A Novel Method for Ocean Wind Speed Detection Based on Energy Distribution of Beidou Reflections

Abstract

The Global Navigation Satellite System Reflectometry (GNSS-R) technique exploits the characteristics of reflected GNSS signals to estimate the geophysical parameters of the earth’s surface. This paper focuses on investigating the wind speed retrieval method using ocean scattered signals from a Beidou Geostationary Earth Orbit (GEO) satellite. Two new observables are proposed by computing the ratio of the low energy zone and the high energy zone of the delay waveform. Coastal experimental raw data from a Beidou GEO satellite are processed to establish the relationship between the energy-related observables and the sea surface wind. When the delay waveform normalized amplitude (this will be referred to as “threshold” in what follows) is 0.3, fitting results show that the coefficient of determination is more than 0.76 in the gentle wind scenario (<10 m/s), with a root mean square error (RMSE) of less than 1.0 m/s. In the Typhoon UTOR scenario (12.7 m/s~37.3 m/s), the correlation level exceeds 0.82 when the threshold is 0.25, with a RMSE of less than 3.10 m/s. Finally, the impact of the threshold and coherent integration time on wind speed retrieval is discussed to obtain an optimal result. When the coherent integration time is 50 milliseconds and the threshold is 0.15, the best wind speed retrieval error of 2.63 m/s and a correlation level of 0.871 are obtained in the UTOR scenario.

Remote sensing and its applications using GNSS reflected signals: advances and prospects

“…There was a good correlation between the observables of the reflected signal and typhoon wind speeds, with the observed maximum wind speeds reaching up to 35 m/s (Li et al, 2016). After then, more observables sensitive to typhoon wind speed were proposed (Wang et al, 2016(Wang et al, , 2019.…”

“…In particular, the Cyclone Global Navigation Satellite System (CYGNSS) mission, launched on December 15, 2016, consisting of eight microsatellites constellation jointly operated by the NASA and the University of Michigan (UM), provided a huge amount of space-borne GNSS-R data and made a number of progress in oceans, e.g. wind speed, sea surface, and significant wave height (Cardellach et al, 2011;Foti et al, 2017;Qiu & Jin, 2020;Wang et al, 2019;Yang et al, 2021a) and the land, e.g., soil moisture and soil freeze/ thaw cycles (Dong & Jin, 2021;Wu et al, 2020).…”

Developing and Testing Models for Sea Surface Wind Speed Estimation with GNSS-R Delay Doppler Maps and Delay Waveforms

“…In this work, NBRCS ( 0 σ ) was used as a surface roughness sensitivity parameter (also known as an observable parameter) to estimate sea surface wind speed. The NBRCS equation is given as follows [34,22,40]:…”