Soil moisture detection method, device and equipment based on multi-source remote sensing technology

Abstract

The present invention relates to the field of geographic information technology, and in particular to a soil moisture detection method based on multi-source remote sensing technology, comprising: obtaining satellite parameters of a target area, obtaining soil backscatter coefficient data of the target area, constructing a soil backscatter coefficient simulation data set according to the satellite parameters of the target area, constructing a soil moisture detection model according to the soil backscatter coefficient simulation data set, inputting the satellite parameters of the target area and the soil backscatter coefficient data into the soil moisture detection model, and obtaining soil moisture data of the target area. Compared with the prior art, the present invention can more comprehensively, accurately and effectively realize the inversion of soil moisture in a target area with high vegetation coverage.

Description

Technical Field

[0001] The present invention relates to the field of geographic information technology, and in particular to a soil moisture detection method, device, equipment and storage medium based on multi-source remote sensing technology. Background Art

[0002] Soil moisture (soil humidity, soil moisture data) is a key parameter of the land surface water cycle, controlling the water and heat energy exchange between the land and the atmosphere. It is of great significance to meteorology, hydrology, ecology, agriculture, forestry, animal husbandry and other research fields. Traditional soil moisture measurement methods such as weighing method, neutron moisture meter method, time domain reflectometry, etc., although with high measurement accuracy, are only suitable for single-point measurement in a small range and cannot meet the requirements of large-scale monitoring of soil moisture. The emergence of remote sensing technology has made it possible to observe soil moisture in real time over a large area with high efficiency and low cost.

[0003] At present, the remote sensing methods used for soil moisture inversion mainly include soil moisture inversion in the visible light band, infrared band, and microwave band. Since optical data has poor penetration and is easily affected by cloud cover, high vegetation cover, sunlight, etc., the applicability of the data is limited. Among microwave remote sensing methods, the resolution of passive microwave remote sensing methods is usually not high, generally tens of kilometers, and is often suitable for large-scale soil moisture inversion such as the global scale, but performs poorly in high-precision soil moisture inversion. Previous soil moisture inversion studies were mostly based on bare soil or sparsely vegetation-covered surfaces in arid and semi-arid areas. However, on surfaces with high vegetation cover, dense vegetation canopies often cause attenuation and scattering of soil microwave radiation signals, greatly reducing the accuracy of soil moisture inversion, and the inversion results are not ideal. Summary of the invention

[0004] Based on this, the purpose of the present invention is to provide a soil moisture detection method, device, equipment and storage medium based on multi-source remote sensing technology. By constructing soil backscattering coefficient simulation data under HH polarization and soil backscattering coefficient simulation data under HV polarization, a soil moisture detection model is constructed, which can realize the inversion of soil moisture in target areas with high vegetation coverage more comprehensively, accurately and effectively.

[0005] In a first aspect, an embodiment of the present application provides a soil moisture detection method based on multi-source remote sensing technology, comprising the following steps:

[0006] Acquire satellite parameters of the target area, wherein the satellite parameters include incident angle parameters;

[0007] Acquire soil backscattering coefficient data of the target area, wherein the soil backscattering coefficient data includes backscattering coefficient data under HH polarization and backscattering coefficient data under HV polarization;

[0008] Constructing a soil backscatter coefficient simulation data set according to the satellite parameters of the target area, wherein the soil backscatter coefficient simulation data set includes soil backscatter coefficient simulation data under HH polarization and soil backscatter coefficient simulation data under HV polarization;

[0009] A soil moisture detection model is constructed according to the soil backscatter coefficient simulation data set, and the satellite parameters and soil backscatter coefficient data of the target area are input into the soil moisture detection model to obtain the soil moisture data of the target area.

[0010] In a second aspect, an embodiment of the present application provides a soil moisture detection device based on multi-source remote sensing technology, comprising:

[0011] A first acquisition module is used to acquire satellite parameters of a target area, wherein the satellite parameters include incident angle parameters;

[0012] A second acquisition module is used to acquire soil backscattering coefficient data of the target area, wherein the soil backscattering coefficient data includes backscattering coefficient data under HH polarization and backscattering coefficient data under HV polarization;

[0013] A soil backscatter coefficient simulation data set construction module, used to construct a soil backscatter coefficient simulation data set according to the satellite parameters of the target area, wherein the soil backscatter coefficient simulation data set includes soil backscatter coefficient simulation data under HH polarization and soil backscatter coefficient simulation data under HV polarization;

[0014] The soil moisture data calculation module is used to construct a soil moisture detection model according to the soil backscatter coefficient simulation data set, input the satellite parameters and soil backscatter coefficient data of the target area into the soil moisture detection model, and obtain the soil moisture data of the target area.

[0015] In a third aspect, an embodiment of the present application provides a computer device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, the steps of the soil moisture detection method based on multi-source remote sensing technology as described in the first aspect are implemented.

[0016] In a fourth aspect, an embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the soil moisture detection method based on multi-source remote sensing technology as described in the first aspect are implemented.

[0017] In an embodiment of the present application, a soil moisture detection method, device, equipment and storage medium based on multi-source remote sensing technology are provided. By constructing soil backscattering coefficient simulation data under HH polarization and soil backscattering coefficient simulation data under HV polarization, a soil moisture detection model is constructed, which can remove the influence of high vegetation coverage on soil moisture inversion and can realize the inversion of soil moisture in the target area more comprehensively, accurately and effectively.

[0018] For better understanding and implementation, the present invention is described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of a flow chart of a soil moisture detection method based on multi-source remote sensing technology provided in one embodiment of the present application;

[0020] Figure 2 A schematic diagram of the process of S2 in a soil moisture detection method based on multi-source remote sensing technology provided in one embodiment of the present application;

[0021] Figure 3 A schematic diagram of the process of S201 in a soil moisture detection method based on multi-source remote sensing technology provided in one embodiment of the present application;

[0022] Figure 4 A schematic diagram of the process of S4 in a soil moisture detection method based on multi-source remote sensing technology provided in one embodiment of the present application;

[0023] Figure 5 A schematic diagram of the structure of a soil moisture detection device based on multi-source remote sensing technology provided by an embodiment of the present application;

[0024] Figure 6 A schematic diagram of the structure of a computer device provided for one embodiment of the present application. DETAILED DESCRIPTION

[0025] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0026] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0027] It should be understood that, although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" / "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determination".

[0028] See also Figure 1 , Figure 1 A flow chart of a soil moisture detection method based on multi-source remote sensing technology provided in one embodiment of the present application, the method comprising the following steps:

[0029] S1: Get satellite parameters of the target area.

[0030] The executor of the soil moisture detection method based on multi-source remote sensing technology is a detection device of the soil moisture detection method based on multi-source remote sensing technology (hereinafter referred to as the detection device). In an optional embodiment, the detection device can be a computer device, a server, or a server cluster composed of multiple computer devices.

[0031] The satellite parameters include incident angle parameters, incident wave number, Fresnel reflection coefficient and band frequency parameters. The detection device can confirm the corresponding radar satellite according to the band frequency parameters.

[0032] In this embodiment, the detection device can confirm the corresponding radar satellite according to the band frequency parameters and obtain the satellite parameters of the target area. The satellite parameters are obtained based on the basic information of the ALOS radar satellite. The detection device can obtain the satellite parameters of the target area in the header file of the radar data.

[0033] S2: Acquire soil backscatter coefficient data of the target area.

[0034] In this embodiment, the detection equipment can obtain an ALOS radar image of the target area through an ALOS radar satellite. The ALOS radar image is remote sensing data obtained based on a high-resolution synthetic aperture radar satellite. The ALOS radar image is analyzed to obtain soil backscatter coefficient data of the target area. It can also establish a data connection with a preset network database to obtain soil backscatter coefficient data of the target area from the network database.

[0035] The soil backscattering coefficient data includes backscattering coefficient data under HH polarization and backscattering coefficient data under HV polarization.

[0036] HH and HV are the polarization modes of the radar remote sensing system. The electric field vector of the energy pulse emitted by the radar can be polarized in the vertical or horizontal plane. Regardless of the wavelength, the radar signal can transmit the horizontal (H) or vertical (V) electric field vector, and receive the return signal of the horizontal (H) or vertical (V) or both. HH is the co-polarization mode, and HV is the anti-polarization mode.

[0037] In an optional embodiment, the soil backscatter coefficient data is a level 1.1 SLC remote sensing image product of the multi-radiation ALOS / PALSAR of the ALOS earth observation satellite launched by Japan in 2006, with a center frequency of 1.274 GHZ, an image spatial resolution of 12.5 m, and a center incidence angle of 38.7°.

[0038] See also Figure 2 , Figure 2 The schematic flow chart of S2 in the soil moisture detection method based on multi-source remote sensing technology provided in one embodiment of the present application includes steps S201 to S202, which are specifically as follows:

[0039] S201: Acquire vegetation moisture content data and total radar backscatter coefficient data of the target area.

[0040] Due to the high vegetation coverage on the surface, the dense vegetation canopy often causes the attenuation and scattering of soil microwave radiation signals, which greatly reduces the accuracy of soil moisture inversion and the inversion results are not ideal.

[0041] In this embodiment, the detection device obtains the vegetation moisture content data of the target area, and obtains the total radar backscatter coefficient data of the target area from the radar image data.

[0042] S202: Acquire the backscatter coefficient data output by the water cloud model according to the vegetation moisture content data, the total radar backscatter coefficient data, the incident angle parameter and the preset water cloud model.

[0043] The water cloud model is constructed based on the WCM (Water Cloud Model) model and is used to eliminate the influence of vegetation on the backscattering coefficient, wherein the water cloud model is:

[0044]

[0045]

[0046]

[0047] In the formula, is the soil backscattering coefficient data, is the total radar backscatter coefficient data, is the vegetation backscatter coefficient data, m veg is the vegetation moisture content data, T 2 is the double-layer attenuation factor, θ is the incident angle parameter, A is the first model parameter, and B is the second model parameter.

[0048] In order to remove the impact of high vegetation coverage in the area and quickly and accurately obtain soil moisture data, in this embodiment, the detection equipment inputs the vegetation moisture content data, total radar backscatter coefficient data, and incident angle parameters into the water cloud model to obtain the backscatter coefficient data output by the water cloud model.

[0049] See also Figure 3 , Figure 3 The flowchart of S201 in the soil moisture detection method based on multi-source remote sensing technology provided in one embodiment of the present application includes steps S2011 to S2013, which are as follows:

[0050] S2011: Acquire a remote sensing image of the target area, and near-infrared band values ​​and mid-infrared band values ​​of the remote sensing image.

[0051] In this embodiment, the detection equipment obtains the remote sensing image of the target area, performs radiation calibration, filtering, atmospheric correction, cropping, reprojection and other preprocessing on the remote sensing data of the target area, and obtains near-infrared band values ​​and mid-infrared band values ​​from the remote sensing data.

[0052] S2012: Obtaining a normalized moisture index of the remote sensing image according to the near-infrared band value, the mid-infrared band value and a preset normalized moisture index calculation algorithm.

[0053] The normalized moisture index calculation algorithm is:

[0054]

[0055] Wherein, NDWI is the normalized moisture index, p(NIR) is the near infrared band value, and p(MIR) is the mid infrared band value;

[0056] In this embodiment, the detection device obtains the normalized moisture index of the remote sensing data according to the near-infrared band value, the mid-infrared band value and a preset normalized moisture index calculation algorithm of the remote sensing data.

[0057] S2013: Obtain the vegetation moisture content according to the normalized moisture index and the preset vegetation moisture content calculation algorithm.

[0058] The vegetation moisture content calculation algorithm is:

[0059] m veg =e 1 NDWI 2 +e 2 NDWI+e 3

[0060] In the formula, m veg is the water content of the vegetation, e 1 、e 2 、e 3 They are respectively the first empirical parameter, the second empirical parameter and the third empirical parameter.

[0061] In this embodiment, the detection device obtains the vegetation moisture content according to the normalized moisture index of the remote sensing data and a preset vegetation moisture content calculation algorithm.

[0062] S3: Constructing a soil backscatter coefficient simulation data set according to the satellite parameters of the target area.

[0063] The soil backscattering coefficient simulation data set includes soil backscattering coefficient simulation data under HH polarization and soil backscattering coefficient simulation data under HV polarization;

[0064] In this embodiment, the detection device constructs a model according to the satellite parameters and a preset soil backscatter coefficient simulation data set to construct the soil backscatter coefficient simulation data set, wherein the model constructed by the soil backscatter coefficient simulation data set is an advanced integral equation model (AIEM). Compared with other models, the AIEM model can simulate a surface with a relatively wide roughness range and can simulate the natural surface more realistically.

[0065] Based on the advanced integral equation model, the expression constructed according to the satellite parameters of the target area is:

[0066]

[0067] In the formula, is the simulated data set of soil backscattering coefficient, k is the incident wave number, s is the preset soil root mean square height, k x , k y , k z , k sz , k sx , k sy are functions of the incident angle parameters, k x =ksinθ, k y , k sy Generally, 0, k z = kcosθ, k sz = kcosθ, k sx =ksinθ,W n is the n-th order Fourier transform of the surface correlation function in the advanced integral equation model, is the polarization incidence coefficient, where The expression is:

[0068]

[0069] In the formula, F pq 、f pq is the Fresnel reflection coefficient.

[0070] S4: constructing a soil moisture detection model according to the soil backscatter coefficient simulation data set, inputting the satellite parameters and soil backscatter coefficient data of the target area into the soil moisture detection model, and acquiring the soil moisture data of the target area.

[0071] Since the AIEM model is relatively complex, it is not possible to directly obtain specific soil moisture. In this embodiment, the detection equipment constructs a soil moisture detection model based on the soil backscattering coefficient simulation data set, inputs the satellite parameters of the target area and the soil backscattering coefficient data into the soil moisture detection model, performs soil moisture data inversion, and obtains the soil moisture data of the target area output by the soil moisture detection model.

[0072] See also Figure 4 , Figure 4 The flowchart of S4 in the soil moisture detection method based on multi-source remote sensing technology provided in one embodiment of the present application includes steps S401 to S402, which are as follows:

[0073] S401: According to the soil backscattering coefficient simulation data set, construct a relationship formula of the soil backscattering coefficient simulation data set under the HH polarization and a relationship formula of the soil backscattering coefficient simulation data set under the HV polarization.

[0074] In this embodiment, the detection device determines the relationship between the soil backscatter coefficient simulation data set and the soil moisture data based on the soil backscatter coefficient simulation data set as follows:

[0075]

[0076] in is the simulated data set of soil backscatter coefficient, A pq is an unknown coefficient related to the incident angle parameter and is independent of the surface roughness, and f((s,l)) is a function of the soil root mean square height and soil correlation length.

[0077] Secondly, to characterize the surface roughness, the combined roughness Zs=s proposed by Zribi et al. can be used 2 / l characterizes the surface roughness, and based on the soil backscattering coefficient simulation data set, the relationship between the soil backscattering coefficient simulation data set and the combined roughness is determined as follows:

[0078]

[0079] Among them B pq is an unknown constant that is independent of soil moisture and related to the incident angle, and f(mv) is a function related to soil moisture.

[0080] Then, according to the soil backscatter coefficient simulation data set, the relationship between the soil backscatter coefficient and the soil moisture data, the combined roughness and the incident angle is determined as follows:

[0081]

[0082] Based on different HH polarization and HV polarization modes, a relationship formula for constructing a simulated data set of soil backscattering coefficient under the HH polarization and a relationship formula for constructing a simulated data set of soil backscattering coefficient under the HV polarization are constructed respectively.

[0083] The relationship between the simulated data set of soil backscattering coefficient under HH polarization is:

[0084]

[0085] In the formula, is the simulated data set of soil backscattering coefficient under HH polarization, A hh , B hh , C hh are the first incidence coefficient, the second incidence coefficient and the third incidence coefficient in the incidence angle parameters under the HH polarization, mv is the soil moisture data, θ is the incidence angle parameter, ln(mv,θ) is a function associated with the soil moisture data, ln(Z s,θ) is a function associated with soil roughness;

[0086] The relationship between the simulated data set of soil backscatter coefficient under HV polarization is:

[0087]

[0088] In the formula, is the simulated data set of soil backscattering coefficient under HH polarization, A hv , B hv , C hv They are respectively the first incidence coefficient, the second incidence coefficient and the third incidence coefficient in the incident angle parameters under the HH polarization.

[0089] S402: Subtract the relationship formula of the soil backscattering coefficient simulation data set under the HH polarization and the relationship formula of the soil backscattering coefficient simulation data set under the HV polarization to construct the soil moisture detection model.

[0090] In this embodiment, the detection device subtracts the relationship between the soil backscattering coefficient simulation data set under the HH polarization and the relationship between the soil backscattering coefficient simulation data set under the HV polarization to eliminate the influence of the combined roughness on the soil backscattering coefficient, and constructs the soil moisture detection model, wherein the soil moisture detection model is:

[0091]

[0092] In the formula, A hh (θ), B hh (θ), C hh (θ) are functions of the first incidence coefficient, the second incidence coefficient and the third incidence coefficient under the HH polarization respectively; A hv (θ), B hv (θ), C hv (θ) are functions of the first incidence coefficient, the second incidence coefficient and the third incidence coefficient under the HV polarization, respectively.

[0093] In an optional embodiment, A hh (θ), B hh (θ), C hh The expression of (θ) is as follows:

[0094] A hh (θ)=3.392*sin(0.882*θ+77.159)

[0095] B hh (θ)=5.337*sin(0.989*θ+18.218)

[0096] C hh (θ)=6.553*sin(2.709*θ+103.299)

[0097] A hv (θ), B hv (θ), C hv The expression of (θ) is as follows:

[0098] A hv (θ)=16.843*sin(0.108*θ+24.46)

[0099] B hv (θ)=5.816*sin(1.44*θ+27.871)

[0100] C hv (θ)=4.137*sin(0.109*θ+119.293)

[0101] Please refer to Figure 5 , Figure 5 A schematic diagram of the structure of a soil moisture detection device based on multi-source remote sensing technology provided by an embodiment of the present application. The device can implement all or part of the soil moisture detection device based on multi-source remote sensing technology through software, hardware or a combination of both. The device 5 includes:

[0102] A first acquisition module 51 is used to acquire satellite parameters of a target area, wherein the satellite parameters include incident angle parameters and band frequency;

[0103] A second acquisition module 52 is used to acquire soil backscattering coefficient data of the target area, wherein the soil backscattering coefficient data includes backscattering coefficient data under HH polarization and backscattering coefficient data under HV polarization;

[0104] A soil backscatter coefficient simulation data set construction module 53 is used to construct a soil backscatter coefficient simulation data set according to the satellite parameters of the target area, wherein the soil backscatter coefficient simulation data set includes soil backscatter coefficient simulation data under HH polarization and soil backscatter coefficient simulation data under HV polarization;

[0105] The soil moisture data calculation module 54 is used to construct a soil moisture detection model according to the soil backscatter coefficient simulation data set, input the satellite parameters and soil backscatter coefficient data of the target area into the soil moisture detection model, and obtain the soil moisture data of the target area.

[0106] In an embodiment of the present application, satellite parameters of the target area are acquired through a first acquisition module, wherein the satellite parameters include incident angle parameters; soil backscattering coefficient data of the target area are acquired through a second acquisition module, wherein the soil backscattering coefficient data include backscattering coefficient data under HH polarization and backscattering coefficient data under HV polarization; a soil backscattering coefficient simulation data set construction module is used to construct a soil backscattering coefficient simulation data set according to the satellite parameters of the target area, wherein the soil backscattering coefficient simulation data set includes soil backscattering coefficient simulation data under HH polarization and soil backscattering coefficient simulation data under HV polarization; a soil moisture detection model is constructed according to the soil backscattering coefficient simulation data set through a soil moisture data calculation module, and the satellite parameters and soil backscattering coefficient data of the target area are input into the soil moisture detection model to acquire soil moisture data of the target area. By constructing the simulated data of soil backscattering coefficient under HH polarization and the simulated data of soil backscattering coefficient under HV polarization, a soil moisture detection model is constructed, which can remove the influence of high vegetation cover on soil moisture inversion and realize the inversion of soil moisture in the target area more comprehensively, accurately and effectively.

[0107] Please refer to Figure 6 , Figure 6 The schematic diagram of the structure of a computer device provided in one embodiment of the present application, the computer device 6 comprises: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61; the computer device may store a plurality of instructions, which are suitable for being loaded and executed by the processor 61 Figures 1 to 4 The method steps of the embodiment shown in the figure can be found in the specific execution process. Figures 1 to 4 The specific description of the illustrated embodiment will not be repeated here.

[0108] Among them, the processor 61 may include one or more processing cores. The processor 61 uses various interfaces and lines to connect various parts in the server, and executes various functions and processes data of the soil moisture detection device 5 based on multi-source remote sensing technology by running or executing instructions, programs, code sets or instruction sets stored in the memory 62, and calling the data in the memory 62. Optionally, the processor 61 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programble Logic Array, PLA). The processor 61 can integrate one or more combinations of a central processing unit 61 (Central Processing Unit, CPU), an image processor 61 (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the touch display; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 61, and it can be implemented by a single chip.

[0109] Among them, the memory 62 may include a random access memory 62 (Random Access Memory, RAM), and may also include a read-only memory 62 (Read-Only Memory). Optionally, the memory 62 includes a non-transitory computer-readable storage medium. The memory 62 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 62 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 62 may also be optionally at least one storage device located away from the aforementioned processor 61.

[0110] The present application also provides a storage medium that can store multiple instructions, which are suitable for the processor to load and execute the above-mentioned Figures 1 to 4 The method steps of the embodiment shown in the figure can be found in the specific execution process. Figures 1 to 4 The specific description of the illustrated embodiment will not be repeated here.

[0111] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0112] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0113] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraint algorithm of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0114] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0115] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0116] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0117] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc.

[0118] The present invention is not limited to the above-mentioned embodiments. If various changes or modifications to the present invention do not depart from the spirit and scope of the present invention, and if these changes and modifications fall within the scope of the claims and equivalent technologies of the present invention, the present invention is also intended to include these changes and modifications.

Claims

1. A soil moisture detection method based on multi-source remote sensing technology, characterized in that: The following steps are involved: Acquire satellite parameters of the target area, wherein the satellite parameters include incident angle parameters; Acquire soil backscattering coefficient data of the target area, wherein the soil backscattering coefficient data includes backscattering coefficient data under HH polarization and backscattering coefficient data under HV polarization; Constructing a soil backscatter coefficient simulation data set according to the satellite parameters of the target area, wherein the soil backscatter coefficient simulation data set includes soil backscatter coefficient simulation data under HH polarization and soil backscatter coefficient simulation data under HV polarization; Based on the soil backscatter coefficient simulation data set, the relationship between the soil backscatter coefficient simulation data set and the soil moisture data is determined as follows: in is the simulated data set of soil backscatter coefficient, A pq is an undetermined coefficient that is independent of the surface roughness and is related to the incident angle parameter, ln(mv, θ) is a function associated with soil moisture data, f((s, l), θ) is a function of a preset soil root mean square height and a soil correlation length, mv is the soil moisture data, and θ is the incident angle parameter; Based on the soil backscatter coefficient simulation data set, the relationship between the soil backscatter coefficient simulation data set and the combined roughness is determined as follows: Among them B pq is an undetermined constant that is independent of soil moisture and is related to the angle of incidence, ln(Z s , θ) is a function related to soil roughness, f(mv, θ) is a function related to soil moisture, and the combined roughness Z s =s 2 / l represents the surface roughness; According to the soil backscatter coefficient simulation data set, the relationship between the soil backscatter coefficient and soil moisture data, combined roughness and incident angle is determined as follows: Based on different HH polarizations and HV polarizations, a relationship formula for constructing a simulated data set of soil backscattering coefficients under the HH polarization and a relationship formula for constructing a simulated data set of soil backscattering coefficients under the HV polarization are constructed, wherein the relationship formula for constructing a simulated data set of soil backscattering coefficients under the HH polarization is: In the formula, is the simulated data set of soil backscattering coefficient under HH polarization, A hh , B hh , C hh are respectively a first incidence coefficient, a second incidence coefficient and a third incidence coefficient in the incident angle parameters under the HH polarization; The relationship between the simulated data set of soil backscatter coefficient under HV polarization is: In the formula, is the simulated data set of soil backscattering coefficient under HH polarization, A hv , B hv , C hv are respectively a first incidence coefficient, a second incidence coefficient and a third incidence coefficient in the incident angle parameters under the HH polarization; The relationship formula of the soil backscattering coefficient simulation data set under the HH polarization and the relationship formula of the soil backscattering coefficient simulation data set under the HV polarization are subtracted to construct a soil moisture detection model, wherein the soil moisture detection model is: In the formula, A hh (θ), B hh (θ), C hh (θ) are functions of the first incidence coefficient, the second incidence coefficient and the third incidence coefficient under the HH polarization respectively; A hv (θ), B hv (θ), C hv (θ) are functions of the first incidence coefficient, the second incidence coefficient and the third incidence coefficient under the HV polarization respectively; The satellite parameters and soil backscatter coefficient data of the target area are input into a soil moisture detection model to obtain soil moisture data of the target area.

2. The soil moisture detection method based on multi-source remote sensing technology according to claim 1 is characterized in that: The step of obtaining soil backscatter coefficient data of the target area comprises the following steps: Acquiring vegetation moisture content data and total radar backscatter coefficient data of the target area; According to the vegetation moisture content data, the total radar backscatter coefficient data, the incident angle parameter and the preset water cloud model, the backscatter coefficient data output by the water cloud model is obtained, wherein the water cloud model is: In the formula, is the soil backscattering coefficient data, is the total radar backscatter coefficient data, is the vegetation backscatter coefficient data, m veg is the vegetation moisture content data, T 2 is the double-layer attenuation factor, θ is the incident angle parameter, A is the first model parameter, and B is the second model parameter.

3. The soil moisture detection method based on multi-source remote sensing technology according to claim 2 is characterized in that: The step of obtaining the vegetation moisture content data and the total radar backscatter coefficient data of the target area comprises the following steps: Acquire a remote sensing image of the target area, as well as near-infrared band values ​​and mid-infrared band values ​​of the remote sensing image; According to the near-infrared band value, the mid-infrared band value and a preset normalized moisture index calculation algorithm, the normalized moisture index of the remote sensing image is obtained, wherein the normalized moisture index calculation algorithm is: Wherein, NDWI is the normalized moisture index, p(NIR) is the near infrared band value, and p(MIR) is the mid infrared band value; The vegetation moisture content is obtained according to the normalized moisture index and a preset vegetation moisture content calculation algorithm, wherein the vegetation moisture content calculation algorithm is: <h2 style=";text-align:left;direction:ltr">m<h2 style=";text-align:left;direction:ltr"> veg <h2 style=";text-align:left;direction:ltr"> =e1NDWI<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +e2NDWI+e3 In the formula, m veg is the moisture content of the vegetation, and e1, e2, and e3 are the first empirical parameter, the second empirical parameter, and the third empirical parameter, respectively.

4. The soil moisture detection method based on multi-source remote sensing technology according to claim 1 is characterized in that: The satellite parameters also include the incident wave number and the Fresnel reflection coefficient.

5. The soil moisture detection method based on multi-source remote sensing technology according to claim 4 is characterized in that: The step of constructing a soil backscatter coefficient simulation data set according to the satellite parameters of the target area comprises the following steps: A model is constructed according to the satellite parameters and a preset soil backscatter coefficient simulation data set to construct the soil backscatter coefficient simulation data set, wherein the expression of the soil backscatter coefficient simulation data set is: In the formula, is the simulated data set of soil backscattering coefficient, k is the incident wave number, s is the preset soil root mean square height, k x , k y , k z , k sz , k sx , k sy are functions of the incident angle parameters, k x =ksinθ, k y , k sy Take 0, k z = kcosθ, k sz = kcosθ, k sx =ksinθ,W n is the nth-order Fourier transform of the surface correlation function in the advanced integral equation model, is the polarization incidence coefficient, where The expression is: In the formula, F pq 、f pq is the Fresnel reflection coefficient.

6. A soil moisture detection device based on multi-source remote sensing technology, characterized in that: include: A first acquisition module is used to acquire satellite parameters of a target area, wherein the satellite parameters include incident angle parameters; A second acquisition module is used to acquire soil backscattering coefficient data of the target area, wherein the soil backscattering coefficient data includes backscattering coefficient data under HH polarization and backscattering coefficient data under HV polarization; A soil backscatter coefficient simulation data set construction module, used to construct a soil backscatter coefficient simulation data set according to the satellite parameters of the target area, wherein the soil backscatter coefficient simulation data set includes soil backscatter coefficient simulation data under HH polarization and soil backscatter coefficient simulation data under HV polarization; The soil moisture data calculation module is used to determine the relationship between the soil backscatter coefficient simulation data set and the soil moisture data based on the soil backscatter coefficient simulation data set as follows: in is the simulated data set of soil backscatter coefficient, A pq is an undetermined coefficient that is independent of the surface roughness and is related to the incident angle parameter, ln(mv,θ) is a function associated with soil moisture data, f((s,l),θ) is a function of a preset soil root mean square height and a soil correlation length, mv is the soil moisture data, and θ is the incident angle parameter; Based on the soil backscatter coefficient simulation data set, the relationship between the soil backscatter coefficient simulation data set and the combined roughness is determined as follows: Among them B pq is an undetermined constant that is independent of soil moisture and is related to the angle of incidence, ln(Z s ,θ) is a function related to soil roughness, f(mv,θ) is a function related to soil moisture, and the combined roughness Z s =s 2 / l represents the surface roughness; According to the soil backscatter coefficient simulation data set, the relationship between the soil backscatter coefficient and soil moisture data, combined roughness and incident angle is determined as follows: Based on different HH polarizations and HV polarizations, a relationship formula for constructing a simulated data set of soil backscattering coefficients under the HH polarization and a relationship formula for constructing a simulated data set of soil backscattering coefficients under the HV polarization are constructed, wherein the relationship formula for constructing a simulated data set of soil backscattering coefficients under the HH polarization is: In the formula, is the simulated data set of soil backscattering coefficient under HH polarization, A hh , B hh , C hh are respectively a first incidence coefficient, a second incidence coefficient and a third incidence coefficient in the incident angle parameters under the HH polarization; The relationship between the simulated data set of soil backscatter coefficient under HV polarization is: In the formula, is the simulated data set of soil backscattering coefficient under HH polarization, A hv , B hv , C hv are respectively a first incidence coefficient, a second incidence coefficient and a third incidence coefficient in the incident angle parameters under the HH polarization; The relationship formula of the soil backscattering coefficient simulation data set under the HH polarization and the relationship formula of the soil backscattering coefficient simulation data set under the HV polarization are subtracted to construct a soil moisture detection model, wherein the soil moisture detection model is: In the formula, A hh (θ), B hh (θ), C hh (θ) are functions of the first incidence coefficient, the second incidence coefficient and the third incidence coefficient under the HH polarization respectively; A hv (θ), B hv (θ), C hv (θ) are functions of the first incidence coefficient, the second incidence coefficient and the third incidence coefficient under the HV polarization respectively; The satellite parameters and soil backscatter coefficient data of the target area are input into a soil moisture detection model to obtain soil moisture data of the target area.

7. A computer device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, the steps of the soil moisture detection method based on multi-source remote sensing technology as described in any one of claims 1 to 5 are implemented.

8. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the soil moisture detection method based on multi-source remote sensing technology as described in any one of claims 1 to 5 are implemented.

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