Multivariate DINEOF Reconstruction for Creating Long-Term Cloud-Free Chlorophyll-a Data Records From SeaWiFS and MODIS: A Case Study in Bohai and Yellow Seas, China
Multivariate DINEOF Reconstruction for Creating Long-Term Cloud-Free Chlorophyll-a Data Records From SeaWiFS and MODIS: A Case Study in Bohai and Yellow Seas, China
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用于从 SeaWiFS 和 MODIS 创建长期无云叶绿素 a 数据记录的多变量 DINEOF 重建：以中国渤海和黄海为例
DOI：
10.1109/jstars.2019.2908182
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发表时间：
2019-05
影响因子：
5.5
通讯作者：
Liu Dongyan
中科院分区：
文献类型：
--
作者：
Wang Yueqi;Gao Zhiqiang;Liu Dongyan
长期可靠的卫星叶绿素a（chl-a）数据记录对于了解海洋生物状况和量化其变化至关重要。创建长期数据记录需要将多个卫星产品组合/合并为一个数据记录，因为任何单一海洋颜色传感器的寿命都是有限的。然而，由于传感器设计、校准和检索模型的差异，在不同的传感器产品之间通常会观察到明显的跨任务偏差。为了获得一致的多传感器chl-a数据记录，观测到的跨任务偏差应在数据组合/合并方案中得到准确处理。在这项研究中，一个多变量的数据插值经验正交函数（M-DINEOF）的方法来创建长期的chl-a记录，通过应用海景宽视场传感器和中等分辨率成像光谱仪产品。在假设单传感器chl-a产品没有虚假的时间伪影并且可以作为代表chl-a实际变异性的参考时间序列的情况下，基于统计t检验和泰勒图分析定量评估来自不同chl-a系列的趋势的差异。与直接连接和线性回归方法相比，M-DINEOF方法更有效地再现了在参考数据序列重叠期间观察到的主要趋势模式。结果突出了跨任务的偏差校正的重要性，结合多传感器卫星数据记录，并建议M-DINEOF重建提供了一个简单而有效的路径，为创建可靠的多传感器海洋颜色记录适合长期趋势分析。
A long-term reliable satellite chlorophyll-a (chl-a) data record is essential in understanding the state of ocean biology and quantifying its changes. Creating a long-term data record requires a combination/merger of multiple satellite products into one data record, since the lifetime of any single ocean color sensor is finite. However, because of differences in sensor design, calibration, and retrieval models, apparent cross-mission biases are usually observed between different sensor products. To attain a coherent multisensor chl-a data record, the observed cross-mission biases should be accurately addressed in the data combination/merging schemes. In this study, a multivariable data interpolating empirical orthogonal functions (M-DINEOF) approach was used to create long-term chl-a records by applying the sea-viewing wide field-of-view sensor and moderate resolution imaging spectroradiometer products. Under the assumption that the single-sensor chl-a product is free from spurious temporal artifacts and can be reference time series representing the actual variability of chl-a, the discrepancies of trends derived from different chl-a series were quantitatively evaluated based on statistical t-test and Taylor diagram analyses. Compared with direct concatenation and linear regression methods, the M-DINEOF method more effectively reproduced the main trend patterns observed in reference data series during their overlapped periods. The results highlight the importance of a cross-mission bias correction when combining multisensor satellite data records and suggest that the M-DINEOF reconstruction provides a simple and effective path forward for creating reliable multisensor ocean color records suitable for long-term trend analysis.
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影响因子：
13.5
作者：
Caiyun Zhang;Chuanmin Hu;S. Shang;F. Muller‐Karger;Yan Li;M. Dai;Bangqin Huang;X. Ning;H. Hong
通讯作者：
Caiyun Zhang;Chuanmin Hu;S. Shang;F. Muller‐Karger;Yan Li;M. Dai;Bangqin Huang;X. Ning;H. Hong
影响因子：
2.2
作者：
J. Beckers;M. Rixen
通讯作者：
J. Beckers;M. Rixen
影响因子：
6
作者：
A. Ali;S. Amin;H. Ramadan;M. Tolba
通讯作者：
A. Ali;S. Amin;H. Ramadan;M. Tolba
DOI：
10.1109/36.701084
发表时间：
1998-07
期刊：
IEEE Trans. Geosci. Remote. Sens.
影响因子：
--
作者：
W. Gregg;R. H. Woodward
通讯作者：
W. Gregg;R. H. Woodward
影响因子：
13.5
作者：
Chuanmin Hu;Lian Feng;Z. Lee
通讯作者：
Chuanmin Hu;Lian Feng;Z. Lee