Soil moisture inversion method and system based on multi-temporal soil line consistency correction

Abstract

The present invention discloses a soil moisture inversion method and system based on multi-phase soil line consistency correction, including: using multi-phase optical image data of the target area, extracting the minimum near-red band value of the red band, and generating a theoretical feature space by fusing these data, and calculating the theoretical soil line slope. Using the theoretical soil line slope and field measurement data, a regression equation model reflecting the relationship between soil moisture and RPDI is established. Based on the reflectivity data of the optical satellite image and the theoretical soil line slope, the RPDI value of the entire target area is calculated, and the model is used to perform an inversion calculation of the soil moisture content. The advantages of the present invention are: reducing the dependence on ground measured data in remote sensing soil moisture estimation, and effectively improving the application value of remote sensing technology in soil moisture monitoring.

Description

Technical Field

[0001] The invention relates to the technical field of soil moisture monitoring and agricultural resource management, and in particular to a soil moisture remote sensing inversion method and system based on a multi-temporally comparable re-corrected drought index RPDI. Background Art

[0002] Since the 1980s, due to the sustained growth of China's economy, the demand for water resources has increased dramatically. At the same time, with the continued drier climate, the frequency of droughts has increased, the duration has prolonged, and the losses in agricultural production have continued to increase. Acquiring relatively continuous surface soil moisture over a large area through remote sensing is of great significance for crop irrigation management, farmland drought early warning, etc.

[0003] According to the characteristics of the data source, remote sensing monitoring methods of soil moisture can be divided into three categories: optical (visible light), thermal infrared and microwave inversion. Among them, optical remote sensing has the advantages of abundant available data and simple calculation. It is often used to invert surface soil moisture content and calculate parameters related to plant growth and state changes. It has strong practical value.

[0004] The inversion of soil moisture using optical remote sensing data is mainly based on the construction of feature space based on multispectral information, and the calculation of drought index based on feature space. Affected by the overall dryness and wetness of the region, vegetation coverage and satellite observation status during the monitoring period, the feature space is prone to instability, resulting in the drought index calculated from multi-temporal images can only represent the relative dryness and wetness of the current period, that is, it is necessary to establish a regression relationship between the drought index and the ground measured data for each phase image, and then convert it into soil moisture content for comparison. This method relies on ground measured data, which brings difficulties to the quantitative inversion of soil moisture content. Summary of the invention

[0005] In view of the defects of the prior art, the present invention provides a soil moisture inversion method and system based on multi-temporal soil line consistency correction.

[0006] In order to achieve the above invention object, the technical solution adopted by the present invention is as follows:

[0007] A soil moisture inversion method based on multi-temporal soil line consistency correction includes the following steps:

[0008] Step 1: Collect optical image data of the target area over a period of time, extract the minimum near-red band value corresponding to the red band of all image data, fuse them into a theoretical feature space, and calculate the corresponding theoretical soil line slope.

[0009] Step 2: Use the theoretical soil line slope obtained in step 1 and the satellite image data of each period to calculate the RPDI value of the pixel where the soil moisture content is measured in the field, and establish a regression equation with the measured surface soil moisture data on the satellite transit date to construct a soil moisture inversion model.

[0010] Step 3: Using the red band and near-red band reflectance of the optical satellite image and the theoretical soil line slope as input, calculate the RPDI values ​​of all pixels in the target area, and use the regression equation obtained in step 2 to invert and calculate the soil moisture content of the entire area.

[0011] Step 4: When there is a new remote sensing image of the target area, repeat step 1 to calculate and evaluate whether there is a significant change in the theoretical soil line slope. If the change is significant, use the new soil line slope and repeat step 2 to obtain a new regression equation; then repeat step 3 to calculate the soil moisture content of the entire area. If the change is not significant, directly repeat step 3 and use the original regression equation to calculate the soil moisture content of the entire area.

[0012] Furthermore, the step 1 includes the following sub-steps:

[0013] Step 11: Collect satellite image data of the target area for at least one full natural year, with no clouds or less clouds, and with red and near-infrared bands, and perform satellite image data preprocessing.

[0014] Step 12: During the data collection period, start from 0 and count 10 -4 The minimum near-red band value corresponding to the red band of all images is extracted for the step length and stored in a two-dimensional matrix. For n scenes of images, a total of n sets of two-dimensional matrices d i (i=1…n), the n-dimensional matrix d i Merge into a two-dimensional matrix A = [d1; d2; ...d n ];

[0015] Step 13: Extract the minimum near-red band value corresponding to all red bands in matrix A, and perform linear fitting on the minimum near-infrared band reflectance value to obtain the soil line in the theoretical feature space:

[0016] R NIR =m t ·R red +c (1)

[0017] In the formula, m t is the slope of the theoretical soil line, c is the intercept of the soil line, R red , R NIR is the reflectivity of the red and near-infrared bands of the image.

[0018] Preferably, in step 11, the spatial resolution of the satellite image data is not less than 16 meters. When the target area is too large or exceeds the coverage of one image, it is divided into multiple sub-areas and pre-processed separately.

[0019] Furthermore, the step 2 includes the following sub-steps:

[0020] Step 21, calculate the RPDI of each image period, the calculation formula is:

[0021]

[0022] Where m t is the slope of the theoretical soil line; R red,v , R NIR,v is the reflectivity of vegetation in the red and near-infrared bands; F v is the vegetation coverage;

[0023] F v Calculated by the following formula:

[0024]

[0025] Where NDVI max and NDVI min They are the maximum and minimum values ​​of NDVI in the image, respectively.

[0026] Step 22: Select the measured soil moisture content on the satellite transit day for model parameter calibration, and the number of measured points in the calibration area shall be no less than 20;

[0027] Step 23: For each sub-area, during the period when the underlying soil does not change significantly, extract the RPDI values ​​of all pixels where the field-measured soil moisture content meets the conditions of step 22, and construct a soil moisture inversion model:

[0028] SM0=a*RPDI+b (5)

[0029] In the formula, SM0 is the soil moisture content of each pixel, and RPDI is the soil drought index of the pixel. The linear relationship between the measured soil moisture content and RPDI is obtained by fitting the least squares method, and the parameters a and b are obtained.

[0030] Step 24: extract the soil moisture inversion results corresponding to all measured points in the sub-area, calculate the unbiased root mean square error ubRMSE according to the following formula, and evaluate the accuracy of the inversion model:

[0031]

[0032] In the formula, x i ,y iThey represent remote sensing inversion and measured soil moisture content respectively, and n is the number of samples involved in the calculation.

[0033] The model ubRMSE should be less than 4%. If the accuracy does not meet the requirements, it is necessary to return to step 11 to reduce the area or step 22 to supplement the ground measured data and recalculate the relevant parameters.

[0034] Furthermore, step 3 includes the following sub-steps:

[0035] Step 31, using the red and near-red band reflectances of the optical satellite image and the theoretical soil line slope as input, calculate the RPDI values ​​of all pixels in each period and sub-region, and use the regression equation obtained in step 23 to inversely calculate the soil moisture content of the sub-region on that date;

[0036] Step 32: The soil moisture results of the sub-regions with the same transit date are merged and spliced ​​to obtain the soil moisture content of the target region on the satellite transit date.

[0037] Furthermore, step 4 includes the following sub-steps:

[0038] Step 41: When there is a new remote sensing image of the target area, the minimum near-red band value corresponding to the red band of the new image is integrated into the existing theoretical feature space to calculate the new theoretical soil line slope. Evaluate whether the change in the slope of the soil line is significant. If the new theoretical soil line slope changes by more than 0.05 compared with the original theoretical soil line slope, it is a significant change and proceed to step 42. Otherwise, jump to step 43.

[0039] Step 42, repeating step 2, using the new theoretical soil line slope, calculating the re-corrected vertical drought index RPDI value of the pixel where the field-measured soil moisture content is located, and constructing a soil moisture inversion model with the ground-measured data of the surface soil moisture content on the satellite transit day;

[0040] Step 43, based on the red and near-red band reflectance of the optical satellite image, the new theoretical soil line slope is used as input if the change is significant, and the original soil line slope is used as input if the change is not significant, and the RPDI values ​​of all pixels in the target area are calculated. If the change is significant, the new regression equation is used to calculate the soil moisture content of the target area, and if the change is not significant, the original regression equation is used to calculate the soil moisture content of the target area.

[0041] Preferably, the soil moisture inversion method is applied to areas with NDVI<0.6.

[0042] The present invention also discloses a soil moisture inversion system based on multi-phase soil line consistency correction, which can be used to implement the above-mentioned soil moisture inversion method, specifically including:

[0043] Data collection module: responsible for collecting optical image data of the target area and extracting the minimum near-red band value corresponding to the red band for subsequent processing.

[0044] Theoretical feature space generation module: Process the collected image data, generate theoretical feature space, and calculate the theoretical soil line slope.

[0045] Model building module: Using the theoretical soil line slope and field measurement data, a regression equation model reflecting the relationship between soil moisture content and RPDI is established.

[0046] Inversion calculation module: Based on the reflectivity data of optical satellite images and the theoretical soil line slope, the RPDI value of the entire target area is calculated, and the soil moisture content is inverted using the model.

[0047] Change detection and update module: Regularly evaluate the changes in the slope of the theoretical soil line. If the changes are significant, update the relevant model parameters to ensure the accuracy and stability of the inversion results.

[0048] The present invention also discloses a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the soil moisture inversion method when executing the program.

[0049] The present invention also discloses a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the soil moisture inversion method is realized.

[0050] Compared with the prior art, the advantages of the present invention are:

[0051] 1. Multi-temporal soil line consistency correction: By integrating the multi-temporal image feature space, the theoretical soil line slope is calculated, and the drought index RPDI of each phase is calculated based on this correction. This method allows the RPDI values ​​of different periods to be directly compared, effectively improving the monitoring accuracy and continuity of multi-temporal soil moisture changes.

[0052] 2. Stable regression relationship: The present invention establishes a stable regression relationship model between RPDI and soil moisture content. This relationship model reduces the dependence on ground measured data, making the soil moisture estimation based on optical remote sensing data more reliable and accurate. Compared with traditional methods, the present invention greatly reduces the demand for field data and improves the application value and practicality of remote sensing technology in soil moisture monitoring.

[0053] 3. Reduce labor and time costs: The present invention uses automated remote sensing data processing and model building technology to significantly reduce labor and time costs. Compared with traditional field measurement methods, frequent field surveys and data collection are not required, saving a lot of human resources and time.

[0054] 4. Improve spatial coverage and resolution: Remote sensing technology can achieve wide spatial coverage, covering large areas of land and regions, and has high spatial resolution. The present invention uses remote sensing data to estimate soil moisture content, which can achieve rapid monitoring and analysis of large areas, improving the spatial coverage and accuracy of monitoring.

[0055] 5. Improve the timeliness and real-time performance of monitoring: Soil moisture monitoring based on remote sensing data has high timeliness and real-time performance. By quickly acquiring and processing remote sensing images, the changes in soil moisture can be reflected in a timely manner, supporting agricultural production decisions and water resources management.

[0056] 6. Environmental friendliness and sustainability: The present invention uses remote sensing technology to monitor soil moisture, which reduces environmental interference and resource consumption, and meets the requirements of sustainable development. Compared with traditional field surveys and field measurement methods, remote sensing technology has less impact on the environment, which is conducive to ecological environmental protection and sustainable agricultural development. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a flow chart of a method for inverting regional soil moisture content according to an embodiment of the present invention;

[0058] Figure 2 is a schematic diagram of the minimum near-red band value extraction result corresponding to the red bands of all images in an embodiment of the present invention;

[0059] Figure 3 is a schematic diagram of the fitting result of the slope of the theoretical soil line in an embodiment of the present invention;

[0060] Figure 4 is a soil moisture model diagram based on RPDI in an embodiment of the present invention;

[0061] Figure 5 It is a soil moisture inversion accuracy evaluation diagram based on RPDI in 2020 in an embodiment of the present invention;

[0062] Figure 6 is a spatial distribution diagram of regional irrigation identification results according to an embodiment of the present invention;

[0063] Figure 7 This is a soil moisture inversion accuracy evaluation chart based on RPDI in 2021 in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples.

[0065] like Figure 1As shown, the present invention provides a soil moisture inversion method based on multi-phase soil line consistency correction, comprising the following steps:

[0066] The soil moisture inversion method includes the following steps:

[0067] Step 1: Collect optical image data of the target area, extract the minimum near-red band value corresponding to the red band of all image data, fuse them into a theoretical feature space, and calculate the corresponding theoretical soil line slope.

[0068] Step 2: Use the theoretical soil line slope obtained in step 1 to calculate the re-corrected vertical drought index RPDI value of the pixel where the soil moisture content is measured in the field, and construct a soil moisture inversion model with the ground-based measured data of the surface soil moisture content on the satellite transit day.

[0069] Step 3: Using the red and near-red band reflectance of the optical satellite image and the theoretical soil line slope as input, calculate the RPDI values ​​of all pixels in the target area, and use the regression equation obtained in step 2 to invert and calculate the soil moisture content of the entire area. Figure 6 , taking the inversion results of soil moisture content in cultivated land area on February 6, 2020 as an example.

[0070] Step 4: When there is a new remote sensing image of the target area, repeat step 1 to evaluate whether there is a significant change in the theoretical soil line slope. If the change is significant, use the new soil line slope and repeat step 2 to obtain a new regression equation; then repeat step 3 to calculate the soil moisture content of the entire area. If the change is not significant, repeat step 3 and use the original regression equation to calculate the soil moisture content of the entire area.

[0071] The step 1 includes the following sub-steps:

[0072] Step 11: The satellite remote sensing data used is the Sentinel-2 satellite, and the target area is the southern Hebei Province covered by the T50SKH scene. According to the scope of the target area, 26 scenes of all non-foggy, cloudless or lightly cloudy day image data in 2020 with red and near-red bands are selected, and the cultivated land range is used for masking. The ground measured data of soil moisture content comes from the soil moisture monitoring results of Hebei Province in 2020. In 2020, there are 60 sets of measured data in the target area for parameter determination of this method.

[0073] It should be noted that: optical image data include but are not limited to remote sensing data products such as Sentinel-2 and GF-6; measured soil moisture data can be obtained by fixed-point continuous observation using an automatic soil moisture observation instrument, or by field measurement using a portable soil moisture detection instrument, or by manual sampling followed by a drying method to determine the soil moisture content.

[0074] Step 12: Start from 0 to 10-4 The minimum near-red band value corresponding to the red band of all images is extracted for the step length and stored in a two-dimensional matrix. A total of 26 sets of two-dimensional matrices d are obtained for the 26 images. i (i=1…26). See the example Figure 2 , merge the above 26-dimensional matrix d i Get the two-dimensional matrix A = [d1; d2; ...d 54 ];

[0075] Step 13: Extract the minimum near-red band value corresponding to all red bands in matrix A, construct a two-dimensional feature space of the red and near-red band combination, and perform linear fitting on these points to obtain the soil line expression:

[0076] y=1.025x-0.067 (1)

[0077] The slope of the soil line is 1.025; see the example Figure 3 .

[0078] The step 2 includes the following sub-steps:

[0079] Step 21, calculate the RPDI of each image period, the calculation formula is:

[0080]

[0081] Where m t is the slope of the soil line, R red , R NIR is the reflectivity of the image in the red and near-infrared bands; R red,v , R NIR,v is the reflectivity of vegetation in the red and near-infrared bands; F v is the vegetation coverage;

[0082] F v It can be calculated by the following formula:

[0083]

[0084]

[0085] Where NDVI max and NDVI min They are the maximum and minimum values ​​of NDVI in the image of this scene, respectively.

[0086] Step 22: Match the measured soil moisture sampling date with the satellite transit date. In 2020, there are 60 pairs of data sets that meet the requirement that the two dates are on the same day, which are used for model parameter calibration.

[0087] Step 23: For the target area, extract the RPDI values ​​of all pixels where the field-measured soil moisture content meets the conditions of step 22, and construct a soil moisture inversion model;

[0088] SM0=a*RPDI+b (5)

[0089] In the formula, SM0 is the measured soil moisture content of each pixel, and RPDI is the pixel soil re-corrected drought index; a scatter plot of RPDI-measured soil moisture content is drawn, and the linear relationship between the measured soil moisture content and RPDI is obtained by using the least squares method to fit, and the parameters a = -39.84, b = 30.47 are obtained. For example, see Figure 4 The soil moisture inversion model can be expressed as:

[0090] SM=-39.84*RPDI+30.47 (6)

[0091] Step 24, extract the soil moisture inversion results corresponding to all measured points in the sub-area, calculate the unbiased root mean square error ubRMSE according to the following formula, and evaluate the accuracy of the inversion model;

[0092]

[0093] In the formula, x i ,y i They represent remote sensing inversion and measured soil moisture content respectively, and n is the number of samples involved in the calculation.

[0094] like Figure 5 Shown is the soil moisture inversion accuracy evaluation chart based on RPDI in 2020.

[0095] In this embodiment, the data involved in the inversion model construction are compared with the measured data, and the ubRMSE is 2.878%<4%, indicating that the model accuracy meets the requirements and the subsequent steps can be carried out.

[0096] Step 31: Using the red and near-red band reflectances of the optical satellite image on February 6, 2020, and the theoretical soil line slope of 1.025 as input, calculate the RPDI values ​​of all pixels in the T50SKH scene area. Use the regression equation (6) obtained in step 23 to invert and calculate the soil moisture content of the target area on that date. The results are shown in Figure 6 ;

[0097] Step 32: For the optical satellite images of other dates in 2020, perform soil moisture inversion period by period to obtain the soil moisture content of the target area on all satellite transit dates.

[0098] The step 4 includes the following sub-steps:

[0099] Step 41, taking the 2021 satellite images as an example, collect the 2021 remote sensing image target area range, and collect 28 scenes of image data of all non-fog days, cloudless days or few cloud days with red and near-red bands. Repeat step 1 to obtain the theoretical soil line slope of 2021 as 1.039, which is less than 0.05 compared with the theoretical soil line slope of 2020. Therefore, jump to step 43, that is, for the 2021 satellite images, it is no longer necessary to calibrate the ground measured data, and the regression equation obtained in step 23 can be directly used to calculate the soil moisture content of the scene image;

[0100] Step 43: Based on the red and near-red band reflectances of the optical satellite image and the theoretical soil line slope of 1.025 as input, the RPDI values ​​of all pixels in the target area are calculated, and equation (6) is still used to invert the soil moisture content of the target area in 2021.

[0101] The inversion results for 2021 were verified, and the results showed that the ubRMSE was 3.051% <4%, which met the accuracy requirements. It can be seen that for the inversion of soil moisture content in the target area in 2021, inversion results that meet the accuracy requirements can be obtained without ground measured data.

[0102] Taking into account the shielding of soil reflectivity by ground vegetation, the method described in this patent is applicable to areas with NDVI < 0.6.

[0103] In another embodiment of the present invention, a soil moisture inversion system based on multi-temporal soil line consistency correction is provided. The system can be used to implement the above-mentioned soil moisture inversion method, specifically including:

[0104] Data collection module: responsible for collecting optical image data of the target area and extracting the minimum near-red band value corresponding to the red band for subsequent processing.

[0105] Theoretical feature space generation module: Process the collected image data, generate theoretical feature space, and calculate the theoretical soil line slope.

[0106] Model building module: Using the theoretical soil line slope and field measurement data, a regression equation model reflecting the relationship between soil moisture content and RPDI is established.

[0107] Inversion calculation module: Based on the reflectivity data of optical satellite images and the theoretical soil line slope, the RPDI value of the entire target area is calculated, and the soil moisture content is inverted using the model.

[0108] Change detection and update module: Regularly evaluate the changes in the slope of the theoretical soil line. If the changes are significant, update the relevant model parameters to ensure the accuracy and stability of the inversion results.

[0109] In another embodiment of the present invention, a terminal device is provided, the terminal device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the soil moisture inversion method.

[0110] In one embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk storage.

[0111] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the soil moisture inversion method in the above embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor.

[0112] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0114] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0116] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the implementation methods of the present invention, and should be understood that the protection scope of the present invention is not limited to such special statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A soil moisture inversion method based on multi-temporal soil line consistency correction, characterized by: The following steps are involved: Step 1: Collect optical image data of the target area over a period of time, extract the minimum near-red band value corresponding to the red band of all image data, fuse them into a theoretical feature space, and calculate the corresponding theoretical soil line slope; Step 1 includes the following sub-steps: Step 11, collect satellite image data of the target area for at least one full natural year, with no clouds or less clouds, and with red and near-infrared bands, and perform satellite image data preprocessing; Step 12: During the data collection period, start from 0 and count 10 -4 The minimum near-red band value corresponding to the red band of all images is extracted for the step length and stored in a two-dimensional matrix. For n scenes of images, a total of n sets of two-dimensional matrices d i (i=1…n), the n-dimensional matrix d i Merge into a two-dimensional matrix A = [d1; d2; ...d n ]; Step 13: Extract the minimum near-red band value corresponding to all red bands in matrix A, and perform linear fitting on the minimum near-infrared band reflectance value to obtain the soil line in the theoretical feature space: R NIR =m t ·R red +c (1) In the formula, m t is the slope of the theoretical soil line, c is the intercept of the soil line, R red , R NIR is the reflectivity of the red and near-infrared bands of the image; Step 2: Using the theoretical soil line slope obtained in step 1 and the satellite image data of each period, the RPDI value of the pixel where the field-measured soil moisture is located is calculated, and a regression equation is established with the measured surface soil moisture data of the satellite transit date to construct a soil moisture inversion model; Step 3: Using the red band and near-red band reflectance of the optical satellite image and the theoretical soil line slope as input, calculate the RPDI values ​​of all pixels in the target area, and use the regression equation obtained in step 2 to invert and calculate the soil moisture content of the entire area; Step 4: When there is a new remote sensing image of the target area, repeat step 1 to calculate and evaluate whether there is a significant change in the theoretical soil line slope; if the change is significant, use the new soil line slope and repeat step 2 to obtain a new regression equation; then repeat step 3 to calculate the soil moisture content of the entire area; If the change is not significant, repeat step 3 directly and use the original regression equation to calculate the soil moisture content of the entire area.

2. The soil moisture inversion method according to claim 1, characterized in that: In step 11, the spatial resolution of the satellite image data is not less than 16 meters; when the target area is too large or exceeds the coverage of one image, it is divided into multiple sub-areas and pre-processed separately.

3. The soil moisture inversion method according to claim 1, characterized in that: The step 2 includes the following sub-steps: Step 21, calculate the RPDI of each phase of the image, the calculation formula is: Where m t is the slope of the theoretical soil line; R red,v , R NIR,v is the reflectivity of vegetation in the red and near-infrared bands; F v is the vegetation coverage; F v Calculated by the following formula: Where NDVI max and NDVI min are the maximum and minimum values ​​of NDVI in the image respectively; Step 22: Select the measured soil moisture content on the satellite transit day for model parameter calibration, and the number of measured points in the calibration area shall be no less than 20; Step 23: For each sub-area, during the period when the underlying soil does not change significantly, extract the RPDI values ​​of all pixels where the field-measured soil moisture content meets the conditions of step 22, and construct a soil moisture inversion model: SM0=a*RPDI+b (5) In the formula, SM0 is the soil moisture content of each pixel, and RPDI is the soil drought index of the pixel. The linear relationship between the measured soil moisture content and RPDI is obtained by fitting the least squares method, and the parameters a and b are obtained. Step 24: extract the soil moisture inversion results corresponding to all measured points in the sub-area, calculate the unbiased root mean square error ubRMSE according to the following formula, and evaluate the accuracy of the inversion model: In the formula, x i ,y i They represent the remote sensing inversion and measured soil moisture content respectively, and n is the number of samples involved in the calculation; The model ubRMSE should be less than 4%; if the accuracy does not meet the requirements, it is necessary to return to step 11 to reduce the area range or step 22 to supplement the ground measured data and recalculate the relevant parameters.

4. The soil moisture inversion method according to claim 1, characterized in that: The step 3 includes the following sub-steps: Step 31, using the red and near-red band reflectances of the optical satellite image and the theoretical soil line slope as input, calculate the RPDI values ​​of all pixels in each period and sub-region, and use the regression equation obtained in step 23 to inversely calculate the soil moisture content of the sub-region on that date; Step 32: The soil moisture results of the sub-regions with the same transit date are merged and spliced ​​to obtain the soil moisture content of the target region on the satellite transit date.

5. The soil moisture inversion method according to claim 1, characterized in that: The step 4 includes the following sub-steps: Step 41, when there is a new remote sensing image of the target area, the minimum near-red band value corresponding to the red band of the new image is integrated into the existing theoretical feature space to calculate the new theoretical soil line slope; evaluate whether the change of the soil line slope is significant. If the new theoretical soil line slope changes by more than 0.05 compared with the original theoretical soil line slope, it is a significant change, and continue to step 42. Otherwise, jump to step 43; Step 42, repeating step 2, using the new theoretical soil line slope, calculating the re-corrected vertical drought index RPDI value of the pixel where the field-measured soil moisture content is located, and constructing a soil moisture inversion model with the ground-measured data of the surface soil moisture content on the satellite transit day; Step 43, based on the red and near-red band reflectance of the optical satellite image, the new theoretical soil line slope is used as input if the change is significant, and the original soil line slope is used as input if the change is not significant, and the RPDI values ​​of all pixels in the target area are calculated. If the change is significant, the new regression equation is used to calculate the soil moisture content of the target area, and if the change is not significant, the original regression equation is used to calculate the soil moisture content of the target area.

6. The soil moisture inversion method according to claim 1, characterized in that: The soil moisture inversion method is applied to areas with NDVI < 0.

6.

7. A soil moisture inversion system based on multi-temporal soil line consistency correction, characterized by: The system can be used to implement the soil moisture inversion method described in any one of claims 1 to 6, specifically including: Data collection module: responsible for collecting optical image data of the target area and extracting the minimum near-red band value corresponding to the red band for subsequent processing; Theoretical feature space generation module: Process the collected image data, generate theoretical feature space, and calculate the theoretical soil line slope; Model building module: using the theoretical soil line slope and field measurement data, a regression equation model reflecting the relationship between soil moisture content and RPDI is established; Inversion calculation module: Based on the reflectivity data of optical satellite images and the theoretical soil line slope, the RPDI value of the entire target area is calculated, and the soil moisture content is inverted using the model; Change detection and update module: Regularly evaluate the changes in the slope of the theoretical soil line. If the changes are significant, update the relevant model parameters to ensure the accuracy and stability of the inversion results.

8. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the soil moisture inversion method according to one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the soil moisture inversion method described in one of claims 1 to 6 is implemented.

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