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由于天气等各种因素,卫星遥感叶绿素数据中的大面积无规律缺失问题一直是遥感数据领域的研究热点,阻碍了卫星数据的应用。因此,卫星遥感数据的重构和再分析成为一个重要课题,在关注海域获得时空连续的完整数据对于扩展遥感数据的应用范围,提高其数据利用效率有着重要意义。针对这一系列问题,基于对东中国海叶绿素时空多尺度(包括天气过程时间尺度)变化机制研究的需要,结合多变量DINEOF方法和最优插值等数学方法的优点,成功构建和发展了多尺度最优插值、二次订正的多变量DINEOF方法,简称DINEOF-OI方法。对于目标缺测数据点重构过程中,如何有效分配时间序列上与空间场中的观测数据对重构数据的影响权重,取决于研究的具体目标问题,是研究的重要思路创新。基于这一方法对东中国海近10a的卫星遥感叶绿素数据成功进行了重构试验,并较成功地刻画了东中国海海表面叶绿素的包括天气尺度在内的多尺度变化特征。
Due to various factors such as the weather, the problem of large area irregularity missing in satellite remote sensing chlorophyll data has been a hot research topic in the field of remote sensing data and hindered the application of satellite data. Therefore, the reconstruction and reanalysis of satellite remote sensing data has become an important issue. It is of great significance to extend the application range of remote sensing data and improve its data utilization efficiency in obtaining the complete spatiotemporal data in the sea of interest. Aiming at these problems, based on the need of studying the temporal and spatial variation of chlorophyll in the East China Sea, including the time scale of weather process, combined with the advantages of multivariate mathematical methods such as DINEOF method and optimal interpolation, we successfully construct and develop multi-scale The optimal interpolation, the second revision of the multivariate DINEOF method, referred to as DINEOF-OI method. How to effectively allocate the influence weight of the observed data on the time-series and the reconstructed data in the spatial field depends on the specific target of the study, which is an important idea innovation of the research. Based on this method, the satellite remote sensing chlorophyll data in the East China Sea near 10 years have been successfully reconstructed, and the multi-scale changes of surface chlorophyll in the East China Sea, including the weather scale, have been successfully described.