Spatiotemporal analysis of ocean primary productivity in Bohai Sea estimated using improved DINEOF reconstructed MODIS data

IF 5.8 2区 环境科学与生态学 Q1 ECOLOGY
Shuhan Jia , Linlin Bei , Yu Li , Quanhua Zhao
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引用次数: 0

Abstract

In this study, a novel multiple spatiotemporal data interpolating empirical orthogonal function (MS-DINEOF) method was employed to solve the problem of missing remote sensing data in the estimation of ocean primary productivity (OPP). The scheme was integrated with a vertically generalized productivity model (VGPM) for estimating OPP. First, a new time-scale feature was defined for effectively preserving spatiotemporal characteristics during the reconstruction of missing remote sensing data. The proposed algorithm, which integrates MS-DINEOF for reconstructing sea surface temperature, chlorophyll-a concentration, photosynthetically active radiation, and diffuse attenuation coefficient at 490 nm data, with VGPM for OPP estimation, was implemented for the Bohai Sea from 2010 to 2021. The main results are as follows: (1) The root mean square error values of the reconstructed data were all less than 0.1, and the absolute error values of the estimated OPP were even smaller. The quality of the reconstructed data using the MS-DINEOF algorithm was high, both for overall and local data. (2) The OPP in the Bohai Sea exhibited obvious seasonal fluctuations. (3) The spatial distribution of OPP exhibited regional characteristics over time. Specifically, OPP in the Bohai Sea showed a decreasing trend from the coastal sea to the distant sea during the periods 2010–2014, 2015–2019, and 2020–2021. The OPPs were higher in the coastal areas than in Bohai Bay and Laizhou Bay and gradually decreased from the coastal sea to the distant sea in July and August during 2015–2019.
基于改进DINEOF重构MODIS数据估算渤海海洋初级生产力的时空分析
为了解决海洋初级生产力估算中遥感数据缺失的问题,提出了一种新的多时空数据插值经验正交函数(MS-DINEOF)方法。该方法结合垂直广义生产力模型(VGPM)估算OPP,首先定义一个新的时间尺度特征,在缺失遥感数据重建过程中有效保留时空特征;该算法将MS-DINEOF和VGPM相结合,用于重建490 nm海面温度、叶绿素-a浓度、光合有效辐射和扩散衰减系数,并在2010 - 2021年对渤海海域进行了OPP估算。主要结果如下:(1)重建数据的均方根误差值均小于0.1,估计OPP的绝对误差值更小。采用MS-DINEOF算法重建的数据质量较高,无论是整体数据还是局部数据。(2)渤海OPP具有明显的季节波动特征。(3) OPP的空间分布呈现区域特征。2010-2014年、2015-2019年和2020-2021年,渤海OPP呈由近海向远海递减的趋势。2015-2019年7月和8月,沿海地区的opp高于渤海湾和莱州湾,由近岸向远海逐渐降低。
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来源期刊
Ecological Informatics
Ecological Informatics 环境科学-生态学
CiteScore
8.30
自引率
11.80%
发文量
346
审稿时长
46 days
期刊介绍: The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.
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