Drainage basin multi-source actual evapotranspiration fusion method considering vegetation ecological hydrological effect

Abstract

The invention discloses a watershed multi-source actual evapotranspiration fusion method considering a vegetation ecological hydrological effect. The method comprises: 1, searching target data; 2, setting a proper target temporal-spatial resolution, preprocessing the data, and calculating a saturated water pressure difference VPD according to the air temperature; 3, calculating actual evapotranspiration data dominantly considering vegetation influence by using sunlight induced chlorophyll fluorescence data SIF, saturated water pressure difference data VPD and surface net radiation data Rn; 4, calculating a multi-source actual evapotranspiration data variance according to a three-source matching method; and 5, according to the multi-source actual evapotranspiration data variance, obtaining a long-series actual evapotranspiration product of the grid drainage basin by using a linear data fusion method. According to the method, other actual evapotranspiration products are fused through the multi-source matching and data fusion technology, it is guaranteed that the vegetation effect is fully considered in the fused products, meanwhile, the advantage of high resolution of mature actual evapotranspiration products is also compatible, and important and accurate data are provided for watershed hydrology and ecological research.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-source data fusion, and in particular to a method for fusing actual evapotranspiration from multiple sources in a watershed taking into account the eco-hydrological effect of vegetation. Background Art

[0002] Evapotranspiration is the main land pathway for precipitation to return to the atmosphere during the water cycle. People have recognized the important role of this water flux in climate regulation, and in recent years, people have realized the importance of evapotranspiration in ecological research. Obtaining accurate evapotranspiration estimates on a large scale has always been a focus of attention. In recent years, with the development of satellite remote sensing technology and inversion algorithms, evapotranspiration products based on satellite remote sensing inversion have become the mainstream data products used in basin evapotranspiration-related research. Compared with station data, current evapotranspiration products have greater global data coverage, making up for the lack of evapotranspiration observation sites. Different satellite products have different accuracies and spatiotemporal resolutions, providing a better reference for people to study the spatiotemporal changes of evapotranspiration on a global scale.

[0003] However, due to the limitations of satellite sensor accuracy and inversion algorithms, the currently publicly available evapotranspiration data still has many defects. First, for satellites with high spatial coverage, due to the limitations of the inversion algorithm, when using such data, it is necessary to grid the data at low resolution to reduce the impact of noise. Even so, low-resolution data still cannot meet the needs of most current research. Limited by the detection accuracy of the sensor itself, it is impossible to further obtain data with less noise and higher spatial resolution. Secondly, the current satellite revisit cycle and evapotranspiration inversion algorithm cannot effectively reflect the role of vegetation activity in evapotranspiration. Therefore, some evapotranspiration products cannot reflect the ecological and hydrological effects of vegetation or the impact of some extreme drought events, and there is an underestimation or overestimation phenomenon. Therefore, it is still difficult to actually use evapotranspiration data in ecological and hydrological data.

[0004] In order to more effectively utilize evapotranspiration data for hydrological or ecological research, researchers have begun to work on improving remote sensing evapotranspiration products. One of the main improvement routes is the data fusion research of multi-source remote sensing satellite products. Current research focuses on integrating existing evapotranspiration data. However, since the public evapotranspiration data still does not fully consider the eco-hydrological effects of vegetation, there is still room for improvement.

[0005] In general, existing studies have failed to effectively consider the role of vegetation ecohydrological effects in evapotranspiration, limiting the feasibility of evapotranspiration data in practical research applications. Therefore, it is necessary to design a basin multi-source actual evapotranspiration fusion method that considers vegetation ecohydrological effects to overcome the above problems. Summary of the invention

[0006] In order to avoid the above problems, a basin multi-source actual evapotranspiration fusion method considering the vegetation eco-hydrological effect is provided. Through multi-source matching and data fusion technology, other evapotranspiration products are integrated to ensure that the vegetation effect is fully considered in the fusion product, while also being compatible with the high-resolution advantages of mature evapotranspiration products, providing important and accurate data for basin hydrological and ecological research.

[0007] The present invention provides a method for fusing actual evapotranspiration from multiple sources in a watershed taking into account the eco-hydrological effect of vegetation, comprising the following steps:

[0008] Step 1: Collect target data, including the currently available land surface temperature data LST, sunlight-induced chlorophyll fluorescence data SIF, and air temperature data T a , surface net radiation data R n , leaf area index data LAI, MODIS land use type data, MODIS evapotranspiration data, GLEAM evapotranspiration data and FLUXNET2015 flux network flux data;

[0009] Step 2: Set the appropriate target spatiotemporal resolution and calculate the solar induced chlorophyll fluorescence data SIF and temperature data T a , 2m dew point temperature data T dew , surface net radiation data R n , pre-process the leaf area index data LAI, and calculate the saturated water vapor pressure difference VPD according to the air temperature;

[0010] Step 3: Use sunlight-induced chlorophyll fluorescence data SIF, saturated water vapor pressure difference data VPD, and surface net radiation data R n The actual evapotranspiration data ET that explicitly considers the influence of vegetation is calculated;

[0011] Step 4: Calculate the variance of multi-source actual evapotranspiration data according to the three-source matching method;

[0012] Step 5: Based on the variance of multi-source actual evapotranspiration data, a linear data fusion method is used to obtain a long series of grid actual basin evapotranspiration products.

[0013] Preferably, in step 1:

[0014] Land surface temperature data LST and air temperature data T a The shortwave net radiation data provided by the ERA5-Land reanalysis data is derived by adding the longwave net radiation data. The calculation formula is:

[0015] R n =R ns +R nl ;

[0016] In the formula, Rn is the net radiation data, unit: J m -2 ; R ns is the shortwave net radiation, unit: J m -2 ; R nl is the long-wave net radiation data, unit: J m -2 ;

[0017] The sunlight-induced chlorophyll fluorescence data SIF uses CSIF sunlight-induced chlorophyll fluorescence data to reconstruct products; the leaf area index data LAI is calculated using the bidirectional reflectance function corrected reflectance MCD43C4, with a temporal and spatial resolution of 0.05° on a daily scale; the MODIS land use type data uses MODIS MCD12C1 land use type data.

[0018] Preferably, step 2 specifically includes:

[0019] 2.1 Based on the basic reflectivity data of each site, select appropriate temporal and spatial resolutions to resample the SIF data;

[0020] 2.2 Collected land surface temperature data LST and air temperature data T a , dew point temperature data T dew , Normalized Difference Vegetation Index data NDVI, Surface Net Radiation data R n , leaf area index data LAI are preprocessed and clipped, and the temporal and spatial resolution of each data is unified with the temporal and spatial resolution of SIF data; the saturated water vapor pressure difference VPD and relative humidity RH are calculated using the processed air temperature and dew point temperature data, and the calculation formula is as follows:

[0021] VPD=e sat ×(1-RH),

[0022]

[0023] Among them, RH can be expressed as:

[0024] That is, VPD is expressed as:

[0025] In the above formula, VPD is the saturated water vapor pressure difference, e sat is the saturated water vapor pressure, e sat，dew is the saturated water vapor pressure at dew point temperature, RH is the relative humidity, T a is the temperature data, T dew It is the dew point temperature data.

[0026] Preferably, step 3 specifically includes:

[0027] 3.1 Using SIF and VPD data to calculate plant transpiration T, the calculation formula is as follows:

[0028]

[0029] Where T is plant transpiration, unit is Wm -2 ; SIF is sunlight-induced chlorophyll fluorescence; NDVI is the normalized difference vegetation index; γ is the psychrometric constant; λ cf is the marginal water use efficiency, which is a constant; O2 is a constant 2.09×104Pa; T a is the temperature, in °C; P a is the atmospheric pressure, in Pa; C a is the atmospheric carbon dioxide concentration, using a constant of 426.9 ppm; α and β are calibration parameters, which are calibrated based on the flux station data in the studied basin;

[0030] 3.2 Soil evaporation is calculated by the following formula:

[0031] In the formula, RH is relative humidity; R n is the net radiation of the surface; Δ is the slope of the saturated water vapor pressure difference; k A is the extinction coefficient, and its value is 0.6; γ is the psychrometric constant; LAI is the leaf area index;

[0032] 3.3 Combining plant transpiration and soil evaporation into actual evapotranspiration: ET = E s +T;

[0033] Where: ET is the actual evapotranspiration; E s is soil evaporation; T is plant transpiration.

[0034] Preferably, step 4 specifically includes:

[0035] 4.1 Based on MODIS, GLEAM, and ET SIF Three different actual evapotranspiration data are used to calculate different data variances. There is a linear relationship between the three different evapotranspiration data and the true value of the evapotranspiration data, that is, the following formula:

[0036]

[0037] In the formula, ET MODIS , E.T. GLEAM , E.T. SIF represent MODIS, GLEAM, and SIF-based evapotranspiration, respectively; among them, ET SIF For ET;

[0038] α1, α2, and α3 are the cumulative deviation coefficients of the evapotranspiration product relative to the true value of the actual evapotranspiration;

[0039] β1, β2, and β3 are the multiplication deviation coefficients of the evapotranspiration product relative to the true value of the actual evapotranspiration;

[0040] ε1, ε2, and ε3 are the errors of the evapotranspiration product relative to the true value of the actual evapotranspiration;

[0041] 4.2 Calculate the error variance of each product:

[0042]

[0043] In the formula, are the error variances of different actual evapotranspiration products, σ1, σ2, σ3 are the errors of different actual evapotranspiration products themselves, σ True is the theoretical true value variance.

[0044] Preferably, step 5 specifically includes: using a fusion principle based on linear least squares method to fuse different data products, the formula is as follows:

[0045] ET 融合 =w1ET MODIS +w2ET GLEAM +w3ET SLF ;

[0046] Where w1, w2, and w3 are MODIS, GLEAM, and ET, respectively. SIF The weight of actual evapotranspiration data in evapotranspiration data fusion, ET 融合 MODIS, GLEAM, ET SIF The fused evapotranspiration data product; w1, w2, and w3 are obtained by the least squares method using ET 融合 The calculation is performed with the minimum error variance as the goal. The error variance of each evapotranspiration product calculated in step 4 is used to quantify w1, w2, and w3 in data fusion:

[0047]

[0048] After processing with the above formula, the evapotranspiration fusion data can be finally obtained.

[0049] Compared with the prior art, the present invention has the following beneficial effects: the present invention provides a method for fusing actual evapotranspiration from multiple sources in a watershed that takes into account the eco-hydrological effect of vegetation. It is scientific, reasonable, and close to the actual research and application. It makes full use of the advantages of the currently disclosed sunlight-induced chlorophyll fluorescence data in accurately reflecting the eco-hydrological effect of vegetation, estimates the evapotranspiration of the watershed, and fuses other evapotranspiration products through multi-source matching and data fusion technology. It not only ensures that the vegetation effect is fully considered in the fused product, but also is compatible with the high-resolution advantages of mature evapotranspiration products, and finally obtains a watershed evapotranspiration remote sensing product that takes into account the eco-hydrological effect of vegetation. Moreover, the fused watershed evapotranspiration product can provide important and accurate data for watershed hydrological and ecological research. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of a method for fusing actual evapotranspiration from multiple sources in a watershed considering the eco-hydrological effect of vegetation according to a preferred embodiment of the present invention;

[0051] Figure 2 It is a schematic diagram of the probability density function of the fused basin actual evapotranspiration data according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0053] A method for fusing multi-source actual evapotranspiration products of a watershed that considers the eco-hydrological effect of vegetation in an embodiment of the present invention first collects publicly available remote sensing sunlight-induced chlorophyll fluorescence observation data, global meteorological reanalysis data, vegetation-related data, and land use type data sets, etc.; then, through the ground basic reflectance station data, through correlation analysis and other methods, the appropriate data sampling spatiotemporal resolution is determined, and the spatiotemporal resolution of the collected data is unified; then, the key variables in the evapotranspiration formula are calculated using the collected remote sensing data; the evapotranspiration is divided into two parts, soil evaporation and plant transpiration, wherein the plant transpiration part uses sunlight-induced chlorophyll fluorescence data and is calculated in combination with meteorological data to obtain evapotranspiration data calculated using sunlight-induced chlorophyll fluorescence data; the three-source matching method is used to analyze the evapotranspiration data obtained from MODIS, GLEAM, and calculations based on sunlight-induced chlorophyll fluorescence to obtain the error variances of three different evapotranspiration products; finally, a fusion product of watershed evapotranspiration is obtained through a data fusion method, and the specific process is described in detail in Figure 1 .

[0054] The technical solution of the present invention is further described in detail below through embodiments and in combination with the accompanying drawings. A method for fusing actual evapotranspiration from multiple sources in a watershed taking into account the eco-hydrological effect of vegetation comprises the following steps:

[0055] Step 1: Collect target data, including the currently available land surface temperature data LST, sunlight-induced chlorophyll fluorescence data SIF, and air temperature data T a , surface net radiation data R n , leaf area index data LAI, MODIS land use type data, MODIS evapotranspiration data, GLEAM evapotranspiration data and FLUXNET2015 flux network flux data; the specific method is:

[0056] As for the surface temperature data and air temperature data, the short-wave net radiation data and long-wave net radiation data provided by the ERA5-Land reanalysis data are added together and derived. The calculation formula is:

[0057] R n =R ns +R nl ;

[0058] In the above formula, R n is the net radiation data (unit: J m -2 ), R ns is the shortwave net radiation (unit: J m -2 ), R nl is the long-wave net radiation data (unit: J m -2 ).

[0059] In terms of sunlight-induced chlorophyll fluorescence data, it is used to characterize the photosynthesis effect in the vegetation eco-hydrological effect. The CSIF sunlight-induced chlorophyll fluorescence data reconstruction product is mainly used; the leaf area index data uses the MODIS leaf area index MCD15A3H, with a temporal and spatial resolution of 500m and 4 days.

[0060] The land use type data adopts MODIS MCD12C1 land use type data.

[0061] Step 2: Set the appropriate target spatiotemporal resolution and calculate the solar induced chlorophyll fluorescence data SIF and temperature data T a , 2m dew point temperature data T dew , surface net radiation data R n , leaf area index data LAI is preprocessed, and saturated water vapor pressure difference VPD is calculated according to the air temperature; the specific method is:

[0062] 2.1 Based on the basic reflectivity data of each site, select the appropriate temporal and spatial resolution to resample the SIF data.

[0063] 2.2 Collected land surface temperature data LST and air temperature data T a , dew point temperature data T dew , Normalized Difference Vegetation Index data NDVI, Surface Net Radiation data R n , leaf area index data LAI are preprocessed and clipped, and the temporal and spatial resolutions of each data are unified with those of the SIF data.

[0064] The saturated water vapor pressure difference VPD and relative humidity RH are calculated using the processed air temperature and dew point temperature data. The calculation formula is as follows:

[0065] VPD=e sat ×(1-RH),

[0066]

[0067] Among them, RH can be expressed as:

[0068] Therefore, VPD can be expressed as:

[0069] In the above formula, VPD is the saturated water vapor pressure difference, e sat is the saturated water vapor pressure, e sat，dew is the saturated water vapor pressure at dew point temperature, RH is the relative humidity, T air is the temperature data, T dew It is the dew point temperature data.

[0070] Step 3: Use sunlight-induced chlorophyll fluorescence data SIF, saturated water vapor pressure difference data VPD, and surface net radiation data R n The data calculation explicitly considers the actual evapotranspiration data ET of the vegetation eco-hydrological effect;

[0071] The actual evapotranspiration is calculated by dividing it into two parts, namely plant transpiration and soil evaporation.

[0072] 3.1 Plant transpiration is calculated using SIF and VPD data, and the calculation formula is as follows:

[0073]

[0074] In the above formula:

[0075] T is plant transpiration, unit is Wm -2 ;

[0076] SIF is sunlight-induced chlorophyll fluorescence;

[0077] NDVI is the Normalized Difference Vegetation Index;

[0078] γ is the psychrometric constant;

[0079] λ cf is the marginal water use efficiency, which is a constant;

[0080] O2 is a constant 2.09×104Pa;

[0081] T a is the air temperature (℃);

[0082] P a is the atmospheric pressure, Pa;

[0083] C a is the atmospheric carbon dioxide concentration, using a constant of 426.9 ppm;

[0084] α and β are calibration parameters, which are calibrated based on the flux station data in the studied basin;

[0085] 3.2 Soil evaporation is calculated by the following formula:

[0086]

[0087] Where:

[0088] RH is relative humidity;

[0089] R n is the net radiation from the surface;

[0090] Δ is the slope of the saturated water vapor pressure difference;

[0091] k A is the extinction coefficient, and its value is 0.6;

[0092] γ is the psychrometric constant;

[0093] LAI is leaf area index;

[0094] 3.3 Combine plant transpiration and soil evaporation into actual evapotranspiration:

[0095] ET=E s +T;

[0096] Where:

[0097] ET is actual evapotranspiration;

[0098] E s for soil evaporation;

[0099] T is plant transpiration;

[0100] This ET is referred to as ET in the following text. SIF .

[0101] Step 4: Calculate the variance of actual evapotranspiration data from multiple sources using the three-source matching method. The specific method is as follows:

[0102] 4.1 Based on MODIS, GLEAM, and ET SIF Three different actual evapotranspiration data are used to calculate the variance of different data. It is assumed that there is a linear relationship between the three different evapotranspiration data and the true value of the land evapotranspiration data, that is, the following formula is obtained:

[0103]

[0104] Where:

[0105] ET MODIS , E.T. GLEAM , E.T. SIF represent MODIS, GLEAM and the evapotranspiration based on SIF mentioned above; among them, ET SIF For ET;

[0106] α1, α2, and α3 are the cumulative deviation coefficients of the evapotranspiration product relative to the true value of evapotranspiration;

[0107] β1, β2, and β3 are the multiplicative deviation coefficients of the evapotranspiration product relative to the true value of evapotranspiration;

[0108] ε1, ε2, and ε3 are the errors of the evapotranspiration product relative to the true value of evapotranspiration.

[0109] 4.2 Calculate the error variance of each product according to the above formula:

[0110]

[0111] In the above formula, are the error variances of different evapotranspiration products, σ1, σ2, σ3 are the errors of different evapotranspiration products themselves, σ True is the theoretical true value variance.

[0112] Step 5: Based on the variance of multi-source actual evapotranspiration data, a linear data fusion method is used to obtain a long series of grid basin actual evapotranspiration products.

[0113] The fusion principle based on linear least squares method is used to fuse different data products. The formula is as follows:

[0114] ET 融合 =w1ETM ODIS +w2ET GLEAM +w3ET SIF ;

[0115] In the above formula, w1, w2, and w3 are MODIS, GLEAM, and ET, respectively. SIFThe weight of the actual evapotranspiration data of the basin in the fusion of evapotranspiration data, ET 融合 MODIS, GLEAM, ET SIF The fused evapotranspiration data product.

[0116] In the formula, w1, w2, and w3 are obtained by the least squares method using ET 融合 The calculation is performed with the minimum error variance as the goal. The error variance of each evapotranspiration product calculated in step 4 is used to quantify w1, w2, and w3 in data fusion:

[0117]

[0118] After processing with the above formula, the evapotranspiration fusion data can be finally obtained.

[0119] Figure 2 This is a comparison chart of the evapotranspiration results of the measured evapotranspiration sites CN-Cng and FI-Hyy after the method of the present invention estimates the evapotranspiration, and the traditional evapotranspiration product PT-JPL. It can be seen from the figure that the estimated results of the patent results at this site are basically distributed near the 1:1 line, while the PT-JPL evapotranspiration estimation deviates significantly from the 1:1 line, indicating that the estimation results of this method are more accurate than those of the traditional remote sensing evapotranspiration estimation method.

[0120] 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 replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A basin multi-source actual evapotranspiration fusion method considering vegetation eco-hydrological effects, characterized by: The steps include: Step 1: Collect target data, including the currently available land surface temperature data LST, sunlight-induced chlorophyll fluorescence data SIF, and air temperature data T a , surface net radiation data R n , leaf area index data LAI, MODIS land use type data, MODIS evapotranspiration data, GLEAM evapotranspiration data and FLUXNET2015 flux network flux data; Step 2: Set the appropriate target spatiotemporal resolution and calculate the solar induced chlorophyll fluorescence data SIF and temperature data T a , 2m dew point temperature data T dew , surface net radiation data R n , pre-process the leaf area index data LAI, and calculate the saturated water vapor pressure difference VPD according to the air temperature; Step 3: Use sunlight-induced chlorophyll fluorescence data SIF, saturated water vapor pressure difference data VPD, and surface net radiation data R n The actual evapotranspiration data ET that explicitly considers the influence of vegetation is calculated; Step 4: Calculate the variance of multi-source actual evapotranspiration data according to the three-source matching method; Step 5: Based on the variance of multi-source actual evapotranspiration data, a linear data fusion method is used to obtain a long series of grid basin actual evapotranspiration products.

2. The method for fusing actual evapotranspiration from multiple sources in a watershed considering the vegetation eco-hydrological effect as claimed in claim 1, characterized in that: In step 1: Land surface temperature data LST and air temperature data T a The shortwave net radiation data provided by the ERA5-Land reanalysis data is derived by adding the longwave net radiation data. The calculation formula is: R n =R ns +R nl ; In the formula, R n is the net radiation data, unit: J m -2 ; R ns is the shortwave net radiation, unit: J m -2 ; R nl is the long-wave net radiation data, unit: J m -2 ; The sunlight-induced chlorophyll fluorescence data SIF uses CSIF sunlight-induced chlorophyll fluorescence data to reconstruct products; the leaf area index data LAI is calculated using the bidirectional reflectance function corrected reflectance MCD43C4, with a temporal and spatial resolution of 0.05° on a daily scale; the MODIS land use type data uses MODIS MCD12C1 land use type data.

3. The method for fusing actual evapotranspiration from multiple sources in a watershed considering the vegetation eco-hydrological effect as claimed in claim 1, characterized in that: Step 2 specifically includes: 2.1 Based on the basic reflectivity data of each site, select appropriate temporal and spatial resolutions to resample the SIF data; 2.2 Collected land surface temperature data LST and air temperature data T a , dew point temperature data T dew , Normalized Difference Vegetation Index data NDVI, Surface Net Radiation data R n , leaf area index data LAI are preprocessed and clipped, and the temporal and spatial resolution of each data is unified with the temporal and spatial resolution of SIF data; the saturated water vapor pressure difference VPD and relative humidity RH are calculated using the processed air temperature and dew point temperature data, and the calculation formula is as follows: VPD=e sat ×(1-RH), Among them, RH can be expressed as: That is, VPD is expressed as: In the above formula, VPD is the saturated water vapor pressure difference, e sat is the saturated water vapor pressure, e sat,deω is the saturated water vapor pressure at dew point temperature, RH is the relative humidity, T a is the temperature data, T dew It is the dew point temperature data.

4. The method for fusing actual evapotranspiration from multiple sources in a watershed considering the eco-hydrological effect of vegetation as claimed in claim 1, characterized in that: Step 3 specifically includes: 3.1 Using SIF and VPD data to calculate plant transpiration T, the calculation formula is as follows: Where T is plant transpiration, unit is Wm -2 ; SIF is sunlight-induced chlorophyll fluorescence; NDVI is the normalized difference vegetation index; γ is the psychrometric constant; λ cf is the marginal water use efficiency, which is a constant; O2 is a constant 2.09×104Pa; T a is the temperature, in °C; P a is the atmospheric pressure, in Pa; C a is the atmospheric carbon dioxide concentration, using a constant of 426.9 ppm; α and β are calibration parameters, which are calibrated based on the flux station data in the studied basin; 3.2 Soil evaporation is calculated by the following formula: Where RH is relative humidity; R n is the net radiation of the surface; Δ is the slope of the saturated water vapor pressure difference; k A is the extinction coefficient, and its value is 0.6; γ is the psychrometric constant; LAI is the leaf area index; 3.3 Combining plant transpiration and soil evaporation into actual evapotranspiration: ET = E s +T; Where: ET is the actual evapotranspiration; E s is soil evaporation; T is plant transpiration.

5. The method for fusing actual evapotranspiration from multiple sources in a watershed considering the vegetation eco-hydrological effect as claimed in claim 1, characterized in that: Step 4 specifically includes: 4.1 Based on MODIS, GLEAM, and ET SIF Three different actual evapotranspiration data are used to calculate different data variances. There is a linear relationship between the three different evapotranspiration data and the true value of the evapotranspiration data, that is, the following formula: In the formula, ET MODIS , E.T. GLEAM , E.T. SIF represent MODIS, GLEAM, and SIF-based evapotranspiration, respectively; among them, ET SIF For ET; α1, α2, and α3 are the cumulative deviation coefficients of the evapotranspiration product relative to the true value of the actual evapotranspiration; β1, β2, and β3 are the multiplication deviation coefficients of the evapotranspiration product relative to the true value of the actual evapotranspiration; ε1, ε2, and ε3 are the errors of the evapotranspiration product relative to the true value of the actual evapotranspiration; 4.2 Calculate the error variance of each product: In the formula, are the error variances of different actual evapotranspiration products, σ1, σ2, σ3 are the errors of different actual evapotranspiration products themselves, σ True is the theoretical true value variance.

6. The method for fusing actual evapotranspiration from multiple sources in a watershed considering the eco-hydrological effect of vegetation as claimed in claim 5, characterized in that: Step 5 is as follows: Use the fusion principle based on linear least squares method to fuse different data products. The formula is as follows: AND 融合 =ω1ET MODIS +ω2ET GLEAM +w3ET SIF ; Where ω1, ω2, and ω3 are MODIS, GLEAM, and ET, respectively. SIF The weight of actual evapotranspiration data in evapotranspiration data fusion, ET 融合 MODIS, GLEAM, ET SIF The fused evapotranspiration data product; w1, w2, and w3 are obtained by the least squares method using ET 融合 The calculation is performed with the minimum error variance as the goal. The error variance of each evapotranspiration product calculated in step 4 is used to quantify w1, w2, and w3 in data fusion: After processing with the above formula, the evapotranspiration fusion data can be finally obtained.

Images