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Potential Applications of Geostationary Ocean Color Imagery forPhysical-Biological Interactions

机译:地球静止海洋彩色图像在物理-生物相互作用中的潜在应用

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Since Geostationary Ocean Color Imager (GOCI) data are not available yet, we used daily MODIS Chlorophyll a (Chla) data to illustrate how GOCI data can be used for physical and biological interaction research. For physical features, we used the daily New Generation Sea Surface Temperature (NGSST) for the Yellow Sea, the South Sea, and the East Sea from January 2005 to December 2009. Since the cloud contamination in ocean color observations are always programmatic, analyzing physical and biological interactions have been limited. In order to examine whether we can use NGSST for Chla using a linear regression, we investigated their relations to obtain cloud free Chla. The results show that the ES and the SS have relatively small root mean error (RMSE) than that in the YS. In addition to time series of two different observations, we applied empirical Mode Decomposition (EMD) to extract different spatial features from both Chla and SST imagery. We selected off the west coast of the ES for a jet like feature on August 13, 2007. The Chla meandering features were different from previously reported upwelling features in the area. The features seem like to be modulated by waves, which were appeared in SST decomposition modes, i.e., Intrinsic Mode Decomposition (IMF).rnAlthough the methods were applied to MODIS observations, which are coarser spatial and temporal resolutions than those of GOCI, these methods will provide better results with GOCI observations because of better resolutions.
机译:由于地球静止海洋彩色成像仪(GOCI)数据尚不可用,我们使用每日MODIS叶绿素a(Chla)数据来说明如何将GOCI数据用于物理和生物相互作用研究。对于物理特征,我们使用2005年1月至2009年12月期间黄海,南海和东海的每日新一代海表温度(NGSST)。由于海洋颜色观测中的云污染始终是程序性的,因此需要对物理特征进行分析。生物相互作用受到限制。为了检查是否可以通过线性回归将NGSST用于Chla,我们调查了它们之间的关系以获得无云的Chla。结果表明,与YS相比,ES和SS的均方根误差(RMSE)相对较小。除了两个不同观测值的时间序列外,我们还应用了经验模式分解(EMD)从Chla和SST影像中提取不同的空间特征。我们于2007年8月13日在ES西海岸附近选择了喷射状特征。Chla的蜿蜒特征与该地区先前报道的上升特征不同。这些特征似乎受到了波的调制,并以SST分解模式(即本征模式分解(IMF))出现。尽管这些方法已应用于MODIS观测,但其时空分辨率比GOCI的粗糙,但这些方法由于分辨率更高,它将对GOCI观测提供更好的结果。

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