Forest underlying surface earth surface evapotranspiration estimation and component segmentation method based on kNDVI
By using a forest surface evapotranspiration segmentation method based on the kNDVI index, combined with the maximum entropy increase model and energy balance equation, the evapotranspiration of bare soil, vegetation transpiration, and canopy intercepted evapotranspiration are calculated. This solves the problem of large estimation errors in forest evapotranspiration models and achieves higher accuracy in evapotranspiration calculation and component classification.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUAZHONG UNIV OF SCI & TECH
- Filing Date
- 2026-01-28
- Publication Date
- 2026-05-15
AI Technical Summary
Existing forest evapotranspiration models have large estimation errors under different environmental conditions, making it difficult to accurately estimate forest evapotranspiration and its components.
Using a method based on the kNDVI index, the surface evapotranspiration of the forest underlying surface is divided into bare soil evaporation, vegetation transpiration and canopy intercepted evapotranspiration. Each component is solved by the maximum entropy increase model and energy balance equation. Leaf area index and rainfall are introduced, and the maximum interception capacity is estimated by machine learning model. Weighted summation is then performed to obtain the total evapotranspiration.
It improves the accuracy of forest evapotranspiration calculation, reduces calculation parameters and operational complexity, is applicable to vegetation management and water resource regulation, and enhances the accuracy of ecological restoration assessment.
Smart Images
Figure CN122045563A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of eco-hydrology technology, and more specifically, relates to a method for estimating and classifying the surface evapotranspiration of forest underlying surfaces based on kNDVI. Background Technology
[0002] Forest evapotranspiration is the overall process by which water from forest ecosystems is converted into water vapor and enters the atmosphere through physical evaporation and plant transpiration. It is the most important water output in the forest water cycle. Accurate estimation of forest evapotranspiration and its components is of great significance for calculating water cycles and ecological flows within watersheds: providing core data for the scientific allocation of water resources, agricultural irrigation, and drought early warning; serving as a key parameter for understanding surface energy balance, simulating regional climate, and predicting global climate change; and forming an indispensable foundation for assessing forest ecosystem health, productivity, and carbon cycle processes. Therefore, it is a fundamental scientific support for achieving sustainable water resource utilization and addressing climate change.
[0003] The complexity and diversity of forest environments pose challenges to the accurate estimation of forest evapotranspiration. Although scientists have developed many models for estimating forest evapotranspiration, many models face problems such as difficulty in data acquisition, overly complex parameters, and imperfect model structures, and often exhibit large estimation errors under different environmental conditions. Summary of the Invention
[0004] To address the shortcomings of existing technologies, the purpose of this application is to provide a method for estimating and classifying the components of forest surface evapotranspiration based on kNDVI, aiming to solve the technical problem of excessive estimation errors in existing forest evapotranspiration models.
[0005] The first aspect of this application relates to a method for estimating and classifying surface evapotranspiration from forest underlying surfaces based on kNDVI, including: The total evapotranspiration of the forest underlying surface is obtained by weighted summation of bare soil evaporation, vegetation transpiration and canopy intercepted evaporation in the forest based on the kNDVI index. The soil evaporation of bare soil is calculated from the surface state parameters of the forest-free area under the constraint of the surface energy balance equation, based on the maximum entropy increase model. The vegetation transpiration rate is calculated from the canopy state parameters of the forest vegetation cover area based on the maximum entropy increase model and under the constraint of the canopy energy balance equation. The canopy interception evaporation is calculated based on the leaf area index and rainfall in the forest vegetation coverage area.
[0006] Preferably, the total evapotranspiration of the forest underlying surface is obtained by weighted summation of bare soil evaporation, vegetation transpiration, and canopy intercepted evaporation based on the kNDVI index. Specifically: Using the kernel function method, the near-infrared band reflectance and red band reflectance of the target area are processed to obtain the kNDVI index; the kNDVI index is normalized to obtain the vegetation index, and the bare soil index is obtained by subtracting the vegetation index from 1. The total evapotranspiration of the forest underlying surface is obtained by weighting the vegetation transpiration and canopy intercepted evaporation with the vegetation index as the weight and the bare soil index as the weight of the bare soil evaporation.
[0007] Preferably, the evaporation of the bare soil is specifically calculated by the following method: the energy distribution function is obtained from the maximum entropy increase model, and the surface state parameters are substituted into the energy distribution function to obtain the relationship between the surface latent heat flux and the surface sensible heat flux. The relationship is then combined with the surface energy balance equation to obtain the evaporation of the bare soil.
[0008] Preferably, the vegetation transpiration is specifically calculated by the following method: obtaining the energy distribution function from the maximum entropy increase model; correcting the canopy state parameters using the water pressure factor, and substituting the corrected canopy state parameters into the energy distribution function to obtain the relationship between the canopy latent heat flux and the canopy sensible heat flux; combining the relationship with the canopy energy balance equation to obtain the vegetation transpiration.
[0009] Preferably, the canopy evaporation interception rate is calculated using the following method: The maximum retention capacity of the canopy is calculated based on the leaf area index. The evaporation ratio of the retention capacity is determined by an exponential function based on the relative magnitude of the rainfall and the maximum retention capacity. Based on whether the rainfall exceeds the maximum interception capacity, the canopy interception evaporation is calculated using a piecewise function: when there is no rainfall, the canopy interception evaporation is zero; when the rainfall does not exceed the maximum interception capacity, the canopy interception evaporation is equal to the product of the rainfall and the interception capacity evaporation ratio; when the rainfall exceeds the maximum interception capacity, the canopy interception evaporation is equal to the product of the maximum interception capacity and the interception capacity evaporation ratio.
[0010] Preferably, the maximum retention capacity of the canopy is calculated based on the leaf area index, specifically as follows: The leaf area index is input into a trained maximum cutoff capacity prediction model, which outputs the maximum cutoff capacity. The maximum cutoff capacity prediction model is trained using the following method: A maximum cut-off capacity prediction model is constructed based on a machine learning model. The input features of the model include leaf area index, and the output target of the model includes the maximum cut-off capacity. Construct a loss function that includes prediction error and physical penalty term, calculate the gradient of the predicted value with respect to the input leaf area index, and if the gradient is negative, increase the loss value of the loss function through the physical penalty term; Train the maximum capacity cutoff prediction model until the model convergence condition is met.
[0011] Preferably, the total evapotranspiration of the forest underlying surface is : ; in, This refers to the evaporation rate of the bare soil. The transpiration rate of the vegetation is [missing information]. The amount of evaporation retained by the canopy. Vegetation index: ; ; in, For the near-infrared reflectance data of the target area, For the infrared reflectance data of the target area, The kNDVI index for the target region. The densest vegetation area within the forest vegetation cover region value, For forest areas without vegetation value.
[0012] Preferably, the surface state parameters are: : ; in, The latent heat of vaporization of water, The specific heat capacity of air at constant pressure. Let be the gas constant of water vapor. This represents the ratio of water vapor turbulent diffusivity to thermal diffusivity in the boundary layer. The surface is wetter. It is the surface temperature; substituting the surface state parameters into the energy distribution function obtained based on the maximum entropy increase model. : ; The soil evaporation rate of the bare soil is then obtained by combining it with the surface energy balance equation. : ; ; ; in, This refers to the evaporation rate of the bare soil. For surface sensible heat flux, Let be the gas constant of water vapor. For surface heat flux, For the thermal inertia of the Earth's surface medium, is the surface sensible heat flux coefficient.
[0013] Preferably, the corrected canopy state parameters are: : ; in, The latent heat of vaporization of water, The specific heat capacity of air at constant pressure. Let be the gas constant of water vapor. The canopy is more humid. It is the canopy temperature. It is the water pressure factor: The corrected canopy state parameters Substituting the energy allocation function obtained from the maximum entropy increase model : ; The vegetation transpiration rate was obtained by combining it with the aforementioned canopy energy balance equation. : ; ; ; in, Net radiation, This refers to the canopy sensible heat flux.
[0014] Preferably, the canopy evaporation retention capacity is : ; in, The rainfall amount, The maximum retention capacity is obtained based on the leaf area index. The evaporation ratio is determined by an exponential function based on the relative magnitude of the rainfall and the maximum retention capacity.
[0015] In a second aspect, this application provides an electronic device, comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute the method described in the first aspect or any possible implementation thereof.
[0016] Overall, the technical solutions conceived in this application have the following beneficial effects compared with the prior art: (1) This application clearly divides the problem of estimating the surface evapotranspiration of forest underlayment into three sub-problems: bare soil evaporation, vegetation evaporation and canopy interception evaporation. The kNDVI index is introduced to weight the estimation of the three sub-problems and finally obtain the surface evapotranspiration of forest underlayment. Compared with the existing evapotranspiration calculation methods, the method of this application can effectively improve the accuracy of evapotranspiration calculation and component classification of forest underlayment, and can be better used for vegetation management, water resource regulation and ecological restoration assessment.
[0017] (2) This application combines the maximum entropy increase model and energy balance equation to calculate the evaporation of bare soil and the transpiration of vegetation. Compared with conventional calculation methods, it involves fewer parameters and has a smaller computational cost.
[0018] (3) This application introduces factors such as leaf area index and rainfall, and uses a hybrid machine learning model that combines physical laws to estimate the maximum interception capacity, thereby more flexibly reflecting the canopy interception evaporation under different vegetation densities or canopy structures, accurately segmenting and solving the evapotranspiration components, and the cost and operational complexity are lower than the traditional methods that require isotopes, vegetation profiles and other technologies. Attached Figure Description
[0019] Figure 1 This is a schematic diagram of the composition of surface evapotranspiration of forest underlayment provided in the embodiments of this application.
[0020] Figure 2 This is a schematic diagram of the forest surface evapotranspiration estimation process provided in the embodiments of this application.
[0021] Figure 3 This is a schematic diagram comparing the estimated and actual evaporation values provided in the embodiments of this application.
[0022] Figure 4 This is a schematic diagram comparing the estimated and actual values of canopy evaporation interception provided in the embodiments of this application.
[0023] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation
[0024] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0025] In this application, the terms "first" and "second," etc., are used to distinguish different objects, not to describe a specific order of objects. For example, "first response message" and "second response message," etc., are used to distinguish different response messages, not to describe a specific order of response messages.
[0026] In this application, the term "electrical connection" can refer to a direct circuit connection or a signal transmission via a communication protocol.
[0027] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.
[0028] In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, for example, multiple processing units means two or more processing units, multiple elements means two or more elements, etc.
[0029] First, the technical terms involved in the embodiments of this application will be introduced.
[0030] The kernel normalized difference vegetation index (kNDVI) is a remote sensing index that improves upon NDVI by introducing a kernel function. By using kernel tricks to handle the nonlinear relationship between near-infrared and red light reflectance, it can effectively alleviate the signal saturation problem of traditional indices in high vegetation cover areas, thus more accurately characterizing vegetation biophysical parameters and photosynthetic dynamics.
[0031] The Normalized Difference Vegetation Index (NDVI) is one of the most commonly used vegetation monitoring indicators in the field of remote sensing. It quantifies the status of surface vegetation cover by analyzing the difference in reflectance between red and near-infrared bands.
[0032] Maximum Entropy Production (MEP) is a framework for estimating evapotranspiration based on non-equilibrium thermodynamics. It posits that the Earth's surface system, under steady-state conditions, spontaneously tends towards a state that maximizes entropy production.
[0033] The embodiments of this application are described below with reference to the accompanying drawings.
[0034] like Figure 1 As shown, in this application, the surface evapotranspiration of the forest underlying surface is divided into three components: bare soil evaporation, vegetation transpiration, and canopy intercepted evaporation. The surface evapotranspiration of the forest underlying surface can be obtained by solving these three components separately and then summing them with weights. The weights are determined by the vegetation index.
[0035] Example 1: See Figure 2 This embodiment discloses the specific steps of a method for estimating surface evapotranspiration over a forest underlying surface, including: 1. First, the flux data and various meteorological data of the forest stations are directly downloaded from the publicly available dataset FLUXNET2015. This includes surface specific humidity, surface temperature, canopy specific humidity, canopy temperature, rainfall, leaf area index, gas constant of water vapor, and net radiation required by the method of this application.
[0036] 2. Calculate the evaporation of bare soil, the transpiration of vegetation, and the evaporation intercepted by the canopy, respectively.
[0037] 2.1 Based on the maximum entropy increase model, under the constraint of the surface energy balance equation, the soil evaporation of bare soil is calculated from the surface state parameters of the forest-free area: Surface state parameters are : ; in, The latent heat of vaporization of water, The specific heat capacity of air at constant pressure. Let be the gas constant of water vapor. This represents the ratio of water vapor turbulent diffusivity to thermal diffusivity in the boundary layer. The surface is wetter. It is the surface temperature; substituting the surface state parameters into the energy distribution function obtained based on the maximum entropy increase model. : ; The soil evaporation rate of the bare soil is then obtained by combining it with the surface energy balance equation. : ; ; ; in, This refers to the evaporation rate of the bare soil. For surface sensible heat flux, Let be the gas constant of water vapor. For surface heat flux, For the thermal inertia of the Earth's surface medium, is the surface sensible heat flux coefficient.
[0038] 2.2 Based on the maximum entropy increase model, under the constraint of the canopy energy balance equation, the vegetation transpiration is calculated from the canopy state parameters of the forest vegetation cover area: The corrected canopy state parameters are: : ; in, The latent heat of vaporization of water, The specific heat capacity of air at constant pressure. Let be the gas constant of water vapor. The canopy is more humid. It is the canopy temperature. It is the water pressure factor: ; in, This represents the current surface water content. For permanent wilting points, Field holding capacity; The corrected canopy state parameters Substituting the energy distribution function obtained from the maximum entropy increase model: ; The vegetation transpiration rate was obtained by combining it with the aforementioned canopy energy balance equation. : ; ; ; in, Net radiation, This refers to the canopy sensible heat flux.
[0039] 2.3. The canopy evaporation interception is calculated from the leaf area index and rainfall in the forest vegetation cover area: The maximum retention capacity of the canopy is calculated based on the leaf area index. In some embodiments, a maximum cutoff capacity prediction model is trained by combining machine learning models and physical constraints, enabling the prediction of maximum cutoff capacity based on input features such as leaf area index. During the training of the prediction model, physical constraints such as the upper limit of the maximum cutoff capacity and the positive gradient of the maximum cutoff capacity with respect to the input leaf area index are incorporated as physical penalty terms into the loss function.
[0040] After training the prediction model, the maximum cutoff capacity is predicted by inputting the feature leaf area index.
[0041] In some embodiments, the maximum retention capacity is calculated from the leaf area index using an empirical formula: ; in, To maximize the retention capacity, The leaf area index is used. Then, based on the relative magnitudes of the rainfall and the maximum retention capacity, the retention capacity-to-evaporation ratio is determined using an exponential function: ; in To retain the capacity evaporation ratio, It is a natural constant.
[0042] Based on whether the rainfall exceeds the maximum retention capacity, the canopy evaporation retention is calculated using a piecewise function. : ; in, The rainfall amount, To maximize the retention capacity, The evaporation ratio is the capacity to be retained.
[0043] 3. The total evapotranspiration of the forest underlying surface is obtained by weighted summation of soil evaporation, vegetation transpiration and canopy intercepted evaporation in the forest based on the kNDVI index.
[0044] 3.1 Obtain the NDVI index of the target area: The reflectance data of the target forest area in the near-infrared and red bands were extracted from multispectral data provided by satellites such as US Landsat, ESA Sentinel-2, and NASA MODIS, and then substituted into the following formula to obtain the NDVI index of the target area: ; in, The NDVI index for the target region. This is reflectance data in the near-infrared band. This is the reflectivity data for the infrared band.
[0045] 3.2. The kNDVI index of the target region is obtained by nonlinearly stretching the NDVI index of the target region using the following formula: ; in, It is the tangent function.
[0046] 3.3. Normalize the kNDVI index to obtain the vegetation index. The bare soil index is obtained by subtracting the vegetation index from 1. : .
[0047] 3.4. Evaporation rate of the bare soil Vegetation transpiration and canopy evaporation The total evapotranspiration of the forest underlying surface is obtained by weighted summation. : .
[0048] Example 2: Taking a certain forest site as an example, the surface evapotranspiration of its forest underlying surface is estimated using the method proposed in this application.
[0049] First, download flux data and various meteorological data for this forest station from the publicly available dataset FLUXNET2015. See Table 1 for specific data. Table 1
[0050] The surface evapotranspiration of the forest underlying surface was estimated using the method described in this application. A comparison between the final estimated and actual total forest underlying surface evapotranspiration can be found in [reference needed]. Figure 3 The results show a high degree of consistency between the two methods, with a slope of 1.007, a coefficient of determination of 0.953, and a root mean square error of only 0.356 mm / day. This indicates that the method proposed in this application can accurately calculate forest surface evaporation.
[0051] The comparison between the canopy evaporation cut-off value estimated using the method of this application and the measured canopy evaporation cut-off value is shown in the figure below. Figure 4 The results showed a high degree of consistency between the two methods, with a slope of 0.892, a coefficient of determination of 0.758, and a root mean square error of only 0.358 mm / day. This indicates that the method proposed in this application can accurately calculate the canopy-retained evaporation of the forest underlying surface.
[0052] Based on the methods in the above embodiments, this application provides an electronic device, see [link to relevant documentation]. Figure 5As shown, the electronic device includes a processor, a communications interface, a memory, and a communication bus, wherein the processor, communications interface, and memory communicate with each other via the communication bus. The processor can invoke logical instructions stored in the memory to execute the methods described in the above embodiments.
[0053] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions 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 described in the various embodiments of this application.
[0054] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.
[0055] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.
[0056] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.
[0057] The method steps in this application embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.
[0058] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).
[0059] It is understood that the various numerical designations used in the embodiments of this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application.
[0060] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.
Claims
1. A method for estimating and classifying the components of surface evapotranspiration over forest underlying surfaces based on kNDVI, characterized in that, include: The total evapotranspiration of the forest underlying surface is obtained by weighted summation of bare soil evaporation, vegetation transpiration and canopy intercepted evaporation in the forest based on the kNDVI index. The soil evaporation of bare soil is calculated from the surface state parameters of the forest-free area under the constraint of the surface energy balance equation, based on the maximum entropy increase model. The vegetation transpiration rate is calculated from the canopy state parameters of the forest vegetation cover area based on the maximum entropy increase model and under the constraint of the canopy energy balance equation. The canopy interception evaporation is calculated based on the leaf area index and rainfall in the forest vegetation coverage area.
2. The method for estimating and classifying surface evapotranspiration of forest underlying surfaces according to claim 1, characterized in that, The total evapotranspiration of the forest underlying surface is obtained by weighted summation of soil evaporation, vegetation transpiration, and canopy intercepted evaporation based on the kNDVI index. Using the kernel function method, the near-infrared band reflectance and red band reflectance of the target area are processed to obtain the kNDVI index; the kNDVI index is normalized to obtain the vegetation index, and the bare soil index is obtained by subtracting the vegetation index from 1. The total evapotranspiration of the forest underlying surface is obtained by weighting the vegetation transpiration and canopy intercepted evaporation with the vegetation index as the weight and the bare soil index as the weight of the bare soil evaporation.
3. The method for estimating and classifying surface evapotranspiration of forest underlying surfaces according to claim 1, characterized in that, The evaporation of the bare soil is specifically calculated by the following method: the energy distribution function is obtained from the maximum entropy increase model, and the surface state parameters are substituted into the energy distribution function to obtain the relationship between the surface latent heat flux and the surface sensible heat flux. The relationship is then combined with the surface energy balance equation to obtain the evaporation of the bare soil.
4. The method for estimating and classifying surface evapotranspiration of forest underlying surfaces according to claim 1, characterized in that, The vegetation transpiration is specifically calculated using the following method: the energy distribution function is obtained from the maximum entropy increase model; the canopy state parameters are corrected using the water pressure factor, and the corrected canopy state parameters are substituted into the energy distribution function to obtain the relationship between the canopy latent heat flux and the canopy sensible heat flux; the relationship is then combined with the canopy energy balance equation to obtain the vegetation transpiration.
5. The method for estimating and classifying surface evapotranspiration of forest underlain surfaces according to claim 1, characterized in that, The canopy evaporation interception rate is specifically calculated using the following method: The maximum retention capacity of the canopy is calculated based on the leaf area index. The evaporation ratio of the interception capacity is determined by an exponential function based on the relative magnitude of the rainfall and the maximum interception capacity. Based on whether the rainfall exceeds the maximum interception capacity, the canopy interception evaporation is calculated using a piecewise function: when there is no rainfall, the canopy interception evaporation is zero; when the rainfall does not exceed the maximum interception capacity, the canopy interception evaporation is equal to the product of the rainfall and the interception capacity evaporation ratio; when the rainfall exceeds the maximum interception capacity, the canopy interception evaporation is equal to the product of the maximum interception capacity and the interception capacity evaporation ratio.
6. The method for estimating and classifying surface evapotranspiration of forest underlying surfaces according to claim 5, characterized in that, The maximum retention capacity of the canopy is calculated based on the leaf area index, specifically as follows: The leaf area index is input into a trained maximum cutoff capacity prediction model, which outputs the maximum cutoff capacity. The maximum cutoff capacity prediction model is trained using the following method: A maximum cut-off capacity prediction model is constructed based on a machine learning model. The input features of the model include leaf area index, and the output target of the model includes the maximum cut-off capacity. Construct a loss function that includes prediction error and physical penalty term, calculate the gradient of the predicted value with respect to the input leaf area index, and if the gradient is negative, increase the loss value of the loss function through the physical penalty term; Train the maximum capacity cutoff prediction model until the model convergence condition is met.
7. The method for estimating and classifying surface evapotranspiration of forest underlying surfaces according to claim 1, characterized in that, The total evapotranspiration of the forest underlying surface is : ； in, This refers to the evaporation rate of the bare soil. The transpiration rate of the vegetation is [missing information]. The amount of evaporation retained by the canopy. Vegetation index: ； ； in, For the near-infrared reflectance data of the target area, For the infrared reflectance data of the target area, The kNDVI index for the target region. The densest vegetation area within the forest vegetation cover region value, For forest areas without vegetation value.
8. The method for estimating and classifying surface evapotranspiration of forest underlying surfaces according to claim 1, characterized in that, The surface state parameters are: : ； in, The latent heat of vaporization of water, The specific heat capacity of air at constant pressure. Let be the gas constant of water vapor. This represents the ratio of water vapor turbulent diffusivity to thermal diffusivity in the boundary layer. The surface is wetter. It is the surface temperature; substituting the surface state parameters into the energy distribution function obtained based on the maximum entropy increase model. : ； The soil evaporation rate of the bare soil is then obtained by combining it with the surface energy balance equation. : ； ； ； in, This refers to the evaporation rate of the bare soil. For surface sensible heat flux, Let be the gas constant of water vapor. For surface heat flux, For the thermal inertia of the Earth's surface medium, is the surface sensible heat flux coefficient.
9. The method for estimating and classifying surface evapotranspiration of forest underlying surfaces according to claim 4, characterized in that, The corrected canopy state parameters are: : ； in, The latent heat of vaporization of water, The specific heat capacity of air at constant pressure. Let be the gas constant of water vapor. The canopy is more humid. It is the canopy temperature. It is the water pressure factor: The corrected canopy state parameters Substituting into the energy allocation function obtained based on the maximum entropy increase model : ； The vegetation transpiration rate was obtained by combining it with the aforementioned canopy energy balance equation. : ； ； ； in, Net radiation, This refers to the canopy sensible heat flux.
10. The method for estimating and classifying surface evapotranspiration of forest underlying surfaces according to claim 1, characterized in that, The canopy evaporation interception rate is: : ； in, The rainfall amount, The maximum retention capacity is obtained based on the leaf area index. The evaporation ratio is determined by an exponential function based on the relative magnitude of the rainfall and the maximum retention capacity.