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61 results for “chlorophyll-a”
Harmonized Chlorophyll-a dataset from Landsat-8/9 OLI and Sentinel 2 MSI in lakes of the Yunnan-guizhou Plateau, China
<p>We generated a harmonized Chl-a dataset for the lakes in the Yunnan–Guizhou Plateau in China from 2013 to 2022 by the Landsat 8/9 and Sentinel-2A/B virtual constellation. Here, we shared the mean chlorophyll-a in nine major lakes in studied area. </p><p>These dataset were aggregated from MSI- and OLI-derived Chl-a images and were subseted using the boundary of each lake. Data format is Geo-Tiff (*.tif) and zero values are INVALID values. More details on Chl-a retrievals can be found in: </p><p>Z. Cao et al., "Harmonized Chlorophyll-a Retrievals in Inland Lakes From Landsat-8/9 and Sentinel 2A/B Virtual Constellation Through Machine Learning," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022, Art no. 4209916, doi: 10.1109/TGRS.2022.3207345.</p>
Fortnight Climatology Average of Corrected Chlorophyll-a Concentration from MODIS Level-2 Data in the Algerian Basin (2003-2018)
<p>We deposited the corrected MODIS Level-2 Chlorophyll data after a specific processing steps applied for the first time to the standard cloud-corrected Level-2 Chlorophyll fields to detect and remove spurious local patterns that affect data quality, even in time series averages.</p> <p>From a purely spatial point of view, the quality of the Chlorophyll field is improved more significantly by the outlier removal than the previous synthetic representation suggests.</p>
Satellite-derived chlorophyll-a concentrations for Lake Hume (Australia) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of Lake Hume (Australia) 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>
Satellite-derived chlorophyll-a concentrations for Western Water Treatment Plant (Melbourne, Australia) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of the Western Water Treatment Plant (Melbourne, Australia) 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>
Fig. 2 in Chlorophyll-a and Suspended Inorganic Material Affecting the Shell Traits of Testate Amoebae Community
Fig. 2. Ordination results of the non-metric multidimensional scaling to combination between environments and testate amoeba species. Symbols represent the environments with lower (EL), intermediate (EI), and higher (EH) concentration of inorganic suspended material. Species: Cyp – Cyphoderia cf. ampulla, Eu – Euglypha acanthophora, Lm – Lesquereusia mimetica, Lt – L. modesta, Lu – L. modesta var. caudata, Lo – L. ovalis, Ls – L. spiralis, Lh – L. spiralis var. hirsuta, Lc – L. spiralis var. caudata, No – Netzelia oviformis, Nt – N. tuberculata, Nw – N. wailesi, He – Heleopera petricola, Ne – Nebela penardiana, Phr – Phryganella hemisphaerica, Ho – Hoogenraadia criptostoma, Pl – Plagiopyxis callida, Cyc – Cyclopyxis kahli, Tr – Trinema lineare. Arcellidae species (As), Centropyxidae species (Cs), and Difflugiidae species (Ds) were grouped to more easily identify the relationships between species and environments.
Figure 6. The Chlorophyll-a in The impact of wildfires on the Lake Skadar/Shkodra environment
Figure 6. The Chlorophyll-a concentration (Chla, mg/m3) in Lake Šas on (a) 11 September 2020, 09:40 GMT, MSI Sentinel-2B; (b) 13 September 2020, 09:30 GMT, MSI Sentinel-2A; (c) 21 September 2020, 09:40 GMT, MSI Sentinel- 2B; (d) 8 October 2020, 09:30 GMT, MSI Sentinel-2B. The River Bojana/Buna passes in the southeastern corner of the frame.
AMT29 optical underway properties and estimates of chlorophyll-a concentration
<p>Dataset of inherent optical properties determined as described in https://doi.org/10.5194/essd-2024-267 and https://doi.org/10.1364/OE.25.0A1079</p>
Extreme values and exceedances of chlorophyll-a calculated from the OBGP MODIS AQUA v2018 dataset's daily files in European seas between 2003 and 2021.
<p>This dataset includes all the files generated to extract the extreme values of chlorophyll-a in European Seas between 2003 and 2021.</p> <p>The thresholds used are the overall's period and the monthly 90th percentiles.</p> <p>These files support a publication recently submitted.</p> <p> </p>
VIIRS-derived annual mean chlorophyll-a in China large lakes from 2012 to 2021
<p>This dataset included annual mean Chl-a in China's large lakes (>50 km<sup>2</sup>) derived from VIIRS-SNPP imagery from 2012 to 2021. The dataset is stored as the Geo-Tiff data.</p> <p>To generate this dataset, we develop a practical deep neural network (DNN) model . The DNN model performed satisfactory performance on Chl-a retrievals (MR = 0.86, MAPE = 32%, Bias = 5%) over the three magnitudes (0.1–300 μg L<sup>−1</sup>) spanning clear/deep to turbid/shallow waters, with significant improvements compared with the existing algorithms and other machine learning approaches. </p> <p>The dataset is related to a under-review manuscript and we will release it when the paper is published.</p>
Most rotifer species have positive responses in abundance caused by the increase in nitrogen, phosphorus and chlorophyll-a in reservoirs using TITAN analysis
<p>Some zooplanktonic species change their abundances according to the primary productivity increases in freshwater lentic environments. Here, we aimed to i) evaluate the concentration threshold of variables related to eutrophication (nitrogen, phosphorus<br> and chlorophyll-a concentrations) that alter the frequency of occurrence and relative abundance of rotifer species, and ii) analyze which Rotifera species are related positively or negatively to the increase in these variables. The rotifer community<br> structure was studied in fifteen reservoirs in La Plata River Basin, the second largest in South America, relating its abundance with nitrogen, phosphorus and chlorophyll-a values using the Threshold Indicator Rate Analysis (TITAN). Seventy-one rotifer<br> species were registered in the reservoirs, and six species were considered as indicators of changes in their frequency of occurrence and relative abundance with points of change of 1.118 µg.L -1 , 22.44 µg.L -1 and 3.89 µg.L -1 of the nitrogen, phosphorus and<br> chlorophyll-a concentrations, respectively. Species with positive responses to the increase in nutrients were Keratella tropica, Plationus patulus, Filinia terminalis and Synchaeta oblonga, and negative responses Conochilus unicornis and Synchaeta stylata,<br> typical of oligotrophic reservoirs. The only species that presented the same response for all variable concentrations was Keratella tropica, which is a very common species in South America. Our results reinforce the assumption that some rotifer species are good<br> indicators to the variables related to the trophic level in reservoirs. The script and study data are attached to this database.</p>
NH and ME Landsat chlorophyll-a retrieval algorithms and in situ measurements 2000 (Landsat 7), 2013-2015 (Landsat 8)
Predicting algal blooms has become a priority for scientists, municipalities, businesses, and citizens. Remote sensing offers solutions to the spatial and temporal challenges facing existing lake research and monitoring programs that rely primarily on high-investment, in situ measurements. Techniques to remotely measure chlorophyll-a (chl-a) as a proxy for algal biomass have been limited to specific large water bodies in particular seasons and narrow chl-a ranges. Thus, a first step toward prediction of algal blooms is generating regionally robust algorithms using in situ and remote sensing data. This study explores the relationship between in-lake measured chl-a data from Maine and New Hampshire lakes and remotely-sensed chl-a retrieval algorithm outputs. Landsat 8 images were obtained and then processed after required atmospheric and radiometric corrections. Six previously developed algorithms were tested on a regional scale on eleven scenes from 2013-2015 covering 192 lakes. Additionally, data related to one Landsat 7 scene (2000) are included in this data set. Boucher, J, K.C. Weathers, H. Norouzi, B. Steele. In Press. Assessing the effectiveness of Landsat 8 chlorophyll-a retrieval algorithms for regional freshwater monitoring. Ecological Applications.
Multidecadal Time Series of Measured Chlorophyll-a in Lakes and Estuarine-Coastal Ecosystems, 1966-2024
The photosynthetic pigment chlorophyll-a is a commonly measured index of phytoplankton biomass and water quality across all aquatic ecosystem types. Some monitoring and research programs have sustained chlorophyll-a measurements for decades at monthly or higher frequency. Each of these time series is an invaluable record of phytoplankton variability at a particular location. The patterns of that variability have been essential for identifying the underlying processes of phytoplankton change at time scales of days, months, seasons, years and decades. Multidecadal series are rare and valuable because they provide empirical records of phytoplankton changes over the recent decades of unprecedented global change. These records also provide an empirical basis for comparing patterns and rates of change across geographic regions and ecosystem types. This data package contains multidecadal time series of measured chlorophyll-a concentration in three ecosystem types: 134 freshwater lakes (including a small number of reservoirs) that do not freeze; 78 high latitude lakes that do freeze; and 176 coastal ecosystems defined as water bodies where freshwater and seawater mix, including estuaries, coastal bays and lagoons, tidal rivers, and the Baltic Sea. Although there are other published compilations of chlorophyll-a time series, this package was compiled specifically to report observations made at monthly or higher frequency and sustained over multiple decades. The mean time series duration in this package is 33 years, and the mean number of sampling dates per site was 503 (range 186 to 2381). Thus, this data package provides an empirical basis for analyses to measure and compare decadal-scale patterns and rates of phytoplankton biomass variability between inland lakes and water bodies at the land-ocean interface. All chl-a measurements reported here were accessed from published repositories, except these four sites. We acknowledge and thank the following data providers for perm
McMurdo Dry Valleys Chlorophyll-A Responses to Short-Term Soil Manipulation Experiment
A short-term soil manipulation experiment has been conducted as part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project. The chlorophyll-a concentration of soil samples was determined. Samples were taken on November 13, 1995, November 17, 1995, November 24, 1995 and December 11, 1995.
McMurdo Dry Valleys Soil Depth Effects on Chlorophyll-a Concentrations
Investigation of the effect of soil depth on soil biota and properties was part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project. Chlorophyll-a concentrations from soil samples collected for organism extraction and identification was monitored at various soil depths in Taylor Valley in order to accomplish this. Samples were taken on 21-Nov-1994 and 26-Dec-1994.
McMurdo Dry Valleys Human Disturbances Effects on Chlorophyll-A
Concerns over environmental disturbance in the McMurdo Dry Valleys are increasing with increasing foot traffic from tourists and scientist. The effect of pedestrian disturbance was monitored by comparing the species composition, depth distribution and soil properties between adjacent high-, low- and no- traffic sites. This study began in the austral summer 1995/1996.
McMurdo Dry Valleys Chlorophyll-a Concentrations for Soil Biota Distribution Experiment
 Investigation of the variation in soil biota and soil properties across the McMurdo Dry Valleys was part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project. Chlorophyll-a concentrations from soil samples collected for organism extraction and identification  was determined. Chlorophyll-a samples were gathered on the following dates:   * December 12 and 22, 1994,  * January 9, 1995,  * November 8, 1995,  * December 7, 1995, and  * January 12, 1998. Â
McMurdo Dry Valleys Soil Properties Effects on Chlorophyll-A Concentrations
Investigation of the effect of short-term variation in soil moisture and soil temperature on nematode anhydrobiosis as part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project. The percent of anhydrobiotic (coiled) nematodes with relation to soil moisture, temperature, and salinity was determined. The study began in the austral summer of 1996/1997. Samples gathered in the south side of the Lakes Hoare and Fryxell, and in a moss site near the Canada Glacier on Jan 1st 1997
McMurdo Dry Valleys Stream Transects - Chlorophyll-A Concentration
Investigation of the effect of hydrological and biogeochemical linkages in the transition zone between streams and surrounding soils on invertebrate community structure as part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project. The invertebrate community structure with relation to soil moisture, salinity, and chlorophyll-a was determined. The study took place on 28 November 1997 and 31 December 1997.
McMurdo Dry Valleys Chlorophyll-A Concentrations in Lake Hoare Benthic Mats 1996-97
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, the growth of benthic cyanobacteria-dominated mats in the lakes have been monitored. This dataset shows chlorophyll-a levels associated with the benthic mats. Values are provided for various depths of Lake Hoare during the 1996-97 field season, both with and without correction for phaeopigments.
ASSESSING THE CHLOROPHYLL-A VARIABILITY IN THE GULF OF GUINEA USING REMOTE SENSING DATA.
<h3>Introduction</h3> <p>The report begins by highlighting the importance of oceans in influencing the Earth’s climate and supporting marine life. It focuses on phytoplankton, which are crucial for the marine food web and global carbon cycle. The study aims to evaluate the variability of chlorophyll-a (Chl-a) and sea surface temperature (SST) in the Gulf of Guinea using satellite remote sensing data.</p> <h3>Materials and Methods</h3> <ul> <li><strong>Study Site</strong>: The Gulf of Guinea, located on the eastern edge of the Atlantic Ocean, bordered by several West African countries.</li> <li><strong>Data</strong>: Monthly Chl-a and SST data from the Aqua-MODIS satellite, covering the period from 2020 to 2022.</li> <li><strong>Methods</strong>: Analysis of satellite images using Python programming to evaluate spatiotemporal variability and conduct time series analysis.</li> </ul> <h3>Results and Discussion</h3> <ul> <li><strong>Chlorophyll-a Variability</strong>: The study found significant spatial and temporal variability in Chl-a concentrations, with higher values near the coastline due to nutrient inputs from rivers and coastal upwelling.</li> <li><strong>Sea Surface Temperature Variability</strong>: SST showed relatively uniform spatial distribution but notable seasonal and interannual variability, influenced by climatic phenomena like the West African Monsoon.</li> <li><strong>Interannual and Monthly Climatology Variability</strong>: The report discusses the seasonal patterns and the influence of environmental factors on Chl-a and SST.</li> </ul> <h3>Conclusion</h3> <p>The study concludes that Chl-a concentrations are higher near the coast due to nutrient inputs and coastal upwelling, while SST shows a consistent seasonal cycle. These findings provide insights into the dynamic nature of marine productivity in the Gulf of Guinea and the influence of environmental factors on phytoplankton biomass.</p>
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