A remote sensing estimation method for surface evapotranspiration in mountainous areas considering topography

Abstract

The present invention discloses a remote sensing estimation method for surface evapotranspiration in mountainous areas taking terrain into consideration. The method comprises: obtaining a data set including pre-processed remote sensing image data, elevation data and pre-processed meteorological data; obtaining surface parameters, terrain parameters and auxiliary data according to the data set; calculating the shortwave net radiation of mountainous areas coupled with terrain effects by using an extended libRadtran radiation transfer model, a random forest model and processed remote sensing image data; calculating the net radiation flux, sensible heat flux and latent heat flux based on the shortwave net radiation of mountainous areas, and calculating the daily evapotranspiration by using the evaporation ratio invariant method. The present invention gets rid of the restriction that the existing remote sensing evapotranspiration model cannot be applied to mountainous areas, expands the applicability of the remote sensing evapotranspiration model, improves the accuracy of estimating surface evapotranspiration in mountainous areas, and is of great significance for accurately obtaining surface evapotranspiration in mountainous areas by remote sensing means.

Description

Technical Field

[0001] The invention belongs to the technical field of remote sensing inversion of surface evapotranspiration, and in particular to a remote sensing estimation method for surface evapotranspiration in mountainous areas taking terrain into consideration. Background Art

[0002] Surface evapotranspiration links the water cycle and surface energy balance and is an important component of water resource consumption. About 60% of global precipitation returns to the atmosphere in the form of evapotranspiration, and more than half of the solar energy absorbed by the land surface is used for evapotranspiration. In the context of water shortage, accurate estimation of evapotranspiration is particularly important for the rational use of water resources, agricultural water management and ecosystem protection.

[0003] Mountain areas account for about 1 / 4 of the world's land surface and provide a variety of ecological services such as water resource protection and biodiversity maintenance. Climate change has a greater impact on mountain areas than other places, which has an impact on the sustainable development of mountain landforms, ecosystems and socio-economic systems. Therefore, accurate monitoring of evapotranspiration in mountain areas is crucial.

[0004] At present, most remote sensing evapotranspiration models are mainly aimed at flat areas. However, in complex terrain areas with undulating surfaces, due to the influence of slope and aspect, the surface energy flux shows different characteristics from the horizontal surface. The existing remote sensing evapotranspiration models cannot be directly used for the inversion of surface evapotranspiration in mountainous areas.

[0005] Therefore, it is urgent to construct a remote sensing evapotranspiration model that takes terrain effects into account, so as to improve the accuracy of surface evapotranspiration estimation in complex terrain areas. Summary of the invention

[0006] In view of the above technical problems, the purpose of the present invention is to propose a remote sensing estimation method for surface evapotranspiration in mountainous areas taking into account the terrain, which achieves accurate estimation of evapotranspiration in complex terrain areas by coupling terrain effects.

[0007] The technical solution adopted by the present invention to solve its technical problem is:

[0008] In a first aspect, the present invention provides a remote sensing estimation method for surface evapotranspiration in mountainous areas taking into account terrain, comprising the following steps:

[0009] Step 1, obtaining a data set including pre-processed remote sensing image data, elevation data and pre-processed meteorological data;

[0010] Step 2: According to the data set in step 1, obtain surface parameters, terrain parameters and auxiliary data;

[0011] Step 3: Calculate the shortwave net radiation in the mountainous area coupled with the terrain effect by using the extended libRadtran radiation transfer model, random forest model and preprocessed remote sensing image data;

[0012] Step 4: Calculate the net radiation flux, sensible heat flux and latent heat flux based on the shortwave net radiation in the mountainous area, and calculate the daily evapotranspiration using the evaporation ratio invariant method.

[0013] Furthermore, in step 1, the preprocessing method includes: performing quality control, radiation calibration, atmospheric correction and cropping on remote sensing image data; and resampling meteorological data.

[0014] Furthermore, in step 2, the surface parameters include the normalized difference vegetation index NDVI , surface temperature T s , surface emissivity e g , surface albedo α ; Terrain parameters include the local angle of incidence i t and sky visibility factor V d ; Auxiliary data include solar zenith angle i s and observation zenith angle i v .

[0015] Furthermore, in step 2, the normalized vegetation index NDVI , surface emissivity e g and surface albedo α Use the following formula to obtain:

[0016]

[0017]

[0018]

[0019]

[0020] in, r NIR is the surface reflectivity in the near-infrared band, r RED is the surface reflectivity in the infrared band; Pv is the vegetation cover; is the broadband top-of-atmosphere reflectivity; α path It is the atmospheric radiation; is the atmospheric single transmittance.

[0021] Further, in step 2, the local incident angle i tIt is calculated as follows:

[0022]

[0023] in, i s is the solar zenith angle; β is the slope; is the solar azimuth; It is the slope direction.

[0024] Further, the step 3 includes the following sub-steps:

[0025] Step 3.1: Couple the libRadtran radiation transfer model with the terrain to construct an extended libRadtran radiation transfer model and calculate the shortwave net radiation in the mountainous area according to the following formula: NSR :

[0026]

[0027] in, α is the surface albedo, , and They are the direct radiation of the mountains, diffuse radiation and reflected radiation of the surrounding environment;

[0028]

[0029]

[0030]

[0031] in, is the shadow factor; and They are direct radiation and diffuse radiation from flat ground respectively; i t is the local angle of incidence; i s is the solar zenith angle; V d is the sky visibility factor; is the average albedo; is the downward shortwave radiation on the horizontal plane;

[0032] Step 3.2, according to the extended libRadtran radiation transfer model, obtain a simulation database with different atmospheric, surface and terrain conditions;

[0033] Step 3.3: Based on the simulation database, use the random forest model to construct the shortwave net radiation in the mountainous area NSR The functional relationship is:

[0034] NSR = f ( i s , i v , Z , i t , V d ,TOA reflectance)

[0035] in, i V To observe the zenith angle, Z is the elevation, TOA reflectance is the top of atmosphere reflectance, f represents a nonlinear function;

[0036] Step 3.4: Use the top of atmosphere reflectivity in remote sensing image data and the trained random forest model to simulate the shortwave net radiation in mountainous areas NSR .

[0037] Further, the step 4 comprises the following steps:

[0038] Step 4.1: Use the data set from step 1 and combine it with the simulated shortwave net radiation in the mountainous area NSR , calculate the net radiation R n :

[0039]

[0040] in, and are the incoming longwave radiation and the outgoing longwave radiation from the surface, e g is the surface emissivity;

[0041] Step 4.2: Obtain the sensible heat flux according to the following formula: H :

[0042]

[0043] in, a , b , c All are coefficients;

[0044] Step 4.3: Using the surface albedo α , surface temperature T s , Normalized Difference Vegetation Index NDVI and the net radiation obtained R n , calculate soil heat flux G :

[0045]

[0046] Step 4.4: Based on the net radiation R n , sensible heat flux H and soil heat flux G , calculate the latent heat flux LE :

[0047]

[0048] Step 4.5: Calculate daily evapotranspiration according to the following formula: ET :

[0049]

[0050] in, R n24 is the daily net radiation flux; l is the latent heat of vaporization of water; EF is the evaporation ratio, calculated as follows:

[0051] .

[0052] In a second aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for remote sensing estimation of surface evapotranspiration in mountainous areas taking into account terrain as described in the first aspect is implemented.

[0053] In a third aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the remote sensing estimation method for surface evapotranspiration in mountainous areas taking into account terrain as described in the first aspect.

[0054] In a fourth aspect, the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements the remote sensing estimation method for surface evapotranspiration in mountainous areas taking into account terrain as described in the first aspect.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention obtains the shortwave net radiation of the mountain area taking the terrain into consideration by constructing a direct estimation model of terrain extension, and obtains the net radiation according to the surface radiation balance equation, and then obtains the sensible heat flux and the soil heat flux by using the relationship between the net radiation and the sensible heat flux and the soil heat flux in turn, and finally estimates the surface evapotranspiration based on the surface energy balance equation, thereby realizing the remote sensing estimation of the surface evapotranspiration in the mountain area. The present invention can effectively solve the limitation that the commonly used remote sensing evapotranspiration model can only be used in flat areas, and is of great significance for accurately obtaining the surface evapotranspiration in the mountain area by remote sensing means. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic flow chart of a method for remote sensing estimation of surface evapotranspiration in mountainous areas taking terrain into consideration according to an embodiment of the present invention;

[0058] Figure 2 A verification diagram of daily evapotranspiration results simulated by an evapotranspiration model taking terrain into consideration provided in an embodiment of the present invention; Figure 2 (a) is the model of this embodiment. Figure 2 (b) is the SEBAL model;

[0059] Figure 3 A time series variation curve diagram of evapotranspiration generated by an embodiment of the present invention;

[0060] Figure 4 A spatial distribution map of evapotranspiration generated for an embodiment of the present invention;

[0061] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0062] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] like Figure 1 As shown, a remote sensing estimation method for surface evapotranspiration in mountainous areas taking into account terrain is provided in an embodiment of the present invention, comprising the following steps:

[0064] Step S1, obtaining a data set including pre-processed remote sensing image data, elevation data and pre-processed meteorological data.

[0065] As a specific embodiment, the remote sensing image data (Landsat 8) used in this embodiment is downloaded from the United States Geological Survey (https: / / earthexplorer.usgs.gov / ), with a spatial resolution of 30m; the elevation data (AWD3D) is provided by the Japan Aerospace Exploration Agency, with a spatial resolution of 30m, and is the world's most accurate three-dimensional map; the meteorological data (ERA5) is downloaded from the European Center for Medium-Range Weather Forecasts website (https: / / www.ecmwf.int / ). This embodiment selects atmospheric temperature data, with a time resolution of hours and a spatial resolution of 0.1°.

[0066] As a specific embodiment, the preprocessing method includes:

[0067] Preprocessing of remote sensing image data:

[0068] First, the image quality is controlled by cloud coverage and visual inspection, and images that do not meet the requirements are eliminated. Secondly, the image is radiometrically calibrated, atmospherically corrected, and cropped. This embodiment selects part of the Santa Catalina Mountains in the United States as the study area, calculates remote sensing images based on a total of 20 images from 2013 to 2020, and estimates the corresponding surface evapotranspiration.

[0069] Preprocessing of meteorological data:

[0070] Bilinear interpolation was used to resample the meteorological data to match the Landsat 8 imagery.

[0071] Step S2: According to the data set of step 1, obtain surface parameters, terrain parameters and auxiliary data.

[0072] As a specific embodiment, the surface parameters include the normalized difference vegetation index NDVI , surface temperature T s , surface emissivity e g and surface albedo α .

[0073] Exemplarily, the acquisition of ground surface temperature T s The surface temperature inversion method based on atmospheric correction is adopted:

[0074]

[0075] in, , K 2=1321.08K;

[0076] B ( Ts ) is the blackbody radiation brightness:

[0077]

[0078] in, is the thermal infrared radiation brightness value received by the satellite sensor, is the transmittance of the atmosphere in the thermal infrared band; is the upward radiance of the atmosphere, is the downward radiance of the atmosphere, e g is the surface emissivity.

[0079] For example, the Normalized Difference Vegetation Index NDVI The calculation formula is as follows:

[0080]

[0081] in, r NIR is the surface reflectivity in the near-infrared band, r RED is the surface reflectivity in the infrared band.

[0082] Then, the surface emissivity is calculated using the normalized vegetation index obtained above: e g , the formula is as follows:

[0083]

[0084]

[0085] in, Pv is the vegetation cover.

[0086] For Landsat 8 data, the surface albedo α That is its wide-band albedo, calculated by the following formula:

[0087]

[0088] in, α path is the atmospheric radiation, taking the empirical value as 0.03; and They are the wide-band top of atmosphere reflectivity and the atmospheric single transmittance, and the calculation formula is as follows:

[0089]

[0090]

[0091] in, r 2. r 4. r 5. r 6. r 7 are the atmospheric apparent reflectances of bands 2, 4, 5, 6, and 7 in Landsat 8; Z It is the elevation.

[0092] As a specific embodiment, the terrain parameters include the local incident angle i t and sky visibility factor V d .

[0093] Local angle of incidence i t It is calculated as follows:

[0094]

[0095] in, i s and They are the solar zenith angle and solar azimuth angle, which can be obtained from the original remote sensing image; β , They are slope and aspect, calculated based on elevation data.

[0096] Sky Visibility Factor V d It can be calculated from elevation data.

[0097] As a specific embodiment, the auxiliary data includes the solar zenith angle i s and observation zenith angle i v In this embodiment, the satellite sensor carried by Landsat is observed at the sub-satellite point, and its phase amplitude is not very wide, and the inconsistency of different pixels is not considered, so the zenith angle is observed. i v Set to 0.

[0098] Step S3: Calculate the mountain surface shortwave net radiation using the extended libRadtran radiation transfer model, random forest model and preprocessed remote sensing image data.

[0099] Step S3 includes the following sub-steps:

[0100] S31, couple the libRadtran radiation transfer model with terrain, construct an extended libRadtran radiation transfer model, and calculate the shortwave net radiation in mountainous areas NSR , as follows:

[0101] In this embodiment, a radiation transfer model considering terrain is constructed based on the libRadtran radiation transfer model. The terrain is determined by the local incident angle. i t and sky visibility factor V d By introducing terrain parameters into the libRadtran radiation transfer model ( i t , V d ) is used to express the libRadtran radiation transfer model and terrain coupling.

[0102] For example, in mountainous areas, the downlink shortwave radiation is composed of direct radiation, diffuse radiation, and reflected radiation from the surrounding environment, and the calculation formula is as follows:

[0103]

[0104]

[0105]

[0106] in, , and They are the direct radiation of the mountains, diffuse radiation and reflected radiation of the surrounding environment; is the shadow factor; and They are direct radiation and diffuse radiation from flat ground respectively; is the average albedo; is the downward shortwave radiation on the horizontal plane.

[0107] Assuming the surface is Lambertian, the radiation received by the satellite sensor over the mountain is Calculated by the following formula:

[0108]

[0109] in, L 0 is the path radiation; T V is the atmospheric transmittance; r m is the Lambertian surface reflectance; S is the hemispherical albedo.

[0110] Furthermore, the top-of-atmosphere reflectivity The calculation formula is as follows:

[0111]

[0112] in, d is the distance between the sun and the earth;E 0 is the solar irradiance.

[0113] Net shortwave radiation in mountainous areas NSR Calculated by the following formula:

[0114]

[0115] Step 3.2: According to the extended libRadtran radiation transfer model, a simulation database with different atmospheric, surface and terrain conditions is obtained as follows:

[0116] Based on the extended coupled terrain radiation transfer model, the DISORT solver in the libRadtran radiation transfer model is used to generate a simulation database. In this embodiment, the band response function of Landsat 8 OLI is used to calculate the corresponding top of atmosphere reflectivity. The formula is as follows:

[0117]

[0118] in, It is Landsat 8 in the band i The reflectivity of the top of the atmosphere at ; l min and l max Bands i The minimum and maximum values ​​of SRF ( l )and r ( l ) is the wavelength l Spectral response function and top-of-atmosphere reflectance at .

[0119] Step 3.3: Based on the simulation database, use the random forest model to construct the shortwave net radiation in the mountainous area NSR The functional relationship is as follows:

[0120] The random forest model is used to construct the shortwave net radiation in the mountainous area using the above simulation database. NSR and the reflectivity of the top of the atmosphere, the solar zenith angle i s , Observation zenith angle i v , elevation, sky visibility factor V d The model randomly selects 70% of the simulated data for training and the remaining 30% for validation. The number of trees in the random forest model is set to 100. The nonlinear relationship established by the random forest model is expressed by the following formula:

[0121] NSR = f ( is , i v , Z , i t , V d ,TOA reflectance)

[0122] in, Z is the elevation, TOA reflectance is the reflectivity of the top of the atmosphere, f Represents a nonlinear function.

[0123] Step 3.4: Use the above trained random forest model to directly estimate the shortwave net radiation in the mountain area coupled with the terrain effect NSR .

[0124] Step S4: Calculate the net radiation flux, sensible heat flux and latent heat flux based on the mountain shortwave net radiation coupled with the terrain effect, and calculate the daily evapotranspiration using the evaporation ratio invariance method.

[0125] Step S4 includes the following sub-steps:

[0126] Step 4.1: Using the dataset from step 1 and the simulated shortwave net radiation of coupled terrain effects NSR , the net radiation is calculated by the surface radiation balance equation R n , including the following steps:

[0127]

[0128]

[0129]

[0130]

[0131] in, and is the incoming longwave radiation and the outgoing longwave radiation from the surface; e a is the atmospheric emissivity; s is the Stefan-Boltzmann constant, which is ; is the atmospheric temperature.

[0132] Step 4.2: Net radiation calculated above R n Based on the sensible heat flux H Calculated according to the following exponential relationship:

[0133]

[0134] in, a is 115.1; b is 0.001629; c It is -171.4.

[0135] Step 4.3: Using the surface albedo α , surface temperature T s , Normalized Difference Vegetation Index NDVI and the net radiation obtained R n , calculate soil heat flux G :

[0136] Soil heat flux G The calculation formula is as follows:

[0137]

[0138] Step 4.4: Using the energy balance equation, the latent heat flux is calculated from the sensible heat flux obtained above, combined with the surface net radiation and soil heat flux. LE , as shown in the following formula:

[0139]

[0140] Step 4.5: Obtain daily evaporation according to the evaporation ratio constant method ET , as shown in the following formula:

[0141]

[0142] in, R n24 is the daily net radiation flux; EF is the evaporation ratio; l is the latent heat of vaporization of water, which can be estimated using the following formula:

[0143]

[0144] The calculation formula for daily net radiation flux is as follows:

[0145]

[0146] in, R ad is the total daily radiation, in this embodiment R adIt was obtained according to the method described in the document “Allen, RG, Trezza,R.,&Tasumi, M. 2006. Analytical integrated functions for daily solarradiation on slopes. Agricultural and Forest Meteorology, 139(1-2), 55-73.”

[0147] The evaporation ratio is calculated using the following formula:

[0148]

[0149] In order to verify the effect of the present invention, the present application uses the eddy covariance system to verify the calculation results of this embodiment and compares them with the SEBAL model results. Figure 2 As shown, Figure 2 (a) is the model of this embodiment. Figure 2 (b) is the SEBAL model. Figure 2 It can be seen that the model accuracy of the coupled terrain is significantly improved compared with the SEBAL model. 2 =0.874, RMSE=0.487mm / d, MAE=0.425mm / d.

[0150] Figure 3 A time series graph of evapotranspiration generated by the method of this embodiment is shown. Figure 3 In the figure, the red curve (T-SEBAL) represents the calculation results of the present invention, the blue curve represents the results of the SEBAL model, and the yellow curve represents the observed values. Figure 3 It can be seen that the evapotranspiration obtained by the present invention is highly consistent with the site observation value, and the change trends during the study period are highly similar, which fully demonstrates the reliability and accuracy of the method of the present invention.

[0151] Figure 4 The spatial distribution diagram of evapotranspiration generated by the method of the present invention is shown. In each group of figures, the figure on the left represents the result of the present invention (T-SEBAL), and the figure on the right represents the result of the SEBAL model. The evapotranspiration data obtained by the present invention shows obvious topographic characteristics in spatial distribution, which fully demonstrates the superiority of the evapotranspiration data generated by the present invention.

[0152] Figure 5 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 5As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530 and a communication bus 540, wherein the processor 510, the communication interface 520 and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute a remote sensing estimation method for surface evapotranspiration in a mountainous area considering terrain, the method comprising:

[0153] Step 1, obtaining a data set including pre-processed remote sensing image data, elevation data and pre-processed meteorological data;

[0154] Step 2: According to the data set in step 1, obtain surface parameters, terrain parameters and auxiliary data;

[0155] Step 3: Calculate the shortwave net radiation in the mountainous area coupled with the terrain effect by using the extended libRadtran radiation transfer model, random forest model and preprocessed remote sensing image data;

[0156] Step 4: Calculate the net radiation flux, sensible heat flux and latent heat flux based on the shortwave net radiation in the mountainous area, and calculate the daily evapotranspiration using the evaporation ratio invariant method.

[0157] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0158] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the remote sensing estimation method for surface evapotranspiration in mountainous areas taking into account the terrain provided by the above-mentioned methods.

[0159] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the remote sensing estimation method for surface evapotranspiration in mountainous areas taking into account terrain provided by the above methods.

[0160] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0161] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0162] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote sensing estimation method for surface evapotranspiration in mountainous areas taking into account topography, characterized in that: The following steps are involved: Step 1, obtaining a data set including pre-processed remote sensing image data, elevation data and pre-processed meteorological data; Step 2: According to the data set in step 1, obtain surface parameters, terrain parameters and auxiliary data; Step 3: Calculate the shortwave net radiation in the mountainous area coupled with the terrain effect by using the extended libRadtran radiation transfer model, random forest model and preprocessed remote sensing image data; Wherein, step 3 comprises the following steps: Step 3.1: Couple the libRadtran radiation transfer model with the terrain to construct an extended libRadtran radiation transfer model and calculate the shortwave net radiation in the mountainous area according to the following formula: NSR : in, α is the surface albedo, , and They are the direct radiation of the mountains, diffuse radiation and reflected radiation of the surrounding environment; in, is the shadow factor; and They are direct radiation and diffuse radiation from flat ground respectively; i t is the local angle of incidence; θ s is the solar zenith angle; V d is the sky visibility factor; is the average albedo; is the downward shortwave radiation on the horizontal plane; Step 3.2, according to the extended libRadtran radiation transfer model, obtain a simulation database with different atmospheric, surface and terrain conditions; Step 3.3: Based on the simulation database, use the random forest model to construct the shortwave net radiation in the mountainous area NSR The functional relationship is: NSR = f ( θ s , θ v , Z , i t , V d ,TOA reflectance) in, θ V To observe the zenith angle, Z is the elevation, TOA reflectance is the reflectivity of the top of the atmosphere, f represents a nonlinear function; Step 3.4, using the top of atmosphere reflectivity in remote sensing image data and the trained random forest model, simulate the shortwave net radiation NSR in mountainous areas; Step 4: Calculate the net radiation flux, sensible heat flux and latent heat flux based on the shortwave net radiation in the mountainous area, and calculate the daily evapotranspiration using the evaporation ratio invariant method.

2. The remote sensing estimation method for surface evapotranspiration in mountainous areas considering terrain according to claim 1, characterized in that: In the step 1, the preprocessing method includes: performing quality control, radiation calibration, atmospheric correction and cropping on the remote sensing image data; and resampling the meteorological data.

3. The remote sensing estimation method for surface evapotranspiration in mountainous areas considering terrain according to claim 1, characterized in that: In step 2, the surface parameters include the normalized difference vegetation index NDVI , surface temperature T s , surface emissivity ε g , surface albedo α ; Terrain parameters include the local angle of incidence i t and sky visibility factor V d ; Auxiliary data include solar zenith angle θ s and observation zenith angle θ v .

4. The remote sensing estimation method for surface evapotranspiration in mountainous areas considering terrain according to claim 3 is characterized in that: In step 2, the normalized vegetation index NDVI , surface emissivity ε g and surface albedo α Use the following formula to obtain: in, ρ NIR is the surface reflectivity in the near-infrared band, ρ RED is the surface reflectivity in the infrared band; Pv is the vegetation cover; is the broadband top-of-atmosphere reflectivity; α path It is the atmospheric radiation; is the atmospheric single transmittance.

5. The remote sensing estimation method for surface evapotranspiration in mountainous areas considering terrain according to claim 3, characterized in that: In step 2, the local incident angle i t It is calculated as follows: in, θ s is the solar zenith angle; β is the slope; is the solar azimuth; It is the slope direction.

6. The remote sensing estimation method for surface evapotranspiration in mountainous areas considering terrain according to claim 1, characterized in that: The step 4 comprises the following steps: Step 4.1: Use the data set from step 1 and combine it with the simulated shortwave net radiation in the mountainous area NSR , calculate the net radiation R n : in, and are the incoming longwave radiation and the outgoing longwave radiation from the surface, ε g is the surface emissivity; Step 4.2: Obtain the sensible heat flux according to the following formula: H : in, a , b , c All are coefficients; Step 4.3: Using the surface albedo α , surface temperature T s , Normalized Difference Vegetation Index NDVI and the net radiation obtained R n , calculate soil heat flux G : Step 4.4: Based on the net radiation R n , sensible heat flux H and soil heat flux G , calculate the latent heat flux LE : Step 4.5: Calculate daily evapotranspiration according to the following formula: ET : in, R n24 is the daily net radiation flux; λ is the latent heat of vaporization of water; EF is the evaporation ratio, which is calculated as follows: 。 7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the remote sensing estimation method for surface evapotranspiration in mountainous areas taking into account terrain is implemented as claimed in any one of claims 1 to 6.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the remote sensing estimation method for surface evapotranspiration in mountainous areas taking into account terrain is implemented as claimed in any one of claims 1 to 6.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the remote sensing estimation method for surface evapotranspiration in mountainous areas taking into account terrain is implemented as claimed in any one of claims 1 to 6.

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