Retrieval of Sea Surface Wind Speed by One-Dimensional Synthetic Aperture Microwave Radiometer |
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| Authors: | AI Wei-hu FENG Meng-yan LU Wen MA Shuo and CHEN Guan-yu |
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| Institution: | 1. College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101 China;
2. PLA Troop 32033, Haikou 570100 China |
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| Abstract: | One-dimensional synthetic aperture microwave radiometers have higher spatial resolution and record measurements at multiple incidence angles. In this paper, we propose a multiple linear regression method to retrieve sea surface wind speed at an incidence angle between 0° ~65° . We assume that a one-dimensional synthetic aperture microwave radiometer operates at frequencies of 6.9, 10.65, 18.7, 23.8 and 36.5 GHz. Then, the microwave radiative transfer forward model is used to simulate the measured brightness temperatures. The sensitivity of the brightness temperatures at 0°~65° to the sea surface wind speed is calculated. Then, vertical polarization channels (VR), horizontal polarization channels (HR) and all channels (AR) are used to retrieve the sea surface wind speed via a multiple linear regression algorithm at 0° ~65° , and the relationship between the retrieval error and incidence angle is obtained. The results are as follows: (1) The sensitivity of the vertical polarization brightness temperature to the sea surface wind speed is smaller than that of the horizontal polarization. (2) The retrieval error increases with Gaussian noise. The retrieval error of VR first increases and then decreases with increasing incidence angle, the retrieval error of HR gradually decreases with increasing incidence angle, and the retrieval error of AR first decreases and then increases with increasing incidence angle. (3) The retrieval error of AR is the lowest and it is necessary to retrieve the sea surface wind speed at a larger incidence angle for AR. |
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| Keywords: | sea surface wind speed high spatial resolution synthetic aperture microwave radiometer multiple incidence angles multiple linear regression algorithm |
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