Soil moisture remote sensing inversion method and system based on surface water balance constraint

Abstract

This invention relates to the field of soil moisture remote sensing inversion technology, providing a method and system for soil moisture remote sensing inversion based on surface water balance constraints. The invention acquires moisture detection data from multiple preset sampling points through remote sensing monitoring to obtain remote sensing image data; constructs a moisture correlation function; calculates the first soil moisture at the remote sensing inversion location based on the moisture detection data; performs thermal inertia analysis on the remote sensing image data, and, combined with the moisture correlation function, calculates the second soil moisture at the remote sensing inversion location; and, based on surface water balance constraints, determines the influencing weight data to calculate the target soil moisture at the remote sensing inversion location. This method can calculate the first soil moisture based on moisture detection data, the second soil moisture based on remote sensing image data, determine the influencing weight data, and calculate the target soil moisture at the remote sensing inversion location without requiring complex calculations involving multiple parameters and models, and can obtain relatively accurate target soil moisture.

Description

Technical Field

[0001] This invention belongs to the field of soil moisture remote sensing inversion technology, and particularly relates to a method and system for soil moisture remote sensing inversion based on surface water balance constraints. Background Technology

[0002] Soil moisture remote sensing inversion is a technique that uses multi-source observation data acquired by remote sensing platforms such as satellites, aerial vehicles, or drones to quantitatively estimate the water content of surface soil. It typically includes remote sensing data preprocessing, feature parameter extraction, inversion model construction, and inversion result verification. The inversion model can be based on radiative transfer theory, surface energy or water balance constraints, or it can adopt data-driven methods such as machine learning.

[0003] Soil moisture remote sensing inversion provides key basic data support for agricultural drought monitoring, crop growth assessment, water resource management, flood and drought early warning, and climate change research. It is an important technical means in modern earth observation and hydrological and ecological research.

[0004] Existing technologies for soil moisture remote sensing inversion typically rely on complex calculations involving multiple parameters and models. The overall implementation process is cumbersome and computationally expensive. Moreover, it often only yields theoretical moisture values, which differ from the actual soil surface moisture. Although it can provide data support for some applications, the data accuracy is limited and cannot meet the needs of higher-precision practical applications. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for remote sensing inversion of soil moisture based on surface water balance constraints, aiming to solve the technical problems existing in the prior art mentioned in the background.

[0006] The embodiments of the present invention are implemented as follows:

[0007] A remote sensing inversion method for soil moisture based on surface water balance constraints, the method specifically includes the following steps:

[0008] Multiple preset sample points are identified in the remote sensing inversion area, humidity detection data of the multiple preset sample points are obtained, and remote sensing monitoring is performed on the remote sensing inversion area to obtain remote sensing image data.

[0009] Based on the remote sensing image data, the thermal inertia values ​​of multiple preset sample points are calculated, and a correlation analysis is performed in conjunction with the humidity detection data to construct a humidity correlation function.

[0010] Determine the remote sensing inversion location within the remote sensing inversion area, and calculate the first soil moisture at the remote sensing inversion location based on the humidity detection data;

[0011] Thermal inertia analysis is performed on the remote sensing image data, and the second soil moisture at the remote sensing inversion location is calculated by combining the humidity correlation function.

[0012] Based on the surface water balance constraint, the influencing weight data are determined, and the target soil moisture at the remote sensing inversion location is calculated by combining the first soil moisture and the second soil moisture.

[0013] As a further limitation of the technical solution of this embodiment of the invention, the steps of determining multiple preset sample points in the remote sensing inversion area, acquiring humidity detection data of multiple preset sample points, and performing remote sensing monitoring on the remote sensing inversion area to acquire remote sensing image data specifically include the following steps:

[0014] Acquire sampling record data for the remote sensing inversion area;

[0015] The sampled data is used to identify sample points to determine multiple preset sample points in the remote sensing inversion area;

[0016] Humidity detection data of multiple preset sample points are extracted from the sampling record data;

[0017] Remote sensing monitoring is performed on the remote sensing inversion area to acquire remote sensing image data.

[0018] As a further limitation of the technical solution of this embodiment of the invention, the step of calculating the thermal inertia values ​​of multiple preset sample points based on the remote sensing image data, and performing correlation analysis in conjunction with the humidity detection data to construct a humidity correlation function specifically includes the following steps:

[0019] The remote sensing image data is identified to determine the surface reflectance of multiple preset sample points;

[0020] Determine the period of influence;

[0021] Obtain surface temperature data of multiple preset sample points during the period of influence;

[0022] Based on the surface temperature data and surface reflectance of the multiple sample points, calculate the thermal inertia values ​​of the multiple preset sample points;

[0023] By combining the humidity detection data and the thermal inertia values ​​of multiple sample points, a correlation analysis of thermal inertia and humidity is performed to construct a humidity correlation function.

[0024] As a further limitation of the technical solution of this embodiment of the invention, the calculation formula for the thermal inertia values ​​of the plurality of preset sample points is as follows:

[0025] ;

[0026] in, Representing the One preset sample point; For the first The thermal inertia value of a preset sample point; For the first The surface reflectance of a preset sample point; For the first The highest surface temperature at each preset sampling point during a given time period; For the first The lowest surface temperature at each preset sampling point during a given time period.

[0027] As a further limitation of the technical solution of this embodiment of the invention, the step of determining the remote sensing inversion location in the remote sensing inversion area and calculating the first soil moisture at the remote sensing inversion location based on the humidity detection data specifically includes the following steps:

[0028] Receive remote sensing inversion requests;

[0029] The remote sensing inversion requirement is located and identified to determine the remote sensing inversion location within the remote sensing inversion area;

[0030] Measure the distance between the remote sensing inversion location and the plurality of preset sample points;

[0031] Based on the humidity detection data and the multiple distances between them, the first soil humidity at the remote sensing inversion location is calculated.

[0032] As a further limitation of the technical solution of this embodiment of the invention, the formula for calculating the first soil moisture is:

[0033] ;

[0034] in, The first soil moisture level; Representing the There are a total of preset sample points, with a total of One preset sample point; For the first Humidity detection values ​​of a preset sample point; For remote sensing inversion location and the first The distance between preset sample points; These are the preset calculation parameters.

[0035] As a further limitation of the technical solution of this embodiment of the invention, the step of performing thermal inertia analysis on the remote sensing image data and calculating the second soil moisture at the remote sensing inversion location in conjunction with the humidity correlation function specifically includes the following steps:

[0036] The remote sensing image data is identified to determine the location and surface reflectance of the remote sensing inversion position;

[0037] Obtain the surface temperature data of the remotely sensed location during the period of influence;

[0038] Calculate the location thermal inertia value of the remote sensing inversion location based on the location surface temperature data and the location surface reflectance;

[0039] By combining the location thermal inertia value and the humidity correlation function, the second soil moisture at the remote sensing inversion location is calculated.

[0040] As a further limitation of the technical solution of this invention, the step of determining the influencing weight data based on surface water balance constraints, and calculating the target soil moisture at the remote sensing inversion location by combining the first soil moisture and the second soil moisture specifically includes the following steps:

[0041] Obtain surface water related data for the remote sensing inversion area;

[0042] An impact analysis of surface water balance constraints was performed on the aforementioned surface water-related data to determine the impact weight data;

[0043] The target soil moisture at the remote sensing inversion location is calculated by combining the first soil moisture, the second soil moisture, and the influence weight data.

[0044] As a further limitation of the technical solution of this embodiment of the invention, the formula for calculating the target soil moisture is:

[0045] ;

[0046] ;

[0047] in, Target soil moisture; The second soil moisture level; , These are the first and second influence weights, respectively.

[0048] A soil moisture remote sensing inversion system based on surface water balance constraints includes a sample data acquisition module, a moisture correlation analysis module, a first moisture calculation module, a second moisture calculation module, and a target moisture calculation module, wherein:

[0049] The sample data acquisition module is used to determine multiple preset sample points in the remote sensing inversion area, acquire humidity detection data of the multiple preset sample points, and perform remote sensing monitoring on the remote sensing inversion area to acquire remote sensing image data.

[0050] The humidity correlation analysis module is used to calculate the thermal inertia values ​​of multiple preset sample points based on the remote sensing image data, and to perform correlation analysis in combination with the humidity detection data to construct a humidity correlation function.

[0051] The first humidity calculation module is used to determine the remote sensing inversion location in the remote sensing inversion area and calculate the first soil humidity at the remote sensing inversion location based on the humidity detection data.

[0052] The second humidity calculation module is used to perform thermal inertia analysis on the remote sensing image data and, in conjunction with the humidity correlation function, calculate the second soil humidity at the remote sensing inversion location.

[0053] The target humidity calculation module is used to determine the influencing weight data based on the surface water balance constraint, and calculate the target soil humidity at the remote sensing inversion location by combining the first soil humidity and the second soil humidity.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] This invention acquires humidity detection data from multiple preset sampling points for remote sensing monitoring to obtain remote sensing image data; constructs a humidity correlation function; calculates the first soil moisture at the remote sensing inversion location based on the humidity detection data; performs thermal inertia analysis on the remote sensing image data, and calculates the second soil moisture at the remote sensing inversion location in conjunction with the humidity correlation function; and determines the influencing weight data based on surface water balance constraints to calculate the target soil moisture at the remote sensing inversion location. It can calculate the first soil moisture based on humidity detection data, the second soil moisture based on remote sensing image data, determine the influencing weight data, and calculate the target soil moisture at the remote sensing inversion location without requiring complex calculations involving multiple parameters and models, and can obtain relatively accurate target soil moisture. Attached Figure Description

[0056] Figure 1 A flowchart of the soil moisture remote sensing inversion method based on surface water balance constraints provided in an embodiment of the present invention is shown.

[0057] Figure 2 The following is an application architecture diagram of the soil moisture remote sensing inversion system based on surface water balance constraints provided in an embodiment of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0059] Understandably, existing soil moisture remote sensing inversion technologies typically rely on complex calculations involving multiple parameters and models. The overall implementation process is cumbersome and computationally expensive. Moreover, it often only yields theoretical moisture values, which differ from the actual soil surface moisture. Although it can provide data support for some applications, the data accuracy is limited and cannot meet the needs of higher-precision practical applications.

[0060] To address the aforementioned issues, this invention discloses a method and system for remote sensing inversion of soil moisture based on surface water balance constraints. This method involves identifying multiple preset sample points within a remote sensing inversion area, acquiring humidity detection data from these points, and performing remote sensing monitoring of the inversion area to obtain remote sensing image data. Based on the image data, the method calculates the thermal inertia values ​​of the preset sample points, performs correlation analysis using the humidity detection data, and constructs a humidity correlation function. It then determines the remote sensing inversion location within the area and calculates the first soil moisture at that location based on the humidity detection data. Next, it performs thermal inertia analysis on the image data, combines it with the humidity correlation function, and calculates the second soil moisture at the location. Finally, based on surface water balance constraints, it determines the influence weight data and, combining the first and second soil moisture, calculates the target soil moisture at the location. This method can calculate the first soil moisture based on humidity detection data, the second soil moisture based on remote sensing image data, determine the influence weight data, and calculate the target soil moisture at the remote sensing inversion location without requiring complex calculations involving multiple parameters and models, and can obtain relatively accurate target soil moisture.

[0061] Specifically, Figure 1 A flowchart of the remote sensing inversion method for soil moisture based on surface water balance constraints provided in an embodiment of the present invention is shown.

[0062] In a preferred embodiment of the present invention, the soil moisture remote sensing inversion method based on surface water balance constraints specifically includes the following steps:

[0063] Step S101: Determine multiple preset sample points in the remote sensing inversion area, acquire humidity detection data of the multiple preset sample points, and perform remote sensing monitoring on the remote sensing inversion area to acquire remote sensing image data.

[0064] In this embodiment of the invention, sampling record data obtained by sampling and recording a remote sensing inversion area in advance is acquired. By identifying sample points in the sampling record data, multiple preset sample points in the remote sensing inversion area are determined. Then, humidity detection data of multiple preset sample points are extracted from the sampling record data. At the same time, remote sensing monitoring is performed on the remote sensing inversion area to acquire remote sensing image data covering the entire remote sensing inversion area.

[0065] Specifically, in another preferred embodiment provided by the present invention, the steps of determining multiple preset sample points in the remote sensing inversion area, acquiring humidity detection data of the multiple preset sample points, and performing remote sensing monitoring on the remote sensing inversion area to acquire remote sensing image data specifically include the following steps:

[0066] Acquire sampling record data for the remote sensing inversion area;

[0067] The sampled data is used to identify sample points to determine multiple preset sample points in the remote sensing inversion area;

[0068] Humidity detection data of multiple preset sample points are extracted from the sampling record data;

[0069] Remote sensing monitoring is performed on the remote sensing inversion area to acquire remote sensing image data.

[0070] Furthermore, the soil moisture remote sensing inversion method based on surface water balance constraints also includes the following steps:

[0071] Step S102: Based on the remote sensing image data, calculate the thermal inertia values ​​of multiple preset sample points, and perform correlation analysis in conjunction with the humidity detection data to construct a humidity correlation function.

[0072] In this embodiment of the invention, by identifying remote sensing image data, the surface reflectance of multiple preset sample points in the remote sensing inversion area is determined, and the influence period is determined. According to the influence period, the surface temperature of the multiple preset sample points is monitored to obtain the surface temperature data. Then, based on the surface temperature data and surface reflectance of the multiple sample points, the thermal inertia values ​​of the multiple preset sample points are calculated. Using the thermal inertia values ​​as multiple input values ​​and humidity detection data as multiple output values, data is substituted into a preset calculator suite, and automatic data correlation function analysis is performed to generate a humidity correlation function related to the thermal inertia values ​​of the multiple sample points and the humidity detection data. This achieves the construction of the humidity correlation function. Specifically, the calculation formula for the thermal inertia values ​​of the multiple preset sample points is as follows:

[0073] ;

[0074] in, Representing the One preset sample point; For the first The thermal inertia value of a preset sample point; For the first The surface reflectance of a preset sample point; For the first The highest surface temperature at each preset sampling point during a given time period; For the first The lowest surface temperature at each preset sampling point during a given time period.

[0075] It is understood that, in the embodiments of the present invention, the period of influence on the cycle can be 0:00-23:59 in 24-hour format.

[0076] Specifically, in another preferred embodiment provided by the present invention, the step of calculating the thermal inertia values ​​of multiple preset sample points based on the remote sensing image data, and performing correlation analysis in conjunction with the humidity detection data to construct a humidity correlation function specifically includes the following steps:

[0077] The remote sensing image data is identified to determine the surface reflectance of multiple preset sample points;

[0078] Determine the period of influence;

[0079] Obtain surface temperature data of multiple preset sample points during the period of influence;

[0080] Based on the surface temperature data and surface reflectance of the multiple sample points, calculate the thermal inertia values ​​of the multiple preset sample points;

[0081] By combining the humidity detection data and the thermal inertia values ​​of multiple sample points, a correlation analysis of thermal inertia and humidity is performed to construct a humidity correlation function.

[0082] Furthermore, the soil moisture remote sensing inversion method based on surface water balance constraints also includes the following steps:

[0083] Step S103: Determine the remote sensing inversion location in the remote sensing inversion area, and calculate the first soil moisture at the remote sensing inversion location based on the humidity detection data.

[0084] In this embodiment of the invention, a remote sensing inversion request uploaded by staff is received. The remote sensing inversion location within the inversion area is determined by locating and identifying the request. Then, the distance between the remote sensing inversion location and multiple preset sample points is measured on a preset electronic map. Based on humidity detection data and these distances, a first soil moisture content at the remote sensing inversion location is calculated. Specifically, the formula for calculating the first soil moisture content is:

[0085] ;

[0086] in, The first soil moisture level; Representing the There are a total of preset sample points, with a total of One preset sample point; For the first Humidity detection values ​​of a preset sample point; For remote sensing inversion location and the first The distance between preset sample points; These are the preset calculation parameters.

[0087] Specifically, in another preferred embodiment provided by the present invention, determining the remote sensing inversion location within the remote sensing inversion area and calculating the first soil moisture at the remote sensing inversion location based on the humidity detection data specifically includes the following steps:

[0088] Receive remote sensing inversion requests;

[0089] The remote sensing inversion requirement is located and identified to determine the remote sensing inversion location within the remote sensing inversion area;

[0090] Measure the distance between the remote sensing inversion location and the plurality of preset sample points;

[0091] Based on the humidity detection data and the multiple distances between them, the first soil humidity at the remote sensing inversion location is calculated.

[0092] Furthermore, the soil moisture remote sensing inversion method based on surface water balance constraints also includes the following steps:

[0093] Step S104: Perform thermal inertia analysis on the remote sensing image data, and calculate the second soil moisture at the remote sensing inversion location by combining the humidity correlation function.

[0094] In this embodiment of the invention, the location surface reflectance of the remote sensing inversion location is determined by identifying remote sensing image data, and the location surface temperature data of the remote sensing inversion location during the influence period is obtained. Then, based on the location surface temperature data and location surface reflectance, the location thermal inertia value of the remote sensing inversion location is calculated. The location thermal inertia value is then substituted into the humidity correlation function to perform humidity correlation calculation, thereby obtaining the second soil moisture of the remote sensing inversion location.

[0095] Specifically, in another preferred embodiment provided by the present invention, the step of performing thermal inertia analysis on the remote sensing image data and calculating the second soil moisture at the remote sensing inversion location in conjunction with the humidity correlation function specifically includes the following steps:

[0096] The remote sensing image data is identified to determine the location and surface reflectance of the remote sensing inversion position;

[0097] Obtain the surface temperature data of the remotely sensed location during the period of influence;

[0098] Calculate the location thermal inertia value of the remote sensing inversion location based on the location surface temperature data and the location surface reflectance;

[0099] By combining the location thermal inertia value and the humidity correlation function, the second soil moisture at the remote sensing inversion location is calculated.

[0100] Furthermore, the soil moisture remote sensing inversion method based on surface water balance constraints also includes the following steps:

[0101] Step S105: Based on the surface water balance constraint, determine the influencing weight data, and calculate the target soil moisture at the remote sensing inversion location by combining the first soil moisture and the second soil moisture.

[0102] In this embodiment of the invention, surface water-related data such as precipitation, evapotranspiration, and surface runoff in the remote sensing inversion area are acquired. Then, an impact analysis of surface water balance constraints is performed on the surface water-related data to determine the influence weight data. Subsequently, combining the first soil moisture, the second soil moisture, and the influence weight data, the target soil moisture at the remote sensing inversion location is calculated. Specifically, the formula for calculating the target soil moisture is:

[0103] ;

[0104] ;

[0105] in, Target soil moisture; The second soil moisture level; , These are the first and second influence weights, respectively.

[0106] It is understandable that the impact analysis of surface water balance constraints on surface water-related data is a process of analyzing the degree of influence of radiation and location on surface water-related data based on surface water balance constraints. If the degree of location influence is greater than that of radiation influence, then the weight of the first influence is greater than that of the second influence; if the degree of radiation influence is greater than that of location influence, then the weight of the first influence is smaller than that of the second influence.

[0107] Understandably, surface water-related data are compared with preset standard data. Based on the degree of deviation of precipitation, evapotranspiration, and surface runoff in the surface water-related data from the corresponding standard values, the influence of radiation and location is analyzed. Among them, precipitation and surface runoff are directly related to location influence, while evapotranspiration is directly related to radiation influence.

[0108] Specifically, in another preferred embodiment provided by the present invention, the step of determining the influencing weight data based on surface water balance constraints, and calculating the target soil moisture at the remote sensing inversion location by combining the first soil moisture and the second soil moisture includes the following steps:

[0109] Obtain surface water related data for the remote sensing inversion area;

[0110] An impact analysis of surface water balance constraints was performed on the aforementioned surface water-related data to determine the impact weight data;

[0111] The target soil moisture at the remote sensing inversion location is calculated by combining the first soil moisture, the second soil moisture, and the influence weight data.

[0112] Furthermore, Figure 2 The following is an application architecture diagram of the soil moisture remote sensing inversion system based on surface water balance constraints provided in an embodiment of the present invention.

[0113] Specifically, in another preferred embodiment provided by the present invention, the soil moisture remote sensing inversion system based on surface water balance constraints includes:

[0114] The sample data acquisition module 101 is used to determine multiple preset sample points in the remote sensing inversion area, acquire humidity detection data of the multiple preset sample points, and perform remote sensing monitoring on the remote sensing inversion area to acquire remote sensing image data.

[0115] In this embodiment of the invention, the sample data acquisition module 101 acquires the sampling record data obtained by sampling and recording the remote sensing inversion area in advance. By identifying the sample record data, multiple preset sample points in the remote sensing inversion area are determined. Then, the humidity detection data of multiple preset sample points are extracted from the sampling record data. At the same time, remote sensing monitoring is performed on the remote sensing inversion area to acquire remote sensing image data covering the entire remote sensing inversion area.

[0116] The humidity correlation analysis module 102 is used to calculate the thermal inertia values ​​of multiple preset sample points based on the remote sensing image data, and to perform correlation analysis in combination with the humidity detection data to construct a humidity correlation function.

[0117] In this embodiment of the invention, the humidity correlation analysis module 102 identifies the surface reflectance of multiple preset sample points in the remote sensing inversion area by identifying remote sensing image data, and determines the influence period. According to the influence period, it monitors the surface temperature of the multiple preset sample points to obtain surface temperature data. Then, based on the surface temperature data and surface reflectance, it calculates the thermal inertia values ​​of the multiple preset sample points. Using the thermal inertia values ​​as multiple input values ​​and the humidity detection data as multiple output values, it substitutes the data into a preset calculator suite and automatically performs data correlation function analysis to generate a humidity correlation function related to the thermal inertia values ​​and humidity detection data. Specifically, the calculation formula for the thermal inertia values ​​of the multiple preset sample points is as follows:

[0118] ;

[0119] in, Representing the One preset sample point; For the first The thermal inertia value of a preset sample point; For the first The surface reflectance of a preset sample point; For the first The highest surface temperature at each preset sampling point during a given time period; For the first The lowest surface temperature at each preset sampling point during a given time period.

[0120] The first humidity calculation module 103 is used to determine the remote sensing inversion location in the remote sensing inversion area and calculate the first soil humidity at the remote sensing inversion location based on the humidity detection data.

[0121] In this embodiment of the invention, the first humidity calculation module 103 receives remote sensing inversion requests uploaded by staff, identifies the remote sensing inversion location within the remote sensing inversion area by locating the requests, and then measures the distances between the remote sensing inversion location and multiple preset sample points on a preset electronic map. Based on humidity detection data and the multiple distances, the first soil moisture at the remote sensing inversion location is calculated. Specifically, the formula for calculating the first soil moisture is as follows:

[0122] ;

[0123] in, The first soil moisture level; Representing the There are a total of preset sample points, with a total of One preset sample point; For the first Humidity detection values ​​of a preset sample point; For remote sensing inversion location and the first The distance between preset sample points; These are the preset calculation parameters.

[0124] The second humidity calculation module 104 is used to perform thermal inertia analysis on the remote sensing image data and, in conjunction with the humidity correlation function, calculate the second soil humidity at the remote sensing inversion location.

[0125] In this embodiment of the invention, the second humidity calculation module 104 identifies the remote sensing image data to determine the location surface reflectance of the remote sensing inversion location, and obtains the location surface temperature data of the remote sensing inversion location during the influence period. Then, based on the location surface temperature data and the location surface reflectance, it calculates the location thermal inertia value of the remote sensing inversion location, and then substitutes the location thermal inertia value into the humidity correlation function to perform humidity correlation calculation, thereby obtaining the second soil humidity of the remote sensing inversion location.

[0126] The target humidity calculation module 105 is used to determine the influence weight data based on the surface water balance constraint, and calculate the target soil humidity at the remote sensing inversion location by combining the first soil humidity and the second soil humidity.

[0127] In this embodiment of the invention, the target humidity calculation module 105 acquires surface water-related data such as precipitation, evapotranspiration, and surface runoff in the remote sensing inversion area, then performs an impact analysis on the surface water-related data based on surface water balance constraints to determine the impact weight data. Subsequently, combining the first soil humidity, the second soil humidity, and the impact weight data, it calculates the target soil humidity at the remote sensing inversion location. Specifically, the calculation formula for the target soil humidity is:

[0128] ;

[0129] ;

[0130] in, Target soil moisture; The second soil moisture level; , These are the first and second influence weights, respectively.

[0131] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A remote sensing inversion method for soil moisture based on surface water balance constraints, characterized in that, The method specifically includes the following steps: Multiple preset sample points are identified in the remote sensing inversion area, humidity detection data of the multiple preset sample points are obtained, and remote sensing monitoring is performed on the remote sensing inversion area to obtain remote sensing image data. Based on the remote sensing image data, the thermal inertia values ​​of multiple preset sample points are calculated, and a correlation analysis is performed in conjunction with the humidity detection data to construct a humidity correlation function. Determine the remote sensing inversion location within the remote sensing inversion area, and calculate the first soil moisture at the remote sensing inversion location based on the humidity detection data; Thermal inertia analysis is performed on the remote sensing image data, and the second soil moisture at the remote sensing inversion location is calculated by combining the humidity correlation function. Based on the surface water balance constraint, the influencing weight data are determined, and the target soil moisture at the remote sensing inversion location is calculated by combining the first soil moisture and the second soil moisture.

2. The method for remote sensing inversion of soil moisture based on surface water balance constraints according to claim 1, characterized in that, The process of determining multiple preset sample points in the remote sensing inversion area, acquiring humidity detection data from the multiple preset sample points, and performing remote sensing monitoring on the remote sensing inversion area to acquire remote sensing image data specifically includes the following steps: Acquire sampling record data for the remote sensing inversion area; The sampled data is used to identify sample points to determine multiple preset sample points in the remote sensing inversion area; Humidity detection data of multiple preset sample points are extracted from the sampling record data; Remote sensing monitoring is performed on the remote sensing inversion area to acquire remote sensing image data.

3. The method for remote sensing inversion of soil moisture based on surface water balance constraints according to claim 1, characterized in that, The process of calculating the thermal inertia values ​​of multiple preset sample points based on the remote sensing image data, and then performing correlation analysis with the humidity detection data to construct a humidity correlation function specifically includes the following steps: The remote sensing image data is identified to determine the surface reflectance of multiple preset sample points; Determine the period of influence; Obtain surface temperature data of multiple preset sample points during the period of influence; Based on the surface temperature data and surface reflectance of the multiple sample points, calculate the thermal inertia values ​​of the multiple preset sample points; By combining the humidity detection data and the thermal inertia values ​​of multiple sample points, a correlation analysis of thermal inertia and humidity is performed to construct a humidity correlation function.

4. The method for remote sensing inversion of soil moisture based on surface water balance constraints according to claim 3, characterized in that, The formula for calculating the thermal inertia values ​​of multiple preset sample points is as follows: ； in, Representing the One preset sample point; For the first The thermal inertia value of a preset sample point; For the first The surface reflectance of a preset sample point; For the first The highest surface temperature at each preset sampling point during a given time period; For the first The lowest surface temperature at each preset sampling point during a given time period.

5. The method for remote sensing inversion of soil moisture based on surface water balance constraints according to claim 1, characterized in that, The process of determining the remote sensing inversion location within the remote sensing inversion area and calculating the first soil moisture at the remote sensing inversion location based on the humidity detection data specifically includes the following steps: Receive remote sensing inversion requests; The remote sensing inversion requirement is located and identified to determine the remote sensing inversion location within the remote sensing inversion area; Measure the distance between the remote sensing inversion location and the plurality of preset sample points; Based on the humidity detection data and the multiple distances between them, the first soil humidity at the remote sensing inversion location is calculated.

6. The method for remote sensing inversion of soil moisture based on surface water balance constraints according to claim 5, characterized in that, The formula for calculating the first soil moisture content is: ； in, The first soil moisture level; Representing the There are a total of preset sample points, with a total of One preset sample point; For the first Humidity detection values ​​of a preset sample point; For remote sensing inversion location and the first The distance between preset sample points; These are the preset calculation parameters.

7. The method for remote sensing inversion of soil moisture based on surface water balance constraints according to claim 3, characterized in that, The process of performing thermal inertia analysis on the remote sensing image data and combining it with the humidity correlation function to calculate the second soil moisture at the remote sensing inversion location specifically includes the following steps: The remote sensing image data is identified to determine the location and surface reflectance of the remote sensing inversion position; Obtain the surface temperature data of the remotely sensed location during the period of influence; Calculate the location thermal inertia value of the remote sensing inversion location based on the location surface temperature data and the location surface reflectance; By combining the location thermal inertia value and the humidity correlation function, the second soil moisture at the remote sensing inversion location is calculated.

8. The method for remote sensing inversion of soil moisture based on surface water balance constraints according to claim 6, characterized in that, The process of determining the influencing weight data based on surface water balance constraints, and calculating the target soil moisture at the remote sensing inversion location by combining the first soil moisture and the second soil moisture, specifically includes the following steps: Obtain surface water related data for the remote sensing inversion area; An impact analysis of surface water balance constraints was performed on the aforementioned surface water-related data to determine the impact weight data; The target soil moisture at the remote sensing inversion location is calculated by combining the first soil moisture, the second soil moisture, and the influence weight data.

9. The method for remote sensing inversion of soil moisture based on surface water balance constraints according to claim 8, characterized in that, The formula for calculating the target soil moisture is: ； ； in, Target soil moisture; The second soil moisture level; , These are the first and second influence weights, respectively.

10. A soil moisture remote sensing inversion system based on surface water balance constraints, characterized in that, The system includes a sample data acquisition module, a humidity correlation analysis module, a first humidity calculation module, a second humidity calculation module, and a target humidity calculation module, wherein: The sample data acquisition module is used to determine multiple preset sample points in the remote sensing inversion area, acquire humidity detection data of the multiple preset sample points, and perform remote sensing monitoring on the remote sensing inversion area to acquire remote sensing image data. The humidity correlation analysis module is used to calculate the thermal inertia values ​​of multiple preset sample points based on the remote sensing image data, and to perform correlation analysis in combination with the humidity detection data to construct a humidity correlation function. The first humidity calculation module is used to determine the remote sensing inversion location in the remote sensing inversion area and calculate the first soil humidity at the remote sensing inversion location based on the humidity detection data. The second humidity calculation module is used to perform thermal inertia analysis on the remote sensing image data and, in conjunction with the humidity correlation function, calculate the second soil humidity at the remote sensing inversion location. The target humidity calculation module is used to determine the influencing weight data based on the surface water balance constraint, and calculate the target soil humidity at the remote sensing inversion location by combining the first soil humidity and the second soil humidity.

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