claimed is:

Claims

1. A method for predicting canopy combustible moisture content by combining remote sensing and meteorological data, characterized in that, Includes the following steps: Step 1: Sensitivity analysis of parameters; obtain key input species parameters that affect the canopy combustible water content (CFMC) simulated by the MEDFATE ecophysical process model, and then perform sensitivity analysis to screen and obtain species parameter vectors; Step 2: Species-based localization correction; Using the vegetation simulation ecological rules of the MEDFATE ecophysical process model as constraints, minimize the difference between CFMC simulated values ​​and field measurements, and use an evolutionary algorithm to optimize the selected species parameter vectors. The optimized species parameter values ​​are used as the standard species parameter inputs for the MEDFATE ecophysical process model. Step 3: Four-dimensional variational data assimilation; using the standard input species parameters of the MEDFATE ecophysical process model as the state field variables for four-dimensional variational data assimilation, the accuracy of the MEDFATE ecophysical process model in simulating CFMC is improved through data assimilation techniques; Step 4: The optimized MEDFATE ecophysical process model is used to make CFMC predictions based on meteorological forecast data.

2. The method for predicting canopy combustible moisture content by combining remote sensing and meteorological data according to claim 1, characterized in that, Step 1 is described in detail as follows: First, key input species parameters affecting the MEDFATE ecophysical process model's simulation of CFMC are obtained, including meteorological data, soil, geographical and vegetation information. Sensitive vegetation parameters for the MEDFATE ecophysical process model's simulation of CFMC include vegetation allometric growth coefficient, vegetation anatomy parameters, radiation balance and water interception parameters, vegetation photosynthetic parameters, as well as related hydraulic and vegetation water storage parameters. The model output is set as the mean and variance of the simulated CFMC. Then, sensitivity analysis was performed using the test method based on the mean and variance of the simulated CFMC to obtain the parameter vector of the screened species, including m sensitivity parameters.

3. The method for predicting canopy combustible moisture content by combining remote sensing and meteorological data according to claim 2, characterized in that, The test method is the extended Fourier amplitude sensitivity test, which selects parameters whose total order sensitivity index shows a significant decrease or is greater than 0.1 as species parameter vectors.

4. The method for predicting canopy combustible moisture content by combining remote sensing and meteorological data according to claim 3, characterized in that, The evolutionary algorithm is specifically the DEoptim global optimization algorithm, and the formula is as follows: ； in, Indicates species parameters as hour, The model in the Japanese simulation ; Indicates the first Japanese Measured value; express Upper bound of the feasible domain for species parameters in the ecological physical process model; express Lower bound of the feasible domain for species parameters in the ecological physical process model; Represents a species parameter vector. For the m-th sensitivity parameter, Indicates the first The default values ​​for each sensitivity parameter, Indicates the first Lower bound of the domain of a sensitivity parameter Indicates the first Upper bound of the domain of each sensitivity parameter; This represents the lower limit of the m-th sensitivity parameter. This represents the upper bound of the m-th sensitivity parameter; n represents... The amount of measured CFMC data used in the localization correction of the ecological physical process model.

5. The method for predicting canopy combustible moisture content by combining remote sensing and meteorological data according to claim 4, characterized in that, The four-dimensional variational data assimilation optimizes the accuracy of the MEDFATE ecophysical process model in simulating CFMC by constructing the following cost function: ； In the formula, The cost function; Represented as a species parameter vector, This represents the value of X in the previous iteration, and is the background field error covariance matrix; Represents the MEDFATE model. These are CFMC remote sensing observations and are only used for background field correction and model validation. This is the observation field error matrix.

6. The method for predicting canopy combustible moisture content by combining remote sensing and meteorological data according to claim 5, characterized in that, The species parameter vector includes eight sensitivity parameters, specifically: maximum live fuel water content, hydraulic vulnerability curve of stem xylem, ratio of leaf-branch weight to leaf weight of 6.35 mm branches, two holographic coefficients of leaf biomass, osmotic potential when leaves are fully expanded, hydraulic vulnerability curve of leaves, and permeable portion of stem tissue.

7. The method for predicting canopy combustible moisture content by combining remote sensing and meteorological data according to claim 6, characterized in that, The meteorological data inputs for the MEDFATE model include daily minimum temperature, daily maximum temperature, daily minimum relative humidity, daily maximum relative humidity, daily precipitation, and daily solar radiation behind clouds.

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