A method for parameterizing land surface conductance and regional evapotranspiration model based on shortwave infrared moisture index

Abstract

The present invention provides a method for parameterizing surface conductivity and regional evapotranspiration model based on shortwave infrared moisture index, including: collection and spatialization of meteorological radiation data in the study area, acquisition of satellite remote sensing data, inversion of remote sensing underlying surface biophysical parameters, moisture index calculation based on shortwave infrared information, time series interpolation of remote sensing underlying surface biophysical parameters and shortwave infrared moisture index, construction of regional evapotranspiration estimation model, and surface conductivity parameterization combined with shortwave infrared moisture index. The improved surface conductivity parameterization scheme and evapotranspiration estimation method provided by the present invention can make full use of satellite remote sensing and reanalysis data to achieve the estimation of surface evapotranspiration in large areas, data-free areas, and complex non-uniform underlying surfaces. The method described in the present invention will support the needs of application fields such as water resource management in arid areas, agricultural water use efficiency evaluation, regional drought monitoring, and agricultural water conservation.

Description

Technical Field

[0001] The invention belongs to the technical field of remote sensing hydrology, and in particular relates to a surface conductance parameterization and regional evapotranspiration model method based on shortwave infrared moisture index. Background Art

[0002] Evapotranspiration is an important process of water and energy exchange between the atmosphere, hydrosphere and biosphere, and is closely related to the cycles of water, energy and carbon on Earth. As a key component of surface energy balance and water balance, the observation and simulation of surface evapotranspiration has important scientific significance and practical value in carbon-water cycle, regional water balance research, climate model parameterization and irrigation area water resources management.

[0003] With the advancement of satellite remote sensing technology and the development of quantitative remote sensing models, remote sensing evapotranspiration models based on energy balance and evapotranspiration models based on surface conductivity parameterization have been widely used in regional evapotranspiration estimation research. The evapotranspiration model based on energy balance uses the surface temperature observed by remote sensing to calculate the sensible heat flux, and the latent heat flux is usually used as the remainder of the energy balance equation. The remote sensing evapotranspiration models based on energy balance mainly include single-layer and double-layer models. The single-layer model assumes the energy exchange interface of the underlying surface as a uniform single-layer "large leaf", and uses residual impedance or empirical methods to reconcile the difference between remote sensing surface temperature and aerodynamic temperature. The reliability and applicability of residual impedance parameterization are still the biggest obstacles to the application of single-layer models. At present, there is still no residual impedance parameterization method suitable for different conditions. The double-layer model considers the differences in water and heat transfer between soil and vegetation respectively, eliminates the problem of additional impedance parameterization of the single-layer model, and can realize the estimation of vegetation transpiration and soil evaporation. The double-layer model improves the empiricism of the single-layer model to a certain extent, and has higher accuracy in evapotranspiration estimation for non-uniform surfaces. However, the two-layer model defines a more complex impedance system, which is greatly affected by vegetation structure, physiological characteristics and soil moisture conditions, becoming the main difficulty in accurately inverting regional evapotranspiration with the two-layer model. The surface evapotranspiration model based on surface temperature also needs to be combined with the time scale expansion method to expand to the daily and monthly scales. Surface temperature is the key driver of the evapotranspiration model based on energy balance, but due to the influence of atmospheric conditions and cloud cover, the difficulty in continuously obtaining surface temperature with high temporal and spatial resolution is still the main factor restricting its widespread application. Compared with the remote sensing evapotranspiration model based on energy balance, the evapotranspiration model based on surface conductivity parameterization does not require surface temperature as a driver, which is suitable for long-term continuous estimation of regional evapotranspiration, and is convenient for remote sensing estimation of regional evapotranspiration and operational application of irrigation areas. The remote sensing evapotranspiration model based on surface conductivity parameterization establishes the relationship between surface conductivity and remote sensing vegetation index or leaf area index, and uses Penman-Monteith, Shuttleworth-Wallace or Priestley formula to achieve regional evapotranspiration estimation. Surface conductance (Gs) is affected by the combined influence of environmental factors and vegetation physiological properties, plays a controlling role in the transmission of surface water and heat flux, and is a key variable in evapotranspiration models. The evapotranspiration model based on surface conductance parameterization has a strong physical mechanism. It combines meteorological data of different time scales and vegetation parameters inverted by remote sensing as a driver. It does not require surface thermal infrared information and effectively avoids the uncertainty caused by the expansion from instantaneous to daily scales. Therefore, it is more suitable for long-term continuous evapotranspiration estimation, and is convenient for business applications and remote sensing estimation of large-scale evapotranspiration. The key to the evapotranspiration model based on surface conductance parameterization is how to accurately characterize the canopy conductance and soil moisture stress factors that play a decisive role in vegetation transpiration and soil evaporation.

[0004] Although the parameterized model of surface conductance has made great progress, studies have shown that due to the lack of accurate description of soil evaporation and vegetation water stress, the estimation of evapotranspiration in arid and semi-arid areas by the parameterized model of surface conductance has large deviations. Therefore, how to describe the water stress information in arid and semi-arid areas and improve the estimation accuracy of the parameterized model of surface conductance in arid and semi-arid areas remains a key scientific issue that needs to be broken through. At present, there is still no method that can be applied to the description of water stress information of non-uniform underlying surfaces in arid areas. In particular, the accurate estimation of evapotranspiration of natural and artificial oases under the influence of human activities in arid areas is currently a hot topic and difficulty in research. Summary of the invention

[0005] In view of the problems existing in the prior art, the purpose of the present invention is to provide a method for parameterizing surface conductance and regional evapotranspiration model based on shortwave infrared moisture index. The constructed model can be used to accurately estimate evapotranspiration in arid areas, which is practical and operable for regional applications.

[0006] The objective of the present invention is achieved through the following technical solutions:

[0007] A method for parameterizing surface conductance and regional evapotranspiration model based on shortwave infrared moisture index includes the following steps:

[0008] Step 1: Collection and spatialization of meteorological radiation data in the study area:

[0009] First, collect daily meteorological radiation data in the study area, including daily temperature, wind speed, atmospheric pressure, precipitation, air humidity and solar radiation;

[0010] Then, the digital elevation model of the study area is used to perform spatial interpolation on the meteorological radiation data collected above, thereby realizing the spatialization of meteorological radiation data;

[0011] Step 2: Acquisition of satellite remote sensing data:

[0012] Obtain multi-source satellite remote sensing data of the study area, including Landsat8, Sentinel-2, Terra / AquaMODIS, and Sentinel-3;

[0013] Step 3: Inversion of remote sensing underlying surface biophysical parameters:

[0014] Obtain the underlying biophysical parameters including normalized difference vegetation index, leaf area index, surface albedo, daily 500-meter resolution snow area data, underlying land cover data, and vegetation height;

[0015] Step 4, moisture index calculation based on shortwave infrared information:

[0016] The moisture index based on shortwave infrared information is constructed, and the calculation formula is as follows:

[0017]

[0018] Where NDSMI is the moisture index, SWIR5 and SWIR7 are the reflectances of the 5th and 7th bands of MODIS, respectively;

[0019] Step 5: Time series interpolation of remote sensing underlying surface biophysical parameters and shortwave infrared moisture index:

[0020] Perform time series interpolation processing on the remote sensing underlying surface biophysical parameters obtained in step 3 and the moisture index of the shortwave infrared information calculated in step 4, so as to obtain the remote sensing underlying surface biophysical parameters and the moisture index of the shortwave infrared information that are continuous in time and space;

[0021] Step 6: Construction of regional evapotranspiration estimation model:

[0022] Considering the difference between evapotranspiration of ice-covered and snow-covered surfaces and soil-covered surfaces, the following formula is used to calculate surface evapotranspiration:

[0023]

[0024] Where ET is the surface evapotranspiration, ET snow_ice is the evapotranspiration from the underlying surface covered with ice and snow, ET i is the interception and evaporation of precipitation, ET s is soil evaporation, ET c for transpiration from the vegetation canopy;

[0025] Step 7, parameterization of surface conductance combined with shortwave infrared moisture index:

[0026] Based on the moisture index of the shortwave infrared information calculated in step 4, the soil moisture stress index WSI and the vegetation canopy moisture stress index S m Parameterization is performed to construct an evapotranspiration estimation model suitable for arid and semi-arid regions;

[0027] Realize WSI, S m The specific parameterization formula is:

[0028]

[0029] Where, NDSMI min and NDSMI max are the maximum and minimum values ​​of NDSMI, K sf It is a characteristic parameter that expresses the water absorption capacity of different vegetation types.

[0030] Furthermore, the method further comprises the following steps:

[0031] Step 8: Calibration of evapotranspiration model parameters:

[0032] Combined with the observations of the ground flux station, the trust region reflectance optimization algorithm is used to calibrate the key parameters of the evapotranspiration estimation model constructed in steps 1-7;

[0033] The key parameters include the following 7: D0, β, η, m, V m25 ,k Q ,k a .

[0034] Step 9, verification and evaluation of regional evapotranspiration:

[0035] The model estimation results are verified and evaluated by combining the regional surface evapotranspiration observation data of the study area and the evapotranspiration results calculated by the water balance method.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. The present invention provides a method for parameterizing surface conductivity and regional evapotranspiration model based on shortwave infrared moisture index. The constructed regional evapotranspiration model can realize accurate estimation of evapotranspiration in arid areas, and has practicality and operability for regional application. Specifically, the surface conductivity parameterization described in the present invention is based on the canopy conductivity parameterization scheme of the PML_V2 carbon-water coupling mechanism, combined with the improvement of canopy conductivity optimized by shortwave infrared moisture index, so that it has the ability to accurately depict the water stress information of the underlying surface, and constructs a remote sensing evapotranspiration model suitable for complex underlying surfaces that takes into account snowmelt sublimation, vegetation interception evaporation, soil evaporation and vegetation transpiration, thereby improving the accuracy of evapotranspiration estimation in arid areas.

[0038] 2. The improved surface conductivity parameterization scheme and evapotranspiration estimation method provided by the present invention can make full use of satellite remote sensing and reanalysis data to estimate surface evapotranspiration over large areas, data-free areas, and complex non-uniform underlying surfaces. The method described in the present invention will support the needs of application areas such as regional water resource management in arid areas, agricultural water use efficiency evaluation, regional drought monitoring, and agricultural water conservation. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0040] Figure 1 It is a schematic flow chart of the method for parameterizing the surface conductivity and regional evapotranspiration model based on the shortwave infrared moisture index described in the embodiment;

[0041] Figure 2Results of evaluating the model described in this application using evapotranspiration data from basin water balance calculations are shown. DETAILED DESCRIPTION

[0042] Example

[0043] like Figure 1 As shown, this embodiment provides a surface conductance parameterization and regional evapotranspiration model method based on shortwave infrared moisture index, including the following steps:

[0044] Step 1: Collection and spatialization of meteorological radiation data in the study area:

[0045] Meteorological radiation data is the basis of the model that drives evapotranspiration. First, collect meteorological radiation data on a daily scale in the study area, including daily temperature, wind speed, atmospheric pressure, precipitation, air humidity and solar radiation;

[0046] Then, according to the research needs, the digital elevation model of the study area is used to perform spatial interpolation on the meteorological radiation data collected above, thereby realizing the spatialization of the meteorological radiation data.

[0047] This embodiment prefers the ERA5-Land climate reanalysis dataset. ERA5-Land is produced by inverting the land part of the ECMWF ERA5 climate reanalysis data, providing higher-resolution long-term continuous observations of land meteorological elements. ERA5-Land can be accessed through CDS (Copernicus Climate Data Store, https: / / cds.climate.copernicus.eu / )get.

[0048] Step 2: Acquisition of satellite remote sensing data:

[0049] Obtain multi-source satellite remote sensing data of the study area, including Landsat8, Sentinel-2, Terra / AquaMODIS, and Sentinel-3.

[0050] Specifically, Landsat8 and Sentinel-2 meet the requirements for regional evapotranspiration calculations with a resolution better than 30 meters, and MODIS remote sensing product data meet the requirements for evapotranspiration calculations with a resolution of 500 meters. The data download address for EOS / MODIS satellite products is: https: / / modis.gsfc.nasa.gov / data / dataprod / .

[0051] This embodiment uses MODIS remote sensing data as an example to illustrate the technical solution of the present invention.

[0052] Step 3: Inversion of remote sensing underlying surface biophysical parameters:

[0053] The inversion of remote sensing underlying surface biophysical parameters is an important step in the remote sensing evapotranspiration estimation model. In this step, remote sensing underlying surface biophysical parameters including normalized vegetation index, leaf area index, surface albedo, daily 500-meter resolution snow area data, underlying surface land cover data, and vegetation height are obtained.

[0054] Specifically, the normalized vegetation index, leaf area index and surface albedo can be obtained through Terra / Aqua MODIS satellite remote sensing data products. The normalized vegetation index is calculated using the latest 6.1 version of MODIS surface reflectance data product (MOD09GA.061). The leaf area index is obtained using the latest 6.1 version of MODIS leaf area index and photosynthetically active radiation data product (MCD15A3H.061). The surface albedo can be obtained through the latest 6.1 version of MODIS surface albedo data product (MCD43A3.061). Daily 500-meter resolution snow area data can be obtained through the MODIS snow data product (MOD10A1). The underlying land cover data can be obtained through the MODIS surface cover data (MCD12Q1.061) product.

[0055] The vegetation height is parameterized using a combination of the leaf area index and the global forest height dataset released by NASA JPL, and the calculation formula is:

[0056]

[0057] Where LAI is the leaf area index, and CPH is the global forest height dataset released by NASA JPL, which is based on the global tree height fused with spatial lidar data from the Earth Science Laser Altimeter System (GLAS) and auxiliary geospatial data.

[0058] Step 4, moisture index calculation based on shortwave infrared information:

[0059] The shortwave infrared spectrum is sensitive to the moisture information of the underlying surface. Compared with visible light and near-infrared spectra, the shortwave infrared spectrum responds better to changes in vegetation moisture content and soil moisture content, and has great potential in characterizing soil and vegetation moisture stress.

[0060] In this step, the moisture index based on shortwave infrared information (NDSMI) is constructed, and the calculation formula is as follows:

[0061]

[0062] Where NDSMI is the moisture index, SWIR5 and SWIR7 are the reflectances of the 5th and 7th bands of MODIS, respectively.

[0063] Since NDSMI is calculated based on two shortwave infrared bands, it is insensitive to changes in non-water information and can better describe the moisture information of the underlying surface. The NDSMI values ​​of vegetation and soil are between 0 and 0.667, but the NDSMI of ice and snow covered surfaces can exceed 0.667.

[0064] Step 5: Time series interpolation of remote sensing underlying surface biophysical parameters and shortwave infrared moisture index:

[0065] Satellite remote sensing data are affected by weather factors such as clouds and fog, and the biophysical parameters and shortwave infrared moisture index calculated based on satellite remote sensing data will inevitably have data missing. Therefore, in order to obtain temporally and spatially continuous remote sensing underlying surface biophysical parameters and shortwave infrared moisture index data, it is necessary to perform time series interpolation processing on these data. The specific method is:

[0066] The remote sensing underlying surface biophysical parameters obtained in step 3 and the moisture index of the shortwave infrared information calculated in step 4 are subjected to time series interpolation processing to obtain the remote sensing underlying surface biophysical parameters and the moisture index of the shortwave infrared information that are continuous in time and space.

[0067] In this embodiment, the nearest neighbor linear interpolation method is used to interpolate the surface albedo data to obtain the spatiotemporally continuous surface albedo data. The leaf area index, normalized difference vegetation index and NDSMI index are processed by the time series reconstruction method of joint neighbor interpolation and Savitzky-Golay filtering. The specific steps are as follows:

[0068] ① Based on the MODIS product quality information, the leaf area index, normalized difference index and NDSMI index time series data with good data quality were screened out.

[0069] ②The missing values ​​of leaf area index, normalized difference index and NDSMI index time series data were interpolated using linear interpolation method, and the seasonal average value was used to replace the missing time series data after linear interpolation.

[0070] ③ Use the Savitzky-Golay filter to smoothly reconstruct the time series data, which can be processed by calling the SciPy package Savitzky-Golay filter of Python.

[0071] Through the above processing, the temporally and spatially continuous remote sensing underlying surface biophysical parameters and shortwave infrared moisture index data are obtained to drive the evapotranspiration model.

[0072] Step 6: Construction of regional evapotranspiration estimation model:

[0073] Considering the difference between evapotranspiration of ice-covered and snow-covered surfaces and soil-covered surfaces, the following formula is used to calculate surface evapotranspiration:

[0074]

[0075] Where ET is the surface evapotranspiration, ET snow_ice is the evapotranspiration from the underlying surface covered with ice and snow, ET i is the interception and evaporation of precipitation, ET s is soil evaporation, ET c Transpiration from the vegetation canopy.

[0076] The evapotranspiration ET of the underlying surface covered by ice and snow snow_ice Use the following formula to calculate:

[0077] ET snow_ice =(0.18+0.098u 10 )VPD hpa

[0078] In the formula, u 10 For 10m wind speed (m / s), VPD hpa is the atmospheric saturation water vapor deficit (hPa).

[0079] The precipitation interception evaporation ET i Parameterize using leaf area index and precipitation data:

[0080]

[0081] I e =λET i

[0082] Where LAI is the leaf area index, P is the precipitation (mm), and f veg is the vegetation coverage, I e Energy consumed by precipitation interception and evaporation (MJ / m 2 ), λ is the latent heat of vaporization of water.

[0083] The energy partitioning of vegetation and soil components is calculated by using Beer's law after deducting the energy intercepted by vegetation canopy precipitation and evaporation:

[0084] R nsi =[(1-α0)R s -L net -I e ]exp(-k a LAI)

[0085] R nci =[(1-α0)R s -L net -I e ][1-exp(-k a LAI)]

[0086] In the formula, R nsi and R nci are the net radiation of soil and vegetation components (MJ / m 2 ), L net is the net longwave radiation (MJ / m 2 ), α0 is the surface albedo, k a is the extinction coefficient of the vegetation canopy, R s It is the downward shortwave radiation.

[0087] The soil evaporation ET s Use the following formula to calculate:

[0088] ET s =WSI·EP s

[0089] Where WSI is the soil water stress index, EP is s is the soil potential evapotranspiration, calculated using the Priestley-Taylor formula.

[0090] The vegetation canopy transpiration ET c Use the following formula to calculate:

[0091]

[0092] ε=Δ / γ

[0093] Where Δ is the slope of the temperature and saturated water vapor pressure curve (kPa / ℃), γ is the humidity calculation constant (kPa / ℃), ε is the ratio of the slope of the temperature and saturated water vapor pressure curve to the humidity calculation constant, and ρ is the air density (kg / m 3 ), c p is the specific heat of air at constant pressure (J / kg·℃), D a is the saturated vapor pressure deficit (kPa), G a is the atmospheric carbon dioxide concentration (μmol mol -1 ). c,r To consider the influence of water stress on vegetation canopy conductance (m / s), the following formula is used for parameterization:

[0094] G c,r =S m ·G c

[0095]

[0096] In the formula, G c is the vegetation canopy conductance (m / s) when water supply is sufficient, S mis the vegetation canopy water stress index, and its parameterization is shown in step 7; m is the stomatal conductance coefficient, k Q is the extinction coefficient of photosynthetically active radiation, LAI is the leaf area index, D0 is the sensitivity parameter of stomatal conductance to leaf saturated water vapor pressure difference (kPa), D is the saturated water vapor pressure difference, and UD is the unit conversion factor, which can realize the conversion of vegetation canopy conductance from μmol m -2 s -1 Convert to ms -1 p1, p2, p3, and p4 are merged variables, which are calculated as follows:

[0097] p1=A m βI o η

[0098] p2=A m βI o

[0099] p3=A m ηC a

[0100] p4=βI o ηC a

[0101] Among them, A m is the maximum photosynthetic rate when the photosynthetically active radiation flux density is saturated (μmol m -2 s -1 ), β is the initial quantum efficiency (μmol m -2 s -1 (μmol m -2 s -1 ) -1 ), Io is the canopy photosynthetically active radiation (μmol m -2 s -1 ), η is the initial rate of carbon dioxide response to photosynthesis (μmol m -2 s -1 ), G a is the atmospheric carbon dioxide concentration (μmol mol -1 ). m Approximately equal to the maximum catalytic rate of Rubisco per unit area (V m ), V m The parameterization is as follows:

[0102]

[0103] Where T is the air temperature (°C), V m,25 V when the air temperature is 25 degrees m The value of .

[0104] Step 7, parameterization of surface conductance combined with shortwave infrared moisture index:

[0105] Compared with visible light and near infrared bands, shortwave infrared is more sensitive to changes in moisture and can directly respond to changes in the moisture characteristics of the underlying surface. The above step 4 obtains the moisture index NDSMI based on shortwave infrared. NDSMI is sensitive to changes in the moisture information of the underlying surface. This step further uses the key feature of NDSMI that is sensitive to changes in the moisture characteristics of the underlying surface. Based on the moisture index of the shortwave infrared information calculated in step 4, the soil moisture stress index WSI and the vegetation canopy moisture stress index S m By parameterizing the model, an evapotranspiration estimation model suitable for arid and semi-arid areas can be constructed. The evapotranspiration model can calculate and solve the surface evapotranspiration components such as vegetation transpiration and soil evaporation.

[0106] Realize WSI, S m The specific parameterization formula is:

[0107]

[0108] Where, NDSMI min and NDSMI max are the maximum and minimum values ​​of NDSMI, K sf It is a characteristic parameter that expresses the water absorption capacity of different vegetation types.

[0109] Step 8: Calibration of evapotranspiration model parameters:

[0110] Combined with the observations of the ground flux station, the trust region reflective optimization algorithm in the Python Scipy package is used to optimize the D0, β, η, m, V in the evapotranspiration estimation model constructed in steps 1-7. m25 , k Q and k a These 7 key parameters are calibrated.

[0111] Step 9, verification and evaluation of regional evapotranspiration:

[0112] After the evapotranspiration model parameters are calibrated, the calculation of regional evapotranspiration and its components can be realized by combining the remote sensing underlying surface biophysical parameters and shortwave infrared moisture index time series interpolation continuous data in step 5. The model estimation results are verified and evaluated by combining the regional surface evapotranspiration observation data of the study area and the evapotranspiration results calculated by the water balance method.

[0113] Example verification

[0114] This paper collects MODIS product data and EAR5-LAND reanalysis data from across the country, estimates evapotranspiration data on a national scale, and evaluates the model using evapotranspiration data from water balance calculations in nine major river basins across the country. The results show that (see Figure 2 ), the evapotranspiration model proposed in the present invention better depicts the spatial pattern of evapotranspiration across the country. The use of water balance evapotranspiration evaluation shows that the evapotranspiration product data estimated by the present invention has good estimation accuracy, R2 is 0.81, and RMSE is 80.65 mm / year. Compared with similar products, the evapotranspiration model improved based on short-wave infrared information can more realistically depict the evapotranspiration pattern in arid and semi-arid areas. The evapotranspiration model proposed in the present invention can provide new ideas for remote sensing estimation of evapotranspiration in arid areas, and the estimated evapotranspiration products can provide a basis for revealing the spatiotemporal distribution and changes of land evapotranspiration in China.

[0115] Finally, it should be noted that the above is only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred arrangement scheme, a person skilled in the art should understand that the technical solution of the present invention (such as the use of various formulas, the sequence of steps, etc.) can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for parameterizing surface conductance and regional evapotranspiration model based on shortwave infrared moisture index, characterized in that: The method comprises the following steps: Step 1: Collection and spatialization of meteorological radiation data in the study area: First, collect daily meteorological radiation data in the study area, including daily temperature, wind speed, atmospheric pressure, precipitation, air humidity and solar radiation; Then, the digital elevation model of the study area is used to perform spatial interpolation on the meteorological radiation data collected above, thereby realizing the spatialization of meteorological radiation data; Step 2: Acquisition of satellite remote sensing data: Obtain multi-source satellite remote sensing data of the study area, including Landsat8, Sentinel-2, Terra / Aqua MODIS, and Sentinel-3; Step 3: Inversion of remote sensing underlying surface biophysical parameters: Obtain remote sensing underlying surface biophysical parameters including normalized difference vegetation index, leaf area index, surface albedo, daily 500-meter resolution snow area data, underlying surface land cover data, and vegetation height; Step 4, moisture index calculation based on shortwave infrared information: The moisture index based on shortwave infrared information is constructed, and the calculation formula is as follows: Where NDSMI is the moisture index, SWIR5 and SWIR7 are the reflectances of the 5th and 7th bands of MODIS, respectively; Step 5: Time series interpolation of remote sensing underlying surface biophysical parameters and shortwave infrared moisture index: Perform time series interpolation processing on the remote sensing underlying surface biophysical parameters obtained in step 3 and the moisture index of the shortwave infrared information calculated in step 4, so as to obtain the remote sensing underlying surface biophysical parameters and the moisture index of the shortwave infrared information that are continuous in time and space; Step 6: Construction of regional evapotranspiration estimation model: Considering the difference between evapotranspiration of ice-covered and snow-covered surfaces and soil-covered surfaces, the following formula is used to calculate surface evapotranspiration: Where ET is the surface evapotranspiration, ET snow_ice is the evapotranspiration from the underlying surface covered by ice and snow, ET i is the interception and evaporation of precipitation, ET s is soil evaporation, ET c for transpiration from the vegetation canopy; Step 7, parameterization of surface conductance combined with shortwave infrared moisture index: Based on the moisture index of the shortwave infrared information calculated in step 4, the soil moisture stress index WSI and the vegetation canopy moisture stress index S m Parameterization is performed to construct an evapotranspiration estimation model suitable for arid and semi-arid regions; Realize WSI, S m The specific parameterization formula is: Where, NDSMI min and NDSMI max are the maximum and minimum values ​​of NDSMI, K sf It is a characteristic parameter that expresses the water absorption capacity of different vegetation types.

2. The method according to claim 1, characterized in that The method further comprises the following steps: Step 8: Calibration of evapotranspiration model parameters: Combined with the observations of the ground flux station, the trust region reflectance optimization algorithm is used to calibrate the key parameters of the evapotranspiration estimation model constructed in steps 1-7; Step 9, verification and evaluation of regional evapotranspiration: The model estimation results are verified and evaluated by combining the regional surface evapotranspiration observation data of the study area and the evapotranspiration results calculated by the water balance method.

3. The method according to claim 1, characterized in that: In step 1, for study areas with sparse stations or lack of station observation data, reanalysis data are combined to obtain regional-scale data including air temperature, humidity, and solar radiation for use in the regional driven evapotranspiration model.

4. The method according to claim 1, characterized in that In step 3, the vegetation height is parameterized using the leaf area index and the global forest height dataset released by NASA JPL, and the calculation formula is: Where LAI is the leaf area index, and CPH is the global forest height dataset released by NASA JPL.

5. The method according to claim 1, characterized in that In step 5, the nearest neighbor linear interpolation method is used to interpolate the surface albedo data to obtain spatiotemporally continuous surface albedo data; the leaf area index, normalized vegetation index and moisture index of shortwave infrared information are interpolated using the time series reconstruction method of joint neighbor interpolation and Savitzky-Golay filtering.

6. The method according to claim 1, characterized in that In step 6, the evapotranspiration ET of the ice-snow covered underlying surface snow_ice , precipitation interception evaporation ET i , soil evaporation ET s , and vegetation canopy transpiration ET c The specific method for calculating is: The evapotranspiration ET of the underlying surface covered by ice and snow snow_ice Use the following formula to calculate: ET snow_ice =(0.18+0.098u 10 )VPD hpa In the formula, u 10 For 10m wind speed, VPD hpa It is the atmospheric saturation water vapor deficit; The precipitation interception evaporation ET i Parameterize using leaf area index and precipitation data: I e =λET i Where LAI is the leaf area index, P is the precipitation, and f veg is the vegetation coverage, I e is the energy consumed by precipitation interception and evaporation, and λ is the latent heat of vaporization of water; The energy partitioning of vegetation and soil components is calculated by using Beer's law after deducting the energy intercepted by vegetation canopy precipitation and evaporation: R nsi =[(1-α0)R s -L net -I e ]exp(-k a LAI) R nci =[(1-α0)R s -L net -I e ][1-exp(-k a LAI)] In the formula, R nsi and R nci are the net radiation of soil and vegetation components, L net is the net longwave radiation, α0 is the surface albedo, k a is the extinction coefficient of the vegetation canopy, R s It is the downlink shortwave radiation; The soil evaporation ET s Use the following formula to calculate: ET s =WSI·EP s Where WSI is the soil water stress index, EP is s is the soil potential evapotranspiration; The vegetation canopy transpiration ET c Use the following formula to calculate: ε=Δ / γ Where Δ is the slope of the temperature and saturated water vapor pressure curve, γ is the humidity calculation constant, ε is the ratio of the slope of the temperature and saturated water vapor pressure curve to the humidity calculation constant, ρ is the air density, c p is the specific heat of air at constant pressure, D a is the saturated vapor pressure deficit, G a is the atmospheric carbon dioxide concentration, G c,r In order to consider the influence of water stress on vegetation canopy conductance, the following formula is used for parameterization: G c,r =S m ·G c In the formula, G c is the vegetation canopy conductance under sufficient water supply, S m is the vegetation canopy water stress index, m is the stomatal conductance coefficient, k is Q is the extinction coefficient of photosynthetically active radiation, LAI is the leaf area index, D0 is the sensitivity parameter of stomatal conductance to the saturated water vapor pressure difference on the leaf surface, D is the saturated water vapor pressure difference, UC is the unit conversion factor; p1, p2, p3, and p4 are combined variables, which are calculated as follows: p1=A m βI o or p2=A m βI o p3=A m hC a p4=βI o hC a In the formula, A m is the maximum photosynthetic rate when the photosynthetically active radiation flux density is saturated, β is the initial quantum efficiency, I o is the canopy photosynthetically active radiation, η is the initial rate of carbon dioxide response to photosynthesis, G a is the atmospheric carbon dioxide concentration, A m Approximately equal to the maximum catalytic rate of Rubisco per unit area of ​​leaves V m Half of V m The parameterization is as follows: Where T is the air temperature (°C), V m,25 V when the air temperature is 25 degrees m The value of .

Images