First records of Ostreopsis heptagona, O. cf. siamensis and O. cf. ovata – in the Azores archipelago, Portugal

Copyright © 2010 IOC HAB Programme.During summer 2008, surveys were carried out around São Miguel island in the Azores archipelago (36–39ºN, 25–31ºW). [...]. Species of Ostreopsis were morphologically characterized with an Olympus BX50 equipped with epifluorescence, following Penna et al. [...].Governo Regional dos Açores

A compilation of global bio-optical in situ data for ocean-colour satellite applications – version three

A global in-situ data set for validation of ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in-situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient and total suspended matter. Data were obtained from multi-project archives acquired via open internet services, or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 151,673 rows, with each row representing a unique station in space and time (cf 136,250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68,641 (cf 59,781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented, and it is also possible to analyse each set of data separately. The compiled data are available at https://doi.pangaea.de/10.1594/PANGAEA.941318 (Valente et al., 2022)

Spatial Variability and Detection Levels for Chlorophyll-a Estimates in High Latitude Lakes Using Landsat Imagery

Monitoring lakes in high-latitude areas can provide a better understanding of freshwater systems sensitivity and accrete knowledge on climate change impacts. Phytoplankton are sensitive to various conditions: warmer temperatures, earlier ice-melt and changing nutrient sources. While satellite imagery can monitor phytoplankton biomass using chlorophyll a (Chl) as a proxy over large areas, detection of Chl in small lakes is hindered by the low spatial resolution of conventional ocean color satellites. The short time-series of the newest generation of space-borne sensors (e.g., Sentinel-2) is a bottleneck for assessing long-term trends. Although previous studies have evaluated the use of high-resolution sensors for assessing lakes’ Chl, it is still unclear how the spatial and temporal variability of Chl concentration affect the performance of satellite estimates. We discuss the suitability of Landsat (LT) 30 m resolution imagery to assess lakes’ Chl concentrations under varying trophic conditions, across extensive high-latitude areas in Finland. We use in situ data obtained from field campaigns in 19 lakes and generate remote sensing estimates of Chl, taking advantage of the long-time span of the LT-5 and LT-7 archives, from 1984 to 2017. Our results show that linear models based on LT data can explain approximately 50% of the Chl interannual variability. However, we demonstrate that the accuracy of the estimates is dependent on the lake’s trophic state, with models performing in average twice as better in lakes with higher Chl concentration (>20 µg/L) in comparison with less eutrophic lakes. Finally, we demonstrate that linear models based on LT data can achieve high accuracy (R2 = 0.9; p-value < 0.05) in determining lakes’ mean Chl concentration, allowing the mapping of the trophic state of lakes across large regions. Given the long time-series and high spatial resolution, LT-based estimates of Chl provide a tool for assessing the impacts of environmental change

20-year satellite observations of phytoplankton functional types (PFTs) in the Atlantic Ocean

Phytoplankton composition structure varies in ocean biomes. Different phytoplankton groups drive differently the marine ecosystem and biogeochemical processes. Therefore, variations in phytoplankton composition influence the entire ocean environment, specifically the ocean energy transfer, the deep ocean carbon export, water quality etc. As one of the algorithms deriving phytoplankton composition from space borne data, the EOF-PFT algorithm was developed using multi-spectral satellite data collocated to an extensive global in-situ PFT data set based on HPLC pigments and sea surface temperature data (Xi et al. 2020, 2021). By using multi-sensor merged products and Sentinel-3 OLCI data, the algorithm provides global chlorophyll a (Chla) data with per-pixel uncertainty for diatoms, haptophytes, dinoflagellates, chlorophytes and prokaryotic phytoplankton spanning the period from 2002 until today, with products available on the EU Copernicus Marine Service (CMEMS). The objectives of this study are to 1) evaluate CMEMS PFT products and improve their continuity along the products derived from different satellite sensors, and 2) 20-year satellite PFT products for time series analysis of climatology, trends, anomaly and phenology of multiple PFTs in the whole Atlantic and its different biogeochemical provinces (Longhurst, 2006)

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

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.peerReviewe

Satellite monitoring of surface phytoplankton functional types in the Atlantic Ocean over 20 years (2002–2021)

An analysis of multi-satellite-derived products of four major phytoplankton functional types (PFTs – diatoms, haptophytes, prokaryotes and dinoflagellates) was carried out to investigate the PFT time series in the Atlantic Ocean between 2002 and 2021. The investigation includes the 2-decade trends, climatology, phenology and anomaly of PFTs for the whole Atlantic Ocean and its different biogeochemical provinces in the surface layer that optical satellite signals can reach. The PFT time series over the whole Atlantic region showed mostly no clear trend over the last 2 decades, except for a small decline in prokaryotes and an abrupt increase in diatoms during 2018–2019, which is mainly observed in the northern Longhurst provinces. The phenology of diatoms, haptophytes and dinoflagellates is very similar: at higher latitudes bloom maxima are reached in spring (April in the Northern Hemisphere and October in the Southern Hemisphere), in the oligotrophic regions in winter time and in the tropical regions during May to September. In general, prokaryotes show opposite annual cycles to the other three PFTs and present more spatial complexity. The PFT anomaly (in percent) of 2021 compared to the 20-year mean reveals mostly a slight decrease in diatoms and a prominent increase in haptophytes in most areas of the high latitudes. Both diatoms and prokaryotes show a mild decrease along coastlines and an increase in the gyres, while prokaryotes show a clear decrease at mid-latitudes to low latitudes and an increase on the western African coast (Canary Current Coastal Province, CNRY and Guinea Current Coastal Province, GUIN) and southwestern corner of North Atlantic Tropical Gyral Province (NATR). Dinoflagellates, as a minor contributor to the total biomass, are relatively stable in the whole Atlantic region. This study illustrated the past and current PFT state in the Atlantic Ocean and acted as the first step to promote long-term consistent PFT observations that enable time series analyses of PFT trends and interannual variability to reveal potential climate-induced changes in phytoplankton composition on multiple temporal and spatial scales

Deriving water quality parameters using sentinel-2 imagery: A case study in the Sado Estuary, Portugal

Monitoring water quality parameters and their ecological effects in transitional waters is usually performed through in situ sampling programs. These are expensive and time-consuming, and often do not represent the total area of interest. Remote sensing techniques offer enormous advantages by providing cost-effective systematic observations of a large water system. This study evaluates the potential of water quality monitoring using Sentinel-2 observations for the period 2018-2020 for the Sado estuary (Portugal), through an algorithm intercomparison exercise and time-series analysis of different water quality parameters (i.e., colored dissolved organic matter (CDOM), chlorophyll-a (Chl-a), suspended particulate matter (SPM), and turbidity). Results suggest that Sentinel-2 is useful for monitoring these parameters in a highly dynamic system, however, with challenges in retrieving accurate data for some of the variables, such as Chl-a. Spatio-temporal variability results were consistent with historical data, presenting the highest values of CDOM, Chl-a, SPM and turbidity during Spring and Summer. This work is the first study providing annual and seasonal coverage with high spatial resolution (10 m) for the Sado estuary, being a key contribution for the definition of effective monitoring programs. Moreover, the potential of remote sensing methodologies for continuous water quality monitoring in transitional systems under the scope of the European Water Framework Directive is briefly discussed.Fil: Sent, Giulia. Universidade Nova de Lisboa; PortugalFil: Biguino, Beatriz. Universidade Nova de Lisboa; PortugalFil: Favareto, Luciane. Universidade Nova de Lisboa; PortugalFil: Cruz, Joana. Universidade Nova de Lisboa; PortugalFil: Sá, Carolina. Universidade Nova de Lisboa; PortugalFil: Dogliotti, Ana Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Palma, Carla. Instituto Hidrográfico; PortugalFil: Brotas, Vanda. Universidade Nova de Lisboa; PortugalFil: Brito, Ana C.. Universidade Nova de Lisboa; Portuga

Phytoplankton functional types from multi-sensor satellite observations – towards a long-term monitoring (2002-2020)

Phytoplankton in the sunlit layer of the ocean act as the base of the marine food web fueling fisheries, and also regulate key biogeochemical processes such as exporting carbon to the deep ocean. Phytoplankton composition structure varies in ocean biomes and different phytoplankton groups drive differently the marine ecosystem. As one of the algorithms deriving phytoplankton composition from space borne data, within the framework of the EU Copernicus Marine Service (CMEMS), OLCI-PFT algorithm was developed using multi-spectral satellite data collocated to an extensive in-situ PFT data set based on HPLC pigments and sea surface temperature data (Xi et al. 2020, 2021). It provides global PFT retrievals including chlorophyll a estimations of diatoms, haptophytes, dinoflagellates, chlorophytes and prokaryotic phytoplankton spanning the period from 2002 until today, by using multi-sensor merged products and OLCI data. These PFT products with per-pixel uncertainty are publicly available on the CMEMS. Due to different lifespans and radiometric characteristics of the ocean color sensors, it is crucial to evaluate the CMEMS PFT products to provide quality-assured data for a consistent long-term monitoring of the phytoplankton community structure. In this study, using in-situ phytoplankton data (HPLC pigment data further evaluated with microscopic, flow cytometry, molecular and hyperspectral optical data) collected from expeditions since 2009 in the tropical, temperate and polar (mainly Fram Strait within the PEBCAO network) regions, we aim to 1) validate the CMEMS PFT products and investigate the continuity of the PFTs data derived from different satellites, and 2) deliver two-decade consistent PFT products for times series analysis. For the latter we determine inter-annual trends and variation of the surface phytoplankton community structure targeting some key sub-regions (e.g.,east Fram Strait) that have been observed being influenced by the changing marine environment

Uncertainty in Ocean-Color Estimates of Chlorophyll for Phytoplankton Groups

Over the past decade, techniques have been presented to derive the community structure of phytoplankton at synoptic scales using satellite ocean-color data. There is a growing demand from the ecosystem modeling community to use these products for model evaluation and data assimilation. Yet, from the perspective of an ecosystem modeler these products are of limited use unless: (i) the phytoplankton products provided by the remote-sensing community match those required by the ecosystem modelers; and (ii) information on per-pixel uncertainty is provided to evaluate data quality. Using a large dataset collected in the North Atlantic, we re-tune a method to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size [pico- (20 μm)]. The method is modified to account for the influence of sea surface temperature, also available from satellite data, on model parameters and on the partitioning of microphytoplankton into diatoms and dinoflagellates, such that the phytoplankton groups provided match those simulated in a state of the art marine ecosystem model (the European Regional Seas Ecosystem Model, ERSEM). The method is validated using another dataset, independent of the data used to parameterize the method, of more than 800 satellite and in situ match-ups. Using fuzzy-logic techniques for deriving per-pixel uncertainty, developed within the ESA Ocean Colour Climate Change Initiative (OC-CCI), the match-up dataset is used to derive the root mean square error and the bias between in situ and satellite estimates of the chlorophyll for each phytoplankton group, for 14 different optical water types (OWT). These values are then used with satellite estimates of OWTs to map uncertainty in chlorophyll on a per pixel basis for each phytoplankton group. It is envisaged these satellite products will be useful for those working on the validation of, and assimilation of data into, marine ecosystem models that simulate different phytoplankton groups.info:eu-repo/semantics/publishedVersio