Authenticity verification method of surface soil moisture products based on pixel averaging method

Abstract

The present application provides a method for verifying the authenticity of a surface soil moisture product based on a pixel averaging method, comprising: obtaining a set of ground measured data points; creating a target area containing all ground measured points; dividing the target area into sub-areas using pixel scale as a unit, and determining the geographic coordinates of designated points in each sub-area; converting the geographic coordinates of designated points in each sub-area and the geographic coordinates of each ground measured point into projection coordinates; calculating the average value of the surface soil moisture values of each ground measured point in each sub-area to obtain a relative true value; calculating the projection coordinates of the center point of each sub-area and converting them into geographic coordinates; obtaining the pixel value corresponding to the center point of each sub-area from the surface soil moisture remote sensing product to be verified, and obtaining a satellite-ground matching data pair. The present application adopts the pixel averaging method to obtain a relative true value at the same pixel scale as the product to be verified. The obtained relative true value is relatively accurate, which is conducive to ensuring the authenticity of the authenticity verification result.

Description

Technical Field

[0001] The present application relates to the technical field of authenticity verification of surface soil moisture products, and in particular to a method for authenticity verification of surface soil moisture products based on a pixel averaging method. Background Art

[0002] The accuracy of surface soil moisture measurements constrains agricultural, ecological, and water resource management. With the continuous accumulation of massive amounts of remote sensing data, surface soil moisture inversion products across various spectral bands are being mass-produced, making product authenticity verification a key application area. Authenticity verification is a key technical step in converting remote sensing data into remote sensing information. Remote sensing data acquisition is influenced by a variety of factors, including atmospheric radiation transmission characteristics, the sensor's operating environment, the sensor's operating status, and the state of the observed target. Authenticity verification is essential to ensure that the remote sensing data received by the sensor meets design requirements, that the inversion products are accurate, and that the remote sensing application products are qualified.

[0003] The direct verification method is the preferred method for authenticity verification of quantitative remote sensing products. It means that based on the remote sensing product to be verified, the relative true value of the ground measured data of the same pixel scale is obtained synchronously, and then the remote sensing product to be verified and the objective reality are compared and analyzed to obtain the consistency and uncertainty of the two. The key link is the determination of the relative true value of the pixel scale. ENVI obtains pixel values based on the longitude and latitude of the ground measured points and adopts the principle of proximity. The derived longitude and latitude are inconsistent with the measured points and ignores the characteristics between multiple points within the pixel scale. ArcGIS uses the method of extracting values to points. Although it avoids the problem of inconsistency between input and output longitude and latitude, it still ignores multiple points within the pixel, resulting in the obtained relative true value of the pixel scale being inaccurate, affecting the authenticity verification results. To this end, this application proposes a method for authenticity verification of surface soil moisture products based on the pixel averaging method. Summary of the Invention

[0004] The purpose of this application is to address the above problems and provide a method for verifying the authenticity of surface soil moisture products based on the pixel averaging method.

[0005] This application provides a method for verifying the authenticity of surface soil moisture products based on the pixel averaging method, the method comprising the following steps:

[0006] Acquire a ground measured data point set; the ground measured data point set includes measured data of multiple ground measured points; the measured data includes the geographic coordinates of the ground measured points and the surface soil moisture value;

[0007] Creating a target area including all ground-measured points based on the geographic coordinates of the ground-measured data point set, and obtaining the boundaries of the target area;

[0008] Divide the target area into a plurality of sub-areas using the pixel scale of the surface soil moisture remote sensing product to be tested as a unit, and determine the geographic coordinates of the designated points of each sub-area according to the four-boundary range and the size of the pixel scale;

[0009] Convert the geographic coordinates of the specified points in each sub-region into projection coordinates;

[0010] Convert the geographic coordinates of each ground measured point into projection coordinates;

[0011] According to the projection coordinates of the designated points in each sub-region, the projection coordinates of each ground measured point and the size of the pixel scale, the ground measured points falling in each sub-region are counted, and the average value of the surface soil moisture value of each ground measured point in each sub-region is calculated to obtain the relative true value;

[0012] Calculate the projection coordinates of the center point of each sub-region;

[0013] Convert the projection coordinates of the center point of each sub-region into geographic coordinates;

[0014] The pixel value corresponding to the center point of each sub-region is obtained from the surface soil moisture remote sensing product to be tested, and a satellite-ground matching data pair is obtained; the satellite-ground matching data pair includes the surface soil moisture detection value of the surface soil moisture remote sensing product to be tested at the same geographical location and the relative true value of the surface soil moisture measured on the ground.

[0015] According to the technical solutions provided in certain embodiments of the present application, the method used to convert the geographic coordinates of designated points in each sub-area into projection coordinates, and the method used to convert the geographic coordinates of each ground measured point into projection coordinates are both Gaussian arithmetic.

[0016] According to the technical solutions provided by certain embodiments of the present application, the method used to convert the projection coordinates of the center point of each sub-region into geographic coordinates is Gaussian inverse calculation.

[0017] According to the technical solutions provided in certain embodiments of the present application, the geographical coordinates of the designated point in the sub-area are the geographical coordinates of the upper left corner of the sub-area.

[0018] Compared with the existing technology, the beneficial effects of the present application are as follows: the present application adopts the pixel averaging method to obtain the relative true value data of the same pixel scale as the product to be tested, and finally obtains the satellite-ground matching data pair; this method takes into account the situation of multiple points within the pixel, and the relative true value obtained is relatively accurate, which is convenient for the subsequent calculation of the accuracy and uncertainty of the surface soil moisture remote sensing product to be tested and the relative true value, and thus helps to ensure the authenticity of the authenticity test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1This is a flowchart of a method for verifying the authenticity of surface soil moisture products based on the pixel averaging method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the technical solution of the present application, the present application is described in detail below with reference to the accompanying drawings. The description in this section is only exemplary and explanatory and should not have any limiting effect on the scope of protection of the present application.

[0021] This embodiment provides a method for verifying the authenticity of surface soil moisture products based on the pixel averaging method. The flowchart of the method is as follows: Figure 1 As shown, the method includes the following steps:

[0022] S1. Obtain a set of ground measured data points; the ground measured data point set includes measured data of multiple ground measured points; the measured data includes the geographic coordinates of the ground measured points and the surface soil moisture value; wherein the geographic coordinates of the ground measured points are the longitude and latitude values of the ground measured points; the surface soil moisture value is the water adsorbed on soil particles and present in soil pores.

[0023] S2. Create a target area including all ground measured points based on the ground measured data point set, and obtain the boundaries of the target area; specifically, create a target area including all ground measured points according to the geographic coordinates of each ground measured point in the ground measured data point set, and the shape of the target area is a rectangle.

[0024] S3. Divide the target area into multiple sub-areas based on the pixel scale of the surface soil moisture remote sensing product to be tested, and determine the geographic coordinates of the designated points of each sub-area according to the four-boundary range and the size of the pixel scale; wherein the geographic coordinates of the designated points of the sub-area are the geographic coordinates of the upper left corner of the sub-area.

[0025] For example, the target area is divided into m×n sub-areas, that is, a total of m rows and n columns of sub-areas. Among these sub-areas, some sub-areas may have multiple ground measurement points, some sub-areas may have only one ground measurement point, and some sub-areas may not have any ground measurement point.

[0026] According to the four boundaries of the target area, the geographic coordinates (i.e., longitude and latitude) of the upper left corner of the target area can be obtained, and then according to the geographic coordinates of the upper left corner of the entire target area and the size of the pixel scale, the geographic coordinates of the upper left corner of each sub-area can be determined; specifically, traverse m×n sub-areas to obtain the longitude and latitude values of the upper left corner of the sub-area in the i-th row and j-th column, where 1≤i≤m, 1≤j≤n.

[0027] S4. Convert the geographic coordinates of the designated points in each sub-area into projection coordinates.

[0028] The Gaussian calculation method is used to convert the geographic coordinates of the upper left corner of each sub-region into projection coordinates, that is, the longitude and latitude values of the upper left corner of each sub-region are converted into x-coordinate values and y-coordinate values in the plane coordinate system.

[0029] S5. Convert the geographic coordinates of each ground measured point into projection coordinates.

[0030] The Gaussian calculation method is used to convert the geographical coordinates of each ground measurement point into projection coordinates, that is, the longitude and latitude values of each ground measurement point are converted into x-coordinate values and y-coordinate values in the plane coordinate system.

[0031] S6. According to the projection coordinates of the designated points in each sub-region, the projection coordinates of each ground measured point and the size of the pixel scale, the ground measured points falling in each sub-region are counted, and the average value of the surface soil moisture value of each ground measured point in each sub-region is calculated to obtain a relative true value.

[0032] Based on the projection coordinates of the upper left corner of each sub-region and the size of the pixel scale, the projection coordinates of the upper right corner, lower left corner, and lower right corner of each sub-region can be obtained. Then, the projection coordinates of each ground measured point can be used to determine in which sub-region each ground measured point falls. As described in step S3, some sub-regions may have multiple ground measured points, some sub-regions may have only one ground measured point, and some sub-regions may have no ground measured point. When calculating the average value of the surface soil moisture value of each ground measured point in each sub-region, the calculated average value of the surface soil moisture value of the sub-region without a ground measured point is 0, and the sub-region without a ground measured point does not participate in the output of the satellite-ground data matching data pair. For a sub-region with only one ground measured point, the calculated average value of the surface soil moisture value is the surface soil moisture value of the ground measured point. For a sub-region with more than one ground measured point, the calculated average value of the surface soil moisture value is the arithmetic mean of the surface soil moisture values of each ground measured point. The calculated average value of the surface soil moisture value of each sub-region is the relative true value of the surface soil moisture of each sub-region.

[0033] For example, assuming that the sub-region has 20 rows and 20 columns, and there are 5 ground-measured points, 3 of which are located in the 20th row and 20th column, 1 ground-measured point is located in the 2nd row and 2nd column, and 1 ground-measured point is located in the 1st row and 1st column, then the number of satellite-ground matching data pairs to be output is 3.

[0034] When the pixel scale of the remote sensing product to be tested is large and the spatial distribution of the ground measured data is relatively concentrated, the above scheme can be used (for the convenience of the following description, this scheme is referred to as Scheme 1) to traverse each sub-region one by one to count the ground measured data and calculate the relative true value of the ground measured data corresponding to each sub-region.

[0035] When the pixel scale of the remote sensing product to be tested is small and the spatial distribution of the ground-based measured data is relatively scattered, the following scheme can be used (for the convenience of the following description, this scheme is referred to as Scheme 2) to calculate the row and column numbers of the sub-region where the ground-based measured points are located, and lock the ground-based measured points that fall in the same sub-region by the same row and column numbers. This scheme specifically includes the following steps:

[0036] (1) Calculate the row and column numbers of each ground measurement point one by one: Assume that the number of ground measurement points is T, and the projection coordinates of the kth (k = 1, 2, 3....T) ground measurement point are (x k ,y k ), the projection coordinates of the upper left corner of the target area are (x0, y0), and the size of the pixel scale is d x ×d y , that is, the width and height of each sub-region are d y and d x , then the row number of the sub-area where the kth ground measured point falls can be calculated as g=|x k -x0| / d x +1, column number is h=|y k -y0| / d y +1; that is, the row number g of the sub-region where the k-th ground measured point falls is: x k Difference from x0, take the absolute value, and divide by d x Then take the integer part and add 1. The column number h of the sub-area where the k-th ground measurement point falls is: y k Difference with y0, take the absolute value, and divide by d y Then take the integer part and add 1.

[0037] (2) Go through each ground measurement point one by one and group the ground measurement points with the same row and column numbers. The ground measurement points with the same row and column numbers will fall in the same sub-area. By calculating the arithmetic mean of the surface soil moisture values of each ground measurement point with the same row and column number, the relative true value of the surface soil moisture in each sub-area can be obtained.

[0038] S7. Calculate the projection coordinates of the center point of each sub-region.

[0039] When the scheme 1 is used in step S6 to calculate the relative true value of the ground measured data corresponding to each sub-region, the projection coordinates of the center point of each sub-region can be calculated based on the projection coordinates of the upper left corner of each sub-region and the size of the pixel scale. Specifically, the projection coordinates of the upper left corner of the sub-region in the i-th row and j-th column are (x i,j ,y i,j ), the pixel size is d x ×d y , then the projection coordinates of the center point of the sub-region are (x i,j -0.5d x ,y i,j +0.5d y ), where the projection coordinates (x i,j ,y i,j ) has already been determined in step S6.

[0040] When the second scheme is used in step S6 to calculate the relative true value of the ground measured data corresponding to each sub-region, according to the projection coordinates (x0, y0) of the upper left corner of the target area and the size of the pixel scale d x ×d y , then the projection coordinates of the center point of the sub-region in the gth row and hth column are (x0-(g+0.5)d x ,y0+(h+0.5)d y ).

[0041] S8. Convert the projection coordinates of the center point of each sub-region into geographic coordinates.

[0042] The Gaussian inverse calculation method is used to convert the projection coordinates of the center point of each sub-region into geographic coordinates, that is, the x-coordinate value and y-coordinate value of the center point of each sub-region are converted into the corresponding longitude and latitude values.

[0043] S9. Obtain pixel values corresponding to the center points of each sub-region from the surface soil moisture remote sensing product to be tested, and obtain a satellite-ground matching data pair; the satellite-ground matching data pair includes the surface soil moisture detection value of the surface soil moisture remote sensing product to be tested at the same geographical location, and the relative true value of the surface soil moisture measured on the ground.

[0044] Among them, the same geographical location refers to having the same geographical coordinates, that is, longitude and latitude values; the satellite-ground matching data pair refers to the surface soil moisture detection value of the surface soil moisture remote sensing product to be tested obtained by satellite measurement and the relative true value of the surface soil moisture measured on the ground at the same geographical location, and also includes the geographical coordinates of the same geographical location (that is, the longitude and latitude values of the geographical location), that is, a satellite-ground matching data pair contains four values: the detection value (pixel value) of the surface soil moisture remote sensing product to be tested, the relative true value, the longitude value and the latitude value.

[0045] The current method for obtaining satellite-ground matching data pairs cannot meet the requirements for obtaining the relative true value of the ground measured data at the pixel scale of the remote sensing product to be tested and the geographical coordinates of the pixel center. The embodiment of the present application provides an authenticity verification method for the surface soil moisture product based on the pixel averaging method. It is a direct verification method based on the Gaussian forward and inverse calculation coupled pixel averaging method, which realizes the acquisition of the arithmetic mean of the ground measured points at the pixel scale and the geographical coordinates of the pixel center, and obtains the pixel value of the surface soil moisture remote sensing product to be tested based on the geographical coordinates (longitude and latitude values) of the pixel center, and finally obtains the satellite-ground matching data pair, which is convenient for subsequent evaluation and calculation of the accuracy and uncertainty of the surface soil moisture remote sensing product to be tested and the relative true value.

[0046] Among them, accuracy evaluation refers to the quantitative expression of the accuracy of the soil moisture remote sensing product to be tested, and the indicators include root mean square error, correlation coefficient, average error, mean absolute error, relative error, etc.; uncertainty evaluation refers to the quantitative expression of the uncertainty of the soil moisture remote sensing product to be tested, and the indicators include standard deviation, variance, covariance, standard uncertainty, etc.

[0047] This application targets the pixel averaging method for ground-based measured data and utilizes the coordinate transformation method of Gaussian forward and inverse calculation to meet the direct verification requirements of all quantitative remote sensing products. It can be used as a general direct verification method for obtaining satellite-ground matching data for quantitative remote sensing products.

[0048] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. The above is only the preferred implementation method of this application. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of the present invention, they can also make several improvements, modifications or changes, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of this application.

Claims

1. A method for verifying the authenticity of surface soil moisture products based on pixel averaging, characterized in that: The method comprises the following steps: Acquire a ground measured data point set; the ground measured data point set includes measured data of multiple ground measured points; the measured data includes the geographic coordinates of the ground measured points and the surface soil moisture value; Creating a target area including all ground-measured points based on the geographic coordinates of the ground-measured data point set, and obtaining the boundaries of the target area; The target area is divided into a plurality of sub-areas using the pixel scale of the surface soil moisture remote sensing product to be tested as a unit, and the geographic coordinates of a designated point of each sub-area are determined according to the four boundaries and the size of the pixel scale; the geographic coordinates of the designated point of the sub-area are the geographic coordinates of the upper left corner of the sub-area; Convert the geographic coordinates of the specified points in each sub-region into projection coordinates; Convert the geographic coordinates of each ground measured point into projection coordinates; According to the projection coordinates of the designated points in each sub-region, the projection coordinates of each ground measured point and the size of the pixel scale, the ground measured points falling in each sub-region are counted, and the average value of the surface soil moisture value of each ground measured point in each sub-region is calculated to obtain the relative true value; Calculate the projection coordinates of the center point of each sub-region; Convert the projection coordinates of the center point of each sub-region into geographic coordinates; The pixel value corresponding to the center point of each sub-region is obtained from the surface soil moisture remote sensing product to be tested, and a satellite-ground matching data pair is obtained; the satellite-ground matching data pair includes the surface soil moisture detection value of the surface soil moisture remote sensing product to be tested at the same geographical location and the relative true value of the surface soil moisture measured on the ground.

2. The authenticity verification method of surface soil moisture products based on pixel averaging method according to claim 1 is characterized in that: The method used to convert the geographical coordinates of the designated points in each sub-region into projection coordinates and the method used to convert the geographical coordinates of each ground measured point into projection coordinates is the same Gaussian arithmetic.

3. The authenticity verification method of surface soil moisture products based on pixel averaging method according to claim 1 is characterized in that: The method used to convert the projection coordinates of the center point of each sub-region into geographic coordinates is Gaussian inversion.

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