Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach

Global Navigation Satellite System-Reflectometry (GNSS-R) as a microwave remote sensing technique can retrieve the Earth’s surface parameters using the GNSS reflected signal from the surface. These reflected signals convey the surface features and therefore can be utilized to detect certain physical properties of the reflecting surface such as soil moisture content (SMC). Up to now, a serial of electromagnetic models (e.g., bistatic radar and Fresnel equations, etc.) are employed and solved for SMC retrieval. However, due to the uncertainty of the physical characteristics of the sites, complexity, and nonlinearity of the inversion process, etc., it is still challenging to accurately retrieve the soil moisture. The popular machine learning (ML) methods are flexible and able to handle nonlinear problems. It can dig out and model the complex interactions between input and output and ultimately make good predictions. In this paper, two typical ML methods, specifically, random forest (RF) and support vector machine (SVM), are employed for SMC retrieval from GNSS-R data of self-designed experiments (in situ and airborne). A comprehensive simulated dataset involving different types of soil is constructed firstly to represent the complex interactions between the variables (reflectivity, elevation angle, dielectric constant, and SMC) for the requirement of training ML regression models. Correspondingly, the main task of soil moisture retrieval (regression) is addressed. Specifically, the post-processed data (reflectivity and elevation angle) from sensor acquisitions are used to make predictions by these two adopted ML methods and compared with the commonly used GNSS-R retrieval method (electromagnetic models). The results show that the RF outperforms the SVM method, and it is more suitable for handling the inversion problem. Moreover, the RF regression model built by the comprehensive dataset demonstrates satisfactory accuracy and strong universality, especially when the soil type is not uniform or unknown. Furthermore, the typical task of detecting water/soil (classification) is discussed. The ML algorithms demonstrate a high potential and efficiency in SMC retrieval from GNSS-R data

GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation

Global navigation satellite system (GNSS)-reflectometry is a type of remote sensing technology and can be applied to soil moisture retrieval. Until now, various GNSS-R soil moisture retrieval methods have been reported. However, there still exist some problems due to the complexity of modeling and retrieval process, as well as the extreme uncertainty of the experimental environment and equipment. To investigate the behavior of bistatic GNSS-R soil moisture retrieval process, two ground-truth measurements with dierent soil conditions were carried out and the performance of the input variables was analyzed from the mathematical statistical aspect. Moreover, the feature of XGBoost method was utilized as well. As a recently developed ensemble machine learning method, the XGBoost method just emerged for the classification of remote sensing and geographic data, to investigate the characterization of the input variables in the GNSS-R soil moisture retrieval. It showed a good correlation with the statistical analysis of ground-truth measurements. The variable contributions for the input data can also be seen and evaluated. The study of the paper provides some experimental insights into the behavior of the GNSS-R soil moisture retrieval. It is worthwhile before establishing models and can also help with understanding the underlying GNSS-R phenomena and interpreting data

Cygnss Soil Moisture Estimation Using Machine Learning Regression

Global Navigation Satellite System-Reflectometry (GNSS-R) can retrieve Earth's surface parameters, such as soil moisture (SM) using the reflected signals transmitted from GNSS constellations. GNSS-R has advantages of non-contact, large coverage area, real-time, and continuity. The CYclone GNSS (CYGNSS) data used for SM retrieval have generated considerable interests. In this paper, estimating SM on a global scale is performed using machine learning (ML) regression. The the optimal XGBoost predicted model with root mean square error (RMSE) of 0.064 cm3/cm3 is adopted. In addition, satisfactory daily SM estimation outcome with an overall correlation coefficient value of 0.86 is achieved at a global scale

The Sensitivity Analysis on GNSS-R Soil Moisture Retrieval

The use of bistatic re°ected global navigation satellite system (GNSS) signals as a means of sensing the Earth's surface is attracting widespread interest. It has the advantages of non-contact, large coverage area, and real-time which have attracted much attention during recent years. These re°ected signals contain the information of the re°ecting surface and therefore were applied to investigate the properties of the observed object, such as soil moisture (SM). Machine learning (ML) methods are featured with °exibility and are good at handling non-linear problems, modeling complex interactions between inputs and outputs, and have been rise attention for the GNSS-R SM retrieval ¯eld. The contribution of di®erent input variables to SM is quite signi¯cant for optimizing the ML-based SM retrieval. In this paper, the typical random forest (RF) algorithm was adopted to evaluate the weight of input variables for ML-based SM retrieval. A simulation data set was built for training RF models, since the simulated data provide su±cient samples and show a more accurate relationship between the inputs and outputs. The SM predictions made by the RF methods are evaluated and compared with the simulation data set. The results show the contribution of a single variable to soil moisture retrieval, which can help with the ML-based GNSS-R SM retrieval to overcome the complex auxiliary variable problem

Evaluation of the Ocean Surface Wind Speed Change following the Super Typhoon from Space-Borne GNSS-Reflectometry

Ocean surface wind speed is an essential parameter for typhoon monitoring and forecasting. However, traditional satellite and buoy observations are difficult to monitor the typhoon due to high cost and low temporal-spatial resolution. With the development of spaceborne GNSS-R technology, the cyclone global navigation satellite system (CYGNSS) with eight satellites in low-earth orbit provides an opportunity to measure the ocean surface wind speed of typhoons. Though observations are made at the extremely efficient spatial and temporal resolution, its accuracy and reliability are unclear in an actual super typhoon case. In this study, the wind speed variations over the life cycle of the 2018 Typhoon Mangkhut from CYGNSS observations were evaluated and compared with European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-5 (ERA-5). The results show that the overall root-mean-square error (RMSE) of CYGNSS versus ECMWF was 4.12 m/s, the mean error was 1.36 m/s, and the correlation coefficient was 0.96. For wind speeds lower and greater than 15 m/s, the RMSE of CYGNSS versus ECMWF were 1.02 and 4.36 m/s, the mean errors were 0.05 and 1.61 m/s, the correlation coefficients were 0.91 and 0.90, and the average relative errors were 9.8% and 11.6%, respectively. When the typhoon reached a strong typhoon or super typhoon, the RMSE of CYGNSS with respect to ERA-5 from ECMWF was 5.07 m/s; the mean error was 3.57 m/s; the correlation coefficient was 0.52 and the average relative error was 11.0%. The CYGNSS estimation had higher precision for wind speeds below 15 m/s, but degraded when the wind speed was above 15 m/s

Improving Modulation Recognition Using Time Series Data Augmentation via a Spatiotemporal Multi-Channel Framework

Automatic modulation recognition technology with deep learning has a broad prospective owing to big data and computing power. However, the accuracy of modulation recognition largely depends on the massive volume of data and the applicability of the model. Here, to eliminate the difficulties of manual feature extraction, a low accuracy, and a small sample dataset, we propose an effective recognition method that combines time series data augmentation with a spatiotemporal multi-channel learning framework. Compared with other advanced network models, the results showed that the method gave a positive index in the order of 93.5% for ten modulation signal types, which was increased by at least 15%. Especially for QAM16 and QAM64 signals, the average recognition accuracy was improved by nearly 50% at SNRs as low as −2 dB, showing a significant recognition performance. The proposed method provides an attractive method for signal modulation recognition in wireless or wired communication fields

Improvement of Earth orientation parameters estimate with Chang'E-1 Delta VLBI observations

Earth orientation parameters (EOP) are essential for the interconnection of different reference systems involved in Chang'E-1 (CE-1) lunar exploration, such as the Earth fixed reference system, celestial reference system and dynamical reference system. To improve the accuracy of predicted EOP values and to reduce their influence on the accuracy of CE-1 orbital parameters, a relativistic mathematical model of differential VLBI (Delta VLBI) time delay observations for the CE-1 transfer orbit is derived in this paper, which is generated by differencing CE-1 time delay observations with a simulated radio source's time delay observations. The CE-1 orbital parameters and FOP are simultaneously estimated with least squares adjustment using the measured time delay observations of the CE-1 transfer orbit. The results show that the accuracy of the CE-1 orbit and EOP estimates is improved by the CE-1 Delta VLBI observations with optimal orbital arc length and the win-win approach is able to improve the accuracy of both the CE-1 orbital parameters and EOP estimates. The estimated CE-1 orbital accuracy can achieve a few hundred meters and the estimated EOP accuracies are better than their predicted values. (C) 2013 Elsevier Ltd. All rights reserved

Improving CyGNSS-Based Land Remote Sensing: Track-Wise Data Calibration Schemes

Cyclone Global Navigation Satellite System (CyGNSS) data have been used for generating several intermediate products, such as surface reflectivity (Γ), to facilitate a wide variety of land remote sensing applications. The accuracy of Γ relies on precise knowledge of the effective instantaneous radiative power (EIRP) of the transmitted GNSS signals in the direction of the specular reflection point, the precise knowledge of zenith antenna patterns which in turn affects estimates of EIRP, the good knowledge of receive antenna patterns etc. However, obtaining accurate estimates on these parameters completely is still a challenge. To solve this problem, in this paper, an effective method is proposed for calibrating the CyGNSS Γ product in a track-wise manner. Here, two different criteria for selecting data to calibrate and three reference options as targets of the calibrating data are examined. Accordingly, six calibration schemes corresponding to six different combinations are implemented and the resulting Γ products are assessed by (1) visual inspection and (2) evaluation of their associated soil moisture retrieval results. Both visual inspection and retrieval validation demonstrate the effectiveness of the proposed schemes, which are respectively demonstrated by the immediate removal/fix of track-wisely noisy data and obvious enhancement of retrieval accuracy with the calibrated Γ. Moreover, the schemes are tested using all the available CyGNSS level 1 version 3.0 data and the good results obtained from such a large volume of data further illustrate their robustness. This work provides an effective and robust way to calibrate the CyGNSS Γ result, which will further improve relevant remote sensing applications in the future

Autonomous navigation of Mars probe using X-ray pulsars: Modeling and results

Autonomous navigation of Mars probe is a main challenge due to the lack of dense ground tracking network measurements. In this paper, autonomous navigation of the Mars probe Orbits is investigated using the X-ray pulsars. A group of X-ray pulsars with high ranging accuracy are selected based on their properties and an adaptive extended Kalman filter is developed to incorporate the Mars probe dynamics and pulsar-based ranging measurements. Results of numerical experiment show that the three-dimensional positioning accuracy can achieve 750m in X-axis, 220m in Y-axis and 230m in Z-axis, which is much better than the positioning results by current Very Long Baseline Interferometry (VLBI) or Doppler observations with the accuracy of 150 km or several kilometers, respectively. (C) 2012 COSPAR. Published by Elsevier Ltd. All rights reserved