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

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Maeda , E E , Lisboa , F , Kaikkonen , L M , Koponen , S , Kallio , K , Brotas , V & Kuikka , O S 2019 , ' Temporal patterns of phytoplankton phenology across high latitude lakes unveiled by long-term time series of satellite data ' , Remote Sensing of Environment , vol. 221 , pp. 609-620 . https://doi.org/10.1016/j.rse.2018.12.006

Title: Temporal patterns of phytoplankton phenology across high latitude lakes unveiled by long-term time series of satellite data
Author: Maeda, Eduardo Eiji; Lisboa, Filipe; Kaikkonen, Laura Maria; Koponen, Sampsa; Kallio, Kari; Brotas, Vanda; Kuikka, Olli Sakari
Contributor organization: TreeD lab - Terrestrial Ecosystem Dynamics
Department of Geosciences and Geography
Ecosystems and Environment Research Programme
Helsinki Institute of Sustainability Science (HELSUS)
Date: 2019-02
Language: eng
Number of pages: 12
Belongs to series: Remote Sensing of Environment
ISSN: 0034-4257
DOI: https://doi.org/10.1016/j.rse.2018.12.006
URI: http://hdl.handle.net/10138/298714
Abstract: Monitoring 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 Köyliönjärvi, 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.
Subject: 1172 Environmental sciences
Chlorophyll a
Remote sensing
Peer reviewed: Yes
Rights: CC BY
Usage restriction: openAccess
Self-archived version: publishedVersion

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