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.
34
datasets available to search
ShareScore release 0.9.0
Dataset results
34 results for “Gross Primary Productivity (GPP)”
MOD17A2H version 6 Gross Primary Productivity (GPP) global mosaics at 500 m resolution and difference 2000-2017
<p>MOD17A2H version 6 Gross Primary Productivity (GPP) global mosaics at 500 m resolution and difference in GPP for the period 2000-2017. Changes in GPP could be used to estimate land degradation or similar. Derived using <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/MOD17A2H">the data.table package and quantile function in R</a>. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod17a2h_v006. Antartica is not included.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>veg = theme: vegetation,</li> <li>gpp = variable: gross primary productivity in kg C m<sup>2</sup>,</li> <li>mod17a2h.oct = determination method: MOD17A2H product, GPP values for October,</li> <li>d = median value / difference = difference between periods / u.975 = aggregation/statistics method: 97.5% probability upper quantile,</li> <li>500m = spatial resolution / block support: 500 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000..2017 = time reference: from 2000 to 2017,</li> <li>v1.0 = version number: 1.0,</li> </ul>
CEDAR-GPP: A Spatiotemporally Upscaled Dataset of Gross Primary Productivity Incorporating CO2 Fertilization
<p>Overview:<br>----------<br>CEDAR-GPP is a global Gross Primary Productivity (GPP) data product, including monthly GPP estimates at 0.05º spatial resolution. These datasets were generated via upscaling eddy covariance measurements with machine learning and satellite data. CEDAR-GPP uniquely incorporated the direct CO2 fertilization effect (CFE) using both data-driven and theoretical approaches. GPP estimates were produced from ten different model setups that vary by temporal span, direct CFE incorporation method, and GPP partitioning approaches. CEDAR stands for ups<strong>C</strong>aling <strong>E</strong>cosystem <strong>D</strong>ynamics with <strong>AR</strong>tificial intelligence.</p> <p>CEDAR-GPP consists of GPP estimates from ten model setups, differing by temporal range, methods for quantifying CO2 fertilization effects, and the partitioning methods used to derive GPP from eddy covariance measurements. Users are encouraged to refer to the user manual for a structured approach to selecting the most appropriate dataset.</p> <p> </p> <p>Authors:<br>----------<br>Yanghui Kang, Maoya Bassiouni, Max Gaber, Xinchen Lu, Trevor Keenan</p> <p> </p> <p>File Structure:<br>----------<br>Each zip file contains GPP data from a CEDAR model setup.</p> <p> </p> <p>File Naming Convention:<br>----------<br>All netCDF files follow this naming convention:<br>CEDAR-GPP_<version>_<model-setup>_<YYYYMM>.nc</p> <p>Where:<br><model-setup> comprises of <temporal_span>_<CFE_option>_<GPP_partitioning><br><temporal_span>: ST denotes short-term (2001 to 2020); LT denotes long-term (1982 to 2020)<br><CFE_option>: 'Baseline' indicates no direct CO2 fertilization effect, 'CFE-ML' represents direct CO2 fertilization incorporated by ML, 'CFE-Hybrid' implies direct CO2 fertilization incorporated by theory<br><GPP_partitioning>: 'NT' for night-time GPP partitioning method, 'DT' for day-time GPP partitioning method</p> <p><br>NetCDF characteristics:<br>----------<br>- Spatial Resolution: 0.05 degree<br>- Temporal Resolution: Monthly<br>- Temporal Coverage: Short-term (ST): 2001-2020; Long-term (LT): 1982 - 2020<br>- Image Dimension: Rows: 3600, Columns: 7200<br>- Units: gCm^-2day^-1<br>- Fill Value: -9999<br>- Multiply By Scale Factor: 0.01<br>- Data Type: uint16<br>- File Size: Approximately 99 MB per file</p> <p><br>Data variables:<br>----------<br>- GPP_mean: monthly gross primary productivity (gCm^-2day^-1), mean from 30 model ensemble<br>- GPP_std: standard deviation of 30 model ensemble</p> <p><br>Support Contact:<br>----------<br>For any queries related to this dataset, please contact:</p> <p>Name: Yanghui Kang<br>Email: kangyanghui@gmail.com</p> <p> </p> <p> </p>
Global gross primary production (GPP) product generated by data fusion based on random forest
<p>Improving the ability of gross primary production (GPP) estimates to capture extreme climate perturbations and reduce the uncertainty of GPP response processes to extreme climate is a new challenge. Based on the random forest algorithm, we integrated the multimodel GPP simulation results published by the Multiscale Synthesis and Terrestrial Model Intercomparison Project, the FLUXNET flux-site-observed GPP, the standardized precipitation index (SPI) and the standardized temperature index (STI) to generate a set of global GPP time-series data products from 2001 to 2010. The new GPP product was named DFRF-GPP, referring to the GPP generated by data fusion based on random forest. DFRF-GPP is highly reliable and can be used as a valuable data source for various applications, especially in high-temperature and drought-related studies.</p>
Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon (Open)
<p>This work was carried out in the scope and with the support of the project: Climate Services Through Knowledge Co-Production: A Euro-South American Initiative for Strengthening Societal Adaptation Response to Extreme Events (CLIMAX).</p> <p>The project consortium includes the following institutions: Centre National de la Recherche Scientifique CNRS/Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI-IFAECI) (Argentina-France); General Coordination of Earth Sciences /National Institute for Space Research (INPE) (Brazil); Institut de Recherche pour le Développement (IRD)/ Unité Mixte de Recherche (UMR 245) (France); Le Laboratoire des Sciences du Climat et de l'Environnement (LSCE) (France); Potsdam Institute for Climate Impact Research (PIK) (Germany); Technical University of Munich (TUM) (Germany) and Wageningen University and Research (WUR) Netherlands). The project is sponsored by the Collaborative Research Action (CRA) on “Climate Predictability and Inter-Regional Linkages” of the Belmont Forum, launched in 2015.</p> <p>Climate variability patterns linking the South American Monsoon region, including Amazonia, with southeastern South America influence climate extremes and impact several societal sectors. More than 200 million people live in the study region, which is also one of the largest agricultural production regions of the world and home to the world’s second largest hydroelectric power plant.</p> <p>The objectives of CLIMAX include better understanding the combined role of remote and local drivers on South American climate variability from sub-seasonal to decadal timescales, and its impact on the occurrence and intensity of extreme events. Special focus is given to an improved understanding of the effects of land use changes from the Amazon to the subtropics and their impact on climate.</p> <ol> <li> <p><strong>EXPERIMENT DESIGN</strong></p> </li> </ol> <p>We used four models that are classified as Dynamic Global Vegetation Models (DGVMs) (Prentice et al., 2007; Rezende et al., 2015): Integrated Model of Land Surface Processes (INLAND) (Tourigny, 2014); Lund-Potsdam-Jena managed Land model version 4 (LPJmL4) (Schaphoff et al., 2018), Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) (Smith et al. 2001, Hickler et al., 2012), and Organising Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE) (Krinner et al., 2005).</p> <p>We used three forcings with climate data (GLDAS, GSWP3, and WATCH+WFDEI), Land Use Change (LUC) data and validation data (FLUXCOM (Remote sensor+meteorological data+artificial neural network approach), FLUXCOM (eddy covariance), MODIS (Light Use Efficiency), GLEAM, and TerraClimate (Rezende et al., 2022).</p> <p>We conducted two sets of simulation experiments with different values of CO2: 1) increasing CO2 from the pre-industrial period to 2010 named <strong>historical CO</strong><strong>2</strong> (<strong>hist CO</strong><strong>2</strong>); 2) constant concentration of 278 ppm of (pre-industrial) atmospheric CO2 named <strong>constant CO</strong><strong>2</strong><strong> (const CO</strong><strong>2</strong><strong>)</strong>. We ran both CO2 experiments under <strong>Land Use Change</strong> (<strong>LUC</strong>) and <strong>Potential Natural Vegetation </strong>(<strong>PNV</strong>) conditions. All combinations of CO2 and land use change resulted in four sets of simulation experiments per climate input: 1. <strong>LUC historical CO</strong><strong>2</strong>; 2. <strong>LUC constant CO</strong><strong>2</strong>; 3. <strong>PNV historical CO</strong><strong>2</strong>; 4. <strong>PNV constant CO</strong><strong>2 </strong> (Rezende et al., 2022).</p> <p><strong>2. DATA DESCRIPTION</strong></p> <p>The complete description of the data, including the climate forcing, LUC, the validation datasets, methodology, simulations, discussion and conclusion is in Rezende et al. (2022). This archive contains only the data description from the simulations (outputs) by the DGVMs.</p> <p><strong>2.1 SOFTWARE </strong></p> <p><br> The data were manipulated, worked, standardized, converted using the software: <strong>Climate Data Operators (cdo) version 1.7.0</strong>, <strong>Grid Analysis and Display System (Grads) (</strong><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a><strong>) version 2.0.2, and RStudio Desktop version</strong> 1.3.1093 <strong>(R Core Team, 2020)</strong>, through command lines and several scripts developed for this purpose. The figures were generated with <strong>Grads,</strong> and <strong>RStudio</strong>, and some images were enhanced with <strong>Gimp version 2.8.22</strong>. All the software used is freeware.</p> <p><strong> 2.2 PRIMARY DATA FROM SIMULATIONS</strong></p> <p>Data originating from the simulations are in monthly resolution, covering South America, with all the forcings. Despite data spanning over 1948-2010 or 1950-2010 our study focuses on the period 1981-2010.</p> <p><strong>Variables</strong>: Gross Primary Productivity (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p>The naming of the files is according to the following rules:</p> <p><strong>DGVM_forcing_vegetation cover_CO2 concentration_attribute</strong></p> <p><strong>DGVMs</strong>;</p> <p> InLand (INLAND)</p> <p> LPJ-G (LPJ-GUESS)</p> <p> LPJmL (LPJmL4)</p> <p> ORCHI (ORCHIDEE)</p> <p>forcings: </p> <p> gld - GLDAS</p> <p> gsw – GSWP3</p> <p> wat – WATCH+WFDEI</p> <p> </p> <p>vegetation cover:</p> <p> LU – Land Use Change</p> <p> PNV – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration:</p> <p> CO2 – historical CO2</p> <p> noCO2 – constant CO2 = 278 ppm</p> <p> </p> <p>variables:</p> <p> E – evaporation</p> <p> Et – transpiration</p> <p> gpp – Gross Primary Productivity</p> <p> npp - Net Primary Productivity (not used in the experiment)</p> <p> </p> <p><strong>Example</strong>:</p> <p> inLand_gld_LU_noCO2_E.nc</p> <p> </p> <p><strong> 2.3 SUPPLEMENTARY DATA </strong></p> <p>These interception loss data (mm month-1) were requested by a reviewer to complement the analysis and are available only for the LUC CO2 scenario and for the study region: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).</p> <p>Files are named according to the following rules:</p> <p>variable_season_forcing_DGVM_vegetation cover CO2 concentration_region</p> <p>variable:</p> <p> inter – loss by interception</p> <p> </p> <p>season:</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>forcings: </p> <p> gl - GLDAS</p> <p> gs – GSWP3</p> <p> wa – WATCH+WFDEI</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or – ORCHIDEE</p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p> inter_D_gl_in_LC_SA.nc</p> <p><strong>2.4 PROCESSED DATA</strong></p> <p>Processed data cover all scenarios and input data sets and are restricted to the study area: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).They are in seasonal resolution with averages for January-February-March-April (JFMA) (rainy season) and averages for June-July-August-September (JJAS). Each of the files contains the Gross Primary Productivity variables (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p> </p> <p>Files are named according to the following rules:</p> <p> </p> <p><strong>season_DGVM_forcing_vegetation cover CO2 concentration_region</strong></p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>Example:</p> <p>D_in_Gs_LN.nc</p> <p><strong>2.5 DERIVED DATA</strong></p> <p> </p> <p>The variable Water Use Efficiency (WUE) (kg m-2 mm-1 month-1) results from rate: GPP / Tr (transpiration) (Eq. 1). </p> <p> </p> <table> <tbody> <tr> <td> <p> WUE = GPP / Tr</p> </td> <td> <p>(Eq. 1)</p> </td> </tr> </tbody> </table> <p> </p> <p>These data refer to the study region: southern Amazon and apply to only one scenario: Land Use Change and historic CO2. Files are named naming of according to the following rules:</p> <p> </p> <p>Variable:</p> <p> WUE – Water Use Efficiency</p> <p> </p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p>wue_D_lm_Gl_LC_SA.nc</p> <p> </p> <p><strong>How to cite this work</strong>:</p> <p>Rezende, Luiz F. C., Aline Castro, Celso Von Randow, Romina Ruscica, Boris Sakschewski, Phillip Papastefanou, Nicolas Viovy, Kirsten Thonicke, Anna Sörensson, Anja Rammig, Iracema F. A. Cavalcanti. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p><strong>References</strong></p> <p><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a>. Center for Ocean-Land-Atmosphere Studies, Institute of Global Environment and Society,<a href="https://en.wikipedia.org/wiki/George_Mason_University"> George Mason University</a>. Archived from<a href="http://www.iges.org/grads/gadoc/"> the original</a> on 7 April 2015. Retrieved 14 March 2015.</p> <p>Hickler T. et al., 2012. Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species based dynamic vegetation model. Glob Ecol Biogeograp 21:50–63, <a href="https://doi.org/10.1111/j.1466-8238.2010.00613.x">https://doi.org/10.1111/j.1466-8238.2010.00613.x</a></p> <p>Krinner, G.et al., 2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochemical Cycles, 19, GB1015, doi:10.1029/2003GB002199.</p> <p>R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. </p> <p>Prentice IC (2007) Dynamic global vegetation modeling: quantifying terrestrial ecosystem responses to large-scale environmental change. In: Canadell J, Pataki D, Pitelka L (eds) Terrestrial ecosystems in a changing world. Springer, Berlin Heidelberg.</p> <p>Rezende, Luiz F. C. et al., 2015. Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2. Int J Biometeorol., 2015, doi: 10.1007/s00484-015-1087-6.</p> <p>Rezende, Luiz F. C. et al., 2022. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research - Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p>Schaphoff, S. et al., 2018. LPJmL4 – a dynamic global vegetation model with managed land –Part 1: Model description. Geosci. Model Dev., 11, 1343–1375, 2018, <a href="https://doi.org/10.5194/gmd-11-1343-2018">https://doi.org/10.5194/gmd-11-1343-2018</a>.</p> <p>Smith B. et al (2001) Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Glob Ecol Biogeograp 10:621–637</p> <p>Tourigny, E. (2014). Multi-scale fire modeling in the neotropics: coupling a land surface model to a high resolution fire spread model, considering land cover heterogeneity. Phd dissertation, Meteorology. INPE. Retrieved from http://urlib.net/sid.inpe.br/mtc-m21b/2014/05.30.00.36</p> <p><br> </p> <p> </p>
eLUE-GPP (MODIS): A global gross primary productivity product based on ecosystem light-use-efficiency model and MODIS EVI
<p>Gross Primary Productivity (GPP) represents the cumulative amount of carbon dioxide (CO<sub>2</sub>) assimilated by green plants through photosynthesis at specific time intervals and spatial scales. It is the main component of the carbon exchange between the terrestrial biosphere and the atmosphere, and has a major influence on global climate and terrestrial ecosystem functioning. Over the last two decades, the continuous and reliable collection of global land surface variables by EOS-MODIS, and the parallel development of the eddy-covariance flux tower network (FLUXNET) have enabled the integration of MODIS observations with tower measurements for the calibration and validation of remote sensing models to obtain global GPP estimates. Despite the significant progress and success to date, current remote sensing GPP models based on the light use efficiency (LUE) concept share several limitations, including the difficulty in accurately predicting LUE variability and the associated use of land cover maps and look-up tables for biome specific maximum LUE, further down-regulated by coarse resolution interpolated meteorological data, which introduce significant uncertainties in the predicted GPP. To address the above limitations, here we applied a simple yet ecologically sound remote sensing GPP model based on the ecosystem light use efficiency (eLUE) concept, using the more than two decades of global MODIS Enhanced Vegetation Index (EVI) product and the publicly available FLUXDATA2015 dataset, to generate a global 5 km, 16-d GPP product (eLUE-GPP) from February 2000 to March 2024. Cross-validation with 202 flux tower sites (1494 site/year) showed favorable accuracy of eLUE-GPP (hereafter GPP<sub>eLUE</sub>) (<em>R</em><sup>2</sup> = 0.71, RMSE = 2.11 g C m<sup>-2</sup> d<sup>-1</sup>). The uncertainty associated with GPP<sub>eLUE</sub> is comparatively lower than that of the other global GPP datasets (MOD17, FluxSat, VPM, among others). We have also calculated the uncertainty analytically for each GPP estimate based on the law of error propagation, which allows quantification of the error budget in applications such as Earth system model benchmarking and atmospheric inversion. Our estimate of global total annual GPP, averaged over the period 2001-2023, was 138.46±13.92 Pg C yr<sup>-1</sup>. Furthermore, we found a significant increasing trend in global total annual GPP at a rate of 0.28±0.05 Pg C yr<sup>-1</sup> (<em>p</em> < 0.001) from 2001 to 2023, particularly in eastern Asia, northern India, Europe, eastern North America, and central South America. We expect that the eLUE-GPP product will enable a more accurate diagnostic analysis of the global carbon budget and thus contribute to climate change research.</p>
eLUE-GPP (MODIS): A global gross primary productivity product based on ecosystem light-use-efficiency model and MODIS EVI
Open the record for dataset details and reuse information.
Effects of anthropogenic activity on global terrestrial gross primary production (GPP)
<p>This data set contains the 100-member ensembles of monthly gross primary production (GPP) estimated using the biosphere model BEAMS and d4PDF data for historical and non-warming climates in 1951-2010/2011, 100-member ensembles of yearly GPP of the historical sensitivity experiment for 7 input variables in 1951-2010, yearly GPP of the extended CO<sub>2</sub> sensitivity experiment using four RCP scenarios in 1951-2300. Data is 0.5625-degree (640×320) 4-byte binary (.raw). Undefined value is -9999.</p> <p>For more details, please check the ReadmeGPP.pdf</p> <p>If you questions, please contact Irina Melnikova (irina.melnikova.russia@gmail.com)</p>
Seasonal Gross Primary Productivity (GPP) Imagery 2005-2020
Open the record for dataset details and reuse information.
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>
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>
North America (SW) Gross Primary Productivity (GPP) Dataset 2018 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2018</p> <p>Region: North America (southwest)</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: 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>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics.</p>
Amazon Gross Primary Productivity (GPP) Dataset 2018 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2018 </p> <p>Region: Amazon </p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: 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: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
Europe Gross Primary Productivity (GPP) Dataset 2018 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2018 </p> <p>Region: Europe</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: As ArcPy (used in generation) classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
North America (SW) Gross Primary Productivity (GPP) Dataset 2018 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2018</p> <p>Region: North America (southwest)</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: 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>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics.</p>
North America (SW) Gross Primary Productivity (GPP) Dataset 2019 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2019</p> <p>Region: North America (southwest)</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: 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: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics.</strong></p>
Amazon Gross Primary Productivity (GPP) Dataset 2019 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2019</p> <p>Region: Amazon</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: 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: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
Europe Gross Primary Productivity (GPP) Dataset 2019 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2019</p> <p>Region: Europe</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>NOTE: As ArcPy (used in generation) classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </p>
Europe Gross Primary Productivity (GPP) Dataset 2020 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2020</p> <p>Region: Europe</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: As ArcPy (used in generation) classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
Australia Gross Primary Productivity (GPP) Dataset 2019 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2019</p> <p>Region: Australia</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: 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: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
North America (SW) Gross Primary Productivity (GPP) Dataset 2020 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2020 </p> <p>Region: North America (southwest)</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: 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: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
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.