| Soil moisture,also known as soil moisture content,is a physical quantity that characterizes the dryness and wetness of soil,it is also an important factor affecting ecosystems.Monitoring of soil moisture is of great value for weather forecasting and climate research.GNSS-MR(GNSS Multipath Reflectometry)is a new kind of remote sensing technology that directly uses the amplitude,frequency and phase of multi-path SNR reflection components within a range of satellite elevation angles to perform near-surface physical parameter retrieval.It has been proved that GNSS-MR can be used to monitor the surface environment such as soil moisture,vegetation index,tidal level change,snow depth,volcanic ash and so on.Meanwhile,it can overcome the disadvantages of traditional soil moisture measurement methods,such as the destruction of monitored objects,the difficulty of data assimilation among instrument types,the limited spatial-temporal resolution and the high cost.Since the signal-to-noise ratio(SNR)of low satellite elevation angles can well carry the rich physical parameters information of near-surface reflectors,the data source of soil moisture inversion based on GNSS-MR is generally the observed SNR in the range of 5 °~30°.However,the motion of GNSS satellites is usually periodic,and the duration of low elevation angle in one day is short.In addition,due to the influence of soil evenness and spatial variability of soil composition,the GNSS-MR based on single system and single satellite often fails to monitor the short-term changes of soil moisture.The multi-system GNSS combination can provide more azimuth coverage,and then can select more SNR combination in each observation period,taking into account the soil smoothness and the spatial difference of soil composition.Therefore,it is expected to achieve high-precision,quasi-real-time dynamic monitoring of soil moisture.What's more,the SNR observations will inevitably be polluted by abnormal noise,which leads to the multipath SNR obtained by least square removing the trend is often not pure,thus affecting the accuracy of the delay phase characterizing the trend of soil moisture change.Finally,the fundamental purpose of GNSS-MR soil moisture inversion is to build a mathematical model with SNR residual series characteristic parameters as independent variables and soil moisture as dependent variables based on the existing soil moisture samples and SNR observations,so as to obtain soil moisture according to the characteristic parameters of SNR residual series.Around the above problems,in order to improve the accuracy,reliability and stability of GNSS-MR soil moisture inversion,the main contents and achievements of this paper are as follows:(1)The characteristics of satellite signals of GPS,BDS,GLONASS,GALILEO and the signal-to-noise ratio of each frequency point of each system are analyzed in detail.(2)The physical model and mathematical model of soil moisture inversion of GNSS-MR SNR and the mathematical model of multipath error are introduced.(3)The modeling principle and flow of the linear regression,BP neural network and SVRM support vector regression machine are analyzed.The modeling accuracy and application are analyzed based on the measured SNR observations and soil moisture samples.(4)Based on the periodicity characteristics of multipath SNR sequence,the selection of effective elevation angle is discussed and analyzed in detail.Considering the spatial heterogeneity of soil moisture and soil composition,in order to ensure the single reflection of satellite signal with the help of the "first Fresnel reflection zone",the satellites in the similar azimuth range are selected by the reflection point trajectory map,and the time arc segment is divided by the duration of the effective elevation angle to realize the effective SNR observation,thus realizing the selection of effective SNR observation.(5)In view of the fact that the PBO(Platform Boundary Observation)P484 station of the continental plate boundary observation network only has GPS SNR observations,in order to reduce the influence of soil moisture,soil flatness,soil composition and the spatial difference of multipath environment in different directions,by limiting the azimuth range of SNR observations and taking into account the period characteristics of a single GPS satellite and the recurrence characteristics of the same satellite in similar directions,the time resolution and accuracy of soil moisture inversion are improved by using multi-GPS satellite combined with GPS-MR.(6)Considering the short duration of the same satellite in a low elevation angle state in a single day,taking into account the spatial difference of soil moisture,soil composition,multipath environment,the GPS/BDS/GLONASS/GALILEO combined SNR observations are used to retrieve soil moisture,which makes up for the shortcomings of low reliability,poor stability and poor practical operability of GNSS-MR soil moisture retrieval based on single system and single satellite.(7)On the basis of multi-system GNSS combination,aiming at the defect that the least square estimation does not have robustness,in order to obtain better delayed phase estimation,simplify complicated satellite selection process and improve the reliability and stability of GNSS-MR soil moisture inversion,an improved algorithm of multi-satellite system combination GNSS-MR soil moisture inversion based on robust estimation is proposed.Firstly,this algorithm combines the multi-system SNR observations based on the satellite azimuth,then uses the IGGIII weight function-based robustness estimation to solve the delay phase,and then uses the delayed phase to characterize the soil moisture change trend.The results show that the delay phase and the soil moisture measurement obtained by the multi-satellite system combined with GNSS-MR based on the robust estimation are in good agreement,the correlation coefficient is better than 0.97,the root mean square error is 0.01,which has better performance in the soil moisture inversion than more satellite combination,multi-system single-satellite combination and the multi-system single-satellite combination based on the robust estimation. |
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