Microwave emission model calibration method based on microwave radiometer observation and related device

Abstract

The invention provides a microwave emission model calibration method based on microwave radiometer observation and a related device. Wherein the electronic equipment carries out synchronous acquisition through a microwave radiometer, a vegetation sensor and a soil sensor which are arranged in a research area, and multiple groups of observation data of the research area in a preset time period are obtained. Wherein each group of observation data comprises microwave brightness temperature collected by a microwave radiometer, vegetation information collected by a vegetation sensor and soil information collected by a soil sensor; and calibrating a plurality of to-be-calibrated parameters in the microwave radiation model by using the plurality of groups of observation data to obtain a calibrated target model. Therefore, the problem that aviation observation and ground observation scales are not matched in a traditional method is solved, and more detailed and accurate data can be provided for ground observation.

Description

Technical Field

[0001] The present application relates to the field of microwave radiometers and surface parameter observations, and more specifically, to a microwave emission model calibration method based on microwave radiometer observations and related devices. Background Art

[0002] Accurately understanding and describing the microwave radiation characteristics of surface soil and vegetation is the key to microwave remote sensing inversion of surface parameters. Microwave radiation models play an important role in this regard. They not only help us understand the basic mechanism of microwave radiation, but also describe the microwave radiation characteristics of the surface. Among them, accurate calibration of model parameters is a key factor to ensure the success of model forward modeling and parameter inversion. Therefore, microwave theoretical modeling and its parameter calibration have always been an important research topic in the field of microwave remote sensing.

[0003] Although significant progress has been made in the calibration and optimization of microwave radiation model parameters in recent years, there are still many shortcomings. First, most existing studies are based on the calibration of microwave radiation models based on aerial microwave observations and ground parameter observations at single or multi-point networks. In this case, the scale of aerial observations does not match that of ground observations, which may lead to uncertainty and inaccuracy in the model calibration results. Summary of the invention

[0004] In order to overcome at least one of the deficiencies in the prior art, the present application provides a microwave emission model calibration method, device, storage medium and electronic device based on microwave radiometer observation, specifically including:

[0005] In a first aspect, the present application provides a microwave emission model calibration method based on microwave radiometer observation, the method comprising:

[0006] Through the microwave radiometer, vegetation sensor and soil sensor arranged in the study area, multiple groups of observation data of the study area within a preset time period are obtained, wherein each group of observation data includes the microwave brightness temperature collected by the microwave radiometer, the vegetation information collected by the vegetation sensor and the soil information collected by the soil sensor;

[0007] A plurality of groups of observation data are used to calibrate a plurality of parameters to be calibrated in the microwave radiation model to obtain a calibrated target model.

[0008] In a second aspect, the present application provides a microwave emission model calibration device based on microwave radiometer observation, the device comprising:

[0009] A data observation module, used to synchronously collect data through microwave radiometers, vegetation sensors and soil sensors arranged in the study area, to obtain multiple groups of observation data of the study area within a preset time period, wherein each group of observation data includes microwave brightness temperature collected by the microwave radiometer, vegetation information collected by the vegetation sensor and soil information collected by the soil sensor;

[0010] The model calibration module is used to calibrate multiple parameters to be calibrated in the microwave radiation model using multiple groups of observation data to obtain a calibrated target model.

[0011] In a third aspect, the present application provides a storage medium storing a computer program, which, when executed by a processor, implements the microwave emission model calibration method based on microwave radiometer observation.

[0012] In a fourth aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the microwave emission model calibration method based on microwave radiometer observation is implemented.

[0013] Compared with the prior art, this application has the following beneficial effects:

[0014] The present application provides a microwave emission model calibration method based on microwave radiometer observation and related devices. Among them, the electronic device collects data synchronously through the microwave radiometer, vegetation sensor and soil sensor arranged in the study area, and obtains multiple groups of observation data in the study area within a preset time period. Among them, each group of observation data includes the microwave brightness temperature collected by the microwave radiometer, the vegetation information collected by the vegetation sensor and the soil information collected by the soil sensor; multiple groups of observation data are used to calibrate multiple parameters to be calibrated in the microwave radiation model to obtain a calibrated target model. In this way, by arranging microwave radiometers, vegetation sensors and soil sensors in the study area for synchronous collection, multiple groups of observation data in the study area within a preset time period are obtained, which overcomes the problem of mismatch between the scales of aerial observations and ground observations in traditional methods, and can provide more detailed and accurate data for ground observations. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1A schematic diagram of a flow chart of a microwave emission model calibration method based on microwave radiometer observation provided in an embodiment of the present application;

[0017] Figure 2 A detailed schematic diagram of a microwave emission model calibration method based on microwave radiometer observation provided in an embodiment of the present application;

[0018] Figure 3A-3E A schematic diagram of the iterative effect provided in the embodiment of the present application;

[0019] Figure 4 The experimental effect diagram provided for the embodiment of the present application;

[0020] Figure 5 A schematic diagram of the structure of a microwave emission model calibration device based on microwave radiometer observation provided in an embodiment of the present application;

[0021] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0024] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0025] In the description of the present application, it should be noted that the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. In addition, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0026] Based on the above statement, as introduced in the background technology, although progress has been made in the calibration and optimization of microwave radiation model parameters in recent years, there are still problems of scale mismatch and insufficient representativeness, mainly because most studies rely on aerial observations and short-term experimental data.

[0027] Based on the discovery of the above technical problems, the inventors have proposed the following technical solutions to solve or improve the above problems through creative work. It should be noted that the defects in the solutions in the above prior art are the results obtained by the inventors after practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed in the embodiments of the present application for the above problems below should all be the contributions made by the inventors to the present application in the process of invention and creation, and should not be understood as technical contents known to those skilled in the art.

[0028] In view of the above technical problems, an embodiment of the present application (hereinafter referred to as the present embodiment) provides a microwave emission model calibration method based on microwave radiometer observation. Figure 1 As shown, the method includes:

[0029] S1, through the microwave radiometers, vegetation sensors and soil sensors arranged in the study area, synchronous data collection is carried out to obtain multiple sets of observation data in the study area within the preset time period.

[0030] Among them, each set of observation data includes microwave brightness temperature collected by microwave radiometer, vegetation information collected by vegetation sensor and soil information collected by soil sensor.

[0031] S2, calibrating multiple parameters to be calibrated in the microwave radiation model using multiple sets of observation data to obtain a calibrated target model.

[0032] In this way, by arranging microwave radiometers, vegetation sensors, and soil sensors in the study area for synchronous collection, multiple sets of observation data in the study area within the preset time period were obtained, overcoming the problem of mismatch between the scales of aerial observations and ground observations in traditional methods, and providing more detailed and accurate data for ground observations. In addition, by controlling the span of the preset time period, the long-term diversity characteristics of the surface can be captured, making the calibrated target model more adaptable.

[0033] It should be understood that the electronic device that implements the microwave emission model calibration method based on microwave radiometer observation of this embodiment may be, but is not limited to, a mobile terminal, a tablet computer, a laptop computer, a desktop computer, and a server, etc. The server may be a single server or a server group. The server group may be centralized or distributed (for example, the server may be a distributed system). In some embodiments, the server may be local or remote relative to the user terminal. In some embodiments, the server may be implemented on a cloud platform; as an example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud (Community Cloud), a distributed cloud, an inter-cloud (Inter-Cloud), a multi-cloud (Multi-Cloud), etc., or any combination thereof. In some embodiments, the server may be implemented on an electronic device having one or more components.

[0034] To make the solution provided by this embodiment clearer, the following uses a server as an electronic device for implementing a microwave emission model calibration method based on microwave radiometer observation. Figure 1 However, it should be understood that the operations of the flowchart may not be implemented in order, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art may add one or more other operations to the flowchart, or remove one or more operations from the flowchart, guided by the content of this application. Figure 1 As shown, the method includes:

[0035] S1, through the microwave radiometers, vegetation sensors and soil sensors arranged in the study area, synchronous data collection is carried out to obtain multiple sets of observation data in the study area within the preset time period.

[0036] Each set of observation data includes microwave brightness temperature collected by a microwave radiometer, vegetation information collected by a vegetation sensor, and soil information collected by a soil sensor. The microwave radiometer can collect microwave brightness temperatures of different frequencies or polarization conditions. The multiple microwave brightness temperatures in the above multiple sets of observation data have the same frequency or polarization condition. The above microwave brightness temperature, vegetation information, and soil information are explained below:

[0037] Microwave brightness temperature refers to the microwave radiation intensity of an object at a specific frequency, reflecting the microwave energy emitted or reflected by the object. In this embodiment, microwave brightness temperature is data collected by a ground-based microwave radiometer, which can help us understand the microwave radiation characteristics of soil and vegetation under different conditions. By analyzing these data, key information such as soil moisture and vegetation water content can be inferred. Microwave brightness temperature is one of the most important observation data in the model calibration process because it directly reflects the output of the model.

[0038] In this embodiment, the microwave brightness temperature is measured by a microwave radiometer, which is installed in the research area (e.g., the test base) and measures the microwave brightness temperature by receiving microwave radiation emitted from the surface. By adjusting the frequency and angle of the radiometer, microwave brightness temperature data under different frequency bands and polarization conditions can be obtained.

[0039] Vegetation information includes various properties of vegetation, such as leaf area index, vegetation water content, crop height, and vegetation coverage. This information reflects the density, health, and growth of vegetation. In this embodiment, vegetation information is part of the model input, and by analyzing this data, the impact of vegetation on microwave radiation can be understood. For example, the water content of vegetation affects the absorption and scattering of microwave radiation, thereby affecting the microwave brightness temperature. By taking vegetation information as input, the model can simulate the microwave brightness temperature more accurately, thereby improving the calibration accuracy of the model.

[0040] In this embodiment, the vegetation information can be obtained through vegetation sensors and manual sampling. For example, vegetation parameters in the study area are collected every five days, including leaf area index, water content, crop height, coverage, etc. Among them, the leaf area index can be measured by a leaf area meter, and the vegetation water content can be measured by a sampling drying method.

[0041] Soil information includes soil moisture at multiple layers, soil temperature, soil bulk density, etc. This information reflects the physical properties and state of the soil. For example, soil moisture and temperature directly affect the transmission of microwave radiation. In this embodiment, soil information is also part of the model input. By analyzing this data, the impact of soil on microwave radiation can be understood. For example, changes in soil moisture content will significantly affect microwave brightness temperature. Therefore, by taking soil information as input, the model can simulate microwave brightness temperature more accurately, thereby improving the calibration accuracy of the model.

[0042] These soil information can be obtained through soil sensors (soil temperature and humidity sensors and soil temperature sensors) or sensors that can simultaneously measure soil temperature and humidity and soil temperature. In order to obtain effective soil temperature, a set of automatic weather stations can also be deployed to provide data on deep soil temperature. Among them, the automatic weather station can provide multiple layers of soil temperature, including depths of 5cm, 10cm, 20cm, 30cm, 40cm, 50cm, 60cm, 70cm, 80cm, 90cm, 100cm, 120cm, etc., and the deep temperature is usually located at a position of 50cm-120cm. In this embodiment, the temperature at the 80cm position is selected as the deep soil temperature.

[0043] In addition, the long-term diversity characteristics of the surface can be captured by controlling the span of the preset time period, so that the calibrated target model has stronger adaptability. In this embodiment, the preset time period can be a complete crop growth cycle. Since the complete crop growth cycle covers all key stages from sowing to harvesting, such as germination, growth period, maturity period and withering period. In these different stages, parameters such as leaf area index, water content, soil moisture and soil temperature of vegetation will change significantly, thereby affecting the radiation characteristics of the surface. By selecting the complete crop growth cycle as the preset time period, these changes can be fully captured, so as to more accurately establish and verify the model. This can not only improve the prediction accuracy of the model, but also enhance the applicability and robustness of the model in different growth stages, ensuring that the model has wide applicability and reliability in practical applications.

[0044] The relationship between the above microwave brightness temperature and the soil, vegetation upward, vegetation downward and sky downward radiation components is:

[0045]

[0046] In the formula, TB P is the total microwave brightness temperature observed by the radiometer, with the subscript P indicating the polarization mode (e.g., horizontal polarization is represented by H or vertical polarization is represented by V); R are the upward radiation from soil and vegetation, the downward radiation from vegetation reflected by soil, and the downward radiation from sky; P is the soil surface reflectivity; γ P and ω P are vegetation attenuation rate and single scattering albedo, respectively; and are the effective temperatures of soil and vegetation respectively. It is not difficult to see that microwave brightness temperature is affected by soil and vegetation. As for the relationship between microwave brightness temperature and soil and vegetation, there are currently a large number of mature models in the industry. In this embodiment, the Tau-omega model is selected as the microwave radiation model.

[0047] It should be understood that the Tau-Omega model is a radiation transmission model widely used in the field of microwave remote sensing, which is used to describe and predict the radiation characteristics of the earth's surface at microwave frequencies. The model simulates the microwave brightness temperature by considering the contribution of different components of the earth's surface (such as soil, vegetation and sky), as well as the interaction between them. In this embodiment, the Tau-Omega model is used to configure the microwave radiation environment of the surface covered with vegetation. The input data of the model include soil moisture, soil temperature, vegetation water content, vegetation optical thickness and other parameters. The model calculates the microwave brightness temperature through these parameters, thereby simulating the radiation characteristics under different frequency bands and polarization conditions.

[0048] Among them, the Tau-Omega model includes the following key parameters:

[0049]

[0050]

[0051] Among them, H RP , Q RP 、N RP 、tt P ,ω P 、b P Parameters such as these need to be calibrated so that the microwave radiation model can achieve the best effect, that is, obtain the target model in this embodiment.

[0052] Based on the description of multiple groups of observation data in the above embodiment, continue to refer to Figure 1 , the step S2 in the figure is described in detail below:

[0053] S2, calibrating multiple parameters to be calibrated in the microwave radiation model using multiple sets of observation data to obtain a calibrated target model.

[0054] In this embodiment, the calibration principle of the microwave radiation model is to synchronously collect multiple sets of observation data of microwave brightness temperature, vegetation information and soil information, and use the optimization algorithm to optimize the model parameters in rounds, especially the highly sensitive parameters, so as to minimize the difference between the model output and the actual observation value until the preset convergence condition is reached, thereby obtaining the optimal parameters and ensuring that the model accurately simulates the microwave radiation characteristics of the surface.

[0055] Therefore, if Figure 2 As shown, Figure 1 Step S2 in may include:

[0056] S2-1, for each set of observation data, the vegetation information and soil information in the observation data are used as input data of the microwave radiation model, and the microwave brightness temperature in the observation data is used as the control data of the output result of the microwave radiation model.

[0057] S2-2, calibrating multiple parameters to be calibrated using each set of input data and reference data to obtain a target model.

[0058] Optionally, the server may optimize the multiple parameters to be calibrated for multiple rounds with a target calibration strategy until a preset convergence condition is met, and use the best calibration values ​​of the multiple parameters to be calibrated in the last round as their respective global best calibration values, wherein the target calibration strategy includes:

[0059] S2-2-1, initializing a plurality of parameters to be calibrated, and determining the plurality of parameters to be calibrated except for the highly sensitive parameters as a low-sensitivity parameter set.

[0060] Among them, the highly sensitive parameters represent the parameters to be calibrated that have the greatest impact on the accuracy of the microwave radiation model.

[0061] Exemplarily, continuing with the above Tau-Omega model, the model includes H RP , Q RP 、N RP 、tt P ,ω P 、b P Parameters such as H need to be calibrated. RP It is expressed as a highly sensitive parameter. It can be understood that H RP The soil surface roughness represented is a parameter that has a significant impact on the microwave brightness temperature output by the model. Therefore, in this embodiment, the soil surface roughness is taken as a free variable, and its parameter value is arranged and combined with the parameter value of each target parameter to obtain multiple numerical combinations.

[0062] S2-2-2, select the parameters that have not been optimized in this round from the low-sensitivity parameter set as the target parameters, and keep the parameters that have not been selected in the low-sensitivity parameter set unchanged;

[0063] S2-2-3, using each set of input data and control data to screen multiple numerical combinations between the target parameter and the highly sensitive parameter, to obtain the best numerical combination between the target parameter and the highly sensitive parameter.

[0064] Among them, the optimal numerical combination minimizes the cost function value of the microwave radiation model. As an optional implementation, the server can combine the parameter values ​​of the target parameter and the highly sensitive parameter within their respective value ranges to obtain multiple numerical combinations; use each set of input data and reference data to screen the multiple numerical combinations, and select the one that minimizes the cost function value of the microwave radiation model as the optimal numerical combination.

[0065] S2-2-4, based on the best numerical combination, obtain the best calibration value of the target parameter in this round.

[0066] S2-2-5, if there are still unoptimized parameters left among the multiple parameters to be calibrated, return to step S2-2-2 until the best calibration value of this round is obtained for the low-sensitivity parameter set.

[0067] Continuing with H in the Tau-Omega model RP , Q RP 、N RP 、tt P ,ω P 、b P In order to obtain the optimal value of the parameter, multiple rounds of calculation are required, and each round of calculation includes 5 steps. In each step, the optimal calibration value of a parameter to be calibrated is determined. In addition, during the iteration process, the highly sensitive parameter H RP As a free variable, it is combined with the parameter value of the target parameter.

[0068] For example, assuming that from b P 10 parameter values ​​were selected within the range of H RP Five parameter values ​​are selected within the range of values. At this time, there are 10*5=50 combinations of the two. Among these 50 combinations, there must be one combination that minimizes the cost function value of the microwave radiation model. Therefore, this combination is called the best numerical combination of this round. Here, it is assumed that H RP The parameter value of b is 1.5, P The parameter value of b is 0.13. Then 0.13 is used as b P The best calibration value in this round.

[0069] Next, b P Updated to 0.13 and remains unchanged, continue to support Q RP Optimize. Here we assume that RP Nine parameter values ​​were selected within the range of H RP Five parameter values ​​are selected within the range of values. At this time, there are 9*5=45 combinations of values. Among these 45 combinations, there must be one combination that minimizes the cost function value of the microwave radiation model. Therefore, this combination is called the best combination of values ​​in this round. Here, it is assumed that H RP The parameter value is 1.01, Q RP The parameter value of is 0.22. Then 0.22 is used as Q RP The best calibration value in this round.

[0070] By analogy, we can get Q RP 、N RP 、tt P ,ω P 、b PThen, the above steps are repeated again until the preset convergence conditions are met, and the best calibration values ​​of the last round of multiple parameters to be calibrated are taken as their respective global best calibration values.

[0071] As an optional implementation, the preset convergence condition may be that the number of iterations reaches a set maximum number of iterations. Alternatively, multiple consecutive cost function values ​​of the microwave radiation model are all less than a preset threshold, wherein the cost function value represents the difference between the output result of the microwave radiation model and the corresponding control data. In this way, after multiple iterations, the optimal calibration value of the highly sensitive parameter will slowly approach stability.

[0072] For example, Figure 3A , 3B , 3C, 3D, 3E, H RP The parameter value of H RV It is not difficult to see that as the number of iterations increases, H RV It is stable around 0.621. Therefore, 0.621 is taken as H RP The global optimal calibration value, and once H RP After being determined, other parameters to be calibrated can use the best calibration value of the last round as their respective global optimal calibration value.

[0073] In order to verify the effectiveness of the above method, experiments were conducted using actual data. Figure 4 As shown in the figure, the line with the subscript Obs represents the measured data, and the line with the subscript Sim represents the predicted data. It is not difficult to see that the model simulation using the calibration parameters shows obvious improvement, which is manifested in that the calibrated model simulation is more consistent with the observed values.

[0074] Based on the same inventive concept as the microwave emission model calibration method based on microwave radiometer observation provided in this embodiment, this embodiment also provides a microwave emission model calibration device based on microwave radiometer observation, which includes at least one software function module that can be stored in a memory or fixed in an electronic device in the form of software. The processor in the electronic device is used to execute the executable module stored in the memory. For example, the software function module and computer program included in the device. Please refer to Figure 5 Functionally, the device 110 may include:

[0075] The data observation module 11 is used to synchronously collect data through the microwave radiometer, vegetation sensor and soil sensor arranged in the study area to obtain multiple groups of observation data in the study area within a preset period of time, wherein each group of observation data includes the microwave brightness temperature collected by the microwave radiometer, the vegetation information collected by the vegetation sensor and the soil information collected by the soil sensor;

[0076] The model calibration module 12 is used to calibrate multiple parameters to be calibrated in the microwave radiation model using multiple groups of observation data to obtain a calibrated target model.

[0077] In this embodiment, the data observation module 11 is used to implement Figure 1 In step S1, the model calibration module 12 is used for the remaining Figure 1 Therefore, for the detailed description of each of the above modules, please refer to the specific implementation of the corresponding step.

[0078] Since the method for calibrating a microwave emission model based on microwave radiometer observation provided in this embodiment has the same inventive concept, it means that the device for calibrating a microwave emission model based on microwave radiometer observation can also implement other steps or sub-steps of the method through the above modules.

[0079] Optionally, the model calibration module 12 is further specifically used for:

[0080] For each set of observation data, the vegetation information and soil information in the observation data are used as the input data of the microwave radiation model, and the microwave brightness temperature in the observation data is used as the control data of the output results of the microwave radiation model;

[0081] Each set of input data and control data is used to calibrate multiple parameters to be calibrated to obtain a target model.

[0082] Optionally, the model calibration module 12 is further specifically used for:

[0083] After multiple rounds of optimization of the parameters to be calibrated are performed with the target calibration strategy until the preset convergence conditions are met, the best calibration values ​​of the parameters to be calibrated in the last round are taken as their respective global best calibration values, wherein the target calibration strategy includes:

[0084] Initializing a plurality of parameters to be calibrated, and determining the parameters other than the highly sensitive parameters among the plurality of parameters to be calibrated as a low-sensitivity parameter set, wherein the highly sensitive parameters represent the parameters to be calibrated that have the greatest impact on the accuracy of the microwave radiation model;

[0085] Select the parameters that have not been optimized in this round from the low-sensitivity parameter set as the target parameters, and keep the parameters that have not been selected in the low-sensitivity parameter set unchanged;

[0086] Using each set of input data and control data, multiple numerical combinations between the target parameter and the highly sensitive parameter are screened to obtain the best numerical combination between the target parameter and the highly sensitive parameter, wherein the best numerical combination minimizes the cost function value of the microwave radiation model;

[0087] According to the best numerical combination, the best calibration value of the target parameter in this round is obtained;

[0088] If there are still unoptimized parameters left in multiple parameters to be calibrated, return to selecting the parameters that have not been optimized in this round from the low-sensitivity parameter set as the target parameters, and keep the unselected parameters in the low-sensitivity parameter set unchanged until the best calibration value of this round is obtained for the low-sensitivity parameter set.

[0089] Optionally, the model calibration module 12 is further specifically used for:

[0090] Combining the parameter values ​​of the target parameter and the highly sensitive parameter within their respective value ranges to obtain a plurality of value combinations;

[0091] Each group of input data and control data is used to screen multiple numerical combinations, and the one that minimizes the cost function value of the microwave radiation model is selected as the optimal numerical combination.

[0092] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0093] It should also be understood that if the above implementation is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0094] Therefore, this embodiment further provides a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, the microwave emission model calibration method based on microwave radiometer observation provided in this embodiment is implemented. Among them, the storage medium can be a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, etc., which can store program codes.

[0095] This embodiment provides an electronic device for implementing a microwave emission model calibration method based on microwave radiometer observation. Figure 6As shown, the electronic device may include a processor 22 and a memory 21. In addition, the memory 21 stores a computer program, and the processor implements the microwave emission model calibration method based on microwave radiometer observation provided in this embodiment by reading and executing the computer program corresponding to the above implementation manner in the memory 21.

[0096] Continue to see Figure 6 The electronic device further includes a communication unit 23. The memory 21, the processor 22 and the communication unit 23 are electrically connected to each other directly or indirectly through a system bus 24 to achieve data transmission or interaction.

[0097] The memory 21 may be an information recording device based on any electronic, magnetic, optical or other physical principle, used to record execution instructions, data, etc. In some embodiments, the memory 21 may be, but is not limited to, a volatile memory, a non-volatile memory, a storage drive, etc.

[0098] In some embodiments, the volatile memory may be a random access memory (RAM); in some embodiments, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), a flash memory, etc.; in some embodiments, the storage drive may be a disk drive, a solid-state drive, any type of storage disk (such as a CD, a DVD, etc.), or a similar storage medium, or a combination thereof, etc.

[0099] The communication unit 23 is used to send and receive data through a network. In some embodiments, the network may include a wired network, a wireless network, a fiber optic network, a telecommunication network, an intranet, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a wide area network (WAN), a public switched telephone network (PSTN), a Bluetooth network, a ZigBee network, or a near field communication (NFC) network, or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network may include a wired or wireless network access point, such as a base station and / or a network switching node, through which one or more components of the service request processing system may be connected to the network to exchange data and / or information.

[0100] The processor 22 may be an integrated circuit chip having a signal processing capability, and the processor may include one or more processing cores (e.g., a single-core processor or a multi-core processor). By way of example only, the processor may include a central processing unit (CPU), an application specific integrated circuit (ASIC), an application specific instruction set processor (ASIP), a graphics processing unit (GPU), a physical processing unit (PPU), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), or a microprocessor, or any combination thereof.

[0101] Understandably, Figure 6The structure shown is for illustration only. The electronic device may also have Figure 6 More or fewer components than shown, or with Figure 6 Different configurations are shown. Figure 6 The components shown may be implemented in hardware, software or a combination thereof.

[0102] It should be understood that the apparatus and method disclosed in the above-mentioned embodiments can also be implemented in other ways. The apparatus embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the apparatus, methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0103] The above are only various implementations of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A microwave emission model calibration method based on microwave radiometer observation, characterized in that: The method comprises: Through the microwave radiometer, vegetation sensor and soil sensor arranged in the study area, multiple groups of observation data of the study area within a preset time period are obtained, wherein each group of observation data includes the microwave brightness temperature collected by the microwave radiometer, the vegetation information collected by the vegetation sensor and the soil information collected by the soil sensor; A plurality of groups of observation data are used to calibrate a plurality of parameters to be calibrated in the microwave radiation model to obtain a calibrated target model.

2. The microwave emission model calibration method based on microwave radiometer observation according to claim 1 is characterized in that: Using multiple groups of observation data to calibrate multiple parameters to be calibrated in the microwave radiation model to obtain a calibrated target model, including: For each group of observation data, the vegetation information and soil information in the observation data are used as input data of the microwave radiation model, and the microwave brightness temperature in the observation data is used as control data of the output result of the microwave radiation model; The plurality of parameters to be calibrated are calibrated using each group of the input data and the control data to obtain the target model.

3. The microwave emission model calibration method based on microwave radiometer observation according to claim 2 is characterized in that: Calibrate the plurality of parameters to be calibrated using each set of the input data and the control data to obtain the target model, including: After the multiple parameters to be calibrated are optimized for multiple rounds with a target calibration strategy until a preset convergence condition is met, the best calibration values ​​of the multiple parameters to be calibrated in the last round are used as their respective global best calibration values, wherein the target calibration strategy includes: Initializing the multiple parameters to be calibrated, and determining the parameters except the highly sensitive parameters among the multiple parameters to be calibrated as a low-sensitivity parameter set, wherein the highly sensitive parameters represent the parameters to be calibrated that have the greatest impact on the accuracy of the microwave radiation model; Selecting parameters that have not been optimized in this round from the low-sensitivity parameter set as target parameters, and keeping the unselected parameters in the low-sensitivity parameter set unchanged; Using each group of the input data and the control data, multiple numerical combinations between the target parameter and the highly sensitive parameter are screened to obtain an optimal numerical combination between the target parameter and the highly sensitive parameter, wherein the optimal numerical combination minimizes the cost function value of the microwave radiation model; According to the optimal numerical combination, the optimal calibration value of the target parameter in this round is obtained; If there are still some parameters that have not been optimized among the multiple parameters to be calibrated, then return to selecting the parameters that have not been optimized in this round from the low-sensitivity parameter set as the target parameters, and keep the unselected parameters in the low-sensitivity parameter set unchanged until the best calibration value of this round is obtained for the low-sensitivity parameter set.

4. The microwave emission model calibration method based on microwave radiometer observation according to claim 3 is characterized in that: The multiple parameters to be calibrated include soil surface roughness, a dependency factor of the soil surface roughness on an incident angle, an influence factor of the soil surface roughness on a polarization mixing ratio, and a dependency factor of vegetation optical thickness on vegetation water content; The highly sensitive parameter is the soil surface roughness.

5. The microwave emission model calibration method based on microwave radiometer observation according to claim 3 is characterized in that: Using each set of the input data and the control data to screen multiple numerical combinations between the target parameter and the highly sensitive parameter to obtain the best numerical combination between the target parameter and the highly sensitive parameter, including: Combining the target parameter and the parameter values ​​of the highly sensitive parameter within their respective value ranges to obtain the multiple value combinations; The plurality of numerical combinations are screened using each group of the input data and the control data, and the combination that minimizes the cost function value of the microwave radiation model is selected as the optimal numerical combination.

6. The microwave emission model calibration method based on microwave radiometer observation according to any one of claims 1 to 5, characterized in that: The microwave radiation model is a Tau-omega model.

7. The microwave emission model calibration method based on microwave radiometer observation according to any one of claims 1 to 5, characterized in that: The preset time period is a complete crop growth cycle.

8. A microwave emission model calibration device based on microwave radiometer observation, characterized in that: The device comprises: A data observation module, used to synchronously collect data through microwave radiometers, vegetation sensors and soil sensors arranged in the study area, to obtain multiple groups of observation data of the study area within a preset time period, wherein each group of observation data includes microwave brightness temperature collected by the microwave radiometer, vegetation information collected by the vegetation sensor and soil information collected by the soil sensor; The model calibration module is used to calibrate multiple parameters to be calibrated in the microwave radiation model using multiple groups of observation data to obtain a calibrated target model.

9. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the microwave emission model calibration method based on microwave radiometer observation according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The electronic device includes a processor and a memory, the memory stores a computer program, and when the computer program is executed by the processor, the microwave emission model calibration method based on microwave radiometer observation according to any one of claims 1 to 7 is implemented.

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