The sea surface temperature (SST) actively impacts the backscattering coefficient measured by scatterometers and the wind retrieval accuracy. However, none of the geophysical model functions (GMFs) currently used in operational wind retrieval considers the effect of the SST. With the HY-2A scatterometer as the research subject, this paper attempts to quantitatively analyze the effect of the SST on the backscattering coefficient for the first time and establish a new GMF by the Fourier series method (containing only cosine terms), which adopts the SST as the independent variable. The collocated Level 2A radar backscatter measurement data of the HY-2A scatterometer and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis wind data are used to build the new SST-dependent GMF that considers the influence of the SST in the model. The study indicates that the SST effects are wind speed dependent and more significant under vertical-vertical (VV) polarization. The backscattering coefficient increases with increasing SST in the wind speed range of 5-15 m/s. In addition, with increasing wind speed, the influence of the SST gradually decreases. Finally, the new GMF was applied to retrieve the ocean surface wind, which was compared to the conventional GMF to validate its performance. The experimental results indicate that the accuracy of wind field retrieval by the new GMF is considerably improved and the systematic deviation in the wind speed is effectively corrected. This study potentially contributes to a better understanding of the microwave backscattering behavior of the ocean surface and provides a way to further improve the wind retrieval accuracy of the HY-2A scatterometer as well as of other Ku-band scatterometers. INDEX TERMS Fourier series, geophysical model function (GMF), wind field retrieval, SST.

Rain affects the wind measurement accuracy of the Ku-band spaceborne scatterometer. In order to improve the quality of the retrieved wind field, it is necessary to identify and flag rain-contaminated data. In this study, an HY-2A scatterometer is used to study rain identification. In addition to the conventional parameters, such as the retrieved wind speed, the wind direction relative to the along-track direction, and the normalized beam difference, the experiment expands the mean deviation of the backscattering coefficient, the beam difference between fore and aft, and the node number of the wind vector cell (WVC) as the sensitive parameters according to the microwave scattering characteristics of rain and the actual measurement situation of the HY-2A. Furthermore, a rain identification model for HY2 (HY2RRM) with the K-Nearest Neighborhood (KNN) algorithm was built. After several tests, the accuracy of the selected HY2RRM approach is found to about 88%, and about 70% of rain-contaminated data can be accurately identified. The research results are helpful for better understanding the characteristics of microwave backscattering and provide a possible way to further improve the wind field retrieval accuracy of the HY-2A scatterometer and other Ku-band scatterometers.

The development of the county economy in China is a complicated process that is influenced by many factors in different ways. This study is based on multi-source big data, such as Tencent user density (TUD) data and point of interest (POI) data, to calculate the different influencing factors, and employed a multiscale geographically weighted regression (MGWR) model to explore their spatial non-stationarity impact on China’s county economic development. The results showed that the multi-source big data can be useful to calculate the influencing factor of China’s county economy because they have a significant correlation with county GDP and have a good models fitting performance. Besides, the MGWR model had prominent advantages over the ordinary least squares (OLS) and geographically weighted regression (GWR) models because it could provide covariate-specific optimized bandwidths to incorporate the spatial scale effect of the independent variables. Moreover, the effects of various factors on the development of the county economy in China exhibited obvious spatial non-stationarity. In particular, the Yangtze River Delta, the Pearl River Delta, and the Beijing-Tianjin-Hebei urban agglomerations showed different characteristics. The findings revealed in this study can furnish a scientific foundation for future regional economic planning in China.