CN114264672A - Deep soil humidity inversion method based on microwave remote sensing - Google Patents
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Abstract
本发明公开一种基于微波遥感的深层土壤湿度反演方法，包括获取深层土壤数据、计算深层土壤发射率和反射率、构建反演模型以及反演深层土壤湿度；本发明先基于微波遥感获取待测深层土壤的亮度温度，并通过土壤测量仪器获取待测深层土壤的物理温度，再由获取的数据计算深层土壤的发射率和反射率，并通过构建的反演模型对土壤的发射率、反射率以及等效介电常数进行反演，从而获得深层土壤湿度，相比传统测量方法机理简单，处理步骤简单，易于操作，应用成本不高，可以满足规定时间内获取大范围的深层土壤湿度空间分布信息，通过对获取数据的预处理，一定程度上保证了测量的精度，从而扩大了应用范围，可以适用于不同的应用场景，值得广泛推广使用。
The invention discloses a deep soil moisture inversion method based on microwave remote sensing, which includes acquiring deep soil data, calculating deep soil emissivity and reflectivity, constructing an inversion model, and inverting deep soil moisture; The brightness temperature of the deep soil is measured, and the physical temperature of the deep soil to be measured is obtained through the soil measuring instrument, and then the emissivity and reflectance of the deep soil are calculated from the obtained data, and the emissivity and reflectance of the soil are calculated through the constructed inversion model. Compared with the traditional measurement method, the mechanism is simple, the processing steps are simple, the operation is easy, and the application cost is not high, and it can meet the requirements of obtaining a wide range of deep soil moisture space within a specified time. The distribution information, through the preprocessing of the acquired data, ensures the accuracy of the measurement to a certain extent, thereby expanding the scope of application, which can be applied to different application scenarios, and is worthy of widespread use.
Description
Technical Field
The invention relates to the technical field of soil moisture content inversion, in particular to a deep soil humidity inversion method based on microwave remote sensing.
Background
The soil water is unsaturated water in soil layer, which is widely distributed on land surface layer and is necessary water source for plant growth, so the soil water resource is important resource related to agricultural production quality, meanwhile, the soil water resource is a link for mutual conversion of surface water and underground water, which can enter into atmosphere in a steaming mode and be supplemented in the soil surface layer in a precipitation mode, so the soil water resource is a core factor in surface energy exchange and is also an important carrier composition of global surface substance energy circulation, the water content in soil is also called soil humidity and is a physical quantity representing the dryness and wetness degree of soil, which is a relative variable of the soil water content, usually expressed by the percentage of the soil water content in dry soil, also called soil moisture, and the soil moisture is an important input parameter thereof in various hydrological models, climate models and ecological models, and in drought, In natural disasters such as flood, debris flow landslide and the like, corresponding models can be established according to the distribution and the space-time change of soil moisture, and better help and support are provided.
At present, a plurality of methods for measuring the moisture in soil exist, but the existing method has a complex mechanism for inverting the moisture of deep soil and complex processing steps, improves the application cost of the method to a certain extent, is inconvenient to popularize, cannot meet the requirement of obtaining large-range spatial distribution information of the moisture of the deep soil within a specified time, cannot ensure the measurement precision, so that the application range is limited, cannot be suitable for different application scenes, and has low applicability, therefore, the invention provides the deep soil moisture inversion method based on microwave remote sensing to solve the problems in the prior art.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a deep soil humidity inversion method based on microwave remote sensing, the method comprises the steps of obtaining the brightness temperature of deep soil to be measured based on microwave remote sensing, obtaining the physical temperature of the deep soil to be measured through a soil measuring instrument, calculating the emissivity and the reflectivity of the deep soil according to the obtained data, and inverting the emissivity, the reflectivity and the equivalent dielectric constant of the soil through a constructed inversion model to obtain the deep soil humidity.
In order to achieve the purpose of the invention, the invention is realized by the following technical scheme: a deep soil humidity inversion method based on microwave remote sensing comprises the following steps:
the method comprises the following steps: firstly, transmitting a radio frequency signal to a deep soil target at a high altitude through a microwave remote sensing transmitter, receiving an echo signal of the deep soil target through a microwave remote sensing receiver, measuring the brightness temperature of the deep soil, then drilling a hole in the monitoring area, wherein the drilling depth is 5-10 m, then extending a probe of a soil measuring instrument into the drilling hole, and measuring the physical temperature of the deep soil;
step two: firstly, preprocessing acquired soil brightness temperature data and soil physical temperature data, including filtering noise reduction, geometric correction and radiation correction, constructing a bispectrum scattering model, calculating soil emissivity according to the preprocessed soil brightness temperature data and the preprocessed soil physical temperature data, and then calculating soil reflectivity according to the soil emissivity;
step three: constructing a soil humidity inversion model comprising an input module, a feature mining module, a numerical simulation module and an output module by taking a full connection layer as a basic unit based on a depth confidence network structure model;
step four: the equivalent dielectric constant of the deep soil in the monitoring area is obtained, then the equivalent dielectric constant of the soil, the emissivity of the soil and the reflectivity of the soil are input into a soil humidity inversion model for inversion, and finally the humidity inversion value of the deep soil is obtained.
The further improvement lies in that: in the first step, the frequency of the radio frequency signal transmitted by the microwave remote sensing transmitter is 4GHz-6GHz, the microwave remote sensing transmitter is provided with a polarized antenna, the polarized antenna adopts vertical receiving polarization and horizontal receiving polarization, and the incident angle of the antenna is 30-45 degrees.
The further improvement lies in that: in the first step, the soil measuring instrument is realized by adopting a mode of heating a temperature sensor by a single chip microcomputer, the temperature sensor is arranged in a probe of the soil measuring instrument, the temperature sensor is a digital temperature sensor or a thermistor temperature sensor, the temperature range is-30-50 ℃, and the probe of the soil measuring instrument is subjected to anti-corrosion and waterproof treatment.
The further improvement lies in that: in the second step, the emissivity of the soil is equal to the ratio of the brightness temperature of the soil to the physical temperature of the soil, and the sum of the reflectivity of the soil and the emissivity of the soil is equal to 1.
The further improvement lies in that: in the second step, the specific step of constructing the double-spectrum scattering model is to construct a random rough surface in a spectrum domain, decompose a related spectrum into the sum of two independent random rough surfaces by dividing, construct a double-spectrum surface by adopting a surface spectrum domain theory on the basis of a kirchhoff model and a small disturbance model, and select a corresponding surface spectrum filter to obtain the random rough surface double-spectrum scattering model.
The further improvement lies in that: in the third step, each neuron in the fully connected layer is fully connected with all neurons in the previous layer, and the features extracted by the fully connected layer in front of the neuron are integrated.
The further improvement lies in that: in the third step, the input module is used for inputting parameter data, the feature mining module is used for extracting features in the input parameter data, the numerical simulation module is used for expressing the mined features, and the output module is used for outputting the soil humidity value.
The further improvement lies in that: in the fourth step, the specific steps of obtaining the equivalent dielectric constant of the deep soil of the monitored area are as follows: and collecting a sample of deep soil in the monitored area, performing component analysis to obtain soil component composition, and calculating the equivalent dielectric constant of the soil by using the soil component composition.
The invention has the beneficial effects that: the method comprises the steps of obtaining the brightness temperature of deep soil to be measured based on microwave remote sensing, obtaining the physical temperature of the deep soil to be measured through a soil measuring instrument, calculating the emissivity and the reflectivity of the deep soil according to obtained data, and inverting the emissivity, the reflectivity and the equivalent dielectric constant of the soil through a constructed inversion model, so that the deep soil humidity is obtained.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method according to a first embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Referring to fig. 1, the present embodiment provides a deep soil moisture inversion method based on microwave remote sensing, including the following steps:
the method comprises the following steps: firstly, a microwave remote sensing transmitter transmits a radio frequency signal to a deep soil target at high altitude to a monitoring area, then a microwave remote sensing receiver receives an echo signal of the deep soil target to measure the brightness temperature of the deep soil, then the monitoring area is drilled, the depth of the drilled hole is 5 meters, then a probe of a soil measuring instrument is extended into the drilled hole to measure the physical temperature of the deep soil, the frequency of the radio frequency signal transmitted by the microwave remote sensing transmitter is 4GHz, the microwave remote sensing transmitter is provided with a polarized antenna, the polarized antenna adopts vertical receiving polarization and horizontal receiving polarization, the incident angle of the antenna is 30 degrees, the soil measuring instrument is realized by adopting a mode of a single chip microcomputer and a temperature sensor, the temperature sensor is arranged in the probe of the soil measuring instrument, the temperature sensor adopts a digital temperature sensor or a thermistor temperature sensor, and the temperature range is between-30 ℃ and 50 ℃, carrying out anti-corrosion and waterproof treatment on a probe of the soil measuring instrument;
step two: firstly, preprocessing acquired soil brightness temperature data and soil physical temperature data including filtering noise reduction, geometric correction and radiation correction, ensuring the measurement precision to a certain extent by preprocessing the acquired data, then constructing a dual-spectrum scattering model, calculating the soil emissivity according to the preprocessed soil brightness temperature data and the soil physical temperature data, then calculating the soil reflectivity according to the soil emissivity, wherein the soil emissivity is equal to the ratio of the soil brightness temperature to the soil physical temperature, the sum of the soil reflectivity and the soil emissivity is equal to 1, constructing the dual-spectrum scattering model by specifically constructing a random rough surface in a spectrum domain, dividing a related spectrum to decompose the random rough surface into the sum of two independent random rough surfaces, and constructing a dual-spectrum surface by adopting a surface spectrum domain theory on the basis of a kirchhoff model and a small disturbance model, selecting a corresponding surface spectrum filter to obtain a random rough surface double-spectrum scattering model;
step three: constructing a soil humidity inversion model comprising an input module, a feature mining module, a numerical simulation module and an output module by taking a full connection layer as a basic unit based on a depth confidence network structure model;
step four: the method comprises the following steps of firstly obtaining the equivalent dielectric constant of deep soil of a monitoring area, inputting the equivalent dielectric constant of the soil, the emissivity of the soil and the reflectivity of the soil into a soil humidity inversion model for inversion, finally obtaining a humidity inversion value of the deep soil, fully connecting each neuron in a full connection layer with all neurons in the previous layer, and integrating the characteristics extracted by the full connection layer in front of the neuron, wherein an input module is used for inputting parameter data, a characteristic mining module is used for extracting the characteristics in the input parameter data, a numerical simulation module is used for expressing the excavated characteristics, an output module is used for outputting a soil humidity value, and the concrete steps of obtaining the equivalent dielectric constant of the deep soil of the monitoring area are as follows: and collecting a sample of deep soil in the monitored area, performing component analysis to obtain soil component composition, and calculating the equivalent dielectric constant of the soil by using the soil component composition.
Example two
Referring to fig. 1, the present embodiment provides a deep soil moisture inversion method based on microwave remote sensing, including the following steps:
the method comprises the following steps: firstly, a microwave remote sensing transmitter transmits a radio frequency signal to a deep soil target at high altitude to a monitoring area, then a microwave remote sensing receiver receives an echo signal of the deep soil target to measure the brightness temperature of the deep soil, then the monitoring area is drilled, the depth of the drilled hole is 10 meters, then a probe of a soil measuring instrument is extended into the drilled hole to measure the physical temperature of the deep soil, the frequency of the radio frequency signal transmitted by the microwave remote sensing transmitter is 6GHz, the microwave remote sensing transmitter is provided with a polarized antenna, the polarized antenna adopts vertical receiving polarization and horizontal receiving polarization, the incident angle of the antenna is 45 degrees, the soil measuring instrument is realized by adopting a mode of a single chip microcomputer and a temperature sensor, the temperature sensor is arranged in the probe of the soil measuring instrument, the temperature sensor adopts a digital temperature sensor or a thermistor temperature sensor, and the temperature range is between-30 ℃ and 50 ℃, carrying out anti-corrosion and waterproof treatment on a probe of the soil measuring instrument;
step two: firstly, preprocessing the acquired soil brightness temperature data and soil physical temperature data, including filtering noise reduction, geometric correction and radiation correction, then constructing a bispectrum scattering model, calculating the emissivity of the soil according to the brightness temperature data of the pretreated soil and the physical temperature data of the soil, then calculating the reflectivity of the soil according to the emissivity of the soil, wherein the emissivity of the soil is equal to the ratio of the brightness temperature of the soil to the physical temperature of the soil, the sum of the reflectivity of the soil and the emissivity of the soil is equal to 1, constructing a dual-spectrum scattering model by constructing a random rough surface in a spectrum domain, dividing a related spectrum to decompose the random rough surface into the sum of two independent random rough surfaces, on the basis of a kirchhoff model and a small disturbance model, a dual-spectrum surface is constructed by adopting a surface spectrum domain theory, and a corresponding surface spectrum filter is selected to obtain a random rough surface dual-spectrum scattering model;
step three: constructing a soil humidity inversion model comprising an input module, a feature mining module, a numerical simulation module and an output module by taking a full connection layer as a basic unit based on a depth confidence network structure model;
step four: the method comprises the following steps of firstly obtaining the equivalent dielectric constant of deep soil of a monitoring area, inputting the equivalent dielectric constant of the soil, the emissivity of the soil and the reflectivity of the soil into a soil humidity inversion model for inversion, finally obtaining a humidity inversion value of the deep soil, fully connecting each neuron in a full connection layer with all neurons in the previous layer, and integrating the characteristics extracted by the full connection layer in front of the neuron, wherein an input module is used for inputting parameter data, a characteristic mining module is used for extracting the characteristics in the input parameter data, a numerical simulation module is used for expressing the excavated characteristics, an output module is used for outputting a soil humidity value, and the concrete steps of obtaining the equivalent dielectric constant of the deep soil of the monitoring area are as follows: and collecting a sample of deep soil in the monitored area, performing component analysis to obtain soil component composition, and calculating the equivalent dielectric constant of the soil by using the soil component composition.
The method comprises the steps of obtaining the brightness temperature of deep soil to be detected based on microwave remote sensing, obtaining the physical temperature of the deep soil to be detected through a soil measuring instrument, calculating the emissivity and the reflectivity of the deep soil according to obtained data, and inverting the emissivity, the reflectivity and the equivalent dielectric constant of the soil through a constructed inversion model, so that the more accurate humidity of the deep soil in a monitoring area is obtained.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
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