GNSS-R deep soil humidity inversion method considering rainfall information

Abstract

The invention belongs to an over-the-ground remote sensing monitoring technology, and relates to a GNSS-R deep soil humidity inversion method considering rainfall information, which comprises the following steps: S1, building a foundation GNSS-R double-antenna monitoring platform; s2, processing the GNSS signal by using a signal processing module; s3, performing soil humidity preliminary inversion; s4, step-by-step correction and elimination of rainfall influence; and S5, obtaining deep soil humidity. According to the GNSS-R deep soil humidity inversion method considering rainfall information, a multi-step correction method is adopted, and the obtained earth surface soil humidity is corrected from multiple aspects of the change rate and the rainfall amount; and finally, further obtaining the soil humidity at a deeper level according to a statistical result obtained from a large amount of data. According to the method, the time needed in the calculation process is greatly shortened, real-time inversion of the soil humidity of a deeper level is achieved, and the agricultural application requirement is met.

Description

Technical Field

[0001] The invention relates to a ground remote sensing monitoring technology, and in particular to a GNSS-R deep soil moisture inversion method considering precipitation information. Background Art

[0002] GNSS-R is one of the emerging means of soil moisture monitoring. Compared with traditional technologies, it has the characteristics of low cost and high spatiotemporal resolution. The low cost enables more agricultural producers and scientific research institutions to use GNSS-R technology to conduct large-scale soil moisture monitoring and research. High spatiotemporal resolution makes the monitoring data more accurate and can reflect the changes in soil moisture in time and space.

[0003] However, due to the weak GNSS-R signal and the wavelength of the L-band signal of about 20cm, the ability to penetrate the soil layer is poor. The greater the humidity of the soil surface, the higher the moisture content, the stronger the attenuation effect on the signal, resulting in a shallower penetration depth of the GNSS-R signal. Therefore, when using GNSS-R technology to invert soil moisture, it is often affected by the relatively large moisture content of the surface soil. Some important crops, such as wheat, have their main root systems distributed at 15-30cm during the important sowing-seedling period of the growth process. Therefore, in agricultural production, more attention is paid to soil moisture at a depth of more than 15cm. Precipitation, as a common weather phenomenon, can cause large changes in soil moisture. Depending on the intensity of the rain and the duration of the precipitation, this change presents complex characteristics. For example, when the rainfall is small, only the surface soil moisture changes, and the soil moisture in the deep soil is almost unaffected. When the rainfall is large, not only does it cause the surface soil moisture to increase rapidly, but it also gradually penetrates downward over time, thereby affecting the soil moisture at a deeper level, so that the soil moisture in each layer has a relatively consistent change trend.

[0004] Therefore, how to use GNSS-R technology to avoid errors caused by precipitation factors and accurately obtain soil moisture at a depth of about 15 cm is one of the technical problems that need to be solved at present. Summary of the invention

[0005] The purpose of the present invention is to solve the above problems and provide a GNSS-R deep soil inversion method taking precipitation information into account.

[0006] To achieve the above object, the present invention provides a GNSS-R deep soil inversion method considering precipitation information, comprising:

[0007] S1. Build a ground-based GNSS-R dual-antenna monitoring platform;

[0008] S2. Processing the GNSS signal using a signal processing module;

[0009] S3, perform preliminary inversion of soil moisture;

[0010] S4, step-by-step correction to eliminate the impact of precipitation;

[0011] S5. Acquisition of deep soil moisture.

[0012] Furthermore, the S1 includes:

[0013] A liftable platform is set up and a waterproof chassis is arranged under the platform. A signal collector and a workstation are arranged in the waterproof chassis. A right-hand circularly polarized antenna and a left-hand circularly polarized antenna are fixed on the chassis. The right-hand circularly polarized antenna faces upward at a 45° angle to receive GNSS direct signals, and the left-hand circularly polarized antenna faces downward at a 45° angle to receive GNSS reflected signals. The right-hand circularly polarized antenna and the left-hand circularly polarized antenna are connected to the corresponding channels of the signal collector.

[0014] Furthermore, the CPU+GPU architecture is used in S2 to process GNSS signals:

[0015] The signal collector outputs the converted intermediate frequency digital signal, which is transmitted from the CPU to the GPU. The GPU performs coarse and fine capture of the signal to obtain the peak power values ​​of the direct and reflected signals. The result is then transmitted to the CPU to obtain the surface reflectivity and output it.

[0016] Furthermore, the coarse capture and fine capture of GNSS-R signals include:

[0017] 1) Calculate the visible satellite range in advance based on the longitude and latitude of the experimental site;

[0018] 2) Input parameters to the CPU, including visible satellite PRN, coherent integration time for coarse capture, coherent and incoherent integration time for fine capture, and Doppler frequency search range and search step size;

[0019] 3) Then use GPU for coarse capture;

[0020] 4) Based on the code phase and frequency information obtained by coarse capture, complete the fine capture of the signal and obtain the direct and reflected signal power peaks.

[0021] Further, the S3 includes:

[0022] Pre-built reflectance and soil moisture lookup tables:

[0023] The GNSS signal is completely reflected after reaching the reflecting surface. The concept of Fresnel reflection coefficient is used to describe the quantitative relationship between the reflected energy and the incident energy during the reflection process of electromagnetic waves:

[0024]

[0025] Where ε is the complex dielectric constant of the soil surface; θ is the satellite altitude angle; Γ is the polarization coefficient; v and h represent vertical linear polarization and horizontal linear polarization respectively, and l and r represent left-hand circular polarization antenna and right-hand circular polarization antenna respectively;

[0026] The reflectivity corresponding to different polarization modes can be calculated using the above Fresnel reflection coefficient:

[0027]

[0028] Where R rl Represents the reflectivity of left-hand circularly polarized signal; R rr Indicates the reflectivity of right-hand circularly polarized signal;

[0029] In order to quantify the reflection characteristics of GNSS satellite signals on the ground, the ratio of the reflected signal power to the direct signal power is calculated and used as the surface reflectivity:

[0030]

[0031] Where P r is the reflected signal power received by the receiver; P d The direct signal power received by the receiver;

[0032] The soil dielectric constant ε is obtained by the relationship between reflectivity R and dielectric constant:

[0033]

[0034] Calculate soil moisture using the Topp model:

[0035]

[0036] Among them, m v represents soil moisture, and a reflectivity and soil moisture lookup table is established based on the obtained results;

[0037] Then, the soil moisture value is directly estimated using the nearest neighbor interpolation method based on the reflectivity obtained in step S2.

[0038] Furthermore, the S4 includes:

[0039] Correction using rate of change:

[0040] Calculate the difference value diff for the inversion results every hour:

[0041] diff i =SM i -SM i-1

[0042] When diff is greater than 0.05 or less than -0.05, all soil moisture values ​​within this period are uniformly multiplied by the coefficient K = 0.85 to correct the soil moisture values:

[0043] SM i =0.85*SM i ,diff i <-0.05or>0.05

[0044] Among them SM i Represents the soil moisture value measured at the i-th hour.

[0045] Furthermore, the S4 further includes:

[0046] Correction using a piecewise model:

[0047] For the soil moisture corrected by the rate of change, the soil moisture value is 0.3m 3 / m 3 With 0.6m 3 / m 3 As the boundary, according to different intervals, the corresponding correction formula is used for accurate calculation:

[0048]

[0049] Furthermore, the S4 further includes:

[0050] Correction using precipitation:

[0051] For the soil moisture results of the completed segmented model correction, when a specific time t n , it has been verified that there is no precipitation at this time and in the previous 2 hours. If the soil moisture at this time is lower than that at the previous time t n-1 However, it shows an upward trend, so the abnormal data is corrected, that is, t n-1 The soil moisture value at time t is taken as n-1 The result of the moment, replacing the original unreasonable t n Invert the value at any time.

[0052] Further, in said S5:

[0053] Calculate the soil moisture at a depth of 15 cm using the formula:

[0054] SM deep =SM+0.03.

[0055] The beneficial effects of the present invention are as follows:

[0056] Compared with traditional monitoring methods such as contact measurement and optical remote sensing, the design method adopted by the present invention has significant advantages over the traditional contact measurement and optical remote sensing. In terms of economic cost, free GNSS signals are used; the temporal and spatial resolution can reach a relatively high level, and continuous monitoring can be achieved around the clock, which is an extremely effective means of monitoring soil moisture. The present invention adopts the CPU+GPU program architecture, which significantly improves the running efficiency of the program; establishes a lookup table with a soil moisture range from 0-1, and directly obtains the soil moisture value according to the surface reflectivity; adopts a multi-step correction method, and corrects the obtained surface soil moisture from multiple aspects such as the rate of change and precipitation; finally, the statistical results obtained from a large amount of data further obtain the soil moisture at a deeper level. The above innovations greatly reduce the time required in the calculation process, realize the real-time inversion of soil moisture at a deeper level, and meet the needs of agricultural applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A data processing flow chart schematically showing a GNSS-R deep soil moisture inversion method considering precipitation information according to the present invention;

[0058] Figure 2 A diagram schematically showing a reflectivity and soil moisture lookup representation of a GNSS-R deep soil moisture inversion method considering precipitation information according to the present invention;

[0059] Figure 3 Schematic diagram of the result verification of the GNSS-R deep soil moisture inversion method considering precipitation information according to the present invention. DETAILED DESCRIPTION

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0061] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments cannot be described one by one here, but the embodiments of the present invention are not therefore limited to the following embodiments.

[0062] Combination Figure 1As shown, the present invention provides a GNSS-R deep soil moisture inversion method considering precipitation information, including: S1, building a ground-based GNSS-R dual-antenna monitoring platform; S2, using a signal processing module to process GNSS signals; S3, performing preliminary inversion of soil moisture; S4, step-by-step correction to eliminate the influence of precipitation; S5, acquiring deep soil moisture.

[0063] Among them, in S1, assuming a lifting platform of about 15m high, a waterproof chassis is set under the platform, and a 62MHz signal collector is placed in the chassis. The right-hand circular polarization antenna and the left-hand circular polarization antenna are fixed to the workstation chassis. The right-hand circular polarization antenna faces upward at a 45° angle to receive the GNSS direct signal, and the left-hand circular polarization antenna faces downward at a 45° angle to receive the GNSS reflected signal. The right-hand circular polarization antenna and the left-hand circular polarization antenna are connected to the corresponding channels of the signal collector. Then the remote control device opens the collector control software on the workstation, modifies the sampling time as required, and receives and stores the GNSS intermediate frequency data.

[0064] Then, the CPU+GPU architecture is used in S2 for GNSS signal processing:

[0065] The signal collector outputs the converted intermediate frequency digital signal, which is transmitted from the CPU to the GPU. The GPU performs coarse capture and fine capture of the signal to obtain the direct and reflected signal power peaks, and then transmits the results to the CPU to obtain the surface reflectivity and output. Among them, the coarse capture and fine capture of GNSS-R signals include: 1) Pre-calculating the visible satellite range based on the longitude and latitude of the experimental site; 2) Inputting parameters to the CPU, including visible satellite PRN, coarse capture coherent integration time, fine capture coherent and incoherent integration time, and Doppler frequency search range and search step; 3) Then using the GPU for coarse capture; 4) Based on the code phase and frequency information obtained by coarse capture, complete the fine capture of the signal to obtain the direct and reflected signal power peaks.

[0066] Then, in S3, the obtained surface reflectance is used to obtain the corresponding soil moisture value according to the pre-established reflectance and soil moisture lookup table. The reflectance and soil moisture lookup table includes:

[0067] Ideally, the GNSS signal is completely reflected after reaching the reflecting surface. The concept of Fresnel reflection coefficient is used to describe the quantitative relationship between the reflected energy and the incident energy during the reflection process of electromagnetic waves:

[0068]

[0069] Where ε is the complex dielectric constant of the soil surface; θ is the satellite altitude angle; Γ is the polarization coefficient; v and h represent vertical linear polarization and horizontal linear polarization respectively, and l and r represent left-hand circular polarization antenna and right-hand circular polarization antenna respectively;

[0070] The reflectivity corresponding to different polarization modes can be calculated using the above Fresnel reflection coefficient:

[0071]

[0072] Where R rl Represents the reflectivity of left-hand circularly polarized signal; R rr Indicates the reflectivity of right-hand circularly polarized signal;

[0073] In order to quantify the reflection characteristics of GNSS satellite signals on the ground, the ratio of the reflected signal power to the direct signal power is calculated and used as the surface reflectivity:

[0074]

[0075] Where P r is the reflected signal power received by the receiver; P d The direct signal power received by the receiver;

[0076] The soil dielectric constant ε is obtained by the relationship between reflectivity R and dielectric constant:

[0077]

[0078] Calculate soil moisture using the Topp model:

[0079]

[0080] Among them, m v represents soil moisture, and then the soil moisture value is directly estimated using the nearest neighbor interpolation method based on the reflectivity obtained in step S2.

[0081] Figure 2 The lookup table of the relationship between soil moisture and reflectivity is used. For the results obtained from the lookup table, under the condition of precipitation, the precipitation accumulates on the soil surface, which will cause the GNSS signal reflectivity to increase significantly, thus causing a large deviation between the soil moisture obtained by inversion and the in-situ data. Further, corrections need to be made in S4, including:

[0082] Correction using rate of change:

[0083] Calculate the difference value diff for the inversion results every hour:

[0084] diff i =SM i-SM i-1

[0085] When diff is greater than 0.05 or less than -0.05, all soil moisture values ​​within this period are uniformly multiplied by the coefficient K = 0.85 to correct the soil moisture values:

[0086] SM i =0.85*SM i ,diff i <-0.05or>0.05

[0087] Among them SM i Represents the soil moisture value measured at the i-th hour. Through this operation, the drastic change of the humidity value can be effectively weakened, making the inversion result closer to the actual soil moisture state.

[0088] Then, the soil moisture data obtained in the previous step is corrected using the segmented model:

[0089] Soil moisture value 0.3m 3 / m 3 With 0.6m 3 / m 3 As the boundary, according to different intervals, the corresponding correction formula is used for accurate calculation:

[0090]

[0091] Furthermore, the S4 further includes:

[0092] Correction using precipitation:

[0093] For the soil moisture results that have been corrected by the segmented model, the rationality judgment operation is implemented by integrating precipitation data: when a certain time t n , it has been verified that there is no precipitation at this time and in the previous 2 hours. If the soil moisture at this time is lower than that at the previous time t n-1 However, it shows an upward trend, so the abnormal data is corrected, that is, t n-1 The soil moisture value at time t is taken as n-1 The result of the moment, replacing the original unreasonable t n Invert the value at any time.

[0094] Finally, in order to obtain deeper soil moisture values, the results after the above series of corrections need to be further processed. Taking into account the special physical location of deep soil moisture, it is relatively less affected by external environmental factors, and compared with the surface soil moisture, its value usually presents a larger characteristic. Based on this, the present invention adopts the method of uniformly adding a constant to the correction result for adjustment. After systematic analysis and research on a large amount of long-term experimental data, it was determined that the constant is 0.03. Through this operation, the inversion results can more accurately reflect the actual situation of deep soil moisture, and effectively improve the availability and scientificity of the data. That is, the deep soil moisture is obtained as:

[0095] SM deep =SM+0.03

[0096] The present invention is described below with specific embodiments:

[0097] The experimental data collection frequency is set to 120 seconds per hour for a total of 48 hours. The intermediate frequency data sampling rate is 62MHz. The QZSS GEO L5 frequency signal is used to monitor soil moisture, including:

[0098] S1. Related parameter settings:

[0099] According to the ground-based GNSS-R experiment, the following settings are made:

[0100] 1) Pre-store the local code and local phase of L5 to reduce the time spent on each repeated generation;

[0101] 2) The incoherent integration time is 50ms;

[0102] 3) Doppler frequency shift range and search step size setting: the search range for coarse capture is [-5000Hz, +5000Hz], the step size is set to 200Hz, and there are 51 search frequency points in total; the search range for fine capture is [-300Hz, 300Hz], the step size is set to 25Hz, and there are 25 search frequency points in total;

[0103] 4) The signal collector stores 120 seconds of data per hour.

[0104] S2. Data acquisition:

[0105] The signal to be processed every second is read into the CPU, and then transmitted from the CPU to the GPU for signal capture. After coarse capture and fine capture, the capture result is transmitted to the CPU.

[0106] S3. Data processing

[0107] The CPU calculates the peak ratio of direct and reflected signals to obtain the surface reflectivity. The nearest neighbor interpolation method is used to obtain the initial soil moisture value based on the lookup table.

[0108] The obtained soil moisture values ​​are corrected in steps:

[0109] 1) First, the differential value diff is calculated for the hourly inversion results. For all soil moisture values ​​in the period when diff transitions from greater than 0.05 to less than -0.05, that is, the moisture values ​​with too drastic changes and not in line with the law of natural changes, they are uniformly multiplied by a coefficient of 0.85.

[0110] 2) The segmented model is corrected by substituting the above corrected results into the formula for correction:

[0111]

[0112] 3) After being corrected by the segmented model, the precipitation is corrected according to the rules described above;

[0113] 4) Based on the long-term observation data of the TDR hygrometer, the difference between the surface soil and the soil about 15 cm deep is about 0.03, which is added to the soil surface moisture after all the corrections mentioned above to obtain the deep soil moisture.

[0114] S4. Result Verification

[0115] like Figure 3 As shown, in order to verify the correctness of the results in this embodiment, the in-situ data of soil moisture in the detected area was collected using a TDR soil moisture meter. The in-situ data with a depth of 15 cm was compared with the soil moisture obtained by detection, and the experimental data accuracy was obtained to be 0.035m 3 / m 3 , which verifies the correctness of this embodiment.

[0116] The GNSS-R deep soil moisture inversion method considering precipitation information of the present invention combines the outstanding capabilities of the CPU in logic control and the unique advantages of the GPU in massive data parallel computing, and utilizes this program architecture to overcome the problem of low computational efficiency of GNSS-R software. This innovative combination greatly improves the program computing speed, allowing the originally time-consuming process to be completed efficiently, realizing real-time calculation of soil moisture inversion, enhancing the practicality of the present invention, and providing solid technical support for GNSS-R soil moisture detection on ground-based platforms.

[0117] The GNSS-R deep soil moisture inversion method considering precipitation information of the present invention can eliminate the interference of precipitation on the soil moisture inversion value caused by precipitation. By setting different judgment limits and combining precipitation data, the abnormally high soil moisture inversion value due to water accumulation on the soil surface is corrected accordingly, thereby obtaining reliable deep soil moisture data.

[0118] In addition, a lookup table was established through forward calculation, and the Topp model was used to invert the experimental data to obtain the soil moisture value calculated based on the soil dielectric constant. According to the corresponding relationship between reflectivity and soil moisture value, the soil moisture was interpolated with a step size of 0.001, and a 0-1cm interpolation was established through the sequence of interpolation first and then extrapolation. 3 / cm 3 This method can directly estimate soil moisture content based on reflectivity, simplifies the intermediate parameter of soil dielectric constant, solves the problem of a large number of inverse operations required in the traditional inversion process, and greatly reduces the time required for solving the original model.

[0119] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A GNSS-R deep soil moisture inversion method considering precipitation information, characterized in that: include: S1. Build a ground-based GNSS-R dual-antenna monitoring platform; S2. Processing the GNSS signal using a signal processing module; S3, perform preliminary inversion of soil moisture; S4, step-by-step correction to eliminate the impact of precipitation; S5. Acquisition of deep soil moisture.

2. The GNSS-R deep soil moisture inversion method considering precipitation information according to claim 1 is characterized in that: The S1 includes: A liftable platform is set up and a waterproof chassis is arranged under the platform. A signal collector and a workstation are arranged in the waterproof chassis. A right-hand circularly polarized antenna and a left-hand circularly polarized antenna are fixed on the chassis. The right-hand circularly polarized antenna faces upward at a 45° angle to receive GNSS direct signals, and the left-hand circularly polarized antenna faces downward at a 45° angle to receive GNSS reflected signals. The right-hand circularly polarized antenna and the left-hand circularly polarized antenna are connected to the corresponding channels of the signal collector.

3. The GNSS-R deep soil moisture inversion method considering precipitation information according to claim 2 is characterized in that: The S2 uses a CPU+GPU architecture for GNSS signal processing: The signal collector outputs the converted intermediate frequency digital signal, which is transmitted from the CPU to the GPU. The GPU performs coarse and fine capture of the signal to obtain the peak power values ​​of the direct and reflected signals. The result is then transmitted to the CPU to obtain the surface reflectivity and output it.

4. The GNSS-R deep soil moisture inversion method considering precipitation information according to claim 3 is characterized in that: Coarse and fine acquisition of GNSS-R signals include: 1) Calculate the visible satellite range in advance based on the longitude and latitude of the experimental site; 2) Input parameters to the CPU, including visible satellite PRN, coherent integration time for coarse capture, coherent and incoherent integration time for fine capture, and Doppler frequency search range and search step size; 3) Then use GPU for coarse capture; 4) Based on the code phase and frequency information obtained by coarse capture, complete the fine capture of the signal and obtain the direct and reflected signal power peaks.

5. The GNSS-R deep soil moisture inversion method considering precipitation information according to claim 4 is characterized in that: The S3 includes: Pre-built reflectance and soil moisture lookup tables: The GNSS signal is completely reflected after reaching the reflecting surface. The concept of Fresnel reflection coefficient is used to describe the quantitative relationship between the reflected energy and the incident energy during the reflection process of electromagnetic waves: Where ε is the complex dielectric constant of the soil surface; θ is the satellite altitude angle; Γ is the polarization coefficient; v and h represent vertical linear polarization and horizontal linear polarization respectively, and l and r represent left-hand circular polarization antenna and right-hand circular polarization antenna respectively; The reflectivity corresponding to different polarization modes can be calculated using the above Fresnel reflection coefficient: Where R rl Represents the reflectivity of left-hand circularly polarized signal; R rr Indicates the reflectivity of right-hand circularly polarized signal; In order to quantify the reflection characteristics of GNSS satellite signals on the ground, the ratio of the reflected signal power to the direct signal power is calculated and used as the surface reflectivity: Where P r is the reflected signal power received by the receiver; P d The direct signal power received by the receiver; The soil dielectric constant ε is obtained by the relationship between reflectivity R and dielectric constant: Calculate soil moisture using the Topp model: Among them, m v represents soil moisture, and then a reflectivity and soil moisture lookup table is established based on the obtained results; Finally, the soil moisture value is directly estimated using the nearest neighbor interpolation method based on the reflectivity obtained in step S2.

6. The GNSS-R deep soil moisture inversion method considering precipitation information according to claim 5 is characterized in that: The S4 includes: Correction using rate of change: Calculate the difference value diff for the inversion results every hour: diff i =SM i -SM i-1 When diff is greater than 0.05 or less than -0.05, all soil moisture values ​​within this period are uniformly multiplied by the coefficient K = 0.85 to correct the soil moisture values: SM i =0.85*SM i ,diff i <-0.05or>0.05 Among them SM i Represents the soil moisture value measured at the i-th hour.

7. The GNSS-R deep soil moisture inversion method considering precipitation information according to claim 6 is characterized in that: The S4 further comprises: Correction using a piecewise model: For the soil moisture corrected by the rate of change, the soil moisture value is 0.3m 3 / m 3 With 0.6m 3 / m 3 As the boundary, according to different intervals, the corresponding correction formula is used for accurate calculation:

8. The GNSS-R deep soil moisture inversion method considering precipitation information according to claim 7 is characterized in that: The S4 further comprises: Correction using precipitation: For the soil moisture results of the completed segmented model correction, when a specific time t n , it has been verified that there is no precipitation at this time and in the previous 2 hours. If the soil moisture at this time is lower than that at the previous time t n-1 However, it shows an upward trend, so the abnormal data is corrected, that is, t n-1 The soil moisture value at time t is taken as n-1 The result of the moment, replacing the original unreasonable t n Invert the value at any time.

9. The GNSS-R deep soil moisture inversion method considering precipitation information according to claim 8 is characterized in that: In S5: Calculate the soil moisture at a depth of 15 cm using the formula: SM deep =SM+0.03。

Images