Jennifer Reynolds

TL;DR: A retrieval algorithm based on the use of an artificial neural network (ANN) is proposed for wind speed estimations from cyclone global navigation satellite system (CYGNSS), highlighting that the ANN approach outperforms the baseline approach for both low and high wind speeds and removes most of the geographical biases.

Abstract: The Institute of Electrical and Electronics Engineers (IEEE) Geoscience and Remote Sensing Society (GRSS) created the GRSS “Standards for Earth Observation Technical Committee” to advance the usability of remote sensing products by experts from academia, industry, and government through the creation and promotion of standards and best practices. In February 2019, a Project Authorization Request was approved by the IEEE Standards Association (IEEE-SA) with the title “Standard for Spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) Data and Metadata Content.” At present, 4 GNSS constellations cover the Earth with their navigation signals: The United States of America (USA) Global Positioning System GPS with 31 Medium Earth Orbit (MEO) operational satellites, the Russian GLObal’naya NAvigatsionnaya Sputnikovaya Sistema GLONASS with 24 MEO operational satellites, the European Galileo with 24 MEO operational satellites, and the Chinese BeiDou-3 with 3 Inclined GeoSynchronous Orbit (IGSO), 24 MEO, and 2 Geosynchronous Equatorial Orbit (GEO) operational satellites. Additionally, several regional navigation constellations increase the number of available signals for remote sensing purposes: the Japanese Quasi-Zenith Satellite System QZSS with 1 GSO and 3 Tundra-type orbit operational satellites, and the Indian Regional Navigation Satellite System IRNSS with 3 GEO and 4 IGSO operational satellites. On the other hand, there are different GNSS-R processing techniques, instruments and spaceborne missions, and a wide variety of retrieval algorithms have been used. The heterogeneous nature of these signals of opportunity as well as the numerous working methodologies justify the need of a standard to further advance in the development of GNSS-R towards an operational Earth Observation technique. In particular, the scope of this working group is to develop a standard for data and metadata content arising from past, present, and future spaceborne missions such as the United Kingdom (UK) TechDemoSat-1 TDS-1, and the National Aeronautics and Space Administration (NASA) CYclone Global Navigation Satellite System CYGNSS constellation coordinated by the University of Michigan (UM). In this article we describe the scene study, including fundamental aspects, scientific applications, and historical milestones. The spaceborne standard is under development and it will be published in IEEE-SA.

TL;DR: In this paper, a standard for Global Navigation Satellite System Reflectometry (GNSS-R) data and metadata content is proposed for the purpose of unifying and documenting measurements, calibration procedures, and product level definitions.

TL;DR: In this paper, a sensitivity analysis is performed to understand the relationship between SAR altimetry backscatter data and land parameters, including vegetation-related parameters, at different bands used by past and existing altimeters.