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12 results for “LAND SURFACE PHENOLOGY”
Intra-urban variations in land surface phenology in a semi-arid environment
<p>Data repository for 'Intra-urban variations in land surface phenology in a semi-arid environment', ERL</p> <p>Contact: Ben Crawford, University of Colorado Denver (benjamin.crawford@ucdenver.edu)</p> <p>Data description:</p> <ul> <li>NDVI.zip: <ul> <li>MODIS NDVI geotif rasters for Denver study area</li> <li>Additional metadata provided in subdirectories</li> </ul> </li> <li>LST.zip: <ul> <li>Landsat LST geotif rasters for Denver study area</li> </ul> </li> <li>Tair.zip <ul> <li>Seasonal modeled air temperatures for Denver study area (as described in the manuscript and supplemental information)</li> </ul> </li> <li>Den470_LandCover_250m_WGS.tif <ul> <li>Denver study area 2018 land cover fractions, derived from 1 m data at https://data.drcog.org/</li> </ul> </li> </ul> <p> </p>
Assessing land surface phenology in Araucaria-Nothofagus forests in Chile with Landsat 8/Sentinel-2 time series - Data and Material
<p>This dataset contains the Enhanced Vegetation Index (EVI) data used in our research work about land surface phenology of Andean Araucaria-Nothofagus forests as well as the phenology information derived from it.</p> <p>Study area: Conguillío National Park, Chile<br> Study period: 2016-2020</p> <p>Description of datasets:</p> <p>conguillio.sen2.lnd8.evi.2016.2020.nc - A raster dataset (NetCDF) of EVI values (resolution 10m). EVI was calculated from Level-2 Sentinel-2 and Landsat 8 data. To ensure harmonization, the Landsat 8 data was resampled and reprojected to Sentinel-2 properties prior to the index calculation.</p> <p>evi_gb_beck_white.tif - A raster dataset (GeoTiff) of phenological metrics per year (resolution 10m). Metrics were derived by fitting a double logistic function (see Beck et al., 2006) to the smoothed and interpolated EVI pixel time series. Subsequently, the main phenological variables SOS (start of season) and EOS (end of season) were extracted using a 50% threshold value. The dataset itself is a result of the R package "greenbrown" and the layers are named accordingly (see https://greenbrown.r-forge.r-project.org/phenology.php). It is available as GeoTIFF and as R rasterfile.</p> <p>Details about the methodology and results describing this dataset can be found in the following publication:<br> Kosczor, E., Forkel, M., Hernández, J., Kinalczyk, D., Pirotti, F. & Kutchartt, E., 2022. Assessing land surface phenology in Araucaria-Nothofagus forests in Chile with Landsat 8/Sentinel-2 time series. Int. J. Appl. Earth Obs. Geoinf. 112, 102862. https://doi.org/10.1016/j.jag.2022.102862</p>
Replication data for: Mapping oak wilt disease from space using land surface phenology
Open the record for dataset details and reuse information.
Land surface phenology based crop maps for Continental United States 2000-2018
<p>This data collection contains annual maps of crop types for Continental United States for the period 2000-2018 at spatial resolution of ~231m derived using <a href="https://doi.org/10.3334/ORNLDAAC/1299">MODIS Land Surface Phenology</a>. This data collection is a companion to the paper <strong><em>Konduri, V., Kumar, J., Hargrove, W. W., Hoffman, F. M., Ganguly, A. R. (2020) <a href="https://doi.org/10.1016/j.rse.2020.112048">Mapping Crops Within the Growing Season Across the United States. Remote Sensing of Environment</a>, Vol 251, 2020 </em></strong><a href="https://doi.org/10.1016/j.rse.2020.112048">https://doi.org/10.1016/j.rse.2020.112048</a>, which describes the methodology for development of these datasets, validation metrics and analysis.</p> <p> </p> <p><strong><strong>Files in collection (28): </strong></strong></p> <ul> <li><strong><em>crop_map_predicted_[YEAR].nc</em>: </strong>Annual crop type maps for YEAR = 2000-2018</li> <li><strong><em>Crop_map_legend.csv</em>: </strong>Legend for crop type categories in the maps (Category numbers and legends for crop types are consistent with those used by <a href="https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php">USDA Crop Data Layer</a>)</li> <li>Maps of earliest date (Day of Year) of classification for eight dominant crop types for year 2015: <ul> <li><strong>earliest_classification_date_corn_CONUS.nc</strong> : Earliest date of classification for corn</li> <li><strong>earliest_classification_date_soybeans_CONUS.nc</strong> : Earliest date of classification for soybeans</li> <li><strong>earliest_classification_date_winter_wheat_CONUS.nc</strong> : Earliest date of classification for winter wheat</li> <li><strong>earliest_classification_date_fallow_CONUS.nc</strong> : Earliest date of classification for fallow</li> <li><strong>earliest_classification_date_other_hay_non_alfalfa_CONUS.nc</strong> : Earliest date of classification for other hay/non-alfalfa</li> <li><strong>earliest_classification_date_alfalfa_CONUS.nc</strong> : Earliest date of classification for alfalfa</li> <li><strong>earliest_classification_date_sorghum_CONUS.nc</strong> : Earliest date of classification for sorghum</li> <li><strong>earliest_classification_date_rice_CONUS.nc</strong> : Earliest date of classification for rice</li> </ul> </li> </ul> <p> </p> <p><strong><strong>Data formats:</strong></strong></p> <ul> <li>All map products are in gridded <a href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF</a> format.</li> <li>Annual crop type maps use category types described in Crop_map_legend.csv. </li> <li>Earliest date of classification maps are encoded as Day of the Year.</li> </ul> <p> </p> <p><strong><strong>Projection for geospatial data</strong> (in <a href="https://live.osgeo.org/en/overview/proj4_overview.html">PROJ4 format</a>):</strong></p> <pre><code class="language-bash">PROJCS["US_National_Atlas_Equal_Area", GEOGCS["sphere", DATUM["unknown", SPHEROID["Spherical_Earth",6370997,"inf"]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]], PROJECTION["Lambert_Azimuthal_Equal_Area"], PARAMETER["latitude_of_center",45], PARAMETER["longitude_of_center",-100], PARAMETER["false_easting",0], PARAMETER["false_northing",0], UNIT["Meter",1]]</code></pre> <p><strong>Paper Citation:</strong></p> <blockquote> <p><strong><em>Konduri, V., Kumar, J., Hargrove, W. W., Hoffman, F. M., Ganguly, A. R. (2020) Mapping Crops Within the Growing Season Across the United States. Remote Sensing of Environment (in revision)</em></strong></p> </blockquote>
Land surface phenology derived from 3 sets of vegetation indices
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Novel representation of leaf phenology improves simulation of Amazonian evergreen forest photosynthesis in a land surface model
<p>This dataset contains the LAI, Litterfall and GPP etc. of the four Amazon FLUX sites (BR-Sa1, BR-Sa3, BR-Ma2 and GF-Guy) simulated using the improved ORCHIDEE model and the corresponding FLUXNET eddy-covariance or ground-measured data. The more detial please the ReadMe.pdf in the zip.</p> <p>Data is organized with netCDF4(.nc).</p> <p><br> If you want to know more detail please contact: chenxzh73@mail.sysu.edu.cn</p>
VIIRS/NPP Land Surface Phenology Yearly L3 Global 500m SIN Grid V002
The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Phenology data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22Q2 data product is derived from time series of the two-band Enhanced Vegetation Index-2 (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 500 meter spatial resolution are identified for up to two detected growing cycles per year. Provided in each VNP22Q2 product are 19 Science Dataset (SDS) layers. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final layer is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22Q2 granule.Important information is provided in Section 5 of the User Guide when comparing VNP22Q2 with the MCD12Q2 data product.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).Improvements/Changes from Previous Versions* Improved calibration algorithm and better coefficients for entire Suomi NPP mission* Improved geolocation accuracy and updates to fix outliers around maneuver periods and other events* Corrections to the aerosol quantity flag (low, average, high) mainly over brighter surfaces in the mid to high latitudes such as desert and tropical vegetation areas. This has an impact on the retrieval of other downstream data products such as VNP13 Vegetation Indices and VNP43 BRDF/Albedo.* Improved cloud mask input product for corrections along coastlines and artifacts from use of coarse resolution climatology data* Replaced the land/water mask input product with MODIS heritage seven class land/water mask* More details can be found in this [VIIRS Land V2 Changes document](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf).
Land Surface Phenology, Eddy Covariance Tower Sites, North America, 2017-2021
This land surface phenology (LSP) dataset provides spatially explicit data related to the timing of phenological changes such as the start, peak, and end of vegetation activity, vegetation index metrics and associated quality assurance flags. The data are for the growing seasons of 2017-2021 for 10-km x 10-km windows centered over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. The dataset is derived at 3-m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. These LSP data can be used to assess satellite-based LSP products, to evaluate predictions from land surface models, and to analyze processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes. The data are provided in NetCDF format along with geospatial area-of-interest information and visualizations of the analysis window for each site in GeoJSON and HTML formats.
VIIRS/NPP Land Surface Phenology Yearly L3 Global 0.05Deg CMG V002
The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Phenology data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22C2 data product is derived from time series of the two-band Enhanced Vegetation Index-2 (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 0.05 degree (~5,600 meters) spatial resolution are identified for up to two detected growing cycles per year. Provided in each VNP22C2 product are 19 Science Dataset (SDS) layers. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final layer is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22C2 granule.Important information is provided in Section 5 of the User Guide when comparing VNP22C2 with the MCD12C2 data product. 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).Improvements/Changes from Previous Versions* Improved calibration algorithm and better coefficients for entire Suomi NPP mission* Improved geolocation accuracy and updates to fix outliers around maneuver periods and other events* Corrections to the aerosol quantity flag (low, average, high) mainly over brighter surfaces in the mid to high latitudes such as desert and tropical vegetation areas. This has an impact on the retrieval of other downstream data products such as VNP13 Vegetation Indices and VNP43 BRDF/Albedo.* Improved cloud mask input product for corrections along coastlines and artifacts from use of coarse resolution climatology data* Replaced the land/water mask input product with MODIS heritage seven class land/water mask* More details can be found in this [VIIRS Land V2 Changes document](https://landweb.modaps.eosdis.nasa.gov/data/userguide/VIIRS_Land_C2_Changes_09152022.pdf).
MuSLI Multi-Source Land Surface Phenology Yearly North America 30 m V011
The Multi-Source Land Surface Phenology (LSP) Yearly North America 30 meter (m) Version 1.1 product (MSLSP) provides a Land Surface Phenology product for North America derived from Harmonized Landsat Sentinel-2 (HLS) data. Data from the combined Landsat 8 Operational Land Imager (OLI) and Sentinel-2A and 2B Multispectral Instrument (MSI) provides the user community with dates of phenophase transitions, including the timing of greenup, maturity, senescence, and dormancy at 30m spatial resolution. These data sets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, and land cover, land use, and land cover change mapping. Provided in the MSLSP product are layers for percent greenness, onset greenness dates, Enhanced Vegetative Index (EVI2) amplitude, and maximum EVI2, and data quality information for up to two phenological cycles per year. For areas where the data values are missing due to cloud cover or other reasons, the data gaps are filled with good quality values from the year directly preceding or following the product year. A low resolution browse image representing maximum EVI is also available for each MSLSP30NA granule.Known Issues* Data are sparse in 2016 and early 2017, as Sentinel-2B was not yet launched, and Sentinel-2A was not fully operational, leading to poorer quality retrievals of phenology in 2016 and 2017. However, poor quality pixels can be masked with Quality Assurance (QA) flags.* Disturbance has not been explicitly accounted for or mapped, which can lead to premature detections of senescence and dormancy when sharp spectral changes occur.* Pixels with more than two growth cycles per year (e.g., alfalfa fields) may not be accurately characterized, especially if they occur in rapid succession.Improvements/Changes from Previous Version* Modest changes were made to the spline fitting algorithm used to estimate the MSLSP30NA product in V011. Only gaps greater than 20 days were filled using observations from the outside of the target year to reduce the computational burden. Sensitivity analyses demonstrated that this change had negligible impact on product results. * The Quality Assurance (QA) fields were updated to reflect the changes in the gap-filling. Version 1 included QA values from 1-7, whereas Version 1.1 includes QA values from 1-6, 9, and 10.* Peak date corresponding to the maximum EVI2 value in a growth cycle (Peak and Peak_2) and number of days with clear observations in calendar year (numObS) layers were added.
MuSLI Multi-Source Land Surface Phenology Yearly North America 30 m V001
MSLSP V1 data was decommissioned on December 14, 2021. Users are encouraged to use the improved [MSLSP V1.1](https://doi.org/10.5067/Community/MuSLI/MSLSP30NA.011) data product.NASA’s Multi-Source Land Imaging (MuSLI) Land Surface Phenology (LSP) Yearly North America 30 meter (m) Version 1 product (MSLSP) provides a Land Surface Phenology product for North America derived from Harmonized Landsat Sentinel-2 (HLS) data. Data from the combined Landsat 8 Operational Land Imager (OLI) and Sentinel 2A and 2B Multispectral Instrument (MSI) provide the user community with dates of phenophase transitions, including the timing of greenup, maturity, senescence, and dormancy. MSLSP30NA is aligned with the Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system)) at 30 m spatial resolution. These datasets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, land cover, land use, and land cover change mapping.Provided in the MSLSP product are variables for percent greenness, onset greenness dates, Enhanced Vegetative Index (EVI2) amplitude, maximum EVI2, and data quality information for up to two phenological cycles per year. For areas where the data values are missing due to cloud cover or other reasons, the data gaps are filled with good quality values from the year directly preceding or following the product year. A low-resolution browse image representing maximum EVI is also available for each MSLSP30NA granule.Known Issues* Data are sparse in 2016 and early 2017, as Sentinel-2B was not yet launched, and Sentinel-2A was not fully operational, leading to poorer quality retrievals of phenology in 2016 and 2017. However, poor quality pixels can be masked with Quality Assurance (QA) flags.* Disturbance has not been explicitly accounted for or mapped, which can lead to premature detections of senescence and dormancy when sharp spectral changes occur.* Pixels with more than two growth cycles per year (e.g., alfalfa fields) may not be accurately characterized, especially if they occur in rapid succession.
Dastaset for: "Rodriguez-Galiano, V.F., Sanchez-Castillo, M., Dash, J., Atkinson, P. and Ojeda-Zujar, J. (2016). Modelling interannual variation in the spring and autumn land surface phenology of the European forest, Biogeosciences, 13
<p>Dastaset for: "Rodriguez-Galiano, V.F., Sanchez-Castillo, M., Dash, J., Atkinson, P. and Ojeda-Zujar, J. (2016). Modelling interannual variation in the spring and autumn land surface phenology of the European forest, Biogeosciences, 13</p>
ScienceDex guides
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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.