A method for constructing a three-source urban evapotranspiration model (TRU) based on remote sensing

Abstract

The present invention belongs to the field of environmental detection, and specifically discloses a construction method of a three-source urban evapotranspiration model (TRU) based on remote sensing, characterized in that the three-source urban evapotranspiration model (TRU) mainly includes three parts: TRU model assumptions: assuming that urban impervious areas and non-impervious areas have independent water vapor diffusion processes; non-impervious area evapotranspiration parameterization design: optimization based on the surface temperature decomposition model (TSEB) based on an iterative process; impervious area evapotranspiration parameterization design: integrating urban anthropogenic heat flux (A) with surface energy balance. Compared with previous model algorithms for estimating urban evapotranspiration, TRU is the first remote sensing evapotranspiration model algorithm that can distinguish between soil evaporation, vegetation transpiration and impervious surface evaporation in urban areas, and has higher simulation accuracy.

Description

Technical Field

[0001] The invention belongs to the field of three-source urban evapotranspiration model calculation, and in particular relates to a construction method of a three-source urban evapotranspiration model (TRU) based on remote sensing. Background Art

[0002] Although regional evapotranspiration models based on remote sensing technology for ecosystems have been widely developed and applied, remote sensing evapotranspiration models customized for urban areas are still scarce. The biggest difference between urban areas and ecosystems is the presence of impervious land surfaces and the impact of human activities. In addition, compared with natural ecosystems, the surface types in urban areas are more complex and the underlying surface heterogeneity is also higher, which makes it more challenging to accurately simulate urban evapotranspiration at a regional scale. For the limited number of urban evapotranspiration models based on remote sensing technology, almost all models use a single-source model structure. These models can only be used to estimate the total evapotranspiration and cannot obtain the evapotranspiration components of urban areas. One of the important reasons is that for most of the current remote sensing-based evapotranspiration models, there is a lack of a reliable parameterization scheme for soil evaporation and impervious surface evaporation. In addition, for the currently available urban regional evapotranspiration models, the simulation of impervious surface evaporation usually adopts the same calculation method as other urban land types, which leads to a significant overestimation of impervious surface evaporation. Regarding the existing urban evapotranspiration models, the common problems are:

[0003] 1) Failure to estimate soil evaporation and vegetation transpiration in urban areas separately

[0004] 2) There is no reasonable method for estimating evapotranspiration in impervious areas

[0005] 3) Almost all models are based on single-source model mechanisms applicable to ecosystems, with minor modifications to the model mechanisms for urban areas. None of these models fully consider the heterogeneity of urban surfaces, nor do they provide a complete and thorough solution for urban areas. Summary of the invention

[0006] In order to overcome the above technical problems, the present invention provides a method for constructing a three-source urban evapotranspiration model (TRU) based on remote sensing. The three-source urban evapotranspiration model (TRU) mainly includes three parts as follows:

[0007] (1) TRU model assumptions

[0008] TRU assumes that urban impervious areas (buildings and traffic areas) and non-impervious areas (forests, farmlands, sparse vegetation, bare soil and water bodies) have independent water vapor diffusion processes. TRU first divides urban underlying surfaces into two categories according to land use type. The evapotranspiration of impervious surfaces is estimated by a single-source evaporation mechanism, while the evapotranspiration of non-impervious areas is estimated by a dual-source evaporation mechanism (soil evaporation and vegetation transpiration).

[0009] (2) Parameterization scheme for evapotranspiration in non-impervious areas

[0010] TRU is improved on the basis of the iterative process based surface temperature decomposition model (TSEB)

[0011] 1) Considering the heterogeneity of urban underlying surfaces, an improved algorithm for net radiation flux and soil heat flux suitable for urban underlying surfaces is proposed; 2) A new soil evaporation parameterization scheme is developed, which uses the complementary correlation theory to estimate soil evaporation and characterizes the soil surface moisture condition by soil surface temperature; 3) The humidity gradient is used to calculate vegetation transpiration to replace the empirical algorithm based on the PT formula in the original TSEB model; 4) A new iterative process is proposed to decompose the surface temperature of mixed pixels into soil surface temperature and vegetation canopy temperature.

[0012] (3) Parameterization scheme of evapotranspiration in impervious areas

[0013] Impervious areas have intensive human activities, so the TRU model algorithm takes urban anthropogenic heat flux (A) into account in the surface energy balance.

[0014] The specific design method is as follows:

[0015] First, the surface energy balance equation is established, as shown in formula (1):

[0016] R n -G=H+LE (1)

[0017] In formula (1), R n is the net radiation flux (W / m 2 ), G is the soil heat flux (W / m 2 ); H is the sensible heat flux (W / m 2 ), LE is the latent heat flux (W / m 2 ). It is estimated according to the method proposed by Nishida et al., as shown in formula (2).

[0018]

[0019] In formula (2), R a ↓(W / m 2 ) and R l↓(W / m 2 ) are solar shortwave radiation and atmospheric longwave radiation respectively; α m is the surface albedo (dimensionless, from Landsat OLI / ETM + Image acquisition, bands 1-5, 7); σ is the Stefan-Boltzmann constant (5.67×10 -8 Wm -2 K -4 );T m (K) is the surface temperature, from ETM + The image inversion results in (10.31-12.36 μm); ε is the emissivity (dimensionless) of four types of urban surfaces; in this study, different estimation methods for ε in TRU are proposed based on the distribution of urban land use, as shown in formula (3).

[0020]

[0021] Then according to Beer's law, R n The net radiation flux distributed to the soil layer (R ns , W / m 2 ) and the net radiation flux of the vegetation layer (R ns , W / m 2 ). Beer's law converts R nv and R ns It is estimated as an exponential function of LAI, as shown in equations (4) and (5).

[0022] R nv =R n (1-exp(-k c LAI)) (4)

[0023] R ns =R n exp(-k c LAI) (5)

[0024] In equations (4) and (5), k c (dimensionless) is the extinction coefficient (0.6); LAI is obtained through the empirical relationship with vegetation coverage. LAI = -ln(1-f c );f c =(NDVI-NDVI min ) / (NDVI max -NDVI min )

[0025] LAI: leaf area index; fc: fractional vegetation cover; NDVI: normalized difference vegetation index.

[0026] In TRU, urban land use information is also used to improve urban soil heat flux (G, W / m 2 ) simulation, G is connected to R n The empirical relationship is obtained, as shown in formula (6).

[0027]

[0028] In formula (6), the coefficient c g Depends on the surface heat capacity and surface thermal conductivity, the higher the c g Values ​​of α represent stronger thermal conductivity properties and higher soil heat flux values. NDVI refers to the Normalized Difference Vegetation Index and α refers to the albedo.

[0029] For the soil layer, R ns By LE s , H s and G (as shown in formula (7)). s It is estimated by the temperature gradient between the surface and the air (as shown in equation (8)), and LE s The estimation of is based on the improved complementary correlation theory, as shown in equations (8)-(16).

[0030] H s +LE s +G=R ns (7)

[0031]

[0032]

[0033] In equations (8) and (9), e s (T a ) is the temperature T a Saturated water vapor pressure at 1000 ℃ (hPa); e a is the actual water vapor pressure (hPa); ρ is the air density (kg / m 3 );C p is the specific heat at constant pressure (1005 J / kg / K); Δ is the slope of the saturated water vapor pressure and air temperature (Pa / K); γ is the psychrometric constant (0.667 hPa / K); r a is the aerodynamic impedance (s / m), calculated as a function of wind speed and roughness length according to equation (10); r s is the blade boundary layer impedance, calculated according to equations (11) and (12).

[0034]

[0035]

[0036] rb = r′ b / (L+S) (12)

[0037] In formula (10), Z U and Z T is the observation height of wind speed and temperature; d is the zero plane displacement calculated based on the canopy height (d = 2 / 3h c );Z OM According to and c Relationship calculation: Z OM =1 / 8h c ; Z OH is the thermodynamic roughness length, Z OH According to and Z OM Relationship calculation: Z OH =1 / 7Z OM ; k is the von Karman constant (0.41); ψ H and ψ M are the thermodynamic stability function and the kinetic stability function, respectively, calculated by the method proposed by Brutsaert. In equations (11) and (12), r′b is the leaf boundary layer impedance (s / m), L and S are the LAI and the stem area index, respectively.

[0038] In formula (9), LE sp represents the potential soil evaporation and uses the coefficient R ef Correction is performed. ef is the relative evaporation ratio, which depends on the wetness of the soil surface and is calculated by formula (13).

[0039]

[0040] In formula (13), "a" and "b" are empirical parameters (dimensionless). According to Granger & Gray, a = 0.028; b = 8.045; SDI is the surface dryness index (dimensionless). According to Granger & Gray, the SDI calculation method is SDI = E p / (E p +R n -G). E p (W / m 2 ) is the atmospheric drying capacity (Formula (14)). Original E p The parameterization scheme mainly relies on air humidity to reflect the soil surface moisture content, but it is not related to the soil surface temperature (T s ), the interaction between air humidity and soil surface humidity is very weak, while T s Faster response to soil moisture changes. sThe higher the value, the lower the soil surface water content and the lower the soil evaporation rate, and vice versa. Therefore, in the energy balance model, T s It can better reflect the soil surface water content. Gao et al. used the actual evaporation (LE sa ) instead of E p The calculation method of SDI was revised. sa The estimation method is T s Coupling to LE sa In the calculation, LE sa The expression of is shown in formula (15). SDI becomes LE sa / (LE sa +R ns -G).LE s The calculation method is as shown in formula (16). In formulas (13)-(16), when SDI increases, R ef decrease, resulting in LE s Reduced, but at the same time E p and LE sp Therefore, the LE proposed in TRU s The parameterization scheme embodies the complementary correlation theory. In addition, the new LE s The parameterization scheme avoids the need for soil moisture data, allowing the model to be used at a regional scale using remote sensing imagery.

[0041]

[0042]

[0043]

[0044] In formula (16), e s (T s ) is the temperature T s Saturated water vapor pressure (hPa) at the time of the vegetation layer. nv By LE v and H v Composition (Formula (17)). LE v and H v It is calculated by temperature and humidity gradient, as shown in equations (18) and (19). It should be noted that the LE used in TRU v The parameterization scheme is different from the Priestley-Taylor empirical formula in the TSEB model.

[0045] H v +LE v =R nv (17)

[0046]

[0047]

[0048] In equations (18) and (19), e s (T v ) is the temperature T v Saturated water vapor pressure (hPa) at r v is the canopy stomatal impedance (s / m), which is calculated from the potential stomatal impedance and T a and VPD correction.

[0049] r v =c L ×m(Tmin)×m(VPD) (20)

[0050]

[0051]

[0052] In formula (20), c L is the potential stomatal resistance per unit leaf area (s / m); mT(min) and m(VPD) are T a and VPD, estimated by equations (21) and (22).

[0053] The only two unknown parameters left in equations (7)-(22) are T s and T v In order to obtain an accurate solution for the component temperature, TRU developed a new iterative algorithm that minimizes T m The difference between and is used as the iteration target, thus replacing the LE in the original TSEB model. s >0 [74,83] The iterative process is based on the Newton-Raphson method.

[0054] 1) T m The iteration interval is set to [250k, 330k], the iteration step is set to 0.1k, and the initial T m Set to 250k;

[0055] 2) For each T m , calculate R according to equations (2), (4) and (5) n , R nv and R ns ;

[0056] 3) For equations (7)-(16), solve the one-variable nonlinear equation and obtain the root T s ; For equations (17)-(22), solve the one-variable nonlinear equation and obtain the root Tv ;

[0057] 4) For each T s and T v Combination, calculate the new T according to formula (23) m (T m，n ). (Assume T m It is T v and T s A nonlinear combination of

[0058]

[0059] 5) Once you get |T m -T m，n |<0.1k, the iteration process is terminated and LE is calculated s , L.E. v , H s and H v ; Otherwise, increase T in steps of 0.1K m value, and repeat steps 1-5.

[0060] Latent heat flux in impermeable areas (LE imp , W / m 2 ) can be calculated by formula (24).

[0061]

[0062] In formula (24), r imp is the impedance of the impervious area (s / m), calculated by equation (25). During the relatively dry noon and afternoon hours, LE imp Close to 0, but close to maximum in the wetter early mornings and evenings.

[0063] r imp =exp(8.206-4.225W) (25)

[0064] W is the wetness of the surface layer, which is close to zero in impermeable areas, so r in (24) imp Set to 3662.9s / m. R of the impervious area n , G and H can be calculated by equations (2), (6) and (8). According to the surface energy balance theory, the anthropogenic heat flux in urban areas can be calculated by the energy balance equation.

[0065] A=LE imp +H imp +G imp -R n,imp (26).

[0066] Advantages of the present invention:

[0067] 1. Compared with the current urban evapotranspiration model, a significant advantage of the TRU model is that it can distinguish between urban soil latent heat flux, vegetation latent heat flux and impervious area evaporation. The multi-source solution can better understand the water consumption of various urban areas, thereby more scientifically guiding the precise irrigation and water resource allocation of urban areas.

[0068] 2. TRU makes full use of urban land use information within the model framework.

[0069] 3. The TRU model is relatively robust to different model input parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0071] Figure 1 : Newton-Raphson iterative process, which is used to convert T m Decompose into T v and T s and estimated LE v and LE s ;

[0072] Figure 2 : Flowchart of the TRU model, where the trapezoids represent the model input variables or parameters, and the rectangles represent the model intermediate variables or parameters. DETAILED DESCRIPTION

[0073] In order to better understand the essence of the present invention, the present invention is further described below in conjunction with specific embodiments and drawings.

[0074] Example 1

[0075] This embodiment provides a method for constructing a three-source urban evapotranspiration model (TRU) based on remote sensing, and the flow chart is as follows: Figure 2 As shown, the details are as follows:

[0076] First, the surface energy balance equation is established, as shown in formula (1):

[0077] R n -G=H+LE (1)

[0078] In formula (1), R n is the net radiation flux (W / m 2), estimated according to the method proposed by Nishida et al., as shown in formula (2).

[0079]

[0080] In formula (2), R a ↓(W / m 2 ) and R l ↓(W / m 2 ) are solar shortwave radiation and atmospheric longwave radiation respectively; α m is the surface albedo (dimensionless, from Landsat OLI / ETM + Image acquisition, bands 1-5, 7); σ is the Stefan-Boltzmann constant (5.67×10 -8 Wm -2 K -4 );T m (K) is the surface temperature, from ETM + The image inversion results in (10.31-12.36 μm); ε is the emissivity (dimensionless) of four types of urban surfaces; in this study, different estimation methods for ε in TRU are proposed based on the distribution of urban land use, as shown in formula (3).

[0081]

[0082] Then according to Beer's law, R n The net radiation flux distributed to the soil layer (R ns , W / m 2 ) and the net radiation flux of the vegetation layer (R ns , W / m 2 ). Beer's law converts R nv and R ns It is estimated as an exponential function of LAI, as shown in equations (4) and (5).

[0083] R nv =R n (1-exp(-k c LAI)) (4)

[0084] R ns =R n exp(-k c LAI) (5)

[0085] In equations (4) and (5), k c (dimensionless) is the extinction coefficient (0.6); LAI is obtained through an empirical relationship with vegetation cover.

[0086] In TRU, urban land use information is also used to improve urban soil heat flux (G, W / m2 ) simulation, G is connected to R n The empirical relationship is obtained, as shown in formula (6).

[0087]

[0088] In formula (6), the coefficient c g Depends on the surface heat capacity and surface thermal conductivity, the higher the c g Values ​​represent stronger thermal conductivity properties and higher soil heat flux values.

[0089] For the soil layer, R ns By LE s , H s and G (as shown in formula (7)). s It is estimated by the temperature gradient between the surface and the air (as shown in equation (8)), and LE s The estimation of is based on the improved complementary correlation theory, as shown in equations (8)-(16).

[0090] H s +LE s +G=R ns (7)

[0091]

[0092]

[0093] In equations (8) and (9), e s (T a ) is the temperature T a Saturated water vapor pressure at 1000 ℃ (hPa); e a is the actual water vapor pressure (hPa); ρ is the air density (kg / m 3 );C p is the specific heat at constant pressure (1005 J / kg / K); Δ is the slope of the saturated water vapor pressure and air temperature (Pa / K); γ is the psychrometric constant (0.667 hPa / K); r a is the aerodynamic impedance (s / m), calculated as a function of wind speed and roughness length according to equation (10); r s is the blade boundary layer impedance, calculated according to equations (11) and (12).

[0094]

[0095]

[0096] r b = r′ b / (L+S) (12)

[0097] In formula (10), Z U and Z T is the observation height of wind speed and temperature; d is the zero plane displacement calculated based on the canopy height (d = 2 / 3h c );Z OM According to and c Relationship calculation: Z OM =1 / 8h c ; Z OH is the thermodynamic roughness length, Z OH According to and Z OM Relationship calculation: Z OH =1 / 7Z OM ; k is the von Karman constant (0.41); ψ H and ψ M are the thermodynamic stability function and the kinetic stability function, respectively, calculated by the method proposed by Brutsaert. In equations (11) and (12), r′b is the leaf boundary layer impedance (s / m), L and S are the LAI and the stem area index, respectively.

[0098] In formula (9), LE sp represents the potential soil evaporation and uses the coefficient R ef Correction is performed. ef is the relative evaporation ratio, which depends on the wetness of the soil surface and is calculated by formula (13).

[0099]

[0100] In formula (13), "a" and "b" are empirical parameters (dimensionless). According to Granger & Gray, a = 0.028; b = 8.045; SDI is the surface dryness index (dimensionless). According to Granger & Gray, the SDI calculation method is SDI = E p / (E p +R n -G). E p (W / m 2 ) is the atmospheric drying capacity (Formula (14)). Original E p The parameterization scheme mainly relies on air humidity to reflect the soil surface moisture content, but it is not related to the soil surface temperature (T s ), the interaction between air humidity and soil surface humidity is very weak, while T s Faster response to soil moisture changes. s The higher the value, the lower the soil surface water content and the lower the soil evaporation rate, and vice versa. Therefore, in the energy balance model, T sIt can better reflect the soil surface water content. Gao et al. used the actual evaporation (LE sa ) instead of E p The calculation method of SDI was revised. sa The estimation method is T s Coupling to LE sa In the calculation, LE sa The expression of is shown in formula (15). SDI becomes LE sa / (LE sa +R ns -G).LE s The calculation method is as shown in formula (16). In formulas (13)-(16), when SDI increases, R ef decrease, resulting in LE s Reduced, but at the same time E p and LE sp Therefore, the LE proposed in TRU s The parameterization scheme embodies the complementary correlation theory. In addition, the new LE s The parameterization scheme avoids the need for soil moisture data, allowing the model to be used at a regional scale using remote sensing imagery.

[0101]

[0102]

[0103]

[0104] In formula (16), e s (T s ) is the temperature T s Saturated water vapor pressure (hPa) at the time of the vegetation layer. nv By LE v and H v Composition (Formula (17)). LE v and H v It is calculated by temperature and humidity gradient, as shown in equations (18) and (19). It should be noted that the LE used in TRU v The parameterization scheme is different from the Priestley-Taylor empirical formula in the TSEB model.

[0105] H v +LE v =R nv (17)

[0106]

[0107]

[0108] In equations (18) and (19), e s (T v ) is the temperature T v Saturated water vapor pressure (hPa) at r v is the canopy stomatal impedance (s / m), which is calculated from the potential stomatal impedance and T a and VPD correction.

[0109] r v =c L ×m(Tmin)×m(VPD) (20)

[0110]

[0111]

[0112] In formula (20), c L is the potential stomatal resistance per unit leaf area (s / m); mT(min) and m(VPD) are T a and VPD, estimated by equations (21) and (22).

[0113] The only two unknown parameters left in equations (7)-(22) are T s and T v In order to obtain an accurate solution for the component temperature, TRU developed a new iterative algorithm that minimizes T m The difference between and is used as the iteration target, thus replacing the LE in the original TSEB model. s >0 [74,83] The iterative process is based on the Newton-Raphson method. Figure 1 shown.

[0114] 1) T m The iteration interval is set to [250k, 330k], the iteration step is set to 0.1k, and the initial T m Set to 250k;

[0115] 2) For each T m , calculate R according to equations (2), (4) and (5) n , R nv and R ns ;

[0116] 3) For equations (7)-(16), solve the one-variable nonlinear equation and obtain the root T s ; For equations (17)-(22), solve the one-variable nonlinear equation and obtain the root T v ;

[0117] 4) For each T s and T v Combination, calculate the new T according to formula (23) m (T m，n ). (Assume T m It is T v and T s A nonlinear combination of

[0118]

[0119] 5) Once you get |T m -T m，n |<0.1k, the iteration process is terminated and LE is calculated s , L.E. v , H s and H v ; Otherwise, increase T in steps of 0.1K m value, and repeat steps 1-5.

[0120] Latent heat flux in impermeable areas (LE imp , W / m 2 ) can be calculated by formula (24).

[0121]

[0122] In formula (24), r imp is the impedance of the impervious area (s / m), calculated by equation (25). During the relatively dry noon and afternoon hours, LE imp Close to 0, but close to maximum in the wetter early mornings and evenings.

[0123] r imp =exp(8.206-4.225W) (25)

[0124] W is the wetness of the surface layer, which is close to zero in impermeable areas, so r in (24) imp Set to 3662.9s / m. R of the impervious area n , G and H can be calculated by equations (2), (6) and (8). According to the surface energy balance theory, the anthropogenic heat flux in urban areas can be calculated by the energy balance equation.

[0125] A=LE imp +H imp +G imp -R n,imp (26).

Claims

1. A method for constructing a three-source urban evapotranspiration model (TRU) based on remote sensing, characterized in that: The three-source urban evapotranspiration model (TRU) consists of three parts as follows: Step 1: TRU model assumptions: It is assumed that the urban impervious area and non-impervious area have independent water vapor diffusion processes; the evapotranspiration of the impervious surface is estimated by a single-source evaporation mechanism, and the evapotranspiration of the non-impervious area is estimated by a dual-source evaporation mechanism; Step 2: Parameterized design of evapotranspiration in non-impervious areas: Optimization based on the surface temperature decomposition model (TSEB) based on an iterative process; Step 3: Parameterized design of evapotranspiration in impervious areas: Integrate urban anthropogenic heat flux with surface energy balance; the parameterized design of evapotranspiration in non-impervious areas is as follows: (1) Optimize the net radiation flux R of the applicable urban underlying surface n and soil heat flux G algorithm; (2) using complementary correlation theory to estimate soil evaporation, and using soil surface temperature to characterize soil surface moisture conditions; (3) using humidity gradient to calculate vegetation transpiration; (4) using an iterative process to decompose the mixed pixel surface temperature into soil surface temperature and vegetation canopy temperature; urban anthropogenic heat flux is calculated by the energy balance equation as follows: A=LE imp +H imp +G imp -R n,imp Among them, the latent heat flux LE of the impermeable area imp , its unit is W / m 2 , calculated as follows: r imp is the impedance of the impervious area, its unit is s / m, and the calculation formula is as follows: During relatively dry noon and afternoon hours, LE imp Close to 0, but close to maximum in the wetter early mornings and evenings; r imp =exp(8.206-4.225W) C p is the specific heat at constant pressure, with the unit of J / kg / K, and is generally taken as a constant of 1005. γ is the psychrometric constant, with the unit of Pa / K. W is the wetness of the surface, which is close to zero in impermeable areas, so r imp Set to 3662.9s / m, R n is the net radiation flux, its unit is W / m 2 .

2. The method for constructing the three-source urban evapotranspiration model (TRU) based on remote sensing according to claim 1 is characterized in that: The equation for the surface energy balance is as follows: R n -G=H+LE Where G is the soil heat flux, and its unit is W / m 2 ; H is the sensible heat flux, its unit is W / m 2 ; LE is the latent heat flux, its unit is W / m 2 .

3. The method for constructing the three-source urban evapotranspiration model (TRU) based on remote sensing according to claim 1, characterized in that: The net radiation flux R n The formula is as follows: Among them, R a ↓ and R l ↓ are solar shortwave radiation and atmospheric longwave radiation, respectively, in W / m 2 ; α m is the surface albedo; σ is the Stefan-Boltzmann constant; T m is the surface temperature, in K; ε is the emissivity of the four types of urban surfaces; NDVI is the Normalized Difference Vegetation Index; The estimation method of ε is as follows:

4. The method for constructing a three-source urban evapotranspiration model (TRU) based on remote sensing according to claim 1, characterized in that: According to Beer's law, the net radiation flux R n The net radiation flux R distributed to the soil layer ns and the net radiation flux of the vegetation layer R nv , the formula is as follows: R nv =R n (1-exp(-k c LOL)) R ns =R n exp(-k c (LAI) Among them, k c is the dimensionless extinction coefficient, which is 0.6, R ns is the net radiation flux of the soil layer, R nv is the net radiation flux of the vegetation layer, in W / m 2 ; LAI is leaf area index.

5. The method for constructing a three-source urban evapotranspiration model (TRU) based on remote sensing according to claim 1, characterized in that: The formula for soil heat flux G is as follows: Among them, the coefficient c g Depends on the surface heat capacity and surface thermal conductivity, the higher the c g The value represents a stronger thermal conductivity performance and a higher soil heat flux value. NDVI refers to the Normalized Difference Vegetation Index. m is the surface albedo, α refers to the albedo, T m is the surface temperature.

6. The method for constructing the three-source urban evapotranspiration model (TRU) based on remote sensing according to claim 5, characterized in that: Net radiation flux R ns By LE s , H s and G, H s It is estimated by the temperature gradient between the surface and the air, while LE s The estimation of is based on the improved complementary correlation theory, and the specific formula is as follows: H s +THE s +G=R ns Among them, LE sp represents potential soil evaporation; R ef is the relative evaporation rate; e s (T a ) is the temperature T a The saturated water vapor pressure at that time is expressed in hPa; a is the actual water vapor pressure, its unit is hPa; ρ is the air density, its unit is kg / m 3 ; C p is the specific heat at constant pressure, its unit is 1005 J / kg / K; Δ is the slope of saturated water vapor pressure and air temperature, its unit is Pa / K; γ is the psychrometric constant, its unit is 0.667 hPa / K; r a is the aerodynamic impedance, its unit is s / m, r s is the leaf boundary layer impedance and G is the soil heat flux, its unit is W / m 2 ; Ts is the soil surface temperature, in °C; R ef is the relative evaporation rate, which depends on the wetness of the soil surface; R ns is the net radiation flux of the soil layer, in W / m 2 ;f c is the vegetation coverage rate, r b =r′ b / (L+S) Among them, Z U and Z T is the observation height of wind speed and temperature; d is the zero plane displacement, calculated as d = 2 / 3h c ; Z OM According to and c Relationship calculation: Z OM =1 / 8h c ; Z OH is the thermodynamic roughness length, Z OH According to and Z OM Relationship calculation: Z OH =1 / 7Z OM ; k is the von Karman constant, which is 0.41; ψ H and ψ M are the thermodynamic stability function and the momentum stability function, r' b is the leaf boundary layer impedance, its unit is s / m, L and S are LAI and stem area index, respectively, and the relative evaporation rate R ef The formula is as follows: Among them, a and b are empirical parameters, dimensionless. According to Granger & Gray, a = 0.028; b = 8.045; SDI is the surface dryness index, dimensionless.

7. The method for constructing a three-source urban evapotranspiration model (TRU) based on remote sensing according to claim 6, characterized in that: The specific calculation of vegetation transpiration using humidity gradient is as follows: H v +THE v =R nv where e s (T v ) is the temperature T v The saturated water vapor pressure at that time is expressed in hPa, e a is the actual water vapor pressure, the unit is hPa, γ is the psychrometric constant, the unit is Pa / K, ρ is the air density, the unit is kg / m 3 , r v is the canopy stomatal impedance, in s / m, which is calculated from the potential stomatal impedance and T a and VPD correction; r v =c L ×m(Tmin)×m(VPD) Among them, c L is the potential stomatal resistance per unit leaf area (s / m); mT(min) and m(VPD) are T a and VPD functions.

8. The method for constructing the remote sensing-based three-source urban evapotranspiration model (TRU) according to claim 7, characterized in that: The iterative process decomposes the mixed pixel surface temperature into soil surface temperature and vegetation canopy temperature as follows: 1) T m The iteration interval is set to [250k, 330k], the iteration step is set to 0.1k, and the initial T m Set to 250k; 2) For each T m , calculate R n , R nv and R ns ; 3) Solve the one-variable nonlinear equation and obtain the root T s and root T v ; 4) For each T s and T v Combination, calculate the new T m (T m，n ); Assume T m It is T v and T s Nonlinear combination of 5) Get |T m -T m，n |<0.1k, the iteration process is terminated and LE is calculated s , L.E. v , H s and H v ; Otherwise, increase T in steps of 0.1K m value, and repeat steps 1-5.

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