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25 results for “Solar-induced Fluorescence”
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>
Tower-based solar-induced fluorescence and vegetation index data for Southern Old Black Spruce forest
<p>Data includes remote sensing products from PhotoSpec (a scanning spectrometer) from September 2019-December 2020 at the Southern Old Black Spruce site in Saskatchewan Canada. We provide half-hourly averaged vegetation indices (NIRv, NDVI, PRI, CCI) and solar-induced fluorescence (SIF) and for a stand-representative mix of black spruce and larch. Additionally, we provide photosynthetically active radiation (PAR), absorbed photosynthetically active radiation (APAR), and the escape fraction of SIF photons (fesc). Version 2 also provides half-hourly averaged SIF and SIFrelative for black spruce (evergreen) and larch (deciduous) separately. </p>
Boreal forest tower-based remote sensing data (solar-induced fluorescence and reflectance-based vegetation indices)
<p>Data includes remote sensing products from PhotoSpec (a scanning spectrometer) from August 2019-December 2021 at the Southern Old Black Spruce site in Saskatchewan Canada and the National Ecological Observatory Network (NEON) Delta Junction. We provide half-hourly averaged vegetation indices (NIRv, NDVI, PRI, CCI) and Solar-Induced Fluorescence (SIF) and for stand-representative targets. Additionally, we provide half-hourly Photosynthetically Active Radiation (PAR), a fraction of direct vs. diffuse radiation (Df), Air Temperature (Tair) and Gross Primary Productivity (GPP). </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>
TanSat Solar-Induced Chlorophyll Fluorescence Product
<p>The TanSat Solar Induce chlorophyll fluorescence (SIF) retrievals acquired from the AGCS spectroradiometer onboard TanSat make use of O<sub>2</sub>-A band L1 data with 0.044 nm spectral resolution and wavelengths between 757.4 and 778.1 nm.</p> <p>The TanSat SIF products contain effective SIF values (including SIF_758nm and SIF_771nm) and other geometric information </p> <p>(Latitude, longitude, angles and so forth). There is one file per day if the corresponding ACGS L1B data is available.</p>
Solar-induced chlorophyll fluorescence study in Bibai Wetland
<p>#SIF study in Bibai Wetland</p><p>The data utilized in the investigation carried out by Buareal et al.,</p><p>titled "Solar-induced chlorophyll fluorescence as a potential proxy for gross primary production and methane emission in a cool-temperate bog in northern Japan," comprised:</p><p>a. Solar-induced chlorophyll fluorescence (SIF)</p><p>b. Eddy flux data and meteorological measurements (specifically FCh4, GPP, Ta, Ts, VPD, Water table depth, Soil water content, and PPFD) obtained from Bibai Mire, a cool-temperate bog in Japan.</p>
Bayesian hierarchical model gridded solar-induced fluorescence (BHM gridded SIF) data product
<p>This archive provides the solar-induced fluorescence (SIF) data product documented in "Estimation of solar-induced chlorophyll fluorescence using Bayesian hierarchical regression". The archive includes daily NetCDF files with the global gridded SIF estimates and associated uncertainties.</p>
SIF and CLM5 outpfile files to support Kunik et al "Satellite-based solar-induced fluorescence tracks seasonal and elevational patterns of photosynthesis in California's Sierra Nevada mountains"
<p>These files contain 0.04° monthly sampled TROPOMI SIF, corrected for length of day and topography ("SIFdc_dem") over the Sierra Nevada region of California, along with Community Land Model (CLM) v5.0 point and regional simulation output. CLM5.0 simulations with prognostic vegetation state (CLM5.0-BGC, files begninning with "clm5_") and with satellite phenology (CLM5.0-SP, files beginning with "clm5_SP_") are provided. </p>
Data from: Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize
Accurately quantifying cropland gross primary production (GPP) is of great importance to monitor cropland status and carbon budgets. Satellite-based light-use efficiency (LUE) models and process-based terrestrial biosphere models (TBMs) have been widely used to quantify cropland GPP at different scales in past decades. However, model estimates of GPP are still subject to large uncertainties, especially for croplands. More recently, space-borne solar-induced chlorophyll fluorescence (SIF) has shown the ability to monitor photosynthesis from space, providing new insights into actual photosynthesis monitoring. In this study, we examined the potential of SIF data to describe maize phenology and evaluated three GPP modeling approaches (space-borne SIF retrievals, a LUE-based Vegetation Photosynthesis Model (VPM), and a process-based Soil Canopy Observation of Photochemistry and Energy flux (SCOPE) model constrained by SIF) at a maize (Zea mays L.) site in Mead, Nebraska, USA. The result shows that SIF captured the seasonal variations (particularly during the early and late growing season) of tower-derived GPP (GPP_EC) much better than did satellite-based vegetation indices (enhanced vegetation index, EVI and land surface water index, LSWI). Consequently, SIF was strongly correlated with GPP_EC than were EVI and LSWI. Evaluation of GPP estimates against GPP_EC during the growing season demonstrated that all three modeling approaches provided reasonable estimates of maize GPP, with Pearson's correlation coefficients (r) of 0.97, 0.94, and 0.93 for the SCOPE, VPM, and SIF models, respectively. The SCOPE model provided the best simulation of maize GPP when SIF observations were incorporated through optimizing the key parameter of maximum carboxylation capacity (Vcmax). Our results illustrate the potential of SIF data to offer an additional way to investigate the seasonality of photosynthetic activity, to constrain process-based models for improving GPP estimates, and to reasonably estimate GPP by integrating SIF and GPP_EC data without dependency on climate inputs and satellite-based vegetation indices.
TroDSIF: an improved spatially downscaled solar-induced chlorophyll fluorescence product of TROPOMI dataset
<p>TroDSIF is an improved spatially downscaled solar-induced chlorophyll fluorescence product of TROPOMI dataset at far-red band (wavelength at 740 nm), with a spatial resolution of 500 m and a temporal resolution of 16 days under clear-sky condition.<br>The original TROPOMI SIF was retrieved by Guanter et al., which was available at https://doi.org/10.5270/esa-s5p_innovation-sif-20180501_20210320-v2.1-202104.</p>
Global Spatially Continuous TanSat Solar-Induced Chlorophyll Fluorescence Product
<p>The global spatially continuous TanSat solar-induced chlorophyll fluorescence product (wavelength at 758 nm) had a spatial resolution of 0.05° and a temporal resolution of four days. Version 2 presents daily SIF under clear-sky and all-sky. <br> The original discontinuous TanSat SIF (referenced at 758 nm and 771 nm) at 13:30 local time was retrieved through singular vector decomposition (SVD). The information about original TanSat SIF can refer to the link (https://zenodo.org/record/3883434). <br> </p> <p> </p> <p> </p>
Data from: Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize
Open the record for dataset details and reuse information.
CMLR: A mechanistic global 0.05° Gross Primary Production dataset using TROPOMI Solar-induced fluorescence observations
<p>CMLR GPP is a mechanistic global Gross Primary Production dataset using TROPOMI Solar-induced chlorophyll fluorescence observations (TROPOSIF). This dataset provided GPP estimates from May 2018 to December 2021 with a 0.05° spatial resolution at 1-day time step. We modified the mechanistic light response (MLR) model proposed by Gu et al. (2019) to apply it to the canopy-scale, and then generated this dataset. The parameterization of q<sub>L</sub> (the fraction of open photosystem II reaction centers) in the MLR framework is accomplished using a random forest model. For the continuous global mapping purpose, we further composited the original CMLR GPP using an 8-day moving window and applied a 2D Gaussian function in 3 × 3 moving windows to fill in the gaps. Pixels with filled data were marked using flag = 1 in the quality control layer.</p>
OCO-2 Level 2 bias-corrected solar-induced fluorescence and other select fields from the IMAP-DOAS algorithm aggregated as daily files, Retrospective processing V11r (OCO2_L2_Lite_SIF) at GES DISC
Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r. The OCO-2 SIF Lite files contain bias-corrected solar induced chlorophyll fluorescence along with other select fields aggregated as daily files. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.This collection encompass the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline.
OCO-2 Level 2 bias-corrected solar-induced fluorescence and other select fields from the IMAP-DOAS algorithm aggregated as daily files, Retrospective processing V11.2r (OCO2_L2_Lite_SIF) at GES DISC
Version 11.2r is the current version of the data set. The OCO-2 SIF Lite files contain bias-corrected solar induced chlorophyll fluorescence along with other select fields aggregated as daily files. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.This collection encompass the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline.
CLM Simulated Solar-Induced Fluorescence, Niwot Ridge, Colorado, USA, 1998-2018
This dataset provides results for simulations of solar-induced chlorophyll fluorescence (SIF) implemented within the terrestrial biosphere Community Land Model (CLM 4.5) for Niwot Ridge, Colorado, USA, from 1998-2018. The data include outputs from three model simulations designed to test the importance of non-photochemical quenching (NPQ), that is, the absorbed light energy dissipated as heat, in determining seasonal SIF.
OCO-3 Level 2 bias-corrected solar-induced fluorescence and other select fields from the IMAP-DOAS algorithm aggregated as daily files, Forward Processing V11 (OCO3_L2_Fwd_SIF) at GES DISC
Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.
L2 Solar-Induced Fluorescence (SIF) from SCIAMACHY, 2003-2012
This dataset provides Level 2 (L2) Solar-Induced Fluorescence (SIF) of chlorophyll estimates derived from the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument on the European Space Agency's (ESA's) Environmental satellite (Envisat) with ~0.5 nm spectral resolution and wavelengths between 734 and 758 nm. SCIAMACHY covers global land between approximately 70 and -57 degrees latitude on an orbital basis at a resolution of approximately 30 km x 240 km. Data are provided for the period from 2003-01-01 to 2012-04-08. Each file contains daily raw and bias-adjusted solar-induced fluorescence along with quality control information and ancillary data.
OCO-3 Level 2 bias-corrected solar-induced fluorescence and other select fields from the IMAP-DOAS algorithm aggregated as daily files, Retrospective processing V10r (OCO3_L2_Lite_SIF) at GES DISC
Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.
High Resolution Land Cover-Specific Solar-Induced Fluorescence, Midwestern USA, 2018
This dataset provides estimated solar-induced chlorophyll fluorescence (SIF) of specific vegetation types and total SIF under clear-sky and real/cloudy conditions at a resolution of 4 km for the Midwest USA. The estimates are 8-day averaged daily means over the 2018 crop growing season for the time period 2018-05-01 to 2018-09-29. SIF of a specific vegetation type (i.e., corn, soybean, grass/pasture, forest) was expressed as the product of photosynthetically active radiation (PAR), the fraction of photosynthetically active radiation absorbed by the canopy (fPAR), and canopy SIF yield (SIFyield) for each vegetation type. Uncertainty of each variable was also calculated and is provided. These components of the SIF model were derived using a TROPOspheric Monitoring Instrument (TROPOMI) dataset, the USDA National Agricultural Statistics Service Cropland Data Layer, and the MODIS MCD15A2H 8-day 500 m fPAR product. These data could be used to improve estimates of vegetation productivity and vegetation stress.
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