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61 results for “chlorophyll-a”
Chlorophyll-a data for the Green Lake 4 buoy, 2018 - ongoing.
High-resolution water quality data are fundamental to observing rapid ecological responses to meteorology, climate, and other disturbance events. Here we describe the deployment of a single buoy line with multiple sensors at fixed-depths from a subsurface float in the water-column of Green Lake 4 (GL4). Sensors on the buoy collect data in both summer and winter, thereby providing valuable insights into lake characteristics beyond our standard sampling period, including key transitional periods such as ice formation and ice break-up.
Discrete water temperature, flow, solar radiation, chlorophyll-a and inundation, Sacramento-San Joaquin Delta, CA, 1999-2019
The objective of our study is to better understand the factors affecting chlorophyll-a production within a floodplain and its transport downstream to determine how lateral connectivity influences longitudinal connectivity. The Yolo Bypass is an engineered floodplain of the Sacramento River that inundates during periods of high outflow via overtopping weirs. Water traveling through the Yolo Bypass flows parallel to the Sacramento River and re-connects to the mainstem at the southern extent of the floodplain. Several monitoring programs in the Sacramento San-Joaquin Delta and Yolo Bypass collect discrete and continuous water quality data, including chlorophyll measurements. For this study, we synthesized available flow, water temperature, chlorophyll and inundation data between March 1999 to December 2019 and modeled the effects of environmental variables and inundation on chlorophyll-a production in the floodplain, the mainstem, and downstream of the floodplain/mainstem.
The Hubbard Brook Stream Ecology Record: Algal Biomass (Chlorophyll-a), 2018 - ongoing
The Hubbard Brook Stream Ecology record is a companion dataset to the Hubbard Brook Watershed Stream and Precipitation Chemistry record. The Stream Ecology record started in 2018 and HBWatER collects ecological samples from seven gauged watersheds: Watersheds 1 through 6 and Watershed 9. HBWatER measures algal biomass, aquatic invertebrate emergence, and stream decomposition by measuring (1) chlorophyll-a on tiles and artificial moss, which approximate algal biomass growth on bare rock and bryophyte mats, (2) preserved algal biomass on artificial moss substrates in Lugol’s Iodine solution, (3) aquatic invertebrate emergence on replicate sticky traps placed above the stream, and (4) stream decomposition through leaf litter pack and cotton strip decay. To complement these ecological records, HBWaTER installed light sensors and field cameras to obtain better information about the light and stream environment daily. Three replicate light sensors that take sub-daily measurements of light level intensity are placed at each watershed at the weir pond (full-sun), and two under the canopy (partial shade). Field cameras take daily photos at noon of the stream canopy and the stream channel. While many studies at Hubbard Brook have measured algal biomass, aquatic invertebrates, and stream decomposition, they are scattered in locations across the valley, were performed at non-continuous times, and use various semi-comparable methods. The HBWatER Stream Ecology record was created to address this gap and systematically measure any long-term changes in the organisms living in the stream. The collection of HBWatER samples is currently sustained by Tammy Wooster (Cary IES) and analyses of these samples has been performed by Heather Malcom (Cary IES), Audrey Thellman (Duke), Steve Anderson (Duke), Geoff Wilson (Cary IES), and Adam Rok (Duke). The dataset is curated and maintained by a team of researchers: Chris Solomon (Cary IES), Emma Rosi (Cary IES), and Emily Bernhardt (Duke). Curr
Enrichment index related to seamounts and islands in the South West Indian Ocean from chlorophyll-a satellite remote sensing data
<p>This data set is the result of the calculation of an original “enrichment index” (EI) from chlorophyll-a (chl-a) remote sensing data (MODIS-Aqua sensor) and initially dedicated to highlight localized chl-a enrichments associated to isolated seamounts and islands in the South West Indian Ocean, in order to estimate their contribution in increasing the local primary productivity. Details and results are described in the DSR-II paper entitled “Satellite observations of phytoplankton enrichments around seamounts in the South West Indian Ocean with a special focus on the Walters Shoal” from Demarcq et al. 2020.<br> 1. Initial data used<br> We used daily L3 data chl-a and sea surface temperature (SST) collected by the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor on board the Aqua platform (downloaded from https://oceancolor.gsfc.nasa.gov/) from January 2003 to December 2018. This has a spatial resolution of 1/24° (ca. 4.5–5 km). The data covers the region (45°S – 10°S / 25°W – 80°W).<br> 2. The calculation method<br> The calculations were done at the pixel level. The EI is the difference (expressed in %) between the value of each ‘candidate pixel’ and its medium range surrounding, defined as the average value of all chl-a values around the candidate pixel between a fix range of distance between 30 and 90 km, the R1 and R2 terms of the equation enclosed.<br> 3. Data sets<br> The data set contains two files:<br> - the monthly climatology (12 frames) of the EI from January to December (2003 to 2018 average), in an internally compressed netCDF-4 format (NC-compliant or almost)<br> - the yearly average of the EI (period 01/2003 - 12/2018)<br> <br> Two images are joined with this data set:<br> - a "technical view" of the yearly average of the index for the full region sub-region (45°S – 10°S / 25°W – 80°W)<br> (file: indsw4_modis_p100_4km_16y_20030101_20181231.R2018.0.enrichment-index.dist-30-90km.png).</p> <p> - a slightly improved view of the yearly average of the index for the sub-region (40°S – 10°S / 30°W – 70°W).<br> (file: Figure-enrichment-index.pdf)<br> <br> An improved version of this index will be available in a near future.</p>
OCNET global daily Chlorophyll-a products
<p>We constructed the Ocean Chl-a Reconstruction Neural Ensemble Network (OCNET) model to generate global daily chlorophyll-a (Chl-a) concentration data. The model utilized the climatological data from the Ocean Color Climate Change Initiative (OCCCI) version 6 as the background field and the NOAA MSL12 spatiotemporally continuous daily-scale data as the target dataset. Sea surface temperature (SST), salinity (SAL), photosynthetically available radiation (PAR), and sea surface pressure (SSP) were selected as the primary environmental factors influencing phytoplankton growth and distribution for data reconstruction. Ultimately, we developed a daily-scale global ocean surface Chl-a concentration dataset for the period 2001–2023, with a spatial resolution of 0.25°. This dataset is spatiotemporally continuous, spans a long time period, and shows high consistency with satellite data products in regions with available data. It also addresses the issue of severe data gaps in satellite daily-scale products, providing critical information for long-term and large-scale studies of marine phytoplankton. Compared with traditional interpolation methods, the OCNET model fully leverages the environmental information provided by ERA5 reanalysis data and MODIS satellite data to reconstruct Chl-a concentration data. Moreover, the model is not limited by the size of the marine area or the temporal coverage of the original Chl-a concentration dataset. Provided that reliable environmental variable data are available, the model can serve as an important reference for reconstructing historical Chl-a data and predicting future changes in Chl-a concentration.</p> <p>The latitude and longitude of the left bottom corner, the number of rows and columns, and grid cells information are all included in each netCDF file. In addition to the Chl-a data generated by the OCNET model, we also provide the OCNET model algorithm (based on Matlab version 2022b), example datasets, and the detection results of Marine High Chlorophyll-a (Chl-a) Events. </p>
HYPSTAR hyperspectral water reflectance and derived water quality products (suspended particulate matter and chlorophyll-a concentration) at the Blankaart surface water reservoir (BE)
<p><strong>Hyperspectral Water Leaving Reflectance spectra (2988)</strong> measured between 2021-02-03 and 2022-08-03 at the Blankaart Surface Water Reservoir (Belgium, 50.98857N, 2.835213E) with the <strong>HYPSTAR®</strong> (ID: HYPSTAR_12120241). Detailed description of the data collection, processing and analysis can be found in <strong>Goyens et al.- Remote Sens. 2022</strong> - 14(21)- 5607; https://doi.org/10.3390/rs14215607.</p> <ol> <li>HYPSTAR_W_BSBE_L2A_REFL_20210203_20220803_v1.csv</li> </ol> <p><strong>Chlorophyll-a (Chl-a) concentration and Suspended Particulate Matter (SPM) </strong>were derived from the above dataset of hyperspectral water reflectance and estimated according to different algorithms found in the litterature, i.e.,</p> <ol> <li>HYPSTAR_W_BSBE_CHLA_SIMIS_20210203_20220803_v1.csv<strong>:</strong> <strong>Chlorophyll-a concentration</strong> estimated from the <strong>HYPSTAR®</strong> reflectance measurements and following the algorithm suggested by Simis et al. (2005; https://doi.org/10.4319/lo.2005.50.1.0237) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_CHLA_CRAT_20210203_20220803_v1.csv: <strong>Chlorophyll-a concentration</strong> estimated with the <strong>HYPSTAR®</strong> reflectance measurements and following the algorithm suggested by Ruddick et al. (2001; https://doi.org/10.1364/AO.40.003575.) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_SPM_20210203_20220803_v1.csv: <strong>Suspended particulate matter </strong>estimated with the <strong>HYPSTAR®</strong> reflectance measurements and following the algorithm suggested by Nechad et al. (2010) at 700 nm</li> </ol>
Dissolved oxygen, temperature, chlorophyll-a, total phosphorus, total nitrogen, and dissolved organic carbon at multiple depths in 822 lakes from 1921-2022
Rapid changes in climate and land use are having substantial and interacting impacts on lake water quality around the world. Here, we synthesized time-series data for dissolved oxygen, temperature, chlorophyll-a, total phosphorus, total nitrogen, and dissolved organic carbon at multiple depths in 822 lakes to facilitate analyses of these changes. The dataset extends from 1921–2022, with a median data duration of 29 years (range 5-102) and a median of 5 unique sampling dates per year at each lake. Lakes in the dataset have a median depth of 12.5 m (range 1.5–480 m), median surface area of 85.4 ha (range: 0.5–237000 ha) and median elevation of 264 m (range: -215–2804). The lakes are located in 18 countries across 5 continents, with latitudes ranging from -42.6 to 68.3. To facilitate interoperability with other large-scale datasets, each lake is linked to a unique hydroLAKES lake ID when possible (n = 683).
Chlorophyll-a concentrations in discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (1993-2025, ongoing)
An important part of the McMurdo Long Term Ecological Research (LTER) project is monitoring of spatial and temporal patterns, and processes that control primary production in perennial ice-covered lakes. This data package addresses this core area of research by quantifying chlorophyll-a concentrations within specific depths along the water column of several lakes located across the McMurdo Dry Valleys region of Antarctica.
Lemming Mesocosm (Denmark): in-situ fluorescence chlorophyll-a calibration, underlying data
<p>These data files include 2 years (2018-2020) of high-frequency in-situ chlorophyll-a and phycocyanin fluorescence sensor (Turner Designs, Cyclops 7F) data, together with data from various tests conducted with these sensors. Morever, in-vitro chlorophyll-a data for 2018-2020 period is also included.</p> <p>These data are collected in Lemming mesocosm site, Denmark, where there are 24 tanks with 2 nutrient and 3 temperature treatments (2x3 factorial design). There is data from 24 in-situ chlorophyll-a and 12 in-situ phycocyanin fluorescence sensors.</p> <p>In this Dataset folder, there are:</p> <ul> <li>one <strong>Metadata </strong>(*.xlsx)<strong> </strong>file with 3 sheets; <ul> <li>"<em>Descriptive</em>": comprises information on location, authors, study period and the main instrument used, </li> <li>"<em>Structural</em>": includes detailed information on each dataset.</li> <li>"<em>Relational</em>": includes a figure showing the relations between the datasets</li> </ul> </li> <li>thirteen files (*.csv) in LemCP_DataORE that were used to; <ul> <li>conduct in-situ fluorescence sensor tests (i.e. blank variation, linearity check, DOC effect check), </li> <li>calibrate 24 in-situ fluorescence chlorophyll-a sensors</li> <li>plot various figures</li> </ul> </li> </ul>
Lemming Mesocosm (Denmark): in-situ fluorescence chlorophyll-a calibration, extended data
<p>These files include a scheme showing the sensor and tank (mesocosm) system, plots from high frequency in-situ chlorophyll-a and phycocyanin fluorescence data and in-vitro chlorophyll-a data collected in Lemming mesocosm site, Denmark, where there are 24 tanks with 2 nutrient and 3 temperature treatments (2x3 factorial design). These plots are based on 24 in-situ chlorophyll-a and 12 in-situ phycocyanin fluorescence sensors from 24 tanks/mesocosms. There is also a table showing the steps for cleaning the high-frequency data.</p>
Satellite monthly surface chlorophyll-a concentration, particulate backscattering, Secchi Disk depth, Mixed Layer Depth, Sea Surface Temperature at 25 km resolution optimally interpolated for the North Atlantic Ocean (1998-2018)
<p>Satellite monthly records of surface chlorophyll-a concentration (CHL), particulate backscattering at 443nm (bbp), Secchi Disk depth (zsd), Mixed Layer Depth (MLD), Sea Surface Temperature (SST) at 25 km resolution optimally interpolated via Multivariate Singular Spectrum Analysis (MSSA) for the North Atlantic Ocean for the period 1998-2018. This dataset has been used for the article "Ultra-oligotrophic waters expansion in the North Atlantic Subtropical Gyre revealed by 21 years of satellite observations" Leonelli et al. 2022, where details of interpolation method are fully explained.</p>
Satellite-derived chlorophyll-a concentrations for Lake Harsha (USA) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of Lake Harsha (USA) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery.</p> <p>Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following:</p> <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. <em>Remote Sensing of Environment, 270</em>, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. <em>Frontiers in Remote Sensing, 1</em></li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. <em>Remote Sensing of Environment, 240</em>, 111604</li> </ul>
A new merged dataset of global ocean chlorophyll-a concentration for better trend detection
<p>Chlorophyll-a concentration (Chla) is recognized as an essential climate variable and is one of the primary parameters of ocean-color satellite products. Ocean-color missions have accumulated continuous Chla data for over two decades since the launch of SeaWiFS in 1997. However, the on-orbit life of a single mission is about five to ten years. To build a dataset with a time span long enough to serve as a climate data record (CDR), it is necessary to merge the Chla data from multiple sensors. The European Space Agency has developed two sets of merged Chla products, namely GlobColour and OC-CCI, which have been widely used. Nonetheless, issues remain in the long-term trend analysis of these two datasets because the intermission differences in Chla have not been completely corrected. To obtain more accurate Chla trends in the global and various oceans, we produced a new dataset by merging Chla records from the Sea-viewing Wide Field-of-view Sensor, Medium-spectral Resolution Imaging Spectrometer, Moderate Resolution Imaging Spectroradiometer, Visible Infrared Imaging Radiometer Suite, and Ocean and Land Colour Instrument with intermission differences corrected in this work. The fitness of the dataset as a CDR was validated by using in situ Chla and comparing the trend estimates to the multi-annual variability of different satellite Chla records. </p>
OLIGOTREND, a global database of multi-decadal timeseries of chlorophyll-a and nutrient concentrations in inland and transitional waters, 1986-2023
The Oligotrend database is a collection of multi-decadal chlorophyll-a and nutrient timeseries in inland and transitional waters. The objective of this Data Package was to explore how inland and transitional aquatic ecosystems respond to oligotrophication trends. Overall, the Oligotrend L1 database is made of 4.3 million valid observations originating from 1,894 stations. There are 238, 687 and 969 stations located in estuaries, lakes and rivers, respectively. The top 3 largest sources of data are the French national water quality monitoring (775 stations), the global database of lake datasets from Naderian et al. 2024 (378 stations), and the Chesapeake Bay Program (199 stations). The data was harmonized through a reproducible data processing pathway. In this Data Package, quality-checked level L1 data is provided, together with data sources, geographical coordinates of the stations, and the output of a trend analysis of all timeseries (level L2).
Satellite derived chlorophyll-a of the Ohio and Illinois Rivers (CHOIR), 1984-2022
Chlorophyll-a is a vital water quality parameter used to quantify concentrations of algal biomass in freshwater systems. However, insufficient field data in the Ohio River Basin has resulted in limited understanding of the development of algal blooms. We built a 38-year (1984 – 2022) dataset of satellite derived chlorophyll-a predictions to support research efforts which aim to quantify the frequency and intensity of river algal blooms. We developed our model by leveraging coinciding in situ chlorophyll-a data and surface reflectance extracted from Landsat Collection 2 Tier 1, referred to as matchups. Matchups were used to train and test our machine learning model. We also extracted Landsat surface reflectance over 6,116 NHD river reaches using similar methods. We then applied our model to this reach-level data to create a comprehensive dataset of chlorophyll-a predictions. This dataset includes the following files: 1) data used to train and test the model (matchups), 2) the model infrastructure, 3) satellite derived chlorophyll-a predictions aggregated over NHD river reaches, and 4) a shapefile of NHD river reaches.
Seasonality of in-lake and meteorological data from seven lakes, including daily measurements of water temperature, chlorophyll-a, dissolved oxygen, ice cover, air temperature, and solar radiation
This data product supports the manuscript "Seasons and seasonality in lakes: a synthesis amid global change" (Lewis et al. 2026; in review). Data were analyzed to understand how seasonality varies among diverse lakes and variables. Specifically, this data publication includes daily mean water temperature, chlorophyll-a, and dissolved oxygen at multiple depths, ice cover (binary), air temperature and solar radiation. Data availability and collection methods differ among lakes, as described in the Methods.
Station data of passive and active fluorescence measurements of chlorophyll-a (Chl), phycoerythrin (PE), chromophoric dissolved organic matter (CDOM), and variable fluorescence (Fv/Fm) from CCE-CalCOFI Augmented cruises in the California Current System, 2012 - October 2020.
Active and passive fluorescence measurements are made using the ALFA5 system (Chekalyuk and Hafez, 2013) on water from the ship’s underway system (these are not samples from bottles!) during CalCOFI cruises while on station. The instrument uses excitation at 405 and 510 nm to measures passively the fluorescence of chlorophyll-a (Chl), three different phycoerythrins (PE1, PE2 and PE3) and chromophoric dissolved organic matter (CDOM). Variable fluorescence (Fv/Fm) is measured actively using pump-during-probe (PDP) measurements of Chl a fluorescence induction. Fluorescence measurements are normalized to the water’s Raman fluorescence. ALF data are merged with CTD and bottle data that were collected by the CalCOFI group.
Continuous passive and active fluorescence measurements of chlorophyll-a (Chl), phycoerythrin (PE), chromophoric dissolved organic matter (CDOM), and variable fluorescence (Fv/Fm) from CCE-CalCOFI Augmented cruises in the California Current System, 2012 - 2020
Active and passive fluorescence measurements are made using the ALFA5 system (Chekalyuk and Hafez, 2013) on water from the ship’s underway system during CalCOFI cruises. The instrument uses excitation at 405 and 510 nm to measures passively the fluorescence of chlorophyll-a (Chl), three different phycoerythrins (PE1, PE2 and PE3) and chromophoric dissolved organic matter (CDOM). Variable fluorescence (Fv/Fm) is measured actively using pump-during-probe (PDP) measurements of Chl a fluorescence induction. Fluorescence measurements are normalized to the water’s Raman fluorescence.
Continuous passive and active fluorescence measurements of chlorophyll-a (Chl), phycoerythrin (PE), chromophoric dissolved organic matter (CDOM), and variable fluorescence (Fv/Fm) from CCE process cruises in the California Current System, 2012 - 2019 (ongoing).
Active and passive fluorescence measurements are made using the ALFA5 system (Chekalyuk and Hafez, 2013) on water from the ship’s underway system during CCE process cruises. The instrument uses excitation at 405 and 510 nm to measures passively the fluorescence of chlorophyll-a (Chl), three different phycoerythrins (PE1, PE2 and PE3) and chromophoric dissolved organic matter (CDOM). Variable fluorescence (Fv/Fm) is measured actively using pump-during-probe (PDP) measurements of Chl a fluorescence induction. Fluorescence measurements are normalized to the water’s Raman fluorescence.
Dataset of processed Sentinel-2 images for chlorophyll-a estimation in high-altitude lakes in the Sierra Nevada, Spain
<p>This dataset contains Sentinel 2 satellite images clipped to 5 high-altitude lakes in the Sierra Nevada Mountain Range, Spain. The images were processed with the following atmospheric correction algorithms:</p><ul><li><a href="https://c2rcc.org/">C2RCC</a> (<a href="https://ui.adsabs.harvard.edu/abs/2016ESASP.740E..54B/abstract">Brockmann et al. 2016</a>)</li><li><a href="https://github.com/MarcYin/SIAC">SIAC</a> (<a href=" https://doi.org/10.5194/gmd-15-7933-2022">Yin et al. 2022)</a></li><li><a href="https://github.com/acolite/acolite/releases/tag/20221114.0">ACOLITE</a> (<a href="https://doi.org/10.1016/j.rse.2018.07.015">Vanhellemont & Ruddick, 2018</a>)</li><li><a href="https://grass.osgeo.org/grass83/manuals/i.atcorr.html">6SV</a> (<a href="https://doi.org/10.1109/36.581987">Vermote et al. 2006</a>)</li></ul><p><strong>Included Lakes and and their IDs:</strong></p><ul><li>Laguna de la Caldera (ID = P-2)</li><li>Laguna-embalse de las Yeguas (ID = D-6)</li><li>Laguna de Río Seco (ID = P-8)</li><li>Laguna Larga (ID = G-7)</li><li>Laguna de la Mosca (ID = G-11)</li></ul>
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