Plateau soil moisture remote sensing monitoring method and system

Abstract

The present invention discloses a remote sensing monitoring method and system for plateau soil moisture, and relates to the technical field of satellite remote sensing monitoring of plateau soil moisture. Step 1: collect remote sensing satellite image data of a plateau target area; Step 2: grid the remote sensing satellite image based on color, and generate soil moisture monitoring point data based on the nodes of the grid outline; Step 3: measure the soil moisture data of the target area based on the geographic coordinate target corresponding to the soil moisture monitoring point data; Step 4: perform soil data processing based on the vegetation water supply index method based on the remote sensing satellite image data and the soil moisture measurement data of the target area. The present invention is applied to the remote sensing business monitoring field of soil moisture in Tibet, and combines the MODIS satellite remote sensing data of the same period to establish a soil moisture inversion method and monitoring model suitable for the plateau of the Tibet Autonomous Region, further improve the remote sensing monitoring accuracy of soil moisture in the Tibet Autonomous Region, and serve the remote sensing monitoring business of plateau agriculture and animal husbandry drought, vegetation growth, etc.

Description

Technical Field

[0001] The invention relates to the technical field of satellite remote sensing monitoring of plateau soil moisture, and in particular to a method and system for remote sensing monitoring of plateau soil moisture. Background Art

[0002] Soil moisture is the link between atmospheric precipitation, water in plants, surface water and groundwater, and is one of the key parameters in the fields of hydrology, agriculture, climate, ecology, etc. Large-area soil moisture monitoring is an important part of agricultural water management and crop drought forecasting. At the same time, soil moisture information from regional to global scales is an indispensable parameter for land surface process models, and plays an important role in improving the prediction results of regional and global climate models. Conventional ground soil moisture measurement methods mainly include drying weighing method, neutron instrument method, electromagnetic technology and TDR method. These methods can quantitatively determine the soil moisture content at the observation point. For example, the soil moisture observation station network established by my country's meteorological department is based on these methods. Its main advantage is the high accuracy of single-point measurement. The disadvantage is that the sampling points are limited and the soil characteristics are heterogeneous. Single-point observation data is difficult to represent the situation of a large area, and the manpower and material resources consumed are also large. Satellite remote sensing can quickly, over a large area and in multiple phases to obtain ground information, providing a new means of observation for soil moisture monitoring.

[0003] Since the 1970s, a large number of studies have been conducted on remote sensing soil moisture monitoring methods at home and abroad. Among them, relatively mature and widely used methods include thermal inertia method, thermal infrared method, vegetation index method, vegetation water supply index method, crop water shortage index method, etc. In the past, domestic and foreign research on remote sensing soil moisture monitoring mainly focused on NOAAAVHRR remote sensing applications. With the rapid development of satellite remote sensing technology, especially the implementation of NASA's Earth Observation System (EOS) program, EOS / MODIS data have been increasingly widely used in the field of large-area soil moisture monitoring.

[0004] The Tibet Autonomous Region has a vast plateau with complex topography, very sparsely distributed meteorological stations, and most of them do not have soil moisture observation projects, which brings many difficulties to the use of ground observation methods to understand the soil moisture and drought severity in the Tibet Autonomous Region.

[0005] Therefore, it is urgent to develop a more effective method to monitor soil moisture and drought conditions over a large area of ​​the Tibetan Plateau through remote sensing. Summary of the invention

[0006] In order to solve the above-mentioned technical problem of remote sensing monitoring of plateau soil moisture, the present invention provides a method and system for remote sensing monitoring of plateau soil moisture. The following technical solutions are adopted:

[0007] The remote sensing monitoring method of plateau soil moisture includes the following steps:

[0008] Step 1, collecting remote sensing satellite image data of the plateau target area;

[0009] Step 2, gridding the remote sensing satellite image based on color, and generating soil moisture monitoring point data based on the nodes of the grid outline;

[0010] Step 3, measuring soil moisture data of the target area based on the geographic coordinate target corresponding to the soil moisture monitoring point data;

[0011] Step 4, based on the remote sensing satellite image data and the soil moisture measurement data of the target area, soil data processing is performed based on the vegetation water supply index method;

[0012] Step 5: Based on GIS software, the processed soil moisture data of the target area is marked correspondingly to the remote sensing satellite image of the target area.

[0013] By adopting the above technical solutions, the biggest advantage of MODIS remote sensing data is that in addition to ground receiving stations around the world receiving satellite data for free according to the characteristics of different regions, NASA provides a series of atmospheric, oceanic and land products and a complete sharing service website for different user needs. Among them, MODIS land products can be downloaded from NASAMODIS Land Product Distribution Center, and its website provides MODIS / Terra global daily surface reflectance data set (MOD09GA). The data has been corrected for the effects of atmospheric gases and aerosols, and the projection method is sinusoidal projection. The data format is EOS-HDF, of which the 500m scientific data set includes MODIS band 1-7 reflectance, quality classification, observation range, number of observations and 250m scanning information; the 1000m scientific data set includes the number of observations, quality status, sensor altitude, solar altitude, geometric positioning mark and orbit pointer, etc.

[0014] Using the remote sensing satellite image data of the above-mentioned plateau target area, the remote sensing satellite image is gridded based on color, and soil moisture monitoring point data is generated based on the nodes of the grid outline, so that the soil moisture data can be collected more evenly;

[0015] GIS software is used to digitally input these basic maps, especially the large-scale land use status map of the Tibet Autonomous Region, and convert them into a projection method consistent with the remote sensing image. On this basis, a large number of surface parameter inversion methods at home and abroad are referred to, and a remote sensing inversion method for surface parameters of the plateau of the Tibet Autonomous Region is established. On this basis, the remote sensing inversion model is verified and improved using independent ground sampling soil moisture data, and finally a soil moisture remote sensing monitoring and estimation model suitable for the Tibetan Plateau is generated. Finally, the research results are applied to the field of remote sensing business monitoring of soil moisture in the Tibet Autonomous Region.

[0016] Optionally, step 2 includes the following sub-steps:

[0017] Step 21, acquiring remote sensing satellite image data, and performing radiation correction and geometric correction;

[0018] Step 22, defining a grid division rule, determining the outline and geometric center point of each grid according to the resolution and color variation range of the remote sensing satellite image data, calculating the curvature value of each point on the contour line of the outline of each grid, and determining the node based on the curvature value and geometric center point of each point on the contour line;

[0019] Step 23, using the gridding tool of the GIS software, creating a grid layer in each area, setting the grid size, grid type and grid interval, and overlaying the generated grid layer on the satellite remote sensing map;

[0020] Step 24, extracting remote sensing data within the grid, overlaying and analyzing the grid layer with the remote sensing satellite image, and extracting remote sensing data within each grid;

[0021] Step 25, data export and analysis, export the extracted grid data into CSV or Excel format;

[0022] Step 26, result visualization, import the grid data into GIS software, and use colors to represent the remote sensing data values ​​in different grids.

[0023] By adopting the above technical solution, grid division is performed on the remote sensing satellite image data that has completed radiation correction and geometric correction. The range division is mainly based on resolution and color change range. For example, color thresholds can be set according to the color change range. These thresholds will be used to distinguish different color areas. In HSV space, thresholds can be set according to different ranges of hue; in Lab space, thresholds can be set according to brightness and color difference (a and b channels), so as to achieve gridding based on color expression. The gridding result obtained in this way is not a uniform grid, but a grid formed by some similar geological blocks. The subsequent regional sampling of soil moisture can better reflect the soil moisture conditions of the actual area represented by the grid area.

[0024] Optionally, the specific method of determining the nodes in step 22 is: sorting the curvature values ​​of each point on the contour line according to size, taking several points with the largest curvature values ​​and the geometric center point to form a node set of the mesh contour.

[0025] By adopting the above technical solution, since the gridding rule is based on color changes, the larger the contour curvature value, the greater the geological difference in the relevant area. Therefore, the node set composed of several points with the largest curvature values ​​and the geometric center point to form the grid contour can better reflect the soil moisture conditions of the actual area represented by the grid area.

[0026] Optionally, the number of points with the largest curvature values ​​is determined based on the area of ​​the mesh contour, and a points with the largest curvature values ​​and the geometric center point are taken to form a node set of the mesh contour;

[0027] The formula for calculating a is:

[0028]

[0029] Among them, the grid outline area is Sn, the total area of ​​all grids is S, and B is the percentage coefficient, which is an integer from 100 to 300.

[0030] By adopting the above technical solution, the ratio of the grid contour area to the total area of ​​all grids determines the number of points with the largest curvature value. This can make the soil moisture field sampling points in the corresponding plateau area more uniform, and the soil moisture data presented in the subsequent remote sensing satellite images more uniform.

[0031] Optionally, the specific method of step 3 is: perform humidity measurement according to the soil moisture monitoring point data obtained in step 2,

[0032] Based on the calculation of NDVI and surface temperature of the plateau remote sensing satellite image data, the vegetation water supply index VSWI of the vegetation growing season in the central plateau is calculated, and the relationship between VSWI and the measured soil moisture is established, the regression model is as follows:

[0033] y=114719x2-4392.6x+59.138 R 2 =0.7118,N=16;

[0034] y=3126.5x-50.492 R 2 =0.6525,N=16;

[0035] Where x is VSWI and y is the weight moisture content of 5 cm soil.

[0036] By adopting the above technical solution, it can be seen from the formula that there is a significant linear relationship between VSWI and the measured soil moisture, which passed the significance test of P<0.001; based on the regression modeling principle with a large determination coefficient R2, the quadratic polynomial regression model is better than the linear regression model.

[0037] Optionally, in step 1, the remote sensing satellite image data is a 500m scientific dataset including MODIS band 1-7 reflectance, quality classification, observation range, number of observations, and 250m scanning information.

[0038] Optionally, in step 3, the soil moisture is measured after sampling using an ML2X soil moisture rapid tester.

[0039] By adopting the above technical solution, soil moisture is sampled using the ML2X soil moisture rapid tester. ML2X uses the FDR (Frequency Domain Reflectometry) principle to measure the volume moisture content of the 0-5 cm surface soil, with a measurement range of 0-100% and an instrument accuracy of ±5%.

[0040] A plateau soil moisture remote sensing monitoring system comprises a remote sensing satellite data acquisition module, an Internet communication module, a memory and a computer. The data input end of the remote sensing satellite data acquisition module is connected to the Internet through the Internet communication module, and remote sensing satellite image data of a plateau target area is obtained based on the Internet and stored in the memory. The computer is pre-installed with a monitoring program designed using a plateau soil moisture remote sensing monitoring method. The computer runs the monitoring program to output geographic coordinate data corresponding to soil moisture monitoring point data. A staff member measures soil moisture data of a target area according to the geographic coordinate data and enters the soil moisture data of the target area into the computer. The computer runs the monitoring program to output remote sensing satellite image data with soil moisture data of the target area.

[0041] Optionally, a touch display is also included, and the computer is communicatively connected to the touch display to control the touch display to visually display remote sensing satellite images with soil moisture data of the target area.

[0042] In summary, the present invention includes at least one of the following beneficial technical effects:

[0043] The present invention can provide a remote sensing monitoring method and system for plateau soil moisture, using the remote sensing satellite image data of the above-mentioned plateau target area, gridding the remote sensing satellite image based on color, and generating soil moisture monitoring point data based on the nodes of the grid outline, so that the soil moisture data can be collected more uniformly; these basic maps are digitally input using GIS software, and converted into a projection method consistent with the remote sensing image. A large number of surface parameter inversion methods at home and abroad are referred to, and a surface parameter remote sensing inversion method for the plateau of the Tibet Autonomous Region is established. On this basis, the remote sensing inversion model is verified and improved using independent ground sampling soil moisture data, and finally a soil moisture remote sensing monitoring and estimation model suitable for the plateau of the Tibet Autonomous Region is generated, which is applied to the remote sensing business monitoring field of soil moisture in the Tibet Autonomous Region. Combined with the MODIS satellite remote sensing data of the same period, a soil moisture inversion method and monitoring model suitable for the plateau of the Tibet Autonomous Region are established, and the accuracy of remote sensing monitoring of soil moisture in the Tibet Autonomous Region is further improved, and remote sensing monitoring services such as drought in plateau agriculture and animal husbandry and vegetation growth are provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the process of the plateau soil moisture remote sensing monitoring method of the present invention;

[0045] Figure 2 It is a schematic diagram of the communication connection principle of the plateau soil moisture remote sensing monitoring of the present invention;

[0046] Figure 3 Schematic diagram of a remote sensing satellite image of a plateau target area in a specific embodiment of the plateau soil moisture remote sensing monitoring method of the present invention;

[0047] Figure 4 It is a node schematic diagram of the grid outline in a specific embodiment of the plateau soil moisture remote sensing monitoring method of the present invention.

[0048] Explanation of the accompanying drawings: 1. Remote sensing satellite data acquisition module; 2. Internet communication module; 3. Memory; 4. Computer; 5. Touch display. DETAILED DESCRIPTION

[0049] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0050] The embodiments of the present invention disclose a plateau soil moisture remote sensing monitoring method and system.

[0051] Reference Figure 1-Figure 4 , a remote sensing monitoring method for plateau soil moisture, comprising the following steps:

[0052] Step 1, collecting remote sensing satellite image data of the plateau target area;

[0053] Step 2, gridding the remote sensing satellite image based on color, and generating soil moisture monitoring point data based on the nodes of the grid outline;

[0054] Step 3, measuring soil moisture data of the target area based on the geographic coordinate target corresponding to the soil moisture monitoring point data;

[0055] Step 4, based on the remote sensing satellite image data and the soil moisture measurement data of the target area, soil data processing is performed based on the vegetation water supply index method;

[0056] Step 5: Based on GIS software, the processed soil moisture data of the target area is marked correspondingly to the remote sensing satellite image of the target area.

[0057] The biggest advantage of MODIS remote sensing data is that in addition to ground receiving stations around the world receiving satellite data for free according to the characteristics of different regions, NASA provides a series of atmospheric, oceanic and land products and a complete sharing service website based on the needs of different users. Among them, MODIS land products can be downloaded from the NASAMODIS Land Product Distribution Center, whose website provides the MODIS / Terra global daily surface reflectance data set (MOD09GA). The data has been corrected for the effects of atmospheric gases and aerosols, and the projection method is sinusoidal projection. The data format is EOS-HDF, of which the 500m scientific data set includes MODIS band 1-7 reflectance, quality classification, observation range, number of observations and 250m scanning information; the 1000m scientific data set includes the number of observations, quality status, sensor altitude, solar altitude, geometric positioning mark and orbit pointer, etc.

[0058] Using the remote sensing satellite image data of the above-mentioned plateau target area, the remote sensing satellite image is gridded based on color, and soil moisture monitoring point data is generated based on the nodes of the grid outline, so that the soil moisture data can be collected more evenly;

[0059] GIS software is used to digitally input these basic maps, especially the large-scale land use status map of the Tibet Autonomous Region, and convert them into a projection method consistent with the remote sensing image. On this basis, a large number of surface parameter inversion methods at home and abroad are referred to, and a remote sensing inversion method for surface parameters of the plateau of the Tibet Autonomous Region is established. On this basis, the remote sensing inversion model is verified and improved using independent ground sampling soil moisture data, and finally a soil moisture remote sensing monitoring and estimation model suitable for the plateau of the Tibet Autonomous Region is generated. Finally, the research results are applied to the field of remote sensing business monitoring of soil moisture in the Tibet Autonomous Region.

[0060] Step 2 includes the following sub-steps:

[0061] Step 21, acquiring remote sensing satellite image data, and performing radiation correction and geometric correction;

[0062] Step 22, defining a grid division rule, determining the outline and geometric center point of each grid according to the resolution and color variation range of the remote sensing satellite image data, calculating the curvature value of each point on the contour line of the outline of each grid, and determining the node based on the curvature value and geometric center point of each point on the contour line;

[0063] Step 23, using the gridding tool of the GIS software, creating a grid layer in each area, setting the grid size, grid type and grid interval, and overlaying the generated grid layer on the satellite remote sensing map;

[0064] Step 24, extracting remote sensing data within the grid, overlaying and analyzing the grid layer with the remote sensing satellite image, and extracting remote sensing data within each grid;

[0065] Step 25, data export and analysis, export the extracted grid data into CSV or Excel format;

[0066] Step 26, result visualization, import the grid data into GIS software, and use colors to represent the remote sensing data values ​​in different grids.

[0067] Grid division is performed on the remote sensing satellite image data that has completed radiation correction and geometric correction. The range division is mainly based on resolution and color change range. For example, color thresholds can be set according to the color change range. These thresholds will be used to distinguish different color areas. In HSV space, thresholds can be set according to different ranges of hue; in Lab space, thresholds can be set according to brightness and color difference (a and b channels), so as to achieve gridding based on color expression. The gridding result obtained in this way is not a uniform grid, but a grid formed by some similar geological blocks. The subsequent regional sampling of soil moisture can better reflect the soil moisture conditions of the actual area represented by the grid area.

[0068] The specific method of determining the nodes in step 22 is: sort the curvature values ​​of each point on the contour line according to size, and take several points with the largest curvature values ​​and the geometric center point to form a node set of the mesh contour.

[0069] Since the gridding rule is based on color changes, the larger the contour curvature value, the greater the geological difference in the relevant area. Therefore, the node set composed of several points with the largest curvature values ​​and the geometric center point to form the grid contour can better reflect the soil moisture conditions of the actual area represented by the grid area.

[0070] The number of points with the largest curvature values ​​is determined based on the area of ​​the mesh contour, and the a points with the largest curvature values ​​and the geometric center point are taken to form a node set of the mesh contour;

[0071] The formula for calculating a is:

[0072]

[0073] Among them, the grid outline area is Sn, the total area of ​​all grids is S, and B is the percentage coefficient, which is an integer from 100 to 300.

[0074] The ratio of the grid contour area to the total area of ​​all grids determines the number of points with the largest curvature value, which can make the field sampling points of soil moisture in the corresponding plateau area more uniform, and the subsequent soil moisture data presented in the remote sensing satellite images more uniform.

[0075] The specific method of step 3 is: perform humidity measurement according to the soil moisture monitoring point data obtained in step 2,

[0076] Based on the calculation of NDVI and surface temperature of the plateau remote sensing satellite image data, the vegetation water supply index VSWI of the vegetation growing season in the central plateau is calculated, and the relationship between VSWI and the measured soil moisture is established, the regression model is as follows:

[0077] y=114719x2-4392.6x+59.138 R 2 =0.7118,N=16;

[0078] y=3126.5x-50.492 R 2 =0.6525,N=16;

[0079] Where x is VSWI and y is the weight moisture content of 5 cm soil.

[0080] It can be seen from the formula that there is a significant linear relationship between VSWI and the measured soil moisture, which passed the significance test of P<0.001; according to the regression modeling principle with a large determination coefficient R2, the quadratic polynomial regression model is better than the linear regression model.

[0081] In step 1, the remote sensing satellite image data is a 500m scientific dataset including MODIS band 1-7 reflectance, quality classification, observation range, number of observations, and 250m scanning information.

[0082] In step 3, the ML2X soil moisture rapid tester is used for sampling and measurement.

[0083] Soil moisture is sampled using the ML2X soil moisture rapid tester. The ML2X uses the FDR (Frequency Domain Reflectometry) principle to measure the volumetric moisture content of the 0-5 cm surface soil, with a measurement range of 0-100% and an instrument accuracy of ±5%.

[0084] A plateau soil moisture remote sensing monitoring system comprises a remote sensing satellite data acquisition module 1, an Internet communication module 2, a memory 3 and a computer 4. The data input end of the remote sensing satellite data acquisition module 1 is connected to the Internet through the Internet communication module 2, and remote sensing satellite image data of a plateau target area is obtained based on the Internet and stored in the memory 3. The computer 4 is pre-installed with a monitoring program designed by a plateau soil moisture remote sensing monitoring method. The computer 4 runs the monitoring program to output geographic coordinate data corresponding to soil moisture monitoring point data. A staff member measures soil moisture data of a target area according to the geographic coordinate data, and enters the soil moisture data of the target area into the computer 4. The computer 4 runs the monitoring program to output remote sensing satellite image data with soil moisture data of the target area.

[0085] The device further comprises a touch display 5, and the computer 4 is in communication connection with the touch display 5, and controls the touch display 5 to visually display a remote sensing satellite image with soil moisture data of a target area.

[0086] The following is a specific example to illustrate the implementation principle of the plateau soil moisture remote sensing monitoring method and system:

[0087] Log in to NASAMODIS Land Product Distribution Center through Remote Sensing Satellite Data Collection Module 1 and download the MODIS / Terra global daily surface reflectance dataset (MOD09GA) of the set date from its website (http: / / e4ftl01.cr.usgs.gov).

[0088] The data has been corrected for the effects of atmospheric gases and aerosols, and the projection method is sinusoidal projection. The data format is EOS-HDF, of which the 500m scientific data set includes MODIS band 1-7 reflectivity, quality classification, observation range, number of observations and 250m scanning information; the 1000m scientific data set includes the number of observations, quality status, sensor altitude, sun altitude, geometric positioning mark and orbit pointer, etc. During the field observation, the study area had clear sky weather, which provided extremely favorable weather conditions for the establishment of soil moisture remote sensing monitoring model.

[0089] Computer 4 runs the monitoring program to select remote sensing satellite image data of the Tibet Autonomous Region, see Figure 3; Grid the remote sensing satellite image based on color, define the grid division rules, determine the outline and geometric center point of each grid according to the resolution and color variation range of the remote sensing satellite image data, calculate the curvature value of each point on the contour line of each grid, and determine the node based on the curvature value and geometric center point of each point on the contour line;

[0090] Specifically, an example is given for determining the node set of the grid numbered 1:

[0091] See also Figure 4 , based on the size of the mesh contour area, determine the number of points with the largest curvature value, and take the a points with the largest curvature value and the geometric center point to form a node set of the mesh contour;

[0092] The formula for calculating a is:

[0093]

[0094] Among them, based on the legend size of the electronic map, the calculated grid outline area is Sn = 4.5, the total area of ​​all grids is 100, the percentage coefficient of B is 100, then S = 4.5, and the integer is 5, then the 5 points with the largest curvature value and the geometric center point are selected, a total of 6 points constitute the node set of the grid outline;

[0095] Using the gridding tool of GIS software, a grid layer is created in each area, and the grid size, grid type and grid interval are set. The generated grid layer is overlaid on the satellite remote sensing map;

[0096] Extract remote sensing data within the grid, overlay and analyze the grid layer with remote sensing satellite images, and extract remote sensing data within each grid;

[0097] Data export and analysis: export the extracted grid data to CSV or Excel format;

[0098] To visualize the results, the grid data was imported into GIS software, and the remote sensing data values ​​in different grids were represented by colors.

[0099] Measure the soil moisture data of the target area based on the geographical coordinates corresponding to the soil moisture monitoring point data; process the soil data based on the vegetation water supply index method based on the remote sensing satellite image data and the soil moisture measurement data of the target area;

[0100] Based on the GIS software, the processed soil moisture data of the target area is marked correspondingly in the remote sensing satellite image of the target area, and finally displayed through the touch display 5. The user can touch the corresponding area on the touch display 5 to obtain the soil moisture data of the area.

[0101] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A remote sensing monitoring method for plateau soil moisture, characterized in that: The following steps are involved: Step 1, collecting remote sensing satellite image data of the plateau target area; Step 2, gridding the remote sensing satellite image based on color, and generating soil moisture monitoring point data based on the nodes of the grid outline; Step 3, measuring soil moisture data of the target area based on the geographic coordinate target corresponding to the soil moisture monitoring point data; Step 4, based on the remote sensing satellite image data and the soil moisture measurement data of the target area, soil data processing is performed based on the vegetation water supply index method; Step 5: Based on GIS software, the processed soil moisture data of the target area is marked correspondingly to the remote sensing satellite image of the target area; Step 2 includes the following sub-steps: Step 21, acquiring remote sensing satellite image data, and performing radiation correction and geometric correction; Step 22, defining a grid division rule, determining the outline and geometric center point of each grid according to the resolution and color variation range of the remote sensing satellite image data, calculating the curvature value of each point on the contour line of the outline of each grid, and determining the node based on the curvature value and geometric center point of each point on the contour line; Step 23, using the gridding tool of the GIS software, creating a grid layer in each area, setting the grid size, grid type and grid interval, and overlaying the generated grid layer on the satellite remote sensing map; Step 24, extracting remote sensing data within the grid, overlaying and analyzing the grid layer with the remote sensing satellite image, and extracting remote sensing data within each grid; Step 25, data export and analysis, export the extracted grid data into CSV or Excel format; Step 26, result visualization, importing the grid data into GIS software, and using colors to represent the remote sensing data values ​​in different grids; The specific method of determining the nodes in step 22 is: sorting the curvature values ​​of each point on the contour line according to size, taking a number of points with the largest curvature values ​​and the geometric center point to form a node set of the mesh contour; The number of points with the largest curvature values ​​is determined based on the area of ​​the mesh contour, and the a points with the largest curvature values ​​and the geometric center point are taken to form a node set of the mesh contour; The formula for calculating a is: Wherein, the mesh outline area is Sn, the total area of ​​all meshes is S, and B is the percentage coefficient, which is an integer between 100 and 300; The specific method of step 3 is: perform humidity measurement according to the soil moisture monitoring point data obtained in step 2, Based on the calculation of NDVI and surface temperature of the plateau remote sensing satellite image data, the vegetation water supply index VSWI of the vegetation growing season in the central plateau is calculated, and the relationship between VSWI and the measured soil moisture is established, the regression model is as follows: y=114719x2-4392.6x+59.138R 2 =0.7118,N=16; <h2 style=";text-align:left;direction:ltr">y = 3126.5x - 50.492R<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> =0.6525,N=16; Where x is VSWI and y is the weight moisture content of 5 cm soil.

2. The plateau soil moisture remote sensing monitoring method according to claim 1 is characterized in that: In step 1, the remote sensing satellite image data is a 500m scientific dataset including MODIS band 1-7 reflectance, quality classification, observation range, number of observations, and 250m scanning information.

3. The plateau soil moisture remote sensing monitoring method according to claim 1 is characterized in that: In step 3, the ML2X soil moisture rapid tester is used for sampling and measurement.

4. A plateau soil moisture remote sensing monitoring system, characterized in that: The invention comprises a remote sensing satellite data acquisition module (1), an Internet communication module (2), a memory (3) and a computer (4); the data input end of the remote sensing satellite data acquisition module (1) is connected to the Internet through the Internet communication module (2), and remote sensing satellite image data of a plateau target area is obtained based on the Internet and stored in the memory (3); the computer (4) is pre-installed with a monitoring program designed using the plateau soil moisture remote sensing monitoring method according to any one of claims 1 to 3; the computer (4) runs the monitoring program to output geographic coordinate data corresponding to soil moisture monitoring point data; a staff member measures soil moisture data of the target area according to the geographic coordinate data, and enters the soil moisture data of the target area into the computer (4); the computer (4) runs the monitoring program to output remote sensing satellite image data with soil moisture data of the target area.

5. A plateau soil moisture remote sensing monitoring system according to claim 4, characterized in that: It also comprises a touch display (5), and the computer (4) is in communication connection with the touch display (5), and controls the touch display (5) to visually display a remote sensing satellite image with soil moisture data of a target area.

Images