This paper develops the models that can be used in different flight scenarios to retrieve the wind speed using the width of the normalized delay waveform (NDW). First, the factors that influence the NDW width, including the wind speed, wind direction, flight height, and elevation angle, are analyzed. The contribution of each independent variable to the regression is explored. The results show that the wind speed, flight height, and elevation angle contribute more significantly than the wind direction so the wind direction can be ignored in the model. The multiple regression, in which the function terms of NWD width, flight height, and elevation angle above are taken as independent variables and wind speed is taken as the dependent variable, is proposed to develop the model of retrieving wind speed. Through the simulation, a root-mean-square error (RMSE) over 3 m/s can be obtained. In order to improve the retrieval performance, a Back-Propagation (BP) network is trained as an alternative to the analytical models above. Better performance is achieved with an RMSE less than 2.5 m/s under the same conditions with the analytical model. The errors of retrieved wind speed are analyzed. The conclusions are that: 1) both analytical model and BP network have inherent regression biases, especially for high wind speed from 15 to 20m/s so that at wind speeds higher than 16 m/s, the tendency for retrieval accuracy to rapidly become worse appears and 2) the number of incoherent averaging should be over 1000 to reduce the impact of thermal and speckle noise. At the end of the paper, airborne data are processed to retrieve wind speed utilizing proposed models and the matching method to compare in-situ wind speed from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). By comparing the retrieval results, the proposed methods could obtain the accuracy with the same level of matching method.INDEX TERMS Wind speed, reflected GNSS signal, normalized delay waveform (NDW), NDW width, multiple regression, neural network.

The aim of this paper is to develop a model which can be used to retrieve sea ice thickness based on global navigation satellite system reflected signals at a shore-based platform. First, the method calculates the intensity ratio of the reflected signal and the direct signal of the global navigation satellite system satellite, which is the ratio of the power of the reflected signal to the power of the direct signal. Then, the information of the sea ice thickness is obtained according to the empirical model of the sea ice thickness. In order to verify the effectiveness of the method, the global navigation satellite system reflected signals were observed in the experiment in the Bayu enclosure of Liaoning Province, China. The results show that the sea ice thickness of the global navigation satellite system reflected signal is 10–20 cm, which is consistent with the synthetic-aperture radar observation.

For preventing the effects of sea ice bring the serious negative impact on the maritime transport, a full-scale detection of sea ice should be performed. The BeiDou GEO satellites could provide stable geometry and better coverage in mid-and low-latitude region where most of the sea ice occur. Based on this consideration, this paper evaluates the usage of BeiDou GEO Satellites reflected signals for accurate real-time Earth observation to study the changes in the sea surface state through remote sensing. BeiDou GEO signals received after reflection from Bohai Bay were analyzed for their sea ice content. The results are in good agreement with the cluster center of the sea ice reflected correlation power and the sea water reflected correlation power. The average of the cluster center on the sea ice surface is much smaller than that on the sea water surface..

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