利用中国南极第24次至第26次(2008年-2010年)考察获取的实测数据和AMSR-E辐射计亮温资料开展南大洋实时海面气温的反演研究, 分析了AMSR-E的各通道亮温与海面气温的相关性, 未发现与海面气温相关性较强的观测通道, 相关性最高的是23.8 GHz 水平通道, 相关系数为0.38。将实测数据与亮温资料进行数据匹配, 得到有效的建模数据集, 然后利用多元回归和神经网络两种方法建立海面气温实时反演模型。基于全通道多元回归建立了高纬、低纬海域AMSR-E亮温的反演模型, 对反演结果利用实测数据进行验证, 高纬海域反演的结果均方根差为0.96 ℃, 相关系数为0.93;低纬海域反演结果均差差为1.29 ℃, 相关性系数0.96。基于Back Propagation(BP)神经网络反演模型的反演结果均方根差为1.26 ℃, 相关系数为0.98。

The AMSR-E satellite data and in-situ data were applied to retrieve sea surface air temperature (Ta) over the Southern Ocean. The in-situ data were obtained from the 24^th-26^th Chinese Antarctic Expeditions during 2008-2010. First, Ta was used to analyze the relativity with the bright temperature (Tb) from the twelve channels of AMSR-E, and no high relativity was found between Ta and Tb from any of the channels. The highest relativity was 0.38 (with 23.8 GHz). The dataset for the modeling was obtained by using in-situ data to match up with Tb, and two methods were applied to build the retrieval model. In multi-parameters regression method, the Tbs from 12 channels were used to the model and the region was divided into two parts according to the latitude of 50°S. The retrieval results were compared with the in-situ data. The Root Mean Square Error (RMS) and relativity of high latitude zone were 0.96℃and 0.93, respectively. And those of low latitude zone were 1.29 ℃ and 0.96, respectively. Artificial neural network (ANN) method was applied to retrieve Ta．The RMS and relativity were 1.26 ℃ and 0.98, respectively. The air-sea interaction over the Southern Ocean is so strong and unstable that it increases the retrieval difficulty and affects the accuracy of the results.