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6 results for “chl-a”
e-shape P5.6 EO based phytoplankton biomass for WFD reporting - samples of Chl-a and WFD classes maps of lakes Lauwersmeer and Saadjärv
<p>The current data set is a sample of the data generated within the e-shape pilot 5.6 called "EO based phytoplankton biomass for WFD reporting" and consists of maps based on earth observation (EO) data from the Sentinel 2 satellite. There is a set of chlorophyll-a (Chl-a) maps, and a set maps of WFD ecological status of phytoplankton biomass, derived from the Chl-a maps by applying local WFD phytoplankton biomass thresholds. These maps can complement the monitoring for the Water Framework Directive (WFD).<br> The lakes within this sample set are lakes Saadjärv (Estonia) and Lauwersmeer (the Netherlands).</p> <p>For more info please see the readme files which are provided with the data files.</p>
Ocean Color Data: Modis-aqua_chl-a (JJA, 2002-2018)
<p>Ocean Color Data: Modis-aqua_chl-a (JJA, 2002-2018) downloaded from the ADAM Platform (https://reliance.adamplatform.eu/) used furing the FORCeS eScience course 'Tools in Climate Science: Linking Observations with Modelling'.</p> <p> </p> <p>MODIS Chlorophyll-a Concentration This algorithm returns the near-surface concentration of chlorophyll-a (chlor_a) in mg m-3, calculated using an empirical relationship derived from in situ measurements of chlor_a and remote sensing reflectances (Rrs) in the blue-to-green region of the visible spectrum. The implementation is contingent on the availability three or more sensor bands spanning the 440 - 670 nm spectral regime. The algorithm is applicable to all current ocean color sensors. The chlor_a product is included as part of the standard Level-2 OC product suite and the Level-3 CHL product suite. The current implementation for the default chlorophyll algorithm (chlor_a) employs the standard OC3/OC4 (OCx) band ratio algorithm merged with the color index (CI) of Hu et al. (2012). As described in that paper, this refinement is restricted to relatively clear water, and the general impact is to reduce artifacts and biases in clear-water chlorophyll retrievals due to residual glint, stray light, atmospheric correction errors, and white or spectrally-linear bias errors in Rrs. As implemented, the algorithm diverges slightly from what was published in Hu et al. (2012) in that the transition between CI and OCx now occurs at 0.15 < CI < 0.2 mg/m3 to ensure a smooth transition.</p> <p> </p>
observed dataset and FVCOM-ERSEM modeling result of environmental factors and Chl-a concentration in CRE
<p>This data set is mainly used in the article "Formation and Breakdown of an Offshore Summer Cold-Water Zone and Its Ecological Effect on Phytoplankton". "2013-2018 data" was the dataset of obversed data in the Changjiang River Esturary (CRE) during the summer of 2013-2018. And the "cje1v_avg_2015.zip" was the result of three-dimensional physical-biogeochemical coupled model(FVCOM-ERSEM).</p>
Input data files for Dietrich et al. Chl-a and nutrient random forest modeling
<p>Input data for the models originally from:</p> <p>EPA, U. S. <em>WSIO Indicator Data Library</em>, <<a href="https://www.epa.gov/wsio/wsio-indicator-data-library">https://www.epa.gov/wsio/wsio-indicator-data-library</a>> (2023).</p> <p>Platt, L. R., Spaulding, S.A., Covert, A., Murphy, J.C., and Raynor, N. A national harmonized dataset of discrete chlorophyll from lakes and streams (2005-2022). (2023). <a href="https://doi.orghttps">https://doi.org:https://doi.org/10.5066/P9J0ZIOF</a></p> <p>Saad, D. A., Argue, D.M., Schwarz, G.E., Anning, D.W., Ator, S.W., Hoos, A.B., Preston, S.D., Robertson, D.M., and Wise, D.R., 2019. Water-quality and streamflow datasets used for estimating long-term mean daily streamflow and annual loads to be considered for use in regional streamflow, nutrient and sediment SPARROW models, United States, 1999-2014. (2019). <a href="https://doi.orghttps">https://doi.org:https://doi.org/10.5066/F7DN436B</a></p> <p> </p>
Figure An3. Distribution of pCO2 (a), Chl-a (b), and Pheo (c). in Phytoplankton assemblages under hydrochemical conditions of the Volga River Delta
Figure An3. Distribution of pCO2 (a), Chl-a (b), and Pheo (c).
Fig. 5. Chl-a in Halocarbon emissions by selected tropical seaweeds exposed to different temperatures
Fig. 5. Chl-a (μg g 1), carotenoid (μg g 1) contents and the Chl-a: carotenoid ratios (average ± standard deviation; n = 12) of the four seaweeds, G. manilaensis (GM), U. reticulata (UR), K. alvarezii (KA) and T. conoides (TC), measured after a 28 h exposure to temperatures of 40, 35, 30, 25 and 20 ◦ C. Data were analysed using a oneway ANOVA. a,b,c, indicate homogeneous groups across temperature based on Tukey's post hoc test (p <0.05).
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