{"title":"Spatiotemporal analysis of ocean primary productivity in Bohai Sea estimated using improved DINEOF reconstructed MODIS data","authors":"Shuhan Jia , Linlin Bei , Yu Li , Quanhua Zhao","doi":"10.1016/j.ecoinf.2024.102920","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102920"},"PeriodicalIF":5.8000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Informatics","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S157495412400462X","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
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.
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.