阅读说明：本技术 一种利用多源卫星亮温数据提取区域日均土壤水分的方法 (Method for extracting regional daily average soil moisture by using multisource satellite brightness temperature data ) 是由 张涛 王光辉 齐建伟 戴海伦 王界 张伟 翟浩然 于 2021-07-19 设计创作，主要内容包括：本发明公开了一种利用多源卫星亮温数据提取区域日均土壤水分的方法,涉及微波遥感技术领域；该方法通过获取一天内不同卫星过境时间的观测亮温数据,利用单通道算法反演不同卫星观测条件下的土壤水分,对反演的土壤水分进行采样深度的归一化；利用地面土壤水分实测数据构建日均土壤水分提取模型并将其应用于土壤水分反演数据,从而得到区域日均土壤水分信息。本发明充分利用现有在轨的不同卫星过境时间的观测数据,提取了区域日均土壤水分,克服了遥感对土壤水分仅是一个时间节点瞬时观测的缺陷,有助于推动微波遥感土壤水分产品在农业、气象、水利等行业的应用拓展。(The invention discloses a method for extracting regional daily average soil moisture by using multisource satellite brightness temperature data, and relates to the technical field of microwave remote sensing; according to the method, observation brightness temperature data of different satellite transit times in one day are obtained, soil moisture under different satellite observation conditions is inverted by using a single-channel algorithm, and sampling depth normalization is carried out on the inverted soil moisture; and (3) constructing a daily average soil moisture extraction model by utilizing the ground soil moisture actual measurement data and applying the daily average soil moisture extraction model to soil moisture inversion data so as to obtain regional daily average soil moisture information. The method fully utilizes the observation data of the existing on-orbit different satellite transit time, extracts the regional daily soil moisture, overcomes the defect that the soil moisture is only instantaneously observed by one time node by remote sensing, and is beneficial to promoting the application and expansion of microwave remote sensing soil moisture products in the industries of agriculture, meteorology, water conservancy and the like.)

1. A method for extracting regional daily average soil moisture by using multisource satellite brightness temperature data is characterized by comprising the following steps:

s1, acquiring observation brightness temperature data and observation parameters of different satellite transit times in one day;

s2, inverting the earth surface soil moisture by using a single-channel algorithm according to different satellite observation conditions;

s3, calculating sampling depths of different satellite observation conditions, and converting inverted soil moisture according to the sampling depths to obtain soil moisture inverted values with fixed depths;

s4, calculating a ground soil moisture measured value corresponding to the satellite transit time, and constructing a daily average soil moisture extraction model;

and S5, applying the daily average soil moisture extraction model to soil moisture inversion data to obtain regional daily average soil moisture information.

2. The method for extracting area daily soil moisture by using multi-source satellite brightness and temperature data according to claim 1, wherein in step S1, observed brightness and temperature data and observation parameters of different satellite transit times in one day are obtained, and the observed brightness and temperature data include orbit-rising and orbit-falling brightness and temperature data observed by satellites in each day in an area, including but not limited to observation time, observation angle and observation frequency.

3. The method for extracting area daily average soil moisture by using multi-source satellite brightness temperature data according to claim 1, wherein in step S2, surface soil moisture is inverted by using a single-channel algorithm according to different satellite observation conditions, and the method specifically comprises the following steps:

s21, obtaining the earth surface H-polarized microwave radiation brightness temperature TB at the satellite observation time t_t(f, theta), theta represents an observation angle, f represents an observation frequency, and t represents an observation time;

s22, obtaining the earth surface temperature T, and irradiating the light temperature TB according to the H-polarized microwave on the earth surface_t(f, theta) and said surface temperature T to calculate a rough surface emissivity E_t(f, θ), the rough surface emissivity

S23, obtaining the radiation characteristics of the earth surface vegetation layer by using the vegetation index, correcting the influence of vegetation coverage, obtaining the soil emissivity, and expressing by adopting a formula (1):

in the formula (I), the compound is shown in the specification,which represents the emissivity of the soil and is,indicating the emissivity of the vegetation, is shown as(1-Lp), taking different empirical values for different vegetation types by omega; lp represents a vegetation attenuation factor, which can be calculated from a vegetation index, and is expressed as Lp ═ e^{-b·vwc·secθ}Where e is a natural constant, b is an empirical parameter, vwc is expressed as a function of the normalized vegetation index NDVI,SF represents an empirical parameter of the contribution of a vegetation wood structure to the optical thickness, NDVI is a normalized difference vegetation index, and both alpha and beta are regression coefficients;

s24, according to the rough surface emissivityCalculating the rough surface reflectivityThe rough surface reflectivity

S25, according to the rough surface reflectivityEstablishing a rough surface microwave radiation model which isCalculating the reflectivity of the smooth earth surface according to the rough earth surface microwave radiation modelh is a roughness parameter, and e is a natural constant;

s26, establishing the dielectric constant epsilon of the soil and the reflectivity of the smooth earth surfaceThe relation betweenAccording to the dielectric constant epsilon of the soil and the reflectivity of the smooth earth surfaceThe dielectric constant of the soil is calculated by the relation between the dielectric constant and the soil

S27, establishing a relation between soil moisture and soil dielectric constant by using a soil mixed dielectric constant model, wherein the relation is shown as the following formula:

wherein the content of the first and second substances,representing remotely inverted soil moisture; p represents the porosity of the soil, and the porosity of the soil is the ratio of the volume weight to the density of solid substances of the soil; epsilon_aAnd ε_rAir dielectric constant and soil particle dielectric constant respectively; subscripts a, s, i, w represent air, soil particles, ice and water, respectively, corresponding to constituent substances in the soil, the dielectric constant of waterε_infDenotes the dielectric constant parameter,. epsilon_w0And e represents a first parameter and a second parameter, respectively, related to the temperature, said first parameter e_w0＝88.045-0.4147·T+6.295·10^-4·T²+1.075·10^-5·T³The second parameter e is 1.1109-10^-10-3.824·10^-12·T+6.938·10^-14·T²-5.096·10^-16·T³，sm_gDenotes the transition water content, gamma is an empirical parameter, the transition water content sm_g0.49 (0.06774-0.00064sand +0.00478clay) +0.165, the empirical parameter γ -0.57 (0.06774-0.00064sand +0.00478clay) +0.481, sand and clay representing the soil sand and clay contents, respectively;

s28, solving the soil moisture at the satellite observation time according to the relation between the soil moisture and the soil dielectric constant, if soThe soil moisturea. b and c are respectively a first coefficient, a second coefficient and a third coefficient, wherein the first coefficientThe second coefficient b ═ epsilon_i-1, said third coefficient c ═ (1-P) epsilon_r+ P- ε; if it isThe soil moisture

4. The method for extracting area daily average soil moisture by using multi-source satellite brightness temperature data according to claim 3, wherein in step S3, sampling depths of different satellite observation conditions are calculated, and inverted soil moisture is converted according to the sampling depths to obtain a soil moisture inversion value with a fixed depth, and the method specifically comprises the following steps:

s31, calculating the soil dielectric constant epsilon of different observation frequencies by using the soil mixed dielectric constant model in the step S27, wherein the dielectric constant is a complex number and comprises a real part and an imaginary part;

s32, calculating sampling depths of different observation frequencies by using the soil dielectric constants of different observation frequenciesV represents the speed of light, and epsilon' represent the real part and the imaginary part of the soil dielectric constant calculated in the step S31 respectively;

s33, calculating the transmission angle tau of the electromagnetic wave in two media of air and soil under different observation angles, wherein the transmission angle tau isθ represents an observation angle;

s34, calculating sampling depth d of the soil layer in the vertical direction under different observation angles, wherein the sampling depth d is eta · cos tau;

s35, soil moisture of soil layer depth D

5. The method for extracting area daily average soil moisture by using multi-source satellite brightness temperature data according to claim 4, wherein in step S4, the ground soil moisture measured value corresponding to the satellite transit time is calculated to construct a daily average soil moisture extraction model, and the method specifically comprises the following steps:

s41, obtaining ground soil moisture measurement data, including soil moisture measurement values at each observation time and at the time corresponding to the soil layer with the depth of D;

and S42, calculating the daily soil moisture of the soil layer with the depth D by using the soil moisture at the N measurement moments, wherein the daily soil moisture is expressed as:in the formula (I), the compound is shown in the specification,the average daily soil moisture is shown,represents t_iSoil moisture measured on the ground at the moment, wherein N represents the number of the measuring moments;

s43, calculating the soil moisture ground measurement data of the satellite transit time synchronization, which is expressed as In the formula (I), the compound is shown in the specification,a soil moisture measurement indicative of a satellite transit time,andrespectively representing the soil moisture measured values at the previous moment and the later moment adjacent to the satellite transit moment;

s44, constructing the relation between the daily soil moisture and the ground actual soil moisture at the satellite transit time by using a multiple linear regression method, wherein the relation is expressed as In the formula, k₁、k₂、k₃、…、k_nAnd b are both regression coefficients, and the measured value is the soil moisture ground measured value corresponding to the satellite transit time.

6. The method for extracting area daily average soil moisture by using multisource satellite brightness temperature data as claimed in claim 1, wherein in step S5, a daily average soil moisture extraction model is applied to soil moisture inversion data to obtain area daily average soil moisture information, and the method specifically comprises the following steps:

and (3) substituting the soil moisture inversion value of the fixed depth obtained by converting the soil moisture inverted by each pixel according to the sampling depth into a daily average soil moisture calculation model to obtain the daily average soil moisture of the region, wherein the calculation formula is expressed as:

in the formula (I), the compound is shown in the specification,the results of the day-averaged soil moisture of the extraction are shown,soil layer soil moisture inversion result, t, representing sampling depth D₁、t₂、t₃、…、t_nRespectively representing the satellite transit observation times, k₁、k₂、k₃、…、k_nAnd b is the coefficient of the daily average soil moisture extraction model.

Technical Field

The invention relates to the technical field of microwave remote sensing, in particular to a method for extracting regional daily average soil moisture by using multisource satellite brightness temperature data.

Background

Due to the huge difference of the dielectric properties of soil and water, the microwave remote sensing signal is very sensitive to the change of the soil moisture content, so that the microwave remote sensing technology also becomes one of the important means for regional soil moisture mapping. Compared with the traditional mode of obtaining soil moisture information by ground measurement, the satellite remote sensing soil moisture product has the advantages of large area, low cost and the like. The passive microwave remote sensing is also an important tool for large-area soil moisture mapping due to the advantages of sensitivity to soil moisture, short coverage period and the like. In recent years, with the rapid development of satellite remote sensing technology, the precision of passive microwave remote sensing inversion of soil moisture is continuously improved, and soil moisture remote sensing products of the method also provide important soil moisture data support for global change, agriculture, water conservancy and other industries.

However, since the satellite observes the ground according to a predetermined orbit every day, the inversion result of the ground surface parameters such as soil moisture is also the instantaneous value of the transit time of the satellite, even if the satellite with the shortest coverage period can only obtain two times of observation data of orbit rising and orbit falling in the same area every day, the overall situation of the soil moisture of the ground surface in the whole day cannot be reflected, and the application field of the soil moisture remote sensing product is limited.

Therefore, how to expand the observation information at a certain moment acquired by satellite remote sensing to the average condition of soil moisture every day is an urgent need for application in industries such as agriculture, meteorology and water conservancy at present and is also an important research topic in the field of passive microwave remote sensing soil moisture retrieval.

Disclosure of Invention

The invention provides a method for extracting daily average soil moisture of an area by using multisource satellite brightness temperature data, so that the problems in the prior art are solved.

A method for extracting regional daily average soil moisture by using multisource satellite brightness temperature data comprises the following steps:

s1, acquiring observation brightness temperature data and observation parameters of different satellite transit times in one day;

s2, inverting the earth surface soil moisture by using a single-channel algorithm according to different satellite observation conditions;

s3, calculating sampling depths of different satellite observation conditions, and converting inverted soil moisture according to the sampling depths to obtain soil moisture inverted values with fixed depths;

s4, calculating a ground soil moisture measured value corresponding to the satellite transit time, and constructing a daily average soil moisture extraction model;

and S5, applying the daily average soil moisture extraction model to soil moisture inversion data to obtain regional daily average soil moisture information.

Further, in step S1, observed brightness and temperature data and observed parameters of different satellite transit times in one day are obtained, where the observed brightness and temperature data include orbit ascending and orbit descending brightness and temperature data observed by satellites in an area every day, and the mainly obtained parameters include observation time, observation angle, observation frequency, and the like.

Further, in step S2, inverting the surface soil moisture by using a single-channel algorithm according to different satellite observation conditions, including the following steps:

s21, obtaining the earth surface H-polarized microwave radiation brightness temperature TB at the satellite observation time t_t(f, theta), theta represents an observation angle, f represents an observation frequency, and t represents an observation time;

s22, obtaining the earth surface temperature T, and irradiating the light temperature TB according to the H-polarized microwave on the earth surface_t(f, theta) and said surface temperature T to calculate a rough surface emissivity E_t(f, θ), the rough surface emissivity

S23, obtaining the radiation characteristics of the earth surface vegetation layer by using the vegetation index, correcting the influence of vegetation coverage, and obtaining the soil emissivity which is expressed as:

in the formula (I), the compound is shown in the specification,which represents the emissivity of the soil and is,indicating the emissivity of the vegetation, is shown as Omega takes different experience values for different vegetation types; lp represents a vegetation attenuation factor, which can be calculated from a vegetation index, and is expressed as Lp ═ e^{-b·vwc·secθ}Where e is a natural constant, b is an empirical parameter, and vwc can be expressed as a function of the normalized vegetation index NDVI. SF represents an empirical parameter of the contribution of a vegetation wood structure to the optical thickness, NDVI is a normalized difference vegetation index, and both alpha and beta are regression coefficients;

s24, according to the rough surface emissivityCalculating the rough surface reflectivityThe rough surface reflectivity

S25, according to the rough surface reflectivityEstablishing a rough surface microwave radiation model which isCalculating the reflectivity of the smooth earth surface according to the rough earth surface microwave radiation modelh is a roughness parameter, and e is a natural constant;

s26, establishing the dielectric constant epsilon of the soil and the reflectivity of the smooth earth surfaceThe relation betweenAccording to the dielectric constant epsilon of the soil and the reflectivity of the smooth earth surfaceThe dielectric constant of the soil is calculated by the relation between the dielectric constant and the soil

S27, establishing a relation between soil moisture and soil dielectric constant by using a soil mixed dielectric constant model Representing remotely inverted soil moisture; p represents the porosity of the soil, and the porosity of the soil is the ratio of the volume weight to the density of solid substances of the soil; epsilon_aAnd ε_rAir dielectric constant and soil particle dielectric constant respectively; subscripts a, s, i, w represent air, soil particles, ice and water, respectively, corresponding to constituent substances in the soil, the dielectric constant of waterε_infDenotes the dielectric constant parameter,. epsilon_w0And e represents a first parameter and a second parameter, respectively, related to the temperature, said first parameter e_w0＝88.045-0.4147·T+6.295·10^-4·T²+1.075·10^-5·T³The second parameter e is 1.1109-10^-10-3.824·10^-12·T+6.938·10^-14·T²-5.096·10^-16·T³，sm_gDenotes the transition water content, gamma is an empirical parameter, the transition water content sm_g0.49 (0.06774-0.00064sand +0.00478clay) +0.165, the empirical parameter γ -0.57 (0.06774-0.00064sand +0.00478clay) +0.481, sand and clay representing the soil sand and clay contents, respectively;

s28, solving the soil moisture at the satellite observation time according to the relation between the soil moisture and the soil dielectric constant, if soThe soil moisturea. b and c are respectively a first coefficient, a second coefficient and a third coefficient, wherein the first coefficientThe second coefficient b ═ epsilon_i-1, said third coefficient c ═ (1-P) epsilon_r+ P- ε; if it isThe soil moisture

Further, in step S3, calculating sampling depths of different satellite observation conditions, and converting inverted soil moisture according to the sampling depths to obtain a soil moisture inversion value with a fixed depth, including the following steps:

s31, calculating the dielectric constant of the soil with different observation frequencies by using the soil mixed dielectric constant model in the step S27; the soil mixed dielectric constant model is

S32, calculating sampling depths of different observation frequencies by using the soil dielectric constants of different observation frequenciesV represents the speed of light, and epsilon' represent the real part and the imaginary part of the soil dielectric constant calculated in the step S31 respectively;

s33, calculating the transmission angle tau of the electromagnetic wave in two media of air and soil under different observation angles, wherein the transmission angle tau isθ represents an observation angle;

s34, calculating sampling depth d of the soil layer in the vertical direction under different observation angles, wherein the sampling depth d is eta · cos tau;

s35, soil moisture of the soil layer at depth D may be expressed as

Further, in step S4, calculating a ground soil moisture measured value corresponding to the satellite transit time, and constructing a daily average soil moisture extraction model, including the following steps:

s41, obtaining ground soil moisture measurement data, including soil moisture measurement values at each observation time and at the time corresponding to the soil layer with the depth of D;

and S42, calculating the daily soil moisture of the soil layer with the depth D by using the soil moisture at the N measurement moments, wherein the daily soil moisture is expressed as:in the formula (I), the compound is shown in the specification,the average daily soil moisture is shown,represents t_iSoil moisture measured on the ground at the moment, wherein N represents the number of the measuring moments;

s43, calculating the soil moisture ground measurement data of the satellite transit time synchronization, which is expressed as In the formula (I), the compound is shown in the specification,a soil moisture measurement indicative of a satellite transit time,andrespectively representing the soil moisture measured values at the previous moment and the later moment adjacent to the satellite transit moment;

s44, constructing the relation between the daily soil moisture and the ground actual soil moisture at the satellite transit time by using a multiple linear regression method, wherein the relation is expressed as In the formula, k₁、k₂、k₃、…、k_nAnd b are both regression coefficients, as satellite transit time pairsGround measurement of the soil moisture.

Further, in step S5, applying the daily average soil moisture extraction model to the soil moisture inversion data to obtain regional daily average soil moisture information; and (3) substituting the soil moisture inversion value of the fixed depth obtained by converting the soil moisture inverted by each pixel according to the sampling depth into a daily average soil moisture calculation model to obtain the daily average soil moisture of the region, wherein the calculation formula is expressed as:

in the formula (I), the compound is shown in the specification,the results of the day-averaged soil moisture of the extraction are shown,soil layer soil moisture inversion result, t, representing sampling depth D₁、t₂、t₃、…、t_nRespectively representing the satellite transit observation times, k₁、k₂、k₃、…、k_nAnd b is the coefficient of the daily average soil moisture extraction model.

The invention has the beneficial effects that:

the invention provides a method for extracting daily average soil moisture of an area by utilizing multisource satellite brightness temperature data, which comprises the steps of acquiring observed brightness temperature data of different satellite transit times in one day; inverting the soil moisture under different satellite observation conditions by using a single-channel algorithm; normalizing the sampling depth of the inverted soil moisture; constructing a daily average soil moisture extraction model by using ground soil moisture actual measurement data; and applying the daily average soil moisture extraction model to soil moisture inversion data to obtain regional daily average soil moisture information. The method fully utilizes the observation data of the existing in-orbit different satellite transit time to extract the regional daily soil moisture, overcomes the defect that the soil moisture is only instantaneously observed by remote sensing at one time node, and is beneficial to promoting the application and expansion of microwave remote sensing soil moisture products in the industries of agriculture, meteorology, water conservancy and the like.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments are briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.

Fig. 1 is a flowchart of a method for extracting area daily average soil moisture by using multi-source satellite brightness temperature data according to embodiment 1 of the present invention;

fig. 2 is a schematic diagram of observed brightness temperature data of the same region of the AMSR-E for rail rise and fall obtained in embodiment 1;

FIG. 3 is soil moisture at two observation times obtained by performing inversion calculation on the observed brightness and temperature data obtained in FIG. 2;

FIG. 4 is a schematic view showing the relationship between the simulated value and the true value of the daily average soil moisture estimation model fitted by the constructed daily average soil moisture extraction model in example 1;

fig. 5 is a schematic diagram of the region daily average soil moisture information obtained by applying the daily average soil moisture extraction model to the soil moisture inversion data in example 1.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention. It is noted that the terms "comprises" and "comprising," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

The embodiment provides a method for extracting regional daily average soil moisture by using multisource satellite brightness temperature data, as shown in fig. 1, the method comprises the following steps:

s1, acquiring observation brightness temperature data and observation parameters of different satellite transit times in one day;

the method specifically comprises the steps of obtaining observation brightness temperature data and observation parameters of the satellite transit time in one day of an area, wherein the observation brightness temperature data comprises orbit rising and orbit falling brightness temperature data observed by the satellite in the area every day, and the main obtained parameters include but are not limited to observation time, observation angle, observation frequency and the like. In the embodiment, the observed brightness and temperature data of the same region of the AMSR-e (advanced Microwave Scanning Radiometer for eos) for rail ascending and descending are obtained, and the obtained data result is shown in fig. 2, where the observation time in the data is 1:30 and 13:30, the observation angle is 55 °, and the observation frequency is C (6.925GHz), X (10.65GHz), Ka (36.5GHz), and the like.

S2, inverting the earth surface soil moisture by using a single-channel algorithm according to different satellite observation conditions, wherein the method specifically comprises the following steps;

s21, obtaining the earth surface H-polarized microwave radiation brightness temperature TB at the satellite observation time t_t(f, theta), theta represents an observation angle, f represents an observation frequency, and t represents an observation time; in this example, H-polarized light temperature data with transit times of 1:30 and 13:30 and an observation frequency of X band (10.65GHz) was used, as shown in fig. 2. In the figure, darker colors indicate lower light temperature values (black is a missing part of satellite observation data), and lighter colors indicate higher light temperature values.

S22, obtaining the earth surface temperature T, and irradiating the light temperature TB according to the H-polarized microwave on the earth surface_t(f, theta) and said surface temperature T to calculate a rough surface emissivity E_t(f, θ), the rough surface emissivityIn this example, the surface temperature was estimated using a 36.5GHz V polarization bright temperature, and the calculation formula was T ═ TB (36.5V) -15.2, where TB (36.5V) represents a frequency of 36.5GHz V polarization bright temperature.

S23, obtaining the radiation characteristics of the earth surface vegetation layer by using the vegetation index, correcting the influence of vegetation coverage, and obtaining the soil emissivity which is expressed as:

in the formula (I), the compound is shown in the specification,which represents the emissivity of the soil and is,indicating the emissivity of the vegetation, is shown as Omega takes different experience values for different vegetation types; lp represents a vegetation attenuation factor, which can be calculated from a vegetation index, and is expressed as Lp ═ e^{-b·vwc·secθ}Where e is a natural constant, b is an empirical parameter, vwc can be expressed as a function of the normalized vegetation index NDVI, SF represents an empirical parameter of the contribution of a vegetation wood structure to the optical thickness, NDVI is a normalized difference vegetation index, and both alpha and beta are regression coefficients; in this embodiment, the empirical parameter ω is 0.05, the b is 0.05, the α is 1.9134, the β is 0.3215, and the SF is 0;

s24, according to the rough surface emissivityCalculating the rough surface reflectivityThe rough surface reflectivity

S25, according to the rough surface reflectivityEstablishing a rough surface microwave radiation model which isCalculating the reflectivity of the smooth earth surface according to the rough earth surface microwave radiation modelh is a roughness parameter, and e is a natural constant;

s26, establishing the dielectric constant epsilon of the soil and the reflectivity of the smooth earth surfaceThe relation betweenAccording to the dielectric constant epsilon of the soil and the reflectivity of the smooth earth surfaceThe dielectric constant of the soil is calculated by the relation between the dielectric constant and the soil

S27, establishing a relation between soil moisture and soil dielectric constant by using a soil mixed dielectric constant model Representing remotely inverted soil moisture; p represents the porosity of the soil, the pores of the soilThe void degree is the ratio of the volume weight to the density of solid substances in the soil; epsilon_aAnd ε_rAir dielectric constant and soil particle dielectric constant respectively; subscripts a, s, i, w represent air, soil particles, ice and water, respectively, corresponding to constituent substances in the soil, the dielectric constant of waterε_infDenotes the dielectric constant parameter,. epsilon_w0And e represents a first parameter and a second parameter, respectively, related to the temperature, said first parameter e_w0＝88.045-0.4147·T+6.295·10^-4·T²+1.075·10^-5·T³The second parameter e is 1.1109-10^-10-3.824·10^-12.T+6.938·10^-14·T²-5.096·10^-16·T³，sm_gDenotes the transition water content, gamma is an empirical parameter, the transition water content sm_g0.49 (0.06774-0.00064sand +0.00478clay) +0.165, the empirical parameter γ -0.57 (0.06774-0.00064sand +0.00478clay) +0.481, sand and clay representing the soil sand and clay contents, respectively;

s28, solving the soil moisture at the satellite observation time according to the relation between the soil moisture and the soil dielectric constant, if soThe soil moisturea. b and c are respectively a first coefficient, a second coefficient and a third coefficient, wherein the first coefficientThe second coefficient b ═ epsilon_i-1, said third coefficient c ═ (1-P) epsilon_r+ P- ε; if it isThe soil moisture The soil moisture at two moments of inversion in this example is shown in fig. 3. The left graph and the right graph in the graph respectively represent soil moisture values inverted at the track ascending time and the track descending time, and the darker the color is, the lower the soil moisture value is (the black is the missing part of satellite observation data); the lighter the color, the higher the soil moisture value.

S3, calculating sampling depths of different satellite observation conditions, and converting inverted soil moisture according to the sampling depths to obtain a soil moisture inversion value with a fixed depth, wherein the method specifically comprises the following steps:

s31, calculating the dielectric constant of the soil with different observation frequencies by using the soil mixed dielectric constant model in the step S27;

s32, calculating sampling depths of different observation frequencies by using the soil dielectric constants of different observation frequenciesV represents the speed of light, and epsilon' represent the real part and the imaginary part of the soil dielectric constant calculated in the step S31 respectively;

s33, calculating the transmission angle tau of the electromagnetic wave in two media of air and soil under different observation angles, wherein the transmission angle tau isθ represents an observation angle;

and S34, calculating the sampling depth d of the soil layer in the vertical direction under different observation angles, wherein the sampling depth d is eta · cos tau.

S35, soil moisture of the soil layer at depth D may be expressed asIn this example, the soil moisture inversion result was converted to a depth of 0-4 cm.

In step S4, the method calculates the measured value of ground soil moisture corresponding to the satellite transit time, and constructs a daily average soil moisture extraction model, specifically including the following steps:

s41, obtaining ground soil moisture measurement data, including soil moisture measurement values at each observation time and at the time corresponding to the soil layer with the depth of D; in this example, D is a depth of 0 to 4 cm.

And S42, calculating the daily soil moisture of the soil layer with the depth D by using the soil moisture at the N measurement moments, wherein the daily soil moisture is expressed as:in the formula (I), the compound is shown in the specification,the average daily soil moisture is shown,represents t_iSoil moisture measured on the ground at the time, N represents the number of measurement times. In this embodiment, N includes 1 time point of each of the rail ascending and the rail descending, and the total N is 2.

S43, calculating the soil moisture ground measurement data of the satellite transit time synchronization, which is expressed as In the formula (I), the compound is shown in the specification,a soil moisture measurement indicative of a satellite transit time,andrespectively representing soil moisture measurements at a previous time and a subsequent time adjacent to the satellite transit time.

S44, constructing the ground actual survey soil of the daily soil moisture and the satellite transit time by using the multiple linear regression methodThe relationship between the moisture of the soil is expressed as In the formula, k₁、k₂、k₃、…、k_nAnd b are both regression coefficients, and the measured value is the soil moisture ground measured value corresponding to the satellite transit time. In this embodiment, the daily average soil moisture model fitted by using 50 ground observation stations acquired in the satellite shooting area in 6-9 months and totaling 2938 data points is represented as:

in the formula (I), the compound is shown in the specification,the average daily soil moisture is shown,andthe relationship between the simulated value and the true value of the fitted daily average soil moisture estimation model is shown in the attached figure 4. Each circle in the graph represents a data point of each simulated value and the actual value of the soil moisture estimation model. The closer the model simulation value is to the true value, the closer the circle is to the 1:1 slope, indicating the higher the accuracy of the soil moisture estimation model, and vice versa.

In step S5, the method includes applying the daily average soil moisture extraction model to soil moisture inversion data to obtain regional daily average soil moisture information, and specifically includes bringing a soil moisture inversion value of a fixed depth, which is obtained by converting soil moisture inverted by each pixel according to a sampling depth, into a daily average soil moisture calculation model to obtain regional daily average soil moisture, where the calculation formula is expressed as:

in the formula (I), the compound is shown in the specification,showing the result of the extracted area daily average soil moisture,soil layer soil moisture inversion result, t, representing sampling depth D₁、t₂Respectively representing the transit observation time of the satellite in orbit rising and in orbit falling. The results of the area average daily soil moisture obtained in this example are shown in FIG. 5, in which darker colors indicate lower soil moisture values and lighter colors indicate higher soil moisture values.

At present, the traditional method can only obtain the instantaneous soil moisture at a certain observation moment similar to that shown in figure 3 due to the limitation that satellite remote sensing is instantaneous shooting. Due to the influence of weather such as wind speed, illumination and the like, the regional soil moisture value continuously changes all the time in one day, and the instantaneous value obtained by remote sensing cannot reflect the whole condition of soil moisture in one day. In addition, due to the limitation of the satellite lifting orbit shooting condition, the problem of large-area satellite observation data loss exists. The two factors greatly limit the application effect of the remote sensing product of the soil moisture. By utilizing the method, the instantaneous observation information of the remote sensing soil moisture is expanded to the daily average condition, the area daily average soil moisture similar to that shown in the graph 5 can be obtained, and the problem of satellite observation value deletion can be avoided to the maximum extent through the complementation of lifting rail data in the constructed daily average model soil moisture estimation model.