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53 results for “chlorophyll fluorescence”
Pulse amplitude modulated (PAM) 5-minute chlorophyll fluorescence (ChlF) with accompanying environmental variables from the GCE-LTER Keenan Field site on Sapelo Island, GA in July 2020
Pulse amplitude modulated (PAM) chlorophyll fluorescence (ChlF) from July 11, 2020 to July 27, 2020 collected over a Spartina alterniflora marsh located on the western side of Sapelo Island bounded by the Duplin River. PAM ChlF were processed in WinControl-3.25. Additional biophysical variables included are photosynthetically active radiation (PAR) from onsite quantum sensors (Licor-192), and tide height from an onsite pressure transducer (Hobo U20).
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, turbidity, and fluorescent dissolved organic matter at discrete depths in Carvins Cove Reservoir, Virginia, USA in 2020-2025
We monitored water quality in Carvins Cove Reservoir (Roanoke, Virginia, USA; 37.3697 -79.958) with high-frequency (10-minute) sensors in 2020-2025. Carvins Cove Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source. This data package consists of datasets from two separate deployments. First, from July 2020 - August 2021, depth profiles of water temperature were measured on 1-meter intervals using HOBO temperature pendant loggers deployed from 0.1 m below the surface of the reservoir to 10 m depth, and also at 15 and 20 m depth. Additionally, water temperature was measured in the Sawmill Branch inflow at 0.5 m depth using HOBO temperature pendant loggers. Second, from 9 April 2021 - 31 December 2025, depth profiles of water temperature were measured on 1-meter intervals from 0.1 m below the surface of the reservoir to 11 m depth and additionally at 15 and 19 m. A YSI EXO2 sonde measured water temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, and fluorescent dissolved organic matter at ~1.5 m depth. A YSI EXO3 sonde measured water temperature, conductivity, specific conductance, total dissolved solids, dissolved oxygen, and fluorescent dissolved organic matter at ~9 m depth, which corresponds to the depth of a water outtake valve. The thermistors, EXO3 sonde, and pressure sensor were deployed at stationary, fixed elevations (referred to as positions) deployed off of the dam near the water outtake valves. Due to variable water levels in the reservoir, the depths of these sensors varied over time. In contrast, the EXO2 was deployed on a buoy from 2021-2022 and remained at 1.5 m depth as the water level fluctuated. However, in 2023, the buoy disappeared in a storm, and after that the EXO2 was deployed at a stationary elevation as the water level fluctuated around the sensor. The EXO2 was redeployed on the buoy in 2024. The monitoring site's maximum de
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, pressure, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths in Falling Creek Reservoir, Virginia, USA in 2018-2025
We monitored water quality in Falling Creek Reservoir (Vinton, Virginia, USA; 37.30325 -79.8373) with high-frequency (10-minute) sensors in 2018-2025. All variables were measured at the deepest site of the reservoir adjacent to the dam. Falling Creek Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source for Roanoke, Virginia. This data product consists of one dataset compiled of depth profiles of water temperature on 1-m intervals from 0.1 to 9 m depth; dissolved oxygen at 5 m and 9 m depth; pressure at 9 m depth; and temperature, dissolved oxygen, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, fluorescent dissolved organic matter, turbidity, and pressure at ~1.6 m depth. The dataset is accompanied by a sensor maintenance log and quality assurance/quality control analysis scripts.
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths, and water level in Beaverdam Reservoir, Virginia, USA in 2009-2025
We monitored water level and water quality in Beaverdam Reservoir (Vinton, Virginia, USA; 37.31288, -79.8159) with visual observations and high-frequency (10- to 15-minute resolution) sensors in 2009-2025. All variables were measured at the deepest site of the reservoir adjacent to the dam. Beaverdam Reservoir is owned and managed by the Western Virginia Water Authority as a secondary drinking water source for Roanoke, Virginia. This data package is comprised of three datasets: 1) bvre-waterlevel_2009_2025.csv, 2) bvre-sensorstring_2016_2020.csv, and 3) bvre-waterquality_2020_2025.csv. 1) bvre-waterlevel_2009_2025.csv contains water level observations of the staff gauge at a platform near the reservoir's dam by both the Western Virginia Water Authority and the Virginia Tech Reservoir Group LTREB field crew. This dataset spans 2009 to 2025, with data collection still ongoing. 2) bvre-sensorstring_2016_2020.csv consists of a water temperature profile at ~1-meter intervals from the surface of the reservoir to 10.5 m below the water, complemented by intermittent data collected by a dissolved oxygen logger deployed at 5 m or 10 m. A sonde measuring water temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, fluorescent dissolved organic matter, and turbidity was additionally deployed at ~1.5 m depth. This dataset spans 2016 to 2020, with no additional data collection beyond the last observation. The third dataset is bvre-waterquality_2020_2025.csv, with data collection still ongoing and an accompanying maintenance log. This dataset contains: a) a temperature string with 13 temperature sensors deployed ~1 m apart from the surface to 0.5 m above the sediments of the reservoir; b) two dissolved oxygen sensors, one in the middle of the string and one sensor above the sediments; and c) a pressure sensor just above the sediments. The same sonde from the first 2016-2020 dataset is also included in this 2020-2025 d
High-frequency measurements of chlorophyll fluorescence, characterizing F.I.Z. bias.
This dataset consists of high-frequency measurements of chlorophyll a fluorescence (Fchl), collected as part of manipulative experiments conducted in Lake George, NY. Experiments were set up to test for the potential of phototactic zooplankton to interfere with Fchl measurements. To test for any bias associated with fluorometer interference by zooplankton (FIZ), fluorometers were placed in the shallows of Lake George, and collected data under various treatment conditions. It was found that excitation light from fluorometers triggered a positive phototactic response during nighttime hours, biasing Fchl data by as much as 31x. Full results gleaned from this dataset can be found in: Moriarty, V.W., Lucius, M.A., Johnston, K.E., Borrelli, J.J., Mattes, B.M., Pezzuoli, A.R., Watson, C.D., Eichler, L.W. and Relyea, R.A. (2021), Fluorometer optical path interference via zooplankton phototaxis: Implications for high‐frequency data collection. Limnol Oceanogr Methods. https://doi.org/10.1002/lom3.10411
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>
Canopy solar-induces chlorophyll fluorescence measurements at seven sites
<p>This dataset will accompany the paper "Direct validation of TROPOMI solar-induced chlorophyll fluorescence products using tower-based measurements reveals shortcomings with the current satellite SIF products" in the Remote Sensing of Environment. This dataset includes the canopy SIF measurements at noon timescale for six sites and a public hourly canopy SIF dataset that accompanies the paper "Mechanistic evidence for tracking the seasonality of photosynthesis with solar induced fluorescence" in the Proceedings of the National Academy of Sciences. All relevant methodological information can be found in the paper: Shanshan, D., Xinjie, L., Jidai, C., Weina, D., and Liangyun, L. in press.</p>
Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ
<p>This repository contains data reported in the below study:</p> <p>Atherton, J., Liu, W. and Porcar-Castell, A., 2019. Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ. <em>Remote Sensing of Environment</em>.</p> <p>Each text file contains the data-set used to produce the relevant figure (see file name). You can find the data to produce A.4. online at https://avaa.tdata.fi/web/smart/smear/ </p> <p>Please pay attention to the following before using this data.</p> <ol> <li><strong>Figure2_lampRadPanel_Wm2srnm.txt</strong>: Note that the shapes are of interest here. The magnitude is not the same as the incident light at top of canopy, as these spectra were measured in a laboratory. See paper section A.1. for more details. </li> <li><strong>Figure3_LEDIFspectra_Wm2srnm.txt</strong>: This data contains the whole observed spectrum including the non-fluorescence regions, which were saturated (warped) in the visible. The fluorescence region is approximately > 650 nm. </li> <li><strong>Figure4_AQYspectra_nm.txt</strong>: As with Figure3 the whole spectrum is included here.</li> <li><strong>FigureA3_repLEDIFspectra_[pmay/psep/usep]._nm.txt</strong>: Data from which the mean spectra (Figure3) were calculated, including the uncorrected red spectra. I have split these by canopy type to avoid name conflicts.</li> </ol> <p> </p>
High-frequency water temperature, chlorophyll fluorescence, wind speed, and photosynthetically active radiation data for 18 globally-distributed lakes 2008 - 2013
Abstract: This dataset was used in the analysis described in the manuscript by Rusak, J. A.J. Tanentzap, J.L. Klug, K. Rose, L.A. Winslow R. Smyth, E. Jennings, D. Pierson, S. Hendricks, A. Laas, E. Ryder, D. White, R. Adrian, L. Arvola, E. de Eyto, H. Feuchtmayr, M. Honti, V. Istanovics, I. Jones, C. McBride, S. Schmidt, G. Zhu. Wind and trophic status explain the temporal and spatial variability of chlorophyll in lakes. In review: Limnology and Oceanography Letters. The variation in chlorophyll fluorescence from 18 globally distributed lakes, was tested at monthly, daily and hourly scales in related to high-frequency measurements of wind, water temperature and radiation within lakes as well as lake productivity and morphometry among lakes. Overall, monthly variation in algal biomass was greater than that expressed at either daily or hourly scales but, combined, these latter time scales were equivalent to seasonal variation. Among lakes, algal biomass variation increased with trophic status while, within-lake variation increased with increasing wind speed variation. Together, our results suggest that predicted changes associated with a changing climate, as well as widespread ongoing cultural eutrophication, have the potential to substantially alter the variability of algal biomass and thus the predictability of the services it provides. This dataset includes the data used in the analysis described above.
High-frequency water quality data for water temperature, dissolved oxygen, specific conductivity, pH, phycocyanin fluorescence and chlorophyll a fluorescence at Lake Lillinonah, Connecticut, USA, 2011-2017.
From 2011 to 2017, an instrumented buoy maintained by Friends of the Lake (FOTL, friendsofthelake.org) and Fairfield University was deployed on Lake Lillinonah (Connecticut, USA) during the summer months to collect high-frequency water quality data. The buoy was deployed early each summer and remained in the lake until late October. The instrumented buoy was equipped with a YSI 6600 sonde at a depth of 1 meter. The YSI 6600 collected water temperature, dissolved oxygen, specific conductivity, pH, chlorophyll a fluorescence and phycocyanin fluorescence data. Additionally, the instrumented buoy is equipped with an In-Situ RDO sensor which collects water temperature and dissolved oxygen readings at a depth of 15 meters. Included in this data package are datasets containing 15-minute data as well as daily averages from all sensors. Following the summer of 2017, the YSI 6600 sonde was replaced with a YSI EXO2 sonde which collected data on the same variables. Data from the YSIEXO2 sonde, as well as data from the In-Situ RDO sensor during the same period, are available in the EDI data package EDI560. Additionally, the instrumented buoy at Lake Lillinonah is equipped with a water temperature thermistor chain . These data can be found in data packages EDI557 (2011-2015) and EDI558 (2018-current).
High-frequency water quality data for water temperature, dissolved oxygen, specific conductivity, pH, phycocyanin fluorescence and chlorophyll a fluorescence at Lake Lillinonah, Connecticut, USA, 2018-current.
Since 2011, an instrumented buoy maintained by Friends of the Lake (FOTL, friendsofthelake.org) and Fairfield University has been deployed on Lake Lillinonah (Connecticut, USA) during the summer months to collect high-frequency water quality data. The buoy is deployed early each summer and remains in the lake until late October. Since 2018, the instrumented buoy has been equipped with a YSI EXO2 sonde at a depth of 1 meter. The YSI EXO2 collects water temperature, dissolved oxygen, specific conductivity, pH, chlorophyll a fluorescence and phycocyanin fluorescence data. Additionally, the instrumented buoy is equipped with an In-Situ RDO sensor which collects water temperature and dissolved oxygen readings at a depth of 15 meters. Included in this data package are datasets containing 15-minute data as well as daily averages from both sensors from 2018 to present. Prior to 2018, the instrumented buoy was equipped with a YSI 6600 sonde, which collected data on the same variables (2011-2017). Data from the YSI 6600 sonde, as well as data from the In-Situ RDO sensor during the same period, are available in the EDI data package EDI559. Additionally, the instrumented buoy at Lake Lillinonah is equipped with a water temperature thermistor chain sensor. These data can be found in data packages EDI557 (2011-2015) and EDI558 (2018-current).
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.
supporting files for "What to Choose for Estimating Leaf Water Status - Spectral Reflectance or in vivo Chlorophyll Fluorescence?"
<p><strong><span>Fig. 1 </span></strong><span>Workflow and parameters used for comparative analysis.</span></p> <p><strong><span>Fig. 2</span></strong><span> Representative spectra of diffusive reflectance measured on the adaxial side (R<sub>D</sub>) of fresh (RWC = 98%), partially desiccated (RWC = 52% or 54%) and severely desiccated leaves (RWC = 5%) of tobacco (A) and barley (E). Comparison of R<sub>D</sub> and R<sub>B</sub> (R from the abaxial leaf side) in fresh and severely desiccated leaves of tobacco (B) and barley (F). Water index (WI = R<sub>900</sub>/R<sub>970</sub>) and relative decrease of R in the 800-1100 nm region (ΔR) estimated from R<sub>D</sub> (indexed by "D") and R<sub>B</sub> (indexed by "B") in desiccating leaves of tobacco (C, D) and barley (G, H).</span></p> <p><strong><span>Fig. 3</span></strong><span> Water index (WI<sub>SWIR </sub>= R<sub>1000</sub>/R<sub>1450</sub>) estimated from measurement of directional R from adaxial side of desiccating leaf samples of tobacco (A) and barley (B). </span></p> <p><strong><span>Fig. 4 </span></strong><span>Equivalent water thickness (EWT) during desiccation of tobacco and barley leaf samples within the RWC interval 100-50%.</span></p> <p><strong><span>Fig. 5 </span></strong><span>Normalized difference vegetation index</span><span> </span><span>(NDVI</span><span> </span><span>=</span><span> </span><span>(R<sub>780</sub>-R<sub>630</sub>)/(R<sub>780</sub>+R<sub>630</sub>)) estimated from measurement of diffusive R from the </span><span>abaxial (</span><span>NDVI</span><sub><span>B</span></sub><span>) and </span><span>adaxial side (NDVI</span><sub><span>D</span></sub><span>) of desiccating leaves of tobacco (A) and barley (D). SPAD-value</span><span>s</span><span> of desiccating leaves of tobacco (B) and barley </span><span>(E)</span><span>. Relative SPAD and NDVI</span><span>΄<sub>D</sub> (estimated from measurement of directional R from the adaxial side</span><span>)</span><span> </span><span>of a representative leaf of tobacco </span><span>(C)</span><span> and barley (F) during its desiccation (in % of the value measured immediately after leaf detachment). For selected data points, the time after the leaf detachment is indicated. </span></p> <p><strong><span>Fig. 6</span></strong><span> Chlorophyll fluorescence parameters of desiccating tobacco and barley leaf samples. (A, D) The maximum quantum yield of PSII photochemistry in the dark-adapted state (F<sub>V</sub>/F<sub>M</sub>) and the effective quantum yield of PSII photochemistry in the light-adapted state (ΦPSII<sub>st</sub>). (B, E) The non-photochemical quenching of chlorophyll fluorescence after 1 min of exposure to actinic light (NPQ<sub>1</sub>). (C, F) The non-photochemical quenching of chlorophyll fluorescence at steady state (NPQ<sub>st</sub>).</span></p> <p><strong><span>Fig. 7 </span></strong><span>Coefficient of reliability (<em>CR</em>), coefficient of sensitivity (<em>CS</em>) and coefficient of inaccuracy (<em>CI</em>) of parameters measured in desiccating leaf samples of tobacco and barley within the RWC interval 100-50%. The parameters have been divided into 5 groups according to the type of leaf characteristics they reflect. A horizontal line in <em>CR</em> plot indicates the reliability threshold (<em>CR</em> = 0.4).</span></p> <p><strong><span>Table 1 </span></strong><span>Parameters measured on desiccating tobacco and barley leaves ranked according to the value of their coefficient of reliability (<em>CR</em>) within the RWC interval 100-50% and the corresponding values of the coefficient of determination (<em>R<sup>2</sup></em>).</span></p> <p><strong><span>Fig. S1 </span></strong><span>Decrease in relative water content (RWC) of leaf samples of tobacco and barley with time after their detachment</span></p> <p><strong><span>Fig. S2 </span></strong><span>Micrographs of leaf structure of fresh tobacco (A) and barley (B) leaves.</span></p> <p><strong><span>Fig. S3</span></strong><span> </span><span>WI<sub>SWIR</sub> images </span><span>o</span><span>f representative desiccating tobacco and barley leaf samples</span><strong><span>. </span></strong><span>Numbers above the samples indicate their RWC in %.</span></p> <p><strong><span>Fig. S4 </span></strong><span>(A)</span><strong><span> </span></strong><span>Leaf area (in % of the area of fresh leaves) and (B) equivalent water thickness (EWT) during desiccation of tobacco and barley leaf samples.</span></p> <p><strong><span>Fig. S5 </span></strong><span>Imaging of chlorophyll fluorescence parameters of representative desiccating tobacco and barley leaf samples. The maximum quantum yield of PSII photochemistry (F<sub>V</sub>/F<sub>M</sub>), the effective quantum yield of PSII photochemistry in the light-adapted state (ΦPSII<sub>st</sub>), the non-photochemical quenching of chlorophyll fluorescence after 1 min of exposure to actinic light (NPQ<sub>1</sub>), and the non-photochemical quenching of Chl fluorescence at steady state (NPQ<sub>st</sub>).</span></p> <p><strong><span>Fig. S6</span></strong><span> Dependencies of</span><strong><span> </span></strong><span>measured parameters on RWC (in interval 100-50%) in desiccating tobacco leaves and segments. The parameters are ranked from most to least reliable according to their coefficient of reliability (<em>CR</em>). All parameters are normalized to their mean value (<em>ȳ</em>).</span></p> <p><strong><span>Fig. S7</span></strong><span> Dependencies of</span><strong><span> </span></strong><span>measured parameters on RWC (in interval 100-50%) in desiccating barley leaves and segments. The parameters are ranked from most to least reliable according to their coefficient of reliability (<em>CR</em>). All parameters are normalized to their mean value (<em>ȳ</em>).</span></p> <p><strong><span>Fig. S8 </span></strong><span>Leaf water potential measured by psychrometry </span><span>(Ψ<sub>psy</sub>) and by pressure chamber (Ψ<sub>press</sub>) in desiccating leaves of tobacco (A) and barley (B).</span></p> <p><strong><span>Table S1 </span></strong><span>Parameters measured on desiccating leaf samples ranked according to their coefficient of sensitivity (<em>CS</em>) within the RWC interval 100-50% in tobacco and barley.</span></p> <p><strong><span>Table S2 </span></strong><span>Parameters measured on desiccating leaf samples ranked according to their coefficient of inaccuracy (<em>CI</em>) within the RWC interval 100-50% in tobacco and barley.</span></p> <p><strong><span>Table S3 </span></strong><span>Ranking of measured parameters according to their coefficient of reliability (<em>CR</em>), sensitivity (<em>CS</em>) and inaccuracy (<em>CI</em>) in desiccating leaf samples of tobacco and barley within the RWC interval 100-50%. The parameters have been divided into 5 groups (the first column) according to the type of leaf characteristics they reflect.</span></p> <p><strong><span>Table S4</span></strong><span> Approximate </span><span>time required for the measurement of the parameters used in the study and the destructiveness/non-destructiveness of the measurement. The parameters that were used for the comparison according to their coefficients of reliability (<em>CR</em>), sensitivity (<em>CS</em>) and inaccuracy (<em>CI</em>) are written in bold.</span></p> <p><strong><span>Fig. 2_spectra of diffusive reflectance </span></strong><span>- source data for each panel (A-H) of Fig. 2: </span><span>Representative spectra of diffusive reflectance measured on the adaxial side (R<sub>D</sub>) of fresh (RWC = 98%), partially desiccated (RWC = 52% or 54%) and severely desiccated leaves (RWC = 5%) of tobacco (A) and barley (E). Comparison of R<sub>D</sub> and R<sub>B</sub> (R from the abaxial leaf side) in fresh and severely desiccated leaves of tobacco (B) and barley (F). Water index (WI = R<sub>900</sub>/R<sub>970</sub>) and relative decrease of R in the 800-1100 nm region (ΔR) estimated from R<sub>D</sub> (indexed by "D") and R<sub>B</sub> (indexed by "B") in desiccating leaves of tobacco (C, D) and barley (G, H).</span></p> <p><strong><span>Fig.3_water index WISWIR</span></strong><span> - source data for each panel (A+B) of Fig. 3: Water index (WISWIR = R1000/R1450) estimated from measurement of directional R from adaxial side of desiccating leaf samples of tobacco (A) and barley (B).</span></p> <p><strong><span>Fig.4_equivalent water thickness</span></strong><span> - source data for Fig. 4: Equivalent water thickness (EWT) during desiccation of tobacco and barley leaf samples within the RWC interval 100-50%.</span></p> <p><strong><span>Fig.5_NDVI</span></strong><span> - source data for each panel (A-F) of Fig. 5: Normalized difference vegetation index (NDVI = (R780-R630)/(R780+R630)) estimated from measurement of diffusive R from the abaxial (NDVIB) and adaxial side (NDVID) of desiccating leaves of tobacco (A) and barley (D). SPAD-values of desiccating leaves of tobacco (B) and barley (E). Relative SPAD and NDVI΄D (estimated from measurement of directional R from the adaxial side) of a representative leaf of tobacco (C) and barley (F) during its desiccation (in % of the value measured immediately after leaf detachment).</span></p> <p><strong><span>Fig.6_chlorophyll fluorescence parameters</span></strong><span> - source data for each panel (A-F) of Fig. 6: Chlorophyll fluorescence parameters of desiccating tobacco and barley leaf samples. (A, D) The maximum quantum yield of PSII photochemistry in the dark-adapted state (FV/FM) and the effective quantum yield of PSII photochemistry in the light-adapted state (ΦPSIIst). (B, E) The non-photochemical quenching of chlorophyll fluorescence after 1 min of exposure to actinic light (NPQ1). (C, F) The non-photochemical quenching of chlorophyll fluorescence at steady state (NPQst).</span></p> <p><strong><span>Fig.7_coefficients of reliability, sensitivity, inaccuracy</span></strong><span> - Coefficient of reliability (CR), coefficient of sensitivity (CS) and coefficient of inaccuracy (CI) of parameters measured in desiccating leaf samples of tobacco and barley within the RWC interval 100-50%.</span></p> <p><strong><span>Fig.S1_relative water content</span></strong><span> - source data for supplementary Fig. 1: Relative water content (RWC) of leaf samples of tobacco and barley with time after their detachment.</span></p> <p><strong><span>Fig.S4_leaf area and equivalent water thickness</span></strong><span> - source data for supplementary Fig. 4: Leaf area (A) and equivalent water thickness (EWT; B) during desiccation of tobacco and barley leaf samples.</span></p> <p><strong><span>Fig.S6_dependencies of parameters on RWC in tobacco</span></strong><span> - source data for each panel (A-N) of supplementary Fig.6: Dependencies of measured parameters (water potential, NPQ, NDVI, reflectance, SPAD, Fv/Fm, water indexes) on RWC (in interval 100-50%) in desiccating tobacco leaves and segments.</span></p> <p><strong><span>Fig.S7_dependencies of parameters on RWC in barley</span></strong><span> - source data for each panel (A-N) of supplementary Fig.7: Dependencies of measured parameters (water potential, NPQ, NDVI, reflectance, SPAD, Fv/Fm, water indexes) on RWC (in interval 100-50%) in desiccating barley leaves and segments.</span></p> <p><strong><span>Fig.S8_leaf water potential</span></strong><span> - source data for supplementary Fig. 8: Leaf water potential measured by psychrometry (Ψpsy) and by pressure chamber (Ψpress) in desiccating leaves of tobacco (A) and barley (B).</span></p>
A downscaled 0.05-Degree Monthly Solar-Induced Chlorophyll Fluorescence Product derived using AVHRR Data in East Asia (1995-2003)
<p>Downscaling techniques offer the opportunity to utilize coarse-spatial-resolution SIF products for investigating carbon cycles and ecological processes at finer resolutions. Here, we generated a new monthly SIF product, DSIF_EA0.05, at a resolution of 0.05° in East Asia from July 1995 to June 2003. The random forest kriging (RFK) approach was employed, incorporating GOME SIF, AVHRR data, ERA5 climate data, and using the optimal explanatory variables. The unit of SIF is mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>. The selected variables were daily maximum values of air temperature (T<sub>air</sub>), skin temperature (T<sub>skin</sub>), fraction of absorbed photosynthetically active radiation (fPAR), near-infrared reflectance of vegetation (NIRv), downward shortwave radiation (SR<sub>down</sub>), and precipitation. To verify the reliability of DSIF_EA0.05 and the advantages over original GOME SIF, this dataset has been validated with the original GOME SIF, ground gross primary productivity (GPP) data from eight flux sites, and two other SIF products at 1-degree and 0.05-degree resolutions from SCIAMACHY SIF and downscaled SCIAMACHY SIF datasets.</p>
First description of in situ chlorophyll fluorescence signal within East Antarctic coastal polynyas during fall and winter
<p>Antarctic coastal polynyas are persistent and recurrent regions of open water located between the coast and the drifting pack-ice. In spring, they are the first polar areas to be exposed to light, leading to the development of phytoplankton blooms, making polynyas potential ecological hotspots in sea-ice regions. Knowledge on polynya oceanography and ecology during winter is limited due to their inaccessibility. This study describes i) the first in situ chlorophyll fluorescence signal (a proxy for chlorophyll-a concentration and thus presence of phytoplankton) in polynyas between the end of summer and winter, ii) assesses whether the signal persists through time and iii) identifies its main oceanographic drivers. The dataset comprises 698 profiles of fluorescence, temperature and salinity recorded by southern elephant seals in 2011, 2019–2021 in the Cape-Darnley (CDP;67˚S-69˚E) and Shackleton (SP;66˚S-95˚E) polynyas between February and September. A significant fluorescence signal was observed until April in both polynyas. An additional signal occurring at 130m depth in August within CDP may result from in situ growth of phytoplankton due to potential adaptation to low irradiance or remnant chlorophyll-a that was advected into the polynya. The decrease and deepening of the fluorescence signal from February to August were accompanied by the deepening of the mixed layer depth and a cooling and salinification of the water column in both polynyas. Using Principal Component Analysis as an exploratory tool, we highlighted previously unsuspected drivers of the fluorescence signal within polynyas. CDP shows clear differences in biological and environmental conditions depending on topographic features with higher fluorescence in warmer and saltier waters on the shelf compared with the continental slope. In SP, near the ice-shelf, a significant fluorescence signal in April below the mixed layer (around 130m depth), was associated with fresher and warmer waters. We hypothesize that this signal could result from potential ice-shelf melting from warm water intrusions onto the shelf leading to iron supply necessary to fuel phytoplankton growth. This study supports that Antarctic coastal polynyas may have a key role for polar ecosystems as biologically active areas throughout the season within the sea-ice region despite inter and intra-polynya differences in environmental conditions.</p>
First description of in situ chlorophyll fluorescence signal within East Antarctic coastal polynyas during fall and winter
Open the record for dataset details and reuse information.
Photoprotective leaf pigments and chlorophyll fluorescence measurements during a greenhouse drought experiment: An evolutionary perspective on functional diversity in co-occuring willow(salix) species
Thirteen willow (Salix) species occur in southeastern Minnesota and often co-occur within the same wetlands. This high local diversity is challenging to explain since closely related species are often functionally similar and density-dependent interactions such as competition and susceptibility to pests and pathogens should limit their co-occurrence. However, if willow species are partitioning resources, or if they are phylogenetically structured so that closely related species rarely co-occur, then the impact of these density-dependent processes could be reduced. In this study, I examined the role of niche partitioning in maintaining local willow diversity by comparing species physiology in a greenhouse.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.