Land surface heat flux estimation method and system based on spectral characteristics of vegetation cover

Abstract

The present application belongs to the technical field of surface heat flux estimation, and particularly relates to a method and system for estimating surface heat flux by considering spectral characteristics of vegetation coverage. The method comprises the following steps: decomposing surface temperature based on spectral characteristics of vegetation coverage; decomposing reference point surface temperature based on spectral characteristics of vegetation coverage and radiation balance; decomposing reference point surface temperature based on spectral characteristics of vegetation coverage and balance of sensible and latent heat fluxes; solving reference point surface temperature and its components; and calculating surface heat flux and its components. The present application realizes the decomposition of surface temperature by using different remote sensing indexes of surface vegetation coverage, reduces the uncertainty of estimating bare soil net radiation flux and vegetation net radiation flux by using the reflection characteristics of different wave bands of vegetation coverage remote sensing, and realizes the accurate positioning of reference dry and wet points in the segmentation of surface heat flux by the different degrees of influence of different wave band characteristics of vegetation coverage on sensible and latent heat fluxes, thereby providing technical support for weather forecasting, agricultural production, water resource management and the like.

Description

Technical Field

[0001] This invention belongs to the field of surface heat flux estimation technology, and in particular relates to a method and system for estimating surface heat flux that takes into account the spectral characteristics of vegetation cover. Background Technology

[0002] Surface heat flux is a crucial component of the Earth's energy cycle system, representing the combined effect of momentum, heat, and moisture exchange between the Earth and the atmosphere. It is a vital physical parameter characterizing the interaction between the Earth and the atmosphere, playing a significant role in the dynamics, thermodynamics, and deposition processes of the atmospheric system, and its characteristics profoundly influence atmospheric changes. Understanding surface heat flux, especially the quantitative analysis of sensible and latent heat fluxes and their components, is of great importance for weather forecasting, climate attribution, water and carbon cycles, agricultural production, and water resource management. Therefore, estimating surface heat flux is one of the core issues of concern in many fields.

[0003] Currently, methods for estimating surface heat flux mainly include land surface model simulation, ground observation, and satellite remote sensing data inversion. Each of these methods has its own advantages and disadvantages. Model simulation has the advantage of continuously simulating sensible and latent heat fluxes and their components in space and time, but its spatial resolution is relatively low. Ground observation mainly includes eddy covariance methods, Bowen ratio-energy balance methods, aerodynamic methods, and scintillation methods. Its advantage is that it can obtain the true observational values ​​of surface heat flux, but its spatial scale is limited to single points or patches. While scintillation estimation can match the scale of land surface model simulation, it is expensive and large-scale deployment is impractical. With the increasing maturity of remote sensing technology, its macroscopic and real-time advantages make the estimation of surface heat flux and its components over large spatial areas more convenient.

[0004] While remote sensing has significant advantages in estimating surface heat flux, it also has limitations. The complexity of surface vegetation cover directly affects the spatial distribution of surface heat flux. Due to differences in the spectral characteristics of vegetation cover, there is a certain degree of uncertainty in remote sensing estimation of surface vegetation cover. The estimation results of surface vegetation cover from different spectral bands differ significantly. Therefore, the surface heat flux obtained based on vegetation cover from different spectral bands all have certain errors to varying degrees. Exploring ways to reduce the uncertainty of remote sensing estimation of surface heat flux based on the spectral characteristics of surface vegetation cover is of great significance.

[0005] To gain a deeper understanding of the impact of vegetation cover spectral characteristics on surface heat flux and to fully integrate remote sensing observation information of different bands of surface vegetation cover, this invention proposes an estimation method for surface heat flux and its components based on vegetation cover spectral characteristics. This invention is proposed to simplify and economically estimate surface heat flux. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for estimating surface heat flux based on vegetation cover spectral characteristics, so as to overcome the shortcomings of existing surface heat flux estimation methods.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0008] In a first aspect, embodiments of the present invention provide a method for estimating surface heat flux based on vegetation cover spectral characteristics, comprising the following steps:

[0009] S1: Decomposition of surface temperature based on vegetation cover spectral characteristics;

[0010] S2: Surface temperature based on vegetation cover spectral characteristics and radiation balance decomposition reference point;

[0011] S3: Surface temperature at reference point based on vegetation cover spectral characteristics and the balance decomposition of apparent and latent heat fluxes;

[0012] S4: Solve for the surface temperature and composition of the reference point based on S2 and S3;

[0013] S5: Calculate the surface heat flux and its composition based on S1 and S4.

[0014] In one embodiment, S1: decomposing surface temperature based on vegetation cover spectral characteristics specifically includes:

[0015]

[0016] In the formula, T O T represents remotely sensed surface temperature (K). V T represents O Composition of vegetation temperature (K), T S T represents O Composition of bare soil temperature (K), {f i O} 1≤i≤I Represents the I-type remote sensing vegetation cover obtained based on the spectral characteristics of vegetation cover at target pixel O, {ε i} 1≤i≤I Indicates based on {f i O} 1≤i≤I The obtained surface emissivity type I, Represents {ε i} 1≤i≤I Component vegetation specific radiation rate, Represents {ε i} 1≤i≤I Emissivity of bare soil.

[0017] In one embodiment, S2: based on vegetation cover spectral characteristics and surface temperature at the reference point of radiation balance decomposition, includes:

[0018] Based on the spectral characteristics of vegetation cover and the reference dry point surface temperature decomposed by radiation balance, specifically:

[0019]

[0020]

[0021] In the formula, T U This represents the reference dry point remote sensing surface temperature (K). T represents U Composition of vegetation temperature (K), T represents U Composition of bare soil temperature (K), {f i U} 1≤i≤I This represents type I remote sensing vegetation cover, obtained based on the spectral characteristics of vegetation cover at location O under extremely dry conditions. i} 1≤i≤I Indicates based on {f i U} 1≤i≤I The obtained surface emissivity type I, Represents {e i} 1≤i≤I Component vegetation specific radiation rate, Represents {e i} 1≤i≤I The emissivity of bare soil components;

[0022] Based on the spectral characteristics of vegetation cover and the reference wet point surface temperature decomposed by radiation balance, specifically:

[0023]

[0024]

[0025] In the formula, T D This represents the reference wet point remote sensing surface temperature (K). T represents D Composition of vegetation temperature (K), T represents D Composition of bare soil temperature (K), {f i D} 1≤i≤I {E} represents the I-type remote sensing vegetation cover obtained based on the spectral characteristics of vegetation cover at location O when the surface is extremely wet. i} 1≤i≤I Indicates based on {f i D} 1≤i≤I The obtained surface emissivity type I, Represents {E i}1≤i≤I Component vegetation specific radiation rate, Represents {E i} 1≤i≤I Emissivity of bare soil.

[0026] In one embodiment, S3: based on vegetation cover spectral characteristics and the surface temperature at the reference point decomposition of manifest and latent heat flux balance, includes:

[0027] Based on the spectral characteristics of vegetation cover and the decomposition of the latent heat flux balance equation, the reference dry point surface temperature is as follows:

[0028]

[0029]

[0030]

[0031] In the formula, S R This represents the solar incident shortwave radiation (W / m²). 2 ), L R This represents the incident longwave radiation from the sky (W / m²). 2 ), where σ represents the Stefan Boltzmann constant, 5.6697 × 10⁻⁶. -8 W / (m 2 ·K 4 ), where α represents the albedo of the land surface. Indicates based on {f i O} 1≤i≤I The albedo of the bare soil obtained from the α component, Indicates based on {f i O} 1≤i≤I The obtained α-component vegetation albedo, where G represents the surface heat flux at point O (W / m²). 2 ), {G i} 1≤i≤I Indicates based on The surface heat flux (W / m²) of the bare soil at point O was obtained. 2 );

[0032] Based on the spectral characteristics of vegetation cover and the decomposition of the latent heat flux balance equation, the reference wet point surface temperature is as follows:

[0033]

[0034] In one embodiment, S4: solving for the reference point surface temperature and its composition based on S2 and S3 includes:

[0035] The reference dry point surface temperature and its composition are calculated based on S2 and S3, specifically as follows:

[0036]

[0037]

[0038] The reference wet point surface temperature and its composition are calculated based on S2 and S3, specifically as follows:

[0039]

[0040]

[0041] In one embodiment, S5: calculating the surface heat flux and its composition based on S1 and S4 includes:

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] In the formula, ET S This indicates the latent heat of the bare soil at point O (W / m²). 2 ), H S This indicates the sensible heat of the bare soil at point O (W / m²). 2 ), ET T Indicates the latent heat of vegetation at location O (W / m²) 2 ), H V The sensible heat of vegetation at location O (W / m²) 2 ), ET represents the latent heat flux at point O (W / m). 2 H represents the sensible heat flux at point O (W / m²). 2 ).

[0049] In a second aspect, embodiments of the present invention provide a surface heat flux estimation system based on vegetation cover spectral characteristics, used to execute the surface heat flux estimation method based on vegetation cover spectral characteristics described in the first aspect above, including: a ground temperature decomposition module, a radiation balance module, a manifest and latent flux module, a reference temperature module, and a surface heat flux module.

[0050] The ground temperature decomposition module is used to perform the decomposition of ground surface temperature based on vegetation cover spectral characteristics as described in the first aspect above.

[0051] The radiation balance module is used to perform the surface temperature based on vegetation cover spectral characteristics and radiation balance decomposition reference point as described in the first aspect above.

[0052] The apparent and latent heat flux module is used to perform the above-mentioned first aspect of decomposing the reference point surface temperature based on vegetation cover spectral characteristics and apparent and latent heat flux balance.

[0053] The reference temperature module is used to perform the above-described first aspect of solving for the surface temperature and composition of the reference point.

[0054] The surface heat flux module is used to perform the calculation of surface heat flux and its components as described in the first aspect above.

[0055] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0056] 1) Based on the reflection characteristics of different bands of remote sensing of surface vegetation cover, a radiation balance equation between surface temperature and its components was constructed. The decomposition of surface temperature was realized by using different indices of remote sensing of surface vegetation cover, which improved the efficiency of obtaining bare soil temperature and vegetation temperature.

[0057] 2) The impact of surface vegetation cover characteristics on the estimation of surface net radiation and its components was quantified. The reflection characteristics of different bands of vegetation cover remote sensing were used to reduce the estimation uncertainty of bare soil net radiation flux and vegetation net radiation flux, and to improve the comprehensiveness of the estimation of surface net radiation and its components.

[0058] 3) A heat flux segmentation method based on surface latent heat balance and vegetation cover characteristics is proposed. By utilizing the different degrees of influence of different band features of vegetation cover on latent heat flux, the accurate positioning of the reference dry point in heat flux segmentation is achieved.

[0059] 4) Based on the relationship equation between surface sensible heat balance and surface vegetation cover spectral characteristics, a reference wet point solution method for surface sensible heat flux decomposition is proposed. The sensible heat flux and its components are estimated by using the wet point surface vegetation cover spectral characteristics.

[0060] 5) It makes full use of the surface vegetation cover information reflected by different remote sensing bands, and realizes the joint solution of surface heat flux and its components, including net radiation of bare soil, net radiation of vegetation, sensible heat of bare soil, sensible heat of vegetation, latent heat of bare soil and sensible heat of vegetation, thereby reducing the complexity of surface heat flux estimation. Attached Figure Description

[0061] Figure 1 A flowchart illustrating a method for estimating surface heat flux based on vegetation cover spectral characteristics, provided in an embodiment of the present invention.

[0062] Figure 2A flowchart illustrating another method for estimating surface heat flux based on vegetation cover spectral characteristics provided in an embodiment of the present invention.

[0063] Figure 3 A flowchart illustrating another method for estimating surface heat flux based on vegetation cover spectral characteristics provided in this embodiment of the invention.

[0064] Figure 4 A flowchart illustrating another method for estimating surface heat flux based on vegetation cover spectral characteristics provided in this embodiment of the invention.

[0065] Figure 5 This is a schematic diagram of a surface heat flux estimation system based on vegetation cover spectral characteristics, provided in an embodiment of the present invention. Detailed Implementation

[0066] In one embodiment, such as Figure 1 As shown, Figure 1 A flowchart illustrating a method for estimating surface heat flux based on vegetation cover spectral characteristics, provided in this embodiment of the invention, includes the following steps:

[0067] S1: Decomposition of surface temperature based on vegetation cover spectral characteristics;

[0068] S2: Surface temperature based on vegetation cover spectral characteristics and radiation balance decomposition reference point;

[0069] S3: Surface temperature at reference point based on vegetation cover spectral characteristics and the balance decomposition of apparent and latent heat fluxes;

[0070] S4: Solve for the surface temperature and composition of the reference point based on S2 and S3;

[0071] S5: Calculate the surface heat flux and its composition based on S1 and S4.

[0072] Based on the above embodiments, further, such as Figure 1 As shown, S1: Decomposing surface temperature based on vegetation cover spectral characteristics, one specific implementation method is as follows:

[0073]

[0074] In the formula, T O T represents remotely sensed surface temperature (K). V T represents O Composition of vegetation temperature (K), T S T represents O Composition of bare soil temperature (K), {f i O} 1≤i≤I Represents the I-type remote sensing vegetation cover obtained based on the spectral characteristics of vegetation cover at target pixel O, {εi} 1≤i≤I Indicates based on {f i O} 1≤i≤I The obtained surface emissivity type I, Represents {ε i} 1≤i≤I Component vegetation specific radiation rate, Represents {ε i} 1≤i≤I Emissivity of bare soil.

[0075] In the formula, T O The thermal infrared bands of remote sensing imagery can be used to obtain land surface temperature based on a land surface temperature retrieval algorithm. For example, the thermal infrared bands of the remote sensing imagery could be Landsat 8TIRS band 10, with a spatial resolution of 100 meters. This is open-source data, and the land surface temperature retrieval algorithm can be a single-channel algorithm. The spectral characteristics of land surface vegetation cover can be characterized using the multispectral bands of remote sensing imagery. For example, {f i O} 1≤i≤I Inversion can be performed using the multispectral bands of Landsat 8OLI remote sensing imagery. For example, specifically:

[0076]

[0077]

[0078]

[0079] In the formula, NIR represents the near-infrared reflectance of the Landsat 8OLI remote sensing image, R represents the red reflectance of the Landsat 8OLI remote sensing image, and B represents the reflectance of the Landsat 8OLI remote sensing image. The reflectance of the blue band of Landsat 8OLI remote sensing image, G represents the reflectance of the green band of Landsat 8OLI remote sensing image, and NDVI represents the Normalized Difference Vegetation Index. It quantifies vegetation cover by measuring the difference between the reflectance of the near-infrared band strongly reflected by vegetation and the reflectance of the red band absorbed by vegetation. It is the best indicator of the spatial distribution density of vegetation and is linearly correlated with vegetation cover. Its value depends on factors such as vegetation cover. EVI can reduce the influence of atmospheric and soil noise at the same time and stably reflect the vegetation cover of the measured pixels. It not only improves the ability to detect sparse vegetation, but also reduces the influence of water vapor. The blue band is introduced to correct the scattering of atmospheric aerosols and soil background. GLI is very sensitive to small changes in sparse vegetation and is more sensitive than the broadband greenness index, especially for dense vegetation. Therefore, the I value is 3.

[0080] The surface emissivity {εi} 1≤i≤I It can be obtained from the 100-meter pixel, globally covered ASTER GEDv3 product. ASTER GEDv3 is based on clear-sky ASTER imagery from 2000-2008, obtained through an algorithm separating surface temperature and surface emissivity, with an accuracy of ~0.01. Based on {f i O} 1≤i≤I ASTER GEDv3 was corrected to integrate the contribution of vegetation temporal variation to surface emissivity, specifically as follows:

[0081]

[0082] In the formula, This indicates the surface emissivity of ASTER GEDv3 band 13. f represents the surface emissivity of ASTER GEDv3 band 14. a Indicates vegetation cover in ASTER imagery;

[0083] In the formula, and From {ε i} 1≤i≤I To obtain, specifically:

[0084]

[0085]

[0086]

[0087] Based on the above embodiments, further, such as Figure 2 As shown, S2: Based on the spectral characteristics of vegetation cover and the surface temperature at the reference point of radiation balance decomposition, one implementation method includes:

[0088] Based on the spectral characteristics of vegetation cover and the reference dry point surface temperature decomposed by radiation balance, specifically:

[0089]

[0090]

[0091] In the formula, T U This represents the reference dry point remote sensing surface temperature (K). T represents U Composition of vegetation temperature (K), T represents U Composition of bare soil temperature (K), {f i U} 1≤i≤IThis represents type I remote sensing vegetation cover, obtained based on the spectral characteristics of vegetation cover at location O under extremely dry conditions. i} 1≤i≤I Indicates based on {f i U} 1≤i≤I The obtained surface emissivity type I, Represents {e i} 1≤i≤I Component vegetation specific radiation rate, Represents {e i} 1≤i≤I The emissivity of bare soil components;

[0092] In the formula, {f i U} 1≤i≤I The reflectance of the near-infrared band, red band, blue band, and green band of the Landsat 8OLI multispectral image at the moment of highest surface temperature retrieved from Landsat 8TIRS band 10 over a period of time at target pixel O can be obtained using the aforementioned vegetation cover calculation formula. For example, the period can be one year, but is not limited to this; those skilled in the art can set it according to specific circumstances. i} 1≤i≤I According to {f i U} 1≤i≤I ASTER GEDv3 was corrected to integrate the contribution of vegetation temporal variation to surface emissivity, specifically as follows:

[0093]

[0094] In the formula, and From {e i} 1≤i≤I To obtain, specifically:

[0095]

[0096]

[0097]

[0098] Based on the spectral characteristics of vegetation cover and the reference wet point surface temperature decomposed by radiation balance, specifically:

[0099]

[0100]

[0101] In the formula, T DThis represents the reference wet point remote sensing surface temperature (K). T represents D Composition of vegetation temperature (K), T represents D Composition of bare soil temperature (K), {f i D} 1≤i≤I {E} represents the I-type remote sensing vegetation cover obtained based on the spectral characteristics of vegetation cover at location O when the surface is extremely wet. i} 1≤i≤I Indicates based on {f i D} 1≤i≤I The obtained surface emissivity type I, Represents {E i} 1≤i≤I Component vegetation specific radiation rate, Represents {E i} 1≤i≤I The emissivity of bare soil components;

[0102] In the formula, {f i D} 1≤i≤I The reflectance of the near-infrared band, red band, blue band, and green band of the Landsat 8OLI multispectral image at the lowest surface temperature moment retrieved from Landsat 8TIRS band 10 over a period of time at the target pixel O can be obtained using the aforementioned vegetation cover calculation formula. For example, the period can be one year, but is not limited to this; those skilled in the art can set it according to specific circumstances. i} 1≤i≤I According to {f i D} 1≤i≤I ASTER GEDv3 was corrected to integrate the contribution of vegetation temporal variation to surface emissivity, specifically as follows:

[0103]

[0104] In the formula, and From {E i} 1≤i≤I To obtain, specifically:

[0105]

[0106]

[0107]

[0108] Based on the above embodiments, further, such as Figure 3 As shown, S3: Based on the spectral characteristics of vegetation cover and the surface temperature at the reference point for the equilibrium decomposition of apparent and latent heat fluxes, one implementation method includes:

[0109] Based on the spectral characteristics of vegetation cover and the decomposition of the latent heat flux balance equation, the reference dry point surface temperature is as follows:

[0110]

[0111]

[0112]

[0113] In the formula, S R This represents the solar incident shortwave radiation (W / m²). 2 ), L R This represents the incident longwave radiation from the sky (W / m²). 2 ), where σ represents the Stefan Boltzmann constant, 5.6697 × 10⁻⁶. -8 W / (m 2 ·K 4 ), where α represents the albedo of the land surface. Indicates based on {f i O} 1≤i≤I The albedo of the bare soil obtained from the α component, Indicates based on {f i O} 1≤i≤I The obtained α-component vegetation albedo, where G represents the surface heat flux at point O (W / m²). 2 ), {G i} 1≤i≤I Indicates based on The surface heat flux (W / m²) of the bare soil at point O was obtained. 2 );

[0114] In the formula, S R L R G, {G i} 1≤i≤I α can be obtained by inverting the selected remote sensing images, referring to relevant literature. and It can be obtained from α, specifically:

[0115]

[0116]

[0117]

[0118] In the formula, N represents the number of pixels in the study area. This represents the regional mean of surface albedo in the study area. Indicates the study area f i O The regional mean.

[0119] Based on the spectral characteristics of vegetation cover and the decomposition of the latent heat flux balance equation, the reference wet point surface temperature is as follows:

[0120]

[0121] Based on the above embodiments, further, such as Figure 4 As shown, S4: Solving for the surface temperature and composition of the reference point based on S2 and S3, one implementation method includes:

[0122] The reference dry point surface temperature and its composition are calculated based on S2 and S3, specifically as follows:

[0123]

[0124]

[0125] The reference wet point surface temperature and its composition are calculated based on S2 and S3, specifically as follows:

[0126]

[0127]

[0128] Based on the above embodiments, further, such as Figure 4 As shown, S5: Calculating the surface heat flux and its composition based on S1 and S4, one implementation method includes:

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135] In the formula, ET S This indicates the latent heat of the bare soil at point O (W / m²). 2 ), H S This indicates the sensible heat of the bare soil at point O (W / m²). 2 ), ET T Indicates the latent heat of vegetation at location O (W / m²) 2 ), HV The sensible heat of vegetation at location O (W / m²) 2 ), ET represents the latent heat flux at point O (W / m). 2 H represents the sensible heat flux at point O (W / m²). 2 ).

[0136] In summary, it is possible to estimate surface heat flux based on the spectral characteristics of vegetation cover.

[0137] In one embodiment, such as Figure 5 As shown, a surface heat flux estimation system based on vegetation cover spectral characteristics is provided to execute the surface heat flux estimation method based on vegetation cover spectral characteristics described in the above embodiment, including: a ground temperature decomposition module 1, a radiation balance module 2, a manifest and latent flux module 3, a reference temperature module 4, and a surface heat flux module 5.

[0138] The ground temperature decomposition module 1 is used to perform the decomposition of ground surface temperature based on vegetation cover spectral characteristics as described in the above embodiment.

[0139] The radiation balance module 2 is used to perform the surface temperature based on vegetation cover spectral characteristics and radiation balance decomposition reference point as described in the above embodiment.

[0140] The apparent and latent heat flux module 3 is used to perform the above embodiment of decomposing the reference point surface temperature based on vegetation cover spectral characteristics and apparent and latent heat flux balance.

[0141] The reference temperature module 4 is used to perform the above embodiment to solve for the reference point surface temperature and its composition.

[0142] The surface heat flux module 5 is used to perform the calculation of surface heat flux and its composition as described in the above embodiments.

[0143] The beneficial effects of the surface heat flux estimation method based on vegetation cover spectral characteristics proposed in this invention are as follows:

[0144] 1) By utilizing the remote sensing band characteristics of surface vegetation cover, a radiation balance bridge was built between surface temperature and its components, achieving equivalence with traditional multi-angle decomposition of surface temperature.

[0145] 2) It realizes the direct solution of reference dry and wet points in the decomposition of surface heat flux based on the remote sensing band characteristics of surface vegetation cover, which greatly facilitates the remote sensing inversion of surface heat flux and its components.

[0146] 3) By using the spectral characteristics of ground vegetation cover observed by multi-band remote sensing, the surface heat flux and its components, including sensible heat flux, latent heat flux, vegetation sensible heat, bare soil sensible heat, vegetation latent heat, and vegetation sensible heat were jointly estimated.

[0147] 4) Coupled vegetation cover spectral characteristics to remote sensing inversion of surface heat flux, further improving the estimation efficiency of surface heat flux;

[0148] 5) It can provide technical support for the estimation of surface heat flux in fields such as weather forecasting, climate attribution, water and carbon cycle, agricultural production and water resource management.

[0149] The implementation steps and effects of the surface heat flux estimation system based on vegetation cover spectral characteristics are similar to those of the method embodiments. For details, please refer to the method embodiments. It should be noted that although the flowcharts of the method and system embodiments are linked in the order of arrows, this does not mean that the present invention must be strictly executed according to the arrows in the flowcharts. Furthermore, the embodiments shown are only preferred embodiments of the present invention, and not all embodiments. Those skilled in the art can make substitutions and modifications to the present invention without creative effort, but these modifications also fall within the protection scope of the present invention.

Claims

1. A method for estimating surface heat flux based on vegetation cover spectral characteristics, characterized in that, Includes the following steps: S1: Decomposition of surface temperature based on vegetation cover spectral characteristics; S2: Surface temperature at reference point based on vegetation cover spectral characteristics and radiation balance decomposition, including: Based on the spectral characteristics of vegetation cover and the reference dry point surface temperature decomposed by radiation balance, specifically: In the formula, T U This indicates the reference dry point remote sensing surface temperature. T represents U Composition vegetation temperature, T represents U Composition of bare soil temperature, { } 1≤i≤I Represents type I remote sensing vegetation cover based on the spectral characteristics of vegetation cover at location O under extremely dry conditions. } 1≤i≤I Indicates based on { } 1≤i≤I The obtained surface emissivity of type I, { } 1≤i≤I express{ } 1≤i≤I Component vegetation specific emissivity, { } 1≤i≤I express{ } 1≤i≤I The emissivity of bare soil components; Based on the spectral characteristics of vegetation cover and the reference wet point surface temperature decomposed by radiation balance, specifically: In the formula, T D This represents the reference wet point remote sensing surface temperature. T represents D Composition vegetation temperature, T represents D Composition of bare soil temperature, { } 1≤i≤I Represents type I remote sensing vegetation cover obtained based on the spectral characteristics of vegetation cover at location O when the surface is extremely wet. } 1≤i≤I Indicates based on { } 1≤i≤I The obtained surface emissivity of type I, { } 1≤i≤I express{ } 1≤i≤I Component vegetation specific emissivity, { } 1≤i≤I express{ } 1≤i≤I The emissivity of bare soil components; S3: Surface temperature at reference point based on vegetation cover spectral characteristics and the balance of manifest and latent heat fluxes, including: Based on the spectral characteristics of vegetation cover and the decomposition of the latent heat flux balance equation, the reference dry point surface temperature is as follows: In the formula, S R L represents the incident shortwave radiation from the sun. R Let represent the incident longwave radiation from the sky, and σ represent the Stefan Boltzmann constant, 5.6697 × 10⁻⁶. -8 α represents the surface albedo, { } 1≤i≤I Indicates based on { } 1≤i≤I The albedo of the bare soil obtained from the α component, { } 1≤i≤I Indicates based on { } 1≤i≤I The obtained α-component vegetation albedo, G represents the surface heat flux at point O, { } 1≤i≤I Indicates based on { } 1≤i≤I The surface heat flux of the bare soil at point O was obtained; Based on the spectral characteristics of vegetation cover and the decomposition of the latent heat flux balance equation, the reference wet point surface temperature is as follows: ； S4: Solve for the surface temperature and composition of the reference point based on S2 and S3; S5: Calculate the surface heat flux and its composition based on S1 and S4.

2. The method according to claim 1, characterized in that, Specifically, S1 is: In the formula, T O T represents remotely sensed surface temperature. V T represents O Composition of vegetation temperature, T S T represents O Composition of bare soil temperature, { } 1≤i≤I This represents type I remote sensing vegetation cover obtained based on the spectral characteristics of vegetation cover at target pixel O. } 1≤i≤I Indicates based on { } 1≤i≤I The obtained surface emissivity of type I, { } 1≤i≤I express{ } 1≤i≤I Component vegetation specific emissivity, { } 1≤i≤I express{ } 1≤i≤I Emissivity of bare soil.

3. The method according to claim 1, characterized in that, The S4 includes: The reference dry point surface temperature and its composition are calculated based on S2 and S3, specifically as follows: ； The reference wet point surface temperature and its composition are calculated based on S2 and S3, specifically as follows: 。 4. The method according to claim 1, characterized in that, Specifically, S5 is: In the formula, ET S H represents the latent heat of the bare soil at point O. S This indicates the sensible heat of the bare soil at point O, ET. T Indicates the latent heat of vegetation at location O, H V Let O represent the sensible heat of the vegetation, ET represent the latent heat flux at O, and H represent the sensible heat flux at O.

5. A surface heat flux estimation system based on vegetation cover spectral characteristics, characterized in that, The method for estimating surface heat flux based on vegetation cover spectral characteristics as described in claim 1 includes: a geothermal decomposition module, a radiation balance module, a manifest and latent flux module, a reference temperature module, and a surface heat flux module. The ground temperature decomposition module is used to perform the decomposition of ground surface temperature based on vegetation cover spectral characteristics. The radiation balance module is used to perform the above-mentioned surface temperature based on vegetation cover spectral characteristics and radiation balance decomposition reference point. The apparent and latent heat flux module is used to perform the above-mentioned surface temperature decomposition based on vegetation cover spectral characteristics and apparent and latent heat flux balance decomposition reference point. The reference temperature module is used to perform the calculation of the reference point surface temperature and its composition. The surface heat flux module is used to perform the calculation of surface heat flux and its components.

Images