Historical evapotranspiration monitoring method

Abstract

The invention discloses a historical evapotranspiration monitoring method, comprising: acquiring first historical data and second historical data within a preset historical time period, the first historical data being a normalized vegetation index, a surface temperature, an actual evapotranspiration and a global 30-arc-second terrain; the second historical data comprises a normalized vegetation index, a surface temperature and a digital terrain model; the spatial resolution of the first historical data is smaller than that of the second historical data; determining a local regression coefficient and a local regression residual error of the multi-scale geographically weighted regression model under the second spatial resolution through dependent variables and independent variables obtained from the first historical data; performing downscaling operation through the local regression coefficient, the local regression residual error and second historical data to obtain a target actual evapotranspiration under a second spatial resolution; and calculating the slope and trend statistic of the target actual evapotranspiration to monitor the change trend of the target actual evapotranspiration in the preset historical time period. According to the method, high-precision actual evapotranspiration data are efficiently obtained.

Description

Technical Field

[0001] The invention relates to the field of hydrology, and relates to, but is not limited to, a method for monitoring historical evapotranspiration. Background Art

[0002] Actual evapotranspiration (ETa), as an important part of the Earth's water cycle, plays a key role in redistributing surface energy, connecting various surface systems, and has a significant impact on changes in soil, ecosystems, agriculture, and natural disasters. Against the backdrop of intensified climate change and increased human activities, ETa has shown a clear trend, which not only reflects the changes in geographical elements, but also promotes the transformation of the surface system.

[0003] In related technologies, global ETa is measured through observation instruments, but this method involves estimation problems that rely on auxiliary data sets, or requires a large number of field surveys to obtain high-precision ETa data, which has the problems of low efficiency and lack of spatial continuity.

[0004] Therefore, how to efficiently obtain high-precision actual evapotranspiration data has become an urgent problem to be solved. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method for monitoring historical evapotranspiration, which at least solves the problem that the acquisition accuracy of actual evapotranspiration data in the related art is low and the acquisition process is highly complex.

[0006] According to a first aspect of an embodiment of the present invention, a method for monitoring historical evapotranspiration is provided, comprising:

[0007] Acquire first historical data and second historical data within a preset historical time period, wherein the first historical data includes a first normalized vegetation index, a first surface temperature, actual evapotranspiration, and a global 30 arc second terrain; the second historical data includes a second normalized vegetation index, a second surface temperature, and a digital terrain model; a first spatial resolution corresponding to the first historical data is smaller than a second spatial resolution corresponding to the second historical data;

[0008] Determining a dependent variable and an independent variable of a multi-scale geographically weighted regression model in the first historical data, and determining a local regression coefficient and a local regression residual of the multi-scale geographically weighted regression model at the second spatial resolution based on the dependent variable and the independent variable;

[0009] Performing a downscaling operation on the local regression coefficient, the local regression residual and the second historical data to obtain a target actual evapotranspiration at the second spatial resolution;

[0010] The slope and the trend statistic between the target actual evapotranspiration are calculated respectively, and the change trend between the target actual evapotranspiration within the preset historical time period is monitored through the slope and the trend statistic.

[0011] According to a second aspect of an embodiment of the present invention, there is provided an electronic device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; the memory is used to store at least one executable instruction, wherein the executable instruction enables the processor to perform an operation corresponding to the method described in the first aspect.

[0012] According to a third aspect of an embodiment of the present invention, there is provided a computer storage medium on which a computer program is stored. When the program is executed by a processor, the method described in the first aspect is implemented.

[0013] The solution provided by the embodiment of the present invention is to obtain the first historical data and the second historical data within a preset historical time period, wherein the first historical data includes the first normalized vegetation index, the first surface temperature, the actual evapotranspiration, and the global 30 arc second terrain; the second historical data includes the second normalized vegetation index, the second surface temperature, and the digital terrain model; the first spatial resolution corresponding to the first historical data is less than the second spatial resolution corresponding to the second historical data; the dependent variable and the independent variable of the multi-scale geographical weighted regression model are determined in the first historical data, and the local regression coefficient and the local regression residual of the multi-scale geographical weighted regression model at the second spatial resolution are determined based on the dependent variable and the independent variable; the process uses the multi-scale geographical weighted regression model, takes the first normalized vegetation index, the first surface temperature, the actual evapotranspiration, and the global 30 arc second terrain as independent variables, and performs regression analysis on the actual evapotranspiration at the first spatial resolution to obtain the local regression coefficient and the local regression residual. The actual evapotranspiration at the first spatial resolution is downscaled to the second spatial resolution through the local regression coefficient, the local regression residual, and the second historical data, and the target actual evapotranspiration is obtained, thereby improving the resolution accuracy of the data. By calculating the slope and trend statistics between the target and actual evapotranspiration, and monitoring the changing trend between the target and actual evapotranspiration within a preset historical time period through the slope and trend statistics, the slope and trend statistics are calculated using higher-resolution target actual evapotranspiration, and the changing trend is monitored based on the calculation results, thereby improving the monitoring accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0015] Figure 1 A schematic diagram of a method for monitoring historical evapotranspiration provided by an embodiment of the present invention Figure 1 ;

[0016] Figure 2 A schematic diagram of a method for monitoring historical evapotranspiration provided by an embodiment of the present invention Figure 2 ;

[0017] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0019] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0020] It should be pointed out that the terms "first\second\third" involved in the embodiments of the present invention are only used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present invention described here can be implemented in an order other than that illustrated or described here.

[0021] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the field to which the embodiments of the present invention belong. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0022] Figure 1 A schematic diagram of a method for monitoring historical evapotranspiration provided by an embodiment of the present invention Figure 1 The historical evapotranspiration monitoring method provided in the embodiment of the present invention may be executed by an electronic device, such as a computer, a server, etc.

[0023] like Figure 1 As shown in the figure, the monitoring method of historical evapotranspiration includes:

[0024] S101. Acquire first historical data and second historical data within a preset historical time period, wherein the first historical data includes a first normalized vegetation index, a first surface temperature, actual evapotranspiration, and a global 30 arc second topography; the second historical data includes a second normalized vegetation index, a second surface temperature, and a digital terrain model; a first spatial resolution corresponding to the first historical data is smaller than a second spatial resolution corresponding to the second historical data.

[0025] In an embodiment of the present invention, the preset historical time period may be the period from 1990 to 2020, and the first historical data may be data from three months in the summer of 2020, including the first normalized vegetation index, the first surface temperature, the actual evapotranspiration, and the global 30 arc second terrain. The second historical data may be data from seven periods from 1990 to 2020, that is, the second historical data is seven groups of data, with a period every five years, that is, 1990 is the first period, 1995 is the second period, and so on, and the data of each period is the average value of three months in the summer of three consecutive years, that is, the data corresponding to the first period in 1990 is obtained by: respectively obtaining data from June, July, and August in three months of 1989, data from June, July, and August in three months of 1990, and data from June, July, and August in three months of 1991, and averaging the data of these nine months to obtain the data corresponding to 1990. The second historical data is historical data from seven periods, and the historical data of each period includes the second normalized vegetation index, the second surface temperature, and the digital terrain model. In addition, the first historical data corresponds to a first spatial resolution, such as the first spatial resolution of the first historical data is 1 km, and the second historical data corresponds to a second spatial resolution, such as the second spatial resolution of the second historical data is 30 m, and the first resolution is smaller than the second resolution.

[0026] In an embodiment of the present invention, a large-scale space remote sensing instrument (Moderate Resolution Imaging Spectroradiometer, MODIS) is mainly used for earth observation, the first normalized vegetation index and the first surface temperature are both derived from MODIS, the Global Information and Early Warning System (on Food and Agriculture, GIEWS) is mainly used to collect, analyze and disseminate information on global food production and demand, and the actual evapotranspiration is derived from the Global Famine Early Warning Network. Landsat is a series of earth observation satellite systems used to detect earth resources and environment, the second normalized vegetation index and the second surface temperature are derived from Landsat Earth Resources Technology Satellites (Landsat), and the digital terrain model (Digital Elevation Model, DEM) is derived from the Shuttle Radar Topography Mission (STRM) data set.

[0027] S102. Determine the dependent variable and the independent variable of the multi-scale geographically weighted regression model in the first historical data, and determine the local regression coefficient and the local regression residual of the multi-scale geographically weighted regression model at the second spatial resolution based on the dependent variable and the independent variable.

[0028] In an embodiment of the present invention, the actual evapotranspiration in the first historical data can be used as the dependent variable of the multi-scale geographically weighted regression model, the first normalized vegetation index, the first surface temperature and the global 30 arc second topography in the first historical data can be used as the independent variables of the multi-scale geographically weighted regression model, and the local regression coefficient and the local regression residual of the multi-scale geographically weighted regression model at the second spatial resolution are determined based on the dependent variable and the independent variable.

[0029] S103, performing a downscaling operation on the local regression coefficient, the local regression residual and the second historical data to obtain a target actual evapotranspiration at a second spatial resolution.

[0030] In an embodiment of the present invention, evapotranspiration refers to the process in which surface water changes from liquid to gas and enters the atmosphere, which is quantified by the amount of water vapor released into the atmosphere within a specific period of time, also known as evapotranspiration. Evapotranspiration can be divided into two categories: potential evapotranspiration and target actual evapotranspiration, and the target actual evapotranspiration includes the total evapotranspiration process occurring under natural environmental conditions, including evaporation of surface water, consumption of soil moisture, and transpiration of plants. The target actual evapotranspiration at the second spatial resolution is obtained by using the local regression coefficient, the local regression residual, and the second historical data at the second spatial resolution, so as to achieve the purpose of downscaling the actual evapotranspiration at the first spatial resolution to the second spatial resolution, and obtain the target actual evapotranspiration at the second spatial resolution.

[0031] S104, respectively calculating the slope and trend statistics between the target actual evapotranspiration, and monitoring the change trend between the target actual evapotranspiration in a preset historical time period through the slope and trend statistics.

[0032] In an embodiment of the present invention, the slope can be used to represent the linear relationship between the target actual evapotranspiration. By calculating the slope between the target actual evapotranspiration, the change trend between them can be understood. The trend statistic can be used to measure the monotonic trend (increase or decrease) in the target actual evapotranspiration. The Sen slope estimation method (Theil-Sen Estimator) is a robust non-parametric statistical method for estimating the trend slope of a set of data, which is suitable for time series data analysis. For the target actual evapotranspiration in multiple historical time periods, the Sen slope method can be used to evaluate the trend between the data, and the differences between these trends can be further compared. The Mann-Kendall test is a non-parametric statistical method for detecting whether there is a monotonic trend (increase or decrease) in time series data, and the trend statistic of the target actual evapotranspiration can be calculated by the Mann-Kendall test method. After calculating the slope between the target actual evapotranspiration by the Sen slope estimation method and the trend statistic between the target actual evapotranspiration by the Mann-Kendall test method, the change trend between the target actual evapotranspiration in the preset historical time period is monitored by the slope and the trend statistic, wherein the change trend can be decreasing or increasing year by year or other situations.

[0033] It can be understood that in the embodiment of the present invention, the first historical data and the second historical data are obtained within a preset historical time period, the first historical data includes the first normalized vegetation index, the first surface temperature, the actual evapotranspiration, and the global 30 arc second terrain; the second historical data includes the second normalized vegetation index, the second surface temperature, and the digital terrain model; the first spatial resolution corresponding to the first historical data is less than the second spatial resolution corresponding to the second historical data; the dependent variable and the independent variable of the multi-scale geographical weighted regression model are determined in the first historical data, and the local regression coefficient and the local regression residual of the multi-scale geographical weighted regression model at the second spatial resolution are determined based on the dependent variable and the independent variable; the process uses the multi-scale geographical weighted regression model, takes the first normalized vegetation index, the first surface temperature, the actual evapotranspiration, and the global 30 arc second terrain as independent variables, and performs regression analysis on the actual evapotranspiration at the first spatial resolution to obtain the local regression coefficient and the local regression residual. The actual evapotranspiration at the first spatial resolution is downscaled to the second spatial resolution through the local regression coefficient, the local regression residual and the second historical data, and the target actual evapotranspiration is obtained, thereby improving the resolution accuracy of the data. By calculating the slope and trend statistics between the target and actual evapotranspiration, and monitoring the changing trend between the target and actual evapotranspiration within a preset historical time period through the slope and trend statistics, the slope and trend statistics are calculated using higher-resolution target actual evapotranspiration, and the changing trend is monitored based on the calculation results, thereby improving the monitoring accuracy.

[0034] In some embodiments of the present invention, determining the local regression coefficients and local regression residuals of the multi-scale geographically weighted regression model at the second spatial resolution based on the dependent variable and the independent variable in S102 can be implemented through S1021 to S1022, which is specifically explained through the following steps.

[0035] S1021. Determine the initial local regression coefficient and the initial local regression residual at the first spatial resolution based on the dependent variable, the independent variable and the model calculation formula corresponding to the multi-scale geographically weighted regression model.

[0036] In some embodiments of the present invention, the calculation formula of the multi-scale geographically weighted regression model is:

[0037]

[0038] In the above formula (1), y i is the dependent variable, i.e., actual evapotranspiration, X ij is the independent variable, namely the first normalized vegetation index, the first surface temperature, and the global 30 arc second topography, i is a data point, each of which contains the second historical data, j is the number of data categories in the independent variable, Indicates bandwidth is b wis the jth initial local regression coefficient, w is the model weight, ε is the initial local regression residual, and (ui,vi) represents the spatial geographic location of the data point.

[0039] S1022. Interpolate the initial local regression coefficient and the initial local regression residual to the second spatial resolution using ordinary Kriging interpolation method to obtain the local regression coefficient and the local regression residual at the second spatial resolution.

[0040] In some embodiments of the present invention, ordinary Kriging interpolation method can be used to predict the value of a new point on a higher resolution grid based on the spatial correlation of the initial local regression coefficient and the initial local regression residual, that is, the initial local regression coefficient and the initial local regression residual are interpolated to the second spatial resolution to obtain the local regression coefficient and the local regression residual at the second spatial resolution.

[0041] Exemplarily, the first spatial resolution corresponding to the initial local regression coefficient and the initial local regression residual is 1 km, and the initial local regression coefficient and the initial local regression residual are interpolated to 30 m (second spatial resolution) using ordinary Kriging interpolation to obtain local regression coefficients and local regression residuals of 30 m.

[0042] It can be understood that in some embodiments of the present invention, the initial local regression coefficients and initial local regression residuals of the multi-scale geographically weighted regression model at the first spatial resolution are determined based on the dependent variable and the independent variable, and the initial local regression coefficients and initial local regression residuals are interpolated to the second spatial resolution using the ordinary kriging interpolation method to obtain the local regression coefficients and local regression residuals at the second spatial resolution. This process uses ordinary kriging interpolation to interpolate the initial local regression coefficients and initial local regression residuals at the first spatial resolution to the local regression coefficients and local regression residuals at the second spatial resolution. It can provide more accurate and reliable estimates for unknown points or sparse points of the local regression coefficients and local regression residuals based on the spatial distribution and correlation information of the initial local regression coefficients and initial local regression residuals, and at the same time, taking into account the spatial autocorrelation of the data, so that the interpolation results are more in line with the actual situation, providing more accurate information support for data analysis and decision-making.

[0043] In some embodiments of the present invention, S103 may be implemented through S1031, which is explained by the following steps.

[0044] S1031. Downscaling the second historical data using the local regression coefficient and the local regression residual to obtain target actual evapotranspiration corresponding to each of the multiple historical time points.

[0045] In some embodiments of the present invention, the preset historical time period includes multiple historical time points, each of which includes a set of historical data, which is named second historical data. The second historical data includes data such as a second normalized vegetation index, a second surface temperature, and a digital terrain model. The second historical data of each period is downscaled using a local regression coefficient and a local regression residual to obtain target actual evapotranspiration corresponding to each of the multiple historical time points, and the target actual evapotranspiration may be multiple.

[0046] For example, the second historical data in 1990 are data such as the second normalized vegetation index, the second surface temperature, and the digital terrain model, and the second historical data in 1995 are data such as the second normalized vegetation index, the second surface temperature, and the digital terrain model. The second historical data in 1990 are downscaled using the local regression coefficient and the local regression residual to obtain the target actual evapotranspiration corresponding to 1990, and the second historical data in 1995 are downscaled using the local regression coefficient and the local regression residual to obtain the target actual evapotranspiration corresponding to 1995.

[0047] It can be understood that in some embodiments of the present invention, the local regression coefficient and the local regression residual are used to perform a downscaling operation on the second historical data to obtain the target actual evapotranspiration corresponding to each of the multiple historical time points. This process uses the local regression coefficient and the local regression residual to perform a downscaling operation on the second historical data, downscaling the actual evapotranspiration at the first spatial resolution to the target actual evapotranspiration at the second spatial resolution, thereby improving the resolution of the data.

[0048] In some embodiments of the present invention, S104 may be implemented through S1041 to S1042, which is described by the following steps.

[0049] S1041. Calculate a slope based on any two target actual evapotranspirations in the target actual evapotranspirations and any two historical time points corresponding to the target actual evapotranspirations.

[0050] In some embodiments of the present invention, the slope calculation formula is:

[0051]

[0052] In the above formula (2), z and k are historical time points, X z and X k is the target actual evapotranspiration corresponding to the historical time point, Q i is the i-th slope.

[0053] S1042. Calculate the difference between any two target actual evapotranspirations, and sum the differences to obtain a trend statistic.

[0054] In some embodiments of the present invention, the trend statistic calculation formula is:

[0055]

[0056] In the above formulas (3) and (4), S is the trend statistic, k and z are historical time points, and X k and X z The target actual evapotranspiration corresponding to the historical time point, and n is the number of target actual evapotranspiration.

[0057] It can be understood that in some embodiments of the present invention, the slope is calculated based on any two target actual evapotranspirations in the target actual evapotranspiration and the historical time points corresponding to any two target actual evapotranspirations; the difference between any two target actual evapotranspirations is calculated, and the difference is summed to obtain the trend statistic. The slope and the trend statistic are calculated based on the target actual evapotranspiration at the second spatial resolution, which improves the accuracy of the slope and trend statistic calculations.

[0058] In an embodiment of the present invention, based on all the above steps, Figure 2 As shown, Figure 2 A schematic diagram of a historical evapotranspiration monitoring method provided by an embodiment of the present invention Figure 2 .exist Figure 2 It is divided into three modules: STEP 1 is data acquisition, and the data acquisition years are between 1990 and 2020, specifically:

[0059] MODIS NDVI (1km): 1km first normalized difference vegetation index derived from MODIS;

[0060] MODIS LST(1km): the first surface temperature at 1km from MODIS;

[0061] GTOPO30 DEM (1km): 1km of global 30 arc-second topography;

[0062] FEWS NET ETA(1km): actual evapotranspiration at 1km;

[0063] Specifically, FEWS NET ETA is an important drought monitoring and early warning data product. It uses evapotranspiration data obtained by satellite remote sensing technology to provide strong support for drought monitoring, early warning and scientific research.

[0064] LANDSAT NDVI (30m): 30m is derived from the second normalized vegetation index of Landsat;

[0065] LANDSAT LST (30m): 30m is the second land surface temperature from Landsat;

[0066] STRM DEM (30m): 30m digital terrain model derived from the STRM dataset of space radar topography.

[0067] STEP2 is regression analysis and interpolation, which mainly inputs the first normalized vegetation index from MODIS at 1km, the first surface temperature at 1km, the global 30 arc second terrain at 1km, and the actual evapotranspiration at 1km into the multi-scale geographically weighted regression model to obtain the 1km regression coefficient and 1km residual, and combines the 1km regression coefficient and 1km residual with the ordinary kriging interpolation method to obtain the 30m regression coefficient and 30m residual.

[0068] STEP3 is downscaling and trend analysis, which mainly downscales the 30m regression coefficient and 30m residual to the 30m second normalized vegetation index, 30m second surface temperature and 30m digital terrain model one by one to obtain the target actual evapotranspiration (ETa) of 30m. The target actual evapotranspiration of 30m is specifically the downscaling result of the corresponding actual evapotranspiration of the seven periods between 1990 and 2020. Finally, the change trend of the actual evapotranspiration in 30 years is further judged according to the target actual evapotranspiration.

[0069] Reference Figure 3 , shows a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. The specific embodiment of the present invention does not limit the specific implementation of the electronic device.

[0070] like Figure 3 As shown, the electronic device may include: a processor (processor) 502, a communication interface (Communications Interface 504, a memory (memory) 506, and a communication bus 508.

[0071] in:

[0072] The processor 502 , the communication interface 504 , and the memory 506 communicate with each other via a communication bus 508 .

[0073] The communication interface 504 is used to communicate with other electronic devices or servers.

[0074] The processor 502 is used to execute the program 510, and specifically can execute the relevant steps in the above method embodiment.

[0075] Specifically, the program 510 may include program codes, which include computer operation instructions.

[0076] The processor 502 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0077] The memory 506 is used to store the program 510. The memory 506 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0078] The program 510 may be specifically used to enable the processor 502 to execute operations corresponding to the methods described in the above method embodiments.

[0079] The specific implementation of each step in program 510 can refer to the corresponding description of the corresponding steps and units in the above method embodiment, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the above method embodiment, which will not be repeated here.

[0080] It should be pointed out that, according to the needs of implementation, the various components / steps described in the embodiments of the present invention can be split into more components / steps, or two or more components / steps or partial operations of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present invention.

[0081] The above-described method according to an embodiment of the present invention may be implemented in hardware, firmware, or as software or computer code that may be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein may be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, processor, or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0082] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present invention.

[0083] The above implementation methods are only used to illustrate the embodiments of the present invention, and are not limitations of the embodiments of the present invention. Ordinary technicians in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the embodiments of the present invention. Therefore, all equivalent technical solutions also belong to the scope of the embodiments of the present invention. The patent protection scope of the embodiments of the present invention should be defined by the claims.

Claims

1. A method for monitoring historical evapotranspiration, characterized in that: include: Acquire first historical data and second historical data within a preset historical time period, wherein the first historical data includes a first normalized vegetation index, a first surface temperature, actual evapotranspiration, and global 30 arc second topography; The second historical data includes a second normalized vegetation index, a second surface temperature, and a digital terrain model; a first spatial resolution corresponding to the first historical data is smaller than a second spatial resolution corresponding to the second historical data; Determining a dependent variable and an independent variable of a multi-scale geographically weighted regression model in the first historical data, and determining a local regression coefficient and a local regression residual of the multi-scale geographically weighted regression model at the second spatial resolution based on the dependent variable and the independent variable; Performing a downscaling operation on the local regression coefficient, the local regression residual and the second historical data to obtain a target actual evapotranspiration at the second spatial resolution; The slope and the trend statistic between the target actual evapotranspiration are calculated respectively, and the change trend between the target actual evapotranspiration within the preset historical time period is monitored through the slope and the trend statistic.

2. The method according to claim 1, characterized in that: Determining the dependent variable and the independent variable of the multi-scale geographically weighted regression model in the first historical data includes: The actual evapotranspiration is used as the dependent variable of the multi-scale geographically weighted regression model, and the first normalized vegetation index, the first surface temperature and the global 30 arc second topography are used as the independent variables of the multi-scale geographically weighted regression model.

3. The method according to claim 1, characterized in that The determining, based on the dependent variable and the independent variable, the local regression coefficient and the local regression residual of the multi-scale geographically weighted regression model at the second spatial resolution comprises: Determine an initial local regression coefficient and an initial local regression residual at the first spatial resolution based on the dependent variable, the independent variable, and a model calculation formula corresponding to the multi-scale geographically weighted regression model; The initial local regression coefficient and the initial local regression residual are interpolated to the second spatial resolution by using ordinary Kriging interpolation method to obtain the local regression coefficient and the local regression residual at the second spatial resolution.

4. The method according to claim 1, characterized in that: The preset historical time period includes multiple historical time points, and the second historical data is the historical data contained in each of the multiple historical time points; The downscaling operation is performed on the local regression coefficient, the local regression residual and the second historical data to obtain the target actual evapotranspiration at the second spatial resolution, including: The second historical data is downscaled using the local regression coefficient and the local regression residual to obtain the target actual evapotranspiration corresponding to each of the multiple historical time points.

5. The method according to claim 1, characterized in that The respectively calculating the slope and trend statistics between the target and actual evapotranspiration comprises: Calculating the slope based on any two target actual evapotranspirations among the target actual evapotranspirations and historical time points corresponding to the any two target actual evapotranspirations; The difference between the two arbitrary target actual evapotranspirations is calculated, and the difference is summed to obtain the trend statistic.

6. The method according to any one of claims 1 to 5, characterized in that: The first normalized difference vegetation index and the first surface temperature are derived from the imaging spectrometer MODIS; the actual evapotranspiration is derived from the Global Famine Warning Network; the second normalized difference vegetation index and the second surface temperature are derived from the Landsat satellite; and the digital terrain model is derived from the space radar terrain mapping STRM data set.

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