Search

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology that uses GNSS signals reflected from the Earth's surface to estimate geophysical parameters. Because of its unique advantages such as high temporal and spatial resolutions, low observation cost, wide coverage and all-weather operation, it has been widely used in land and ocean remote sensing fields. Ocean wind monitoring is the main objective of the recently launched Cyclone GNSS (CYGNSS). In previous studies, wind speed was usually retrieved using features extracted from delay-Doppler maps (DDMs) and empirical geophysical model functions (GMFs). However, it is a challenge to employ the GMF method if using multiple sea state parameters as model input. Therefore, in this article, we propose an improved deep learning network framework to retrieve global sea surface wind speed using spaceborne GNSS-R data, named GloWS-Net. GloWS-Net considers the fusion of auxiliary information including ocean swell significant wave height (SWH), sea surface rainfall and wave direction to build an end-to-end wind speed retrieval model. In order to verify the improvement of the proposed model, ERA5 and Cross-Calibrated Multi-Platform (CCMP) wind data were used as reference for extensive testing to evaluate the wind speed retrieval performance of the GloWS-Net model and previous models (i.e., GMF, fully connected network (FCN) and convolutional neural network (CNN)). The results show that, when using ERA5 winds as ground truth, the root mean square error (RMSE) of the proposed GloWS-Net model is 23.98% better than that of the MVE method. Although the GloWS-Net model and the FCN model have similar RMSE (1.92 m/s), the mean absolute percentage error (MAPE) of the former is improved by 16.56%; when using CCMP winds as ground truth, the RMSE of the proposed GloWS-Net model is 2.16 m/s, which is 20.27% better than the MVE method. Compared with the FCN model, the MAPE is improved by 17.75%. Meanwhile, the GloWS-Net outperforms the FCN, traditional CNN, modified CNN (MCNN) and CyGNSSnet models in global wind speed retrieval especially at high wind speeds. [ABSTRACT FROM AUTHOR]

Reliable rainfall information is very important for hydro climatology research, industrial and agricultural production, construction, and drought and flood prediction. As a new remote sensing technology, Global Navigation Satellite System-reflectometry (GNSS-R) can be used to retrieve geophysical parameters due to its unique advantages of high spatial and temporal resolution, low cost, large coverage, and all-weather operation. Existing studies have confirmed that GNSS-R technology can detect rainfall on the ocean surface, but there is no further study on the magnitude of rainfall intensity (RI). Therefore, this article aims to study the utilization of GNSS-R in the retrieval of sea surface RI based on the observables derived from GNSS-R delay-Doppler maps (DDMs). First, a wavelet denoising method is proposed for DDM denoising to improve the quality of DDM data. Then, three models were developed based on the four GNSS-R observables, which were divided into three categories according to the number of observables. The performance of RI retrieval based on the three models was evaluated against reference data IMERG-F extensively. The experimental results show that under very low wind speed (WS) (< 5 m/s), the following holds. First, the retrieval performance of the single observable model based on leading-edge slope of the normalized integrated delay waveform (LES-NIDW) proposed in the first category of model is the worst [root mean square error (RMSE) $=3.69$ mm/h and correlation coefficient (CC) $=0.71$ ]. However, right edge waveform summation of NIDW (REWS-NIDW) is the best, and RMSE and CC are better than 3.17 mm/h and 0.79, respectively. Second, compared with LES-NIDW observable, the model retrieval accuracy based on DDM Average (DDMA), leading-edge waveform summations of NIDW (LEWS-NIDW), and REWS-NIDW observables is improved by 7.05%, 7.96%, and 12.29%, respectively. Third, when using the combined model, compared with LES-NIDW single observable model, the retrieval accuracy of the model based on two observables (i.e., LES-NIDW and REWS-NIDW) combination and three observables (i.e., LES-NIDW, LEWS-NIDW, and REWS-NIDW) combination is improved by 10.34% and 13.60%, respectively. Therefore, considerable performance gain is obtained. [ABSTRACT FROM AUTHOR]

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 individual NBRCS- and LES-based methods. [ABSTRACT FROM AUTHOR]