Sensitivity to Soil Moisture and Observation Geometry of Spaceborne GNSS-R Delay-Doppler Maps

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Thanks to the successful operations of the UK TDS-1 and NASA CYGNSS GNSS-R missions, a wealth of Delay-Doppler Maps (DDM) are being measured from the ocean, but also from land reflections. Using the land reflected DDM, several studies are being conducted to retrieve the land geophysical parameters, such as soil moisture, vegetation depth, and biomass. Although they have shown the dependence of the land geophysical parameters on the DDM, it is also shown that many other parameters impact the DDM. This work presents the impacts of some parameters on the DDM. For the systematical and efficient study, an E2E simulator is used. The simulator generates the synthesized DDM reflected over land varying the input parameters, which are the specular point position on the Earth, the elevation angle at the specular points, soil moisture, etc. From the simulation results, the relation between the input parameters and the DDM is individually analyzed, providing the clue to the retrieval algorithm of the geophysical parameters.Peer ReviewedPostprint (author's final draft

The GRSS standard for GNSS-reflectometry

In February 2019 a Project Authorization Request was approved by the Institute of Electrical and Electronics Engineers (IEEE) Standards Association with the title “Standard for Global Navigation Satellite System Reflectometry (GNSS-R) Data and Metadata Content”. A Working Group has been assembled to draft this standard with the purpose of unifying and documenting GNSS-R measurements, calibration procedures, and product level definitions. The Working Group (http://www.grss-ieee.org/community/technical-committees/standards-or-earth-observations/) includes members, collaborators, and contributors from academia, international space agencies, and private industry. In a recent face-to-face meeting held during the ARSI+KEO 2019 Conference, the need was recognized to develop a standard with a wide range of operations, providing procedure guidelines independently of constraints imposed by current limitations on geophysical parameters retrieval algorithms. As such, this effort aims to establish the fundamentals of a potential virtual network of satellites providing inter-comparable data to the scientific community.Peer ReviewedPostprint (author's final draft

Estimation of swell height using spaceborne GNSS-R data from eight CYGNSS satellites

Global Navigation Satellite System (GNSS)-Reflectometry (GNSS-R) technology has opened a new window for ocean remote sensing because of its unique advantages, including short revisit period, low observation cost, and high spatial-temporal resolution. In this article, we investigated the potential of estimating swell height from delay-Doppler maps (DDMs) data generated by spaceborne GNSS-R. Three observables extracted from the DDM are introduced for swell height estimation, including delay-Doppler map average (DDMA), the leading edge slope (LES) of the integrated delay waveform (IDW), and trailing edge slope (TES) of the IDW. We propose one modeling scheme for each observable. To improve the swell height estimation performance of a single observable-based method, we present a data fusion approach based on particle swarm optimization (PSO). Furthermore, a simulated annealing aided PSO (SA-PSO) algorithm is proposed to handle the problem of local optimal solution for the PSO algorithm. Extensive testing has been performed and the results show that the swell height estimated by the proposed methods is highly consistent with reference data, i.e., the ERA5 swell height. The correlation coefficient (CC) is 0.86 and the root mean square error (RMSE) is 0.56 m. Particularly, the SA-PSO method achieved the best performance, with RMSE, CC, and mean absolute percentage error (MAPE) being 0.39 m, 0.92, and 18.98%, respectively. Compared with the DDMA, LES, TES, and PSO methods, the RMSE of the SA-PSO method is improved by 23.53%, 26.42%, 30.36%, and 7.14%, respectively.This work was supported in part by the National Natural Science Foundation of China under Grant 42174022, in part by the Future Scientists Program of China University of Mining and Technology under Grant 2020WLKXJ049, in part by the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant KYCX20_2003, in part by the Programme of Introducing Talents of Discipline to Universities, Plan 111, Grant No. B20046, and in part by the China Scholarship Council (CSC) through a State Scholarship Fund (No. 202106420009).Peer ReviewedPostprint (published version

An assessment of CyGNSS v3.0 level 1 observables over the ocean

Global Navigation Satellite System Reflectometry (GNSS-R) is a rapidly developing Earth observation technology that makes use of signals of opportunity from Global Navigation Satellite Systems that have been reflected off the Earth’s surface. The Cyclone Global Navigation Satellite System (CyGNSS) is a constellation of eight small satellites launched by NASA in 2016, carrying dedicated GNSS-R payloads to measure ocean surface wind speed at low latitudes (±35° North/South). The ESA ECOLOGY project evaluated CyGNSS v3.0 products, which were recently released following various calibration updates. This paper examines the performance of the new calibration by evaluating CyGNSS v3.0 Level-1 Normalised Bistatic Radar Cross Section (NBRCS) and Leading Edge Slope (LES) data from individual CyGNSS units and different GPS transmitters under constant ocean wind conditions. Results indicate that L1 NBRCS from individual CyGNSS units are well inter-calibrated and remarkably stable over time, a significant improvement over previous versions of the products. However, prominent geographical biases reaching over 3 dB are found in NBRCS, linked to factors including the choice of GPS transmitter and the bistatic geometry. L1 LES shows similar anomalies as well as a secondary geographical pattern of biases. These findings provide a basis for further improvement of CyGNSS Level-2 wind products and have wider applicability to improving the calibration of GNSS-R sensors for the remote sensing of non-ocean Earth surfaces

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

Investigating the Sensitivity of Spaceborne GNSS-R Measurements to Ocean Surface Winds and Rain

Earth remote sensing using reflected Global Navigation Satellite System (GNSS) signals is an emerging trend, especially for ocean surface wind measurements. GNSS-Reflectometry (GNSS-R) measurements of ocean surface scattering cross section are directly related to the surface roughness at scale sizes ranging from small capillary waves to long gravity waves. These roughness scales are predominantly due to swell, surface winds and other meteorological phenomena such as rain. In this study we are interested in understanding and characterizing the impact of these phenomena on GNSS-R signals in order to develop a better understanding of the geophysical parameters retrieved from these measurements. In the first part of this work, we look at GNSS-R measurements made by the NASA Cyclone Global Navigation Satellite System (CYGNSS) for developing an effective wind retrieval model function for GNSS-R measurements. In a fully developed sea state, the wind field has a constant speed and direction. In this case, a single Fully Developed Seas (FDS) Geophysical Model Function (GMF) is constructed which relates the scattering cross-section to the near surface wind speed. However, the sea age and fetch length conditions inside a hurricane are in general not consistent with a fully developed sea state. Therefore, a separate empirical Young Sea Limited Fetch (YSLF) GMF is developed to represent the conditions inside a hurricane. Also, the degree of under development of the seas is not constant inside hurricanes and conditions vary significantly with azimuthal location within the hurricane due to changes in the relative alignment of the storms forward motion and its cyclonic rotation. The azimuthal dependence of the scattering cross-section is modelled and a modified azimuthal YSLF GMF is constructed using measurements by CYGNSS over 19 hurricanes in 2017 and 2018. Next, we study the impact of rain on CYGNSS measurements. At L-band rain has a negligible impact on the transmitted signal in terms of path attenuation. However, there are other effects due to rain, such as changes in surface roughness and rain induced local winds, which can significantly alter the measurements. In this part of the study we propose a 3-fold rain model for GNSS-R signals which accounts for: 1) attenuation; 2) surface effects of rain; and 3) rain induced local winds. The attenuation model suggests a total of 96% or greater transmissivity at L-Band up to 30mm/hr of rain. A perturbation model is used to characterize the other two rain effects. It suggests that rain is accompanied by an overall reduction in the scattering cross-section of the ocean surface and, most importantly, this effect is observed only up to 15 m/s of surface winds, beyond which the gravity capillary waves dominate the scattering in the quasi-specular direction. This work binds together several rain-related phenomena and enhances our overall understanding of rain effects on GNSS-R measurements. Finally, one of the important objectives for the CYGNSS mission is to provide high quality global scale GNSS-R measurements that can reliably be used for ocean science applications. In this part of the work we develop a Neural Network based quality control filter for automated outlier detection for CYGNSS retrieved winds. The primary merit of the proposed Machine Learning (ML) filter is its ability to better account for interactions between the individual engineering, instrument and measurement conditions than can separate threshold quality flags for each one.PHDClimate and Space Sciences and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/166140/1/rajibala_1.pd

Spaceborne GNSS-Reflectometry for ocean winds: First results from the UK TechDemoSat-1 mission

First results are presented for ocean surface wind speed retrieval from reflected GPS signals measured by the Low-Earth-Orbiting UK TechDemoSat-1 satellite (TDS-1). Launched in July 2014, TDS-1 provides the first new spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data since the pioneering UK-Disaster Monitoring Mission experiment in 2003. Examples of onboard-processed delay Doppler Maps reveal excellent data quality for winds up to 27.9 m/s. Collocated ASCAT scatterometer winds are used to develop and evaluate a wind speed algorithm based on Signal-to-Noise ratio (SNR) and the Bistatic Radar Equation. For SNR greater than 3 dB, wind speed is retrieved without bias and a precision around 2.2 m/s between 3–18 m/s even withoutcalibration. Exploiting lower SNR signals however requires good knowledge of the antenna beam, platform attitude and instrument gain setting. This study demonstrates the capabilities of low-cost, low-mass, low-power GNSS-R receivers ahead of their launch on the NASA CYGNSS constellation in 2016

Soil moisture estimation synergy using GNSS-R and L-Band microwave radiometry data from FSSCat/FMPL-2

The Federated Satellite System mission (FSSCat) was the winner of the 2017 Copernicus Masters Competition and the first Copernicus third-party mission based on CubeSats. One of FSSCat’s objectives is to provide coarse Soil Moisture (SM) estimations by means of passive microwave measurements collected by Flexible Microwave Payload-2 (FMPL-2). This payload is a novel CubeSat based instrument combining an L1/E1 Global Navigation Satellite Systems-Reflectometer (GNSS-R) and an L-band Microwave Radiometer (MWR) using software-defined radio. This work presents the first results over land of the first two months of operations after the commissioning phase, from 1 October to 4 December 2020. Four neural network algorithms are implemented and analyzed in terms of different sets of input features to yield maps of SM content over the Northern Hemisphere (latitudes above 45° N). The first algorithm uses the surface skin temperature from the European Centre of Medium-Range Weather Forecast (ECMWF) in conjunction with the 16 day averaged Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate SM and to use it as a comparison dataset for evaluating the additional models. A second approach is implemented to retrieve SM, which complements the first model using FMPL-2 L-band MWR antenna temperature measurements, showing a better performance than in the first case. The error standard deviation of this model referred to the Soil Moisture and Ocean Salinity (SMOS) SM product gridded at 36 km is 0.074 m3/m3. The third algorithm proposes a new approach to retrieve SM using FMPL-2 GNSS-R data. The mean and standard deviation of the GNSS-R reflectivity are obtained by averaging consecutive observations based on a sliding window and are further included as additional input features to the network. The model output shows an accurate SM estimation compared to a 9 km SMOS SM product, with an error of 0.087 m3/m3. Finally, a fourth model combines MWR and GNSS-R data and outperforms the previous approaches, with an error of just 0.063 m3/m3. These results demonstrate the capabilities of FMPL-2 to provide SM estimates over land with a good agreement with respect to SMOS SM.This work was supported by the 2017 ESA S3 challenge and Copernicus Masters overall winner award (“FSSCat” project). This work was (partially) sponsored by project SPOT: Sensing with Pioneering Opportunistic Techniques grant RTI2018-099008-B-C21 / AEI / 10.13039/501100011033, and by the Unidad de Excelencia Maria de Maeztu MDM-2016-0600. This work was also (partially) sponsored by the Spanish Ministry of Science and Innovation through the project ESP2017-89463-C3, by the Centro de Excelencia Severo Ochoa (CEX2019-000928-S), and by the CSIC Plataforma Temática Interdisciplinar de Teledetección (PTI-Teledetect). Joan Francesc Munoz-Martin received support from the grant for the recruitment of early-stage research staff FI-DGR 2018 of the AGAUR - Generalitat de Catalunya (FEDER), Spain; Christoph Herbert received the support of a fellowship from “la Caixa” Foundation (ID 100010434) with the fellowship code LCF/BQ/DI18/11660050 and funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 713673; David Llavería received support from an FPU fellowship from the Spanish Ministry of Education FPU18/06107.Peer ReviewedPostprint (published version

Desert roughness retrieval using CYGNSS GNSS-R data

The aim of this paper is to assess the potential use of data recorded by the Global Navigation Satellite System Reflectometry (GNSS-R) Cyclone Global Navigation Satellite System (CYGNSS) constellation to characterize desert surface roughness. The study is applied over the Sahara, the largest non-polar desert in the world. This is based on a spatio-temporal analysis of variations in Cyclone Global Navigation Satellite System (CYGNSS) data, expressed as changes in reflectivity (G). In general, the reflectivity of each type of land surface (reliefs, dunes, etc.) encountered at the studied site is found to have a high temporal stability. A grid of CYGNSS G measurements has been developed, at the relatively fine resolution of 0.03° x 0.03°, and the resulting map of average reflectivity, computed over a 2.5-year period, illustrates the potential of CYGNSS data for the characterization of the main types of desert land surface (dunes, reliefs, etc.). A discussion of the relationship between aerodynamic or geometric roughness and CYGNSS reflectivity is proposed. A high correlation is observed between these roughness parameters and reflectivity. The behaviors of the GNSS-R reflectivity and the Advanced Land Observing Satellite-2 (ALOS-2) Synthetic Aperture Radar (SAR) backscattering coeffcient are compared and found to be strongly correlated. An aerodynamic roughness (Z0) map of the Sahara is proposed, using four distinct classes of terrain roughness