An efficient imaging algorithm for GNSS-R bi-static SAR

Global Navigation Satellite System Reflectometry (GNSS-R) based Bi-static Synthetic Aperture Radar (BSAR) is becoming more and more important in remote sensing, given its low power, low mass, low cost, and real-time global coverage capability. Due to its complex configuration, the imaging for GNSS-R BSAR is usually based on the Back-Projection Algorithm (BPA), which is very time consuming. In this paper, an efficient and general imaging algorithm for GNSS-R BSAR is presented. A Two Step Range Cell Migration (TSRCM) correction is firstly applied. The first step roughly compensates the RCM and Doppler phase caused by the motion of the transmitter, which simplifies the SAR data into the quasi-mono-static case. The second step removes the residual RCM caused by the motion of the receiver using the modified frequency scaling algorithm. Then, a cubic phase perturbation operation is introduced to equalize the Doppler frequency modulation rate along the same range cell. Finally, azimuth phase compensation and geometric correction are completed to obtain the focused SAR image. A simulation and experiment are conducted to demonstrate the feasibility of the proposed algorithm, showing that the proposed algorithm is more efficient than the BPA, without causing significant degradation in imaging quality

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

Engineering Calibration and Physical Principles of GNSS-Reflectometry for Earth Remote Sensing

The Cyclone Global Navigation Satellite System (CYGNSS) is a NASA mission that uses 32 Global Positioning System (GPS) satellites as active sources and 8 CYGNSS satellites as passive receivers to measure ocean surface roughness and wind speed, as well as soil moisture and flood inundation over land. This dissertation addresses two major aspects of engineering calibration: (1) characterization of the GPS effective isotropic radiated power (EIRP) for calibration of normalized bistatic radar cross section (NBRCS) observables; and (2) development of an end-to-end calibration approach using modeling and measurements of ocean surface mean square slope (MSS). To estimate the GPS transmit power, a ground-based GPS constellation power monitor (GCPM) system has been built to accurately and precisely measure the direct GPS signals. The transmit power of the L1 coarse/acquisition (C/A) code of the full GPS constellation is estimated using an optimal search algorithm. Updated values for transmit power have been successfully applied to CYGNSS L1B calibration and found to significantly reduce the PRN dependence of CYGNSS L1 and L2 data products. The gain pattern of each GPS satellite’s transmit antenna for the L1 C/A signal is determined from measurements of signal strength received by the 8-satellite CYGNSS constellation. Determination of GPS patterns requires knowledge of CYGNSS patterns and vice versa, so a procedure is developed to solve for both of them iteratively. The new GPS and CYGNSS patterns have been incorporated into the science data processing algorithm used by the CYGNSS mission and result in improved calibration performance. Variable transmit power by numerous Block IIF and IIR-M GPS space vehicles has been observed due to their flex power mode. Non-uniformity in the GPS antenna gain patterns further complicates EIRP estimation. A dynamic calibration approach is developed to further address GPS EIRP variability. It uses measurements by the direct received GPS signal to estimate GPS EIRP in the specular reflected direction and then incorporates them into the calibration of NBRCS. Dynamic EIRP calibration instantaneously detects and corrects for power fluctuations in the GPS transmitters and significantly reduces errors due to GPS antenna gain azimuthal asymmetry. It allows observations with the most variable Block IIF transmitters (approximately 37% of the GPS constellation) to be included in the standard data products and further improves the calibration quality of the NBRCS. A physics-based approach is then proposed to examine potential calibration errors and to further improve the Level 1 calibration. The mean square slope (mss) is a key physical parameter that relates the ocean surface properties (wave spectra) to the CYGNSS measurement of NBRCS. An approach to model the mss for validation with CYGNSS mss data is developed by adding the contribution of a high frequency tail to the WAVEWATCH III (WW3) mss. It is demonstrated that the ratio of CYGNSS mss to modified WW3 mss can be used to diagnose potential calibration errors that exist in the Level 1 calibration algorithm. This approach can help to improve CYGNSS data quality, including the Level 1 NBRCS and Level 2 ocean surface wind speed and roughness. The engineering calibration methods presented in this dissertation make significant contributions to the spatial coverage, calibration quality of the measured NBRCS and the geophysical data products produced by the NASA CYGNSS mission. The research is also useful to the system design, science investigation and engineering calibration of future GNSS-reflectometry missions.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168052/1/wangtl_1.pd

New Applications of Satellite-Measured Tropical Cyclone Wind Speeds

Major hurricanes are becoming more frequent due to climate change and the warming of the oceans. Now more than ever, the ability to model hurricanes accurately and provide advanced warning to affected areas is crucial. However, while hurricane track forecasting has greatly improved, there has been little improvement in intensity forecasting, partly due to inadequate observations and modeling of the inner core of the storm. CYGNSS, an experimental NASA satellite mission launched in December 2016, is designed to frequently measure surface wind speeds in hurricanes via GPS reflections. These additional measurements should improve forecasting of hurricane rapid intensification. This dissertation looks at new ways to use and interpret remotely sensed wind speeds in hurricanes. First, we look at how estimated storm intensity is affected by the spatial resolution of the satellite. Satellite-measured wind speeds are not point measurements—a single measurement is of a broad area on the surface where contributions from each part of the surface are given weight according to the antenna pattern. Hurricane intensity is usually determined by the maximum wind speed (Vm). Because satellite-measured wind speeds are effectively averages over a large area, the satellite-measured Vm is lower than the true Vm. Unless this is corrected for, hurricane wind fields from satellites will systematically underestimate storm intensity. This work explores how information from any satellite-measured wind field can be used to improve the estimated storm intensity via a scale factor correction. Next, hurricane parametric wind speed models are examined. Parametric wind models are established using meteorological principles combined with simplifying assumptions about the environment or storm structure. These models tune several free parameters to wind speed measurements by minimizing the difference between the measurements and the wind field “seen” by the model. Parametric wind models are especially useful for filling in gaps between measurements—once the free parameters are optimized, the model can report a wind speed estimate everywhere in the storm. Hurricane wind field characteristics such as Vm, radial distance to Vm, azimuthal information, and more are easily determined from a full wind field but are more difficult to estimate from a gap-filled wind field. Also, many modeling applications are enabled by having a full wind field. This dissertation discusses limitations of existing parametric wind models, and new parameters are added to allow for improved representation of a wider variety of storms. A parametric wind model is then used to find the center location of a hurricane using the principle that the model fit is best when the correct storm center is assumed. The storm center is the location that optimizes the model fit. Most storm center fixes are done manually—this is one of few automated storm center fix techniques and the only one not using cloud structure seen in satellite imagery. Accurate storm center locations are necessary for forecasting hurricanes, hurricane research, and historical record keeping. Lastly, a new CYGNSS data product is described which reports gridded surface wind speeds with increased convenience and reliability for users of hurricane-specific data. This product, which will be released to the public in early 2021, processes the CYGNSS wind speeds in a way that allows for self-consistency checks of the data. This new CYGNSS hurricane wind speed product is shown to be in excellent agreement with wind speeds from models and a well-established satellite.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168082/1/drmayers_1.pd