Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
101
datasets available to search
ShareScore release 0.7.1
Dataset results
101 results for “Gross primary productivity”
Model simulations for " Potential impacts of LUCC and climate change on evapotranspiration and gross primary productivity in the Haihe River Basin, China"
<p>Experiment_1.rar, Experiment_2.rar, and Experiment_3.rar are the model simulations from experiment 1, experiment 2, and experiment 3, respectively. All the simulations are original from the CLM5 model in netcdf format.</p> <p>More details on these data can be found in the paper "Potential impacts of LUCC and climate change on evapotranspiration and gross primary productivity in the Haihe River Basin, China". </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>
MUSES 500m Global Annual Gross and Net Primary Productivity Dataset
<p>The MUltiscale Satellite remotE Sensing (MUSES) 500m global annual vegetation productivity dataset includes gross primary productivity (GPP) and net primary productivity (NPP) data from 2001 to 2019. GPP and NPP were estimated with a light use efficiency (LUE) model and GLASS leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) products.</p> <p>The MUSES product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/).</p> <p>The detail information of the MUSES 500m global annual GPP and NPP dataset is as below:</p> <p>Name: MUSES 500m global annual GPP and NPP dataset</p> <p>Period: 2001-2019</p> <p>Projection: Sinusoidal projection;</p> <p>Spatial resolution: 463.3127165 m;</p> <p>Temporal resolution: annual;</p> <p>Data format: zipped GeoTiff file;</p> <p>Data type: unsigned short integer (16bit);</p> <p>Image size: 86400 columns, 36000 rows</p> <p>Upper left coordinates: ULX = -20015109.354 m, ULY = 10007554.677 m;</p> <p>Scale factor: 10. NPP = DN / scale factor; GPP = DN / scale factor;</p> <p>Unit: gC/m<sup>2</sup>/yr.</p> <p> </p> <p>Citation (Please cite these papers when these data are used)</p> <p>1. Wang, J.M., Sun, R., Zhang, H.L., Xiao, Z.Q., Zhu A.R., Wang, M.J., Yu, T., Xiang, K.L., New global MuSyQ GPP/NPP remote sensing products from 1981 to 2018. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14, 5596-5612.</p> <p>2. Wang, M.J.; Sun, R.; Zhu, A.R.; Xiao, Z. Q. Evaluation and Comparison of Light Use Efficiency and Gross Primary Productivity Using Three Different Approaches. Remote Sensing. 2020, 12, 1003.</p> <p>3. Yu, T.; Sun, R.; Xiao, Z.Q. ;Zhang , Q.; Liu, G.; Cui, T.X.; Wang, J.M. Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data. Remote sensing. 2018, 10, 327.</p>
A global 0.05° gross primary productivity of sunlit and shaded leaves dataset via combining two-leaf light use efficiency model with random forest over 2002~2020
<p>The TL-CRF model generated a global 0.05´0.05° product for eight-day gross primary productivity (GPP) of sunlit and shaded canopies from 2002 to 2020 by embedding the random forest (RF) submodule into the two-leaf light use efficiency (TL-LUE) model while considering the seasonal differences in the clumping index. The RF technique was used to integrate various environmental stress factors including meteorological, hydrological, soil properties, and elevation, thereby improving the overall scale of the complex environmental conditions to the maximum LUE. This novel GPP product could support further research on spatial and temporal patterns of the carbon cycle and its association with climate change.</p> <p> </p> <p> </p> <p>Variable: GPP, GPP<sub>sh</sub>, and GPP<sub>su</sub></p> <p>Spatial coverage: global</p> <p>Temporal coverage: 2002 to 2020</p> <p>Spatial resolution: 0.05×0.05°</p> <p>Temporal resolution: eight-day</p> <p>Unite: g C m<sup>−2</sup> d<sup>−1</sup></p> <p>Data format: raster (.tif)</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>
The linkage between methane fluxes and gross primary productivity at diurnal and seasonal scales on a rice paddy field in Eastern China
<p>The data includes the hourly and seasonal data (eddy flux and meteorological data)</p>
Gross primary production and ecosystem respiration measurments based on the Terrestrial Ecosystem Model (TEM)
Gross primary production and ecosystem respiration measurments based on the Terrestrial Ecosystem Model (TEM).
Drought risk of global terrestrial gross primary productivity in recent 40 years detected by a remote sensing-driven process model
<p class="Abstract"><span>Gross primary productivity (GPP) is the largest flux in the global terrestrial carbon cycle and affected by multiple factors. In recent decades, drought has significantly impacted global terrestrial GPP and been projected to occur with increasing frequency and intensity. However, the drought risk of global terrestrial GPP has not been well investigated. In this study, global terrestrial GPP over the period from 1981 to 2016 was simulated with the process-based Boreal Ecosystem Productivity Simulator (BEPS) model. Then, the drought risk of terrestrial GPP was quantified as the product of frequency of drought and reduction of GPP caused by drought, which were determined using the standardized precipitation evapotranspiration index (SPEI). During the study period, the drought risk of terrestrial GPP exhibited detectable spatial heterogeneity, high in southeastern United States, most of South America, southern Europe, central and eastern Africa, eastern and southeastern Asia, and eastern Australia. In these regions, the maximum reduction of GPP might be above 30% in drought years relative to that in normal years. The drought risk of GPP was low at high latitudes of the Northern Hemisphere, in which terrestrial GPP increased slightly in drought years. The spatial pattern of the drought risk of GPP simulated by the BEPS model was close to that of FLUXCOM GPP, which was scaled from tower observations with a machine learning algorithm forced by remote sensing and meteorological data. This study advances our understanding on the impact of drought on terrestrial GPP over the globe.</span></p>
Carbon flux and gross primary production of a scrub of Mexico
<p><span>Vegetation fixes C in its biomass through photosynthesis or might release it into the atmosphere through respiration. Measurements of these fluxes would help us to understand ecosystem functioning. This data is related to carbon flux (NEE) colected with the eddy covariance technique (EC_Data). The NEE is the balance between gross primary production (GPP) and ecosystem respiration (Reco) both calculated using the recent R package Reddyproc (Code Reddyproc). The database also include estimates of GPP of MOD17A2H product of MODIS Satellite for four pixels of a site with scrub vegetation in Mexico (GPP_MODIS). The programming code include the pre-procecing of NEE and the Theil-Sen and Linear model to describe the relationship between GPP of EC and GPP of MODIS (Code Theil-Sen).</span></p>
A global gross primary productivity dataset of sunlit and shaded leaves via combining two-leaf light use efficiency model with random forest from 2002 to 2020
<p><span>The TL-CRF model generated a global </span><span>0.05</span><span><span>´</span></span><span>0.05<span>°</span></span><span> product for eight-day gross primary productivity (GPP) of sunlit and shaded canopies from 2002 to 2020 by embedding the random forest (RF) submodule into the two-leaf light use efficiency (TL-LUE) model while considering the seasonal differences in the clumping index. The RF technique was used to integrate various environmental stress factors including meteorological, hydrological, soil properties, and elevation, thereby improving the overall scale of the complex environmental conditions to the maximum LUE. Eight-day GPP was then aggregated into monthly, seasonal, and annual GPP. This novel GPP product could support further research on spatial and temporal patterns of the carbon cycle and its association with climate change. </span></p>
Global Monthly Gross Primary Production
<p>This is global monthly gross primary production from 2001-2016 derived from P model using pyrealm python package. Unit for GPP is gC/m2 day (monthly daily mean value). Detailed description of data can be found in Shen et al. 2023. For detailed description of P model please refer to Wang et al. 2017, Cai and Prentice 2020 and Stocker et al. 2020.</p>
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>
Regional estimates of gross primary production applying the process-based model 3D-CMCC-FEM vs. multiple datasets
<p>This repository contains the model 3D-CMCC-FEM v5.6 executable (Testolin et al.2023), model inputs and model outputs in the folder RUN_BASILICATA; scripts to prepare model inputs and perform model outputs post-processing in SCRIPTS; remote-sensing based data and forcing in DATA; tables and post-processed files in OUTPUT; figures in FIGURE, related to the manuscript entitled “Regional estimates of gross primary production applying the process-based model 3D-CMCC-FEM vs. multiple datasets”</p>
Carbon flux and gross primary production of a scrub of Mexico
Open the record for dataset details and reuse information.
Drought risk of global terrestrial gross primary productivity in recent 40 years detected by a remote sensing-driven process model
Open the record for dataset details and reuse information.
VIIRS/NPP Gross Primary Productivity and Net Photosynthesis Gap-Filled 8-Day L4 Global 500m SIN Grid V002
The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Gross Primary Productivity (GPP) and Net Photosynthesis (PSN) Gap-Filled (VNP17A2GF) Version 2 product is a cumulative composite of GPP values based on the radiation use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VNP17A2GF is an 8-day composite at 500 meters (m) spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection. The VNP17A2GF will be generated at the end of each year when the entire yearly 8-day VNP15A2H is available. Hence, the gap-filled VNP17A2GF is the improved VNP17A2, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get VNP17A2GF in near-real time because it will be generated only at the end of a given year. Provided in the VNP17A2GF product are layers for GPP, PSN, along with a quality control layer. A low resolution browse image for GPP is also available for each VNP17A2GF granule.Known Issues* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).
VIIRS/JPSS1 Gross Primary Productivity and Net Photosynthesis 8-Day L4 Global 500m SIN Grid V002
The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Gross Primary Productivity (GPP) and Net Photosynthesis (PSN) (VJ117A2) Version 2 data product is a cumulative composite of GPP values based on the radiation use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VJ117A2 is an 8-day composite at 500 meter spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection. Provided in the VJ117A2 product are layers for GPP, PSN, along with a quality control layer. A low resolution browse image for GPP is also available for each VJ117A2 granule.Known Issues* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).
VIIRS/NPP Gross Primary Productivity and Net Photosynthesis 8-Day L4 Global 500m SIN Grid V002
The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Gross Primary Productivity (GPP) and Net Photosynthesis (PSN) (VNP17A2) Version 2 data product is a cumulative composite of GPP values based on the radiation use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VNP17A2 is an 8-day composite at 500 meter (m) spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection. Provided in the VNP17A2 product are layers for GPP, PSN, along with a quality control layer. A low resolution browse image for GPP is also available for each VNP17A2 granule.Known Issues* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).
MODIS/Aqua Gross Primary Productivity Gap-Filled 8-Day L4 Global 500m SIN Grid V061
The MYD17A2HGF Version 6.1 Gross Primary Productivity (GPP) Gap-Filled product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.The MYD17A2HGF will be generated at the end of each year when the entire yearly 8-day [MYD15A2H](https://doi.org/10.5067/modis/myd15a2h.061) is available. Hence, the gap-filled MYD17A2HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A2HGF in near-real time because it will be generated only at the end of a given year.Known Issues* Operational and uncertainty issues are provided under Section 2 in the User Guide. * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).Improvments/Changes from Previous Version* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.
VIIRS/JPSS1 Gross Primary Productivity and Net Photosynthesis Gap-Filled 8-Day L4 Global 500m SIN Grid V002
The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Gross Primary Productivity (GPP) and Net Photosynthesis (PSN) Gap-Filled (VJ117A2GF) Version 2 data product is a cumulative composite of GPP values based on the radiation-use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VJ117A2GF is an 8-day composite at 500 meter spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection.The VJ117A2GF will be generated at the end of each year when the entire yearly 8-day VJ115A2H is available. Hence, the gap-filled VJ117A2GF is the improved VJ117A2, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get VJ117A2GF in near-real time because it will be generated only at the end of a given year. Provided in the VJ117A2GF product are layers for GPP, PSN, along with a quality control layer. A low resolution browse image for GPP is also available for each VJ117A2GF granule.Known Issues* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=VIIRS).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.