Satellite-borne GNSS-R soil humidity rapid inversion method and equipment and computer readable medium

Abstract

The embodiment of the invention provides a satellite-borne GNSS-R soil humidity rapid inversion method and device and a computer readable medium, and the scheme utilizes the movement of an unmanned aerial vehicle on the elevation to replace the change of the lead angle of a satellite, and artificially accelerates the change of the phase difference of a synthesized signal, so that the characteristics of the signal can periodically change more rapidly, and the accuracy of the signal inversion is improved. Therefore, more obvious satellite signals easy to analyze can be obtained, and the accuracy and efficiency of soil humidity detection are improved. Besides, as the unmanned aerial vehicle is not fixedly arranged on the earth surface, the environment of a monitored place cannot be damaged, daily maintenance is also facilitated, the position of a target to be measured can be conveniently changed, the soil humidity of different places can be rapidly measured, and the unmanned aerial vehicle is suitable for large-scale earth surface soil humidity detection tasks.

Description

Technical Field

[0001] This application relates to the field of information technology, and in particular, to a method, device, and computer-readable medium for rapidly retrieving soil moisture from spaceborne GNSS-R. Background Art

[0002] Data such as soil moisture and vegetation water content are of great value in agricultural, meteorological, and ecological environment monitoring. In terms of current technologies: (1) Traditional manual sampling is time-consuming, laborious, and costly. (2) Ground-embedded monitoring sensors measure accurately, but require a large number of installations, are costly, difficult to maintain, and have a huge data communication capacity, making it difficult to achieve wide coverage. (3) The NASA (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) satellite can provide high-precision, spatio-temporally continuous soil moisture estimates globally and has been successfully used to calibrate hydrological models and CYGNSS, GNSS-R models; however, the spatial resolution of the SMAP satellite is 9 - 36 km, and the temporal resolution is global coverage every 2 - 3 days. Therefore, it is suitable for calibration tasks but not for complex engineering tasks.

[0003] GNSS-R (Global Navigation Satellite System-Reflectometry) technology is an emerging microwave remote sensing method. By receiving the direct signal from a navigation satellite and the reflected signal from the earth's surface, it can retrieve the dielectric properties of the earth's surface and then calculate the soil moisture. In current solutions for measuring soil moisture using GNSS-R technology, the typical scenario is to use a near-field GNSS antenna fixedly installed at the position to be measured to receive the direct and reflected parts of the satellite signal in real time, and calculate the dielectric constant of the surface soil based on the characteristics of the periodic change information formed by the received signal, and then determine the soil moisture. Although such solutions can complete the measurement of soil moisture, they have the following disadvantages:

[0004] Since the measurement solution relies on the satellite signal of the navigation satellite, and the relative position change speed between some navigation satellites and the earth's surface is slow, the change rate of the satellite elevation angle relative to the GNSS antenna is slow, which will result in a long characteristic change period of the signal received by the GNSS antenna, leading to a long measurement response time and low temporal resolution, affecting the accuracy and efficiency of the measurement. And because GEO (Geosynchronous Orbit) satellites hardly change their positions relative to the earth's surface and cannot receive periodically changing satellite signals, they cannot participate in such measurement solutions, resulting in insufficient applicability of the solutions.

[0005] In addition, since the fixedly installed GNSS antenna cannot be moved, the monitoring coverage area is small, large-scale surface soil moisture measurement cannot be quickly achieved, and fixedly installing a GNSS antenna at the monitoring site will cause certain damage to the local environment, and it is not easy to maintain when the device is located in a remote area. Summary of the Invention

[0006] An object of the present application is to provide a spaceborne GNSS-R soil moisture rapid inversion method, device and computer-readable medium to solve the problems existing in the prior art solutions.

[0007] To achieve the above object, an embodiment of the present application provides a spaceborne GNSS-R soil moisture rapid inversion method, the method includes:

[0008] Control the unmanned aerial vehicle (UAV) to move and hover at the target position to be detected, and collect GNSS observation data corresponding to the current flight altitude through the UAV, where the GNSS observation data includes positioning information;

[0009] Control the UAV to change the flight altitude, and during the process of the UAV changing the flight altitude, collect GNSS observation data signal feature information corresponding to the current flight altitude through the UAV at a preset sampling frequency;

[0010] Calculate the surface GNSS-R reflectivity of the target position according to the signal feature information and positioning information collected during the change of the flight altitude;

[0011] Determine the surface soil moisture of the target position according to the surface GNSS-R reflectivity and the surface environment type of the target position.

[0012] Further, the signal feature information in the GNSS observation data includes at least any one of the following: pseudorange observation value, carrier phase observation value, SNR data.

[0013] Further, when collecting GNSS observation data corresponding to the current flight altitude through the UAV, real-time kinematic carrier phase differential technology is adopted.

[0014] Further, when the real-time kinematic carrier phase differential technology is adopted, the signal feature information in the GNSS observation data includes the residual of the carrier phase observation value.

[0015] Further, the positioning information in the GNSS observation data includes satellite elevation angle, satellite azimuth angle, and the current position of the UAV.

[0016] Further, the method further includes:

[0017] Generate a spatio-temporal database that maps the surface GNSS-R reflectivity, surface environmental types, and surface soil moisture through measured data in advance;

[0018] Determine the surface soil moisture at the target location based on the surface GNSS-R reflectivity and the surface environmental type of the target location, including:

[0019] Use the surface GNSS-R reflectivity and the surface environmental type of the target location as indexes to query in the spatio-temporal database and obtain the surface soil moisture at the target location.

[0020] Further, calculate the surface GNSS-R reflectivity of the target location based on the signal feature information and positioning information collected during the change of flight altitude, including:

[0021] Calculate the periodic change information of the phase difference between the composite signal and the direct part at the corresponding flight altitude according to the signal feature information collected during the change of flight altitude, where the composite signal is the signal composed of the direct part and the reflected part of the satellite signal received by the unmanned aerial vehicle;

[0022] Calculate the phase delay of the satellite signal received by the unmanned aerial vehicle at the corresponding flight altitude according to the positioning information collected during the change of flight altitude;

[0023] Calculate the surface GNSS-R reflectivity of the target location according to the periodic change information of the phase difference of the composite signal and the phase delay of the satellite signal during the change of flight altitude.

[0024] Further, after determining the surface soil moisture at the target location based on the surface GNSS-R reflectivity and the surface environmental type of the target location, it further includes:

[0025] Set other target locations to be detected, and repeat the spaceborne GNSS-R soil moisture rapid inversion method to complete the large-scale surface soil moisture detection task.

[0026] Some embodiments of the present application also provide a spaceborne GNSS-R soil moisture rapid inversion device, where the device includes a memory for storing computer program instructions and a processor for executing the computer program instructions. When the computer program instructions are executed by the processor, the device is triggered to execute the foregoing spaceborne GNSS-R soil moisture rapid inversion method.

[0027] Some other embodiments of the present application also provide a computer-readable medium with computer program instructions stored thereon, and the computer program instructions can be executed by a processor to implement the foregoing spaceborne GNSS-R soil moisture rapid inversion method.

[0028] Compared with the prior art, in a spaceborne GNSS-R soil moisture rapid inversion scheme provided by an embodiment of the present application, a drone is used to replace a fixedly arranged GNSS antenna. After the drone is started, the drone can be controlled to move and hover at a target position to be detected. GNSS observation data corresponding to the current flight altitude is collected by the drone, and then the drone is controlled to change the flight altitude. During the process of the drone changing the flight altitude, GNSS observation data corresponding to the current flight altitude is collected by the drone at a preset sampling frequency; wherein, the GNSS observation data includes signal feature information and positioning information. Thus, the surface GNSS-R reflectivity of the target position can be calculated according to the signal feature information and positioning information collected during the change of the flight altitude, and then the surface soil moisture of the target position can be determined according to the surface GNSS-R reflectivity and the surface environment type of the target position. This scheme uses the movement of the drone in the elevation direction to replace the change of the satellite elevation angle, artificially accelerating the change of the phase difference of the synthesized signal, so that the characteristics of the signal can change periodically faster, thereby obtaining a more significant and easily analyzable satellite signal, thus improving the accuracy and efficiency of soil moisture detection.

[0029] At the same time, since this scheme does not depend on the change of the relative position between the navigation satellite and the surface, a GEO satellite can be used as the signal source. Due to the low elevation angle and stable position of these GEO satellites, they can provide a more stable and reliable monitoring ability compared with MEO (Medium Earth Orbit) satellites.

[0030] In addition, since the drone is not fixedly arranged on the surface, it will not damage the environment at the monitoring site, is also convenient for daily maintenance, and can easily change the target position to be measured, quickly measure the soil moisture at different locations, and is suitable for large-scale surface soil moisture detection tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objects, and advantages of the present application will become more apparent:

[0032] Figure 1 It is a schematic processing flow diagram of a spaceborne GNSS-R soil moisture rapid inversion method provided by an embodiment of the present application;

[0033] Figure 2 It is a schematic diagram of satellite signal propagation in an embodiment of the present application;

[0034] Figure 3Schematic diagram of the direct part, reflected part and synthesized signal of the satellite signal in the embodiments of the present application;

[0035] Figure 4 Schematic diagram of the change in path delay caused by the change in the flight altitude of the unmanned aerial vehicle in the embodiments of the present application;

[0036] Figure 5 Schematic diagram of the change in the phase relationship between the direct part, reflected part and synthesized signal of the satellite signal in the embodiments of the present application;

[0037] Figure 6 Schematic diagram of the changes in the azimuth and elevation angles of the satellite in the actual scenario, where the numbers represent accuracy and dimension, and the numbers represent the satellite numbers of the GPS system or Beidou system, G is the GPS satellite, and C is the Beidou satellite;

[0038] The same or similar reference numerals in the drawings represent the same or similar components. Detailed implementation manners

[0039] The present application will be further described in detail below with reference to the drawings.

[0040] In a typical configuration of the present application, the devices of the terminal and the service network both include one or more processors (CPUs), input / output interfaces, network interfaces, and memories.

[0041] The memory may include non-permanent memory in the computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of, for example, read-only memory (ROM) or flash memory (flash RAM). The memory is an example of the computer-readable medium.

[0042] The computer-readable medium includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer program instructions, data structures, program devices, or other data. Examples of the computer storage medium include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0043] The embodiment of the present application provides a method for rapidly retrieving soil moisture by spaceborne GNSS-R. This method utilizes the movement of an unmanned aerial vehicle (UAV) in elevation to replace the change in satellite elevation angle, artificially accelerating the change in the phase difference of the synthesized signal, enabling the characteristics of the signal to change periodically faster, thereby obtaining a more significant and easily analyzable satellite signal, and thus improving the accuracy and efficiency of soil moisture detection. At the same time, since it does not rely on the change in the relative position between the navigation satellite and the ground surface, GEO satellites can be used as signal sources. Due to the low elevation angle and stable position of these GEO satellites, they can provide a more stable and reliable monitoring ability compared to MEO satellites. In addition, since the UAV is not fixedly deployed on the ground surface, it will not damage the environment at the monitoring site, is also convenient for daily maintenance, and can easily change the target position to be measured, quickly measuring the soil moisture at different locations, and is suitable for large-scale surface soil moisture detection tasks.

[0044] In an actual scenario, the execution entity of this method can be a user device, a network device, or a device formed by integrating the user device and the network device through a network, or it can also be an application program running on the above devices. The user device includes but is not limited to various terminal devices such as computers, mobile phones, and tablet computers; the network device includes but is not limited to being implemented such as a network host, a single network server, a set of multiple network servers, or a computer set based on cloud computing. Here, the cloud consists of a large number of hosts or network servers based on cloud computing (Cloud Computing), where cloud computing is a type of distributed computing and consists of a virtual computer formed by a group of loosely coupled computer sets.

[0045] Figure 1 The processing flow of a method for rapidly retrieving soil moisture by spaceborne GNSS-R provided by the embodiment of the present application is shown. The method at least includes the following processing steps:

[0046] Step S101, control the UAV to move and hover at the target position to be detected, and collect GNSS observation data corresponding to the current flight altitude through the UAV.

[0047] In an actual scenario, after starting the UAV, it can be positioned near the ground surface to be detected, and the UAV is controlled to hover at a certain flight altitude. At this time, the position where the UAV is located is the target position described in this solution, and the soil moisture of the corresponding ground surface at the target position can be detected.

[0048] When the UAV collects GNSS observation data, it can use an on-board GNSS receiver to complete the collection of GNSS observation data through a right-hand circular polarization (RHCP) antenna, which can specifically include signal feature information and positioning information. Among them, the signal feature information is used to analyze the periodic oscillation of the received signal, which can specifically include at least any one of the following: pseudorange observation value, carrier phase observation value, SNR (Signal-to-Noise Ratio) data, and the positioning information is used to calculate the relative position relationship between the UAV and the satellite transmitting the satellite signal, which can include satellite elevation angle, satellite azimuth angle, and the current position of the UAV, etc.

[0049] Step S102, control the UAV to change its flight altitude, and during the process of the UAV changing its flight altitude, collect GNSS observation data corresponding to the current flight altitude through the UAV at a preset sampling frequency.

[0050] After collecting the GNSS observation data corresponding to this flight altitude in the hover state, change the flight altitude of the UAV and continue to collect the GNSS observation data corresponding to the current flight altitude at a preset sampling frequency. When the flight altitude of the UAV changes, it will cause a change in the path delay between the reflected part and the direct part of the satellite signal, resulting in a periodic oscillation of the received signal. Among them, when changing the flight altitude of the UAV, a uniform linear descent or ascent method can be adopted, so that the flight altitude of the UAV changes uniformly. At the same time, the sampling frequency needs to meet the sufficient sampling condition to ensure the accuracy of the detection result. For example, the sampling frequency can be set to be at least greater than a certain value, such as 5Hz, 10Hz, etc.

[0051] Step S103, calculate the surface GNSS-R reflectivity of the target position according to the signal feature information and positioning information collected during the flight altitude change process.

[0052] Through the above steps, a series of GNSS observation data during the flight altitude change process can be obtained. Since the signal feature information is used to analyze the periodic oscillation of the received signal, and the positioning information is used to calculate the relative position relationship between the UAV and the satellite transmitting the satellite signal, through the correlation between the two, the reflection intensity of the surface of the target position to the satellite signal, that is, the surface GNSS-R reflectivity, can be calculated. The specific calculation principle is as follows:

[0053] As Figure 2 shown, the satellite signal received by the GNSS receiver of the UAV includes two parts, the direct part S d and the reflected part S mAmong them, the extra distance that the reflected part travels compared to the direct part is called the path delay Δs. Based on the carrier modulation frequency L of the satellite signal and the speed of light c, the wavelength λ of the satellite signal at frequency point L can be obtained as λ = c / L. At this time, the phase delay ψ of the reflected part compared to the direct part can be calculated as ψ = -Δs / λ, where the negative sign is because the signal changes from right-handed circular polarization to left-handed circular polarization after reflection. According to Figure 2 the geometric relationship in, if the elevation of the UAV is H and the current satellite elevation angle is α, then the path delay Δs = 2H·sinα can be obtained. From this, the phase delays of the direct part and the reflected part can be calculated as follows:

[0054]

[0055] The direct part S of the satellite signal received by the GNSS receiver of the UAV d and the reflected part S m can be combined into a composite signal S c , and this composite signal follows the vector interference principle, as shown in Figure 3 . Taking the case of short multipath conditions as an example, the relationship between the phase difference between the composite signal and the direct part and the phase delay ψ can be deduced as follows:

[0056]

[0057] Among them, the difference in phase between the composite signal and the direct part, and R is the surface GNSS-R reflectivity of the target position to be measured. From the above two formulas, it can be seen that when the satellite elevation angle changes, the phase delay will also change, resulting in a periodic change in the phase difference between the composite signal and the direct part, and then calculating the surface GNSS-R reflectivity of the target position. In the embodiments of the present application, by changing the flight altitude of the UAV, the path delay Δs is changed. For example, Figure 4 when the flight altitude of the UAV increases, the path delay can change from Δs to Δs'. Thus, it can replace the way of changing the satellite position to quickly change the satellite elevation angle, thereby artificially accelerating the change of the phase difference of the composite signal, making the characteristics of the signal change periodically faster, so as to obtain a more significant and easy-to-analyze satellite signal, as shown in Figure 4 .

[0058] Specifically, when calculating the surface GNSS-R reflectivity of the target position according to the signal feature information and positioning information collected during the flight altitude change process in the solution of the embodiment of the present application, the periodic change information of the phase difference between the composite signal and the direct part at the corresponding flight altitude can be calculated according to the signal feature information collected during the flight altitude change process. At the same time, according to the positioning information collected during the flight altitude change process, the phase delay of the satellite signal received by the UAV at the corresponding flight altitude can be calculated. Then, the surface GNSS-R reflectivity of the target position can be calculated according to the periodic change information of the phase difference of the composite signal and the phase delay of the satellite signal during the flight altitude change process.

[0059] In the prior art, in view of the relatively slow change speed of the relative position between some navigation satellites and the ground surface, the satellite elevation angle change rate is slow, which will result in a long change period of the phase difference, resulting in a long measurement response time and low time resolution, making it difficult to quickly and accurately calculate the surface GNSS-R reflectivity. In the solution of the embodiment of the present application, a UAV is used to move quickly in altitude (the flight altitude rises or falls) to replace the change of the satellite elevation angle caused by the change of the satellite position, so as to artificially accelerate the change of the phase difference of the composite signal, so that the characteristics of the signal can change periodically faster, so as to obtain a more significant and easy-to-analyze satellite signal, and thus quickly and accurately calculate the corresponding surface GNSS-R reflectivity at the target position.

[0060] Step S104, determine the surface soil humidity of the target position according to the surface GNSS-R reflectivity and the surface environment type of the target position.

[0061] In the solution of the embodiment, a one-to-one spatio-temporal database about the surface GNSS-R reflectivity, the surface environment type, and the surface soil humidity can be generated in advance through measured data. For example, if the reflectivity R of the surface soil to the signal is:

[0062]

[0063] where P r is the power of the reflected part of the satellite signal, P d is the power of the direct part of the satellite signal, and the corresponding relationship between the two can be obtained by calculating the fluctuation amplitude of the SNR data. θ is the signal incident angle, and thus the correlation relationship between the surface GNSS-R reflectivity and the dielectric constant ε of the surface reflection surface can be determined. In the soil dielectric model, the dielectric constant ε is determined by the soil water content m vIt is certain that various existing empirical models can be used to represent the relationship between the two, such as the Topp empirical model, the Hallikainen empirical model, the Wang empirical model, etc. In addition, different types of surface environmental factors, such as vegetation, forests, soil, water surfaces, and environmental quantities such as surface roughness, will also affect the dielectric constant ε under different surface humidities. Therefore, a spatio-temporal database can be established in advance based on actual data to construct a one-to-one corresponding relationship among surface GNSS-R reflectivity, surface environmental types, and surface soil humidity.

[0064] Based on this spatio-temporal database, when this solution determines the surface soil humidity at the target location according to the surface GNSS-R reflectivity and the surface environmental type at the target location, the surface GNSS-R reflectivity and the surface environmental type at the target location can be used as indexes to query in the spatio-temporal database to obtain the surface soil humidity at the target location. Thus, the accuracy and efficiency of soil humidity detection can be improved. At the same time, since it does not depend on the change of the relative position between the navigation satellite and the surface, GEO satellites can be used as signal sources. Because the elevation angles of these GEO satellites are low and their positions are stable, they can provide more stable and reliable monitoring capabilities compared to MEO satellites. In addition, since the unmanned aerial vehicle is not fixedly deployed on the surface, it will not damage the environment at the monitoring site and is also convenient for daily maintenance.

[0065] In some embodiments of the present application, when collecting GNSS observation data corresponding to the current flight altitude by the unmanned aerial vehicle, real-time kinematic (RTK) technology can be used. RTK technology can more accurately locate the position of the unmanned aerial vehicle. Combining with the elevation data of the precise map, the precise position coordinates of the signal reflection point can be determined from the coordinates of the unmanned aerial vehicle, the satellite elevation angle and azimuth, and the precise map, thereby further improving the accuracy of the detection result.

[0066] When using the real-time kinematic technology, differential data can be provided by a nearby base station as an aid to achieve the RTK high-precision positioning function. At this time, the signal feature information in the GNSS observation data can use the residuals of the carrier phase observations. When using the real-time kinematic technology for precise positioning, the range of the residuals of the carrier phase observations divided by the wavelength on the carrier phase observations (for example, the wavelength of the GPS L1 frequency point is about 19.05 cm, representing a whole cycle of 2π) can obtain the maximum phase difference between the composite signal and the direct part. For the scenario of using the residuals of the carrier phase observations, the composite signal S c and the composite signal S c When the phase difference between them is the largest, it corresponds to the reflected part S m and the composite signal Sc The geometric configuration in the vertical state. Taking Figure 5 the phase relationship shown as an example, when the phase of the reflected part S m is perpendicular to the phase of the synthesized signal S c , it corresponds to the case where the synthesized signal S c is tangent to the circle formed by the dynamic change of the phase of the reflected part S m .

[0067] When the real-time dynamic carrier phase differential technology is not adopted, a single-point positioning method without the assistance of base station differential data can be used. At this time, SNR data can be used as the signal feature information of GNSS observation data. Since the SNR data represents the signal amplitude, the numerical value of the SNR data is the largest when the reflected part S m and the direct wave part S d are in the same direction, and the numerical value of the SNR data is the smallest when the reflected part S m and the direct wave part S d are in the opposite direction. Thus, taking Figure 5 the phase relationship shown as an example, the geometric configurations when the reflected part S m and the direct wave part S d are in the same direction and in the opposite direction corresponding to the maximum and minimum SNR data.

[0068] When using SNR data as the signal feature information of GNSS observation data, for the convenience of calculation, the calibration method of SNR data can be converted from logarithmic scale to linear scale. For example, in the actual scenario, the original data of SNR data generally uses decibel (dB) as the calibration unit. In this embodiment, it can be converted into a linear scale and expressed as the amplitude y. The corresponding relationship between the two can be expressed by the following conversion formula:

[0069] ydb = 20log 10 (y)

[0070] where ydb is the SNR data represented by the logarithmic scale, and y is the SNR data represented by the linear scale. Thus, the following conversions can be performed, such as 30dB = 31.6228, 40dB = 100, 50dB = 316.2278, and the SNR data in linear scale has no unit.

[0071] In the actual scenario, the signal feature information in GNSS observation data can be selected from among pseudorange observation values, carrier phase observation values, and SNR data, or several of them can also be selected. For example, in the embodiment of the present application, SNR data, satellite elevation angle, satellite azimuth angle, the current position of the unmanned aerial vehicle, etc. can be selected as GNSS observation data, and at the same time, the carrier phase observation value can be recorded in the RTK mode, and the carrier phase observation value residual can be used as the alternative signal feature information.

[0072] In some other embodiments of the present application, after detecting the surface soil moisture at one target location, other target locations to be detected can be set, and the on-board GNSS-R soil moisture rapid inversion method is repeatedly executed to complete the large-scale surface soil moisture detection task and achieve large-scale rapid inversion measurement of surface soil moisture.

[0073] The solution of the embodiment of the present application can also adopt some enhanced and auxiliary technical means. For example, by setting up GNSS base stations in the monitoring area network, or in areas with network RTK functions, high-precision positioning services for drones with RTK functions can be satisfied; combined with high-precision remote sensing maps, drones can be more precisely scheduled and positioned, making the inversion measurement task highly automated and intelligent.

[0074] In addition, based on the technical principle of the solution of the present application, the solution of the present application can use GEO satellites that cannot be used in the prior art as signal sources. For example, some satellites in the Beidou Navigation Satellite System (BDS) are GEO satellites, and the positions of GEO satellites in the aerospace map remain almost unchanged, such as Figure 6 the C01, C04, and C59 satellites in the figure, so these satellites could not originally participate in the existing on-board GNSS-R soil moisture inversion solutions. In the technical solution provided by the embodiment of the present application, since the signal acquisition method is changed, these GEO satellites with low satellite elevation angles can now also be added to the on-board GNSS-R soil moisture inversion solution, and because these satellites have low elevation angles and stable positions, they can provide more stable and reliable measurement capabilities compared to MEO.

[0075] In addition, the embodiment of the present application also provides an on-board GNSS-R soil moisture rapid inversion device, which includes a memory for storing computer program instructions and a processor for executing the computer program instructions. When the computer program instructions are executed by the processor, the device is triggered to execute the foregoing on-board GNSS-R soil moisture rapid inversion method.

[0076] Specifically, the method and / or embodiment in the embodiment of the present application can be implemented as a computer software program. For example, the embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the method shown in the flowchart. When the computer program is executed by the processing unit, the above functions defined in the method of the present application are executed.

[0077] It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, the computer-readable medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device.

[0078] In this application, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0079] The computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0080] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of devices, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0081] As another aspect, the present application also provides a computer-readable medium, which may be included in the devices described in the above embodiments; or may exist separately without being assembled into the device. The above computer-readable medium carries one or more computer program instructions, and the computer program instructions can be executed by a processor to implement the methods and / or technical solutions of the foregoing multiple embodiments of the present application.

[0082] It should be noted that the present application can be implemented in software and / or a combination of software and hardware. For example, it can be implemented using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In some embodiments, the software program of the present application can be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including related data structures) can be stored in a computer-readable recording medium, such as a RAM memory, a magnetic or optical drive, or a floppy disk and similar devices. Additionally, some steps or functions of the present application can be implemented using hardware, for example, as a circuit that cooperates with a processor to execute each step or function.

[0083] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or basic features of the present application. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present application is limited by the attached claims rather than the above description, so it is intended to include all changes that fall within the meaning and scope of the equivalent elements of the claims in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A method for rapid inversion of soil moisture from spaceborne GNSS-R, characterized in that, The method includes: Controlling the UAV to move and hover at the target position to be detected, and collecting GNSS observation data corresponding to the current flight altitude through the UAV, where the GNSS observation data includes signal feature information and positioning information; Controlling the UAV to change the flight altitude, and during the process of the UAV changing the flight altitude, collecting GNSS observation data corresponding to the current flight altitude through the UAV at a preset sampling frequency; Calculating the surface GNSS-R reflectivity of the target position according to the signal feature information and positioning information collected during the flight altitude change process; Determining the surface soil humidity of the target position according to the surface GNSS-R reflectivity and the surface environment type of the target position.

2. The method according to claim 1, wherein The signal feature information in the GNSS observation data includes at least any one of the following: pseudorange observation value, carrier phase observation value, SNR data.

3. The method according to claim 1, wherein When collecting GNSS observation data corresponding to the current flight altitude through the UAV, the real-time kinematic carrier phase differential technology is adopted.

4. The method according to claim 3, characterized in that, When the real-time kinematic carrier phase differential technology is adopted, the signal feature information in the GNSS observation data includes the residual of the carrier phase observation value.

5. The method according to claim 1, wherein The positioning information in the GNSS observation data includes satellite elevation angle, satellite azimuth angle, and the current position of the UAV.

6. The method according to claim 1, wherein The method further includes: Pre-generating a one-to-one corresponding spatio-temporal database about surface GNSS-R reflectivity, surface environment type, and surface soil humidity through measured data; Determining the surface soil humidity of the target position according to the surface GNSS-R reflectivity and the surface environment type of the target position, including: Using the surface GNSS-R reflectivity and the surface environment type of the target position as indexes to query in the spatio-temporal database to obtain the surface soil humidity of the target position.

7. The method according to claim 1, wherein Calculating the surface GNSS-R reflectivity of the target position according to the signal feature information and positioning information collected during the flight altitude change process, including: Calculating the periodic change information of the phase difference between the composite signal and the direct part during the flight altitude change according to the signal feature information collected during the flight altitude change process, where the composite signal is the signal synthesized by the direct part and the reflected part of the satellite signal received by the UAV; Calculating the phase delay of the satellite signal received by the UAV at the corresponding flight altitude according to the positioning information collected during the flight altitude change process; Calculating the surface GNSS-R reflectivity of the target position according to the periodic change information of the phase difference of the composite signal and the phase delay of the satellite signal during the flight altitude change process.

8. The method according to any one of claims 1 to 7, characterized in that After determining the surface soil humidity of the target position according to the surface GNSS-R reflectivity and the surface environment type of the target position, it further includes: Setting other target positions to be detected, and repeating the on-board GNSS-R soil moisture rapid inversion method to complete the large-scale surface soil moisture detection task.

9. An on-orbit GNSS-R soil moisture rapid inversion device, wherein, The device includes a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein when the computer program instructions are executed by the processor, the device is triggered to execute the method according to any one of claims 1 to 8.

10. A computer-readable medium having computer program instructions stored thereon, the computer program instructions being executable by a processor to implement the method according to any one of claims 1 to 8.

Images