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Temporal patterns of phytoplankton phenology across high latitude lakes unveiled by long-term time series of satellite data

dc.contributor.authorMaeda, Eduardo Eiji
dc.contributor.authorLisboa, Filipe
dc.contributor.authorKaikkonen, Laura
dc.contributor.authorKallio, Kari
dc.contributor.authorKoponen, Sampsa
dc.contributor.authorBrotas, Vanda
dc.contributor.authorKuikka, Sakari
dc.date.accessioned2019-06-25T10:44:31Z
dc.date.available2019-06-25T10:44:31Z
dc.date.issued2019
dc.description.abstractMonitoring temporal changes in phytoplankton dynamics in high latitude lakes is particularly timely for understanding the impacts of warming on aquatic ecosystems. In this study, we analyzed 33-years of high resolution (30 m) Landsat (LT) data for reconstructing seasonal patterns of chlorophyll a (chl a) concentration in four lakes across Finland, between 60°N and 64°N. Chl a models based on LT spectral bands were calibrated using 17-years (2000–2016) of field measurements collected across the four lakes. These models were then applied for estimating chl a using the entire LT-5 and 7 archives. Approximately 630 images, from 1984 to 2017, were analyzed for each lake. The chl a seasonal patterns were characterized using phenology metrics, and the time-series of LT-based chl a estimates were used for identifying temporal shifts in the seasonal patterns of chl a concentration. Our results showed an increase in the length of phytoplankton growth season in three of the lakes. The highest increase was observed in Lake Koylionjarvi, where the length of growth season has increased by 28 days from the baseline period of 1984–1994 to 2007–2017. The increase in the length of season was mainly attributed to an earlier start of phytoplankton blooms. We further analyzed surface temperature (Ts) and precipitation data to verify if climatic factors could explain the shifts in the seasonal patterns of chl a. We found no direct relationship between Ts and chl a seasonal patterns. Similarly, the phenological metrics of Ts, in particular length of season, did not show significant temporal trends. On the other hand, we identify potential links between changes in precipitation patterns and the increase in the phytoplankton season length. We verified a significant increase in the rainfall contribution to the total precipitation during the autumn and winter, accompanied by a decline in snowfall volumes. This could indicate an increasing runoff volume during the beginning of spring, contributing to an earlier onset of the phytoplankton blooms, although further assessments are needed to analyze historical streamflow values and nearby land cover data. Likewise, additional studies are needed to better understand why chl a patterns in some lakes seem to be more resilient than in others.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.rse.2018.12.006pt_PT
dc.identifier.issn0034-4257
dc.identifier.urihttp://hdl.handle.net/10451/38813
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectChlorophyll apt_PT
dc.subjectLandsatpt_PT
dc.subjectEutrophicationpt_PT
dc.subjectRemote sensingpt_PT
dc.subjectFinlandpt_PT
dc.titleTemporal patterns of phytoplankton phenology across high latitude lakes unveiled by long-term time series of satellite datapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage620pt_PT
oaire.citation.startPage609pt_PT
oaire.citation.titleRemote Sensing of Environmentpt_PT
oaire.citation.volume221pt_PT
person.familyNameLisboa
person.familyNameBrotas
person.givenNameFilipe
person.givenNameVanda
person.identifier418368
person.identifier.ciencia-id9919-B6D9-65C3
person.identifier.ciencia-id8E1C-3DB2-13FD
person.identifier.orcid0000-0002-9143-0692
person.identifier.orcid0000-0001-8612-4167
person.identifier.ridO-7997-2016
person.identifier.ridA-2410-2012
person.identifier.scopus-author-id57188741576
person.identifier.scopus-author-id6602078736
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationc1d38d53-ced4-4de3-af29-8842808939d7
relation.isAuthorOfPublicatione0a1924d-aefc-4c25-a932-1f7602f55b53
relation.isAuthorOfPublication.latestForDiscoveryc1d38d53-ced4-4de3-af29-8842808939d7

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