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83 results for “GPP”
Seasonal Gross Primary Productivity (GPP) Imagery 2005-2020
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2019-2020 AR station alpine meadow ecosystem tower-based observation spectra, GPP and meteorological data
<p> This is the dataset used in the <em>Investigating the Performance of Red and Far-Red SIF for Monitoring GPP of Alpine Meadow Ecosystems</em> paper. The dataset contains canopy red and far-red SIF data, GPP data, NDVI data, photosynthetically active radiation(PAR) data, temperature(Ta) data, and vapor pressure deficit(VPD) data during the 2019 and 2020 growing seasons in the alpine meadow ecosystem at the AR site(100.4643 E, 38.0473 N, altitude 3033 m).</p>
Data set containing the energy landscapes for GPO and GPP tropocollagen models under pulling forces
<p>Energy landscapes (databases of minima and transition states) for GPO and GPP repeat collagen models under constant pulling forces as explored with OPTIM and PATHSAMPLE with an AMBER force field.</p> <p>The systems are seven GPO or GPP per chain capped with ACE and NME.</p> <p> The forces applied are 0 pN (F0), 10 pN (F1), 50 pN (F2), 100 pN (F3), 250 pN (F4), 500 pN (F5) and 750 pN (F6).</p> <p>The folders contains numerous analysis scripts and graphs. Most of these assume python with numpy and pandas, as well as cpptraj from AMBERTools.</p>
Global Gross Primary Productivity (GPP) Dataset 2020 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2020</p> <p>Region: Global (in MODIS tile grid)</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publications: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE:</strong> This dataset has been produced in the MODIS tile format, to allow comparison to existing products. </p>
Data for: Global GPP estimates at 8-day/monthly/annual temporal resolution generated by the PTEC model
<p>PTEC provides spatiotemporally estimates of Gross Primary Productivity based on a two-leaf light use efficiency model incorporating plant water status and phenology. PTEC integrates a set of satellite and climate variables within a parsimonious modeling framework to be simple yet robust and grounded on eco-physiological principles. Available at 8-day/monthly/annual and 0.05° resolution from 2001 to 2021, PTEC shows superior performance compared to benchmark products.</p>
Three datasets of global monthly gross primary productivity (GPP) during 2003-2018 derived from SIF, NIRv and LAI and their best-matching environmental factors
<p>As the largest source of uncertainty in carbon cycle studies, accurate quantification of gross primary productivity (GPP) is critical for the global carbon budget in the context of global climate change. Numerous remote sensing vegetation indices (VIs) have participated in the estimation of global GPP. However, the relative performance of various VIs in estimating GPP and what additional factors should be combined with them to reveal the photosynthetic capacity of vegetation mechanistically better are still poorly understood.</p> <p>We used the Random Forest (RF) algorithm to identify the factors with the most powerful explanation of GPP and to explore the importance of these predictors. We trained six RF models to select features, i.e., two types of models (Plant Functional Type [PFT]-specific and universal) for each vegetation index (SIF, NIRv, and LAI). Each model comprised 100 decision trees, was sampled without replacement, and was trained using 70% of the data. Model performance was evaluated using out-of-bag (OOB) R-squared (R<sup>2</sup>) and root mean square error (RMSE) values. The predictor with the lowest importance score in the iteration was removed and the whole procedure was then repeated until only the vegetation index, CO<sub>2</sub>, and PFTs were left. The predictors used to estimate GPP were identified based on the performance curve of OOB R<sup>2</sup> and RMSE. The determination of the model is based on the principle that further reductions in the number of predictors would considerably reduce model performance, while increasing the number of predictors would not significantly improve model performance.</p> <p>Here we provide a set of high-spatial resolution (1/12°) global gridded products of monthly GPP for 2003-2018 generated for each vegetation index based on a generic model with an optimal configuration, i.e., an optimal combination of VI and other relevant variables using the RF algorithm. R<sup>2</sup> of three optimal VI-based GPP estimation models ranges from 0.84 to 0.85, and RMSE ranges from 1.51g C·m<sup>−2</sup>·d<sup>−1</sup> to 1.54g C·m<sup>−2</sup>·d<sup>−1</sup>. More information about the datasets can be found in Zhao and Zhu (2022) <strong><em>Remote Sensing</em></strong>.</p> <p><em>Zhao W, Zhu Z. Exploring the Best-Matching Plant Traits and Environmental Factors for Vegetation Indices in Estimates of Global Gross Primary Productivity[J]. Remote Sensing, 2022, 14(24): 6316.</em></p>
Soil moisture and GPP trends across the Avocado "Green Gold" Belt in central Mexico (2001-2018)
<p>Data processing in RStudio for soil moisture and Gross Primary Productivity trends across the Avocado “Green Gold” Belt in central Mexico (2001-2018).</p>
Data from: Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize
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The code and data used for Mango-GPP
<p>The source code, inputs, and outputs of Mango-GPP and observed GPP at NS and ZJ sites.</p>
Future forest flux (e.g., GPP, NPP, NEP) simulated by the optimized InTEC model under four SSP-RCP scenarios
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Improving intra- and inter-annual GPP predictions by using individual-tree inventories and leaf growth dynamics
<p>Carbon sequestration is a key ecosystem service provided by forests. Inventory data based on individual trees are considered to be the most accurate method for estimating forest productivity. However, estimations of forest photosynthesis itself from inventory data remains understudied, particularly when considering the growth and development of individual trees under the background of global change. Here, we used the leaf growth process with phenology and non-structural carbohydrates (NSC) storage to revise an individual-tree based carbon model, FORCCHN. This model couples leaf development and biomass to quantify gross primary productivity (GPP) in the forests, where growth is decoupled from photosynthesis in daily step. The model was initialized with inventory-based forest data rather than the more widely used satellite-based data. We tested the model against measured aboveground woody biomass, growth of leaf biomass, daily gross ecosystem exchange (GEE), and yearly GEE at five representative forest sites in the Northern Hemisphere. We also compared the results from the original model and the revised model at five forest sites. Including leaf growth dynamics and inventory-based initialization improved the predicted performance (r2) of GPP by an average of 33%. Synthesis and applications. Our results suggest that the appropriate vegetation data sources (i.e. inventory or satellite selection) and the effective predictions of the growth process should be considered when developing future carbon cycle models and forest carbon estimation options. Applying and improving such carbon models to evaluate carbon sequestration is an important part of forest carbon sink management.</p>
Global SIF-based GPP estimates
<p>This dataset represents the attempt done within the ESA Sen4GPP project to produce a global dataset of terrestrial gross primary production (GPP) based on retrievals of sun-induced fluorescence derived from the Sentinel-5P/TROPOMI mission (Guanter et al. 2022).</p> <p>SIF is scaled to GPP using empirical linear models derived and applied on a per biome basis. For details on the SIF:GPP scaling algorithm, the reader is directed to the ATBD document of the Sen4GPP project.</p> <p>The dataset provide global GPP data for the period 2018-05-11 until 2021-12-29. Spatial sampling is 0.0833º and temporal sampling is 8 days. The data are stored as NetCDF files containing data on an annual basis.</p> <p>Further information of some of the variables:</p> <ul> <li> <p>GPP_SIF_COR_ALL: SIF-based GPP values from a universal SIF-to-GPP linear scaling.</p> </li> <li> <p>GPP_SIF_COR: SIF-based GPP values from a biome-based SIF-to-GPP linear scaling (reference GPP variable).</p> </li> <li> <p>sig_GPP_SIF_COR_ALL: precision error of GPP_SIF_COR_ALL</p> </li> <li> <p>sig_GPP_SIF_COR: precision error of GPP_SIF_COR</p> </li> <li> <p>cloud_frac_av: 8-day average cloud fraction for each gridbox</p> </li> <li> <p>n: number of GPP values used to calculated the average GPP for each gridbox</p> </li> </ul> <p> </p>
Study to Assess the Efficacy, Safety and Tolerability of Secukinumab in Japanese Subjects With Generalized Pustular Psoriasis (GPP)
ClinicalTrials.gov study NCT01952015. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Improving intra- and inter-annual GPP predictions by using individual-tree inventories and leaf growth dynamics
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CMS: MODIS GPP, fPAR, and SST, and ENSO Index, Baja California, Mexico, 2000-2013
This data set provides data for MODIS-derived (1) gross primary productivity (GPP) for the years 2000-2010, (2) fraction of photosynthetically active radiation (fPAR) for the years 2003-2013, (3) sea surface temperature (SST) for the years 2003-2013, and (4) the NOAA-source Multivariate ENSO Index (MEI) data for the years 2003-2013 (as a measure of the El Nino/Southern Oscillation). The study areas were three transects on the Baja California Peninsula, Mexico, and the adjacent Pacific Ocean. The terrestrial transects, in order from North to South, West to East included Punta Colonet (three sites-PC1, PC2, PC3), Punta Abreojos (two sites-PA1, PA2), and Magdalena Bay (three sites-MB1, MB2, MB3).
ABoVE: Light-Curve Modelling of Gridded GPP Using MODIS MAIAC and Flux Tower Data
This dataset contains gridded estimations of daily ecosystem Gross Primary Production (GPP) in grams of carbon per day at a 1 km2 spatial resolution over Alaska and Canada from 2000-01-01 to 2018-01-01. Daily estimates of GPP were derived from a light-curve model that was fitted and validated over a network of ABoVE domain Ameriflux flux towers then upscaled using MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) data to span the extended ABoVE domain. In general, the methods involved three steps; the first step involved collecting and processing mainly carbon-flux site-level data, the second step involved the analysis and correction of site-level MAIAC data, and the final step developed a framework to produce large-scale estimates of GPP. The light-curve parameter model was generated by upscaling from flux tower sub-daily temporal resolution by deconvolving the GPP variable into 3 components: the absorbed photosynthetically active radiation (aPAR), the maximum GPP or maximum photosynthetic capacity (GPPmax), and the photosynthetic limitation or amount of light needed to reach maximum capacity (PPFDmax). GPPmax and PPFDmax were related to satellite reflectance measurements sampled at the daily scale. GPP over the extended ABoVE domain was estimated at a daily resolution from the light-curve parameter model using MODIS MAIAC daily reflectance as input. This framework allows large-scale estimates of phenology and evaluation of ecosystem sensitivity to climate change.
Daily FluxSat GPP of biomass over Land, Based on MODIS Terra and Aqua adjusted reflectance Collection 6.1, on a Global 0.5 by 0.625 Degree Grid, Level 3 Version 2.2
This dataset provides global gridded daily estimates of gross primary production (GPP) and uncertainties at 0.5 deg latitude by 0.625 deg longitude resolution for the period March 2000 to present. The GPP is derived from MODerate-resolution Imaging Spectroradiometer (MODIS) instruments on NASA Terra and Aqua satellites. GPP is derived using the MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectances (NBAR) product, which is used as input to neural network models to globally upscale GPP estimated from selected collocated FLUXNET 2015 and OneFlux eddy covariance tower sites used for model training. Version 2.2 is the current version of the dataset.If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
Daily FluxSat GPP of biomass over Land, Based on MODIS Terra and Aqua adjusted reflectance Collection 6.1, on a Global 0.05 by 0.05 Degree Grid, Level 3 Version 2.2
This dataset provides global gridded daily estimates of gross primary production (GPP) and uncertainties at 0.05 deg latitude by 0.05 deg longitude resolution for the period March 2000 to present. The GPP is derived from MODerate-resolution Imaging Spectroradiometer (MODIS) instruments on NASA Terra and Aqua satellites. GPP is derived using the MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectances (NBAR) product, which is used as input to neural network models to globally upscale GPP estimated from selected collocated FLUXNET 2015 and OneFlux eddy covariance tower sites used for model training. Version 2.2 is the current version of the dataset.If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
BigFoot GPP Surfaces for North and South American Sites, 2000-2004
The BigFoot project gathered Gross Primary Production (GPP) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. At this time we are archiving Northern Old Black Spruce (NOBS - BOREAS NSA, Canada) and Harvard Forest LTER (HARV - Massachusetts, USA) data collected in 2001.The GPP surfaces were produced by a spatial version of an ecosystem process model named, Biome-BGC. Inputs to the model included Landsat ETM+ derived Land Cover and LAI, tower derived meteorological variables, and a set of site level ecophysical parameters. The model was calibrated using field measured NPP and validated by tower derived estimates of GPP. For an in depth discussion of methods used to produce these surfaces, please see Turner et al. (2003).Each BigFoot GPP product covers a 7 x 7 km extent and consists of the GPP surface in BIP format (280 rows by 280 columns by 365 bands at 25 meter resolution) and an accompanying text file which provides metadata specific to the image (such as projection, data type, etc). Additional information on GPP surface development can be found on the BigFoot website at http://www.fsl.orst.edu/larse/bigfoot/ovr_mthd.html.BigFoot Project Background: Reflectance data from MODIS, the Moderate Resolution Imaging Spectrometer onboard NASA's Earth Observing System (EOS) satellite Terra (http://landval.gsfc.nasa.gov/MODIS/index.php), is used to produce several science products including land cover, leaf area index (LAI), gross primary production (GPP) and net primary production (NPP). The overall goal of the BigFoot Project was to provide validation of these products. To do this, BigFoot combined ground measurements, additional high resolution remote sensing data, and ecosystem process models at nine flux tower sites representing different biomes to evaluate the effects of the spatial and temporal patterns of ecosystem characteristics on MODIS products. BigFoot characterized up to a 7 x 7 km area (49 MODIS pixels) surrounding the CO2 flux towers located at each of the nine sites. We collected multi-year, in situ measurements of ecosystem structure and functional characteristics related to the terrestrial carbon cycle. Our sampling design allowed us to examine scales and spatial pattern of these properties, the inter-annual variability and validity of MODIS products, and provided for a field-based ecological characterization of the flux tower footprint. BigFoot was funded by NASA's Terrestrial Ecology Program.For more details on the BigFoot Project, please visit the website: http://www.fsl.orst.edu/larse/bigfoot/index.html.
Urban Biogenic CO2 fluxes: GPP, Reco and NEE Estimates from SMUrF, 2010-2019
This dataset contains estimates of biogenic CO2 flux components at 0.05 degree resolution from the Solar-Induced Fluorescence (SIF) for Modeling Urban biogenic Fluxes (SMUrF) model. Estimates were produced for the following regions and periods: eastern and western CONUS (2010-2019), western Europe (2010-2014 and 2017-2018), eastern Asia, eastern China, eastern Australia, South America, and Central Africa (2017-2018). Modeled CO2 flux components include gross primary production (GPP), ecosystem respiration (Reco), and net ecosystem exchange (NEE). Four-day means of GPP are estimated from solar-induced fluorescence (SIF) and biome-specific GPP-SIF relationships. Daily estimates of Reco are included. In addition, GPP and Reco were downscaled to hourly estimates and used to generate hourly NEE. Uncertainties for 4-day GPP and daily Reco estimates are provided. The input data streams included 500 m MODIS-based annual land cover classification, 0.05 degree spatiotemporally contiguous SIF, above-ground biomass (AGB) from GlobBiomass, eddy-covariance (EC) flux measurements, and gridded products of air and soil temperatures.
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