摘要
基于1998年1月至2018年12月期间的SeaWIFS、Terra-MODIS、Aqua-MODIS、MERIS和VIIRS共5个传感器的叶绿素a浓度数据,设计了基于小波变换与Kalman滤波技术相结合的多源遥感数据融合技术和基于查找表法与最大值合成法相结合的融合产品研制技术,研发了月、季、年共三种尺度的全球海洋表面叶绿素a浓度融合数据集(1998–2018)。该数据集的空间分辨率为4 km×4 km,时间分辨率为月/季/年,数据格式为TIFF,由357个数据文件组成,数据量为50.1 GB(压缩为64个数据文件,压缩后数据量为19.4 GB)。将该数据产品与实测值和欧空局GSM数据产品的对比分析,结果显示该数据集2008年数据产品与实测值的拟合度为79%,而GSM与实测值的拟合度仅为35%,低于本融合数据集的数据精度。
Based on the Chlorophyll-a concentration products retrieved from SeaW IFS,Terra-MODIS,Aqua-MODIS,MERIS and VIIRS from January 1998 to December 2018.This paper combines the wavelet transform and Kalman filtering to develop the multi-source remote sensing data fusion technology,and integrates the look-up table and the maximum value composite to design the data fusion method.The algorithm completes the global sea surface chlorophyll-a concentration fusion dataset(1998-2018)at three temporal scales:monthly,seasonal and annual.The spatial resolution of the dataset is 4 km×4 km,the data format is TIFF.The dataset includes 357 data files and the data size is 50.1 GB,and the compressed dataset is 23 data files and the compressed data size is 19.4 GB.Comparisons with the measured data and GSM(Graver-Siegel-Maritorena)products from European Space Agency show that the correlation between the fusion product and the measured data is 79%in 2008,while the correlation between the GSM product and the measured data is only 35%.The results prove the advantages of this fused products.
作者
李连伟
付宇轩
薛存金
崔建勇
张源榆
徐洋峰
Li,L.W.;Fu,Y.X.;Xue,C.J.;Cui,J.Y.;Zhang,Y.Y.;Xu,Y.F.(College of Oceanography and Space Informatics,China University of Petroleum,Qingdao 266580,China;Key Laboratory of Digital Earth Science,Chinese Academy of Sciences,Beijing 100094,China;Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China)
出处
《全球变化数据学报（中英文）》
CSCD
2021年第2期219-225,219-225,共14页
Journal of Global Change Data & Discovery
基金
中国科学院战略性A类型先导专项(XDA19060103)
