摘要
海洋一号 C 卫星(HY-1C)是服务于海洋水色水温、海岸带和海洋灾害与环境监测的业务化应用卫星,是海洋遥感数据的重要来源,对中国HY-1C卫星红外辐射计在东南亚海域的SST观测数据开展评估与校正工作具有重要意义.本研究基于多源卫星SST数据和ARGO浮标测量温度数据,对中国HY-1C卫星在东南亚海域的SST数据进行了质量评估和改进工作.HY-1C SST数据的质量评估结果表明:HY-1C白天的平均偏差、标准差和均方根误差分别为-0.73℃、1.38℃和 1.56℃,夜晚数据偏差分别为-0.95℃、1.57℃和1.83℃.与其他同类红外辐射计的数据质量对比发现HY-1C SST数据精度低于其他红外辐射计.采用月平均差值校正、SST分区域差值校正、SST分段校正 3种校正方法对HY-1C SST数据质量进行改进,其中HY-1C SST分区域校正的质量提升最明显,标准差、均方根误差接近 1℃,夜晚均方根误差比校正前降低了约 0.8℃,白天和晚上均方根误差分别降低了 32.52%和 42.04%.
Abstract
The Ocean-1C satellite(HY-1C)is an operational application satellite serving the monitoring of ocean water color and temperature,coastal zones,and marine disasters and environment.It is an important source of ocean remote sensing data,which is of great significance for the assessment and correction of the SST observation data of Chinese HY-1C infrared radiometer in Southeast Asian waters.In this study,the quality assessment and improvement work of Chinese HY-1C satellite SST data in the Southeast Asian sea area is carried out based on multi-source satellite SST data and ARGO buoy measurement temperature data.The quality assessment results of the HY-1C SST data show that the mean bias,standard deviation and root mean square error of HY-1C during daytime are-0.73℃ ,1.38℃ and 1.56℃ ,respectively,and the nighttime data deviations are-0.95℃ ,1.57℃ and 1.83℃ ,respectively.Comparison of the data quality with other similar infrared radiometers reveals that the accuracy of the HY-1C SST data is lower than that of other infrared radiometers.Three correction methods,namely,monthly mean difference correction,SST subarea difference correction,and SST segmentation correction,are used to improve the data quality of HY-1C SST,among which the quality improvement of HY-1C SST subarea correction is the most obvious,with the standard deviation and the root mean square error close to 1℃ ,and the nighttime root mean square error reduced by about 0.8℃ compared with that before the correction,and the daytime and nighttime root mean square errors reduced by respectively 32.52%,and 42.04%.
国家科技期刊平台
NSTL
万方数据