Retrieval of Sea Surface Rainfall Intensity Using Spaceborne GNSS-R Data

Abstract

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) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=3.69$ </tex-math></inline-formula> mm/h and correlation coefficient (CC) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=0.71$ </tex-math></inline-formula> ]. 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.