Soil moisture inversion method, device, medium and computing equipment based on GNSS-R

Abstract

The present invention discloses a soil moisture inversion method, device, medium and computing equipment based on GNSS-R, wherein the method comprises: obtaining satellite data and soil moisture products in the same target area and the same period, wherein the soil moisture products comprise soil moisture data, physical quantity data and environmental quantity data capable of describing each position of the target area; calculating the surface reflectance of each position of the target area based on the satellite data; clustering each position of the target area based on the soil moisture product, taking at least one of the physical quantity data and the environmental quantity data as a classification basis, and obtaining a preset number of classification types, wherein each classification type comprises a plurality of positions in the target area; matching the soil moisture data with the surface reflectance in the same time and space dimension, and calculating the mapping relationship between the soil moisture data of each classification type and the surface reflectance, so as to perform soil moisture inversion. The method refers to the data of each position of the target area and takes into account the geographical regularity characteristics of the target area, so that the inversion result is more comprehensive and accurate.

Description

Technical Field

[0001] The present invention relates to the field of soil moisture monitoring, and in particular to a soil moisture inversion method, device, medium and computing equipment based on GNSS-R. Background Art

[0002] Soil moisture is a key element in the water cycle. Soil moisture information is very important to scientific research and production fields such as hydrology, climate, ecology, and agriculture. Accurate measurement of soil moisture is of great significance.

[0003] At present, the commonly used small-scale soil moisture measurements (such as gravimetric measurement and time domain reflectometry measurement) are all based on point measurements, representing a very small spatial range of soil moisture information. For large-area soil moisture measurements, the main existing remote sensing methods are microwave remote sensing and optical remote sensing. Commonly used optical remote sensing methods can invert soil moisture by identifying factors such as soil color and surface temperature, but optical remote sensing is usually easily affected by clouds and fog, and the revisit time is relatively long. L-band microwave remote sensing has become the main means of soil moisture observation. Taking passive microwave remote sensing as an example, the soil dielectric constant is usually obtained by measuring the backscattering coefficient, and then the soil moisture is obtained from the correlation between the soil dielectric constant and soil moisture, but it usually takes 2-3 days or even more time to complete the measurement of soil moisture in the range.

[0004] Since Martín-Neira proposed using GPS reflected signals to measure sea level in 1993, the Global Navigation Satellite System Reflectometry (GNSS-R) remote sensing technology has gradually emerged and developed rapidly. Using the long-term stable, high-temporal and spatial resolution L-band microwave signals provided by GNSS (Global Navigation Satellite System) satellites, GNSS-R has become an effective means of detecting surface soil moisture due to its sensitivity to soil moisture, ability to penetrate clouds and medium vegetation cover, and dual-base radar structure.

[0005] Most of the existing satellite-based GNSS-R soil moisture inversion methods are based on single pixel inversion. The inversion results are relatively limited and highly dependent on the amount of data. Since soil moisture is unevenly distributed in space and changes dynamically over time, insufficient data may be encountered. Therefore, this inversion result cannot accurately predict soil moisture in various geographical areas. Summary of the invention

[0006] The main purpose of the present invention is to propose a soil moisture inversion method, device, medium and computing equipment based on GNSS-R, aiming to solve the problems mentioned in the background technology.

[0007] To achieve the above object, the present invention proposes a soil moisture inversion method based on GNSS-R, comprising:

[0008] Acquire satellite data and soil moisture products in the same target area and in the same period, wherein the soil moisture products include soil moisture data, physical quantity data, and environmental quantity data that can describe each location of the target area;

[0009] Calculating the surface reflection coefficient of each position in the target area based on the satellite-borne data;

[0010] Based on the soil moisture product, taking at least one of the physical quantity data and the environmental quantity data as a classification basis, clustering the various locations in the target area to obtain a preset number of classification types, wherein each classification type includes a plurality of locations in the target area;

[0011] In the same space-time dimension, the soil moisture data is matched with the surface reflectance, and the mapping relationship between the soil moisture data of each classification type and the surface reflectance is calculated to perform soil moisture inversion.

[0012] Optionally, after acquiring the satellite data of the target area, the method further includes:

[0013] Preprocessing and screening the satellite-borne data based on preset parameters;

[0014] The surface reflection coefficient of each position in the target area is calculated based on the screened satellite data.

[0015] Optionally, the preset parameters include at least one of the following: signal-to-noise ratio and incident angle;

[0016] When screening based on signal-to-noise ratio, select data with a signal-to-noise ratio greater than 5dB;

[0017] When filtering based on the incident angle, select data with an incident angle less than 45°.

[0018] Optionally, calculating the surface reflection coefficient of each position of the target area based on the satellite-borne data includes:

[0019] Based on the satellite-borne data, using a bistatic radar equation, calculating the surface reflectivity of each position in the target area;

[0020] Based on the physical quantity data and environmental quantity data of each position in the target area, the surface reflectivity of each position is corrected to obtain the surface reflection coefficient of each position in the target area.

[0021] Optionally, physical quantity data and / or environmental quantity data affecting the soil moisture inversion result are selected for clustering;

[0022] The environmental quantity data affecting the soil moisture inversion result include vegetation optical thickness;

[0023] The physical quantity data affecting the soil moisture inversion result include the surface roughness coefficient.

[0024] Optionally, based on the physical quantity data and environmental quantity data of each position in the target area, the surface reflectivity of each position is corrected, including:

[0025] Based on the vegetation optical thickness and the surface roughness coefficient at each location in the target area, the surface reflectivity at each location is corrected.

[0026] Optionally, based on the soil moisture product, taking at least one of the physical quantity data and the environmental quantity data as a classification basis, clustering the locations of the target area includes:

[0027] Based on the duration of the soil moisture product, calculate the average vegetation optical thickness and the average surface roughness coefficient at each location in the target area;

[0028] At least one of the average vegetation optical thickness and the average surface roughness coefficient of each position in the target area is used as a classification basis, and each position in the target area is clustered according to a preset number of classification types.

[0029] Optionally, in the same spatiotemporal dimension, the soil moisture data is matched with the surface reflectance, and a mapping relationship between the soil moisture data of each classification type and the surface reflectance is calculated, including:

[0030] Select the surface reflectance and soil moisture products of the same period;

[0031] In the spatial dimension, matching the surface reflectance and the soil moisture product;

[0032] According to the classification types, soil moisture data and corresponding surface reflectance coefficients of various locations included in each classification type are respectively obtained to calculate a mapping relationship between soil moisture data and surface reflectance coefficients of various classification types.

[0033] Optionally, before calculating the mapping relationship between the soil moisture data of each classification type and the surface reflection coefficient, the method further includes: deleting the surface reflection coefficient and water body data in the soil moisture data.

[0034] Optionally, the method further comprises:

[0035] The soil moisture data of each location contained in each classification type and the corresponding surface reflectance coefficient are fitted respectively to obtain the mapping relationship between the soil moisture data of each classification type and the surface reflectance coefficient for soil moisture inversion.

[0036] The present invention also proposes a soil moisture inversion device based on GNSS-R, comprising:

[0037] An acquisition module, used to acquire satellite data and soil moisture products in the same target area and in the same period, wherein the soil moisture products include soil moisture data, physical quantity data and environmental quantity data that can describe each position of the target area;

[0038] A calculation module, used for calculating the surface reflection coefficient of each position of the target area based on the satellite data;

[0039] A clustering module, configured to cluster the locations of the target area based on the soil moisture product and at least one of the physical quantity data and the environmental quantity data as a classification basis to obtain a preset number of classification types, wherein each classification type includes a plurality of locations in the target area;

[0040] The matching module is used to match the soil moisture data with the surface reflection coefficient in the same time and space dimensions, and calculate the mapping relationship between the soil moisture data of each classification type and the surface reflection coefficient, so as to perform soil moisture inversion.

[0041] Optionally, the device further comprises a screening module, configured to pre-process and screen the satellite-borne data based on preset parameters;

[0042] The surface reflection coefficient of each position in the target area is calculated based on the screened satellite data.

[0043] Optionally, the preset parameters include at least one of the following: signal-to-noise ratio and incident angle;

[0044] The screening module is configured to:

[0045] When screening based on signal-to-noise ratio, select data with a signal-to-noise ratio greater than 5dB;

[0046] When filtering based on the incident angle, select data with an incident angle less than 45°.

[0047] Optionally, the calculation module is configured to:

[0048] Based on the satellite-borne data, using a bistatic radar equation, calculating the surface reflectivity of each position in the target area;

[0049] Based on the physical quantity data and environmental quantity data of each position in the target area, the surface reflectivity of each position is corrected to obtain the surface reflection coefficient of each position in the target area.

[0050] Optionally, the clustering module is configured to:

[0051] Select physical quantity data and / or environmental quantity data that affect the soil moisture inversion result for clustering;

[0052] The environmental quantity data affecting the soil moisture inversion result include vegetation optical thickness;

[0053] The physical quantity data affecting the soil moisture inversion result include the surface roughness coefficient.

[0054] Optionally, the calculation module is configured to:

[0055] Based on the vegetation optical thickness and roughness coefficient at each location in the target area, the surface reflectivity at each location in the target area is corrected.

[0056] Optionally, the clustering module is further configured to:

[0057] Based on the duration of the soil moisture product, calculate the average vegetation optical thickness and the average surface roughness coefficient at each location in the target area;

[0058] At least one of the average vegetation optical thickness and the average surface roughness coefficient of each position in the target area is used as a classification basis, and each position in the target area is clustered according to a preset number of classification types.

[0059] Optionally, the matching module is further configured to:

[0060] Select the surface reflectance and soil moisture products of the same period;

[0061] In the spatial dimension, the surface reflectance and the soil moisture product are matched to obtain a matching result;

[0062] According to the classification type, the soil moisture data and the corresponding surface reflection coefficient of each position included in each classification type are respectively obtained to calculate the mapping relationship between the soil moisture data and the surface reflection coefficient of each position.

[0063] Optionally, the matching module is further configured to delete the surface reflection coefficient and water body data in the soil moisture data before calculating the mapping relationship between the soil moisture data of each classification type and the surface reflection coefficient.

[0064] Optionally, the matching module is further configured to:

[0065] The soil moisture data of each location contained in each classification type and the corresponding surface reflectance coefficient are fitted respectively to obtain the mapping relationship between the soil moisture data of each classification type and the surface reflectance coefficient for soil moisture inversion.

[0066] The present invention also provides a medium on which a computer program is stored, and the computer program implements the above method when executed by a processor.

[0067] The present invention also provides a computing device, which includes a processor, and the processor is used to implement the above method when executing a computer program stored in a memory.

[0068] The technical solution of the present invention is based on the satellite data of the entire target area and the soil moisture product that can describe the surface type of each location in the target area and the soil moisture data of the corresponding location. In the inversion process, the geographical natural law characteristics of the target area, including physical quantity characteristics and environmental quantity characteristics, are fully considered, so that the soil moisture of the entire target area can be fully reflected as a whole, and the inversion is more accurate locally. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] 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 or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0070] Figure 1 This is a step diagram of a soil moisture inversion method based on GNSS-R according to an embodiment of the present invention;

[0071] Figure 2 A global surface clustering map provided by an embodiment of the present invention;

[0072] Figure 3 This is a schematic diagram of the working principle of space-borne GNSS-R;

[0073] Figure 4 The soil moisture inversion result and the SMAP soil moisture spatial distribution map in one embodiment of the present invention.

[0074] Figure 5 It is a schematic diagram of soil moisture inversion results and SMAP soil moisture fitting results in one embodiment of the present invention.

[0075] Figure 6 is a schematic diagram of the structure of a computer storage medium in one embodiment of the present invention;

[0076] Figure 7 It is a schematic diagram of the structure of a computing device in one embodiment of the present invention.

[0077] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0078] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0079] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a device, apparatus, method or computer program product. Therefore, the present disclosure may be implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0080] According to an embodiment of the present invention, a soil moisture inversion method, apparatus, medium and computing device based on GNSS-R are proposed.

[0081] Exemplary Methods

[0082] In this exemplary embodiment, a soil moisture inversion method based on GNSS-R is proposed, such as Figure 1 As shown, the method comprises the following steps:

[0083] Step S100: acquiring satellite data and soil moisture products in the same target area and the same period, wherein the soil moisture products include soil moisture data, physical quantity data and environmental quantity data that can describe each position of the target area;

[0084] Step S200: Calculating the surface reflection coefficient of each position in the target area based on the satellite-borne data.

[0085] Step S300: Based on the soil moisture product, taking at least one of the physical quantity data and the environmental quantity data as a classification basis, clustering the various locations in the target area to obtain a preset number of classification types, wherein each classification type includes multiple locations in the target area.

[0086] Step S400: In the same spatial and temporal dimensions, the soil moisture data is matched with the surface reflectance, and a mapping relationship between the soil moisture data of each classification type and the surface reflectance is calculated to perform soil moisture inversion.

[0087] In step S100, the satellite data and soil moisture products of the same target area and the same period are obtained, such as Figure 3 As shown, the satellite-borne data can be a signal transmitted by a GNSS-R satellite transmitter to the target area, which is received by a satellite-borne GNSS-R receiver after being reflected by the target area, such as the domestic Fengwu-1 experimental A / B dual satellite. In addition, the satellite-borne data can be data of the target area for a period of time.

[0088] After acquiring the satellite data, a soil moisture product is acquired according to the location and duration of the target area. The soil moisture product and the satellite data are related data about the same target area and the same period of time, and the soil moisture product includes soil moisture data, physical quantity data, and environmental quantity data that can describe each location of the target area. In this embodiment, the soil moisture product can be a SMAP (Soil Moisture Active and Passive) soil moisture product. In other embodiments, it can also be other soil moisture products that include soil moisture data, physical quantity data, and environmental quantity data for each location of the target area, such as SMOS and AMSR2. The technical solution does not limit the type of soil moisture product.

[0089] Then, step S200 is performed. In this step, the surface reflection coefficient of each position of the target area is calculated based on the satellite data obtained in step S100. The specific calculation process includes the following steps:

[0090] Step S210: Based on the satellite data, the surface reflectivity of each position in the target area is calculated using the bistatic radar equation. The following bistatic radar equation can be used in this step:

[0091]

[0092]

[0093] Among them, P coh is the coherent received power; λ is the wavelength of the navigation satellite signal; P t Navigation satellite signal transmission power; G t is the transmitting antenna gain; G γ is the receiving antenna gain; R st is the distance from the mirror reflection point to the transmitter; R sγ is the distance from the mirror reflection point to the receiver; Γ θ is the specular reflectivity at the incident angle θ; P inc represents the incoherent received power; σ represents the radar scattering cross section (BRCS).

[0094] Assuming that the coherent receiving power and incoherent receiving power of the onboard GNSS-R are consistent, the surface reflectivity of the target area can be calculated as follows:

[0095] Therefore, when P coh =P inc When , we can get from equation (1) and equation (2)

[0096]

[0097] Using the above formula (3), according to the distance from the mirror reflection point to the receiver, the distance from the mirror reflection point to the transmitter, and the radar scattering cross section, the surface reflectivity of the reflection point on the target area at the incident angle can be calculated.

[0098] After obtaining the surface reflectance of each position in the target area, step S220 is performed: based on the physical quantity data and environmental quantity data of each position in the target area, the surface reflectance of each position is corrected to obtain the surface reflection coefficient of each position in the target area. The physical quantity data and environmental quantity data of each position in the target area can be obtained from the soil moisture product of the area, such as from the SMAP (Soil Moisture Active and Passive) soil moisture product. In one embodiment, the environmental quantity data can be the optical thickness of vegetation at each position in the target area, and the physical quantity data can be the surface roughness coefficient of each position in the target area. Based on the surface roughness coefficient and the optical thickness of vegetation at each position in the target area, the surface reflectance of each position in the target area can be corrected using the following formula. The formula is as follows:

[0099]

[0100] Where, γ represents the influence of vegetation (vegetation optical thickness); [exp(-hcos 2 (θ))] represents the effect of surface roughness (surface roughness coefficient); Represents the Fresnel reflection coefficient.

[0101] Using formula (4), the surface reflectivity can be corrected and the Fresnel reflection coefficient of each reflection point can be obtained. Under the clustering condition of this method, it can be regarded as the surface reflection coefficient of each position in the target area.

[0102] for For example,

[0103] Where γ represents the influence of vegetation (transmittance); [exp(-hcos 2 (θ))] represents the effect of surface roughness; Represents the Fresnel reflection coefficient. Fresnel reflection coefficient generally refers to the complex reflectivity of right-handed emission and left-handed reception. and the vertical polarization complex reflectivity Complex reflectivity of horizontal polarization Related:

[0104]

[0105] The subscripts RL, V, and H represent right-hand circular polarization, vertical linear polarization, and horizontal linear polarization, respectively. and They are:

[0106]

[0107] Here ε represents the dielectric constant, so the reflectivity can be used to invert soil moisture.

[0108] Since Γ θ The unit is generally expressed in dB, which can be obtained from formula (3):

[0109] Γ θ =10log(σ)+20log(R st +R sr )-10log(4π)-20log(R st R sr ) ⑻

[0110] After calculating the surface reflectance of each position in the target area, the next step is to perform step S300, based on the soil moisture product, clustering each position in the target area with at least one of the physical quantity data and the environmental quantity data as a classification basis to obtain a preset number of classification types, wherein each classification type includes multiple positions in the target area. In this embodiment, the soil moisture product takes the SMAP soil moisture product as an example, and clusters all soil moisture data in the SMAP soil moisture product with the physical quantity data or the environmental quantity data in the SMAP soil moisture product as the classification basis, or with both the physical quantity data and the environmental quantity data as the classification basis. When selecting the physical quantity and the environmental quantity, reference can be made to the following: Figure 2The global surface clustering map shown is selected from the physical quantity and environmental quantity that affect the clustering. For example, in one embodiment, the physical quantity is the vegetation optical thickness, the environmental quantity is the surface roughness, and the vegetation optical thickness is used as the classification basis. First, according to the duration of the SMAP soil moisture product, the average vegetation optical thickness of each position in the target area is calculated, and then the average optical thickness of each position in the target area is used as the classification basis. The positions of the target area are clustered according to a preset number of classification types. The K-Means algorithm can be used for clustering. All soil moisture data in the SMAP soil moisture product are clustered according to a preset number of classification types. After the clustering is completed, each classification type contains multiple positions in the target area. For example, 200 classification types are preset, then each position in the target area is classified into these 200 classification types, and at the same time, the soil moisture data of each position is divided into 200 categories. In other embodiments, other classification algorithms can also be used for clustering, such as: i-kmeans algorithm, etc. This technical solution does not limit the clustering algorithm used.

[0111] In the above embodiment, the optical thickness of vegetation at each position in the target area is used as the classification basis, and the K-Means algorithm is used to cluster the various positions in the target area. After clustering, the soil moisture data of each position under the optical thickness of vegetation of each classification type is obtained, that is, each classification type in the clustering result includes multiple positions, and also includes the soil moisture data corresponding to the multiple positions. It should be noted that in other embodiments, the surface roughness coefficient can also be used as the classification basis, or the surface roughness coefficient and the optical thickness of vegetation can be used as the classification basis for clustering. The specific method is consistent with the above classification based on the optical thickness of vegetation, and will not be repeated here. At the same time, in other embodiments, other physical quantities and environmental quantities can also be selected for clustering. The present application does not limit the physical quantities and environmental quantities selected as the classification basis for clustering.

[0112] Then, step S400 is performed. In this step, the soil moisture data is matched with the surface reflectance in the same time and space dimension, and the mapping relationship between the soil moisture data of each classification type and the surface reflectance is calculated so as to perform soil moisture inversion, which specifically includes the following steps:

[0113] Step S410: Select the surface reflectance and soil moisture products of the same time period. In step S200, the surface reflectance of each position in the target area is calculated based on the satellite data. In this step, it is first determined whether the satellite data and soil moisture products obtained in step S100 are in the same time period. If the satellite data and soil moisture products obtained are in the same time period, then the surface reflectance obtained in step S200 also has the same time period as the soil moisture product. In this step, all surface reflectances and all soil moisture products can be selected; if the satellite data and soil moisture products obtained in step S100 overlap only in part of the time period, then the surface reflectance obtained in step S200 also overlaps with the soil moisture product in part of the time period. In this step, it is necessary to select the surface reflectance and soil moisture products of the overlapping time period.

[0114] Step S420: In the spatial dimension, the surface reflection coefficient and the soil moisture product are matched to obtain a matching result; in step S410, the surface reflection coefficient and the soil moisture product of the same time period have been judged and selected, that is, the selected surface reflection coefficient and the soil moisture product are the same in the time dimension. In this step, the surface reflection coefficient and the soil moisture product are matched in the spatial dimension. For example, the pixels of the two data can be matched, and the pixels of the two data in the target area are matched one by one, so that the soil moisture data and the surface reflection coefficient at any position in the target area at a certain time can be obtained.

[0115] Step S430: According to the classification type, the soil moisture data and the corresponding surface reflectance coefficient of each location contained in each classification type are obtained respectively. In step S420, the soil moisture data and the surface reflectance coefficient have been matched in the time dimension and the space dimension. In this step, data is selected according to each classification type. For example, 200 classification types are preset in step S200. Then, in this step, the surface reflectance coefficient and soil moisture data of each location in each classification type can be selected from the matching results according to the 200 classification types. Finally, 200 groups of soil moisture data and surface reflectance coefficient data can be obtained, which represents the mapping relationship between 200 types of soil moisture data and surface reflectance coefficient.

[0116] In another embodiment of the present embodiment, after step S420, step S421 is further included: after matching the surface reflection coefficient and the soil moisture product, the corresponding surface reflection coefficient and the matched soil moisture product are deleted based on the water body proportion field of the matching result. Considering that there may be water bodies in the target area, if there are water bodies, the humidity data of the water body position will inevitably affect the inversion result. Therefore, based on the water body proportion field of the surface reflection coefficient calculated based on the satellite data, it can be learned which surface reflection coefficients belong to the position with a large water body proportion in the target area. Then, after the surface reflection coefficient and the soil moisture product are matched in time and space in step S420, the surface reflection coefficient corresponding to the water body proportion field and the matched soil moisture product can be deleted, so that the reflection coefficient and soil moisture data of the water body position in the target area are completely deleted, and then the remaining data is taken to perform step S430. Since the soil moisture data and the water body data in the surface reflection coefficient are deleted, the inversion result will be more accurate.

[0117] It should be noted that the SMAP soil moisture product is used in this embodiment, and after the soil moisture data is matched with the surface reflectance coefficient, the water body data in the soil moisture data and the surface reflectance coefficient are deleted. In other embodiments, the water body data in the surface reflectance coefficient can also be deleted before matching. For example, when the GSW global water classification product is selected, the surface reflectance coefficient data of the water body part in the GSW water classification data can be deleted first, and then matching is performed, and finally the mapping relationship of each classification type is calculated. This application does not limit whether the water body data is deleted before or after matching.

[0118] Next, a fitting method can be used to calculate the mapping relationship between soil moisture data and surface reflection coefficient. For example, in this implementation, according to the classification type during clustering, the soil moisture data and surface reflection coefficient of each location contained in each classification type are fitted respectively. For example, a linear fitting method, an NDVI fitting method or a piecewise linear regression fitting method can be used. This technical solution does not limit the specific fitting algorithm used. After fitting, the soil moisture k (slope of linear fitting) and b (intercept coefficient) coefficients in each classification type in the target area can be obtained. Therefore, soil moisture inversion can be performed based on the soil moisture k and b coefficients of each classification type.

[0119] This technical solution clusters the soil moisture data of each location in the target area. When clustering, the geographical features of the target area, such as environmental quantity data and physical quantity data, are taken into account, and clustering is performed based on this. Therefore, each classification type includes multiple locations in the target area, and the source of data is not just a single pixel. Then, the soil moisture data and the surface reflection coefficient in the clustering result are fitted to obtain the mapping relationship between the surface reflection coefficient and soil moisture in each classification type. Therefore, when inverting soil moisture, the soil moisture can be inverted using the above mapping relationship based on the surface reflection coefficient. This inversion method not only takes into account the natural geographical features of the target area, but also the data source is not limited to a single pixel, so the inversion result is more accurate and comprehensive. Figure 4 In the embodiment shown, based on the satellite data obtained by the Fengwu-1 experiment A / B dual satellites, the global soil moisture spatial distribution map obtained by the GNSS-R-based soil moisture inversion method and the global moisture spatial distribution map under the SMAP soil moisture product are basically consistent. Figure 5 In the embodiment shown, the GNSS-R based soil moisture inversion method is used to compare the GNSS-R soil moisture with the SMAP soil moisture product reference soil moisture. It can be concluded that the accuracy of the inversion method is compared with the SMAP soil moisture product. The correlation coefficient R is about 0.816, and the root mean square error RMSE is about 0.07cm 3 cm -3 Therefore, it can be seen that the soil moisture inversion method based on GNSS-R proposed in this application has high accuracy.

[0120] In another embodiment of the present implementation, after the satellite data is acquired in step S100, the acquired satellite data may be credibility screened based on preset parameters, and then the surface reflection coefficient of each position in the target area may be calculated based on the screened satellite data.

[0121] For example, in one embodiment, the preset parameters include at least one of the following: signal-to-noise ratio and incident angle; when screening based on the signal-to-noise ratio, data with a signal-to-noise ratio greater than 5 dB are selected; when screening based on the incident angle, data with an incident angle less than 45° are selected. By screening the acquired satellite data, only the screened data with higher credibility is used for inversion, which can improve the accuracy of the inversion result.

[0122] Exemplary Devices

[0123] After introducing the method of the exemplary embodiment of the present invention, the exemplary GNSS-R-based soil moisture inversion device of the present invention is described below, and the device includes:

[0124] An acquisition module, used to acquire satellite data and soil moisture products in the same target area and in the same period, wherein the soil moisture products include soil moisture data, physical quantity data and environmental quantity data that can describe each position of the target area;

[0125] A calculation module, used for calculating the surface reflection coefficient of each position of the target area based on the satellite data;

[0126] A clustering module, configured to cluster the locations of the target area based on the soil moisture product and at least one of the physical quantity data and the environmental quantity data as a classification basis to obtain a preset number of classification types, wherein each classification type includes a plurality of locations in the target area;

[0127] The matching module is used to match the soil moisture data with the surface reflection coefficient in the same time and space dimensions, and calculate the mapping relationship between the soil moisture data of each classification type and the surface reflection coefficient, so as to perform soil moisture inversion.

[0128] In this exemplary embodiment, the apparatus further includes a screening module for pre-processing and screening the satellite-borne data based on preset parameters;

[0129] The surface reflection coefficient of each position in the target area is calculated based on the screened satellite data.

[0130] In this exemplary embodiment, the preset parameters include at least one of the following: signal-to-noise ratio and incident angle;

[0131] The screening module is configured to:

[0132] When screening based on signal-to-noise ratio, select data with a signal-to-noise ratio greater than 5dB;

[0133] When filtering based on the incident angle, select data with an incident angle less than 45°.

[0134] In this exemplary embodiment, the calculation module is configured to:

[0135] Based on the satellite-borne data, using a bistatic radar equation, calculating the surface reflectivity of each position in the target area;

[0136] Based on the physical quantity data and environmental quantity data of each position in the target area, the surface reflectivity of each position is corrected to obtain the surface reflection coefficient of each position in the target area.

[0137] In this exemplary embodiment, the calculation module is configured as follows:

[0138] Based on the vegetation optical thickness and roughness coefficient at each location in the target area, the surface reflectivity at each location in the target area is corrected.

[0139] In this exemplary embodiment, the clustering module is configured to:

[0140] Select physical quantity data and / or environmental quantity data that affect the soil moisture inversion result for clustering;

[0141] The environmental quantity data affecting the soil moisture inversion result include vegetation optical thickness;

[0142] The physical quantity data affecting the soil moisture inversion result include the surface roughness coefficient.

[0143] In this exemplary embodiment, the clustering module is further configured to:

[0144] Based on the duration of the soil moisture product, calculate the average vegetation optical thickness and the average surface roughness coefficient at each location in the target area;

[0145] At least one of the average vegetation optical thickness and the average surface roughness coefficient of each position in the target area is used as a classification basis, and each position in the target area is clustered according to a preset number of classification types.

[0146] In this exemplary embodiment, the matching module is further configured to:

[0147] Select the surface reflectance and soil moisture products of the same period;

[0148] In the spatial dimension, the surface reflectance and the soil moisture product are matched to obtain a matching result;

[0149] According to the classification type, the soil moisture data and the corresponding surface reflection coefficient of each position included in each classification type are respectively obtained to calculate the mapping relationship between the soil moisture data and the surface reflection coefficient of each position.

[0150] In this exemplary embodiment, the matching module is further configured to: after matching the surface reflection coefficient and the soil moisture product, based on the water body proportion field of the surface reflection coefficient, delete the corresponding surface reflection coefficient and the matching soil moisture product.

[0151] In this exemplary embodiment, the matching module is further configured to:

[0152] The soil moisture data of each location contained in each classification type and the corresponding surface reflectance coefficient are fitted respectively to obtain the mapping relationship between the soil moisture data of each classification type and the surface reflectance coefficient for soil moisture inversion.

[0153] Exemplary Media

[0154] After introducing the method and apparatus of the exemplary embodiment of the present invention, next, reference is made to Figure 6 A computer-readable storage medium according to an exemplary embodiment of the present invention is described.

[0155] Please refer to Figure 6 , the computer-readable storage medium shown is a CD 70, on which a computer program (i.e., a program product) is stored. When the computer program is executed by the processor, each step recorded in the above method implementation is implemented, such as obtaining satellite data and soil moisture products in the same target area and the same period, wherein the soil moisture product includes soil moisture data, physical quantity data, and environmental quantity data that can describe each position of the target area; based on the satellite data, the surface reflection coefficient of each position of the target area is calculated; based on the soil moisture product, the various positions of the target area are clustered with at least one of the physical quantity data and the environmental quantity data as the classification basis to obtain a preset number of classification types, wherein each classification type includes multiple positions in the target area; in the same spatiotemporal dimension, the soil moisture data is matched with the surface reflection coefficient, and the mapping relationship between the soil moisture data of each classification type and the surface reflection coefficient is calculated, so as to perform soil moisture inversion. The specific implementation method of each step will not be repeated here.

[0156] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0157] Exemplary Computing Devices

[0158] After introducing the method, apparatus and medium of the exemplary embodiments of the present invention, next, reference is made to Figure 7 A computing device 80 according to an exemplary embodiment of the present invention is described.

[0159] Figure 7 A block diagram of an exemplary computing device 80 , which may be a computer system or a server, is shown that is suitable for use in implementing embodiments of the present invention. Figure 7The computing device 80 shown is only an example and should not bring any limitation to the functionality and scope of use of the embodiments of the present invention.

[0160] like Figure 7 As shown, the components of the computing device 80 may include, but are not limited to: one or more processors or processing units 801, a system memory 802, and a bus 803 connecting different system components (including the system memory 802 and the processing unit 801).

[0161] The computing device 80 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 80, including volatile and non-volatile media, removable and non-removable media.

[0162] The system memory 802 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 8021 and / or cache memory 8022. The computing device 70 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the ROM 8023 may be used to read and write non-removable, non-volatile magnetic media ( Figure 7 is not shown in the Figure 7 As shown in FIG. 8 , a disk drive for reading and writing a removable non-volatile disk (e.g., a “floppy disk”) and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 803 via one or more data medium interfaces. System memory 802 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of various embodiments of the present invention.

[0163] A program / utility 8025 having a set (at least one) of program modules 8024 may be stored, for example, in system memory 802, and such program modules 8024 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. Program modules 8024 generally perform the functions and / or methods of the embodiments described herein.

[0164] The computing device 80 may also communicate with one or more external devices 804 (e.g., a keyboard, a pointing device, a display, etc.). Such communication may be performed via an input / output (I / O) interface. Furthermore, the computing device 80 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 806. Figure 7 As shown, the network adapter 806 communicates with other modules (such as the processing unit 801, etc.) of the computing device 80 via the bus 803. It should be understood that although Figure 7 Not shown, other hardware and / or software modules may be used in conjunction with computing device 80 .

[0165] The processing unit 801 executes various functional applications and data processing by running the program stored in the system memory 802, for example, obtaining satellite data and soil moisture products in the same target area and the same period of time, wherein the soil moisture product includes soil moisture data, physical quantity data and environmental quantity data that can describe each position of the target area; based on the satellite data, calculating the surface reflection coefficient of each position of the target area; based on the soil moisture product, clustering each position of the target area with at least one of the physical quantity data and the environmental quantity data as the classification basis, obtaining a preset number of classification types, wherein each classification type includes multiple positions in the target area; in the same spatiotemporal dimension, matching the soil moisture data with the surface reflection coefficient, calculating the mapping relationship between the soil moisture data of each classification type and the surface reflection coefficient, so as to perform soil moisture inversion. The specific implementation method of each step will not be repeated here. It should be noted that although several units / modules or subunits / submodules of the soil moisture inversion device based on GNSS-R are mentioned in the above detailed description, such division is only exemplary and not mandatory. In fact, according to the embodiments of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be embodied.

[0166] In addition, although the operations of the method of the present invention are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0167] Although the spirit and principle of the present invention have been described with reference to several specific embodiments, it should be understood that the present invention is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined to benefit, and such division is only for the convenience of expression. The present invention is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the attached claims.

[0168] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

[0169] Through the above description, the embodiments of the present invention provide the following technical solutions, but are not limited thereto:

[0170] 1. A soil moisture inversion method based on GNSS-R, comprising:

[0171] Acquire satellite data and soil moisture products in the same target area and in the same period, wherein the soil moisture products include soil moisture data, physical quantity data, and environmental quantity data that can describe each location of the target area;

[0172] Calculating the surface reflection coefficient of each position in the target area based on the satellite-borne data;

[0173] Based on the soil moisture product, taking at least one of the physical quantity data and the environmental quantity data as a classification basis, clustering the various locations in the target area to obtain a preset number of classification types, wherein each classification type includes a plurality of locations in the target area;

[0174] In the same space-time dimension, the soil moisture data is matched with the surface reflectance, and the mapping relationship between the soil moisture data of each classification type and the surface reflectance is calculated to perform soil moisture inversion.

[0175] 2. The soil moisture inversion method based on GNSS-R as described in technical solution 1, wherein after obtaining the satellite data of the target area, it also includes:

[0176] Preprocessing and screening the satellite-borne data based on preset parameters;

[0177] The surface reflection coefficient of each position in the target area is calculated based on the screened satellite data.

[0178] 3. The soil moisture inversion method based on GNSS-R as described in technical solution 1 or 2, wherein the preset parameters include at least one of the following: signal-to-noise ratio and incident angle;

[0179] When screening based on signal-to-noise ratio, select data with a signal-to-noise ratio greater than 5dB;

[0180] When filtering based on the incident angle, select data with an incident angle less than 45°.

[0181] 4. The GNSS-R-based soil moisture inversion method according to any one of technical solutions 1 to 3, wherein the surface reflection coefficient of each position in the target area is calculated based on the satellite data, including:

[0182] Based on the satellite-borne data, using a bistatic radar equation, calculating the surface reflectivity of each position in the target area;

[0183] Based on the physical quantity data and environmental quantity data of each position in the target area, the surface reflectivity of each position is corrected to obtain the surface reflection coefficient of each position in the target area.

[0184] 5. The soil moisture inversion method based on GNSS-R as described in any one of technical solutions 1 to 4, wherein physical quantity data and / or environmental quantity data affecting the soil moisture inversion result are selected for clustering;

[0185] The environmental quantity data affecting the soil moisture inversion result include vegetation optical thickness;

[0186] The physical quantity data affecting the soil moisture inversion result include the surface roughness coefficient.

[0187] 6. The soil moisture inversion method based on GNSS-R as described in any one of technical solutions 1 to 5, wherein the surface reflectivity of each position is corrected based on the physical quantity data and environmental quantity data of each position in the target area, including:

[0188] Based on the vegetation optical thickness and the surface roughness coefficient at each location in the target area, the surface reflectivity at each location is corrected.

[0189] 7. The GNSS-R-based soil moisture inversion method according to any one of technical solutions 1 to 6, wherein, based on the soil moisture product, each location of the target area is clustered based on at least one of the physical quantity data and the environmental quantity data, including:

[0190] Based on the duration of the soil moisture product, calculate the average vegetation optical thickness and the average surface roughness coefficient at each location in the target area;

[0191] At least one of the average vegetation optical thickness and the average surface roughness coefficient of each position in the target area is used as a classification basis, and each position in the target area is clustered according to a preset number of classification types.

[0192] 8. The GNSS-R-based soil moisture inversion method according to any one of technical solutions 1 to 7, wherein the soil moisture data is matched with the surface reflection coefficient in the same time and space dimensions, and the mapping relationship between the soil moisture data of each classification type and the surface reflection coefficient is calculated, including:

[0193] Select the surface reflectance and soil moisture products of the same period;

[0194] In the spatial dimension, matching the surface reflectance and the soil moisture product;

[0195] According to the classification types, soil moisture data and corresponding surface reflectance coefficients of various locations included in each classification type are respectively obtained to calculate a mapping relationship between soil moisture data and surface reflectance coefficients of various classification types.

[0196] 9. A GNSS-R-based soil moisture inversion method as described in any one of technical solutions 1-8, wherein, before calculating the mapping relationship between soil moisture data of each classification type and the surface reflection coefficient, it also includes: deleting the surface reflection coefficient and the water body data in the soil moisture data.

[0197] 10. The soil moisture inversion method based on GNSS-R as described in any one of technical solutions 1 to 9, further comprising:

[0198] The soil moisture data of each location contained in each classification type and the corresponding surface reflectance coefficient are fitted respectively to obtain the mapping relationship between the soil moisture data of each classification type and the surface reflectance coefficient for soil moisture inversion.

[0199] 11. A soil moisture inversion device based on GNSS-R, comprising:

[0200] An acquisition module, used to acquire satellite data and soil moisture products in the same target area and in the same period, wherein the soil moisture products include soil moisture data, physical quantity data and environmental quantity data that can describe each position of the target area;

[0201] A calculation module, used for calculating the surface reflection coefficient of each position of the target area based on the satellite data;

[0202] A clustering module, configured to cluster the locations of the target area based on the soil moisture product and at least one of the physical quantity data and the environmental quantity data as a classification basis to obtain a preset number of classification types, wherein each classification type includes a plurality of locations in the target area;

[0203] The matching module is used to match the soil moisture data with the surface reflection coefficient in the same time and space dimensions, and calculate the mapping relationship between the soil moisture data of each classification type and the surface reflection coefficient, so as to perform soil moisture inversion.

[0204] 12. The GNSS-R-based soil moisture inversion device as described in technical solution 11 further includes a screening module for pre-processing and screening the satellite-borne data based on preset parameters;

[0205] The surface reflection coefficient of each position in the target area is calculated based on the screened satellite data.

[0206] 13. In the GNSS-R based soil moisture inversion device as described in technical solution 11 or 12, the preset parameters include at least one of the following: signal-to-noise ratio and incident angle;

[0207] The screening module is configured to:

[0208] When screening based on signal-to-noise ratio, select data with a signal-to-noise ratio greater than 5dB;

[0209] When filtering based on the incident angle, select data with an incident angle less than 45°.

[0210] 14. The GNSS-R based soil moisture inversion device according to any one of technical solutions 11 to 13, wherein the calculation module is configured as follows:

[0211] Based on the satellite-borne data, using a bistatic radar equation, calculating the surface reflectivity of each position in the target area;

[0212] Based on the physical quantity data and environmental quantity data of each position in the target area, the surface reflectivity of each position is corrected to obtain the surface reflection coefficient of each position in the target area.

[0213] 15. The GNSS-R based soil moisture inversion device according to any one of technical solutions 11 to 14, wherein the clustering module is configured as follows:

[0214] Select physical quantity data and / or environmental quantity data that affect the soil moisture inversion result for clustering;

[0215] The environmental quantity data affecting the soil moisture inversion result include vegetation optical thickness;

[0216] The physical quantity data affecting the soil moisture inversion result include the surface roughness coefficient.

[0217] 16. The GNSS-R based soil moisture inversion device according to any one of technical solutions 11 to 15, wherein the calculation module is configured as follows:

[0218] Based on the vegetation optical thickness and the surface roughness coefficient at each location in the target area, the surface reflectivity at each location in the target area is corrected.

[0219] 17. The GNSS-R based soil moisture inversion device according to any one of technical solutions 11 to 16, wherein the clustering module is further configured as:

[0220] Based on the duration of the soil moisture product, calculate the average vegetation optical thickness and the average surface roughness coefficient at each location in the target area;

[0221] At least one of the average vegetation optical thickness and the average surface roughness coefficient of each position in the target area is used as a classification basis, and each position in the target area is clustered according to a preset number of classification types.

[0222] 18. The GNSS-R based soil moisture inversion device according to any one of technical solutions 11 to 17, wherein the matching module is further configured as:

[0223] Select the surface reflectance and soil moisture products of the same period;

[0224] In the spatial dimension, the surface reflectance and the soil moisture product are matched to obtain a matching result;

[0225] According to the classification type, the soil moisture data and the corresponding surface reflection coefficient of each position included in each classification type are respectively obtained to calculate the mapping relationship between the soil moisture data and the surface reflection coefficient of each position.

[0226] 19. A GNSS-R based soil moisture inversion device as described in any one of technical solutions 11-18, wherein the matching module is further configured to delete the surface reflection coefficient and the water body data in the soil moisture data before calculating the mapping relationship between the soil moisture data of each classification type and the surface reflection coefficient.

[0227] 20. The GNSS-R based soil moisture inversion device according to any one of technical solutions 11 to 19, wherein the matching module is further configured as:

[0228] The soil moisture data of each location contained in each classification type and the corresponding surface reflectance coefficient are fitted respectively to obtain the mapping relationship between the soil moisture data of each classification type and the surface reflectance coefficient for soil moisture inversion.

[0229] 21. A medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the method described in any one of technical solutions 1-10 is implemented.

[0230] 22. A computing device, characterized in that the computing device comprises a processor, and the processor is used to implement the method as described in any one of technical solutions 1-10 when executing a computer program stored in a memory.

Claims

1. A soil moisture inversion method based on GNSS-R, comprising: Acquire satellite data and soil moisture products in the same target area and in the same period, wherein the soil moisture products include soil moisture data, physical quantity data, and environmental quantity data that can describe each position of the target area; Calculating the surface reflection coefficient of each position in the target area based on the satellite-borne data; Based on the soil moisture product, taking at least one of the physical quantity data and the environmental quantity data as a classification basis, clustering the various locations in the target area to obtain a preset number of classification types, wherein each classification type includes a plurality of locations in the target area; In the same space-time dimension, the soil moisture data is matched with the surface reflection coefficient, a mapping relationship between the soil moisture data of each classification type and the surface reflection coefficient is calculated, and soil moisture inversion is performed based on the mapping relationship between the soil moisture data of each classification type and the surface reflection coefficient; The step of calculating the surface reflection coefficient of each position of the target area based on the satellite-borne data includes: Based on the satellite-borne data, using a bistatic radar equation, calculating the surface reflectivity of each position in the target area; Based on the physical quantity data and environmental quantity data of each position in the target area, the surface reflectivity of each position is corrected to obtain the surface reflection coefficient of each position in the target area; the bistatic radar equation is as follows: P coh is the coherent received power; λ is the wavelength of the navigation satellite signal; P t Navigation satellite signal transmission power; G t is the transmitting antenna gain; G r is the receiving antenna gain; R st is the distance from the mirror reflection point to the transmitter; R sr is the distance from the mirror reflection point to the receiver; Γ θ is the specular reflectivity at the incident angle θ; P inc represents the incoherent received power; σ represents the radar cross section.

2. The soil moisture inversion method based on GNSS-R according to claim 1, wherein: After acquiring the spaceborne data of the target area, it also includes: Preprocessing and screening the satellite-borne data based on preset parameters; The surface reflection coefficient of each position in the target area is calculated based on the screened satellite data.

3. The soil moisture inversion method based on GNSS-R as claimed in claim 2, wherein: The preset parameters include at least one of the following: signal-to-noise ratio and incident angle; When screening based on signal-to-noise ratio, select data with a signal-to-noise ratio greater than 5dB; When filtering based on the incident angle, select data with an incident angle less than 45°.

4. The soil moisture inversion method based on GNSS-R according to claim 1, wherein: Select physical quantity data and / or environmental quantity data that affect the soil moisture inversion result for clustering; The environmental quantity data affecting the soil moisture inversion result include vegetation optical thickness; The physical quantity data affecting the soil moisture inversion result include the surface roughness coefficient.

5. The soil moisture inversion method based on GNSS-R as claimed in claim 4, wherein: Based on the physical quantity data and environmental quantity data of each position in the target area, the surface reflectivity of each position is corrected, including: Based on the vegetation optical thickness and the surface roughness coefficient at each location in the target area, the surface reflectivity at each location is corrected.

6. The soil moisture inversion method based on GNSS-R as claimed in claim 5, wherein: Based on the soil moisture product, taking at least one of the physical quantity data and the environmental quantity data as a classification basis, clustering the locations of the target area includes: Based on the duration of the soil moisture product, calculate the average vegetation optical thickness and the average surface roughness coefficient at each location in the target area; At least one of the average vegetation optical thickness and the average surface roughness coefficient of each position in the target area is used as a classification basis, and each position in the target area is clustered according to a preset number of classification types.

7. The soil moisture inversion method based on GNSS-R according to claim 1, wherein: In the same space-time dimension, the soil moisture data is matched with the surface reflectance coefficient, and the mapping relationship between the soil moisture data of each classification type and the surface reflectance coefficient is calculated, including: Select the surface reflectance and soil moisture products of the same period; In the spatial dimension, matching the surface reflectance and the soil moisture product; According to the classification types, soil moisture data and corresponding surface reflectance coefficients of various locations included in each classification type are respectively obtained to calculate a mapping relationship between soil moisture data and surface reflectance coefficients of various classification types.

8. The soil moisture inversion method based on GNSS-R as claimed in claim 7, wherein: Before calculating the mapping relationship between the soil moisture data of each classification type and the surface reflection coefficient, the method further includes: deleting the surface reflection coefficient and the water body data in the soil moisture data.

9. The soil moisture inversion method based on GNSS-R according to claim 7, further comprising: The soil moisture data of each location contained in each classification type and the corresponding surface reflectance coefficient are fitted respectively to obtain the mapping relationship between the soil moisture data of each classification type and the surface reflectance coefficient for soil moisture inversion.

10. A soil moisture inversion device based on GNSS-R, comprising: An acquisition module, used to acquire satellite data and soil moisture products in the same target area and in the same period, wherein the soil moisture products include soil moisture data, physical quantity data and environmental quantity data that can describe each position of the target area; A calculation module, used for calculating the surface reflection coefficient of each position of the target area based on the satellite data; A clustering module, configured to cluster the locations of the target area based on the soil moisture product and at least one of the physical quantity data and the environmental quantity data as a classification basis to obtain a preset number of classification types, wherein each classification type includes a plurality of locations in the target area; A matching module, used to match the soil moisture data with the surface reflection coefficient in the same time and space dimension, calculate the mapping relationship between the soil moisture data of each classification type and the surface reflection coefficient, and perform soil moisture inversion based on the mapping relationship between the soil moisture data of each classification type and the surface reflection coefficient; The computing module is configured as follows: Based on the satellite-borne data, using a bistatic radar equation, calculating the surface reflectivity of each position in the target area; Based on the physical quantity data and environmental quantity data of each position in the target area, the surface reflectivity of each position is corrected to obtain the surface reflection coefficient of each position in the target area; The bistatic radar equation is as follows: P coh is the coherent received power; λ is the wavelength of the navigation satellite signal; P t Navigation satellite signal transmission power; G t is the transmitting antenna gain; G r is the receiving antenna gain; R st is the distance from the mirror reflection point to the transmitter; R sr is the distance from the mirror reflection point to the receiver; Γ θ is the specular reflectivity at the incident angle θ; P inc represents the incoherent received power; σ represents the radar cross section.

11. The GNSS-R-based soil moisture inversion device according to claim 10, further comprising a screening module for pre-processing and screening the satellite-borne data based on preset parameters; The surface reflection coefficient of each position in the target area is calculated based on the screened satellite data.

12. The GNSS-R based soil moisture inversion device according to claim 11, wherein the preset parameters include at least one of the following: signal-to-noise ratio and incident angle; The screening module is configured to: When screening based on signal-to-noise ratio, select data with a signal-to-noise ratio greater than 5dB; When filtering based on the incident angle, select data with an incident angle less than 45°.

13. The soil moisture inversion device based on GNSS-R according to claim 10, wherein: The clustering module is configured to: Select physical quantity data and / or environmental quantity data that affect the soil moisture inversion result for clustering; The environmental quantity data affecting the soil moisture inversion result include vegetation optical thickness; The physical quantity data affecting the soil moisture inversion result include the surface roughness coefficient.

14. The soil moisture inversion device based on GNSS-R according to claim 10, wherein: The computing module is configured as follows: Based on the vegetation optical thickness and the surface roughness coefficient at each location in the target area, the surface reflectivity at each location in the target area is corrected.

15. The GNSS-R based soil moisture inversion device according to claim 14, wherein: The clustering module is further configured to: Based on the duration of the soil moisture product, calculate the average vegetation optical thickness and the average surface roughness coefficient at each location in the target area; At least one of the average vegetation optical thickness and the average surface roughness coefficient of each position in the target area is used as a classification basis, and each position in the target area is clustered according to a preset number of classification types.

16. The GNSS-R based soil moisture inversion device according to claim 15, wherein: The matching module is further configured to: Select the surface reflectance and soil moisture products of the same period; In the spatial dimension, the surface reflectance and the soil moisture product are matched to obtain a matching result; According to the classification type, the soil moisture data and the corresponding surface reflection coefficient of each position included in each classification type are respectively obtained to calculate the mapping relationship between the soil moisture data and the surface reflection coefficient of each position.

17. The GNSS-R based soil moisture inversion device according to claim 16, wherein: The matching module is further configured to: before calculating the mapping relationship between the soil moisture data of each classification type and the surface reflection coefficient, delete the surface reflection coefficient and the water body data in the soil moisture data.

18. The GNSS-R based soil moisture inversion device according to claim 16, wherein: The matching module is further configured to: The soil moisture data of each location contained in each classification type and the corresponding surface reflectance coefficient are fitted respectively to obtain the mapping relationship between the soil moisture data of each classification type and the surface reflectance coefficient for soil moisture inversion.

19. A medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

20. A computing device, characterized in that The computing device comprises a processor, and the processor is configured to implement the method according to any one of claims 1 to 9 when executing a computer program stored in a memory.

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