Sea Surface Wind Speed Estimation From the Combination of Satellite Scatterometer and Radiometer Parameters - Xiang, Kunsheng; Zhou, Wu; Bao, Qingliu (Journal of geophysical research. Machine learning and computation, 2024)
Satellite research on global sea surface winds is crucial for understanding and monitoring the dynamics of earth's oceans, providing valuable insights into weather patterns, climate changes, and oceanographic processes. This study investigates the fusion of active (microwave scatterometer) and passive (microwave radiometer) satellite data using Machine learning (ML) for sea surface wind speed estimation. Employing Random Forest Regression (RF), Convolutional Neural Network Regression (CNN), and Multiple Linear Regression (MLR). Evaluation against reference data sets (Advanced Scatterometer, ERA5, Cross‐Calibrated Multi‐Platform (CCMP), buoy wind speeds) highlights the robustness of the proposed models. The research findings indicate that both RF and CNN exhibit superior accuracy in the active, passive, and joint active‐passive models compared to the simplistic MLR model. The joint models of the three regression methods outperform the individual active or passive models. The root mean square deviation (RMSD) accuracy of RF and CNN joint models, when compared with ASCAT, is in the order of 0.7 m/s, and when compared with buoys, the RMSD accuracy is around 1.1 m/s. The ML models, especially RF and CNN, demonstrate superior performance, providing accurate and reliable estimations crucial for meteorological and oceanographic applications. These findings underscore the potential operational use of ML techniques in enhancing remote sensing applications. Plain Language Summary As widely recognized, sea surface wind speed is a crucial oceanographic parameter, and satellite remote sensing plays a vital role in obtaining global ocean surface wind speed information. In this study, we utilized observational data from the HY2B satellite, which is equipped with both microwave scatterometer and radiometer. Leveraging a joint approach using passive and active microwave observations, we employed various ML techniques (random forest regression, convolutional neural network regression, multiple linear regression) to construct a hybrid model for sea surface wind speed retrieval. The model's accuracy was assessed using wind field data from ERA5, CCMP, buoys, and other sources. The results demonstrated that the hybrid model outperforms wind speeds obtained solely from either scatterometer or radiometer, offering a promising avenue for enhancing global sea surface wind speed accuracy. Key Points The joint active‐passive retrieval accuracy of microwave scatterometer and radiometer surpasses that of individual scatterometer or radiometer retrievals Random forest and convolutional neural network demonstrate robust performance in wind speed retrieval The findings have implications for oceanographic applications, advancing remote sensing methodologies for improved wind speed estimation