For a foam-covered sea surface, it is difficult to retrieve sea surface salinity (SSS) with L-band brightness temperature (1.4 GHz) because of the effect of a foam layer with wind speeds stronger than 7 m/s, especially at low sea surface temperature (SST). With foam-controlled experiments, emissivities of a foam-covered water surface at low SST (−1.4 °C to 1.7 °C) are measured for varying SSS, foam thickness, incidence angle, and polarization. Furthermore, a theoretical model of emissivity is introduced by combining wave approach theory with the effective medium approximation method. Good agreement is obtained upon comparing theoretical emissivities with those of experiments. The results indicate that foam parameters have a strong influence on increasing emissivity of a foam-covered water surface. Increments of experimental emissivities caused by foam thickness of 1 cm increase from about 0.014 to 0.131 for horizontal polarization and 0.022 to 0.150 for vertical polarization with SSS increase and SST decrease. Contributions of the interface between the foam layer and water surface to the foam layer emissivity increments are discussed for frequencies between 1 and 37 GHz.

The spaceborne microwave scatterometer (SCAT) is a radar for quantitatively measuring the backscatter coefficient of the Earth’s surface. Its main application is the measurement of wind speed and direction near the sea surface with a wide swath and high precision. So far, many SCATs have been launched in orbit, including SeaWinds by the United States, the ASCAT series by Europe, and the HY-2 series scatterometers by China, which contribute to improved weather forecasts and typhoon positioning services. The Fengyun-3E satellite wind scatterometer or WindRad, launched by China in July 2021, is the first Ku and C dual-band SCAT with the highest spatial resolution. In this study, the rotation characteristics and calibration parameters of WindRad in the prelaunch stage and after its early test and verification in orbit were investigated. The prelaunch test results demonstrated the excellent performance of WindRad, which can fully meet its quantitative application requirements. In-orbit early tests demonstrated the stable operation of WindRad, with signal characteristics consistent with the design specifications. The Ku and C dual-band earth surface backscatter coefficient projection and the sea surface wind field inversion were also investigated. The preliminary results confirmed the ability of WindRad to provide a high-quality ocean wind measurement and the improved capability for different applications, including sea ice detection and soil moisture measurement.

The HY-2B satellite, equipped with the scanning microwave radiometer (SMR), was launched on 25 October 2018 and began observation on 30 October 2018. With the SMR data, we retrieved the sea surface temperature (SST), along with the ocean wind speed (OWS), total columnar water vapour (TCWV), and cloud liquid water (TCLW), within an optimal estimation (OE) Framework. Through the comparison with Argo profiling floats data, mean difference of 0.01°C, and standard deviation of 0.47 ${ }^{\circ }\mathrm{C}$ are inferred for SST within the latitude range of 60°S–60°N. The contrast against the WindSat standard products also shows an encouraging result, in which the standard deviation for SST, OWS, TCWV, and TCLW are 0.49 $$°\text{C}$$, 0.64 m s⁻¹, 1.23 mm, and 0.029 mm, respectively. To investigate the effect of oceanic and atmospheric state on SST retrieval, the sensitivities of brightness temperature (TB) at considered frequency to SST, OWS, TCWV, and TCLW are carefully analysed, and seven combinations of SMR frequencies are tested to retrieve different parameters. The results emphasize the importance of low-frequency observations in retrieving accurate SST and suggest that the influence of wind speed and atmospheric vapour content is non-negligible. It is also indicated that the role of the higher frequency (23.8 and 37 GHz) is limited for SST retrieval. This study presents an assessment for SST retrieval from SMR measured data and provides a reference about frequency selection for measuring SST from passive microwave radiometer.

For one-dimensional aperture synthesis (1-D AS) radiometers, truncated sampling occurs in the frequency domain due to the system baseline limitation. Therefore, there is an obvious Gibbs oscillation in the reconstructed image. To solve this problem, an imaging method based on a one-dimensional convolutional neural network (1-D CNN) is proposed in this paper. Compared with deep learning methods based on two-dimensional convolutions, the 1-D convolution not only reduces the amount of computation but also produces further performance improvements. The input data of the network are the 1-D visibility function samples, and the output data are the 1-D brightness temperature (BT) samples. The network learns the mapping relationship from the training of the 1-D visibility function samples and 1-D BT samples to complete 1-D AS imaging without any prior knowledge. To verify the performance of this imaging method, simulations and experiments based on the airborne C-band one-dimensional microwave interferometric radiometer (ACMIR) system are implemented. The simulation and experimental results demonstrate that the proposed AS-CNN method achieves higher performance than the IFFT method in terms of image quality and Gibbs phenomenon suppression. In the case of an antenna failure and missing baseline, the AS-CNN method proposed in this paper can still obtain a BT image with high imaging quality, which shows that the robustness of the network is better than that of the IFFT method.

Sea surface temperature (SST) and wind speed (SSWS) are two significant parameters in the coupled ocean-atmosphere system. Nowadays, space-borne microwave radiometers are the main approaches to measuring global SST and SSWS with high coverage and high accuracy. For conventional processing of passive microwave data, radiometer calibration is conducted to convert the radiometer raw output voltage data into the top of atmosphere (TOA) brightness temperatures (TBs). After calibration, sea surface parameters are retrieved from TBs using statistically or physically based retrieval methods. Considering the complex procedures of instrument calibration and retrieval, a novel estimation method based on deep learning is proposed to obtain SST and SSWS directly from radiometer voltages. A comprehensive matchup dataset, including the data measured by the scanning microwave radiometer (SMR) onboard the Chinese HY-2B satellite, the European reanalysis 5 (ERA5) products and the WindSat measurements, is used to develop and test the deep learning models. Validation against ERA5 and WindSat products indicates that the deep learning models perform well. To compare with traditional methods, we also retrieve SST and SSWS from brightness temperature data of SMR using a statistical regression retrieval algorithm. The comparison suggests that the deep learning models provide results close to and sometimes better than the regression algorithm. Furthermore, the feature importance of deep learning models and the dependence of their performances on sea state are analyzed. In this paper, it is demonstrated that the deep learning method is a reliable and feasible tool for SST and SSWS estimation from the radiometer voltages with high accuracy, which can simplify the data processing procedure and improve the processing efficiency.