ORCID Profile
0000-0001-9412-3121
Current Organisation
Kunming University of Science and Technology
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: MDPI AG
Date: 16-11-2020
DOI: 10.3390/RS12223760
Abstract: This paper focuses on sea surface wind speed estimation based on cyclone global navigation satellite system reflectometry (GNSS-R) data. In order to extract useful information from delay-Doppler map (DDM) data, three delay waveforms are presented for wind speed estimation. The delay waveform without Doppler shift is defined as central delay waveform (CDW), and the integral of the delay waveforms with different Doppler shift values is defined as integral delay waveform (IDW), while the difference between normalized IDW (NIDW) and normalized CDW (NCDW) is defined as differential delay waveform (DDW). We first propose a data filtering method based on threshold setting for data quality control. This method can select good-quality DDM data by adjusting the root mean square (RMS) threshold of cleaned DDW. Then, the normalized bistatic radar scattering cross section (NBRCS) and the leading edge slope (LES) of IDW are calculated using clean DDM data. Wind speed estimation models based on NBRCS and LES observations are then developed, respectively, and on this basis, a combination wind speed estimation model based on determination coefficient is further proposed. The CYGNSS data and ECMWF reanalysis data collected from 12 May 2020 to 12 August 2020 are used, excluding data collected on land, to evaluate the proposed models. The evaluation results show that the wind speed estimation accuracy of the piecewise function model based on NBRCS is 2.3 m/s in terms of root mean square error (RMSE), while that of the double-parameter and triple-parameter models is 2.6 and 2.7 m/s, respectively. The wind speed estimation accuracy of the double-parameter and triple-parameter models based on LES is 3.3 and 2.5 m/s. The results also demonstrate that the RMSE of the combination method is 2.1 m/s, and the coefficient of determination is 0.906, achieving a considerable performance gain compared with the in idual NBRCS- and LES-based methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 08-2023
Publisher: MDPI AG
Date: 21-07-2022
DOI: 10.3390/RS14143507
Abstract: This paper focuses on sea surface wind speed estimation using L1B level v3.1 data of reflected GNSS signals from the Cyclone GNSS (CYGNSS) mission and European Centre for Medium-range Weather Forecast Reanalysis (ECMWF) wind speed data. Seven machine learning methods are applied for wind speed retrieval, i.e., Regression trees (Binary Tree (BT), Ensembles of Trees (ET), XGBoost (XGB), LightGBM (LGBM)), ANN (Artificial neural network), Stepwise Linear Regression (SLR), and Gaussian Support Vector Machine (GSVM), and a comparison of their performance is made. The wind speed is ided into two different ranges to study the suitability of the different algorithms. A total of 10 observation variables are considered as input parameters to study the importance of in idual variables or combinations thereof. The results show that the LGBM model performs the best with an RMSE of 1.419 and a correlation coefficient of 0.849 in the low wind speed interval (0–15 m/s), while the ET model performs the best with an RMSE of 1.100 and a correlation coefficient of 0.767 in the high wind speed interval (15–30 m/s). The effects of the variables used in wind speed retrieval models are investigated using the XGBoost importance metric, showing that a number of variables play a very significant role in wind speed retrieval. It is expected that these results will provide a useful reference for the development of advanced wind speed retrieval algorithms in the future.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: MDPI AG
Date: 27-03-2022
DOI: 10.3390/RS14071605
Abstract: This article presents a review on spaceborne Global Navigation Satellite System Reflectometry (GNSS-R), which is an important part of GNSS-R technology and has attracted great attention from academia, industry and government agencies in recent years. Compared with ground-based and airborne GNSS-R approaches, spaceborne GNSS-R has a number of advantages, including wide coverage and the ability to sense medium- and large-scale phenomena such as ocean eddies, hurricanes and tsunamis. Since 2014, about seven satellite missions have been successfully conducted and a large number of spaceborne data were recorded. Accordingly, the data have been widely used to carry out a variety of studies for a range of useful applications, and significant research outcomes have been generated. This article provides an overview of these studies with a focus on the basic methods and techniques in the retrieval of a number of geophysical parameters and the detection of several objects. The challenges and future prospects of spaceborne GNSS-R are also addressed.
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