Method and system for GNSS-IR snow depth retrieval towards beidou stations

Abstract

The application discloses an optimal strategy and system for GNSS-IR snow depth inversion of a Beidou station. Existing snow depth inversion is mostly based on a fixed single model, and the optimal model cannot be selected for specific conditions to perform snow depth inversion. The application comprises obtaining standard format data received by a ground observation station from a satellite; obtaining observation values of a Beidou satellite from the standard format data, and combining coordinate information of a mirror reflection point of the Beidou satellite and satellite elevation angle and azimuth angle information to form a Beidou observation quantity file; screening the Beidou observation quantity file to select observation values meeting preset conditions; determining corresponding snow depth inversion models based on the number of observation values of each type in the observation values meeting the preset conditions; and performing snow depth inversion according to the determined snow depth inversion models and the corresponding observation values. The most suitable snow depth inversion model is determined through the type and number of observation values, and snow depth inversion is efficiently performed.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of satellite remote sensing, and more particularly, embodiments of the present application relate to a Beidou station-oriented GNSS-IR snow depth inversion method and system. BACKGROUND

[0002] This section is intended to provide a background or context to the embodiments of the application recited in the claims. The description herein does not constitute an admission that any of the information provided herein is prior art.

[0003] Snow depth is an important parameter for providing snow water equivalent, and accurate determination of snow depth can provide reliable scientific basis for climate change, water resource management, etc. In traditional techniques, there are multiple ways to obtain snow depth: one is ground measurement, using a steel ruler or a laser snow depth measurement sensor to measure the average value of a certain area at local points, which represents the snow depth condition of the area, the precision is affected by the number and distribution of monitoring stations; two is optical remote sensing, the precision is affected by solar radiation and cloud conditions; three is microwave remote sensing, among which passive microwave remote sensing is the main source of snow depth products, but it has the characteristics of low spatial resolution, generally 10-25 km; active microwave remote sensing is commonly used in mountainous areas, and has the limitation of time resolution.

[0004] Global Navigation Satellite System Reflectometry (GNSS-IR) remote sensing makes full use of existing GNSS monitoring stations and carries out remote sensing detection based on L-band microwave signals, which has large coverage and high time resolution. There are multiple models for GNSS-IR observation of snow depth, which are divided into SNR (Signnal-to-Noise Ratio) model class and carrier phase model class based on different observation signal-to-noise ratio (SNR) and carrier phase. The SNR model type includes classic SNR model, SNR_COM (triple-frequency SNR combination) model; the carrier phase model class includes L4 (geometry-free linear combinations of the phase measurements) model, F3 (triple-frequency phase combination) model, F2C (combination of pseudorange and carrier phase of dual-frequency signals) model.

[0005] The SNR model method is simple but the original station SNR observation value can not be in the observation range; the SNR_COM also faces the problem of whether the SNR observation value exists, and there is a requirement for the number of observation types of SNR observation. The F3 model has high inversion accuracy, but there is a requirement for the number of carrier phase observation types. The L4 model is limited by the ionospheric error, and the F2C model is limited by the low accuracy of the pseudo-range observation itself, and the inversion accuracy of the two is low.

[0006] In the past two years, the number of snow days in northern China has increased significantly compared with the same period, and snow detection is a key and difficult task of the China Meteorological Administration. The snow depth product is in urgent demand. On the one hand, China's Beidou navigation satellite, as a rising star of navigation satellite system, has uniqueness and complexity in algebra, type, frequency, etc., which increases the difficulty of selecting the optimal parameters of GNSS-IR. On the other hand, various Beidou stations in China are faced with problems such as missing observation data and observation types that cannot be unified. In the face of numerous Beidou stations with complex advantages and situations, it is urgent to establish an optimization strategy and system for GNSS-IR inversion of snow depth specifically for Beidou satellites. How to select the optimal GNSS-IR inversion model to obtain accurate snow depth results based on the observation data of the station and the received Beidou satellite signals is a problem to be solved. SUMMARY

[0007] In a first aspect of the embodiments of the present application, an optimal method for GNSS-IR snow depth inversion for Beidou stations is provided, comprising:

[0008] Obtaining standard format data received by a ground observation station from a satellite;

[0009] Obtaining observation values of a Beidou satellite from the standard format data, and combining coordinate information of a specular reflection point of the Beidou satellite and satellite elevation angle and azimuth angle information to form a Beidou observation file;

[0010] Screening the Beidou observation file to select observation values meeting preset conditions;

[0011] Determining a corresponding snow depth inversion model based on the number of observation values of each type in the observation values meeting the preset conditions;

[0012] Performing snow depth inversion according to the determined snow depth inversion model and the corresponding observation values.

[0013] In an embodiment of the present application, the observation values of the Beidou satellite obtained from the standard format data include at least one of the following types: signal-to-noise ratio, carrier phase.

[0014] In one embodiment of the present application, the Beidou observation file is screened to select observation values meeting preset conditions of parameters, including:

[0015] According to the screening range of the preset parameters, observation values of satellite elevation angles and ground observation station sampling rates are selected.

[0016] In one embodiment of the present application, the screening range of the satellite elevation angles is preferably 5-30° and / or 5-25°.

[0017] The screening range of the ground observation station sampling rate includes 0-120s.

[0018] In one embodiment of the present application, based on the number of observation values of signal-to-noise ratios and / or carrier phase in the observation values meeting the preset conditions of parameters, a corresponding snow depth inversion model is determined.

[0019] In one embodiment of the present application, when the carrier phase type observation values exist and the signal-to-noise ratio type observation values exist, it is determined to use one or more of the SNR model, the SNR_COM model and the F3 model for snow depth inversion.

[0020] When the carrier phase type observation values exist and the signal-to-noise ratio type observation values do not exist, based on the number of carrier phase type observation values, one or more of the F3 model, the L4 model and the F2C model is determined to be used for snow depth inversion.

[0021] In one embodiment of the present application, when the carrier phase type observation values exist and the signal-to-noise ratio type observation values exist, it is determined to use one or more of the SNR model, the SNR_COM model and the F3 model for snow depth inversion, including:

[0022] When the signal-to-noise ratio type observation values are greater than or equal to 3, the SNR model and / or the SNR_COM model is used for snow depth inversion, and the SNR_COM is preferably used for snow depth inversion;

[0023] When the carrier phase type observation values are greater than or equal to 3, one or more of the L4 model, the F2C model and the F3 model is used for snow depth inversion, and the F3 model is preferably used for snow depth inversion;

[0024] When the signal-to-noise ratio type observation values are less than 3, the SNR model is used for snow depth inversion.

[0025] In one embodiment of the present application, when the carrier phase type observation values exist and the signal-to-noise ratio type observation values do not exist, a corresponding snow depth inversion model is determined based on the number of carrier phase type observation values, including:

[0026] When the number of carrier phase type observations is greater than or equal to 3, one or more of the F3 model, the L4 model and the F2C model are used to perform snow depth inversion, and the F3 model is preferably used to perform snow depth inversion.

[0027] When the number of carrier phase type observations is greater than or equal to 2, the L4 model or the F2C model is used to perform snow depth inversion.

[0028] In an embodiment of the present application, after obtaining the snow depth inversion results of multiple single satellites, the method further comprises:

[0029] Based on the snow depth inversion results of multiple single satellites, a final snow depth inversion result is determined.

[0030] In a second aspect of the embodiments of the present application, a GNSS-IR snow depth inversion optimal strategy and system for a Beidou ground station are provided, comprising:

[0031] A data preprocessing module configured to obtain standard format data received by a ground observation station from a satellite; and

[0032] Obtain observation values of Beidou satellites from the standard format data, and combine the coordinate information of the specular reflection points of the Beidou satellites and the satellite elevation angle and azimuth angle information to form a Beidou observation file; and

[0033] Select observation values whose parameters meet the preset conditions by screening the Beidou observation file;

[0034] A snow depth inversion module configured to determine a corresponding snow depth inversion model based on the number of each type of observation values in the observation values whose parameters meet the preset conditions; and

[0035] Perform snow depth inversion according to the determined snow depth inversion model and the corresponding observation values.

[0036] In a third aspect of the embodiments of the present application, a computer readable storage medium is provided, the storage medium stores a computer program, and the computer program can implement the method of any one of the first aspect when executed by a processor.

[0037] In a fourth aspect of the embodiments of the present application, a computing device is provided, comprising: a processor; a memory for storing instructions executable by the processor; and the processor is configured to execute the method of any one of the first aspect.

[0038] The GNSS-IR snow depth inversion optimal strategy and system for the Beidou ground station according to the embodiment of the present application can determine a suitable snow depth inversion model through the type and number of observation values, and obtain more accurate snow depth results through the snow depth inversion model. BRIEF DESCRIPTION OF DRAWINGS

[0039] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which a number of embodiments of the present application are shown by way of example, and in which:

[0040] FIG. 1 A flowchart of the GNSS-IR snow depth inversion optimal strategy and system for the Beidou ground station according to the embodiment of the present application is schematically shown;

[0041] FIG. 2 A comparison example of the sampling rate according to an embodiment of the present application is schematically shown;

[0042] FIG. 3 A comparison example of the satellite algebra according to an embodiment of the present application is schematically shown;

[0043] FIG. 4 A model result example of the snow depth inversion based on the Beidou data according to an embodiment of the present application is schematically shown;

[0044] FIG. 5 A structure diagram of the GNSS-IR snow depth inversion optimal strategy and system for the Beidou ground station according to an embodiment of the present application is schematically shown;

[0045] FIG. 6 A structure diagram of a medium according to an embodiment of the present application is schematically shown;

[0046] FIG. 7 A structure diagram of a computing device according to an embodiment of the present application is schematically shown;

[0047] In the drawings, the same or corresponding reference numbers denote the same or corresponding parts. DETAILED DESCRIPTION

[0048] The principles and spirits of the present application will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are given only to enable those skilled in the art to better understand and implement the present application, and do not limit the scope of the present application 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.

[0049] Those skilled in the art know that the embodiments of the present application can be implemented as a system, a device, an apparatus, a method or a computer program product. Therefore, the present disclosure can be embodied in the form of entire hardware, entire software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0050] According to the embodiments of the present application, a Beidou station-oriented GNSS-IR snow depth inversion optimal strategy, system, medium and computing device are proposed.

[0051] In addition, the number of any elements in the drawings is used for illustration and not limitation, and any naming is only used for differentiation and does not have any limiting meaning.

[0052] The principles and spirits of the present application will be explained in detail below with reference to several representative embodiments of the present application.

[0053] Exemplary method

[0054] The principles and spirits of the present application will be explained in detail below with reference to several representative embodiments of the present application. FIG. 1 The Beidou station-oriented GNSS-IR snow depth inversion optimal strategy according to the exemplary embodiments of the present application will be described below.

[0055] The present application will be further described below in combination with specific implementation cases.

[0056] The embodiments of the present application provide a Beidou station-oriented GNSS-IR snow depth inversion optimal strategy and system, comprising:

[0057] In step S110, standard format data received by a ground observation station from a satellite is acquired;

[0058] In the present embodiment, the ground observation station refers to a ground station capable of receiving satellite data, which can be a fixed station provided with a receiver or a mobile station, and the present embodiment does not make any limitation as long as it can receive satellite data.

[0059] In one embodiment of the present embodiment, the standard format data is a RINEX (Receiver Independent Exchange Format) file, which stores data in a text file and the data record format is independent of the manufacturer and specific model of the receiver. It can be understood that in other embodiments of the present embodiment, it can also be other standard format types of data as long as it is data exported by a satellite receiver.

[0060] In step S120, observation values of Beidou satellites are acquired from the standard format data, and combined with the coordinate information of the specular reflection points of the Beidou satellites and the satellite elevation angle and azimuth angle information to form a Beidou observation file.

[0061] In one embodiment of the present embodiment, the observation values of the Beidou satellite obtained from the standard format data include at least one of the following types:

[0062] Signal-to-noise ratio SNR and carrier phase.

[0063] In one embodiment of the present embodiment, the satellite elevation angle and azimuth angle information of the specular reflection point of the Beidou satellite are obtained from the RINEX file, and the coordinate information is calculated and obtained;

[0064] Step S130, filtering the Beidou observation file to select observation values meeting the preset conditions;

[0065] In one embodiment of the present embodiment, the Beidou observation file is filtered to select observation values meeting the preset conditions, including:

[0066] According to the filtering range of the preset parameters, one or more of the satellite elevation angle, the sampling rate of the ground observation station, the satellite algebra, the satellite frequency and the satellite type are selected to meet the observation values.

[0067] In one embodiment of the present embodiment, the filtering range of the satellite elevation angle includes 5-30° and / or 5-25°;

[0068] The satellite elevation angle is the angle between the direction line from the ground observation station to the satellite and the horizontal plane, and the angle range is between 0-90°. At low elevation angle, the signal-to-noise ratio SNR is greatly affected by multipath, and GNSS-IR is used to model the snow depth, which needs to select the low elevation angle range. The inventors have conducted many experiments, as shown in Table 1, which shows the residual error of the calculated snow depth at different satellite elevation angles. The experiments are based on the ALTT site and the ALTS site with S6I as an example. From the results shown in Table 1, it can be seen that the residual error of the snow depth inversion result calculated in the elevation angle range of 5-30° or 5-25° is the smallest, and thus the satellite elevation angle range of 5-30° or 5-25° is the most appropriate.

[0069] Table 1

[0070]

[0071] In the present embodiment, the inventors have conducted many experiments, as shown in Table 1, which shows the residual error of the calculated snow depth at different satellite elevation angles. The experiments are based on the ALTT site and the ALTS site with S6I as an example. From the results shown in Table 1, it can be seen that the residual error of the snow depth inversion result calculated in the elevation angle range of 5-30° or 5-25° is the smallest, and thus the satellite elevation angle range of 5-30° or 5-25° is the most appropriate. FIG. 2 FIG. 2 ​The probability curve of PNR and the number of points under different sampling rates are shown; a1 and b1 are ALTT stations, a2 and b2 are ALTS stations, b1 and b2 show the distribution range of the vertical height under different sampling rates, and the solid box line is the area where the abnormal values are concentrated. As can be seen from the results shown in the figure, the screening range of the ground observation station sampling rate includes 0-120s, that is, the observation values within the sampling rate within 120s can be used for snow depth inversion. It can be understood that those skilled in the art can select within the range of 1-120s according to the actual application scene needs.

[0072] In the present embodiment, the screening range of the satellite algebra includes the second-generation satellite and the subsequent generation satellite. After many experiments by the inventor, as shown in the a1 and a2 of FIG. 3 FIG. 3 The comparison of the signals of two stations BDS S6I\S2I and the measured snow depth is shown; including ALTT S2I (upper left); ALTT S6I (lower left); ALTS S2I (upper right); ALTS S6I (lower right); according to the experimental results, it can be found that the performance of the third-generation satellite (BDS-3, PRN17 after) is comparable to that of the second-generation satellite (BDS-2, PRN17 before).

[0073] BDS-2 provides three public service signals B1I, B2I and B3I in B1, B2 and B3 three frequency bands. Among them, the center frequency of B1 frequency band is 1561.098MHz, B2 is 1207.140MHz, and B3 is 1268.520MHz. BDS-3 provides B1I, B1C, B2a, B2b and B3I five public service signals. Among them, B1I, B2b and B3I follow BDS-2 B1I, B2I and B3I;

[0074] In the present embodiment, after many experiments by the inventor, as shown in the a1 and a2 of FIG. 4 , the satellite frequency can be used.

[0075] ​The Beidou No. 2 basic system constellation adopts the form of 5GEO (Geostationary Earth Orbit) + 5IGSO (Inclined Geosynchronous Satellite Orbit) + 4MEO (Medium Earth Orbit); the Beidou No. 3 basic system constellation adopts the constellation form of 3GEO + 3IGSO + 24MEO. Among them, the GEO satellite is a geosynchronous satellite, and there is no change in satellite elevation angle, which cannot be used for GNSS-IR inversion of snow depth, and the MEO medium orbit earth satellite and the IGSO inclined geosynchronous orbit satellite can be selected. In the present embodiment, the inventors have carried out many experiments, as shown in Table 2, in which the PNR (Peak-to-Noise Ratio) of two stations for one month is selected as the research object. The value represents the level of background noise, and the larger the value, the lower the noise level. Through the experimental results, it can be obtained that the MEO and IGSO both have good performance, and the IGSO has a slight advantage.

[0076] Table 2

[0077]

[0078]

[0079] In Table 2, site represents a station, ALTT and ALTS are two different satellite observation stations, and Mean PNR represents the background noise level of the SNR inversion snow depth corresponding to different types of satellites.

[0080] In step S140, the corresponding snow depth inversion model is determined based on the number of each type of observation value in the observation value in which the parameter meets the preset condition.

[0081] In an embodiment of the present application, the corresponding snow depth inversion model is determined based on the number of SNR and / or carrier phase observation values in the observation value in which the parameter meets the preset condition.

[0082] In an embodiment of the present application, when the carrier phase type observation value exists and the SNR type observation value exists, one or more of the SNR model, the SNR_COM model and the F3 model are determined to be used for snow depth inversion.

[0083] When the carrier phase type observation value exists and the SNR type observation value does not exist, one or more of the L4 model, the F2C model and the F3 model are determined to be used for snow depth inversion based on the number of carrier phase type observation values.

[0084] In one embodiment of the present application, when the carrier phase type observation value exists and the signal-to-noise ratio type observation value exists, it is determined to use one or more of the SNR model, the SNR_COM model and the F3 model for snow depth inversion, comprising:

[0085] When the signal-to-noise ratio type observation value is greater than or equal to 3, the SNR model and / or the SNR_COM model are used for snow depth inversion, and the SNR_COM is preferably used for snow depth inversion;

[0086] When the carrier phase type observation value is greater than or equal to 3, one or more of the L4 model, the F2C model and the F3 model are used for snow depth inversion, and the F3 model is preferably used for snow depth inversion;

[0087] When the signal-to-noise ratio type observation value is less than 3, the SNR model is used for snow depth inversion.

[0088] In one embodiment of the present application, when the carrier phase type observation value exists and the signal-to-noise ratio type observation value does not exist, the corresponding snow depth inversion model is determined based on the number of carrier phase type observation values, comprising:

[0089] When the carrier phase type observation value is greater than or equal to 3, one or more of the L4 model, the F2C model and the F3 model are used for snow depth inversion, and the F3 model is preferably used for snow depth inversion;

[0090] When the carrier phase type observation value is greater than or equal to 2, the L4 model and / or the F2C model are used for snow depth inversion.

[0091] In order to facilitate the understanding of the above-mentioned embodiments, the selection method of the snow depth inversion model is shown in the following table:

[0092] Table 3

[0093]

[0094] The following describes each model for snow depth inversion in detail:

[0095] 1. SNR model

[0096] 1) First, the satellite signal is received by the BDS receiver, and the received is an interference signal superimposed by the direct signal and the reflected signal, the amplitude of the interference signal is A c Then linearize the interference signal;

[0097] The relationship between SNR and A c is:

[0098] The amplitudes of the direct signal and the reflected signal are A dand A m , Q is the angle between the direct signal and the reflected signal;

[0099] 2) In the SNR model, in order to obtain the reflected signal reflecting the ground information, a low-order polynomial needs to be used to eliminate the direct signal, and the amplitude of the reflected signal is A m , λ is the carrier wavelength, h is the vertical reflection height, E is the elevation angle, is the phase value less than one period, and the direct signal can be eliminated by the following formula:

[0100]

[0101] 3) The frequency f of the multi-path reflected signal A can be obtained by performing L-S spectrum analysis on the SNR residual sequence of the reflected signal.

[0102] 4) The vertical reflection distance h is calculated by the following formula, and the snow depth is calculated by calculating the difference between the vertical reflection heights before and after snowfall:

[0103] 2、SNR_COM model

[0104] The pre-process is the same as the SNR model. The difference is that the input of the SNR model is a single SNR observation value, and the input of the SNR_COM model is the joint observation value of the same system (SNR after linearization), which is called SNR com . This joint observation value is used instead of single-frequency SNR data to obtain the respective residual sequence, and then the power of the SNR com sequence extracted by the LSP spectrum analysis method is used to solve the ground snow depth inversion value.

[0105] SNR 1,i , i is the i-th satellite, and 1, 2, and 3 represent different frequencies.

[0106] SNR com,i = [SNR 1,i SNR 2,i SNR 3,i ]

[0107] 3、F3 model

[0108] 1) The observation equation of the BDS carrier phase measurement is: ρ is the geometric distance from the satellite to the station, I(f1) is the ionospheric error related to the frequency. T is the tropospheric error, M Li is the multi-path error corresponding to the L i band, and noise i is the integer ambiguity corresponding to the L i band.

[0109] L1 = ρ + I(f1) + T + ML1 +noise1

[0110] L2 = p + I(f2) + T + M L2 +noise2

[0111] L3 = p + I(f3) + T + M L3 +noise3

[0112] 2) Ionospheric error is proportional to the square of the electromagnetic wavelength, eliminate ionospheric error by multiplying wavelength coefficient, get the multipath error sequence f3 of three-frequency combination:

[0113]

[0114] 3) Get the frequency of the multipath error sequence of three-frequency combination through spectrum analysis.

[0115] 4) Calculate the vertical reflection distance through the relationship of f and h.

[0116] 4、F2C model

[0117] 1) The observation equation of BDS pseudorange measurement is:M ci C i The multipath error corresponding to the pseudorange frequency band.

[0118] c1 = p + I(f1) + T + M c1

[0119] The observation equations L1 and L2 of BDS carrier phase measurement are shown in the 3, F3 model;

[0120] 2) Ionospheric error is proportional to the square of the electromagnetic wavelength, eliminate ionospheric error by multiplying wavelength coefficient, get the multipath error sequence f of double-frequency carrier and pseudorange combination: 2c

[0121]

[0122] 3) Get the frequency of the multipath error sequence of three-frequency combination through spectrum analysis.

[0123] 4) Calculate the vertical reflection distance through the relationship of f and h.

[0124] 5、L4 model

[0125] 1) The observation equation of BDS carrier phase measurement has been given.

[0126] 2) Compared with multipath error, ionospheric error can be regarded as a low-frequency signal, and the double-frequency carrier model can be suppressed by a high-order polynomial.

[0127] 3) The multipath error sequence L4 of the dual-frequency carrier combination is obtained:

[0128] L4 = L1 - L2 = I(f1) - I(f2) + M L1 -M L2

[0129] +noise1-noise2

[0130] 4) The frequency of the multipath error sequence of the triple-frequency combination can be obtained through spectrum analysis.

[0131] 5) The vertical reflection distance is calculated through the relationship between the formula f and h.

[0132] After introducing how each model calculates the snow depth, step S150 can be performed to perform snow depth inversion according to the determined snow depth inversion model and the corresponding observation value.

[0133] In order to verify the effectiveness of the present application, the inventors used the ALTT, ALTS site data based on the above different models to perform snow depth inversion, and compared with the actual measured snow depth, as shown in FIG. 4 FIG. 4 Figures a1-e1 show the comparison of BDS snow depth and actual measured snow depth of ALTT, ALTS site of different models; a1-e1 are related models of ALTT site, a2-d2 are related models of ALTS site; a1, a2 are SNR models; b1, b2 are SNR_COM models; c1, c2 are F3 models; d1, d2 are L4 models; e1 is a F2C model; in each subgraph, the black '+' represents the in-situ measurement, and the solid circle represents the BDS result. The gray line is the number of satellites used for snow depth calculation. It can be seen from the experimental results shown in the figure that the SNR model, the SNR_COM model and the F3 model are more accurate in snow depth inversion, and the L4 model and the F2C model are less accurate in snow depth inversion.

[0134] In an embodiment of the present application, after obtaining the snow depth inversion results of a plurality of single satellites, the method further comprises:

[0135] jointly determining the final snow depth inversion result based on the snow depth inversion results of a plurality of single satellites.

[0136] ​The optimal strategy and system for Beidou station-oriented GNSS-IR snow depth inversion according to the embodiment of the present application can determine a suitable snow depth inversion model through the type and quantity of observation values, and obtain more accurate snow depth results through the snow depth inversion model, thereby effectively and efficiently realizing the snow depth estimation modeling and result output using Beidou satellites, guiding the processing of a large number of Beidou ground station network data with different observation data conditions, and further realizing convenient and efficient data preprocessing, snow depth model selection and snow depth product production, which has high reference value for practical engineering applications.

[0137] Exemplary system

[0138] After introducing the method of the exemplary embodiment of the present application, next, with reference to FIG. 5 The optimal system for Beidou station-oriented GNSS-IR snow depth inversion of the exemplary embodiment of the present application is described, and the system comprises:

[0139] The data preprocessing module 610 is configured to obtain standard format data received by a ground observation station from a satellite; and

[0140] Obtain observation values of a Beidou satellite from the standard format data, and combine the coordinate information of the specular reflection point of the Beidou satellite and the satellite elevation angle and azimuth angle information to form a Beidou observation file; and

[0141] Screen the Beidou observation file, and select observation values meeting preset conditions in parameters;

[0142] The snow depth inversion module 620 is configured to determine a corresponding snow depth inversion model based on the quantity of each type of observation values in the observation values meeting the preset conditions in parameters; and

[0143] According to the determined snow depth inversion model and the corresponding observation values, perform snow depth inversion.

[0144] Exemplary medium

[0145] After introducing the method and system of the exemplary embodiment of the present application, next, with reference to FIG. 6 The computer readable storage medium of the exemplary embodiment of the present application is described, please refer to FIG. 6The computer readable storage medium shown is an optical disc 70, on which a computer program (i.e. program product) is stored, which, when run by a processor, implements each step described in the above method embodiments, such as obtaining standard format data received by a ground observation station from a satellite; obtaining observation values of a Beidou satellite from the standard format data, and combining the coordinate information of a specular reflection point of the Beidou satellite and satellite elevation angle and azimuth angle information to form a Beidou observation file; screening the Beidou observation file to select observation values whose parameters meet preset conditions; determining a corresponding snow depth inversion model based on the number of observation values of each type in the observation values whose parameters meet preset conditions; and performing snow depth inversion according to the determined snow depth inversion model and the corresponding observation values. The specific implementation of each step is not repeated here.

[0146] It should be noted that examples of the computer readable storage medium can 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, magnetic storage media, which are not repeated here.

[0147] Exemplary computing device

[0148] After introducing the method, system and medium of the exemplary embodiments of the application, next, with reference to FIG. 7 The optimal device for Beidou site-oriented GNSS-IR snow depth inversion of the exemplary embodiments of the application.

[0149] FIG. 7 A block diagram of an exemplary computing device 80 suitable for implementing embodiments of the application is shown, which can be a computer system or a server. FIG. 7 The computing device 80 shown is merely one example and should not be construed as limiting the scope of functionality or use of embodiments of the application.

[0150] As shown in FIG. 7 The components of computing device 80 can include, but are not limited to, one or more processors or processing units 801, a system memory 802, and a bus 803 that couples various system components including system memory 802 and processing unit 801.

[0151] The computing device 80 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by the computing device 80 and includes both volatile and non-volatile media, removable and non-removable media.

[0152] The system memory 802 can 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 can further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, a ROM 8023 can be used to read from and write to a non-removable, non-volatile magnetic media (commonly referred to as a "hard drive" or "hard drive" as shown in FIG. 8). Although not shown, a magnetic disk drive can also be used to read from and write to a removable, non-volatile magnetic disk (e.g., a "floppy disk" or other similar disk), and an optical disk drive can be used to read from and write to a removable, non-volatile optical disk (such as a CD-ROM, DVD-ROM or other optical media). In such instances, each can be connected to the bus 803 by one or more data media interfaces. The system memory 802 can include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application. FIG. 7 The system memory 802 can 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 can further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, a ROM 8023 can be used to read from and write to a non-removable, non-volatile magnetic media (commonly referred to as a "hard drive" or "hard drive" as shown in FIG. 8). Although not shown, a magnetic disk drive can also be used to read from and write to a removable, non-volatile magnetic disk (e.g., a "floppy disk" or other similar disk), and an optical disk drive can be used to read from and write to a removable, non-volatile optical disk (such as a CD-ROM, DVD-ROM or other optical media). In such instances, each can be connected to the bus 803 by one or more data media interfaces. The system memory 802 can include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application. FIG. 7 The system memory 802 can 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 can further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, a ROM 8023 can be used to read from and write to a non-removable, non-volatile magnetic media (commonly referred to as a "hard drive" or "hard drive" as shown in FIG. 8). Although not shown, a magnetic disk drive can also be used to read from and write to a removable, non-volatile magnetic disk (e.g., a "floppy disk" or other similar disk), and an optical disk drive can be used to read from and write to a removable, non-volatile optical disk (such as a CD-ROM, DVD-ROM or other optical media). In such instances, each can be connected to the bus 803 by one or more data media interfaces. The system memory 802 can include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application.

[0153] The program / utility 8025 having a set (at least one) of program modules 8024, can be stored in system memory 802 by way of example, and can include an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, can include an implementation of a networking environment. The program modules 8024 generally carry out the functions and / or methodologies of embodiments of the application as described herein.

[0154] The computing device 80 can also communicate with one or more external devices 804 such as a keyboard or a pointing device, a display, etc. through an input / output (I / O) interface(s). Furthermore, the computing device 80 can communicate with one or more networks such as a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet, through a network adapter 806. As FIG. 7 illustrated, the network adapter 806 can be communicatively coupled to the other components of the computing device 80, such as the processing unit 801, via the bus 803. It should be appreciated that the network adapter 806 can be ​ implemented as part of the processing unit 801. Alternatively, the network adapter 806 can be implemented as a separate and distinct component in communication with the processing unit 801.

[0155] The processing unit 801 performs various functional applications and data processing by running programs stored in the system memory 802, such as obtaining standard format data received by a ground observation station from a satellite; obtaining observation values of Beidou satellites from the standard format data, and combining the observation values with coordinate information of specular reflection points of the Beidou satellites and satellite elevation angle and azimuth angle information to form a Beidou observation file; screening the Beidou observation file to select observation values meeting preset conditions in parameters; determining a corresponding snow depth inversion model based on the number of observation values of each type in the observation values meeting the preset conditions in parameters; and performing snow depth inversion according to the determined snow depth inversion model and the corresponding observation values. The specific implementation of each step will not be repeated here. It should be noted that although several units / modules or sub-units / sub-modules of the optimal system for GNSS-IR snow depth inversion of Beidou sites are mentioned in the foregoing detailed description, such division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, 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 several units / modules for embodiment.

[0156] It should be noted that although several units / modules or sub-units / sub-modules of the optimal system for GNSS-IR snow depth inversion of Beidou sites are mentioned in the foregoing detailed description, such division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, 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 several units / modules for embodiment.

[0157] In addition, although the operations of the method of the present application are described in a specific order in the accompanying drawings, this does not require or imply that the operations must be performed in this specific order, or that all of the shown operations must be performed to achieve the desired result. Additionally or alternatively, certain steps can be omitted, a plurality of steps can be combined into one step, and / or one step can be divided into a plurality of steps.

[0158] Although the spirit and principles of the present application have been described with reference to several specific embodiments, it should be understood that the present application is not limited to the disclosed specific embodiments, and the division of aspects does not mean that the features in these aspects cannot be combined for benefit, but is only for the convenience of expression. The present application is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the appended claims.

[0159] Through the above description, the embodiments of the present application provide the following technical solutions, but are not limited thereto:

[0160] 1. An optimal method for GNSS-IR snow depth inversion for a Beidou station, comprising:

[0161] acquiring standard format data received by a ground observation station from a satellite;

[0162] acquiring observation values of a Beidou satellite from the standard format data, and combining coordinate information of a specular reflection point of the Beidou satellite and satellite elevation angle and azimuth angle information to form a Beidou observation file;

[0163] screening the Beidou observation file to select observation values that meet preset conditions in terms of parameters;

[0164] determining a corresponding snow depth inversion model based on the number of observation values of each type in the observation values that meet the preset conditions in terms of parameters;

[0165] performing snow depth inversion according to the determined snow depth inversion model and the corresponding observation values.

[0166] 2. The optimal method for GNSS-IR snow depth inversion for a Beidou station according to claim 1, wherein the observation values of the Beidou satellite acquired from the standard format data include at least one of the following types: signal-to-noise ratio and carrier phase.

[0167] 3. The optimal method for GNSS-IR snow depth inversion for a Beidou station according to claim 1, wherein screening the Beidou observation file to select observation values that meet preset conditions in terms of parameters comprises:

[0168] selecting observation values that meet the satellite elevation angle and ground observation station sampling rate according to the screening range of preset parameters.

[0169] 4. The optimal method for GNSS-IR snow depth inversion for a Beidou station according to claim 3, wherein the screening range of the satellite elevation angle is preferably 5-30° and / or 5-25°.

[0170] The screening range of the ground observation station sampling rate includes 0-120s.

[0171] 5. The optimal method for GNSS-IR snow depth inversion for a Beidou station according to claim 1, wherein a corresponding snow depth inversion model is determined based on the number of observation values of signal-to-noise ratio and / or carrier phase in the observation values that meet the preset conditions in terms of parameters.

[0172] 6. The optimal method for GNSS-IR snow depth inversion for Beidou station according to claim 5, wherein when the carrier phase type observation exists and the signal-to-noise ratio type observation exists, it is determined to use one or more of the SNR model, the SNR_COM model and the F3 model for snow depth inversion.

[0173] When the carrier phase type observation exists and the signal-to-noise ratio type observation does not exist, based on the number of carrier phase type observations, it is determined to use one or more of the F3 model, the L4 model and the F2C model for snow depth inversion.

[0174] 7. The optimal method for GNSS-IR snow depth inversion for Beidou station according to claim 6, wherein when the carrier phase type observation exists and the signal-to-noise ratio type observation exists, it is determined to use one or more of the SNR model, the SNR_COM model and the F3 model for snow depth inversion, comprising:

[0175] When the signal-to-noise ratio type observation is greater than or equal to 3, the SNR model and / or the SNR_COM model are used for snow depth inversion, and the SNR_COM is preferably used for snow depth inversion;

[0176] When the carrier phase type observation is greater than or equal to 3, one or more of the L4 model, the F2C model and the F3 model are used for snow depth inversion, and the F3 model is preferably used for snow depth inversion;

[0177] When the signal-to-noise ratio type observation is less than 3, the SNR model is used for snow depth inversion.

[0178] 8. The optimal method for GNSS-IR snow depth inversion for Beidou station according to claim 6, wherein when the carrier phase type observation exists and the signal-to-noise ratio type observation does not exist, based on the number of carrier phase type observations, the corresponding snow depth inversion model is determined, comprising:

[0179] When the carrier phase type observation is greater than or equal to 3, one or more of the F3 model, the L4 model and the F2C model are used for snow depth inversion, and the F3 model is preferably used for snow depth inversion;

[0180] When the carrier phase type observation is greater than or equal to 2, the L4 model or the F2C model is used for snow depth inversion.

[0181] 9. The optimal method for GNSS-IR snow depth inversion for Beidou station according to claim 1, wherein after obtaining the snow depth inversion results of a plurality of single satellites, the method further comprises:

[0182] The final snow depth inversion result is determined based on snow depth inversion results of multiple single satellites.

[0183] 10. A Beidou station-oriented optimal system for GNSS-IR snow depth inversion, comprising:

[0184] a data preprocessing module configured to acquire standard format data received by a ground observation station from a satellite; and

[0185] acquire observation values of Beidou satellites from the standard format data, and combine coordinate information of specular reflection points of the Beidou satellites and satellite elevation angle and azimuth angle information to form a Beidou observation file; and

[0186] select observation values with parameters meeting preset conditions by screening the Beidou observation file;

[0187] a snow depth inversion module configured to determine a corresponding snow depth inversion model based on a number of observation values of each type in the observation values with the parameters meeting the preset conditions; and

[0188] perform snow depth inversion according to the determined snow depth inversion model and the corresponding observation values.

[0189] 11. A computer readable storage medium, the storage medium storing a computer program, the computer program being used to execute any of the methods in the above technical solutions 1-9.

[0190] 12. A computing device, comprising:

[0191] a processor;

[0192] a memory for storing processor-executable instructions;

[0193] the processor is configured to execute any of the methods in the above technical solutions 1-9.

Claims

1. A method for GNSS-IR snow depth inversion for BeiDou stations, comprising: Acquire standard-format data received by ground observation stations from satellites; The observation values ​​of the BeiDou satellite are obtained from the standard format data, and combined with the coordinate information of the specular reflection point of the BeiDou satellite and the satellite elevation angle and azimuth angle information to form a BeiDou observation data file; The BeiDou observation files are filtered to select observation values ​​whose parameters meet preset conditions; Based on the number of observations of each type among the observations that meet the preset conditions, the corresponding snow depth inversion model is determined. Snow depth inversion is performed based on the determined snow depth inversion model and the corresponding observation values. The BeiDou satellite observations obtained from the standard format data include at least one of the following types: signal-to-noise ratio and carrier phase; Based on the number of observations of signal-to-noise ratio and / or carrier phase among the observations that meet the preset conditions, the corresponding snow depth inversion model is determined. When both carrier phase type observations and signal-to-noise ratio type observations exist, it is determined that one or more of the SNR model, SNR_COM model, and F3 model will be used for snow depth inversion. When carrier phase type observations exist but signal-to-noise ratio type observations do not exist, based on the number of carrier phase type observations, determine whether to use one or more of the F3 model, L4 model and F2C model for snow depth inversion. When both carrier phase type and signal-to-noise ratio type observations exist, one or more of the following models—SNR model, SNR_COM model, and F3 model—are selected for snow depth inversion: When there are 3 or more observations of the signal-to-noise ratio type, the SNR model and / or the SNR_COM model are used to retrieve snow depth. When there are three or more carrier phase type observations, one or more of the L4 model, F2C model and F3 model are used for snow depth inversion. When there are fewer than 3 observations of the signal-to-noise ratio type, the SNR model is used to retrieve snow depth. When carrier phase type observations exist but signal-to-noise ratio type observations do not, the corresponding snow depth inversion model is determined based on the number of carrier phase type observations, including: When there are three or more carrier phase type observations, one or more of the F3 model, L4 model, and F2C model are used for snow depth inversion. When there are two or more carrier phase type observations, the L4 model or F2C model is used for snow depth inversion.

2. The method for GNSS-IR snow depth inversion for BeiDou stations as described in claim 1, wherein, The BeiDou observation files are filtered to select observations whose parameters meet preset conditions, including: Based on the preset parameter filtering range, select observation values ​​that meet the requirements of satellite elevation angle and ground observation station sampling rate.

3. The method for GNSS-IR snow depth inversion for BeiDou stations as described in claim 2, wherein, The satellite elevation angle selection range is 5-30° and / or 5-25°; The sampling rate of the ground observation station is selected within the range of 0-120s.

4. The method for GNSS-IR snow depth inversion for BeiDou stations as described in claim 1, wherein, After obtaining snow depth inversion results from multiple individual satellites, the method further includes: The final snow depth inversion result is determined by jointly analyzing the snow depth inversion results from multiple individual satellites.

5. A system for GNSS-IR snow depth inversion for BeiDou stations, comprising: The data preprocessing module is configured to acquire standard-format data received by the ground observation station from the satellite; as well as The observation values ​​of the BeiDou satellite are obtained from the standard format data, and combined with the coordinate information of the specular reflection point of the BeiDou satellite and the satellite elevation angle and azimuth angle information to form a BeiDou observation data file; as well as The BeiDou observation files are filtered to select observation values ​​whose parameters meet preset conditions; The snow depth inversion module is configured to determine the corresponding snow depth inversion model based on the number of observations of each type among the observations that meet the preset conditions. as well as Snow depth inversion is performed based on the determined snow depth inversion model and the corresponding observation values. The BeiDou satellite observations obtained from the standard format data include at least one of the following types: signal-to-noise ratio and carrier phase; Based on the number of observations of signal-to-noise ratio and / or carrier phase among the observations that meet the preset conditions, the corresponding snow depth inversion model is determined. When both carrier phase type observations and signal-to-noise ratio type observations exist, it is determined that one or more of the SNR model, SNR_COM model, and F3 model will be used for snow depth inversion. When carrier phase type observations exist but signal-to-noise ratio type observations do not exist, based on the number of carrier phase type observations, determine whether to use one or more of the F3 model, L4 model and F2C model for snow depth inversion. When both carrier phase type and signal-to-noise ratio type observations exist, one or more of the following models—SNR model, SNR_COM model, and F3 model—are selected for snow depth inversion: When there are 3 or more observations of the signal-to-noise ratio type, the SNR model and / or the SNR_COM model are used to retrieve snow depth. When there are three or more carrier phase type observations, one or more of the L4 model, F2C model and F3 model are used for snow depth inversion. When there are fewer than 3 observations of the signal-to-noise ratio type, the SNR model is used to retrieve snow depth. When carrier phase type observations exist but signal-to-noise ratio type observations do not, the corresponding snow depth inversion model is determined based on the number of carrier phase type observations, including: When there are three or more carrier phase type observations, one or more of the F3 model, L4 model, and F2C model are used for snow depth inversion. When there are two or more carrier phase type observations, the L4 model or F2C model is used for snow depth inversion.

6. A computer-readable storage medium storing a computer program for performing the method of any one of claims 1-4.

7. A computing device, the computing device comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the method described in any one of claims 1-4.

Images